lectures/lecture12/lecture12_answer_key.ipynb

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   "source": [
    "# Lecture 12: Working with tabular data using `R` and `tidyverse`\n",
    "\n",
    "[Rasi Subramaniam](http://rasilab.org/)\n",
    "\n",
    "To interactively work with the code below, open [lecture12.ipynb](./lecture12.ipynb) in VSCode. Make sure to select the `kernel` for `R` so that you can execute `R` code. You should have already set this up following the software installation instructions [here](../../software/README.md)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`R` is the second programming language after `Python` that we will learn in this course. We will use `R` over the next 5 lectures.\n",
    "\n",
    "`R` is particularly well suited for reading, manipulating, and visualizing data in tabular and biological sequence formats. \n",
    "Many statistical tests are also available out of the box in `R`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While \"base\" `R` is used widely, I almost exclusively use `R` for its two excellent package collections:\n",
    "\n",
    "1. [Tidyverse](https://www.tidyverse.org/) - suited for tabular data\n",
    "2. [Bioconductor](https://www.bioconductor.org/) - suited for biology-aware analyses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Today we will learn a few basic functions from `tidyverse` for working with tabular data.\n",
    "\n",
    "Unlike `pandas` which is a single package with lot of functionality, `tidyverse` is a collection of packages that are focused on specific tasks.\n",
    "\n",
    "1. [ggplot2](https://ggplot2.tidyverse.org/) - for plotting data\n",
    "2. [dplyr](https://dplyr.tidyverse.org/) - for filtering, aggregating, and transforming data\n",
    "3. [readr](https://readr.tidyverse.org/) - for reading and writing data\n",
    "4. [tidyr](https://tidyr.tidyverse.org/) - for cleaning and transforming data\n",
    "5. [stringr](https://stringr.tidyverse.org/) - for manipulating strings\n",
    "6. [purrr](https://purrr.tidyverse.org/) - for manipulating lists of R objects\n",
    "7. [forcats](https://forcats.tidyverse.org/) - for manipulating categorical data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can load all the above packages in one go:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "vscode": {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.3.1 ──\n",
      "\n",
      "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.6     \u001b[32m✔\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.4\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.1.7     \u001b[32m✔\u001b[39m \u001b[34mdplyr  \u001b[39m 1.0.9\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtidyr  \u001b[39m 1.2.0     \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n",
      "\u001b[32m✔\u001b[39m \u001b[34mreadr  \u001b[39m 2.1.2     \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.5.1\n",
      "\n",
      "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(tidyverse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading data\n",
    "\n",
    "Various options for reading and writing data are in package [`readr`](https://readr.tidyverse.org/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m16\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m7\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \"\\t\"\n",
      "\u001b[31mchr\u001b[39m (3): strain, insert_sequence, kozak_region\n",
      "\u001b[32mdbl\u001b[39m (4): mean_yfp, mean_rfp, mean_ratio, se_ratio\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A spec_tbl_df: 16 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>strain</th><th scope=col>mean_yfp</th><th scope=col>mean_rfp</th><th scope=col>mean_ratio</th><th scope=col>se_ratio</th><th scope=col>insert_sequence</th><th scope=col>kozak_region</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>schp688</td><td>1748</td><td>20754</td><td>0.755</td><td>0.066</td><td>10×AGA</td><td>A</td></tr>\n",
       "\t<tr><td>schp684</td><td>3294</td><td>20585</td><td>1.437</td><td>0.021</td><td>10×AGA</td><td>B</td></tr>\n",
       "\t<tr><td>schp690</td><td>3535</td><td>20593</td><td>1.541</td><td>0.018</td><td>10×AGA</td><td>C</td></tr>\n",
       "\t<tr><td>schp687</td><td>4658</td><td>20860</td><td>2.004</td><td>0.021</td><td>10×AGA</td><td>D</td></tr>\n",
       "\t<tr><td>schp686</td><td>5000</td><td>21171</td><td>2.121</td><td>0.023</td><td>10×AGA</td><td>E</td></tr>\n",
       "\t<tr><td>schp685</td><td>7379</td><td>22956</td><td>2.893</td><td>0.050</td><td>10×AGA</td><td>F</td></tr>\n",
       "\t<tr><td>schp683</td><td>9365</td><td>23866</td><td>3.522</td><td>0.110</td><td>10×AGA</td><td>G</td></tr>\n",
       "\t<tr><td>schp689</td><td>8693</td><td>22649</td><td>3.424</td><td>0.125</td><td>10×AGA</td><td>H</td></tr>\n",
       "\t<tr><td>schp679</td><td>2528</td><td>19906</td><td>1.149</td><td>0.056</td><td>10×AAG</td><td>A</td></tr>\n",
       "\t<tr><td>schp675</td><td>3687</td><td>20438</td><td>1.621</td><td>0.036</td><td>10×AAG</td><td>B</td></tr>\n",
       "\t<tr><td>schp681</td><td>3705</td><td>20227</td><td>1.645</td><td>0.021</td><td>10×AAG</td><td>C</td></tr>\n",
       "\t<tr><td>schp678</td><td>4378</td><td>20630</td><td>1.906</td><td>0.010</td><td>10×AAG</td><td>D</td></tr>\n",
       "\t<tr><td>schp677</td><td>3967</td><td>20604</td><td>1.728</td><td>0.030</td><td>10×AAG</td><td>E</td></tr>\n",
       "\t<tr><td>schp676</td><td>2657</td><td>20223</td><td>1.177</td><td>0.048</td><td>10×AAG</td><td>F</td></tr>\n",
       "\t<tr><td>schp674</td><td>1270</td><td>20316</td><td>0.561</td><td>0.004</td><td>10×AAG</td><td>G</td></tr>\n",
       "\t<tr><td>schp680</td><td>1117</td><td>19377</td><td>0.519</td><td>0.010</td><td>10×AAG</td><td>H</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A spec\\_tbl\\_df: 16 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " strain & mean\\_yfp & mean\\_rfp & mean\\_ratio & se\\_ratio & insert\\_sequence & kozak\\_region\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t schp688 & 1748 & 20754 & 0.755 & 0.066 & 10×AGA & A\\\\\n",
       "\t schp684 & 3294 & 20585 & 1.437 & 0.021 & 10×AGA & B\\\\\n",
       "\t schp690 & 3535 & 20593 & 1.541 & 0.018 & 10×AGA & C\\\\\n",
       "\t schp687 & 4658 & 20860 & 2.004 & 0.021 & 10×AGA & D\\\\\n",
       "\t schp686 & 5000 & 21171 & 2.121 & 0.023 & 10×AGA & E\\\\\n",
       "\t schp685 & 7379 & 22956 & 2.893 & 0.050 & 10×AGA & F\\\\\n",
       "\t schp683 & 9365 & 23866 & 3.522 & 0.110 & 10×AGA & G\\\\\n",
       "\t schp689 & 8693 & 22649 & 3.424 & 0.125 & 10×AGA & H\\\\\n",
       "\t schp679 & 2528 & 19906 & 1.149 & 0.056 & 10×AAG & A\\\\\n",
       "\t schp675 & 3687 & 20438 & 1.621 & 0.036 & 10×AAG & B\\\\\n",
       "\t schp681 & 3705 & 20227 & 1.645 & 0.021 & 10×AAG & C\\\\\n",
       "\t schp678 & 4378 & 20630 & 1.906 & 0.010 & 10×AAG & D\\\\\n",
       "\t schp677 & 3967 & 20604 & 1.728 & 0.030 & 10×AAG & E\\\\\n",
       "\t schp676 & 2657 & 20223 & 1.177 & 0.048 & 10×AAG & F\\\\\n",
       "\t schp674 & 1270 & 20316 & 0.561 & 0.004 & 10×AAG & G\\\\\n",
       "\t schp680 & 1117 & 19377 & 0.519 & 0.010 & 10×AAG & H\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A spec_tbl_df: 16 × 7\n",
       "\n",
       "| strain &lt;chr&gt; | mean_yfp &lt;dbl&gt; | mean_rfp &lt;dbl&gt; | mean_ratio &lt;dbl&gt; | se_ratio &lt;dbl&gt; | insert_sequence &lt;chr&gt; | kozak_region &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| schp688 | 1748 | 20754 | 0.755 | 0.066 | 10×AGA | A |\n",
       "| schp684 | 3294 | 20585 | 1.437 | 0.021 | 10×AGA | B |\n",
       "| schp690 | 3535 | 20593 | 1.541 | 0.018 | 10×AGA | C |\n",
       "| schp687 | 4658 | 20860 | 2.004 | 0.021 | 10×AGA | D |\n",
       "| schp686 | 5000 | 21171 | 2.121 | 0.023 | 10×AGA | E |\n",
       "| schp685 | 7379 | 22956 | 2.893 | 0.050 | 10×AGA | F |\n",
       "| schp683 | 9365 | 23866 | 3.522 | 0.110 | 10×AGA | G |\n",
       "| schp689 | 8693 | 22649 | 3.424 | 0.125 | 10×AGA | H |\n",
       "| schp679 | 2528 | 19906 | 1.149 | 0.056 | 10×AAG | A |\n",
       "| schp675 | 3687 | 20438 | 1.621 | 0.036 | 10×AAG | B |\n",
       "| schp681 | 3705 | 20227 | 1.645 | 0.021 | 10×AAG | C |\n",
       "| schp678 | 4378 | 20630 | 1.906 | 0.010 | 10×AAG | D |\n",
       "| schp677 | 3967 | 20604 | 1.728 | 0.030 | 10×AAG | E |\n",
       "| schp676 | 2657 | 20223 | 1.177 | 0.048 | 10×AAG | F |\n",
       "| schp674 | 1270 | 20316 | 0.561 | 0.004 | 10×AAG | G |\n",
       "| schp680 | 1117 | 19377 | 0.519 | 0.010 | 10×AAG | H |\n",
       "\n"
      ],
      "text/plain": [
       "   strain  mean_yfp mean_rfp mean_ratio se_ratio insert_sequence kozak_region\n",
       "1  schp688 1748     20754    0.755      0.066    10×AGA          A           \n",
       "2  schp684 3294     20585    1.437      0.021    10×AGA          B           \n",
       "3  schp690 3535     20593    1.541      0.018    10×AGA          C           \n",
       "4  schp687 4658     20860    2.004      0.021    10×AGA          D           \n",
       "5  schp686 5000     21171    2.121      0.023    10×AGA          E           \n",
       "6  schp685 7379     22956    2.893      0.050    10×AGA          F           \n",
       "7  schp683 9365     23866    3.522      0.110    10×AGA          G           \n",
       "8  schp689 8693     22649    3.424      0.125    10×AGA          H           \n",
       "9  schp679 2528     19906    1.149      0.056    10×AAG          A           \n",
       "10 schp675 3687     20438    1.621      0.036    10×AAG          B           \n",
       "11 schp681 3705     20227    1.645      0.021    10×AAG          C           \n",
       "12 schp678 4378     20630    1.906      0.010    10×AAG          D           \n",
       "13 schp677 3967     20604    1.728      0.030    10×AAG          E           \n",
       "14 schp676 2657     20223    1.177      0.048    10×AAG          F           \n",
       "15 schp674 1270     20316    0.561      0.004    10×AAG          G           \n",
       "16 schp680 1117     19377    0.519      0.010    10×AAG          H           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data <- read_tsv(\"data/example_dataset_1.tsv\")\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tabular data structure is called a `tibble` in `tidyverse`, and is a souped-up version of the [data.frame](https://en.wikipedia.org/wiki/Data_frame) R data structure with additional nice features.\n",
    "\n",
    "The ` <- ` assignment operator is equivalent to the ` = ` assignment operator and can be used interchangeably. However, using the ` <- ` operator is more conventional."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data, aes(x = kozak_region, y = mean_ratio)) +\n",
    "  geom_point()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Anatomy of a `ggplot2` plot**\n",
    "\n",
    " - Begins with `ggplot` function with a `tibble` argument as the first argument.\n",
    " - `aes` specifies the variables to plot.\n",
    " - `geom` specifies the type of plot.\n",
    " - `+` adds additional `layers` to the plot.\n",
    "\n",
    "\n",
    "**Key differences with `Python`**\n",
    "  - No need to specify variables within quotes.\n",
    "  - Indentation convention is different."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change size of plot globally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "options(repr.plot.width = 5, repr.plot.height = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How do we show multiple experimental variables?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting a point graph with color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0+LBdNE5DQZicnMztnCWELFmypPYTlEplUlLSI1YAAOCCrLdLLCyDO/r27ZudnX3gwIGjR48eP358y5Yt8+fPP336NLc5xTBMWFjYhg0bai+hbdu2ltsPuweytkcvo/bbrDGApc71UOPisTa+34ZXKcuydT7hobponIaC8MUXX4yJiSGEDB48eMmSJdzOaw5FURqNJjo62tPT8xErAAB4HGk0msTExMTEREJIUlLS6NGjV65c+fHHHxNCwsPD09PTIyIi6tu+qU8DSeCIMlq3bn316lWz2WzZKGQYJisry/o5Pj4+hJCioiLrxmvXrlnfbfT7tdamTZvLly9XVFTUuRC7dFGfho4RhoeHDxo0aNCgQfPnzx89evQgK88//3xsbCxSEACEqUYwdO3a1bpx3LhxRqNx9uzZNbau7ty50/BiudGeNRbuuDLi4+MLCgq++eYby0Pffvttbm6u9ZPbtGlDCDlw4IClJTk5uUZYNvr9WnvppZeqq6vnzZtn3WhZoF26qI9Nx2kXLFjw6D0BALiNpk2bDho0qFOnTs2aNcvPz1+7dq1YLB47diz36PTp09PS0latWnXu3LmhQ4c2adIkJyfn9OnTFy5caPiAVlRUlEKh+Oyzz2QymZeXl5+fX9++fR1XxsyZMzdv3jxt2rTz588/8cQTFy5c+Pbbb9u1a3fjxg1LF126dOnateuKFSvKy8sjIiIuXry4Z8+eyMhI6+c0+v1amz59+t69e5cvX37+/PmBAwd6enpevXo1NTU1PT3dXl3Ux9YBSyzLpqWl/fzzz0VFRTVmd1u+fPkjFgEA8HiZNWvWsWPHli1bVlpa6ufn17lz53Xr1nXr1o17VCKR7NmzZ82aNevXr//www/NZnNAQEB0dPSyZcsaXqxWq92yZcvChQvfeOMNg8HQu3fvhoPwEcvQaDQnTpx48803v/vuu2+//bZTp06pqanLli2zDjlCyLZt21577bWkpCSWZbt373706NF58+ZZP6fR79eaVCpNSUlZvnz5xo0b58+fL5VKg4ODuV2+9uqiPjbNR1heXj5w4MCTJ0/W+agdJ/zDfIR2hPkIMR8hwXyE8PASEhL27t2r1+v5LsR5bDqPkBuGtGTJksuXLxNC9u7de/z48f79+3fu3PnmzZuOLRAAAMCRbArCXbt2jRgx4p133uHOetHpdLGxsfv372dZ9vPPP3dwhQAAAsWyrL5+NY5SQaPZFIS5ubm9evUif57kz+29FIvFo0aN2r59u0PrAwAQrEuXLinrt23bNr4LdBM2DZZRqVRc+MlkMoVCYRmu6unpee/ePQdWBwAgYKGhofVdPo0Q0rp1a0d0umPHDkcs1pXZFIQhISGWs0aioqKSkpJGjBhB0/TWrVubN2/uyPIAAIRLoVBwVzUBh7Jp12j//v137tzJbRROmjRp9+7doaGhYWFhhw8fbnjyegAAABdnUxC+/fbbhw8f5g7MTpo0aenSpQqFQq1WL1iw4O2333ZwhQAAAA5k065RrVbLXR2cM2vWrFmzZjmsJAAAAOd58BZhVVXV22+/ffbsWSdUAwAA4GQPDkKlUrls2TJ7XfAFAADApTx41yhFUS1atLh7964TqgEAcLLy8nJHLJabRwIeCzYNlhk7duzy5ctrzMQIAADgBmwaLNOuXbv169d36NBhwoQJwcHBNWZVHjZsmGNqA3gkNMtm6/Vqmn7oqU4BQEhsCsKRI0dyN955553aj/I1vQNAfRiWrCwoXH6/qJJhKEKe1ag/CvQLkkn5rgsAXJFNQYgLisLj5d+FRYvz/jvvD0vIwfKKXJMptXVLOYWNQwCoyaYgTEhIeOBzGIbZtm3bgAEDvL29H7kqgMYzMuz/5RfWaLykN+wpLRvhpa3zJQAgZDYNlrGF0WgcPXr09evX7bVAgMbJM5sr6pqe5pqBnwmKAcDF2S0IAVyEl1hc58daJ7Fp/wcACA2CENyNRix6zlNdo1EtEj2vqdkIAEAQhOCW/q9ZQLRSYbnrKRZ93jygOUaNgsv4+eef4+Pjg4ODKYqaNGlSjUf37dsXHR2tUCiCgoLmz5/fiJnoy8vLPT09PTw8CgtrHi9v+NGqqqqPPvqoU6dOGo1Go9FERERMnTr1999/f9gCHi/YWQRuyEcsTg1pebxan00ob4r0kkp8JWK+i4LHGcuKMi9ReXeJXMGEtmGb+D3i8iorK0NDQxMSEubPn1/joTNnzgwdOnTq1KkbN248d+7c1KlTaZr+4IMPuEd/++23Vq1a+fj4WJXGHjly5JlnnrFeyKZNm6KiouRy+bp162bPnl2ji/oeLS4u7tOnz61bt956663OnTvrdLqbN2+ePn163Lhx586de8S37Mooe50FqNfrlUrlL7/88ijTSJaWltrroqZarVYqlRYWFvJ1mqNarVYoFMXFxTRN81KAUqlUqVRlZWVGIz+DRORyuUajqaysrK6u5qUAiUTi5eWl1+srKip4KYCiKJ1OZzKZSktLeSmAEOLj48OybHFxMV8FcN/EgoICey3Q19fXXoviPPASa5TBIN70tehu7n/vSyR0n2fprj0bfpWNl1iLjo6OiYlZu3atpWX48OEZGRmXL1/m7s6bN+/TTz/Nz8/38PAghAwePDgnJyctLY1bDyzLTp06NTk5OTMzU6fTWRYSFRX1+uuvy+XyBQsWXLlyhfrriUP1PTpp0qQtW7ZcvHgxNDTU+vk0TYvF7vynJHaNAgA0RHxo//9SkBBiNonTUqjc2w7q7uTJk3FxcZa7cXFxlZWVlg2ypKQkb2/vPn365OXlMQwzYcKE3bt3Hz161DoFT548ef369REjRsTHx9+/fz8tLa3G8ut81Gg0JiUljR8/vkYKEkLcOwWJo4MwLS1t3rx5Y8eOHTFixBtvvHHo0CGHdgeuo5xm1hcUzblxc01+QSFP28QAdsCy1KULf22iCCGiyxcd0RvDMHl5eQEBAZYW7vadO3e4uyqVav/+/YGBgb179x45cmRqauqxY8ciIiKsF/Lll18mJiaq1WqlUjlq1KivvvrKlkdv375dWVlpvahXXnlF8qdLly454v26CMceIzxy5EiHDh2GDh3q4eFx6tSpzz77zGw2Dxw40KGdAu/S9YZRN2/n/XmV9gVi8TdBgbFqFb9VATQGw1B1zTdAGfTOrMJ636ZSqUxOTg4ODt6xY8fBgwfbtWtn/cyCgoIdO3ZYtjomTpzYo0ePO3fuNG3atOFHax9FWrRo0YwZM86cOTN58mT3vpSm3bYI5XL577//XuMPkyVLlowZMyYmJqZ9+/aTJk2KjIw8efKkvXoE10Sz7Ks5d/LMZvLnF6eUpqfdvldOP/TINwD+icWst652M9vE3xG9iUQif3//e/fuWVq424GBgZYWo9E4btw4T0/P5557bvLkydnZ2dZLWLduncFgePrpp7ktue7du5vNZssxyAYeDQoK8vDwSE9PtyyqWbNmERERrVq1csQ7dSkPt0XIMEx5eXmNPw28vLwIIRRF1UjB2oxGo5/fX0ZbFRUVXbt2zXKX+594qJLqw/0BJeHvHGqRSEQIkUql3A3n43brSyQSJ/8pd6mq+ip3DRerw/P5ZvNpg+F5raczK+HWgEgkkkr5OXGC+xBSFMVXAZYaeCzA8kXgq4BHR/eLk2zbZN3C+uiYJzs7qLsePXocOHBg2bJl3N0DBw6oVKonnniCu2swGIYPH56VlXX8+PGAgIAxY8bExsYePXqUO7DHsuyqVatmzJgxceJEywK/++67NWvWvPfeeyKRqIFHZTLZqFGj1q9fP3PmzNqHCd2bTaNGGYZZtWrVypUrb9y4UXsIoo2/s2lpaV988cW//vWvsLAwS+PRo0fffPNNy91///vfXbp0sa1ycFFHikufuZBeu31d27DxAY866BzA7myZmFd06aL42CGquIiIxUzrcLr/86zXAy6q3PCo0erq6qysLELIyJEjO3To8P7771MUFRUVRQg5c+ZMz549p06dOmXKlPPnz0+ZMmXmzJmW0yfi4+MvX7585MgRblcnTdMvv/zy0aNHL168qNPpUlNT4+LiMjMz27RpY+krJyenZcuWu3btUigUDTw6dOjQoqKi2NjY3Nxc7vQJX1/fnJycNWvW7N27NzMzMzw8/IEr6jFlUxAuWrRo/vz5YWFhnTp10mprXra4xpHYOv34448rVqx47bXXYmNjrdtv3Lixb98+y93nnnuO+999dHK5XCQS8TVwnxAik8nEYrHBYGjEybB2IZFIpFKp0Wh08vkb90ym0N9+r/2eT0a0fUJln819G4lEIrlcbjab7XVOTiMolUqGYQwGA18FKBQKQohe79QDWtbs/k1UKpX2WhTH9hnqKb2elUqJbUMoGw7C8+fPWzbyOGKx2DL5+d69e+fOnZuRkdGkSZOJEycuWLDAsmPp1KlTrVu39vf/345ZhmF++OGHoUOHEkKGDRt269at2uf89ezZU6PRyOXyBh5NSUkhhFRWVq5YsWLHjh1ZWVlms7lp06a9e/eePn16586O2gJ2BTYFYVBQ0IABA1avXt24vXwpKSlff/317Nmzu3bt2vAzcR6hHfF4HuH8e/f/XVD05z2WEOoFrWZ1kH3+xLEdziMkOI/QBrYH4UOx8TxCcAU2HULLy8ubPHly41IwKSkpOTl53rx53FY/CMF7fr6eYtGqwuJiM60Wicf6aOf42fnHCwDAXmwKwhYtWjTuT9o1a9bs37//1Vdf1Wg0N27cIIRIpdKgoKBGLAoeIzIRNauJ7t3mTY0Kpdxo0PO3gxoA4IFsCsKJEyeuXLny2WefpR5ygu9jx47RNP3ll19aWgICAlavXv1wNcJjiCorFV+64FlRbvZQi9p2YHTYIgQAF2VTEIaHh3/99dddu3YdM2ZMUFBQjTgcNmxYfS/cvHnzoxYIjyHxHzeVO7+jzCaGEBEhHj//ZHhumKltB77rAgCog01BmJiYSAi5cePG2bNnaz/q3lccgIdG08r9uyjz/wY9UTQtP7jX3DKYVTp11CgAgC1sCsLt27c7ug5wG6K8u1StYXiUwSD546apTXteSgIAaIBNQZiQkODoOsBt1HlhRkIIy9/JfAAADcA0TGBndBO/Os84ZgKbOb8YAIAHsvVSnCzLpqWl/fzzz0VFRTUulbJ8+XIHFAaPLaWHoWcf+fHD1m3GJ7tg4CgAuCabgrC8vHzgwIH1TRyBIIQajJ27Mx5q+W9nRcWFrNbb2PEJY3QM30UBANTNpiCcP3/+6dOnlyxZMmzYsPbt2+/du1ej0SxevLi4uBjjaKAOFGWOiBJ36qLRaCorK404oR4AXJhNxwh37do1YsSId955Jzg4mBCi0+liY2P379/Psuznn3/u4AoBAAAcyKYgzM3N7dWrF/lzajHuuthisXjUqFHYIgQAgMeaTUGoUqm48JPJZAqF4s6dO1y7p6en9UzKAAAAjx2bgjAkJISbQ5IQEhUVlZSUxLKs2WzeunVr8+bNHVkeAACAY9kUhP3799+5cye3UThp0qTdu3eHhoaGhYUdPnx4woQJDq4QAADAgWwKwrfffvvw4cPc6YOTJk1aunSpQqFQq9ULFix4++23HVwhAACAA9l0+oRWq9VqtZa7s2bNmjVrlsNKAgAAcJ6HuMSa2Wz+z3/+k5KSUlJS4riCAOyDpsWXLppT91JnfhKV4hMLruXnn3+Oj48PDg6mKGrSpEk1Ht23b190dLRCoQgKCpo/f36Ni3nZory83NPT08PDo7CwsMZDVVVVH330UadOnTQajUajiYiImDp16u+//27jy92SrUH43XffNW/ePCYm5rnnnsvMzCSE3Llzx8/Pb9OmTY4sD6AxqIpy1fqvZN/voI8cJCl7PL75tyT9At9FwWOs0GxemHtv+NXsCdl/7CguefSZ5yorK0NDQxcvXhwaGlrjoTNnzgwdOrRnz56//PLL4sWLP/nkk/fff9/y6G+//VZUVGT9fJZlDx8+XGMhmzZtioqK6t69+x4pYwYAAB+YSURBVLp166zbi4uLu3Xr9tFHHw0fPjw5Ofn48eOLFi3SaDTjxo2z5eXuirJlNsHU1NSBAwc++eSTo0aNevPNN0+fPt21a1dCSL9+/TQaza5du+xVTWlpqclOcxRotVqpVFpYWMjXdIlqtVqhUBQXF9M0zUsBSqVSpVKVlZUZjUZeCpDL5dyVZaqdfmUZ5Y4tkuxr1i2sRFo1YSrj5e3MMiiK0ul0JpOptLTUmf1a8/HxYVm2uLiYrwK4b2JBQYG9Fujra+eL1pbXmjWshj+Mpt4ZVwutplUZ5eO9Ojio4VdpNBpbeo+Ojo6JiVm7dq2lZfjw4RkZGZcvX+buzps379NPP83Pz/fw8CCEDB48OCcnJy0tjVsPLMtOnTo1OTk5MzNTp9NZFhIVFfX666/L5fIFCxZcuXLFMpv6pEmTtmzZcvHixRoBTNO02Opa+fW93F3ZtEW4ZMmS6OjoM2fO/P3vf7du79at24UL+EMbXAul10tuXq/ZaDZJrmbyUg887mbcul3418nFkoqK95WUOai7kydPxsXFWe7GxcVVVlaeO3fuv10nJXl7e/fp0ycvL49hmAkTJuzevfvo0aPWKXjy5Mnr16+PGDEiPj7+/v37aWlpXLvRaExKSho/fnztzVDrFKzv5W7MpiD8z3/+89JLL0kkNUfWtGjR4u7duw6oCuARGPSkzt0AelzyFB4aw5Jj5RW12w+XPWA7spHdMUxeXl5AQIClhbttuYyJSqXav39/YGBg7969R44cmZqaeuzYsYiICOuFfPnll4mJiWq1WqlUjho16quvvuLab9++XVlZaf3kV155RfKnS5cuNfxyN2ZTENI0LZfLa7fn5+dLpVJ7lwTwSFi1hlUo62hv4u/8YuBxxxCWruvvKrNzj7lY75xUKpXJyckFBQU7duzYsGFDu3btrJ/JtU+cOJG7O3HixO+//57L0drHiRYtWnT+/PmvvvqKpmnu0QZe7sZsCsLw8PCffvqpRiPLst9//32Nv0QA+CcWG3o9XaONadrcFNaWl3LgsSahqBiVR+32rmqVI7oTiUT+/v7Wl67kbgcGBlpajEbjuHHjPD09n3vuucmTJ2dnZ1svYd26dQaD4emnn+a287p37242m7ljkEFBQR4eHunp6ZYnN2vWLCIiolWrVra83I3ZFIQvv/zytm3brIcPVVRUTJs27ezZs+PHj3dUafDoHn7UtXswRcfon32OVWsIIUQiNXfoWPXCSGJ1FATAdv/XoplC9Jefyl4a9SgfRw286tGjx4EDByx3Dxw4oFKpnnjiCe6uwWCIj49PT08/fvz47t27u3TpEhsbe+3af4eGsSy7atWqGTNmnLfy7rvvrlmzhqZpmUw2atSo9evXW55fQ8Mvd9D7dQU2jRo1m81DhgxJSUnx8/PLz88PCwu7deuW0WgcPHjw7t27RaKHOBmxYRg1aheU2SQ9/aM8/QKprGC9vA0xXU1RnYjTx33xOGqUI5FIvGQyPSEVVVW8FIBRo8QtRo0SQjKq9R/fyz9XWaUVi5/38nzNv4nyQb97DY8ara6u5i7gPHLkyA4dOrz//vsURUVFRRFCzpw507Nnz6lTp06ZMuX8+fNTpkyZOXPmBx98wL0wPj7+8uXLR44cadq0KSGEpumXX3756NGjFy9e1Ol0qampcXFxmZmZbdq0sfSVk5PTsmXLXbt2DR06tKioKDY2Njc396233urcubOvr29OTs6aNWv27t2bmZmZnZ3d8MsfuKIeUzYFISGEpulVq1Zt2LAhIyODYZjw8PBx48b9/e9/F9v1r2wEoV0o9u6SZvzl9FhDr77Grj2dXIZLBKGXl16vr6ioY7CDEyAIibsEYSM0HITnz5+3bORxxGKx+c+xqXv37p07d25GRkaTJk0mTpy4YMECy/bGqVOnWrdu7e//vwPeDMP88MMPXEoNGzbs1q1bliGmFj179tRoNCkpKYSQysrKFStW7NixIysry2w2N23atHfv3tOnT+/cubMtL3dLtgahc7hJEDKM9PyviutXqeoqk87X0KUH08TPaZ2L7uSqNn9ds1UsrvjbzDqHkDgOghBBSBCE8Diw6Vqj8FAU+3ZJMy8RQhhCJHl3xVmXq0aOY5o94PRbexHfz6ujlaZFhQW0s2oAAHiMPEQQ3r9///r167W3sQYNGmTvqh5jkuxrXAoSQriDchRNKw/urZwwzUkVyOo5oUVWxwkwAABgUxAWFxdPnz5969atdV771aV2rvJOfPuP2o2igvtUdTWrdMaeSXPLEFauoAx660bGR0f7NnFC7wAAjx2bgnDatGlbt2594YUX+vTp4+Pj4+iaHmv1/lHgrDGbrIfKEDdYvm8XZbkolNJDPyje+aNGAQAeCzYF4b59+1566aWNGzc6uho3QLcMIWdqXnyA9g905kAVU3g72j9QeTVTWlVp0Hjq20Y4Z2MUAOBxZFMQisXimJgYR5fiHugWrUwdn5Re/M3SwkqkhrghTi6D0XoxvZ6WqFRVZWUsT7NPAAA8FmwKwj59+vz2228Pfh4QQgjR93+ebtFSfv2quLrK6OOrj+nKar2cXEOOyfx9SV4+S4IokqBReeGiKgAA9bApCJcuXdqzZ8+vvvrq1VdfteN1ZNwWRZnaRco7d5MqFBXFxazTT6jfW1o+7fZd/Z+DmD4Ri3cEB0UqMGoUAKAONgVhaGjoF198kZiY+Oabb7Zs2bLGfEznz593TG3QGAVm+o07eXqrobxFND0l585PocEiDJcBqAVnvoNNQbht27bRo0ezLKtUKs1ms/mvc1SCSzlRWVlaaxv0qsGYaTC0x0YhAEAtNgXh/Pnzg4KC9u3b16FDB0cXBI+ogq57xokKoc5EAQDQMJsO+GVnZ0+dOhUp+FjoUNdmn0xEhctlzi8GAMD12RSELVq0MGII/mOik4fyBW3NYx5vNvHFwFEAgDrZNPvEZ599tmLFivPnz6vVaodWY8dpCuRyuUgk4mveA0KITCYTi8UGg6HO69I5VBXN/OvuvW/vF+YZTa3k8tcD/Sb7NXH+SBmxWCyTyUwmE18HlUUikVwuN5vN9prSpBGUSiXDMAaDga8CFAoFIUSv1z/wmQ5i92+iEleHAHuz6RhhUFCQv79/ZGTk1KlTW7duXWPU6LBhw+xVDcuy9ooNLuB5nFWZYRixWEzTtPODUE7IvED/f7YMYiUS1mg0m80sw8OKoCiKEMIwDF//C9xngGVZvgrg1gCPBXD4LYD3byLAA9m0RUg1eJlKO150203mIySEEGJQKMpEYp2+WsTTKBWlUqlSqcrKyvjarY35CDEfIXkc5iMEsGmLcPv27Y6uw51cNxhn3sk7VVlFCPEQiWY00b3exAen8AEAuCabgjAhIcHRdbiNSoYZ80fudcN/N8KqGGZx3n2FiJqq8+a3MAAAqBOul2ZnO0vKLClosTS/0IxZGwEAXBKC0M5uGOs4xllK04UYLAAA4JIQhHamq+t0PZmI0uI0PgAAl4QgtLNhXp6qWhN0xGs9FZggHgDAJSEI7SxIKvm8eaCn+H8rtrvKY0mAH48lAQBAA2waNQoPZZCnuqtHyM9mupiQ1izTVanAxiAAgMtCEDqEr0Q80kurUCiKi4txTQ0AAFeGXaMAACBoCEIAABA0BCEAAAgaghAAAAQNQQgAAIKGIAQAAEFDEAIAgKAhCAEAQNAQhAAAIGgIQgAAEDQEIQAACBqCEAAABA1BCAAAgoYgBAAAQcM0TACOQdOSSxfNpcWsVCpuFkQHteK7IACoG4IQwP4ovd4jab3ofj5NCEuIByHGmK6Gp/vzXRcA1MF9d43SNDEY+C4CBEp+7JDofj53myKEECL79Ywk+xqPJQFAfdxwi1BUVKA4nMr8kW1gGA/fJobe/cwhYXwXBcIiuZpZR+OVDHNwqPOLAYCGudsWIVVd7bFtozj7OmEYQoio4L5y53finFt81wXCQpmMdbSaTE4vBAAezN2CUPqfM1R5+X/3Rv1JceIwT+WAQNF+AbUbmboaAYB37haE4sL7tRupP4/WADhH7XExjI+v6YnOvBQDAA1ztyBkZfI6WhUKpxcCgkY3C6oaNY5p3oJIJMTDw9whqmrkWFYq5bsuAKiDuw2WMbeLkKZfqNFoahfBSzEgZHRQq6oXJ+h8fExmc3VpKd/lAEC93G2L0NyqtaFrT+sWukUrY88+PJUDgkdRD34OAPDK3bYICSHGXn3p8Haqe7kis7nSW2cKDsWPEQAA1McNg5AQQvsHUuFtJVKpubCQsCzf5QAAgOtyt12jAAAADwVBCAAAgoYgBAAAQXPsMcIrV67s3Lnz+vXr+fn5zz777D/+8Q+HdgcAAPCwHLtFqNfrAwMDx44dGxgY6NCOAAAAGsexW4QdO3bs2LEjISQ5OdmhHQEAADQOjhECAICg8Xwe4aVLlzZu3Gi5O378+ODgYLssWSwWE0LUarVdltYIEomEEKJSqVieTmTk1oBSqZTL67r+quOJRCJCiFwu51aF81EURQiRSqUajYaXAjhisZjHAiiKoiiKxwK4zyG//wUADeM5CPPz89PS0ix34+Pj7furzVcGWMhkMn4LkPJ9oWeJRMJXEHLEYjH3W8wXkUjE++cQBQA0gOcg7Nq16549eyx35XJ5cXGxXZas0WgkEom9ltYIHh4ecrm8rKyMpmleClAoFEqlsqKiwsTTfLAymUylUlVXV+v1el4KEIvFnp6eBoOhqqqKlwIoivLy8jKZTBUVFbwUQAjRarWEkFL+rvpt92+it7e3vRYFwOE5CJVKZbNmzSx3S0tL7fWrze2QZBiGrz2TXL80TfMVhAzDcP8KtgBu1yjLsvwWQAjhqwAOj2uAWH0R+CoA4IEcG4RGo/H27dvcjYqKihs3blAUZa+jgAAAAI/OsUF4+/btN954g7udm5t7+vRpkUi0e/duh3YKAABgO8cGYUhIyPfff+/QLgAAAB4FziMEAABBQxACAICgIQgBAEDQEIQAACBoCEIAABA0BCEAAAgaghAAAAQNQQgAAIKGIAQAAEFDEAIAgKAhCAEAQNAQhAAAIGg8z0foIIU0fayo2EioUNocIuN5inYAAHBlbhiESSVl79zJq2AY7u4EH6+PAv1FFL9FAQCAi3K3XaMXqvWzc+9ZUpAQsq6oZFVhMY8lAQCAK3O3INxUXGpg2RqN64oQhAAAUDd3C8J8M127Ma+uRgAAAOJ+QdhCWsdRz5Z1NQIAABD3C8JXdN5q0Z9v6s9dpG/46fiqBwAAXJy7BWErmXR9i6YtuVMmKKIWif4Z6Bev9eS7LgAAcFFuuM+wt1p1Oiw4XyY3UKIAQ7UHhTMnAACgXm4YhIQQKUW191BKpdLCQgNbaxApAACAhbvtGgUAAHgoCEIAABA0BCEAAAgaghAAAAQNQQgAAIKGIAQAAEFDEAIAgKAhCAEAQNDc84R6AKAMeunZU3TeXZZQssBmpi7dWKmM76IAXBGCEMANUQa9x4Y1opJibopqefY16ZWMqrGvsBIpz5UBuB7sGgVwQ7KTx0Ulf5mPWlSQLz39I1/1ALgyBCGAGxL/kV27UZpzy/mVALg+7BoFAIegykrlx9OYW9kGs8kjoKk+9hmmaXO+iwKoA7YIAdwQHdSqdqMpqKXTCqAMeo+kb6WZl0h1FTGZxDm3PLZuEOffc1oBALZDEAK4IWOPPozWy7qF0fmauvZyWgHSX8+ISkusWyizWX70kNMKALAddo0CuCFWoaga96rs7CnFvVyWEH3TIFOX7qzUeUNGxfl5tRtF2CIEl4QgBHBPrEJhiO2r8vFhWdZYXPzgF9i39zpDV4YTGcEVUS41gbvZbBaLxXZZFEVRhBAe3x3vBXA1oAAi7I8BXwUwF8+Zt6yv0Sju0Vs8OP4Rl8y9IwA7cq0twsrKSpPJZJdFabVaqVRaVFTE12+QWq1WKBQlJSU0TfNSgFKpVKlU5eXlRqORlwLkcrlGo6mqqqqurualAIlE4uXlpdfrKyoqeCmAoiidTmcymUpLS3kpgBDi4+PDsmyx07cISbMWisgnpL+fszTQ/oEVnbuzhYWPuGBfX99HXAJADa4VhADgNvRxg01t2qvu5YpMpgofX3P7SCLC6DxwRQhCAHAUOrg1Ff2kRCo1FxTwXQtAvfAHGgAACBqCEAAABA1BCAAAgoYgBAAAQUMQAgCAoCEIAQBA0BCEAAAgaAhCAAAQNNe61qgdbdq06Y8//pgzZ469Ll76sPbt23fhwoXJkyc3adKElwJOnTp17NixhISE8PBwXgrIyMjYtWvXM88889RTT/FSQF5e3tdff/3kk0/GxcXxUoDRaFy6dGmrVq1efPFFXgoghKxYsUIqlf7tb3/jq4ANGzbcvn377bffFuGyMuCq3PajeeLEieTkZL6u80kI+e2335KTk8vLy/kqICsrKzk5+e7du3wVkJubm5ycfPXqVb4KKCkpSU5OPn/+PF8FmM3m5OTkkydP8lUAIWTfvn0HDhzgsYDjx48nJyfzWADAA7ltEAIAANgCQQgAAIKGIAQAAEFz28EyAAAAtsAWIQAACBqCEAAABA1BCAAAguaeM9RXV1ePHz+epul169ZpNBpndr1mzZoffviBuy2Xy/39/QcOHPj88887swaDwbBnz56TJ0/euXNHIpEEBgZ27tx5yJAhKpXKCb1b1gBFUUqlMjAwsGPHjs8//7yfn58Teq9Rg7Vly5aFhobyWENcXJzTTmw3GAw//PDDyZMnc3NzCSF+fn7t27d//vnnW7Zs6ZwC1qxZc+rUqXXr1lk3vvrqq9HR0Tye3Q9QJ/cMwmPHjgUHB0ul0rS0tBdeeMHJvXt5eS1ZsoQQotfrT506tWrVKi8vrx49ejin98rKynfeeef+/fuJiYlhYWEqlSonJ+fgwYMKhcJpq4JbAyzLVldXZ2dn79+/PyUlZe7cuR07dnROAYQQrVa7cOFC65bmzZs7rfc6a/D09HROvxUVFe+++25+fv7w4cPDwsI0Gk1+fn5mZuann366fPly59QA8BhxzyBMSUkZMmSIRCL57rvvhg0bRlGUM3sXi8WW39zQ0NDU1NRr1645LQjXrVt3586dzz77LDAwkGsJCQnp3bt3aWmpcwogf10D4eHhzzzzzLvvvvvpp5+uXr1aKpU6pwaJRBISEuKcvlythtqfgdatW3fr1o1hGOcXA+D63PAYYUZGxr1793r27Nm9e/fS0tILFy7wVQlN0z/99FNFRUW7du2c1uOJEyeeeeYZyy+ghVardU4NtUkkkoSEhMLCwsuXL/NVg3CYzeYff/yxzs8ArvYJUCc33CJMSUnp0aOHQqEghMTGxqakpERHRzuzgMLCwuHDhxNCzGazSCQaO3Zsly5dnNP1/fv39Xq9044D2a5Vq1aEkHv37kVFRTmnx8LCwiFDhljuikSi3bt3O6fr+mpYu3atEw6UFhQU1PgMrFy58siRI5bbLVq0cHQNnBpvH8BluVsQlpWVnTx5ctGiRdzdfv36zZkzp6ioyMfHx2k1WI4RGo3GrKysb775RqVSDRw40GkFuCDnX7ehxvE5J+8er7MGZ34IrY0ZM2bo0KFZWVmff/65M/8jah+m/eCDD5zWO4Dt3C0I09LSTCbTe++9Z2lhGObgwYOjRo1yWg3WR8hCQkKys7M3b97snCD09fVVKBS3bt1yQl8P5ebNm4SQ2jvrHEewxwh9fX3lcrn1Z0Cn0+l0upKSEidXUvvtSyTu9oMD7sGtPpcsyx44cGDo0KH9+vWzNJ44ceLgwYMjRozg8QCJ0WhkWdYJGyUSiaRXr16HDx8eNmxYjdQpLS3l6zCh2WzeuXOnr69v+/bteSlAUBr4DABAndwqCM+dO3fv3r24uLhmzZpZGgcOHLhjx45ffvnFadPD0jR9+/Zt8ueu0SNHjvTs2dNpu+bGjx+flZU1c+bMxMTE8PBwDw8P7vSJmJgYp50+YVkD3OkT+/btu3v37ty5c525QWA2m2/cuGHdEhAQ4OHh4bQCeDRhwoQrV67MnDnTcvpEQUHBwYMHKYrCNhlAbW71rUhJSQkJCbFOQUKIr69v27ZtU1JSnBaEJSUl3CnDUqnU19d3yJAhI0eOdE7XhBCNRrN06dI9e/YcO3Zsy5YtUqm0adOmsbGxAwYMcFoN3BrgTqgPCAiIiop67733nHlCPSGktLT0jTfesG6ZM2eO005i4Rf3Gfjhhx9++umnrVu30jTt4+MTERGxdOnSGt8OACCYfQIAAAQO5xUBAICgIQgBAEDQEIQAACBoCEIAABA0BCEAAAgaghAAAAQNQSgsO3bsoCjK+ZefdoXe65OWlkZR1Pr16/kuBAD4gSAEAABBc6srywA0Qt++faurq502YzAAuBpsEcLjqqqqyi7LEYlECoVCLBbbZWkA8NhBEArdpk2b5HL5gAEDysvLCSElJSWzZs0KDg6Wy+X+/v5jxoy5du2a5ckSiYSqJSIignu0tLR07ty5Tz31FDcTUEhIyOzZsysqKmzvvQHc8cVt27YtXLgwLCxMJpNxs06azeZly5ZFR0crlUqNRtOnT5+DBw9av/DOnTsvvfSSt7e3Wq3u06fP6dOnExISuHmbObWPETa8ErhKdu7c+fHHH4eHh8vl8hYtWixevBhXKwR4TGHXqKAtWbLkvffemzhx4qpVqyQSSWVlZWxs7O+//z5mzJju3btfvXr1yy+/TElJOX36dJs2bQgh+/fvZxjG8vKTJ09+8MEH3OzzhJCcnJzVq1cnJCSMHj1aJpOdOHFi2bJlZ8+ePX78eJ2Tb9To3ZaC58yZ06xZsyVLlgQEBEilUpqmhwwZkpqampiYOGnSJL1ev2nTpri4uM2bN48ePZoQUlZWFhsbm52dPWXKlCeeeOLixYsDBgwICgpqoIsHrgTOW2+9FR4evnLlSi8vr7Vr186dO1en002dOtWWdwEAroUFIdm+fTshZNeuXSaTafLkyYSQBQsWWB7l5hPnNm44qamphJABAwbUXtS1a9d8fX1DQkLu37/Ptej1em7mRYvFixcTQg4dOmRL77ZUHh4ebjKZLI1ffPEFIeSbb76xtBiNxieffNLf35972rx58wghq1evtjxhw4YNhBC5XG5pOXToECFk3bp1Nq4ErpKYmBiGYbgWmqbDwsLatWtn43sBAJeCIBQW7kd848aNAwcOlEql69evt360Y8eOarW6urraurFbt24ikai0tNS6saioqE2bNl5eXhkZGXV2ZDQaq6urL1++TAj55z//aUvvtlT+4YcfWjc+9dRTfn5+1X/1ySefEEJ+/fVXlmUjIyN9fX3NZrPlJQzDNG/evIEgfOBK4CpZsWKF9RO4jWCapm1/RwDgIrBrVIimT59eVla2ffv2hIQE6/YbN260bt3a+vgZISQyMvL06dM3b97s2LEj12IymeLj42/cuHHgwIG2bdtaP3n9+vWrV6++cOGC9UiWoqIiW3q3RXBwsPXdjIyMsrIypVJZ+5n5+fncO4qMjLQeCENRVJs2be7fv19fFzauhBr7Vz09PY1GY3l5uVarfdg3BQD8QhAKUUJCwubNmz/66KOnn35ap9NZ2lmWrfNgXg2vvvrqsWPH1q5d27dvX+v2ZcuWzZo1a/DgwWvXrm3atKlcLi8sLBw0aJD1YcUGereFXC63vsswTFhYGLe3swZLQtvyjqzZuBLqfA6L8TIAjyEEoRANHjx4xIgRL7zwQp8+fdLS0vz9/bn21q1bX7t2Ta/XW28Ppaeni0Qiy4iYxYsXr1+/fs6cOa+88kqNxX799dfBwcF79uyxhMSPP/5oe++NEB4enp6eHhERoVar63xCSEjI1atXaZq2bBSyLHvlypUGlmnLSgAAd4LTJwRqwIABKSkpN2/ejI2NvX37NtcYHx9fUVGxfPlyy9PS0tJOnTrVr18/T09PQsjWrVvnzZs3fPjwDz/8sPYyRSIRy7I0TXN3aZpesmSJ7b03wrhx44xG4+zZs2tsit25c4e7MXTo0IKCAutTI7Zs2ZKTk9PAMh+4EgDAzWCLULh69+596NChgQMHxsbGHj58ODg4ePbs2Tt27HjnnXcuXbpkOXPA29t7xYoVhJDs7Ozx48f7+PiMGTOGG0jJ0Wg0PXr0IIQkJCQsWLBg4MCBI0aMKC8vT0pKamBXYe3eG/EWpk+fnpaWtmrVqnPnzg0dOrRJkyY5OTmnT5++cOECd4xw9uzZmzdvnjJlyrlz56Kjoy9evLh+/fr27dvfuHGjvmU2vBIAwA3xOFAHnM9yAoOl5bfffvP19W3evHlWVhbLssXFxTNmzGjZsqVUKm3SpMno0aOvXr3KPfP06dN1foQ6dOjAPcFkMn3wwQetW7eWyWRBQUEzZszIzs4mhLz++us29v5QlXNomv7qq6+6du2qVqsVCkWrVq2GDRu2ceNGyxNu3749evRorVbr4eHRs2fPn376qX///t7e3pYn1Bg12vBKqK+SKVOmEEKKi4sbfhcA4IIoFof3QWBCQkK0Wu25c+f4LgQAXAKOEYKb0+v11ne3bt2anZ09YMAAvuoBAFeDLUJwCSzLGgyG+h6VyWQiUSP/aOvbt29wcHBMTIxUKj179uw333wTEBBw7ty5Jk2aNLZYAHArCEJwCenp6ZGRkfU9+t13340aNapxS/7kk082b9588+bNyspKf3//AQMGLFy4sHnz5o2tFADcDYIQXIJer09PT6/v0datW3t7ezuzHgAQDgQhAAAIGgbLAACAoCEIAQBA0BCEAAAgaAhCAAAQNAQhAAAIGoIQAAAE7f8BKoaQyZdY+OUAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 180,
       "width": 300
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data, aes(x = kozak_region,\n",
    "                 y = mean_ratio,\n",
    "                 color = insert_sequence)) +\n",
    "  geom_point()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting a line graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 180,
       "width": 300
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data, aes(x = kozak_region,\n",
    "                 y = mean_ratio,\n",
    "                 color = insert_sequence,\n",
    "                 group = insert_sequence)) +\n",
    "  geom_line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting point and line graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 180,
       "width": 300
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data, aes(x = kozak_region,\n",
    "                 y = mean_ratio,\n",
    "                 color = insert_sequence,\n",
    "                 group = insert_sequence)) +\n",
    "  geom_line() +\n",
    "  geom_point()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ‘Faceting’ – Plotting in multiple panels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 180,
       "width": 360
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 6, repr.plot.height = 3)\n",
    "\n",
    "ggplot(data, aes(x = kozak_region,\n",
    "                 y = mean_ratio,\n",
    "                 group = insert_sequence)) +\n",
    "  geom_line() +\n",
    "  geom_point() +\n",
    "  facet_grid(~ insert_sequence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In-class `ggplot2` exercises\n",
    "\n",
    "(20 min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Make X, Y, legend labels into nice strings\n",
    "\n",
    "See https://ggplot2.tidyverse.org/reference/labs.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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K8OCOUJYCAGgMqFIBANjFUpZSIkrvBAcM03q5OhwAgIeBFg4AQMNkBT2Tk3+ZF6bovZ+C2VIAAI0HCQcAoGFvFRYfqDL0UbMfhAS5OhYAgEeChAMA0ICvyys/KSkLp8j1UJYCAGgqSDgAAPX5xcjNzyv0wvFNkWFQlgIAaDLoNAoAuKNswTztZo6oKGsjQqAsBQDQHJBwAADqViPLj93MLRGl/0BZCgCg2eCWCgCgDpaylEscP1nvPQfKUgAAzQYJBwCgDu8UleyvMvRWs/8LhbIUAIADQMIBAKjtm4rKFcWl4RS5PiKUxqAsBQDgAJBwAAD+5hej6YXcQi8c3xQV7kdCWQoAwDEa12m0qqoqMzMTIRQVFeXt7e2UiAAArpMjmJ+4mSsqyprwkI4M7epwAACth70tHGlpaQ888EC7du2SkpKSkpLatWs3fPjw9PR0pwYHAGhJnKI8mZ1XLEpvBPkP94ayFACAI9nVwnH16tW77767vLy8X79+iYmJCKGLFy8mJyf369fvzJkzsbGxTg4SAOB0CkL/yMk/b+ImtdPN8/NxdTgAgNbGroTjjTfeMBqNycnJw4YNsy784YcfRo0atWjRok2bNjktPABAC1lSWPJtZXUvtWppSKCrYwEAtEJ23VJJSUmZO3eubbaBEBo2bNgzzzyTkpLinMAAAC3nu6rq5cWl4TS1ISIMylIAAM5gV8JRUVERFxd3+/K4uLiKigpHhwQAaFGpJu4fOflqHN8YEQplKQAAJ7HrlkpISMiJEyeeeeaZWstPnDgREhLiwGhwHCdaanYogiAURWmZYzUHhmGoZd+Z5sBxHHlatBiGeUS0Vo6NtsAsPn4zl5eVDVFhiRq1A/ds+dP1lPfW+kFzdSB28awPGgAWdiUcY8eO/fDDDzt37vz888+rVCqEEMdxS5cu3bRp0/z58x0YDcMwLMs6cId1snxEvbw8oxO+5cqiVqs9KD1SqVQ07QEVlZZoKYrSarWujsVeBEE4MFqTLE+7cCnfLL4bFTEpLNhRu7XAcRzDME95b62XBY/4oFkuCyzLWi7IAHgEzJ5PV0VFxT333HPx4kUvL6/Y2FhFUa5du2YwGBITE3/88Ue9Xu+oaCorK81ms6P2dic6nY6iqNLSUo+4smg0GpZlW+adaT6GYbRarcFg4DjO1bE0jCRJvV7PcZzBYHB1LHbx8/MTRdFR9zEVhJ7KzttdWT1Rr/skzPHjl+v1eoIgSktLHb5nZ9BqtQzDlJeXS5Lk6lgaxrKsRqOpqqoSBMHZx/Lz83P2IUAbYVf7oV6vP3Xq1OLFi9u3b5+RkXHt2rX27du/+eabJ0+edGC2AQBoSe8WFu+urO6lVn0YCmUpAACns3ekUY1Gs2jRokWLFjk1GgBAy/iuqnpZcVk4Ta2HshQAQIvwjB5SAAAHspalfBUR6g9lKcAVfv31VwzDZs2a5epA0JUrVxYvXnzhwoUGX5mfn79w4cIuXbpotVqdThcfHz9p0iQYicp+d2zhWLduHULo8ccfJwjC8vhOpk+f7uCgAABOUyiKj9/M5WRlVXhwZxXj6nAAcLErV668+eabsbGxXbt2redlV69e7devX3l5+cMPPzx58mSCIK5du3bo0KH09PRHH320xaL1aHdMOJ588kmEkOVttTy+E0g4APAUnKJMu5mbbxZfC/QbrfOM+hEAnMRoNKrV9paCL1mypKSkZPXq1TNnzrRdDnOK2e+Ot1QOHjx48OBBS3HjwXq1YLQAgKZTEHo2J/+skRvlrX3W39fV4QDwl+3bt2MYtmPHjvfeey8+Pp5hmIiIiHfeece2llAUxffffz8xMVGr1Wq12ri4uOnTp1dXV9u+YOnSpd26dWNZVqvVDh48+Icffqh1iG+++ebNN9+Mi4ujafqtt95avHjxyJEjEUKPP/44hmEYhg0ePLjOCDMyMhBCo0ePrrU8ISHB9mn9MSCECgsLn3jiCR8fH41GM2jQoBMnTowfP962vHnZsmUYhv3666+2W40ZM6bWUA72nGz976dlJ8uWLevRo4dGo9FqtV27drXtqdnguTTWHVs47r///jofAwA81PtFJbsqq5NY1cdhwdBNFLihl156KT4+fsWKFXq9fvXq1a+99pqvr++cOXMsa1955ZUPPvhg6tSpzz77LI7jWVlZe/furaqqsoz1IknSqFGjkpOTJ0yYMGvWLI7jNm7cOHz48E2bNk2ZMsV6iH/961+hoaFLliwJCgqiKCooKIhhmFdfffXVV18dOnQoQuhOpZexsbE///zzxo0bn3/++TvF32AMBoNh0KBBGRkZs2fP7tGjx7lz54YNGxYREdHYN8rOk63//RRFccSIEcnJyYMGDXrjjTe8vb3T0tK2bdv25ptv2n+IRrGrSmXw4MHLli3r1q1breWHDx9+6623jh492rRjAwBazN4qw/+KSoMo8quIUBaHfAO4Ix8fn3379llG5Ovdu/ePP/64YsUK6xfkjh077r33XttOmm+99Zb18cqVK/fv3//ll19a+wA899xzffv2feGFFyZMmECSt77saJo+evSo9SlCyDIFeseOHe/UtmHx6quv7tq164UXXvj000/vvffe7t279+/fv0uXLravaTCGpUuXpqenf/bZZ9aT6t69++zZsxmmcb2p7DzZ+t/Pjz/+ODk5+Z///Ofy5cuxP0vVZFlu1CEaxa4qlWPHjtU51lBRUdGxY8eacFQAQEu6YOLm5uQxGLYhIjSYasqVAoAWYLmvYXmM43jPnj2vXbtm/QrU6/WXL1/+5Zdf6tx2w4YNAQEBU6ZM4f4kSdKUKVMKCwtTU1OtL3vyySeb9mUZFxd34cKF+fPnYxj2xRdfzJkzJzExsWvXrsePH7c/hh07dvj6+trW5syYMSM0NLSxwdh5svW/nxs3bmRZdsmSJZhNYbx1dH87D9Eozbr0VFRUwMC6ALg5a1nKF+Ehd7HwgQXuKzw83Papt7e3IAjV1dU6nQ4h9MEHH0ycOLF3794REREDBgy4//77J02aZO31efny5aqqqjonxygqKrI+jo6ObnJ4ERER//vf//73v/9VVlaeOXNm69at69ate/jhh3///XdL5A3GcO3atcTERNuMB8fxDh06/Pzzz42KxM6Trf/9vHLlSmxs7J1m+bDzEI1SX8Jx4cIFa2nywYMHc3JybNeWlZV99NFHHTt2bNqBAQAtwFKWkmcWXw30HwNlKcC9YXWNQWft53jffffduHHjwIEDR44cOXbs2ObNmxctWnTy5ElLC4Esy3FxcRs2bLh9Dx06dLA+buzNizrpdLqhQ4cOHTrUz8/v/fff37Jly0svvWRnDLefY62OnHW+CaIo2j6182Trfz8VRanzBY06RKPUl3Ds3LnT0nkEIbRkyZLbX8Cy7NatW5t2YACAsykIPZdTYClLed7fx9XhANBcWq12woQJEyZMQAht3bp1ypQpK1aseO+99xBC8fHxFy9e7NKlS2Mn5qznS7dBPXr0QAhlZ2dbnjYYQ0xMTEZGhiiK1kYOWZZrFdb6+PgghMrKymwXXr161fZpk0/WVkJCwqVLlwwGQ507ccghaqmvD8fUqVP37NmzZ88ehNCSJUv22Ni7d++xY8cKCgpGjRrlqFAAAI71QVHpzsoqKEsBrUOt7+C+ffvaLpw2bZogCAsXLqzVYJCXl1f/bi1FLrV2fru9e/dWVlbaLlEUZcuWLQihTp062RnD2LFjS0pKvvzyS+uq9evX5+bm2r7YUmd74MAB65KdO3fWSkqafLK2HnvsMZPJ9Prrr9c6KQceopb6Wjji4+Pj4+MRQosWLZoyZUpUVFSTDwMAaGF7K6v/r6gEylJAqxESEjJixIgePXqEhoYWFRWtXr2aIIjHH3/csnbevHkpKSkrV648d+7c6NGj/f39s7OzT548mZqaWn+fg6SkJJVK9dFHH9E0rdfrAwIC7rvvvttftmzZsokTJ95///3du3fX6XTFxcV79uy5ePFi165dn3jiCTtjmD9//qZNm5555pnz58/fddddqamp69ev79ix4/Xr160H6t27d9++fZcvX15dXd2lS5cLFy58++23iYmJtq9p8snamjdv3t69e5ctW3b+/PkHH3zQ29s7IyMjOTn54sWLjjpELXZ1Gl28eHHT9g4AcInfOX5ebgGUpYDWZMGCBUePHl26dGllZWVAQECvXr3Wrl3br18/y1qSJL/99ttVq1atW7fu3XffFUUxKCioW7duS5curX+3Op1u8+bNb7755vPPP8/z/KBBg+pMOP773//u3LnzyJEjK1euLCkpUavV8fHxb7/99nPPPWftuNpgDFqt9scff3zxxRe3bNmyfv36Hj16JCcnL1261DaZQAh98803zz777NatWxVFufvuu48cOfL666/bvqbJJ2uLoqj9+/cvW7bsq6++WrRoEUVR0dHRlttVjjpELVit1pI7URQlJSXl9OnTZWVl1qIai2XLljX58LVUVlaazWZH7e1OdDodRVGlpaV2nrtraTQalmVb5p1pPoZhtFqtwWDgOM7VsTSMJEm9Xs9xnMFgcHUsdvHz8xNFsc4adVtFojjs2s08s3lleMgjrusoqtfrCYIoLS11VQCNotVqGYYpLy+XJMnVsTSMZVmNRlNVVSUIgrOP5efn5+xDtHHjx4/fu3evR1wzm8munz7V1dUPPvigbbWxLQcmHACAZuIVZdrNvFyz+eVAPxdmGwAAUItdA39ZSo+WLFly6dIlhJClx+iwYcN69eqVmZnp3AABAHZTEHout+A3o2mkt9d8mC0FAOBO7Eo4du3aNXHixFdeecUyZIqvr+/AgQP37dunKMrHH3/s5AgBAPZaWly6o6KqK6v6JCwEuokCANyKXQlHbm7uPffcg/4c9NTSmYAgiMmTJ2/bts2p8QEA7PR9VfX7hSWBJJSlAOBJtm/f3hY6cCA7Ew6NRmNJMmiaVqlU1jJcb2/vgoICJ0YHALDPRY6fm1NAY9iGiNAQKEsBALgfuxKO9u3bW0cdSUpKstTqiKL49ddfh4WFOTM8AEDDikTxsaxckywvDwvqrobZUgAA7siuhGPYsGE7duywNHLMmjVr9+7dsbGxcXFxhw4dsk5cCwBwCV5RnriZl2s2LwzwHavzdnU4AABQN7sSjpdffvnQoUOW4TdmzZr1wQcfqFQqLy+vxYsXv/zyy06OEABwRwpCL+QW/Go0jdBpXwyA8RIAAO7Lrnu9Op3OMputxYIFCxYsWGB5bDQarYOsAQBa2PLism0VVYkq5pPQIOgmCgBwZ3a1cNSJ5/kVK1bExMQ4MBoAgP32VVW/W1gcQJJfRYaq8aZ/lgEAoAU00MKhKMqPP/6YkZHh6+s7bNgwjUaDEJIkac2aNf/5z39ycnIsE+kCAFrYHxw/N6eAwrANESGhFOXqcABohOrqamfs1jLvK3Bb9SUc1dXVw4cPP3HihOVpYGDg4cOHGYaZMGHCuXPn/Pz83nnnnX/+858tEicA4C/FovTYzVyjLH8WFtxDzbo6HAAAaFh9CceSJUtOnDjRvXv3oUOHXr16dceOHXPnzs3Ozi4qKlqyZMmzzz5rafAAALSMmxzPKIokKzOzc3ME8wJ/33F6KEsBAHiG+hKO3bt3JyYmnj59miRJhNC///3vJUuWhIWFpaamtm/fvqUiBACgnZVViwqKC8wiQsiHJMpEaYRO+xKUpQAAPEd9Hc1u3LgxYsQIS7aBEHrkkUcQQs8//zxkGwC0pCPVNU9n51uyDYRQmSgRGHotwA+GLwcAeJD6Eg6e5237hPr5+SGELPO3AQBazHvFpbWWSApaX17hkmAAAKBpGj3nAg7VdwC0iHJR+oPnL3H876Y6Jna6ygstHxIAADRZAwnHtm3b0tLSLI8NBgNC6JNPPtm7d6/ta1avXu2k4ABoOyRFuSaYL3H8RY7/g+MucXzen/dQ6uRLwgxtAABP0sA168yZM2fOnLFdkpKSUus1kHAA0ARVknyZ59M5Po3nU8O4yzgAACAASURBVE387xxnkhXrWh1B9FazSSzTgWHOcdzGsspam0+EaVMAAB6lvoTjl19+abE4AGj1CsxiKselmrh0TkgXhCscb80vSAwLpcgEhklimSRW1YFhIum/xvKaqiiVorSnymB5SmPYvwJ87/GCKQUAaITTp0+/9957586dy8zMnDlzZq2fyt9///2///3vtLQ0f3//GTNmLFq0qLH9B6qrq0NDQ0VRzM7O9vX1tX+t0WhcsWLFtm3brly5ghCKjIwcMGDAvHnzEhMTm3Si7qu+hKNnz54tFgcArUytBoyLHG+UZetaHUH0+rMBI0HFJLEqFXbHmhMSw76MCP3FyF3CMBbDeuKoPU23yEkA4DJ4QR52PQOZRSU0TI6JR3f+gNippqYmNjZ2/PjxixYtqrXq1KlTo0ePnjNnzldffXXu3Lk5c+ZIkvT2229b1p49ezYqKsq2hEJRlMOHDw8ZMsR2Jxs3bkxKSmIYZu3atQsXLqx1iDutLS8vHzx4cFZW1ksvvdSrVy9fX9/MzMyTJ09Omzbt3LlzzTxld4MpitLwq1pKZWWl2Wx29lF0Oh1FUaWlpW517nei0WhYlm2Zd6b5GIbRarUGg4Hj6ujn6G5IktTr9RzHWfonNVOjGjAiaKoJl08/Pz9RFCsqPKM+Ra/XEwRRWlq7xMY9abVahmHKy8slSXJ1LA1jWVaj0VRVVQmC0/sOW+oTHavBoc2JoweJn49anypRMebJjyOygSH87RzavFu3bj179rRt4Rg3btzly5cvXbpkefr6669/+OGHRUVFlqlJR44cmZ2dnZKSYnkrFEWZM2fOzp0709LSbNsqkpKSnnvuOYZhFi9efOXKFezvGdKd1s6aNWvz5s0XLlyIjY21fb0kSQRB2HM6HgT6nQHQFNWSfF0Q0jg+leNub8DwJvBearaDiklg6CRW1VWlYmHQDADsg1/PsM02EEJY5jXi2CFpyHAnHfH48eNTp061Ph0+fPjbb7997ty5/v37I4S2bt06YsSIwYMHHzp0yHLDZf/+/UeOHLHNNo4fP37t2rWJEycSBDFv3ryUlJShQ4c2uFYQhK1bt06fPr1WtoEQan3ZBnJ2wpGSknLs2LHMzEye50NCQh5++GHb/wMAXEiQlZ3llZlllXoMG8JQtn0m6lRPAwaBYWEUmcCom9mAAUCbgmVnEb/XcdcAz7px+0Li19MYX0e7qRwcJt/VrLv/siwXFhYGBQVZl1ge5+XlWZ5qNJp9+/aNGjVq0KBBiYmJP//889GjRzt27Gi7k88++2zChAleXl4IocmTJ3/++ee2X3Z3WpuTk1NTU9OlSxfrK2fOnLl+/XrL49TU1M6dOzfn1NyNcxOOw4cPd+7cefTo0Wq1+sSJEx999JEoig8++KBTDwpAgwrM4iOZ2dahLBgM+yAkcHI7nfUFtRow/uD4GmjAAMChsNJi/KzdpQlmoe4Xd+abmXDcie09EZZld+7cGR0dvX379h9++KFWtlFSUrJ9+/aDBw9ans6YMaN///55eXkhISH1r739tv5bb731wgsvnDp1avbs2R5x079RnJtwLFmyxPq4U6dON27cOH78OCQcwOVeyCuwHTiLV5QX8wsFhArMZksDRgbHW/MLSwNGf0adxDIdVEw8TSeoGMgvAGgmpVNXc1TM7cvx0z8Tv5yq/eKgYHH8o3XshGqgbbJBOI4HBgYWFBRYl1geBwcHW5cIgjBt2jRvb+8+ffrMnj37yJEjtoNur127luf5e++917pEkqTVq1e/8cYb9a8NDw9Xq9UXL160rgoNDQ0NDbUNpjVp0T4cgiAEBATYLikrK7t69ar1qeXdd3YYlryVoiiPyB8tpVmecj/PMvMOQRBUs68CzlMpSYeqa2ot5GRlQe6tD7meIPp5abqwqs6sKpFVday3hKSFYRjmzu+tLesHzdWB2MXyQSNJ0iMGU7ZcEEiS9IiL2J0oNI3qqreS7hlCpF1CVVXI5mMnDn1Y0bdzUiT9+/c/cODA0qVLLU8PHDig0Wjuuusuy1Oe58eNG5eenn7s2LGgoKBHH3104MCBR44csXS8UBRl5cqVL7zwwowZM6w73LJly6pVq/7973/jOF7PWpqmJ0+evG7duvnz59/ejaP1abkqlZSUlE8++eT999+Pi4uzLjxy5MiLL75offrpp5/27t27ZeIBbdZNjo889evtywfrvReEh3bVaCJUTMtHBUDb0WCVClZSTCTvxbOuI1lW/AOk+x6Q4zo0uNv6q1RMJlN6ejpCaNKkSZ07d37jjTcwDEtKSkIInTp1asCAAXPmzHn66afPnz//9NNPz58/31oWO3bs2EuXLh0+fNhyi0SSpCeeeOLIkSMXLlzw9fVNTk4ePnx4WlpaQkKC9VjZ2dmRkZG7du1SqVT1rB09enRZWdnAgQNzc3MtZbF+fn7Z2dmrVq3au3dvWlpafHx8g2ftQRqXcMiyXF1dXWsTvV7f4IY//fTT8uXLn3322YEDB9ouv379+vfff299+tBDD1n+R52KYRgcx00mk7MP5BAURZEkyfO8bNOHwG0RBEHTtNlsFsX6huV2LU6WQ367wN32fq6PjZ7g66yfUA7BsqwsyzzPuzoQu3jWB42maYIgOI7ziDYDkiQpihIEoQWKeFmWdfg+G0w4bpEkTDQrjMrO3dafcJw/f97aaGFBEIT1SrV3797XXnvt8uXLljqUxYsXW9u6Tpw4ERMTExgYaN1QluU9e/aMHj0aITRmzJisrKzbx8wYMGCApda6nrX79+9HCNXU1Cxfvnz79u3p6emiKIaEhAwaNGjevHm9evWy88Q9hV0JhyzLK1euXLFixfXr128v+25wD/v371+zZs3ChQv79u1b/ythHI7bwTgcjpUtmOfk5J8x1v4W7KFm90SHU25z66ROMA6H88A4HHfiknE4msbOcTiAq9jVh+Ptt99etGhRXFzc2LFjdTpdwxvY2Lp1686dO19//XVLyxUALvRdVfWC3MIKSXrYW9tHrfq8tDzPLKpwfJS3dnGQv5tnGwAA4NHsSjhWrVo1c+bML774orHdqVatWrVv376nnnpKq9Vev34dIURRVHh4eFMiBaAZDLK8qKB4Q1mFCsPeCQ54yrcdQuifQQGKlxdtNptqavchBQAA4Fh2JRyFhYWzZ89uQufto0ePSpL02WefWZcEBQV98cUXjd0PAM1x3sQ9nZ1/XRA6qJgvwkM6MjRCCDOZqN/P0ZUVMssS7eOkkDBXhwkAAK2ZXQlHREREZWXt2bHtsWnTpiZsBYCjKAitKi1/s7DYLCuzffWLgwJoDEMI4WWl6s1fYiaTjBBSkPrkT/zA+4Q+A1wdLwAAtFp2NVrMmDFjxYoVHtHFEgCrYlGanJnz7/wiLYZvigxbEhxI/9lLgz3wHWatnsAQQoj58TBRXOiiSAEAoPWzq4UjPj5+zZo1ffv2ffTRR8PDw2tNgjdmzBjnxAZA0+2vMjyfV1AmSoO8NB+HBgVRf/2pYyYTnpt9+ybktQzJP/D25QAAAJrProRjwoQJCKHr16+fOXPm9rXQ8gHcCq8obxYUrS6tIDHsxQDfhf5+teY5wSrK6tyQ/P2cQpJiVIzs598SgQIAQFtiV8Kxbds2Z8cBgEOkc/xTOfmXOD6Wob8ID0m0HTNUUcgb16jzv5I3rta5LV5Rzhz5gUFI0XqLUTFSdIwYEa04YdQjAABog+xKOMaPH+/sOABovq/LK1/MLzTJykS97v2QAM2fdVWYsYb6/TyV+hteWYEQkn39pMAQ6tIF223F9rHcAyOJzOvk9Qwy6wb1+znq93MIw6SAICmqvRgRLYVHIg+Z0QYAANxQi07eBoCTlEnSszkFydUGbwJfFh40VudtWU4U5tO/nSbT/kCSpBCEOaGTuWt3Kao9Qkjs1IU5+RNeWozUXnxCR3OfAQpFiV2SxC5JSJaJ4kIi8zp58waRnUUU5tOnjysULYeGie3jzLEJiq7h4fwBAADYsjfhUBQlJSXl9OnTZWVltSb1WLZsmRMCA8BePxmMc3PzC8xiTzX7eVhwJE1hPEemXaLPncGLixBCcjsfc+Jd5q7dbe+PiNGxKK6DXq/nOE4wGP62RxyXAoOlwGChT3/MZCJv3iCyrpM3rhGZ14nM68zhZFmnl6LaS5HtxagYhYGZ3gAAoGF2JRzV1dUPPvjg8ePH61wLCQdwFVFRlhaX/q+oFMewFwN8F/j70kUFVOpv5B+/Y6IZWZs0IqNRU4ctV1jWnNDJnNAJIYRXVhCZ14ms6+SNq1TqWSr1LMJxKThUjImXotpLAUFNPgoAALR6diUcixYtOnny5JIlS8aMGdOpU6e9e/dqtdp33nmnvLwc+pMCV7lpNs/Jzv/FaAqjqc8D/QZkXqX27yCKChFCilYrdOot3NVL0Xo78IiyTi8ndTcndcdEEc+5Sd68QWReJ/JyiNxs9OMhRa0RwyOlyPZiTJziBZNIAQDA39iVcOzatWvixImvvPKKZQpQX1/fvn379u/fv2/fvh9//PH//d//OTlIAGr7urzyX/lFNbI8SkV9nH3Vf983mFlAGCZFtTd37W6O64AaPxK//RSSlKLaS1Ht0cAhmLGGzM4ir10hrmdQ6Zeo9EvQ1RQAAG5nV8KRm5t7zz33IIQs06lY5kknCGLy5MkfffQRJBygJVVL8kv5hdsrqlQIvV9489mzJxFCisZL6N7L3K2n7N242YybT1Frbt1zURSiqOC2rqaUHBouRkSLsQmyr+On+QYAAE9hV8Kh0WgsSQZN0yqVKi8vz7Lc29u7oKDAidEB8Hdnjdycmzk3RKlHdcX6c8djjTW3mjRiE1zfkIBhf3U1NQt4bg557Qp17cqtrqY/HrLpatpeYVQujhYAAFqWXQlH+/bt09PTLY+TkpK2bt06ceJESZK+/vrrsDCYYxO0BEkUP8/IeEfERITm3cxYciMdj+9Q072Pe44KqlC05Z4LP2T4X11NM6/91dXUP1CMiZdi46GrKQCgjbAr4Rg2bNiXX365fPlyiqJmzZo1e/bs2NhYWZYzMzPffvttZ4cI2ji8orzwYupTGP2zzjfAzK3MvnpvVLT5gQcV0jNGkbF2NUWyTOTnkpY2j6ICojAfnTimsGoxIkqKbC+2j1O00NUUANBq2dWx7uWXXz506JBl+I1Zs2Z98MEHKpXKy8tr8eLFL7/8spMjBG2VohCZ11XfbT+4Z/cAje/POt8hvPHH0MABo8eZk7p7SrbxNzguhYbzA4cYp802zJ3PjRpvTuqOcJxKv6T6Ya/X5x9q1n7G/HiIyLyOSVLtbRWFvJ4h/rBP+fEQUZjviugB8GynT58eO3ZsdHQ0hmGzZs2qtfb777/v1q2bSqUKDw9ftGhRreGm7FFdXe3t7a1Wq0tLS2utMhqN//3vf3v06KHVarVabZcuXebMmfP777/buXmrYddVW6fT6XR/9cVbsGDBggULnBYSaOuwGgN1MZU+/ytXY3g5oesndyWqEHonwHd2QEKruffwV1fToX/rakqfPk6fPq6QlBwWLkZES1HtpcBgTJLYnVuIzOuWNESNkNB3AH/PfS4+BwCcRlbQxtKyQ1WGGlnqqVbPDfT3Jppbd1ZTUxMbGzt+/PhFixbVWnXq1KnRo0fPmTPnq6++Onfu3Jw5cyRJsrbfnz17NioqysfHx/p6RVEOHz48ZMgQ251s3LgxKSmJYZi1a9cuXLjQury8vHzw4MFZWVkvvfRSr169fH19MzMzT548OW3atHPnzjW4eWuC2T/XqyiKqampRUVF/fr10+udMrRzZWWlpXeqU+l0OoqiSktLPWKeW41Gw7Jsy7wzzccwjFarNRgMlgrqxlEUIusGdeEslZGGZPmizmdar4GXKCaeob8ID+mscvyAniRJWkYaNdQaadRFMLMZz80mb94gr6bjpSWWhbJOrzCMZXwRW6ZxU8X2sS0eYyPo9XqCIDzl55pWq2UYpry8XLq9ecn9sCyr0WiqqqoEQXD2sfz8HF9dVV1dXc9aBaFJVzMPVFZZl4TR9LEOsf5UA7+QtfbdlOzWrVvPnj1Xr15tXTJu3LjLly9funTJ8vT111//8MMPi4qK1Go1QmjkyJHZ2dkpKSmWt0JRlDlz5uzcuTMtLc3X19e6k6SkpOeee45hmMWLF1+5cgX7s2/WrFmzNm/efOHChdjYv31gJUkibLq632nz1sTenHHLli1hYWE9e/Z86KGH0tLSEEJ5eXkBAQEbN250ZnigTcCMNfSpnzWrPlJv20ilXxJ9/JYNG313/2GXKGaiXpcSE+WMbMMNKRQlRbXnBw6pmTG3ZtY8bshwMSYeM9bcnm0ghKjLF1s+QgBawObScttsAyGUIwiv5OQ574jHjx8fPny49enw4cNramqszQ9bt25t167d4MGDCwsLZVl+8sknd+/efeTIEdts4/jx49euXZs4ceLYsWOLi4tTUlIsywVB2Lp16/Tp02tlGwgh22zjTpu3MnbdUklOTn700Ue7d+++cOHCF1980bIwJCSka9euO3bseOyxx5wZIWi9FIXIzqJSf6My0pAkKQQpdu6a27XHPyTsYHWND44vDw0a7u3l6ihdQ27nK7fzNXfvjSTJ6+MPMIGv/Qq+8W1IALiT7yuq3suvI5nO4utottlZXnmFy7h9+RBv7aLQoOaEIctyYWFhUNBfO7E8tg4AodFo9u3bN2rUqEGDBiUmJv78889Hjx7t2LGj7U4+++yzCRMmeHl5IYQmT578+eefDx06FCGUk5NTU1PTpUsX6ytnzpy5fv16y+PU1NTOnTvXs3krY1fCsWTJkm7dup06dUoURWvCgRDq16/fpk2bnBYbaLUwk4n6I5VKPYuXlSCE5Ha+5m49hM5dj0nKvJyCQlEcoFF/GhYc3FALaptAELJ/AJGbXWux7APDiAHPZpDlzLpyi5q6OmxKilLni4tF0fGRIYQQsr2pwbLszp07o6Ojt2/f/sMPP9TKNkpKSrZv337w4EHL0xkzZvTv3z8vLy8kJOT2G/dvvfXWCy+8cOrUqdmzZ1vW1rO5k07NVey6oP/2229vvfUWSZLi3/9rIyIi8vOhwzxoBKIw/6/J1XBcjIkXuveWIqMFhN4rKv24+K9p2IjWeAuzafhB96s3r621kMy+IRhrFLXGJSEB0HyTfPSTfOroDvhJUckr2bVvoAzTabfFRjsjDBzHAwMDbQextDwODg62LhEEYdq0ad7e3n369Jk9e/aRI0eio/8KZu3atTzP33vvvdYlkiStXr36jTfeCA8PV6vVFy/+dQM0NDQ0NDTU9nD1bO7oc3Uxu/pwSJLE1DUHd1FREUVRjg4JeLi6fp1ggkClnlWvX6nesIpKPYtYldCnv2H2P01jJ0tR7TME8/BrWSuKS0Mp8rvoiJcC/CDbsCWFhpvGTZX9AhBCCMelyGgxOhYvyFdvXGNpIgKgNZnt55ukZm2XeOH4e+Ghzjti//79Dxw4YH164MABjUZz1113WZ7yPD927NiLFy8eO3Zs9+7dvXv3Hjhw4NWrVy1rFUVZuXLlCy+8cN7Gq6++umrVKkmSaJqePHnyunXrrK+vpf7NnXfKLmFXlUpSUlKnTp22bNnCcRzLsidPnuzbt6+iKH379iUI4sSJE46KBqpUbucxVSqyTP92mj57BquqRN46PqmH0KsfIgi8tIQ6/yv1+/lbk6tFRteaXO3r8sqX8ouMsjzKW7s0NFDXgiOUu1uVSoP8vDQiwioMBqQo9IkfmRPHFBVrGj1BiohydWh1gCoV52ndVSoIIYMsf5BflFJZZZDl3hrNKyGB0Qzd4G7rr1IxmUyWIbMnTZrUuXPnN954A8OwpKQkhNCpU6cGDBgwZ86cp59++vz5808//fT8+fOtZbFjx469dOnS4cOHLfc4JEl64oknjhw5cuHCBV9f3+Tk5OHDh6elpSUkJFiPlZ2dHRkZuWvXrtGjR5eVlQ0cODA3N9dSFuvn55ednb1q1aq9e/empaXduHGj/s0bPGsPYlfCsXTp0hdffHH16tVTpkyxJBxdunRZuHDhypUrV65c+dRTTzkqGkg4bucpCQdz9CD9y0nbJVJUDEIKkXkdIaRovMxdkmpNrlYlyQvzCnZVVmsJ/L3gwAl6R04lbw/PSzj8/ERRrKiosDylfj+nOrgPIWQaNkLskuTS0OoACYfztPqEo2nqTzjOnz9vbbSwIAjC2k9g7969r7322uXLl/39/WfMmLF48WL8zx9FJ06ciImJCQwMtG4oy/KePXss2cCYMWOysrJsR9SwGDBggFar3b9/P0KopqZm+fLl27dvT09PF0UxJCRk0KBB8+bN69Wrlz2btxp2JRyiKI4aNWr//v0BAQFFRUVxcXFZWVmCIIwcOXL37t244+YBh4TjbxSFunhedeUyVmMQffz4Xv2kwOCGt3IFrKrSa+XyulZgUmS0OamHOSa+1uRqvxpNT2fn3zSb72JVK8NDomkX3Jvz9IQDIURmXlN9tx0TBL7vPUL/QW41LQskHM4DCUed7ByHA7iKXZ1GSZLcs2fPypUrN2zYwHFcfn5+ly5dpk2b9o9//MOB2QaoRXVwH5X6G0JIQYgoLFBfvmgc/6gUHePAQ2A8h2QFE3gkSZhZQKKIzGZMEDBFRjyHZBnjeUySkGhGZgGTJMTzSJZxgUeSjAQekyRkFjCzGYl1p4n8vQ8IPXrXWigqytLi0qXFZbKizPbVvxkUQLnT16RnEaNiTFOfZHdsYU7+iFeU8w+OUlw+ay4AANTF3rJDgiDmzp07d+5cp0YDrPDcbEu2YYtN3lPzxFNIEDBFwXhOkWRM4DFJxEQRmQUkyRhvsk0gMLOoSCIm8JiiIJMJyTIm8JgsIbOImQXUzF9yGKYwKoTjCkUhisbq+qUl2wwGbJEtmJ/JyT9tNIVQ5GdhwXdr1M2KASAk+QXUPDaT3bmVuvw7XlVhfGQSYuFdBQC4HRjnwE2RuTdvX4hVV3l9/EGT96kwKoRjCs0gNS1TekSSiKIUilZwHFlSB4ZRcAJRFKJohSAQwyAcl2kGETiiGYUgEEUrFIVwXGFUtq33mCRqVn2MVf9tcEBFrZHCImyX7KkyzM8tqJCkh7y1H4YE+pDwW9wxFI2XacoTqr27yIw0zcYvTeOnyO18G94MAABaUH0Jh53TYahUKgcFA2xgdd+rEiOiFZZFFI0I/FYDA80gglAoWiFJjCQVmlEwDLGsZZWCE4giFYpGzmxpVwjSNOIRdudWjLcMiKkoFGN6eIxC3epYbpDlRQXFG8oqGAx7JzjgKd92zgumbVJIyjR6AnP0IP3rKc2mtcYxE6SwSFcHBQAAf6kv4WBZtp61Vu7e9dJD1dWtQdb7mCY+5lYdA62ksMiaWf9g0/6gDdVmL60xvqPidasD13kT93R2/nVB6KBivggP6WhHeRtoCgzj7x0m+/qrUvapv9nonqUrAIA2q4FbKiqVyjLYRstEAxBCmCTRRw/S535RcAyT/0rmFILkHhrtntmGhaLWSP3uIbVazmBQOA4hpCC0qrT8zcJis6xM89G/ExygcuP4Wwdz17tkb2/2ux3sge+EshL+nvvc+W8GANB21JdwxMTEXLt27cqVK9OnT58xY0ZMjCPrI0Cd8LJS1Z7tRFGh7OPLjRyPVZSrMi4TNQazjx/Xo7eb35gvFqWdFcV5BcXBGDZGwxIY9o+c/MOGGl+CWBEZNEzbRqdha3lSVIxxynT1rq306eNYRTn/0BiFhN5aAAAXq28cDkVRjh49umbNmp07d3IcN3jw4JkzZ44dO9bOWy1N0MbH4SAvpqpS9mNmQezclRv6kKUDhKcM/HXKaHo0K6dKujWuOYthDI5XSNIgL83HoUFBbjkNWysYh6MeWI2B3bmVKMiTQ8ONj0xSWrx0BcbhcB4Yh6NOMA6Hm7Nr4K+KiorNmzevWbPm7Nmzer1+6tSpM2fO7N69u8OjabMJB2YWVAf3kX9cUGiau/8hsXNX6yqPSDh4Relz5UbubRE+5+/zaoA/7q4t+q074UAIYWazau9O8mq6rG9nGjelhSeYhYTDeTw94QBtk13Ddun1+rlz5/7222/nzp177LHHtmzZ0qNHjw8+aHp9JrBFFOar168i/7ggBQYbpz1lm214ipMG0+3ZBkKon0btttlGW6BQlGnMRP7uQXhFuXrzWiI7y9URAQDarsY1dMfGxnbr1u3UqVO//vqrp/wodHPkxVTVwe8xURQ7d+WGjfCIe+2VkpTBC+m8kMEL6Tx/hROy79D6YpDqmDkWtCgME/oPUrTeqpR96m0bTQ+M9MSMFgDQCtj79Xb8+PE1a9Z88803NTU1/fr1W7169aRJk5waWauH8RyTvJdKv6QwKu6hMeaETq6OqG4lopTG8xm8cIUXrvD8FV4oMIu2L9ATRFcVk2ri0W2NGZ1VTMsFCu7M3PUuWevN7tnB7v9WKC2G0hUAQMtrIOEoKCjYsGHDl19+mZ6eHhAQMGfOnJkzZ3bs2LFlgmvF8Lxcdu8OvLJCDgk1jRgn6/SujuiWCklK54V0jk/j+Su8+TLHF4m104vearaDiomkyAQV04FhImgKQ2hRQfGnJWW2r5zh2y4WhtxwG1J0jGnqdHbHllulKw+PUQgPaE4DALQa9XUaHT169L59+xRFGTZs2MyZM0eNGkVRzp3S02QyOXX/FgzD4DjeMseqg6Jgp49jB/chWVZ6360Me7j+MUApiiJJkud5WXb87Yl8wXzZZLps4iz/LhpN1X/vMRdEUZ3UbBRDd2RVHVlVF7U64A71JmZF+Si/6PPC4hxBCKappwL8nw8OYNx7bj8cxxmGEUXRnTvk2mJZVpZl/tZwrk1iqMY2r8PyclB4pDzlCaTWOC662lz8QWskmqYJguA4zn36kteDJEmKogRBaIEurs4rSwRtTX0JB4ZhKpVqgB+PDQAAIABJREFUzJgxoaGh9ezCgb1HjUajM75Wa2FZliCImpoaF1xZagzkt9uwaxlI4yWOnqDExDW4BcMwFEWZTKZmXlnMipIrmC9zXJqJu2zi0zguneOMNn0sSAwLo6lohunAMh1Vqg6sKlHNejUyYyBJEqNpZDZ7xFc4juNqtdpsNjfrK7wFeXl5SZLUzK9wzGzGd27Fr1xW2vlIU6Yrvs6qQVCr1RiG1dTUOGn/jqVSqUiSbJlLUPPRNE3TNMdx4t8bIJ3BywtG0AGO0UDCYc8uHPi13brLYonM6+y+3ViNQYpqb3pojKKx62NsVqnKMdxfEDCpEVcWQVauC8IVQUjj+HROyDKbL3O8YHO+FIaFUGQCw3RQMfE01UHFxDMM2+ySEoZhtFqtwWCwcyIe12r1ZbF3JMvMz0fo08cVljWNniiFO2XWFSiLdR4oiwWeqL6buL/88kuLxdHKyTJ98ifm5I8Iw/i7Bwl3D7Sny162YF6YV3jYUIMQYnD8GV/9vwL8yLo2rJSkTMGcxvFXBHMmL6QLQgbH2/5MozEsmqETaDpBRXdQMfE0HcfQBHQbbLNwnB84RNa3Ux3cx27byA0fKXaC0hUAgHPVl3D07NmzxeJoxbDKCvX3u/DcbEWnNz78iBwabs9WgqJMu5l7kbvV1M/L8rLiMhLD/hXgZ+3XmWUW0zg+nedvCmbbthodQSSyqgSGSWAoS7/OcIqC8TBALeau3WWtjt2znd33LV9ebmceDAAATQPd1J2LunKZSd6DcZw5viP/wEhFpbJzw/1VBmu2YbW0qHRVaXnl3we3CKLIgV6aOIZOYOg4hk5gGD8SJtsDdpGiY0xTn2R3bGFOHMPLSviHRkPpCgDASeDi4iyYJNJHU+izZxSS5O97QOjRp1GbXxfq6MsiI6TBiV5qtgPDxDF0PEPHM4w34daVIMDNSX4BNY/NZHdsodL+wKurTI9MVqAqAQDgBJBwOAVeWqL6bjtRUiT7+nGjxkt+AY3dg29daQSG0JHYSJ96y2gBaCxF42WaMt0y64p64xrTuCmyj1vPSwwA8ETw49jxyIup6g2riJIisXNX47TZTcg2EELDvbXMbTfUh3t7QbYBnEGhKNPoCUKf/nhFmXrzl0QOzLoCAHAwSDgcCeN51Z4d7P5vEYFzI8eZHhqjkE0cKu3jklJeUWwLSbqrVR+GBjkoUgBug+P8wCHcsBEYz7PfbCQv/e7qgAAArQrcUnEYoiBPtWcHXlEuBYVwI8fJ+nZN3tWSwuLPSsrb0/SmqNAMGZUgFI2UuxkGKk2As5mTuis6neq77ey+3Xx5GZSuAAAcBRIOR1AU+uwZ5lgKkmWhe29+8ND6Ryuv3xel5R8Wl4VS1PaosHCaStJoWJZtmSHRAEAIiVE2pStVFdywEc35ewYAAAtIOJoLMxlV3+8mb1xVWDX38BgxOrY5e9taUfVafpEfSViyDUcFCUCjSH4BNY/OYHdupS6m4uVlULoCAGg+6MPRLMTNTM26leSNq1JEVM30p5uZbeytMjyfW+BNENuiwmGeVeBaipfWNOUJMSaeyM1Wb1yDl3vGCOUAALcFCUdTyTJ9/Jj6m68wYw1/9yDjxMcVL21z9pdcbXgqO4/BsM2RoV1UjKPCBKDJFIo2jZkodO+NV5RpNq0lcm66OiIAgAeDWypNgVVVsnt3ErnZirfONGKsZN9o5fX4ucY482YehqF14SG91dB2DdwGjvNDhst+AaqUfew3X/EPjjJ3THR1TAAAjwQJR6ORGWmq5D2YySTGdeAeGNn8e9tnjdzjWbkyQuvCQ+7VahwSJAAOZE7qrnjrVHu2q77fjZWVCf0HuToiAIDngVsqjYBJInPoAPvtNiSY+fseMI2e0Pxs4zIvTLmZY5Tlj8OChmntmrAegJYnRseYpjypaL2ZE8dU+79FnjCHOwDArUALh73wslLVnu1EUaHs48uNHC8FBDZ/nzcE84Qb2eWi9H8hgWN13s3fIQDOI/nblK5UVZlGT7B/MkIAAIAWDrvcGq28qPDWaOWOyDbyzOK4GzcLRfH1QP8nfPTN3yEAzvZX6crNG+qvVuPlZa6OCADgMSDhaAAmCOz3u9n93yIMmR4eY3pojEI5oGC1VJImZGZnm8UF/r7/9Pdp/g4BaBl/lq70wivKNJu+hNIVAICd4JZKfYjCfNWeHXh5mRQYzI0cJ7dzTGZQJckTM3Ou8MJM33YvB/o5ZJ8AtBwc54c8qOh9mKMHoXQFAGAnSDjuQFHos2foYymYLAvdewuDhyoOGt3ZJCuPZuVcMHET9bolQU2ZSBYAdyD06CP7+Km+2walKwAAe8AtlTpgJhO762vmcDKiadMjk/ghwx2VbQiy8uTN3FNG00Pe2uWhgTAZG/BoYnSMaeqMP0tXvoPSFQBAPaCFozYiO4v9fidWXS2FR5oeHqtomzV+qC1JUZ7JyT9kqBnkpVkVHkzCJJzA890qXdmxhbp4Hq+uNI2C0hUAQN2ghcOGotCnj6u/+QozGIQ+/Y0TH3dgtqEgtCCv8Luq6l5qdn1ECA3ZBmgtFC+taep0MSaeyLqh3gilKwCAukELxy2YsYb9fjeReU1Ra0wPj5GiYhy7/zfyizaVV3ZWMZsjQzU45HmgVbGUrqiO/ECdPaPZ9KXxkUkYZ5IL85EsU77+5g6dEWTYALR5kHAghBCReY39fjdmrJGiYkwPj1HUDh5f/J3Cks9Ly9vT9LaocL2DuoMA4F5wnBsyXNa3Y478wG5ZhymKgpCEkAoh6vyvpomPO6ojFADAQ7XJn9qShHj+1mPLpK/bN2Ocib97kHH8VIdnGytLy5cVl4ZS1PaoMH8SrrmgNRN69BG69cAUxXYhkXOTOvmjq0ICALiJttXCgZeXMocOyDczeUlS+/iZe/YhU88Shfmy3ocbNU4KDHb4EbdUVL2eX+RHEtujwsJpyuH7B8Dd4JWVtRcpiMpIEwbc64pwAADuog0lHBhnUn+zEau6dTXEy0qYH75HCJk7JvLDHlZoB4wfWsv2iqrnc/K9CWJbVHgs4/j9A+CGMLP5tkV1LQT/3969x0VV5n8Af86Z2zkz3BnkqnJJvCOWaaWhqSuQiRdAJdKo3GrXX7tlVr/Uflub2u7WS63ddtc7ppiGUkSJIpqaypaWecsUBZKLykUYHOZ6Lr8/pmYJEYdkODPM5/3XzONhzmeOZ+Z855znPA+Ah/GgSyrKE8fs1Yad4O1jemS6M6qN3c36P1RfVdN0bmTEEEbV5a8P4JranWmI74Ux7gA8nQcVHFR93c2NtKHFGev6Um+YV1kjI2RL3/DhLIYlAA9iuf9BUePVppEymiiOkyQPALgIDyo4iKqd0wyiquurgW8MxrmXqwVC1vcJG61Rd/nrA7gykVUbHs3iYgcQhiUKJd8nUggLl1VdZndspawWqdMBgGQ8qA+HdcAQxclv2zYOHNK1azlntmRcrjYIwr97h03ybvs7D8ATCH4Bxqkz/fz8ZDTdcP064Xm2YKe89Ad22wfGtEyRZaUOCAAS8KAzHHyfyDbzS/ERfSwJE7pwFeUWa3p5ZRPHvx0WPN23y0YpBXBXtvG+ZDJjSho3KE52tYb9aDNlNEgdCwAk4EFnOAgh5gfGcv0GaGqqaM7a4hdgje7XhQMg1li51PLL1zjuTyFBcwP8uuplAXoCmjYmpzA0rTjznfrDbMPMOaIXKnIAz+JZBQchhA8Kpu6KlSsUXEMD+eXwRHeigefTKyorrdxLvQL/RxvQVS8L0HPQtClpiqhUKL89pt660ThrruCLuhzAg3jQJRXnaeaFmRVVF8yWeYH+L/fSSh0HwFVRlHl8kmXEfbSuSb1tE92Ead4APAgKjjtlFMTMH6tOGU2z/H2XhWCwAYAOUZT5oUnmhAlUs069NZuuq5U6EAB0ExQcd8QiiFmXq/9jME728V4VFkxjRkwAB1hGjTYnTKBa9OqPNsvqrkkdBwC6g3P7cFy4cGHnzp2XLl2qra39zW9+89xzzzl1dd2MF8Vnq2r261vGeWnW9A6VYwJuAIdZRo0WlUpm32522yZD6qNCWITUiQDAuZx7hsNkMoWGhs6ZMyc0tOvnRZOWSMiCmmsFzfp71eymPmFKVBsAnWQdfq9p0mTKbFZ/tEV2uULqOADgXM4tOOLi4rKyssaOHcswPW1479eu1G5t1A1mVFv7hqtpXJkC+DWscXebJk+neI7d+aGs4pLUcQDAiXCk/DWWXqtf3dAYo1LmRvb2k8mkjgPgxqwDhxinzSSiwOZtk5f+IHUcAHAWicfhOHv27ObNm+1Ps7KyoqKinL1SmUxGCPHy+pXjjv+t+sq7dQ29Vcrdgwf0dv6k83K5nBCiVqsFQXD2uu6cbdsyDKNQKKTOcnsURRFCFAqFt7fbDEJF07S7pJXJZBRFOZQ2/h6i0ZBtH7AFO0nao2RwnPPTtWX7oGk0GrHrhudxHtsHjWVZVXtTRAG4JokLjtra2uLiYvvTGTNmdNvn59et6J/VV/7vcnWYSnkgfmh0N04D6xbHbzu5XG77+nYLMplM5j6nqWiadq9jjKNphwwTnvydNXs1yc2RC4JsxCgn52qfUun0nxBdyL2+FgAkPircd999+fn59qcqlaqxsdHZK/Xy8lIoFE1NTZ39KfNRo+65y9WBcvmOyN7+JmOjyeikhK2xLMswzI0bNzh3mN1bqVRqNBqDwWA2m6XOcnsymczHx8dsNhsM7jG7h7+/P8dxN27ckDqIQ3x8fGiabmpqcvQP/APp9MdUuVu4HVsNTY3cPd1ac2g0GqVSqdPp3OJUIsMwLMvq9Xqr1ersdfn7+zt7FeAhJC44WJYNDw+3P9XpdN3w+bHheb5TBcfuZv3/VNZoZPS2vuH9FHKe552XrTVbSEEQum2Nd8L2Ze0uaW2XVERRdIu0du6S1rbrdiotHxouzJrL5m5RFheKHGcZcZ/T0rWFDxqAszm306jFYikrKysrK7NYLHq9vqysrLy83KlrdJIv9YZ5lTVyQrb0CY/vxispAJ6GDw41zM4S1RrVF0WqoweljgMAXca5Zziqqqqef/552+Pq6uqSkhKapj/55BOnrrTLfWMwzrlcLRCyqU/4Axq11HEAejhBG2SY/bj6o83KIwcJx5kTJkidCAC6gHMLjujo6E8//dSpq3C2c2ZLxuVqkyj+KyL0N94aqeMAeAQhINDw6BPs9g+UXx2hLBbThCSCsfUA3BzG4ehIucWaVl7ZxPFvhwVP93WPexEBegbBx9cwa67gH6g4cYwp+py4w92qANABFBy3VGPlUssv13Lcn0KC5vj7Sh0HwOOIPr6GR7P4oF6KU98yn39M3OH+EQC4FRQc7Wvg+bSKykor93Iv7XxtgNRxADyUqNYYZz/Oh4Yrzp1h83Mp3JQB4LZQcLSjmRfSyytLzZZ5AX4v9QqUOg6ARxMZ1pieyYdFyC+eZz7ZTrnDgDQAcDMUHG0ZBfHRH6tOm8yz/X2XhwVLHQcAiKhijDPn8JHR8rKL7I6tlNUidSIA6DQUHL9gEcTHL1d9ZTA+4uu9MiwY3eIBXISoUBhnZHB39ZdVVrC5OZQ7DGULAK2h4PgvXhSfrar5Qm8Y56VZHREqx214AK5ElMmMKWlc7EBZdSW7/QNidI8B6QHABgXHT0RCXqi+WtCsv1fNbOoTpkS1AeCCZDLjlFRu8DDZtSuabZuoFr3UgQDAUSg4CCFEJOTlmmsfNjUPYVQf9o1Q09gsAK6Kpo1JU6xDh9P1deptmyg3mcoOAHBkJYSQpdfqs6833aVS5kb29nWfmcoBPBRNmxIfsdwzir7eoN66gdY5PCEtAEgHBQdZWXf9vbqGSKXi48jeWjmqDQB3QFHm8Ynm+xPoZp162ya68brUgQDgNjy94NjQ0Lj8Wl2oQr4zqneIwrkzywBA17KMGWdOmEA169RbN8rqa6WOAwAd8bhD7HWeP3S9yUhIDM+dMBhfvVIbIJPl9o3oo1BIHQ0AOs0yajShKNWhfeyHm4xpj/Kh4VInAoD2eVbBkdvU/L9XrjXzP83IQBOioentkRH9GZW0wQDgV7OMfEBUKpniQnZHjmFGhhDeW+pEANAOD7qkctZkfqH6qr3aIIQIhEz384lnGQlTAcCds8aPMD48jbJY1LlbZD+WSx0HANrhQQVHTmOT+aYZrvfrWyQJAwBdixs01DR5OiUIbN42ecUlqeMAQFseVHDUcu3MM3nNiomgAHoI64DBxqnpRBTZvG3yC+ekjgMAv+BBBUdfpbK9RvQVBeg5uJhY0/RZIkWxBTvlZ09JHQcA/suDCo4sf9+bB/X6ozZAkjAA4CRcVIwxPVOUy9ndnypOfyd1HAD4iQcVHL2Vik19wqJ+PqWhpuk/hQTN9veVNhUAdDk+oq9x1lxRpWL2FCi/+UrqOABAiKfdFjtaoz7aL6pWqTJSVKjZpMYMbQA9FB8SZpw5h/1oi2r/HspiMd//oNSJADydB53hsJFT1EA1O8rHW4MZ2gB6NL5XiCEjS/TyVh7+QnVon9RxADwdDroA0GMJgVrDo08Ivn7Kr46oDhZLHQfAo6HgAICeTPD1M8x+XPALUH59lNm7i9w0GA8AdA8UHADQw4k+voZHs4SgXorvjrO78okg3P5vAKCroeAAgJ5P1HgZZs7hg4Ll359iPv+Y8O0MAwgAToWCAwA8gqjWGGc/LoRFKH44y+bnUjxGGQboVig4AMBTiAxjmPkY3ydSfukC8/F2ikPNAdB9UHAAgAcRFUpjagYfGSMvv8TmbqEsFqkTAXgKFBwA4FlEucI4YzbXb4Cs6jK7/QPKaJQ6EYBH8KyRRgEACCGiTGacksp8lqe4cI7N3cINGkoqf7RaTMoArXnkaMHXT+qAAD0QCg4A8EgymWlKKtlToDhzUnbtCiFEIER++UfZmVPGx57ig3pJnQ+gp8ElFQDwVDTN9RvQpo3irExRgSRxAHo2nOEAAM8lq/zx5ka6ppot/FRQa0SVSmQYwrAiw4gqRvz5AcFMTACdh4IDAKAt+ZnvOvhXUakkDCuoVIRhRRUjMozIsETFiAwrMCrbA1s7YVhR3pVfsxTPKb8+Kj9z0tysU/oHiPfcZ40bTjDxNbgDFBwA4Ln4PpHk+H/aNvYKMU2eRpnNlMlIjEbabCImE2Uy0maTyHEUx1EmI2U20QYDVV/nyOQsokxOGEZkbVUISxhGaPWAsKzIsKJKJTKsqNZ0fPpEVfS54sxJ22OqoZ4p+owyGSyjxvzqLQDQbVBwAIDn4mJirf0HKc5/b28R5Qrzw1MFrUOdRimeJ2bTT3WJxUyZjMRkos1mYjJSJuNPJYvJSJlMlNlM19fd/hVpmqhs5Qjz35MoP1/Qoc1Ge7Vhpzpy0Bp3j8iynXnfABJAwQEAHs30yAy+bxRzqZQyGa2BQeZRowU/fwf/VpTJiFojqjWEEEdmhKM47he1yC9Pn9geUGYTZTTSN3SE52WOhOB5uqGWj+jrYGYAqaDgAADPRtPWYfcwY8YpVaqWxkbBmfO6iXI58fIWvbwdWZgym4nZRJtNP58jMckvV8jPnmpnUYWyi4MCOAElOnABsttwHCeTOVTT3wmKogghLvXGO+BeaQkhFOVaO1XH3GvzIq3zuEVasUVvfXspMf1iaFQqUKt4cbHzbpyh0CMVuohrHRt0Op3VanX2Wnx9fRUKRUNDg0u991vRaDQsy3bPlrlzKpXK29tbr9ebTCaps9yeXC738/MzmUx6vV7qLA7RarUcxzU1NUkdxCF+fn4ymayhoUHqIA7x9vZWqVSNjY28a89cr7hwTvXZx/apbkWGMaRmCmHhzlujVqt13ouDR8ElFQAAt2GNHcg/GcJe+F7R0mLx8TUOGiqyaqlDATgEBQcAgDsR/PyFsRPlGo2huVnEbLfgPjBeHgAAADgdCg4AAABwOhQcAAAA4HQoOAAAAMDpUHAAAACA06HgAAAAAKdDwQEAAABOh4IDAAAAnM61hjbvHlu3bq2oqFi4cKFS6QYzHu3bt++rr76aO3duRESE1Flu7/Tp0wUFBUlJSXfffbfUWW7v2rVr69evHz58eHJystRZbk8Uxbfeeis8PPzxxx+XOotD1q5d29TU9NJLL0kdxCH5+flnz5793e9+5+/v6FSxEiopKfniiy9SU1P79+8vdRYAR3niGY4jR47k5eVxHCd1EIecPn06Ly/PXSakuHz5cl5e3qVLl6QO4hCdTpeXl/fdd99JHcRReXl5Bw8elDqFo/bt2/fpp59KncJRx48fz8vLa2lpkTqIQ0pLS/Py8qqrq6UOAtAJnlhwAAAAQDdDwQEAAABOh4IDAAAAnM4TO40CAABAN8MZDgAAAHA6FBwAAADgdCg4AAAAwOnkUgfobkajMSsri+f5jRs3ent7Sx3nltauXVtQUGB7rFKpgoODk5OTJ0+eLG2qjpnN5vz8/CNHjtTU1Mjl8tDQ0HvvvTclJUWj0Ugd7Rfs25aiKJZlQ0ND4+LiJk+e3KtXL6mjta/1zmC3YsWKu+66S5I8t3Vz4KSkpN///vdS5emY2WwuKCg4cuSIbViLXr16DRo0aPLkyX379pU6Wltr1649evToxo0bWzc+/fTT8fHxLrt5Aew8ruA4cOBAVFSUQqEoLi6ePn261HE64ufnt3z5ckKIyWQ6evTo6tWr/fz8Ro8eLXWu9rW0tLz66qt1dXXp6en9+vXTaDSVlZVFRUUMw7jgdrZtW1EUjUZjeXn5rl27CgsLlyxZEhcXJ3W09vn6+r7xxhutW1x85Nk2gX18fCQM0wG9Xr9o0aLa2trU1NR+/fp5e3vX1tb+8MMPK1euXLVqldTpAHoUjys4CgsLU1JS5HL5hx9+OG3aNIqipE50SzKZzH5Queuuu/bs2XPx4kWXLTg2btxYU1Pz97//PTQ01NYSHR09duxYnU4nbbB2td62sbGxEyZMWLRo0cqVK9esWaNQKKTN1i65XB4dHS11ik5wl8A377cxMTH333+/IAjSBgPoeTyrD8e5c+euXr06ZsyYBx54QKfTnTx5UupEDuF5/vDhw3q9fuDAgVJnaR/P84cOHZowYYL9W9vO19dXkkidIpfL09LSGhoavv/+e6mzQPfhOO7LL79sd7+lac/6bgToBp51hqOwsHD06NEMwxBCEhISCgsL4+PjpQ51Sw0NDampqYQQjuNomp4zZ87IkSOlDtW+uro6k8nkgte8HRcZGUkIuXr16rBhw6TO0o6GhoaUlBT7U5qmP/nkEwnz3FabwOvWrXPBLjL19fVt9tv33ntv//799sd9+vSRKNottdmwAG7EgwqO5ubmI0eO/PnPf7Y9nThx4iuvvHL9+vWAgABpg92KvQ+HxWI5f/78hg0bNBqNW8xr6o5cfAS8Nl0iXPlSoE2bwC77KWsjMzNz6tSp58+f/8c//uGau8TNvXmWLl0qVRiATvGggqO4uNhqtS5evNjeIghCUVHR7NmzJUzVgdb9DKKjo8vLy3Nyclyz4NBqtQzD/Pjjj1IH+fUqKioIITefWncR7tIlws4tAmu1WpVK1Xq/DQwMDAwMbGpqkjBVx27esHK5B32Ng1vzlD1VFMXdu3dPnTp14sSJ9sZDhw4VFRXNnDnTXa7XWiwWURRd8NetXC5/8MEH9+3bN23atDbHbJ1O5/rdODiO27lzp1arHTRokNRZoPt0sN8CQJfzlILjxIkTV69eTUpKCg8PtzcmJyfv2LHj2LFjo0aNkjDbrfA8X1VVRX6+pLJ///4xY8a4YLVhk5WVdf78+QULFqSnp8fGxqrVatttsSNGjHDB22Lt29Z2W+znn39+5cqVJUuWuOyPRY7jysrKWreEhISo1Wqp8vQYTzzxxIULFxYsWGC/Lba+vr6oqIiiKJfdGQDclKd8ogoLC6Ojo1tXG4QQrVY7YMCAwsJC1yw4mpqabIP5KBQKrVabkpIya9YsqUPdkre39zvvvJOfn3/gwIGtW7cqFIqwsLCEhITExESpo7XDtm1tA3+FhIQMGzZs8eLFLtir0U6n0z3//POtW1555RWXvUfajdj224KCgsOHD2/fvp3n+YCAgCFDhrzzzjttvi4A4A5htlgAAABwOvfouwAAAABuDQUHAAAAOB0KDgAAAHA6FBwAAADgdCg4AAAAwOlQcAAAAIDToeAAuI0dO3ZQFOWak6UVFxdTFJWdnS11EACA20DBAa7u+PHjFEXNmzevdWN1dfWQIUPkcvn69eulCtYBW2Y7lmUHDx68ZMkSvV4vdTQAAGl4ykij0JNcuHBh0qRJ165d27Fjx7Rp06SOc0sjR45MT08nhNTX13/22WfLli07ePDgoUOHunB8+vHjxxuNRoVC0VUvCADgJCg4wM188803ycnJZrN59+7dY8eOlTpOR4YOHbpw4ULb46VLl44cOfLw4cOHDh3qwtg0TTMM01WvBgDgPLikAu5k//79Dz30EE3TBw8ebH3YbmpqevHFF6OiolQqVXBwcGZm5sWLF+3/KpfLqZsMGTLE9q86nW7JkiWjRo2yTVYeHR29cOHCjq99bNmyRaVSJSYm3rhxw8Hkcrl80qRJhJDS0lJ7I8dxK1asiI+PZ1nW29t73LhxRUVFrf+qpqbmscce8/f39/LyGjduXElJSVpaWusK4+Y+HB1vClt/lJ07d/71r3+NjY1VqVR9+vRZtmwZpjgAAGfDGQ5wGzt37szMzAwPDy8qKoqJibG3t7S0JCQknD59OjMz84EHHigtLf3Xv/5VWFhYUlLSv39/QsiuXbsEQbAvf+TIkaVLl0ZGRtqeVlZWrlmzJi0tLSMjQ6lUHjp0aMWKFV9//fXBgwfbvfaxfPnyxYsXP/nkk6tXr+7UhKK2A39gYKDtKc/zKSkpe/bsSU9Pnzdvnslk2rJlS1JSUk5OTkZGBiGkubk5ISGhvLz8mWeeGT58+KlTpxKlUXqzAAAGgElEQVQTE3v37t3BKm67KWxefvnl2NjY9957z8/Pb926dUuWLAkMDHz22Wcdfy8AAJ0mAri2Y8eOEUIiIyNpmh42bNiVK1faLPDGG28QQmw/02327NlDCElMTLz51S5evKjVaqOjo+vq6mwtJpPJYrG0XmbZsmWEkL1799qe5ubmEkI+/vhjq9X629/+lhDy+uuvO5J51qxZ5eXl5eXlx48fX7RoESEkODhYr9fblnn//fcJIRs2bLD/lcViufvuu4ODg61WqyiKr732GiFkzZo19gU++OADQohKpbK37N27lxCyceNGBzeF7b2MGDFCEARbC8/z/fr1GzhwYMfvCADgDqHgAFdnO3izLEsISUpKMhqNbRaIi4vz8vJq037//ffTNK3T6Vo3Xr9+vX///n5+fufOnWt3XRaLxWg0fv/994SQN99809ZoO0hv3rw5OTlZoVBkZ2c7mLmNhISEs2fP2pcZNWpUr169jL/09ttvE0KOHz8uiuLQoUO1Wi3HcfY/EQQhIiKig4LjtpvC9l7efffd1gvYTu3wPH/b9wUA8Kvhkgq4h4yMDJlMtnbt2pSUlPz8fFv9YVNWVhYTE9Om7+TQoUNLSkoqKiri4uJsLVardcaMGWVlZbt37x4wYEDrhbOzs9esWXPy5EmDwWBvvH79eutl5s+f39zcnJubm5aW5mDmiRMnzp8/XxCES5curVq1qqqqqnXIc+fONTc3t34jdrW1tbb3NXToUJlMZm+nKKp///51dXW3WqODm6LNdRkfHx+LxXLjxg1fX18H3xoAQGeh4AD3QFHU6tWrFQrFP//5z4cffvizzz7TaDS2fxJF0ZEbTZ9++ukDBw6sW7du/PjxrdtXrFjx4osvTpkyZd26dWFhYSqVqqGh4ZFHHmnd7YMQkpaWlpOT85e//OWhhx6y98PoWN++fe137c6YMSM+Pj4tLe3YsWO2GkIQhH79+tmukrRhr4c6ewOtg5ui3WVE9BsFAGdCwQFug6Ko999/X6lUrlq1KikpadeuXd7e3oSQmJiYixcvmkym1r/sz5w5Q9O0vWfosmXLsrOzX3nllaeeeqrNy65fvz4qKio/P99+GP7yyy9vXvuUKVNmzpw5ffr0cePGFRcXBwcHdyp8TEzMokWLFi1atH79+qeffpoQEhsbe+bMmSFDhnh5ebX7J9HR0aWlpTzP209yiKJ44cKFjtdy200BACAJ3BYLbmblypUvvfTS4cOHJ02apNPpCCEzZszQ6/WrVq2yL1NcXHz06NGJEyf6+PgQQrZv3/7aa6+lpqa+9dZbN78gTdOiKPI8b3vK8/zy5cvbXXViYmJhYWFFRUVCQkJVVVVnkz/33HOBgYFvvvmm2WwmhMydO9disSxcuLDNqYWamhrbg6lTp9bX17e+5XXr1q2VlZUdrOK2mwIAQCo4wwHu529/+5tSqVy2bNmECRP27t27cOHCHTt2vPrqq2fPnrXfC+rv7//uu+8SQsrLy7OysgICAjIzM223bNh4e3uPHj2aEJKWlvb6668nJyfPnDnzxo0b27Zt6+DiwtixY/fu3ZucnJyQkLBv376oqCjHY3t5eS1YsGDx4sWrV6/+wx/+MH/+/OLi4tWrV584cWLq1KlBQUGVlZUlJSUnT5609eFYuHBhTk7OM888c+LEifj4+FOnTmVnZw8aNKisrOxWq+h4UwAASEnCDqsAjrDd8fHUU0+1aX/99dcJIfHx8XV1dY2NjS+88ELfvn0VCkVQUFBGRkZpaaltsZKSknb3/MGDB9sWsFqtS5cujYmJUSqVvXv3fuGFF8rLywkhf/zjH20L2G+Lta/622+/1Wq1ERER58+f71Tm5ubmgICAkJCQlpYWURR5nv/3v/993333eXl5MQwTGRk5bdq0zZs325evqqrKyMjw9fVVq9Vjxoyxndfx9/e3L9DmLhVRFDvYFO2+F1EUn3nmGUJIY2Pjrf8TAADuFCWipxiA+4iOjvb19T1x4oTUQQAAOgd9OABcl8lkav10+/bt5eXliYmJUuUBAPjVcIYDwHWNHz8+KipqxIgRCoXi66+/3rBhQ0hIyIkTJ4KCgqSOBgDQOSg4AFzX22+/nZOTU1FR0dLSEhwcnJiY+MYbb0REREidCwCg01BwAAAAgNOhDwcAAAA4HQoOAAAAcDoUHAAAAOB0KDgAAADA6VBwAAAAgNOh4AAAAACnQ8EBAAAATvf/AquoikOtnecAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 180,
       "width": 360
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data, aes(x = kozak_region,\n",
    "                 y = mean_ratio,\n",
    "                 color = insert_sequence,\n",
    "                 group = insert_sequence)) +\n",
    "  geom_line() +\n",
    "  geom_point() +\n",
    "  labs(x = \"Kozak Region\",  # Custom x-axis label\n",
    "       y = \"Mean Ratio\",    # Custom y-axis label\n",
    "       color = \"Insert Sequence\") # Custom legend title for color"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Add title to the above plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 180,
       "width": 360
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data, aes(x = kozak_region,\n",
    "                 y = mean_ratio,\n",
    "                 color = insert_sequence,\n",
    "                 group = insert_sequence)) +\n",
    "  geom_line() +\n",
    "  geom_point() +\n",
    "  labs(x = \"Kozak Region\",  # Custom x-axis label\n",
    "       y = \"Mean Ratio\",    # Custom y-axis label\n",
    "       color = \"Insert Sequence\",  # Custom legend title for color\n",
    "       title = \"Mean Ratios in Different Kozak Regions\") # add title "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Change the plot look to a `classic` theme\n",
    "\n",
    "See https://ggplot2.tidyverse.org/reference/ggtheme.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5c2ZISAg5JJGVlZVmsYqKCvJrws7ObsaMGdOmTTM3N7e2tn777bc7JuEgnk/54eTkRE7WQD0k8rvezMwsLCwsIiIiIiKivr6efKnJoYrIr2ahUBgeHh4SEkKOCqU9VBHZ+8Dd3X3q1KkLFiwYN24cOSxBeHh480enp4SDbCQ4evToIUOGeHp6apq5jB8/XmfghyZjoJ5wEJQ/MM0cWrtspPH5aeZdbtLLPmk6OUeLnweJREJWaYwfP351I5pWn69yBZEhWVhYtNhAkvrlQ/0tSEhIIA+5b9++8+fPHz16NJ1OJzvOCAQC7SXbdjU1ecgknXe5zdcdAF1Xt0o4CIKQSCRbt24dNmyYmZkZk8m0sbHp37//ypUrr127pr1YaWnpggULnJyc2Gy2o6PjkiVLKioqoqOjOyzhaGhoIOcy1fw2ohiSXC5fs2aNu7u7ZhJdza+6xl+RBEEUFBQsXrzYycmJyWSamJgEBwfHxsZqfz+ePn06KioqMDCQHKPa0dExNDT02LFjLQ7NpKeEQ4PP59vZ2Y0cOXL16tUvGwTiFRMOgvIHppmE49U30vj8NPMuN6mZT5pOztH856HJscA1IiIiNJt9lSto69atZDfX5qtwWnX5UHwLCIK4devWm2++aWJiwuVy+/btu3v37qKiIoSQi4uL9mJtu5pedshEo3e5zdcdAF0XRrSpBwQAAHQPhw4dmj9/flhY2NGjRw0dCwDdWRdoNAoAAO2isrKSHHVD4+7du6tWrUIIkfeDAAD60wW6xQIAQLv4+++/w8PD+/fv7+zsTKfTCwoKyGkToqKiyN4uAAD9gVsqAICeoqCg4Ntvv/3rr78ePXokFotNTU379u0bGRlJTq4GANArSDgAAAAAoHfQhgMAAAAAegcJBwAAAAD0DhIOAAAAAOgdJBwAAAAA0DtIOAAAAACgd5BwAAAAAEDvIOEAAAAAgN5BwgEAAAAAvYOEAwAAAAB614kSjm+++SYkJKSiosLQgQAAAACgnXWihCMjIyMpKUkikRg6EAAAAAC0s06UcAAAAACgu4KEAwAAAAB6BwkHAAAAAPQOEg4AAAAA6B0kHAAAAADQO0g4AAAAAKB3DEMHAAAABibD8ZQG8VOl0p/Pd+SwDR0OAN0TJBwAgB7tj7r6t7PzimVy8ukSW+v/ursyMMywUQHQ/cAtFQBAz1UhV0zPyNZkGwihXRWVXxSXGjAkALorSDgAAD3Xoarqp0qVTuHW8gqcMEg4AHRncEsFANBzlckVjQufqdQBd1I8eFw3DseN+88/RzabDvdZAHgFkHAAAHouezarcSENQ7kSabr4hXmdmBjmrJV/aHIRDg3qiQGgBBIOAEDPxW4qXVjr5LDB2bFWpSqUygplsgyxJFMsLZTJ8qXSPKlUZ2EzBsOVy3HlcFy5HFcOW/O4Q8IHoCuBhAMA0EN9+7Dsk8ISIzpdQKc/UvxzbyXK1vpzJweEkBmD0U9g1E9gFNb731U0WUihVFYok5OPU0QNd0UN2ltuMgtx4sBNGdCjQcIBAOhxVASxLK9wV0WlHZt1xs/bk8e93yB+olT5G/Ec2M2Nw6HJQrQL5TheLldoZyEZEsn9BrFOFsKiYfZstk4W4sXj8eituymjJoh7DeIyuVzI5frxea1aFwADgoQDANCzNKjVMzNzzj6t9ePzzvr7kM04BhoL2rxBNo3myuW4cjnI7N9CFUE8lMl16kJyJdJCqQzVvrA6WR3izeP58LlkFiLkck0Y9Cb3lSORzsrMSWkQk09DzEwPe3v0ZjLbHDwAHQYSDgBAD/JIoZjwIOueqGG0mckJH6+X/V1/dQwMa5yFoJfclLlL7aaMLZs1PSNbuzXrxdrat7PzEvy89XQUALQjSDgAAD1Fpljy5oPMEpl8obXlLk8h0xAtKpq8KVOvUhdIpQUyWYH0+b+mmoZwaTQpjr+4PezMk9p8qUwIzVRBpwcJBwCgR7j+TDTpQdYTpXK1o/3Xrk6dqvWmCYMeJDAKejELUeBEmfyFmzK3RA0lMpnuyhgqk8sh4QCdHyQcAIDu79fqJwuy89QEscdTGGFjZehwKGHRdG/KJNbUjU3L0F2OQE4w4RzoCmDIGgBAN7e1rGJ2Zg4Dw075enWVbKNJI01N+hrxdQqnWpq7cKB6A3QBkHAAALotNUG8l1e4PL/IksW6Gug73tys5XU6MRYNO+bTZ7CmQw2BJlv02u0hNGhQAFAFt1QAAN2THMfnZ+cdrX4i5HLO+ft0j1YOblzOtSD/DLHkoUzuyeO6dYuDAj0EJBwAgG6oRqmanJ71d/2zQcaC035eFt1opAoMIV8+zxeG/AJdDSQcAIDupkgmG5eWmSORTu1tHuvlwYX51QDoBCDhAAB0K7dFDRMeZFYrlO/b2/zo5krrVP1fAejBIOEAAHQfiTV1YRnZDWr1t67OHzvaGTocAMC/IOEAAHQTex9VReUW0DEszttzpqWFocMBALwAEg4AQJdHILSh+OGG4tJeTMYpX69hJsaGjggAoAsSDgBA16bAiYicvNiqxy4czll/7z48rqEjAgA0ARIOAEAXJlKrwzKyE2vq+guMEvy8rVjdp/srAN0MJBwAgK6qQq4Y/yAztUEc2sv0uE8fAV1fc80DAF4dJBwAgC4pXSx5My2zVC5fZGO1w8PNIHPNAwCog4QDAND1XK6tn5qR9UyljnZ2WO/saOhwAAAtg4QDANDFHKqqjszJxwm001O4uCvP/gpAjwIJBwCgK9laVvFhfhGfTj/m6zm2V9ee/RWAHgUSDgBA10DONb+9otKGxTrj793XiG/oiAAArQAJBwCgCxCr1eGZuQlPa3z4vLN+3o4ctqEjAgC0DiQcAIDO7qlSNSk9M7leNNLU5KRvH1MGfHEB0PXAdQsA6NQKpLJxaZl5Uun03uaHvDw4MNc8AF0TJBwAgM7rxjPRpAdZj5Uw1zwAXR4kHACATurUk6ezM3MVBPFfd9f/2NkYOhwAwCuBhAMA0BltK3v0YX4hh077zafPRPNehg4HAPCqIOEAAHQumrnmzZmM//l6DYG55gHoFiDhAAB0InIcX5idF1/9xJXLOefn7QFzzQPQXUDCAQDoLGpVqinpWX/WPRtoLPjd18sS5poHoBuBhAMA0CkUy+RvpmVkSaSTLXod8fLk0aH7KwDdCiQcAADDS2sQv/kgs1yuiLSx2u7hxoC55gHodvSbcPz111+///57eXm5XC43NzcfNmxYeHg4kwnVpACAf12srZuekS2CueYB6Nb0m3DQ6fQxY8bY2tqyWKz8/Pxffvnl2bNny5Yt0+tOAQBdyIHK6iU5+RiGYr08Zlv1NnQ4AAB90W/C8frrr2see3p6lpSUpKWl6XWPAICuQtP91YzB+M23T7CpiaEjAgDoUQe14cBxvLi4ODU1tW/fvh2zRwBAZ6YiiGV5hbsqKu3YrDN+3gEw1zwA3Z3eEw6lUhkWFkYQBEEQoaGhS5Ys0X71xo0bX3/9Nfm4urqaz4cvHQC6vwa1ekZGzrmaWj8+76y/jz2bZeiIAAB6p/eEg8FgbN26ValU5uXlxcbGGhsbz58/X/OqSqUSiUTkYxzHMWiaDkB390ihmPAg656oYYyZ6QmfPsYMuqEjAgB0BIwgiA7bWWJi4v/93/8dPnzYyMio8avz5s2LjY3Ny8sTCoUdFhIAoCNliiVvPsgskckXWFvu9hQy4TcGAD1Gh47DoVKpCIJQqVQduVMAQCeRXC+anJ71RKlc7Wj/tasT5BoA9Cj6TTh27drl4eFhZWWF43hubm58fHz//v1NTU31ulMAQCd04vHTuVm5SoLY4eG21Nba0OEAADqafhMODodz7Nix6upqGo1maWkZFhY2ceJEve4RANBJKHACRwSHRkMIbS2rWJFfxKXTj/v0GW9uZujQAAAGoN+EY/78+dpNRAEAPUFag3h5ftHf9c9whPoa8R3Z7JNPnlqzWAl+Xv0ETbTfAgD0BDCXCgCgPZXLFaPupz9V/tNU646o4Y6owYHNuhzoJ+RyDBsbAMCAYD5GAEB7+vphmSbb0OjD50G2AUAPBwkHAKA9pYsljQtzJNKOjwQA0KlAwgEAaE8mTQ3kZUKH0b0A6Okg4QAAtKdpFuaNC8MtYRpYAHo6SDgAAO1GTRAJT2t1Csebm33saGeQeAAAnQf0UgEAtA8CoajcgmOPnwwyFnzh4nit/pkCJ4aZGo/rBQNvAAAg4QAAtJPVBcV7HlX58Xln/Lx7MRkhZjCmMADgX3BLBQDQDr59WPZ9abkbl5MY4NOLCb9kAAC6IOEAALyqXyqr1xSWWLKYZ/28bVgsQ4cDAOiMIOEAALySU0+eRubkGzPoif4+HjyuocMBAHRSkHAAANrucm19eGYui4Yl+HkHGvENHQ4AoPOCW60AgDa6LWqYnJ6FE8Rvvl5DTYwNHQ4AoFODhAMA0Ba5EumEB5kStfqwtyd0fAUAtAgSDgBAq5XK5SFpGdUK5RahS7ilhaHDAQB0AdCGAwDQOo+VytD7GQ9l8o0uTh/Y2xo6HABA1wAJBwCgFZ6p1GPTMrMl0nftbD5zsjd0OACALgMSDgAAVVIcn5SedU/UMNeq91ahq6HDAQB0JZBwAAAoURPE3Kzcq3X1E8177e/jTsMMHRAAoEuBhAMA0DICoSW5BScfP33dRBDv7cnAIN0AALQOJBwAgJatLCja96jK34h/xs+bR4fvDQBAq8EXBwCgBV8Ul24urRByORf8fUwZ0JceANAWkHAAAJqzvaIyuvihHZt1McDXisU0dDgAgK6qdT9Wnj17VlxcjBBydnY2NoaRjAHo5uKqH7+bW2DOZFzw93HmsA0dDgCgC6Naw5Gdnf3GG2+YmZkFBAQEBASYmZmNHTs2JydHr8EBAAwoqbbu7ex8Dp32u6+3N59n6HAAAF0bpRqO/Pz8119/vba2dvDgwX5+fgih9PT0xMTEwYMH37p1SygU6jlIAEBHu/lMNCU9m0DESR/v100Ehg4HANDlUUo41q1bJ5FIEhMTQ0NDNYUXLlyYNGlSdHT04cOH9RYeAMAA0sWSNx9kynA8ztvjjV6mhg4HANAdULqlkpSU9J///Ec720AIhYaGvvPOO0lJSfoJDABgGIVSWej9jFqlaoeHW1hvmJgNANA+KNVw1NXVubu7Ny53d3evq6tr75AAAAZTrVCOe5D5SKH41tU50sbK0OEAALoPSjUctra2ycnJjcuTk5NtbWGuSAC6iXqV+o20jFyJdLWj/ceOdoYOBwDQrVBKOKZOnRobG/vNN9/IZDKyRCaTxcTEHD58eOrUqfoMDwDQQaQ4PuFBZmqDeIG15deuToYOBwDQ3WAEQbS4UF1d3bBhw9LT042MjIRCIUEQBQUFDQ0Nfn5+f/75p6lp+7QpmzdvXmxsbF5eHnR7AaCDKQliSnrW2ae1ky16HffpA1OlAADaHaUaDlNT0xs3bqxfv97V1TUvL6+goMDV1XXDhg3Xr19vr2wDAGAoOIHmZ+WefVo7yswEJmYDAOgJ1ZFG+Xx+dHR0dHS0XqMBAHS8FQVF8dVPBgiMTvl6cWgw3QEAQC/gywWAHm1tUcnWsgoPHjfBz1tApxs6HNBT3LlzB8OwyMhIQweCcnNz169fn5aW1uKSjx49Wrlypa+vr0AgMDEx8fDwmDlzJoxERd1LazgOHDiAEJo3bx6dTicfv8zChQvbOSgAQIf4b/mjr0rKHNjsi/4+ljAxG+iRcnNzN2zYIBQK/f39m1ksPz9/8ODBtbW148ePDw8Pp9PpBQUFly5dysnJmTNnTodF26W9NOF4++23EULkaSUfvwwkHAB0RbFVj9/PK+zNZF4I8HGEidlAzyORSHg8qpMExcTEPHnyZM+ePREREdrlMKcYdS+9pXLx4sWLFy+yWCzN45fpwGgBAO3j9NOat7PzjBj0c/7efXhcQ4cDerrjx49jGHbixIlvv/3Ww8ODzWY7Ojp+9dVX2v0oVSrVd9995+fnJxAIBAKBu7v7wq6JfOgAACAASURBVIULRSKR9gKbN28ODAzkcrkCgWDEiBEXLlzQ2cXRo0c3bNjg7u7OYrG++OKL9evXT5w4ESE0b948DMMwDBsxYkSTEebl5SGEJk+erFPu6emp/bT5GBBCVVVVCxYs6NWrF5/PDw4OTk5Onj59OofD0SywZcsWDMPu3LmjvdaUKVOMjIyo74jK+SQ3smXLln79+vH5fIFA4O/vr91Ss8Vjaa2X1nCMGTOmyccAgK4uuV4UnpnDxLDffb36CYxaXgGADvHxxx97eHhs27bN1NR0z549a9euNTc3j4qKIl9ds2bNDz/8MHv27Pfff59Go5WUlCQkJDx79kwgECCE1Gr1pEmTEhMTw8LCIiMjZTJZbGzs2LFjDx8+PGvWLM0uVq9ebWdnFxMTY21tzWQyra2t2Wz2p59++umnn4aEhCCEXtb1UigU/v3337GxscuXL39Z/C3G0NDQEBwcnJeXt3jx4n79+qWkpISGhjo6Orb2RFE82ObPp0qlmjBhQmJiYnBw8Lp164yNjbOzs48dO7Zhwwbqu2gdgoLg4OCUlJTG5ZcuXQoODqayBSrmzp2LEMrLy2uvDQIAGrsvajD96wb9j2snqp8YOhbQc92+fRshFBERQT49duwYQqh///44jpMlarXa3d3dy8tLs4qLi8vIkSNftsH//ve/CKF9+/ZpShQKRVBQkJWVlVKp1OzCw8ODfKpx+vRphNChQ4eaDzg3N9fExAQh5O7uvmTJkh07djx48KC1MZB/y7dv365ZYPfu3QghNputKfnxxx8RQrdv39be8uTJk/l8fmsPtvnzSe7ovffe0yxDLkZxF21AqZfK1atXm5wzpbq6+urVq23MdAAAHS5fKnsjLbNepdrl4Ta1t7mhwwHgBeR9DfIxjUbr379/QUEBjuNkiampaVZWFpmpNHbw4EFLS8tZs2bJnlOr1bNmzaqqqrp//75msbfffpvBoDoehDZ3d/e0tLQVK1ZgGLZr166oqCg/Pz9/f/9r165Rj+HEiRPm5ubafXMWLVpkZ9fqaQQoHmzz5zM2NpbL5cbExGBaQ+/QnneMp7iLVmnLedeoq6vTvvMEAOjMyuWKkPvplQrFD27Oi2BiNtD5ODg4aD81NjZWKBQikYisWvjhhx9mzJjx2muvOTo6Dh06dMyYMTNnztS0+szKynr27BmX20SDpOrqas1jFxeXNofn6Oi4adOmTZs21dfX37p1Kz4+/sCBA+PHj3/w4AEZeYsxFBQU+Pn5aWc8NBqtT58+f//9d6sioXiwzZ/P3NxcoVCo0zSktbtoleYSjrS0NE3X5IsXL5aVlWm/WlNT89NPP3l5ebVtxwCAjvRUqQpNyyiWyT93cvjIASZmA50R1tQot8Tzdo6jRo0qKio6f/78lStXrl69euTIkejo6OvXr5M1BDiOu7u7Hzx4sPEW+vTpo3nMZrdDhywTE5OQkJCQkBALC4vvvvsuLi7u448/phhD42MkXmzI2eRJUKlU2k8pHmzz55MgiCYXaNUuWqW5hOPkyZPkDSeEUExMTOMFuFxufHx823YMAOgwEjU+KT0zUyx5x9b6C5dWt1ADoJMQCARhYWFhYWEIofj4+FmzZm3btu3bb79FCHl4eKSnp/v6+r7sJ/vLNPNHt0X9+vVDCJWWlpJPW4zBzc0tLy9PpVJpKjlwHNfpWNurVy+EUE1NjXZhfn6+9tM2H6w2T0/PzMzMhoaGJjfSLrvQ0VwbjtmzZ58+fZpsUBMTE3NaS0JCwtWrVysrKydNmtReoQAA9EGBE1MzspLrRVN7m//k7mrocABoI52/wYMGDdIunD9/vkKhWLlypU6FQUVFRfObJTu56Gy8sYSEhPr6eu0SgiDi4uIQQt7e3hRjmDp16pMnT/bt26d56ZdffikvL9demOxne/78eU3JyZMndZKSNh+strlz50ql0s8//1znoNpxFzqaq+Hw8PDw8PBACEVHR8+aNcvZ2bnNuwEAGISaIOZm5SbW1I0xMz3i5UGHidlAl2VrazthwoR+/frZ2dlVV1fv2bOHTqfPmzePfHXZsmVJSUk7d+5MSUmZPHly7969S0tLr1+/fv/+/ebbHAQEBHA4nJ9++onFYpmamlpaWo4aNarxYlu2bJkxY8aYMWOCgoJMTEweP358+vTp9PR0f3//BQsWUIxhxYoVhw8ffuedd1JTU/v27Xv//v1ffvnFy8ursLBQs6PXXntt0KBBW7duFYlEvr6+aWlp//vf//z8/LSXafPBalu2bFlCQsKWLVtSU1PHjRtnbGycl5eXmJiYnp7eXrvQ1bbOLfoA3WIBaF84QSzOzkNX/h54975IpTJ0OAD8q8lusb/99pv2MkuXLkUI1dbWkk8//fTT119/3cLCgslk2tnZTZkyJTk5WXt5tVq9Y8eOQYMGGRkZcTgcZ2fnKVOmaPq7NrkL0smTJwMCAsjmHS8b6+H27dtr1qwZNGiQtbU1g8EwNjbu37//xo0bRSIR9RgIgnj06NHcuXNNTU15PN6wYcOuXbs2bdo07W6xBEE8fPiQHOmLz+eHhISkpaXpdItt28HqnE+CIBQKBTmWGofDIQf+Wr9+PfVjaS2MeLG2pJm8JCkp6ebNmzU1NZpONaQtW7a0Mdl50bx582JjY/Py8oRCYbtsEIAebnVh8XcPy335vKuBfr2Yr9QlDQCgJ9OnT09ISJDJZIYORO8ofQeJRKJx48Zp9zbW1l4JBwCgHX37sOy7h+WuXM6FAB/INgAABkdp4C+y61FMTExmZiZCiGwxGhoaOmDAgOLiYv0GCABovYOV1WsKSyxZzHN+3jYslqHDAQAAagnHb7/9NmPGjDVr1pBDppibmw8fPvzs2bMEQfz88896jhAA0Dr/e1ITkZNvzKCf9/fxgInZAACdA6WEo7y8fNiwYej5oKdKpRIhRKfTw8PDyZYpAIBO4nJtPTkx22k/775GfEOHAwBowfHjx3tCAw5EsQ0Hn88nkwwWi8XhcDTdcI2NjSsrK/UYHQCgNW6LGqakZ6kJ4pSv1zATY0OHAwAA/6JUw+Hq6qoZdSQgICA+Pp4gCJVK9euvv9rb2+szPAAAVXlS6YQHmWK1+qCXx5vmZoYOBwAAXkAp4QgNDT1x4gRZyREZGXnq1CmhUOju7n7p0qW3335bzxECAFpWJleE3M+oVig3C13CLS0MHQ4AAOiilHB88sknly5dIoffiIyM/OGHHzgcjpGR0fr16z/55BM9RwgAaMFjpTLkfnqJTP6li+MH9raGDgcAAJpAqQ2HiYkJOZst6aOPPvroo4/IxxKJRDM7MACg4z1TqcelZWZLpMvsbNY6ObS8AgAAGAKlGo4myeXybdu2ubm5tWM0AIBWkeL4pPSsu6KGuVa9twlhYjYAQOfVQg0HQRB//vlnXl6eubl5aGgon89HCKnV6r1793755ZdlZWXkRLoAgI6nJoh5WblX6+onmPfa38edBvOyAQA6seYSDpFINHbs2OTkZPKplZXV5cuX2Wx2WFhYSkqKhYXFV1999d5773VInACAFxAILcktOPH46WBjQby3BwOmgQUAdG7NJRwxMTHJyclBQUEhISH5+fknTpz4z3/+U1paWl1dHRMT8/7775MVHgCAjreqoHjfoyp/I/4Zf28+nW7ocAAAoAXNJRynTp3y8/O7efMmg8FACH322WcxMTH29vb37993dYW7xQB0KJxAxTIZn063YjG/LCndVFou5HIS/b3NGDAxGwCgC2iu0WhRUdGECRMYz7/O3nrrLYTQ8uXLIdsAoIPFVj22vX7L7eZd6+RbDtfvrCt6aMdmXQzwtYaJ2QAAXURzv43kcrl2m1ALCwuEEDl/GwCgw5yrqZ2Xlat5WiaXYxg62MfdmcM2YFQAANAqre4WS87fBgDoMOuLH+qUEARKeFprkGAAAKBtWrj7e+zYsezsbPJxQ0MDQui///1vQkKC9jJ79uzRU3AA9FgNanW2RJoplmRKpCkiceMFcqXSjo8KAADarIWE49atW7du3dIuSUpK0lkGEg4AXlG9Sp0lkWSIJdkSaYZYkiWRlMjkRLOr9GYyOyg4AABoD80lHLdv3+6wOADoOepUqgKpLEMsyZRIM8SSTImkSCrTTi9MGYwggZE3j+fD53rzeVdrn20qK9fZyAJry46MGYCu7ubNm99++21KSkpxcXFERITOT+UzZ8589tln2dnZvXv3XrRoUXR0dGvbD4hEIjs7O5VKVVpaam5uTv1ViUSybdu2Y8eO5ebmIoScnJyGDh26bNkyPz+/Nh1o59VcwtG/f/8OiwOA7qpWpcoQSzLFkkKZnEwvCqUy7QXMGIzXTYx9+DxXDtubz/Ph8Vy4HO1hvN7sZVaukMdXPyGfsmm0jS6OI0xNEADdF/HkMV6QixQKzNGZ5tQOnRXEYrFQKJw+fXp0dLTOSzdu3Jg8eXJUVNShQ4dSUlKioqLUavXGjRvJV+/du+fs7KzdhYIgiMuXL48ePVp7I7GxsQEBAWw2e//+/StXrtTZxctera2tHTFiRElJyccffzxgwABzc/Pi4uLr16/Pnz8/JSXl1Y+6U8EIovmK244zb9682NjYvLw8oVBo6FgAaCNNepEhkWSKpeliSaVCob2AGYPhzef58HnePK4Pn+fL51Hs2nrrWcNNkYhDo402NXHlcvQTPgCdgvrPy6rEBKRSkU9pvgHMOW+jduqyEBgY2L9/f+0ajmnTpmVlZWVmZpJPP//88x9//LG6upqcmnTixImlpaVJSUlkV02CIKKiok6ePJmdna1dVxEQEPDBBx+w2ez169fn5uZiLw7++7JXIyMjjxw5kpaWpvOHT61W07vdgH4wZBAAbVchV2RKJOTNkUKp7H6D+LFSqb2ADYs1xszUlcsh0wt/Pt+S1ca2F68ZG71mbNQeUQPQqeFF+aozp14oSb+vupTICBmnpz1eu3Zt9uzZmqdjx47duHFjSkrKkCFDEELx8fETJkwYMWLEpUuXyBsu586du3Llina2ce3atYKCghkzZtDp9GXLliUlJYWEhLT4qkKhiI+PX7hwYeOf2d0v20D6TjiSkpKuXr1aXFwsl8ttbW3Hjx+v/R4AYEDPVOo9j6oyJBJLJjOst3mQoOW/5drpRYZYcr9B3KBWay9AphfefK4Pj+fN5wUY8QXd8VsDgHZBlD1UP0htXI7nZDZR+PcfKpWycTnNxo4W2O9VwsBxvKqqytraWlNCPq6oqCCf8vn8s2fPTpo0KTg42M/P7++///7jjz+8vLy0N7J9+/awsDAjIyOEUHh4+I4dO7T/2L3s1bKyMrFY7Ovrq1kyIiLil19+IR/fv3/fx8fnVQ6ts9FvwnH58mUfH5/JkyfzeLzk5OSffvpJpVKNG6evLBUAigqlsqEpDx49v9nxzcOyzUKXD+1tNQuoCOKhTJ4hkWSKJRliaaZEki2RirXSCwaGOXLYI3gmmrYXfY34MKcJANThj8rVf+h2e3wZQiZtcmEiIOgVE46X0b4nwuVyT5486eLicvz48QsXLuhkG0+ePDl+/PjFixfJp4sWLRoyZEhFRYWtrW3zrzZu0vDFF198+OGHN27cWLx4cedp8NBe9JtwxMTEaB57e3sXFRVdu3YNEg5gcG/n5D1SKBCB0POvlDWFJUwME6vVZHqRKZZIcVyzPBPDHDhsb56Jpu2FN5/HhUHwAHgFNG9/1vv2jcvV166q797SKcQsrZjh85vYCpf3qmHQaFZWVpWVlZoS8rGNjY2mRKFQzJ8/39jYeODAgYsXL75y5Yr2oNv79++Xy+UjR4789xDU6j179qxbt675Vx0cHHg8Xnp6uuYlOzs7Ozs77WC6kw5tw6FQKCwtX+jLV1FRcePGDfJxbW0tE4YWAPpXo1T9WfcMoX+zDYSQHMffyyskH3NptD48rhef58Pj9eFxffg8Ny4H5n8HoH1hfD5qaspx+tiJeHYGIX5hvDvG+LcwOwc9RTJkyJDz589v3ryZfHr+/Hk+n9+3b1/yqVwunzZtWk5OztWrV62trefMmTN8+PArV66QDS8Igti5c+eHH364aNEizQbj4uJ279792Wef0Wi0Zl5lsVjh4eEHDhxYsWJFT+gt0XEJR1JSUn5+/pIlS7QLc3JytGtBOBxoew/0TqJVdaFttKnpBw423jyeC4dDg+wCAAPBjE2Yi95R/naUKHuIEMIExvTxU2h9vF9xs1KpNCcnh3xQU1OTmpqKYVhAQABCaNWqVUOHDn333XeXLl2ampq6adOmFStWkF1UEEKzZs3Kz8+/evUqeYskLi5uwYIFwcHBaWlp5ubmFy5cKCgoWLp0qaenp2ZfUVFRX3/9dUJCAofDaebVyZMnf//99zdv3hwwYADZLdbCwqK0tHT37t0YhrG63dSMresWi+O4SCTSWcXU1LTFFf/666+tW7e+//77w4cP1y7XruHYt2/fhQsXMjMze0KiBwworUHc/+59ZaNP/knfPm9Z6A7XAwAwFEIiRnI5Ztar5UUpSE1N1VRakOh0uup5z9uEhIS1a9dmZWWR/VDWr1+vGfgrOTnZzc3NyspKsyKO46dPn548eTJCaMqUKSUlJY3HzBg6dKhAIGCz2c28eu7cOYSQWCzeunXr8ePHc3JyVCqVra1tcHDwsmXLBgwY0C4H3nlQSjhwHN+5c+e2bdsKCwsVLw4qgBBqcQvnzp3bu3fvypUrBw0a1MxiMA4H0DclQWwuLY8uLpU3quQYY2aa6O8DFRsAAKAnlG6pbNy4MTo62t3dferUqSYmrRvfMD4+/uTJk59//jlZcwWAoaQ0iCOy81IaxFYs5s/uHnQMbSwpSxeLLZnMmZa91zk5QLYBAAD6Qynh2L17d0RExK5du1o7tvzu3bvPnj27ZMkSgUBQWFiIEGIymQ4O+mr4A0CTpDi+obj0h9JyNUGE9bbY7uFmzmQgqXRSwxOisgIJjOn2lhgDerQCAIAeUUo4qqqqFi9e3NpsAyH0xx9/qNXq7du3a0qsra137drV2u0A0GZ/1T+LzMnPlUidOeydHsLQXqYIIeLpY+X/bSEaROQy6quXGOOn0IePMmikAADQnVFKOBwdHevr69uw9cOHD7dhLQDaRb1K/XFh8e6KSoTQElvrTW7ORs8H5lIdPazJNv4pSTxDc++D2dg2sSEAAACvjFKlxaJFi7Zt29b9Rj0D3VjC0xrf2/d2VVQKudwrgb47Pdw02QYhEePFhborqJR4dkZHRwkAAD0GpRoODw+PvXv3Dho0aM6cOQ4ODjqT4E2ZMkU/sQHQFlUK5aqC4kNV1QwMW+1ov8HZga19N5Ag8MK8JldUJV8lxA00N3easxvicjsoXAAA6BkodYvFmh1jsb1qPqBbLHh1xx4/+U9u4ROlMtCIv9dTqD0lG/GkWn3vDn7vFlFb0/TKNBoiu8vSaJiNHc1VSHMV0lzcXn3sZAAAAJRqOI4dO6bvOAB4RRVyxX/yCv73pIZLo33j6rTSwY5OJspSqTrzAZ5yG8/PRQSBGEyaly/N3EL19x/aq9M8vJhzF+EPi/HiQqKkEC8qUJeXqv+6gjAMs7SiObvShJ6Y0APjNTESMwAAgBZRSjimT5+u7zgAaDMCod0VlSsLikVq9VAT4z2eQk8eF6nVeG4WnpaifpCKlEqEYTQnF5pfIC1oAJk0YEIP1aVE4lEFJhDQ/IMYY95ALDbN3ZPm7okQQkoFXlz0b/JRVam+mYwQwnqZ09w9IfkAAIDW6tDJ2wBod/lS2ZKc/Ct19SYM+hahy3t2tlhFqSrpFp56lxA3IISw3pa0gH70fq9hvV4Ytpzm5cvy8n3pdpmsF5KP8jKiuBDPz8GLCtQ3kyH5AACA1qKacBAEkZSUdPPmzZqaGvzFYaG3bNmih8AAaIGKIDaVlq8vLpXh+JvmZtutLewy0lRx+4gn1QghxOXRB75OC3qN5uSCXnGiVyaL5uyKnF3pI8a0nHy4eWBNTYAJAAA9HKVGoyKRaNy4cdeuXWvyVWg0CjpeWoM4Iif/jqjBjE6PYaCIjLvPm2gwaO596EEDaD7+iK7nwUNfTD7Q81mgIPkAAIDGKNVwREdHX79+PSYmZsqUKd7e3gkJCQKB4KuvvqqtrYX2pKCDyXD8m4dlXz8sU+DEdJl4y+2rFg0iHCHMzoEeNIDWtz/GN2p5K+3ipTUfhVDzAQAAOijVcLi4uAwaNCguLk4mk3G53OvXrw8aNEitVg8aNGjEiBHff/99u4QCNRygRdfqn0VmZGcrlNYK+bb025OqyjFTM1pgP/qAwZhFb0NH95xSiZeXapIPpFKSxWTygTm50oSeWCsnQQQAgK6OUg1HeXn5sGHDEELkdCpKpRIhRKfTw8PDf/rpp/ZKOABohri27osHD35QYwRCEWVFXxflmHl60iZOoQk9XrWJRrtjMrVqPl5IPtQ3k5FWzQfm5EoTemAmpoaOGAAA9I5SwsHn88kkg8VicTiciooKstzY2LiyslKP0QGgVOJZ6WeyspaZ9C7l8FylDdtrq0Z796GHz0RMlqGDo0A7+VCricoKPC+nzckHUV+HMVmIBwORAQC6HkoJh6ura05ODvk4ICAgPj5+xowZarX6119/tbe312d4oKciCLykCL9360l62loXr70OrgyCeA+pYoYOMjLusjcj6HTMzoFu50AfMQbhOPGovLnkw80dMzXTrIrfv6c68xtRX48QwuwdmW/NwOwdDXYgAADQepQSjtDQ0H379m3dupXJZEZGRi5evFgoFOI4XlxcvHHjRn2HCHoUoqpSnZaC371J1NacsHFYPjjkMZPlz2bt8fUaIOio1qAdgEajnnwgjKb69aBmVaLsoXLvdubyT6AhCACgC6E0W+wnn3xy6dIlcviNyMjIH374gcPhGBkZrV+//pNPPtFzhKBHIETP1H9dUWz5VrE5Rp107hGOzxo9cU7g63Us9mpH+9sD+3erbEMHmXyMGMOMXMbe8A1z6fuMkHE0V3fi2TP1zWTV0VjtbINESMTqv68YJFgAuqibN29OnTrVxcUFw7DIyEidV8+cORMYGMjhcBwcHKKjo3WGm6JCJBIZGxvzeLynT5/qvCSRSL755pt+/foJBAKBQODr6xsVFfXgwQOKq3cblGo4TExMTLR+S3300UcfffSR3kICPYlSiWfcV9+7g+dlIxxHNBrm6R3n23eFEtUoVa+bCPZ4unvxetLErQwmzVWIXIX0MQipVHhpMVGQp7p0AeEqhF5oG0tUVxkqRgA6wMnHTxNr68Rq9UBjwWIbKw6N0s/jZojFYqFQOH369OjoaJ2Xbty4MXny5KioqEOHDqWkpERFRanVak39/b1795ydnXv16qVZniCIy5cvjx49WnsjsbGxAQEBbDZ7//79K1eu1JTX1taOGDGipKTk448/HjBggLm5eXFx8fXr1+fPn5+SktLi6t0JpW6xJJVKdf/+/erq6sGDB5uatn+7eugW24MQBF6Uj9+9rU5PRTIZQgizsaP3e63Yy29peeWl2noenbbOyeHfCdh6NsV3XxJPH+sU0vu9xpgx1yDxAKBvc7Jyj1T9+5n35HFvBPmbMtpnLo7AwMD+/fvv2bNHUzJt2rSsrKzMzEzy6eeff/7jjz9WV1fzeDyE0MSJE0tLS5OSkiwsLBBCBEFERUWdPHkyOzvb3PzfCRMCAgI++OADNpu9fv363NxczSzrkZGRR44cSUtL0/nTplar6VqDE75s9e6Eas4YFxdnb2/fv3//N998Mzs7GyFUUVFhaWkZGxurz/BAd0M8rlJdPKf47gvlzp/Ud25gTBZ9SDDr/VWMD1bvdfMKzMq/VFv/Ri/TzAFBqx3tIdsg0fsNaFyIP32CZNKODwYAfYuvfqKdbSCEciTSlQXF+tvjtWvXxo4dq3k6duxYsVisqX6Ij483MzMbMWJEVVUVjuNvv/32qVOnrly5op1tXLt2raCgYMaMGVOnTn38+HFSUhJZrlAo4uPjFy5c2PiHtHa28bLVuxlKCWNiYuKcOXOCgoJWrly5atUqstDW1tbf3//EiRNz58LPLIAQQogg8OICoqYGM+tFc3ZF2lWgUok6LRW/dwsvKfpnjnj/vvSgATRPb0SjpYslESn3bz1rMGUwdnq4Lba1hkRDG31kKP6oAn+Q+vw5HTM2JooLFT9tYs6LwKxtDBodAG10vqZ2U2lF4/J0saRx4aGq6hKZvHH5CFOTz5xeqbMkjuNVVVXW1taaEvKxZgAIPp9/9uzZSZMmBQcH+/n5/f3333/88YeXl5f2RrZv3x4WFmZkZIQQCg8P37FjR0hICEKorKxMLBb7+v47T2RERMQvv/xCPr5//76Pj08zq3czlBKOmJiYwMDAGzduqFQqTcKBEBo8ePDhw4f1FhvoSoiap8rYfUR5KfkUs7Flzl2EmfbC87LV927jGWlIrf5njvig1+iB/RCbjRBSEsTmh2Xrih8qcGKCea8dHm527K4wukYHo9GYcxfhJUVESRFisWhCD6yXhSoxQf1HkuK/m5nTwmmB/QwdIgCtVi5XJNXWUVxYgRNNLmzB1Nec59o3Nbhc7smTJ11cXI4fP37hwgWdbOPJkyfHjx+/ePEi+XTRokVDhgypqKiwtbVt3Gjhiy+++PDDD2/cuLF48WLy1WZW19OhGQqlt+ru3btffPEFg8FQPZ+eiuTo6Pjo0SP9BAYQQgjJZHh+DtEgwiytaS5unW5ITQ2CUMUd0GQbCCGiokLxfz9iBCIkYoQQZmlF8w/SmSP++jNRZE5+plhizWL97O46rbd5E1sGz9GcXJCTi+YpY9wkmo2t8kS8Mu4XemEeY3KY3ierA6BdRdhYRdhYNS7/5mHZmsISncIxZqYXA3z0EQaNRrOystIexJJ8bGPzb92hQqGYP3++sbHxwIEDFy9efOXKFReXfy/G/fv3y+XykSNH5gsHqQAAIABJREFUakrUavWePXvWrVvn4ODA4/HS09M1L9nZ2dnZ2WnvrpnV2/tYDYxSwqFWq9lsduPy6upqJpPZ3iGBf+D5uaq4X4gGEfmU5uzKWLAY4xl0AjCZFBEEIZMhgkBSCUKIkEoRQRDlpfjDF78gMITEYoLLow8dQQ8agNk5aL8oxfENxaU/lJarCSKst8V2Dzdzvf1M6cZogf1ZNnbKQ3vVN5OJRxWMOW9rjxUGQBf1vp3twcrqLMm/TZR4dNpmoUszq7yiIUOGnD9/fvPmzeTT8+fP8/n8vn37kk/lcvm0adNycnKuXr1qbW09Z86c4cOHX7lyhWyWQRDEzp07P/zww0WLFmk2GBcXt3v37s8++4zFYoWHhx84cGDFihVN9odofnV69/oVQamXSkBAgLe3t87kbQRBDBo0iE6nJycnt0so0EtFGyFuUG6KIcQN2oU0v0Dm3EUvW+VfCjlSqwm5HOE4kssRribkcqRW//sYx5FchnCckMsQjiOZDBH4P2mE5vHzciSTEQRBphqtPQrmrAWNa/v/rHsWmZOfJ5W6cDg7Pd1CzGAmkVcjlyuPH8HTUjC+EWPWfJp7H0MHBMCrqlYo1xU/TKypFanVg42Nv3RxDDR61d9aUqmUHDJ75syZPj4+69atwzAsICAAIXTjxo2hQ4dGRUUtXbo0NTV16dKlK1as0HSLnTp1amZm5uXLl8l7HGq1esGCBVeuXElLSzM3N09MTBw7dmx2dranp6dmX6WlpU5OTr/99tvkyZNramqGDx9eXl5Odou1sLAoLS3dvXt3QkJCdnZ2UVFR86u/4lF3KpQSjs2bN69atWrPnj2zZs0iEw5fX9+VK1fu3Llz586dS5YsaZdQIOHQpr6VrDoRr1uKYTTfQIQhpFAgtQopFIRK9e9jtZpMNdonAiYLMRgYi4noDMRiIToDY7EQg/G8nIXodMRiIzodY7GRRKJqahwq1rsfYQ5Omqd1KtXqwpLdFZUYQpG21pvcnI26V/5uQOq//1Cd/R8iCMaYsfRRb3Teu28AGEhqaqqm0oJEp9M17QQSEhLWrl2blZXVu3fvRYsWrV+/nva82XtycrKbm5uV1b93f3AcP336NJkNTJkypaSkRHtEDdLQoUMFAsG5c+cQQmKxeOvWrcePH8/JyVGpVLa2tsHBwcuWLRswYACV1bsNSgmHSqWaNGnSuXPnLC0tq6ur3d3dS0pKFArFxIkTT506RXvl8VhIkHBoU1++oEpMaHk5Oh2x2BiDgZhMMhvAGP/kB4jFel7OJP/Hni+DGAyMydS8hGmWYTKx5wu0NmDlgV141gPtwaloQg9m5DLNX77TT2v+k1tQJlf48Hl7PYUDjQWt3QVoHl5cqDqyn6ivp/XxZs6cD3O8AQA6FUp/VxgMxunTp3fu3Hnw4EGZTPbo0SNfX9/58+e/++677ZVtAF1N/smn01kfrEYcDvY8pejwsF6KETZHdTIeT79PPqV5+TKmzyKzjSqFclVB8aGqaiaGrXa03+DswIaPjR7QnF2Z73+sOvILnp2p2PYdc+4imOANANB5tGKkUX2DGo5/4Lj6z8uqi2eQSvfmCH1kCGPsRIMERRFRX0/UPMHMemlaLx57/OSd3IKnSlWgEX+vpzCoG0+J0knguOpSovrSeUSnM8ZNog8dYeiAAAAAIYo1HKDDEJWPlMcOE2UPMSMBLXg0kZ2JF+QihBCdTh8SzAh509ABNudo9ZNt5Y+KZDLnOvF/bFWvmxhH5eZfqKnj0mjfuDrBOOUdhEZjhIyj2Tsof41VnT5JlJUyps7sVJVhAICeqbmEQyaTUdkEh8Npp2B6tn8qNs4ilYrm35cxJQzjG6HhowjRMyQSYRa9O/nfjO9Lyz9+PvZwhVyRXC9iYzQ5gQ8zMd7jKfToUROwdQI0L1/W8tXK2H3qlNt4+UPmvEjMsokBDwAAoMM0l3BwuZT+SHSemzJdF/GoQnnsMFFeihkJGFPCaH6BmpcwgTESGBswNiqqFcq1RboD9cgJPNrJYZ2zIw3qNQwBMzVjRb2vOvu7+tpVxc8/MKfPpvn3bXk1AADQjxZuqXA4HHKwjY6JpidSq9V/XVFdOIPUapp/X8aUGRjfoEN7tVKpXJ4nkZ168lSBN5F3+hrxIdswJAaTMWkaZueg+u2o8sgBenEhY/wUGJAUAGAQzSUcbm5uBQUFubm5CxcuXLRokZubW4eF1UMQj8qVRw8TFWWYwJgxZQbN19/QETWnSqHMlUrzJNI8qSxPKs2XyvKkUokaRwghRGh3iNVgQaONToDe7zWajZ0ydq/62lWioowxeyFmbGLooAAAPU5zvVQIgvjjjz/27t178uRJmUw2YsSIiIiIqVOnUrzV0lo9q5eKTsXGWzMMPGb5i2pVqkKprFAmK5TKCmXyDLEkXSyuf7HXDIuG2bPZrhyON58r5HA+LXrY8OKYYwI6vXhQ/14wZnknIZMpjx/BH6RifCPG7IU0oYehAwIA9CyUusXW1dUdOXJk79699+7dMzU1nT17dkRERFBQUPuG0nMSDvxhserYYaK6ChMYM6bOpHn7GTAYOY7nS2WZEgmZW2jyDO1lmBjmwGG7cjiuXI4rh+3N5/nweE4ctnavk5OPn87KytG+sXLYy2O2Ve+OOxLQIoJQX7tKDkhKH/UGY8xYGJAUANBhWjcOR2pq6t69ew8fPlxbW/v999+vXLmyHUPpEQmHSqm6eE7952WE4zT/vsy3ZnbkcJAKnCiTywtlsgyxJFMiJXOLIqlM5xNgw2L58HlkbuHK5bhyOD58HofCUF0ZYsn2isoimcyJzV5qax3wynMfAH3Ai/JVhw8Qomc0L1/mzHlIPxWWAACgo3XV3UKhMDAw8MaNG3fu3GloaGh5BaAFLylSHTtCPK7CzHoxpoVTmWSrTqVKqq2vUii8+bwRpibUf40qCaJUJifrKjIkkkyxtFAmK5bK8BcXM2MwggRG3jyeD59L5hZePB6P3sZhQH34vJ/dXdu2LugwNBch871VqiP78ax0xU8/MOdFYDa2hg4KAND9UU04rl27tnfv3qNHj4rF4sGDB+/Zs2fmzJl6jaxbUSpVSefUf15GBEEf+Dpj/FuIzW5xpaTaujlZudUKJfn0dRPB777ejadxVxHEw+e5BXlPJEMiyZFI1S/WXZkxGH0FRmSTCx8+z5XD8eRxYe60ngkzMWEufV+VmKC+eknx8ybG5On01wYbOigAQDfXQsJRWVl58ODBffv25eTkWFpaRkVFRUREeHl5dUxw3QNeXKg6doR4Uo2Z9WJMn02xsV61QjkrM/eJUqkpSa5/tiQ3f4+nMEMsyRRLNO0tMsUSKf5CzYUZgxFoxNducuHH55swILcAWmg0xrhJNCcX5dFY1Yk4oiifMTUcMZmGDgsA0G01l3BMnjz57NmzBEGEhoZ+9dVXkyZNYsL3UasoFaqk8+qrlxBC9IGvMya8hVgtV2yQ/ve0RjvbQAghhJ18/PTk46faRcYMujef587luHO57jyOB5frzuVCxxBAEc3bj/XuR8pD+9T3buOVj5jzIrBe5oYOCgDQPTX3l+n333/ncDhTpkyxs7O7fv369evXm1zshx9+0E9sXRteVKA6foR48hjrZc6YPpvm5t6q1TV3UnSE9jINMjJy53LceVwPLteKBSkgeCWYhSXr3Y9U/zumvn1DsfU7ZticTj4eDACgi2rhp7BMJouPj29+GUg4dMlkqgtn1Ml/on8qNqa2YRoUIbeJGWrYNNpJnz58aHgB2heTyZg+G3NwVv3vmDJ2Lz14NOONCYhCvyQAAKCuuYTj9u3bHRZHt4HnZqtOxBF1tZi5BWP6bJprG7v42rNZdAzTafj5kYMtZBv/396dB0RRN3wA/83szLLc9yUgl4AiV77mHaKiyGPiBZZhZWZp+VRq9vim9rz6pPWUPV5l5QmVmOadGorkg+bxpJaBCIIEJCDIJfcuOzs77x9bGw8ih7LMLvv9/MX+GHe+Oy7sl5nfzICOSIaOoN09uN27+LRU4fZvTPwcysJS7FAA0Hu0VzgGDx7cYzl6A4Vc9d1R/vIl8gg7NjTO1tROvp5NCAk0M8tqaiKESGlqkXuf1V59uzEvQCuUmwf72lLV3i/VOdnc5nVM/Au0p7fYoQCgl8Dswu6hzslWHdor1NyjnF3ZuGcoD8+Hfqrk6nszMm9ygpDY32+2s2MFx5U2K/uZmj705TEAOo8yM2dfWMCf/V518hj3+SYm6klJRKTYoQCgN0DheGRyuSr5KP/jRULTkohIZvxfCPPwW/XbyuqZWTmEkP0DA6Y62BNCHFnWEScHQU+iKElEJOXRV7XnC1Xyt+qiQjYunshwQVIAeCQoHI9EfTNLdWivUFtDubiycfGU+yMd8thzt+L5m7ekNHUkaMB4W5vuCgnwEGhff/a1paqkBHVmhvJuGTv7RcrFVexQAGDAsJf+YcnlqkN7uYTPhfo6SUSk9LW3HrFtbLtT9mx2roymjwcHom2APqBsbNn5r0tGjhYqypVb1qvTfxY7EQAYMOzheBjq7EzVoX1CXS3l2oeNi6fcPB7xCbeUlL52K9+GYZJDAoda4dQA0BsMw8TMoDw8VYf2cnsSJb/mMlPiCE6VAoCuQ+HoGqGhnj9xhP/5CpFIJBGRzIRJj/7L94Pbxf+b/5uzlE0JGRiCO6yC/pE8Nph29+C+2sn/eFFdUszOnkvZ2okdCgAMDApHF6gzrqmO7BcaGyhXN3ZmPNXH/dGf8+8Ft9/9rcjDxCQ1dKC/GeblgZ6iHJ2lC9/kDuxRZ1zjPl7HPP087d/x7Y4BALRQODpFaKhXHdmvvv4LYVhJZDQzdsKj79gQCFmcV7Cp+I6XzCQ1NMi3rUuLAugRExM2/gW+X4Dq6H5u12eS0eOYiZMJRYkdCwAMAwpHx9QZ11RHvhEaG+m+XkxcPOXk/OjPyQvCy7m/7iq929/MNDU0yM3kIS8RBtDDJENHUM4uqqQEPi1VKCtln3qWmJmJHQoADADOUmmPUF/HfbWDS0oQmpVMdAz7yqJuaRsqQXjhZt6u0rthFubnHgtG2wDDQnv5sIuW0X4B6ps3lJs+UN/+TexEAGAAsIfjgdQZ11SHvxGaGmlPbybuGcqxG6oGIUSpFp7OyjlcWTXY0uJkyEB73EoeDBBlbsHOfUX1/Sn++5Pc1k1MdIxkVITYoQBAr+HTrg1CfZ3q0D511nXCskx0jCR8bHfdObOJV0+7kZ1SXRNuY3UsKNCKwemFYLBomhkfTbt7cPt2q44dEkqKmOlPERa76wCgbSgcrakzrnGH95GmJtrLh4l7hnJw6q5nbuD5mOvZ/66pnWhneyiovylu/w2Gjx4QJH3jb9zuXfzPV9Sld9hn51L2jmKHAgB9hMLxJ+FetergXvWtm4SVMtExktHjunEG/j2VKjoj68e6+sn2dt8MDJChbUBvQdnaSV95Q3X0IH/5onLzOjb2GTo4jKhUQvldouYpJ5eHvm0yAPQmKByEEEIEgb98SXXiMGlupr19mdhnKIfu/CvtrpKbkHEjo6HxaSeHLwf4sziTEHoZhmVmPE15+agOf8MlJdABgUJJkVBfRwghpqZM9BTJ0BFiRwQAkaFwEOFeterAHnVeLpHJmMnTJSNHd++lBUqVysj0G1mNTS+5On/u349G2YBeSvI/Q2jXPlzidvXNG3+OyuWqQ3spaxu6f6B40QBAfMZVOISGev7UCXVOltDcTHv0ZSZMUhfdVp38liiVtP8AZsbTlI1t966xUNEcmZ75q1zxqpvrJ34+KBvQu1F93Gkvbz79XqtxPu00CgeAkTOmwsFx3LZPhLulhBBCBPWtHGXeLSKoicyUiX1GMnhot18z8WaTPDI9s6RZuayv+z99PLv3yQH0k1BX28bgveqeTwIAesWICgd/6Yc/2gYhhCKEEEFNmZmxi96mrK27fXW/NDROSL9RwXGrvPr+n9ej3k4WwGBYtfHTRFlZ9XwQANArRnSuhLr49v2DgpLTRdu4Wt8QmZ5ZyXEb+nmjbYBRkQwbef+gQGiiUvV8GADQH0ZUOKg2L0nEst2+onM1dePSM2tU/PaAfovc+3T78wPoM9rHj5kS++cVwCQSYmUl3C7gdmwhTU2iRgMAMRnRIRU6MIi/+p9Wg5KBwd27lpPV96Zn3lQKwq6Afs+5dNtFwwAMiGREOB3ymFBYQNQ85eFJmVtwX3+hzrqu/OQj9oUFlCN+LgCMkRHt4aAHhkiGDG85Qjk6MU9O68ZVHKuqnpZ5kyfCN4EBaBtgzCgLSzoohA55jLK1I1Ip+9w8SWS0UFWp/HS9uiBP7HQAIAIj2sNBCGFmzKIHhqpv3iDKZsrDU/L4MMJ02yGVveWVz2XnsjR1ZGBglJ1Ndz0tQG9AUcz4aMrKWnXkG27Hp2zcM3TYYLEzAUCPMq7CQQih+wfq4noA20vvLsjJM5VIvg0aMNa2+2ehAvQCkqEjKFtbLimB2/uVpKKCGR8tdiIA6DlGdEhFdz4tKZ2fk2fFMKmhA9E2ANpB+w+QLlhEWdvwqcmqA3sIz4udCAB6CArHo/rgdvHCW/mOUjYtLGiYlaXYcQD0HeXah124hOrjzl/5D5fwOVHIxU4EAD1Bt4UjNzf3/fffnzdvXkxMzMcff6zTdYliVeHt/83/zUUq/T40KNTCXOw4AIaBsrKWvvIG3X+g+laO8tONuA4pgDHQbeFQKBSurq7PPvusq6urTlfU8wRCFucVrC4s8pSZ/PBYcJC5mdiJAAyK1IR9/iXJ8CeEu6XclvVCW9flA4DeRLeFIyQkZM6cOaNHj5bJZDpdUQ/jBeGlnLyNxXcCzEzPPxbSz7RXvTqAHkLTzNQ4ZvJ0oaFeuXWzOuu62IEAQIcwh6PLeEGYm5O3s/RuoLnZmdAgd5O2LmAKAJ0jGRXBxr9ABIH7aieflip2HADQFZFPiz1//vzf//53zdeNjY0WFhbi5umQUi3Mys45VFH1P5YWJ0MCHXRwZXQAY0MHh7HWtqovtqmSvxWqK5mpMwmNv4UAehuRf6oZhrH8A03TgiCIm6d9Tbw6JjPrUEXVKGurM6FBaBsA3YXu68kuXEI5OvM/XuQSt5LmZrETAUA3E7lwDBs27Ogf+vXr19jYKG6edjTy/OTMrFPVNRE21skhgVaMROxEAL0KZWcvXbiY9vFT52QrP9sg1NaInQgAuhP2W3ZKjUoVmX7jzL3aSfa2ySGBFhK0DQAdMDVj570qGfS4UHqH2/Iv4U6x2IEAoNvotnAolcr8/Pz8/HylUtnQ0JCfn19QUKDTNepCuZKL+CXzP3X1Tzk5HA4aIMPRZQDdkUiYmbMlkdFCXZ3ys03qm1liBwKA7qHbSaPFxcWLFi3SfF1SUnLp0iWapo8cOaLTlXavMqVyfPqNzMam2c6OCf39GIoSOxFAb0dRzPho2smZ+yaJ+2IbMyVWMmyU2JkA4FHptnD4+Ph8++23Ol2FTv2maI5Mz8yTK17p4/KJny+NsgHQU+jQQay1jerL7arD3wjld5nJ0wnqPoAhw9GBB8ppko+6lpEnVyzr6/6pP9oGQE+jvXzYhUsoB0f+wlkuKYFwnNiJAODhoXC0LauxaWx6ZnGzcllf93/6eIodB8BIUfaO7MIltJeP+vov3LaPhYZ6sRMBwENC4WjDT/UNo3/JLG1W/svXG20DQFyUmTn70l/p0EHq24XclvVCxV2xEwHAw0DhaO18bd3Y9Mwqjtvs57PEo4/YcQCAEIZhZz0viYwWqquUWzao82+JHQgAugyF47+k1dRGZ2Q18uqE/n5/dettd7gFMGAUxYyPZmJnEWUzt+NT/ucrYgcCgK5B4fjTiap70RlZSkG9LzDgeRcnseMAQGuSx4ezL8wnrFT1zW7V6WSi3zdDAICWUDh+t6+8clpmtkDI/sD+MxztxY4DAG2j/fpLX1lEWdvwqcmq/UmE58VOBACdgsJBCCG771bMzs6V0vTx4AExDnZixwGA9lAuruxf36TcPPifLnPbPhb0+B5MAKCFwkE+v1P2fHauhURyOnRgpK2N2HEAoGOUpZV0wRv0gCB1YT63Zb1QWSF2IgDogLEXjg9vl7yS+6sNy6SEDhxuZSl2HADoNKmUfW6eZGS4UFXBbVmvLswXOxAAtEe3lzbXN028+qOikhPV1Y28erClhTPLflhU4iKVpoQODDY3EzsdAHQRTTMxsZSdg+r4YW77FnZmPB06SOxMANA2IyocKkEYn5F5sfb3KxXeaGwihPSRSs8+FtzPVCZqNAB4eJJREZStHff1l9zXX0jK7zLjo8VOBABtMKJDKjtK72rbhlY/MxnaBoChoweGSBe8TllY8qnJqoNf49QVAD1kRIXjfG3d/YNX6xt6PgkAdDvKvS/72ltUHzf+8iUuYStRKMROBAD/xYgKh6Ste1tLCG4CC9BLUNbW0gWL6IBA9a2bys82CjX3xE4EAH8yosIRaWt9/+AEO5wHC9CLmJiwc16WDBsllN3hPvmXUFIkdiAA+J0RFY7Zzk5P2v/XRb2cpeymfj5i5QEAnaBpZtpMZvJ0oaFe+flmdXam2IEAgBCjOkuFIuRIUP9dZeXfVd2r41VDLC2XerjZs0a0BQCMh2RUBGVtw+37ivtyBzN5umREuNiJAIydcX3cSijqJVfnl1ydxQ4CADpHB4exVtaqL7arjh4QKiuYydNJWxO5AKBnGNEhFQAwNrSnN7twCeXoxF84y+3eSTil2IkAjBcKBwD0ZpS9g/TVJbS3rzozQ7n1Y6Gh9cV4AKBnoHAAQG9nZsbOW0g/Nlgo+o375F/C3TKxAwEYIxQOADACDMM+9awkMlq4V63c8i91TrbYgQCMDgoHABgHimLGRzNxzxCVikvcyv94QexAAMYFhQMAjIhk8DB27ivExER1aJ/q24NEEMROBGAsUDgAwLjQ/fylryyibO34C2e5PYmE4wghRBBwDguAThnXdTgAAAghlLMru3AJl7hNnXGNq64iNnbqnCzCKSkHJ2bCX+jQQWIHBOiFsIcDAIwRZWklnf86PWCguvi2OvMXze4NobKc25Oovv6L2OkAeiEUDgAwVlIp3T/o/mHV8cM9nwWg18MhFQAwXkJ5aRuDNfeU7/8fkZlSMhmRmf7xhYwyNSMyGSUzJTKZ9ruUiYyYmvZo5spy/qfLpLaGcnCihwynLCx7cu0ADw2FAwCMmImsjUGKImq1UF0pKDs9jdTUlJKZEhNZxx3FREbJTB+6o6jTf+a+2U1UKkIIIQI5+z07dwHt6f1wzwbQk1A4AMB4SYLD+DMprQbp4DA2/oXfH3CcIG8i8iZBLifyJsJxAsf9+fCPLwR5E+E40tigvlva2VNtGZYyNSVmZoRhKZYlpmaUqSkxNdN+QZmZEYYlLPv7QwtLoamRO7T3j7ZBCKGIQq7a+6X0rXcIjePjoO9QOADAeFF93Jknp6mSvyU8TwRCKEI5uzJTZ/65BMtSrDWxsu7sfWbVaqJQCAo5kTcJzQqiUAhyOVEoSLNCUMiJQi7I5UQh/30ZhUK4d48omzt7MRCplNy300WorhJKSyg3j04+B4BYUDgAwKhJnhhD+/VXZ2cKjQ2Um7skZBCRSB7+6WiamJlRZmaE2He2o5DO7kdRV1fdXzgIIYKyuQvrAhAJCgcAGDvKxVXi4ipmgs7tRxHuFCs3fdh6VCKhXfroLBlAt8FhPwAAw0D1cZc8PrzVIBM1iZiaiZIHoEuwhwMAwGAwU+Moewf+8kWhtoZycJQ8MUYyeJjYoQA6BYUDAMBwMIxkzHjJmPFi5wDoMhxSAQAAAJ1D4QAAAACdQ+EAAAAAnUPhAAAAAJ1D4QAAAACdQ+EAAAAAndO702K3bdtmb28vdgoAACCEkClTpvTv31/sFNAb6FHhmDt37uXLl9etW6frFTk5OZmamhYVFanVal2v69HZ2tpaWVmVlZU1NzeLnaVj5ubmDg4O1dXV9fX1YmfpmFQqdXV1ra+vr66uFjtLp3h6ejY3N5eVlYkdpFNcXV0ZhikqKhI7SKc4ODiYm5uXlJSo/rwXq/6ysrKytbWtqKhoamrS9bq8vLxQOKBbUEIn76TcI9LS0ioqKnS9ln379hUWFi5evFgqlep6XY/u3//+9+XLl2fPnu3m5iZ2lo5lZmaeOHFi/PjxgwYNEjtLx8rLyxMSEsLCwqKiosTO0jFBED788EM3N7fZs2eLnaVTdu3aVVNTs2TJErGDdMrx48dv3Lgxf/58GxsbsbN07Mcff0xLS5s6dWpAQICu1zVs2DAPD9yKFrqBHu3hIIRERET0wFrS0tIKCwunTZtmZmYANyAoKSm5fPnymDFjQkNDxc7SMTMzsxMnTgwaNCguLk7sLB3Lzc1NSEjw9fU1iLSawmFvb28QaQkhBw8ebGhoMJS0GRkZN27ciI6Odnd3FztLx+RyeVpa2ogRI8aOHSt2FoDOwqRRAAAA0DkUDgAAANA5/ZrDAQAAAL0S9nAAAACAzqFwAAAAgM6hcAAAAIDO6ddpsT1ALpfPmTOH5/mEhARLS0ux4zzQ9u3bjx07pvnaxMTE2dk5Ojp60qRJ4qZqX3Nz89GjRy9cuHDnzh2GYVxdXR9//PGYmBhzc3Oxo/0X7balKMrU1NTV1TUkJGTSpElOTk5iR2tbyzeD1vr16/v16ydKng7dH3jixImvvvqqWHna19zcfOzYsQsXLpSUlBBCnJycAgMDJ02a5OnpKXa01rZv337x4sWEhISWgy+//HJYWJjebl4ALaMrHGlpad7e3izLpqamTps2Tew47bGxsXnvvfcIIQqF4uLFi1u3brWxsRk5cqTYudrW2Nj49ttvV1RUxMXF+fn5mZubFxUVpaSkyGQyPdzOmm0rCIJcLi8oKPjuu++Sk5NXrlwZEhIidrS2WVtbr169uuWInl8uolVgKytGN6NwAAAMw0lEQVQrEcO0o6GhYfny5eXl5TNmzPDz87O0tCwvL7958+aGDRs2btwodjqAXsXoCkdycnJMTAzDMF9//fXUqVMpihI70QNJJBLth0q/fv1OnTqVl5ent4UjISHhzp07H3/8saurq2bEx8dn9OjRtbW14gZrU8tt6+/vP27cuOXLl2/YsGHbtm0sy4qbrU0Mw/j4+IidogsMJfD971tfX9/hw4cbxH0PAAyLcc3hyM7OLisrGzVq1IgRI2pra9PT08VO1Ck8z58/f76hoWHAgAFiZ2kbz/Pnzp0bN26c9re2lrW1tSiRuoRhmNjY2KqqqqysLLGzQM9RqVQ//PBDm+9bmjau340APcC49nAkJyePHDlSJpMRQsLDw5OTk8PCwsQO9UBVVVUzZswghKhUKpqmn3322SFDhogdqm0VFRUKhUIPj3l3npeXFyGkrKxMPy8hX1VVFRMTo31I0/SRI0dEzNOhVoF37Nihh1NkKisrW71vN2/efObMGe3Xffv2FSnaA7XasAAGxIgKR11d3YULF/7xj39oHkZGRi5btqy6utrOzk7cYA+incOhVCpzcnJ27dplbm4eHR0tdq7eSc+vgNdqSoQ+HwrUaBVYb3/KWomPj58yZUpOTs4nn3yin2+J+2fzrFmzRqwwAF1iRIUjNTWV47gVK1ZoR9RqdUpKytNPPy1iqna0nGfg4+NTUFCQlJSkn4XDwcFBJpP99ttvYgd5eIWFhYSQ+3et6wlDmRKhZRCBHRwcTExMWr5v7e3t7e3ta2pqREzVvvs3LMMY0a9xMGjG8k4VBOHkyZNTpkyJjIzUDp47dy4lJWXmzJmGcrxWqVQKgqCHf90yDPPEE098//33U6dObfWZXVtbq//TOFQq1cGDBx0cHAIDA8XOAj2nnfctAHQ7Yykc165dKysrmzhxopubm3YwOjr6wIEDV65cGTp0qIjZHoTn+eLiYvLHIZUzZ86MGjVKD9uGxpw5c3JycpYsWRIXF+fv729mZqY5LXbw4MF6eFqsdttqTos9ceJEaWnpypUr9faPRZVKlZ+f33LExcXFzMxMrDy9xgsvvJCbm7tkyRLtabGVlZUpKSkURentmwHAQBnLT1RycrKPj0/LtkEIcXBw6N+/f3Jysn4WjpqaGs3FfFiWdXBwiImJeeqpp8QO9UCWlpYfffTR0aNH09LS9uzZw7Jsnz59wsPDo6KixI7WBs221Vz4y8XFJTQ0dMWKFXo4q1GrtrZ20aJFLUeWLVumt+dIGxDN+/bYsWPnz5/ft28fz/N2dnZBQUEfffRRq18XAPCIcLdYAAAA0DnDmLsAAAAABg2FAwAAAHQOhQMAAAB0DoUDAAAAdA6FAwAAAHQOhQMAAAB0DoUDoAMHDhygKEo/b5aWmppKUVRiYqLYQQAAOoDCAfru6tWrFEXNmzev5WBJSUlQUBDDMDt37hQrWDs0mbVMTU0HDhy4cuXKhoYGsaMBAIjDWK40Cr1Jbm7uhAkT7t69e+DAgalTp4od54GGDBkSFxdHCKmsrDx+/PjatWvPnj177ty5brw+/dixY+VyOcuy3fWEAAA6gsIBBuann36Kjo5ubm4+efLk6NGjxY7TnuDg4KVLl2q+XrNmzZAhQ86fP3/u3LlujE3TtEwm665nAwDQHRxSAUNy5syZMWPG0DR99uzZlh/bNTU1b775pre3t4mJibOzc3x8fF5enva7DMNQ9wkKCtJ8t7a2duXKlUOHDtXcrNzHx2fp0qXtH/vYvXu3iYlJVFRUfX19J5MzDDNhwgRCyK1bt7SDKpVq/fr1YWFhpqamlpaWERERKSkpLf/VnTt3Zs+ebWtra2FhERERcenSpdjY2JYN4/45HO1vCs18lIMHD37wwQf+/v4mJiZ9+/Zdu3YtbnEAALqGPRxgMA4ePBgfH+/m5paSkuLr66sdb2xsDA8Pv379enx8/IgRI27duvXZZ58lJydfunQpICCAEPLdd9+p1Wrt8hcuXFizZo2Xl5fmYVFR0bZt22JjY2fNmiWVSs+dO7d+/frLly+fPXu2zWMf77333ooVK+bOnbt169Yu3VBU88Fvb2+vecjzfExMzKlTp+Li4ubNm6dQKHbv3j1x4sSkpKRZs2YRQurq6sLDwwsKCubPn//YY49lZGRERUV5eHi0s4oON4XG3/72N39//82bN9vY2OzYsWPlypX29vYLFizo/GsBAOgyAUC/XblyhRDi5eVF03RoaGhpaWmrBVavXk0I0fyZrnHq1ClCSFRU1P3PlpeX5+Dg4OPjU1FRoRlRKBRKpbLlMmvXriWEnD59WvNw//79hJDDhw9zHPfSSy8RQlatWtWZzE899VRBQUFBQcHVq1eXL19OCHF2dm5oaNAss2XLFkLIrl27tP9KqVQOGjTI2dmZ4zhBEN555x1CyLZt27QLfPnll4QQExMT7cjp06cJIQkJCZ3cFJrXMnjwYLVarRnhed7Pz2/AgAHtvyIAgEeEwgH6TvPhbWpqSgiZOHGiXC5vtUBISIiFhUWr8eHDh9M0XVtb23Kwuro6ICDAxsYmOzu7zXUplUq5XJ6VlUUIeffddzWDmg/pr776Kjo6mmXZxMTETmZuJTw8/MaNG9plhg4d6uTkJP9v69atI4RcvXpVEITg4GAHBweVSqX9J2q12t3dvZ3C0eGm0LyWTZs2tVxAs2uH5/kOXxcAwEPDIRUwDLNmzZJIJNu3b4+JiTl69Kimf2jk5+f7+vq2mjsZHBx86dKlwsLCkJAQzQjHcdOnT8/Pzz958mT//v1bLpyYmLht27b09PSmpibtYHV1dctlFi5cWFdXt3///tjY2E5mjoyMXLhwoVqt/vXXXzdu3FhcXNwyZHZ2dl1dXcsXolVeXq55XcHBwRKJRDtOUVRAQEBFRcWD1tjJTdHquIyVlZVSqayvr7e2tu7kSwMA6CoUDjAMFEVt3bqVZdlPP/30L3/5y/Hjx83NzTXfEgShMyeavvzyy2lpaTt27Bg7dmzL8fXr17/55puTJ0/esWNHnz59TExMqqqqnnzyyZbTPgghsbGxSUlJ//znP8eMGaOdh9E+T09P7Vm706dPDwsLi42NvXLliqZDqNVqPz8/zVGSVrR9qKsn0HZyU7S5jIB5owCgSygcYDAoitqyZYtUKt24cePEiRO/++47S0tLQoivr29eXp5CoWj5l31mZiZN09qZoWvXrk1MTFy2bNmLL77Y6ml37tzp7e199OhR7cfwDz/8cP/aJ0+ePHPmzGnTpkVERKSmpjo7O3cpvK+v7/Lly5cvX75z586XX36ZEOLv75+ZmRkUFGRhYdHmP/Hx8bl16xbP89qdHIIg5Obmtr+WDjcFAIAocFosGJgNGza89dZb58+fnzBhQm1tLSFk+vTpDQ0NGzdu1C6Tmpp68eLFyMhIKysrQsi+ffveeeedGTNmvP/++/c/IU3TgiDwPK95yPP8e++91+aqo6KikpOTCwsLw8PDi4uLu5r8tddes7e3f/fdd5ubmwkhzz33nFKpXLp0aatdC3fu3NF8MWXKlMrKypanvO7Zs6eoqKidVXS4KQAAxII9HGB4PvzwQ6lUunbt2nHjxp0+fXrp0qUHDhx4++23b9y4oT0X1NbWdtOmTYSQgoKCOXPm2NnZxcfHa07Z0LC0tBw5ciQhJDY2dtWqVdHR0TNnzqyvr9+7d287BxdGjx59+vTp6Ojo8PDw77//3tvbu/OxLSwslixZsmLFiq1bt77++usLFy5MTU3dunXrtWvXpkyZ4ujoWFRUdOnSpfT0dM0cjqVLlyYlJc2fP//atWthYWEZGRmJiYmBgYH5+fkPWkX7mwIAQEwiTlgF6AzNGR8vvvhiq/FVq1YRQsLCwioqKu7du7d48WJPT0+WZR0dHWfNmnXr1i3NYpcuXWrznT9w4EDNAhzHrVmzxtfXVyqVenh4LF68uKCggBDyxhtvaBbQnharXfXPP//s4ODg7u6ek5PTpcx1dXV2dnYuLi6NjY2CIPA8//nnnw8bNszCwkImk3l5eU2dOvWrr77SLl9cXDxr1ixra2szM7NRo0Zp9uvY2tpqF2h1loogCO1sijZfiyAI8+fPJ4Tcu3fvwf8JAACPihIwUwzAcPj4+FhbW1+7dk3sIAAAXYM5HAD6S6FQtHy4b9++goKCqKgosfIAADw07OEA0F9jx4719vYePHgwy7KXL1/etWuXi4vLtWvXHB0dxY4GANA1KBwA+mvdunVJSUmFhYWNjY3Ozs5RUVGrV692d3cXOxcAQJehcAAAAIDOYQ4HAAAA6BwKBwAAAOgcCgcAAADoHAoHAAAA6BwKBwAAAOgcCgcAAADoHAoHAAAA6Nz/A4eGZ1MCzfzjAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 180,
       "width": 360
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data, aes(x = kozak_region,\n",
    "                 y = mean_ratio,\n",
    "                 color = insert_sequence,\n",
    "                 group = insert_sequence)) +\n",
    "  geom_line() +\n",
    "  geom_point() +\n",
    "  labs(x = \"Kozak Region\",  # Custom x-axis label\n",
    "       y = \"Mean Ratio\",    # Custom y-axis label\n",
    "       color = \"Insert Sequence\",  # Custom legend title for color\n",
    "       title = \"Mean Ratios in Different Kozak Regions\") +  # add title\n",
    "  theme_classic()   # Apply classic theme "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Change Y axis to log scale\n",
    "\n",
    "See https://ggplot2.tidyverse.org/reference/scale_continuous.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 180,
       "width": 360
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data, aes(x = kozak_region,\n",
    "                 y = mean_ratio,\n",
    "                 color = insert_sequence,\n",
    "                 group = insert_sequence)) +\n",
    "  geom_line() +\n",
    "  geom_point() +\n",
    "  labs(x = \"Kozak Region\",  # Custom x-axis label\n",
    "       y = \"Mean Ratio\",    # Custom y-axis label\n",
    "       color = \"Insert Sequence\",  # Custom legend title for color\n",
    "       title = \"Mean Ratios in Different Kozak Regions\") +  # add title\n",
    "  theme_classic() +  # Apply classic theme\n",
    "  scale_y_log10()   # Apply logarithmic scale to Y axis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Change Y scale to go linearly from 0 to 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 180,
       "width": 360
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data, aes(x = kozak_region,\n",
    "                 y = mean_ratio,\n",
    "                 color = insert_sequence,\n",
    "                 group = insert_sequence)) +\n",
    "  geom_line() +\n",
    "  geom_point() +\n",
    "  labs(x = \"Kozak Region\",  # Custom x-axis label\n",
    "       y = \"Mean Ratio\",    # Custom y-axis label\n",
    "       color = \"Insert Sequence\",  # Custom legend title for color\n",
    "       title = \"Mean Ratios in Different Kozak Regions\") +  # add title\n",
    "  theme_classic() +  # Apply classic theme\n",
    "  scale_y_continuous(limits = c(0, 5))   # Set Y axis to go from 0 to 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transforming data\n",
    "\n",
    "Uses functions from the [`dplyr`](https://dplyr.tidyverse.org/) package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m16\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m7\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \"\\t\"\n",
      "\u001b[31mchr\u001b[39m (3): strain, insert_sequence, kozak_region\n",
      "\u001b[32mdbl\u001b[39m (4): mean_yfp, mean_rfp, mean_ratio, se_ratio\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A spec_tbl_df: 16 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>strain</th><th scope=col>mean_yfp</th><th scope=col>mean_rfp</th><th scope=col>mean_ratio</th><th scope=col>se_ratio</th><th scope=col>insert_sequence</th><th scope=col>kozak_region</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>schp688</td><td>1748</td><td>20754</td><td>0.755</td><td>0.066</td><td>10×AGA</td><td>A</td></tr>\n",
       "\t<tr><td>schp684</td><td>3294</td><td>20585</td><td>1.437</td><td>0.021</td><td>10×AGA</td><td>B</td></tr>\n",
       "\t<tr><td>schp690</td><td>3535</td><td>20593</td><td>1.541</td><td>0.018</td><td>10×AGA</td><td>C</td></tr>\n",
       "\t<tr><td>schp687</td><td>4658</td><td>20860</td><td>2.004</td><td>0.021</td><td>10×AGA</td><td>D</td></tr>\n",
       "\t<tr><td>schp686</td><td>5000</td><td>21171</td><td>2.121</td><td>0.023</td><td>10×AGA</td><td>E</td></tr>\n",
       "\t<tr><td>schp685</td><td>7379</td><td>22956</td><td>2.893</td><td>0.050</td><td>10×AGA</td><td>F</td></tr>\n",
       "\t<tr><td>schp683</td><td>9365</td><td>23866</td><td>3.522</td><td>0.110</td><td>10×AGA</td><td>G</td></tr>\n",
       "\t<tr><td>schp689</td><td>8693</td><td>22649</td><td>3.424</td><td>0.125</td><td>10×AGA</td><td>H</td></tr>\n",
       "\t<tr><td>schp679</td><td>2528</td><td>19906</td><td>1.149</td><td>0.056</td><td>10×AAG</td><td>A</td></tr>\n",
       "\t<tr><td>schp675</td><td>3687</td><td>20438</td><td>1.621</td><td>0.036</td><td>10×AAG</td><td>B</td></tr>\n",
       "\t<tr><td>schp681</td><td>3705</td><td>20227</td><td>1.645</td><td>0.021</td><td>10×AAG</td><td>C</td></tr>\n",
       "\t<tr><td>schp678</td><td>4378</td><td>20630</td><td>1.906</td><td>0.010</td><td>10×AAG</td><td>D</td></tr>\n",
       "\t<tr><td>schp677</td><td>3967</td><td>20604</td><td>1.728</td><td>0.030</td><td>10×AAG</td><td>E</td></tr>\n",
       "\t<tr><td>schp676</td><td>2657</td><td>20223</td><td>1.177</td><td>0.048</td><td>10×AAG</td><td>F</td></tr>\n",
       "\t<tr><td>schp674</td><td>1270</td><td>20316</td><td>0.561</td><td>0.004</td><td>10×AAG</td><td>G</td></tr>\n",
       "\t<tr><td>schp680</td><td>1117</td><td>19377</td><td>0.519</td><td>0.010</td><td>10×AAG</td><td>H</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A spec\\_tbl\\_df: 16 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " strain & mean\\_yfp & mean\\_rfp & mean\\_ratio & se\\_ratio & insert\\_sequence & kozak\\_region\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t schp688 & 1748 & 20754 & 0.755 & 0.066 & 10×AGA & A\\\\\n",
       "\t schp684 & 3294 & 20585 & 1.437 & 0.021 & 10×AGA & B\\\\\n",
       "\t schp690 & 3535 & 20593 & 1.541 & 0.018 & 10×AGA & C\\\\\n",
       "\t schp687 & 4658 & 20860 & 2.004 & 0.021 & 10×AGA & D\\\\\n",
       "\t schp686 & 5000 & 21171 & 2.121 & 0.023 & 10×AGA & E\\\\\n",
       "\t schp685 & 7379 & 22956 & 2.893 & 0.050 & 10×AGA & F\\\\\n",
       "\t schp683 & 9365 & 23866 & 3.522 & 0.110 & 10×AGA & G\\\\\n",
       "\t schp689 & 8693 & 22649 & 3.424 & 0.125 & 10×AGA & H\\\\\n",
       "\t schp679 & 2528 & 19906 & 1.149 & 0.056 & 10×AAG & A\\\\\n",
       "\t schp675 & 3687 & 20438 & 1.621 & 0.036 & 10×AAG & B\\\\\n",
       "\t schp681 & 3705 & 20227 & 1.645 & 0.021 & 10×AAG & C\\\\\n",
       "\t schp678 & 4378 & 20630 & 1.906 & 0.010 & 10×AAG & D\\\\\n",
       "\t schp677 & 3967 & 20604 & 1.728 & 0.030 & 10×AAG & E\\\\\n",
       "\t schp676 & 2657 & 20223 & 1.177 & 0.048 & 10×AAG & F\\\\\n",
       "\t schp674 & 1270 & 20316 & 0.561 & 0.004 & 10×AAG & G\\\\\n",
       "\t schp680 & 1117 & 19377 & 0.519 & 0.010 & 10×AAG & H\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A spec_tbl_df: 16 × 7\n",
       "\n",
       "| strain &lt;chr&gt; | mean_yfp &lt;dbl&gt; | mean_rfp &lt;dbl&gt; | mean_ratio &lt;dbl&gt; | se_ratio &lt;dbl&gt; | insert_sequence &lt;chr&gt; | kozak_region &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| schp688 | 1748 | 20754 | 0.755 | 0.066 | 10×AGA | A |\n",
       "| schp684 | 3294 | 20585 | 1.437 | 0.021 | 10×AGA | B |\n",
       "| schp690 | 3535 | 20593 | 1.541 | 0.018 | 10×AGA | C |\n",
       "| schp687 | 4658 | 20860 | 2.004 | 0.021 | 10×AGA | D |\n",
       "| schp686 | 5000 | 21171 | 2.121 | 0.023 | 10×AGA | E |\n",
       "| schp685 | 7379 | 22956 | 2.893 | 0.050 | 10×AGA | F |\n",
       "| schp683 | 9365 | 23866 | 3.522 | 0.110 | 10×AGA | G |\n",
       "| schp689 | 8693 | 22649 | 3.424 | 0.125 | 10×AGA | H |\n",
       "| schp679 | 2528 | 19906 | 1.149 | 0.056 | 10×AAG | A |\n",
       "| schp675 | 3687 | 20438 | 1.621 | 0.036 | 10×AAG | B |\n",
       "| schp681 | 3705 | 20227 | 1.645 | 0.021 | 10×AAG | C |\n",
       "| schp678 | 4378 | 20630 | 1.906 | 0.010 | 10×AAG | D |\n",
       "| schp677 | 3967 | 20604 | 1.728 | 0.030 | 10×AAG | E |\n",
       "| schp676 | 2657 | 20223 | 1.177 | 0.048 | 10×AAG | F |\n",
       "| schp674 | 1270 | 20316 | 0.561 | 0.004 | 10×AAG | G |\n",
       "| schp680 | 1117 | 19377 | 0.519 | 0.010 | 10×AAG | H |\n",
       "\n"
      ],
      "text/plain": [
       "   strain  mean_yfp mean_rfp mean_ratio se_ratio insert_sequence kozak_region\n",
       "1  schp688 1748     20754    0.755      0.066    10×AGA          A           \n",
       "2  schp684 3294     20585    1.437      0.021    10×AGA          B           \n",
       "3  schp690 3535     20593    1.541      0.018    10×AGA          C           \n",
       "4  schp687 4658     20860    2.004      0.021    10×AGA          D           \n",
       "5  schp686 5000     21171    2.121      0.023    10×AGA          E           \n",
       "6  schp685 7379     22956    2.893      0.050    10×AGA          F           \n",
       "7  schp683 9365     23866    3.522      0.110    10×AGA          G           \n",
       "8  schp689 8693     22649    3.424      0.125    10×AGA          H           \n",
       "9  schp679 2528     19906    1.149      0.056    10×AAG          A           \n",
       "10 schp675 3687     20438    1.621      0.036    10×AAG          B           \n",
       "11 schp681 3705     20227    1.645      0.021    10×AAG          C           \n",
       "12 schp678 4378     20630    1.906      0.010    10×AAG          D           \n",
       "13 schp677 3967     20604    1.728      0.030    10×AAG          E           \n",
       "14 schp676 2657     20223    1.177      0.048    10×AAG          F           \n",
       "15 schp674 1270     20316    0.561      0.004    10×AAG          G           \n",
       "16 schp680 1117     19377    0.519      0.010    10×AAG          H           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data <- read_tsv(\"data/example_dataset_1.tsv\")\n",
    "\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select specific columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 16 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>strain</th><th scope=col>mean_ratio</th><th scope=col>insert_sequence</th><th scope=col>kozak_region</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>schp688</td><td>0.755</td><td>10×AGA</td><td>A</td></tr>\n",
       "\t<tr><td>schp684</td><td>1.437</td><td>10×AGA</td><td>B</td></tr>\n",
       "\t<tr><td>schp690</td><td>1.541</td><td>10×AGA</td><td>C</td></tr>\n",
       "\t<tr><td>schp687</td><td>2.004</td><td>10×AGA</td><td>D</td></tr>\n",
       "\t<tr><td>schp686</td><td>2.121</td><td>10×AGA</td><td>E</td></tr>\n",
       "\t<tr><td>schp685</td><td>2.893</td><td>10×AGA</td><td>F</td></tr>\n",
       "\t<tr><td>schp683</td><td>3.522</td><td>10×AGA</td><td>G</td></tr>\n",
       "\t<tr><td>schp689</td><td>3.424</td><td>10×AGA</td><td>H</td></tr>\n",
       "\t<tr><td>schp679</td><td>1.149</td><td>10×AAG</td><td>A</td></tr>\n",
       "\t<tr><td>schp675</td><td>1.621</td><td>10×AAG</td><td>B</td></tr>\n",
       "\t<tr><td>schp681</td><td>1.645</td><td>10×AAG</td><td>C</td></tr>\n",
       "\t<tr><td>schp678</td><td>1.906</td><td>10×AAG</td><td>D</td></tr>\n",
       "\t<tr><td>schp677</td><td>1.728</td><td>10×AAG</td><td>E</td></tr>\n",
       "\t<tr><td>schp676</td><td>1.177</td><td>10×AAG</td><td>F</td></tr>\n",
       "\t<tr><td>schp674</td><td>0.561</td><td>10×AAG</td><td>G</td></tr>\n",
       "\t<tr><td>schp680</td><td>0.519</td><td>10×AAG</td><td>H</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 16 × 4\n",
       "\\begin{tabular}{llll}\n",
       " strain & mean\\_ratio & insert\\_sequence & kozak\\_region\\\\\n",
       " <chr> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t schp688 & 0.755 & 10×AGA & A\\\\\n",
       "\t schp684 & 1.437 & 10×AGA & B\\\\\n",
       "\t schp690 & 1.541 & 10×AGA & C\\\\\n",
       "\t schp687 & 2.004 & 10×AGA & D\\\\\n",
       "\t schp686 & 2.121 & 10×AGA & E\\\\\n",
       "\t schp685 & 2.893 & 10×AGA & F\\\\\n",
       "\t schp683 & 3.522 & 10×AGA & G\\\\\n",
       "\t schp689 & 3.424 & 10×AGA & H\\\\\n",
       "\t schp679 & 1.149 & 10×AAG & A\\\\\n",
       "\t schp675 & 1.621 & 10×AAG & B\\\\\n",
       "\t schp681 & 1.645 & 10×AAG & C\\\\\n",
       "\t schp678 & 1.906 & 10×AAG & D\\\\\n",
       "\t schp677 & 1.728 & 10×AAG & E\\\\\n",
       "\t schp676 & 1.177 & 10×AAG & F\\\\\n",
       "\t schp674 & 0.561 & 10×AAG & G\\\\\n",
       "\t schp680 & 0.519 & 10×AAG & H\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 16 × 4\n",
       "\n",
       "| strain &lt;chr&gt; | mean_ratio &lt;dbl&gt; | insert_sequence &lt;chr&gt; | kozak_region &lt;chr&gt; |\n",
       "|---|---|---|---|\n",
       "| schp688 | 0.755 | 10×AGA | A |\n",
       "| schp684 | 1.437 | 10×AGA | B |\n",
       "| schp690 | 1.541 | 10×AGA | C |\n",
       "| schp687 | 2.004 | 10×AGA | D |\n",
       "| schp686 | 2.121 | 10×AGA | E |\n",
       "| schp685 | 2.893 | 10×AGA | F |\n",
       "| schp683 | 3.522 | 10×AGA | G |\n",
       "| schp689 | 3.424 | 10×AGA | H |\n",
       "| schp679 | 1.149 | 10×AAG | A |\n",
       "| schp675 | 1.621 | 10×AAG | B |\n",
       "| schp681 | 1.645 | 10×AAG | C |\n",
       "| schp678 | 1.906 | 10×AAG | D |\n",
       "| schp677 | 1.728 | 10×AAG | E |\n",
       "| schp676 | 1.177 | 10×AAG | F |\n",
       "| schp674 | 0.561 | 10×AAG | G |\n",
       "| schp680 | 0.519 | 10×AAG | H |\n",
       "\n"
      ],
      "text/plain": [
       "   strain  mean_ratio insert_sequence kozak_region\n",
       "1  schp688 0.755      10×AGA          A           \n",
       "2  schp684 1.437      10×AGA          B           \n",
       "3  schp690 1.541      10×AGA          C           \n",
       "4  schp687 2.004      10×AGA          D           \n",
       "5  schp686 2.121      10×AGA          E           \n",
       "6  schp685 2.893      10×AGA          F           \n",
       "7  schp683 3.522      10×AGA          G           \n",
       "8  schp689 3.424      10×AGA          H           \n",
       "9  schp679 1.149      10×AAG          A           \n",
       "10 schp675 1.621      10×AAG          B           \n",
       "11 schp681 1.645      10×AAG          C           \n",
       "12 schp678 1.906      10×AAG          D           \n",
       "13 schp677 1.728      10×AAG          E           \n",
       "14 schp676 1.177      10×AAG          F           \n",
       "15 schp674 0.561      10×AAG          G           \n",
       "16 schp680 0.519      10×AAG          H           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "select(data, strain, mean_ratio, insert_sequence, kozak_region)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine operations using the ` %>% ` operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m16\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m7\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \"\\t\"\n",
      "\u001b[31mchr\u001b[39m (3): strain, insert_sequence, kozak_region\n",
      "\u001b[32mdbl\u001b[39m (4): mean_yfp, mean_rfp, mean_ratio, se_ratio\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    }
   ],
   "source": [
    "data <- read_tsv(\"data/example_dataset_1.tsv\") %>% \n",
    "  select(strain, mean_ratio, insert_sequence, kozak_region)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above is the same as the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m16\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m7\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \"\\t\"\n",
      "\u001b[31mchr\u001b[39m (3): strain, insert_sequence, kozak_region\n",
      "\u001b[32mdbl\u001b[39m (4): mean_yfp, mean_rfp, mean_ratio, se_ratio\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    }
   ],
   "source": [
    "data <- read_tsv(\"data/example_dataset_1.tsv\") %>% \n",
    "  select(., strain, mean_ratio, insert_sequence, kozak_region)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `%>%` operator lets you `chain` different data analysis tasks together and makes the analysis logic easier to understand.\n",
    "\n",
    "Side note: You can create keyboard shortcuts for ` <- ` and ` %>% ` in VSCode as explained [here](https://code.visualstudio.com/docs/getstarted/keybindings). \n",
    "\n",
    "I use `Alt + -` for ` <- ` and `Alt + Shift + m` for ` %>% ` following `RStudio` convention."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can get a view of the transformed data using `print()` as the last step in a chain of commands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m16\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m7\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \"\\t\"\n",
      "\u001b[31mchr\u001b[39m (3): strain, insert_sequence, kozak_region\n",
      "\u001b[32mdbl\u001b[39m (4): mean_yfp, mean_rfp, mean_ratio, se_ratio\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[90m# A tibble: 16 × 4\u001b[39m\n",
      "   strain  mean_ratio insert_sequence kozak_region\n",
      "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m        \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m           \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m       \n",
      "\u001b[90m 1\u001b[39m schp688      0.755 10×AGA          A           \n",
      "\u001b[90m 2\u001b[39m schp684      1.44  10×AGA          B           \n",
      "\u001b[90m 3\u001b[39m schp690      1.54  10×AGA          C           \n",
      "\u001b[90m 4\u001b[39m schp687      2.00  10×AGA          D           \n",
      "\u001b[90m 5\u001b[39m schp686      2.12  10×AGA          E           \n",
      "\u001b[90m 6\u001b[39m schp685      2.89  10×AGA          F           \n",
      "\u001b[90m 7\u001b[39m schp683      3.52  10×AGA          G           \n",
      "\u001b[90m 8\u001b[39m schp689      3.42  10×AGA          H           \n",
      "\u001b[90m 9\u001b[39m schp679      1.15  10×AAG          A           \n",
      "\u001b[90m10\u001b[39m schp675      1.62  10×AAG          B           \n",
      "\u001b[90m11\u001b[39m schp681      1.64  10×AAG          C           \n",
      "\u001b[90m12\u001b[39m schp678      1.91  10×AAG          D           \n",
      "\u001b[90m13\u001b[39m schp677      1.73  10×AAG          E           \n",
      "\u001b[90m14\u001b[39m schp676      1.18  10×AAG          F           \n",
      "\u001b[90m15\u001b[39m schp674      0.561 10×AAG          G           \n",
      "\u001b[90m16\u001b[39m schp680      0.519 10×AAG          H           \n"
     ]
    }
   ],
   "source": [
    "data <- read_tsv(\"data/example_dataset_1.tsv\") %>% \n",
    "  select(strain, mean_ratio, insert_sequence, kozak_region) %>% \n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m16\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m7\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \"\\t\"\n",
      "\u001b[31mchr\u001b[39m (3): strain, insert_sequence, kozak_region\n",
      "\u001b[32mdbl\u001b[39m (4): mean_yfp, mean_rfp, mean_ratio, se_ratio\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    }
   ],
   "source": [
    "data <- read_tsv(\"data/example_dataset_1.tsv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A spec_tbl_df: 2 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>strain</th><th scope=col>mean_yfp</th><th scope=col>mean_rfp</th><th scope=col>mean_ratio</th><th scope=col>se_ratio</th><th scope=col>insert_sequence</th><th scope=col>kozak_region</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>schp688</td><td>1748</td><td>20754</td><td>0.755</td><td>0.066</td><td>10×AGA</td><td>A</td></tr>\n",
       "\t<tr><td>schp679</td><td>2528</td><td>19906</td><td>1.149</td><td>0.056</td><td>10×AAG</td><td>A</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A spec\\_tbl\\_df: 2 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " strain & mean\\_yfp & mean\\_rfp & mean\\_ratio & se\\_ratio & insert\\_sequence & kozak\\_region\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t schp688 & 1748 & 20754 & 0.755 & 0.066 & 10×AGA & A\\\\\n",
       "\t schp679 & 2528 & 19906 & 1.149 & 0.056 & 10×AAG & A\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A spec_tbl_df: 2 × 7\n",
       "\n",
       "| strain &lt;chr&gt; | mean_yfp &lt;dbl&gt; | mean_rfp &lt;dbl&gt; | mean_ratio &lt;dbl&gt; | se_ratio &lt;dbl&gt; | insert_sequence &lt;chr&gt; | kozak_region &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| schp688 | 1748 | 20754 | 0.755 | 0.066 | 10×AGA | A |\n",
       "| schp679 | 2528 | 19906 | 1.149 | 0.056 | 10×AAG | A |\n",
       "\n"
      ],
      "text/plain": [
       "  strain  mean_yfp mean_rfp mean_ratio se_ratio insert_sequence kozak_region\n",
       "1 schp688 1748     20754    0.755      0.066    10×AGA          A           \n",
       "2 schp679 2528     19906    1.149      0.056    10×AAG          A           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data %>% \n",
    "  filter(kozak_region == \"A\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A spec_tbl_df: 1 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>strain</th><th scope=col>mean_yfp</th><th scope=col>mean_rfp</th><th scope=col>mean_ratio</th><th scope=col>se_ratio</th><th scope=col>insert_sequence</th><th scope=col>kozak_region</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>schp688</td><td>1748</td><td>20754</td><td>0.755</td><td>0.066</td><td>10×AGA</td><td>A</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A spec\\_tbl\\_df: 1 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " strain & mean\\_yfp & mean\\_rfp & mean\\_ratio & se\\_ratio & insert\\_sequence & kozak\\_region\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t schp688 & 1748 & 20754 & 0.755 & 0.066 & 10×AGA & A\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A spec_tbl_df: 1 × 7\n",
       "\n",
       "| strain &lt;chr&gt; | mean_yfp &lt;dbl&gt; | mean_rfp &lt;dbl&gt; | mean_ratio &lt;dbl&gt; | se_ratio &lt;dbl&gt; | insert_sequence &lt;chr&gt; | kozak_region &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| schp688 | 1748 | 20754 | 0.755 | 0.066 | 10×AGA | A |\n",
       "\n"
      ],
      "text/plain": [
       "  strain  mean_yfp mean_rfp mean_ratio se_ratio insert_sequence kozak_region\n",
       "1 schp688 1748     20754    0.755      0.066    10×AGA          A           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data %>%\n",
    "  filter(kozak_region == \"A\", insert_sequence == \"10×AGA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A spec_tbl_df: 1 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>strain</th><th scope=col>mean_yfp</th><th scope=col>mean_rfp</th><th scope=col>mean_ratio</th><th scope=col>se_ratio</th><th scope=col>insert_sequence</th><th scope=col>kozak_region</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>schp688</td><td>1748</td><td>20754</td><td>0.755</td><td>0.066</td><td>10×AGA</td><td>A</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A spec\\_tbl\\_df: 1 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " strain & mean\\_yfp & mean\\_rfp & mean\\_ratio & se\\_ratio & insert\\_sequence & kozak\\_region\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t schp688 & 1748 & 20754 & 0.755 & 0.066 & 10×AGA & A\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A spec_tbl_df: 1 × 7\n",
       "\n",
       "| strain &lt;chr&gt; | mean_yfp &lt;dbl&gt; | mean_rfp &lt;dbl&gt; | mean_ratio &lt;dbl&gt; | se_ratio &lt;dbl&gt; | insert_sequence &lt;chr&gt; | kozak_region &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| schp688 | 1748 | 20754 | 0.755 | 0.066 | 10×AGA | A |\n",
       "\n"
      ],
      "text/plain": [
       "  strain  mean_yfp mean_rfp mean_ratio se_ratio insert_sequence kozak_region\n",
       "1 schp688 1748     20754    0.755      0.066    10×AGA          A           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data %>%\n",
    "  filter(kozak_region == \"A\") %>% \n",
    "  filter(insert_sequence == \"10×AGA\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Arrange (sort) rows in a specific order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A spec_tbl_df: 16 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>strain</th><th scope=col>mean_yfp</th><th scope=col>mean_rfp</th><th scope=col>mean_ratio</th><th scope=col>se_ratio</th><th scope=col>insert_sequence</th><th scope=col>kozak_region</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>schp680</td><td>1117</td><td>19377</td><td>0.519</td><td>0.010</td><td>10×AAG</td><td>H</td></tr>\n",
       "\t<tr><td>schp674</td><td>1270</td><td>20316</td><td>0.561</td><td>0.004</td><td>10×AAG</td><td>G</td></tr>\n",
       "\t<tr><td>schp688</td><td>1748</td><td>20754</td><td>0.755</td><td>0.066</td><td>10×AGA</td><td>A</td></tr>\n",
       "\t<tr><td>schp679</td><td>2528</td><td>19906</td><td>1.149</td><td>0.056</td><td>10×AAG</td><td>A</td></tr>\n",
       "\t<tr><td>schp676</td><td>2657</td><td>20223</td><td>1.177</td><td>0.048</td><td>10×AAG</td><td>F</td></tr>\n",
       "\t<tr><td>schp684</td><td>3294</td><td>20585</td><td>1.437</td><td>0.021</td><td>10×AGA</td><td>B</td></tr>\n",
       "\t<tr><td>schp690</td><td>3535</td><td>20593</td><td>1.541</td><td>0.018</td><td>10×AGA</td><td>C</td></tr>\n",
       "\t<tr><td>schp675</td><td>3687</td><td>20438</td><td>1.621</td><td>0.036</td><td>10×AAG</td><td>B</td></tr>\n",
       "\t<tr><td>schp681</td><td>3705</td><td>20227</td><td>1.645</td><td>0.021</td><td>10×AAG</td><td>C</td></tr>\n",
       "\t<tr><td>schp677</td><td>3967</td><td>20604</td><td>1.728</td><td>0.030</td><td>10×AAG</td><td>E</td></tr>\n",
       "\t<tr><td>schp678</td><td>4378</td><td>20630</td><td>1.906</td><td>0.010</td><td>10×AAG</td><td>D</td></tr>\n",
       "\t<tr><td>schp687</td><td>4658</td><td>20860</td><td>2.004</td><td>0.021</td><td>10×AGA</td><td>D</td></tr>\n",
       "\t<tr><td>schp686</td><td>5000</td><td>21171</td><td>2.121</td><td>0.023</td><td>10×AGA</td><td>E</td></tr>\n",
       "\t<tr><td>schp685</td><td>7379</td><td>22956</td><td>2.893</td><td>0.050</td><td>10×AGA</td><td>F</td></tr>\n",
       "\t<tr><td>schp689</td><td>8693</td><td>22649</td><td>3.424</td><td>0.125</td><td>10×AGA</td><td>H</td></tr>\n",
       "\t<tr><td>schp683</td><td>9365</td><td>23866</td><td>3.522</td><td>0.110</td><td>10×AGA</td><td>G</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A spec\\_tbl\\_df: 16 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " strain & mean\\_yfp & mean\\_rfp & mean\\_ratio & se\\_ratio & insert\\_sequence & kozak\\_region\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t schp680 & 1117 & 19377 & 0.519 & 0.010 & 10×AAG & H\\\\\n",
       "\t schp674 & 1270 & 20316 & 0.561 & 0.004 & 10×AAG & G\\\\\n",
       "\t schp688 & 1748 & 20754 & 0.755 & 0.066 & 10×AGA & A\\\\\n",
       "\t schp679 & 2528 & 19906 & 1.149 & 0.056 & 10×AAG & A\\\\\n",
       "\t schp676 & 2657 & 20223 & 1.177 & 0.048 & 10×AAG & F\\\\\n",
       "\t schp684 & 3294 & 20585 & 1.437 & 0.021 & 10×AGA & B\\\\\n",
       "\t schp690 & 3535 & 20593 & 1.541 & 0.018 & 10×AGA & C\\\\\n",
       "\t schp675 & 3687 & 20438 & 1.621 & 0.036 & 10×AAG & B\\\\\n",
       "\t schp681 & 3705 & 20227 & 1.645 & 0.021 & 10×AAG & C\\\\\n",
       "\t schp677 & 3967 & 20604 & 1.728 & 0.030 & 10×AAG & E\\\\\n",
       "\t schp678 & 4378 & 20630 & 1.906 & 0.010 & 10×AAG & D\\\\\n",
       "\t schp687 & 4658 & 20860 & 2.004 & 0.021 & 10×AGA & D\\\\\n",
       "\t schp686 & 5000 & 21171 & 2.121 & 0.023 & 10×AGA & E\\\\\n",
       "\t schp685 & 7379 & 22956 & 2.893 & 0.050 & 10×AGA & F\\\\\n",
       "\t schp689 & 8693 & 22649 & 3.424 & 0.125 & 10×AGA & H\\\\\n",
       "\t schp683 & 9365 & 23866 & 3.522 & 0.110 & 10×AGA & G\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A spec_tbl_df: 16 × 7\n",
       "\n",
       "| strain &lt;chr&gt; | mean_yfp &lt;dbl&gt; | mean_rfp &lt;dbl&gt; | mean_ratio &lt;dbl&gt; | se_ratio &lt;dbl&gt; | insert_sequence &lt;chr&gt; | kozak_region &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| schp680 | 1117 | 19377 | 0.519 | 0.010 | 10×AAG | H |\n",
       "| schp674 | 1270 | 20316 | 0.561 | 0.004 | 10×AAG | G |\n",
       "| schp688 | 1748 | 20754 | 0.755 | 0.066 | 10×AGA | A |\n",
       "| schp679 | 2528 | 19906 | 1.149 | 0.056 | 10×AAG | A |\n",
       "| schp676 | 2657 | 20223 | 1.177 | 0.048 | 10×AAG | F |\n",
       "| schp684 | 3294 | 20585 | 1.437 | 0.021 | 10×AGA | B |\n",
       "| schp690 | 3535 | 20593 | 1.541 | 0.018 | 10×AGA | C |\n",
       "| schp675 | 3687 | 20438 | 1.621 | 0.036 | 10×AAG | B |\n",
       "| schp681 | 3705 | 20227 | 1.645 | 0.021 | 10×AAG | C |\n",
       "| schp677 | 3967 | 20604 | 1.728 | 0.030 | 10×AAG | E |\n",
       "| schp678 | 4378 | 20630 | 1.906 | 0.010 | 10×AAG | D |\n",
       "| schp687 | 4658 | 20860 | 2.004 | 0.021 | 10×AGA | D |\n",
       "| schp686 | 5000 | 21171 | 2.121 | 0.023 | 10×AGA | E |\n",
       "| schp685 | 7379 | 22956 | 2.893 | 0.050 | 10×AGA | F |\n",
       "| schp689 | 8693 | 22649 | 3.424 | 0.125 | 10×AGA | H |\n",
       "| schp683 | 9365 | 23866 | 3.522 | 0.110 | 10×AGA | G |\n",
       "\n"
      ],
      "text/plain": [
       "   strain  mean_yfp mean_rfp mean_ratio se_ratio insert_sequence kozak_region\n",
       "1  schp680 1117     19377    0.519      0.010    10×AAG          H           \n",
       "2  schp674 1270     20316    0.561      0.004    10×AAG          G           \n",
       "3  schp688 1748     20754    0.755      0.066    10×AGA          A           \n",
       "4  schp679 2528     19906    1.149      0.056    10×AAG          A           \n",
       "5  schp676 2657     20223    1.177      0.048    10×AAG          F           \n",
       "6  schp684 3294     20585    1.437      0.021    10×AGA          B           \n",
       "7  schp690 3535     20593    1.541      0.018    10×AGA          C           \n",
       "8  schp675 3687     20438    1.621      0.036    10×AAG          B           \n",
       "9  schp681 3705     20227    1.645      0.021    10×AAG          C           \n",
       "10 schp677 3967     20604    1.728      0.030    10×AAG          E           \n",
       "11 schp678 4378     20630    1.906      0.010    10×AAG          D           \n",
       "12 schp687 4658     20860    2.004      0.021    10×AGA          D           \n",
       "13 schp686 5000     21171    2.121      0.023    10×AGA          E           \n",
       "14 schp685 7379     22956    2.893      0.050    10×AGA          F           \n",
       "15 schp689 8693     22649    3.424      0.125    10×AGA          H           \n",
       "16 schp683 9365     23866    3.522      0.110    10×AGA          G           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data %>%\n",
    "  arrange(mean_ratio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create new columns using `mutate`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m16\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m3\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \"\\t\"\n",
      "\u001b[31mchr\u001b[39m (1): strain\n",
      "\u001b[32mdbl\u001b[39m (2): mean_yfp, mean_rfp\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[90m# A tibble: 16 × 3\u001b[39m\n",
      "   strain  mean_yfp mean_rfp\n",
      "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m      \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
      "\u001b[90m 1\u001b[39m schp688     \u001b[4m1\u001b[24m748    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m754\n",
      "\u001b[90m 2\u001b[39m schp684     \u001b[4m3\u001b[24m294    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m585\n",
      "\u001b[90m 3\u001b[39m schp690     \u001b[4m3\u001b[24m535    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m593\n",
      "\u001b[90m 4\u001b[39m schp687     \u001b[4m4\u001b[24m658    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m860\n",
      "\u001b[90m 5\u001b[39m schp686     \u001b[4m5\u001b[24m000    \u001b[4m2\u001b[24m\u001b[4m1\u001b[24m171\n",
      "\u001b[90m 6\u001b[39m schp685     \u001b[4m7\u001b[24m379    \u001b[4m2\u001b[24m\u001b[4m2\u001b[24m956\n",
      "\u001b[90m 7\u001b[39m schp683     \u001b[4m9\u001b[24m365    \u001b[4m2\u001b[24m\u001b[4m3\u001b[24m866\n",
      "\u001b[90m 8\u001b[39m schp689     \u001b[4m8\u001b[24m693    \u001b[4m2\u001b[24m\u001b[4m2\u001b[24m649\n",
      "\u001b[90m 9\u001b[39m schp679     \u001b[4m2\u001b[24m528    \u001b[4m1\u001b[24m\u001b[4m9\u001b[24m906\n",
      "\u001b[90m10\u001b[39m schp675     \u001b[4m3\u001b[24m687    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m438\n",
      "\u001b[90m11\u001b[39m schp681     \u001b[4m3\u001b[24m705    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m227\n",
      "\u001b[90m12\u001b[39m schp678     \u001b[4m4\u001b[24m378    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m630\n",
      "\u001b[90m13\u001b[39m schp677     \u001b[4m3\u001b[24m967    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m604\n",
      "\u001b[90m14\u001b[39m schp676     \u001b[4m2\u001b[24m657    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m223\n",
      "\u001b[90m15\u001b[39m schp674     \u001b[4m1\u001b[24m270    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m316\n",
      "\u001b[90m16\u001b[39m schp680     \u001b[4m1\u001b[24m117    \u001b[4m1\u001b[24m\u001b[4m9\u001b[24m377\n"
     ]
    }
   ],
   "source": [
    "data <- read_tsv(\"data/example_dataset_2.tsv\") %>% \n",
    "  print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[90m# A tibble: 16 × 4\u001b[39m\n",
      "   strain  mean_yfp mean_rfp mean_ratio\n",
      "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m      \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m      \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
      "\u001b[90m 1\u001b[39m schp688     \u001b[4m1\u001b[24m748    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m754     0.084\u001b[4m2\u001b[24m\n",
      "\u001b[90m 2\u001b[39m schp684     \u001b[4m3\u001b[24m294    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m585     0.160 \n",
      "\u001b[90m 3\u001b[39m schp690     \u001b[4m3\u001b[24m535    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m593     0.172 \n",
      "\u001b[90m 4\u001b[39m schp687     \u001b[4m4\u001b[24m658    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m860     0.223 \n",
      "\u001b[90m 5\u001b[39m schp686     \u001b[4m5\u001b[24m000    \u001b[4m2\u001b[24m\u001b[4m1\u001b[24m171     0.236 \n",
      "\u001b[90m 6\u001b[39m schp685     \u001b[4m7\u001b[24m379    \u001b[4m2\u001b[24m\u001b[4m2\u001b[24m956     0.321 \n",
      "\u001b[90m 7\u001b[39m schp683     \u001b[4m9\u001b[24m365    \u001b[4m2\u001b[24m\u001b[4m3\u001b[24m866     0.392 \n",
      "\u001b[90m 8\u001b[39m schp689     \u001b[4m8\u001b[24m693    \u001b[4m2\u001b[24m\u001b[4m2\u001b[24m649     0.384 \n",
      "\u001b[90m 9\u001b[39m schp679     \u001b[4m2\u001b[24m528    \u001b[4m1\u001b[24m\u001b[4m9\u001b[24m906     0.127 \n",
      "\u001b[90m10\u001b[39m schp675     \u001b[4m3\u001b[24m687    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m438     0.180 \n",
      "\u001b[90m11\u001b[39m schp681     \u001b[4m3\u001b[24m705    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m227     0.183 \n",
      "\u001b[90m12\u001b[39m schp678     \u001b[4m4\u001b[24m378    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m630     0.212 \n",
      "\u001b[90m13\u001b[39m schp677     \u001b[4m3\u001b[24m967    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m604     0.193 \n",
      "\u001b[90m14\u001b[39m schp676     \u001b[4m2\u001b[24m657    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m223     0.131 \n",
      "\u001b[90m15\u001b[39m schp674     \u001b[4m1\u001b[24m270    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m316     0.062\u001b[4m5\u001b[24m\n",
      "\u001b[90m16\u001b[39m schp680     \u001b[4m1\u001b[24m117    \u001b[4m1\u001b[24m\u001b[4m9\u001b[24m377     0.057\u001b[4m6\u001b[24m\n"
     ]
    }
   ],
   "source": [
    "data <- data %>%\n",
    "  mutate(mean_ratio = mean_yfp / mean_rfp) %>%\n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use mutate to modify existing columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 16 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>strain</th><th scope=col>mean_yfp</th><th scope=col>mean_rfp</th><th scope=col>mean_ratio</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>schp688</td><td>1748</td><td>20754</td><td>0.08</td></tr>\n",
       "\t<tr><td>schp684</td><td>3294</td><td>20585</td><td>0.16</td></tr>\n",
       "\t<tr><td>schp690</td><td>3535</td><td>20593</td><td>0.17</td></tr>\n",
       "\t<tr><td>schp687</td><td>4658</td><td>20860</td><td>0.22</td></tr>\n",
       "\t<tr><td>schp686</td><td>5000</td><td>21171</td><td>0.24</td></tr>\n",
       "\t<tr><td>schp685</td><td>7379</td><td>22956</td><td>0.32</td></tr>\n",
       "\t<tr><td>schp683</td><td>9365</td><td>23866</td><td>0.39</td></tr>\n",
       "\t<tr><td>schp689</td><td>8693</td><td>22649</td><td>0.38</td></tr>\n",
       "\t<tr><td>schp679</td><td>2528</td><td>19906</td><td>0.13</td></tr>\n",
       "\t<tr><td>schp675</td><td>3687</td><td>20438</td><td>0.18</td></tr>\n",
       "\t<tr><td>schp681</td><td>3705</td><td>20227</td><td>0.18</td></tr>\n",
       "\t<tr><td>schp678</td><td>4378</td><td>20630</td><td>0.21</td></tr>\n",
       "\t<tr><td>schp677</td><td>3967</td><td>20604</td><td>0.19</td></tr>\n",
       "\t<tr><td>schp676</td><td>2657</td><td>20223</td><td>0.13</td></tr>\n",
       "\t<tr><td>schp674</td><td>1270</td><td>20316</td><td>0.06</td></tr>\n",
       "\t<tr><td>schp680</td><td>1117</td><td>19377</td><td>0.06</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 16 × 4\n",
       "\\begin{tabular}{llll}\n",
       " strain & mean\\_yfp & mean\\_rfp & mean\\_ratio\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t schp688 & 1748 & 20754 & 0.08\\\\\n",
       "\t schp684 & 3294 & 20585 & 0.16\\\\\n",
       "\t schp690 & 3535 & 20593 & 0.17\\\\\n",
       "\t schp687 & 4658 & 20860 & 0.22\\\\\n",
       "\t schp686 & 5000 & 21171 & 0.24\\\\\n",
       "\t schp685 & 7379 & 22956 & 0.32\\\\\n",
       "\t schp683 & 9365 & 23866 & 0.39\\\\\n",
       "\t schp689 & 8693 & 22649 & 0.38\\\\\n",
       "\t schp679 & 2528 & 19906 & 0.13\\\\\n",
       "\t schp675 & 3687 & 20438 & 0.18\\\\\n",
       "\t schp681 & 3705 & 20227 & 0.18\\\\\n",
       "\t schp678 & 4378 & 20630 & 0.21\\\\\n",
       "\t schp677 & 3967 & 20604 & 0.19\\\\\n",
       "\t schp676 & 2657 & 20223 & 0.13\\\\\n",
       "\t schp674 & 1270 & 20316 & 0.06\\\\\n",
       "\t schp680 & 1117 & 19377 & 0.06\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 16 × 4\n",
       "\n",
       "| strain &lt;chr&gt; | mean_yfp &lt;dbl&gt; | mean_rfp &lt;dbl&gt; | mean_ratio &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| schp688 | 1748 | 20754 | 0.08 |\n",
       "| schp684 | 3294 | 20585 | 0.16 |\n",
       "| schp690 | 3535 | 20593 | 0.17 |\n",
       "| schp687 | 4658 | 20860 | 0.22 |\n",
       "| schp686 | 5000 | 21171 | 0.24 |\n",
       "| schp685 | 7379 | 22956 | 0.32 |\n",
       "| schp683 | 9365 | 23866 | 0.39 |\n",
       "| schp689 | 8693 | 22649 | 0.38 |\n",
       "| schp679 | 2528 | 19906 | 0.13 |\n",
       "| schp675 | 3687 | 20438 | 0.18 |\n",
       "| schp681 | 3705 | 20227 | 0.18 |\n",
       "| schp678 | 4378 | 20630 | 0.21 |\n",
       "| schp677 | 3967 | 20604 | 0.19 |\n",
       "| schp676 | 2657 | 20223 | 0.13 |\n",
       "| schp674 | 1270 | 20316 | 0.06 |\n",
       "| schp680 | 1117 | 19377 | 0.06 |\n",
       "\n"
      ],
      "text/plain": [
       "   strain  mean_yfp mean_rfp mean_ratio\n",
       "1  schp688 1748     20754    0.08      \n",
       "2  schp684 3294     20585    0.16      \n",
       "3  schp690 3535     20593    0.17      \n",
       "4  schp687 4658     20860    0.22      \n",
       "5  schp686 5000     21171    0.24      \n",
       "6  schp685 7379     22956    0.32      \n",
       "7  schp683 9365     23866    0.39      \n",
       "8  schp689 8693     22649    0.38      \n",
       "9  schp679 2528     19906    0.13      \n",
       "10 schp675 3687     20438    0.18      \n",
       "11 schp681 3705     20227    0.18      \n",
       "12 schp678 4378     20630    0.21      \n",
       "13 schp677 3967     20604    0.19      \n",
       "14 schp676 2657     20223    0.13      \n",
       "15 schp674 1270     20316    0.06      \n",
       "16 schp680 1117     19377    0.06      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data %>%\n",
    "  mutate(mean_ratio = round(mean_ratio, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine tables using `join` functions\n",
    "\n",
    "Variants: `inner_join`, `left_join`, `right_join`, `full_join`\n",
    "\n",
    "See [https://dplyr.tidyverse.org/reference/mutate-joins.html](https://dplyr.tidyverse.org/reference/mutate-joins.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m17\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m3\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \"\\t\"\n",
      "\u001b[31mchr\u001b[39m (3): strain, insert_sequence, kozak_region\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A spec_tbl_df: 17 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>strain</th><th scope=col>insert_sequence</th><th scope=col>kozak_region</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>schp674</td><td>10×AAG</td><td>G </td></tr>\n",
       "\t<tr><td>schp675</td><td>10×AAG</td><td>B </td></tr>\n",
       "\t<tr><td>schp676</td><td>10×AAG</td><td>F </td></tr>\n",
       "\t<tr><td>schp677</td><td>10×AAG</td><td>E </td></tr>\n",
       "\t<tr><td>schp678</td><td>10×AAG</td><td>D </td></tr>\n",
       "\t<tr><td>schp679</td><td>10×AAG</td><td>A </td></tr>\n",
       "\t<tr><td>schp680</td><td>10×AAG</td><td>H </td></tr>\n",
       "\t<tr><td>schp681</td><td>10×AAG</td><td>C </td></tr>\n",
       "\t<tr><td>schp683</td><td>10×AGA</td><td>G </td></tr>\n",
       "\t<tr><td>schp684</td><td>10×AGA</td><td>B </td></tr>\n",
       "\t<tr><td>schp685</td><td>10×AGA</td><td>F </td></tr>\n",
       "\t<tr><td>schp686</td><td>10×AGA</td><td>E </td></tr>\n",
       "\t<tr><td>schp687</td><td>10×AGA</td><td>D </td></tr>\n",
       "\t<tr><td>schp688</td><td>10×AGA</td><td>A </td></tr>\n",
       "\t<tr><td>schp689</td><td>10×AGA</td><td>H </td></tr>\n",
       "\t<tr><td>schp690</td><td>10×AGA</td><td>C </td></tr>\n",
       "\t<tr><td>control</td><td>NA    </td><td>NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A spec\\_tbl\\_df: 17 × 3\n",
       "\\begin{tabular}{lll}\n",
       " strain & insert\\_sequence & kozak\\_region\\\\\n",
       " <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t schp674 & 10×AAG & G \\\\\n",
       "\t schp675 & 10×AAG & B \\\\\n",
       "\t schp676 & 10×AAG & F \\\\\n",
       "\t schp677 & 10×AAG & E \\\\\n",
       "\t schp678 & 10×AAG & D \\\\\n",
       "\t schp679 & 10×AAG & A \\\\\n",
       "\t schp680 & 10×AAG & H \\\\\n",
       "\t schp681 & 10×AAG & C \\\\\n",
       "\t schp683 & 10×AGA & G \\\\\n",
       "\t schp684 & 10×AGA & B \\\\\n",
       "\t schp685 & 10×AGA & F \\\\\n",
       "\t schp686 & 10×AGA & E \\\\\n",
       "\t schp687 & 10×AGA & D \\\\\n",
       "\t schp688 & 10×AGA & A \\\\\n",
       "\t schp689 & 10×AGA & H \\\\\n",
       "\t schp690 & 10×AGA & C \\\\\n",
       "\t control & NA     & NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A spec_tbl_df: 17 × 3\n",
       "\n",
       "| strain &lt;chr&gt; | insert_sequence &lt;chr&gt; | kozak_region &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| schp674 | 10×AAG | G  |\n",
       "| schp675 | 10×AAG | B  |\n",
       "| schp676 | 10×AAG | F  |\n",
       "| schp677 | 10×AAG | E  |\n",
       "| schp678 | 10×AAG | D  |\n",
       "| schp679 | 10×AAG | A  |\n",
       "| schp680 | 10×AAG | H  |\n",
       "| schp681 | 10×AAG | C  |\n",
       "| schp683 | 10×AGA | G  |\n",
       "| schp684 | 10×AGA | B  |\n",
       "| schp685 | 10×AGA | F  |\n",
       "| schp686 | 10×AGA | E  |\n",
       "| schp687 | 10×AGA | D  |\n",
       "| schp688 | 10×AGA | A  |\n",
       "| schp689 | 10×AGA | H  |\n",
       "| schp690 | 10×AGA | C  |\n",
       "| control | NA     | NA |\n",
       "\n"
      ],
      "text/plain": [
       "   strain  insert_sequence kozak_region\n",
       "1  schp674 10×AAG          G           \n",
       "2  schp675 10×AAG          B           \n",
       "3  schp676 10×AAG          F           \n",
       "4  schp677 10×AAG          E           \n",
       "5  schp678 10×AAG          D           \n",
       "6  schp679 10×AAG          A           \n",
       "7  schp680 10×AAG          H           \n",
       "8  schp681 10×AAG          C           \n",
       "9  schp683 10×AGA          G           \n",
       "10 schp684 10×AGA          B           \n",
       "11 schp685 10×AGA          F           \n",
       "12 schp686 10×AGA          E           \n",
       "13 schp687 10×AGA          D           \n",
       "14 schp688 10×AGA          A           \n",
       "15 schp689 10×AGA          H           \n",
       "16 schp690 10×AGA          C           \n",
       "17 control NA              NA          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "annotations <- read_tsv(\"data/example_dataset_3.tsv\")\n",
    "\n",
    "annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 16 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>strain</th><th scope=col>mean_yfp</th><th scope=col>mean_rfp</th><th scope=col>mean_ratio</th><th scope=col>insert_sequence</th><th scope=col>kozak_region</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>schp688</td><td>1748</td><td>20754</td><td>0.08422473</td><td>10×AGA</td><td>A</td></tr>\n",
       "\t<tr><td>schp684</td><td>3294</td><td>20585</td><td>0.16001943</td><td>10×AGA</td><td>B</td></tr>\n",
       "\t<tr><td>schp690</td><td>3535</td><td>20593</td><td>0.17166027</td><td>10×AGA</td><td>C</td></tr>\n",
       "\t<tr><td>schp687</td><td>4658</td><td>20860</td><td>0.22329818</td><td>10×AGA</td><td>D</td></tr>\n",
       "\t<tr><td>schp686</td><td>5000</td><td>21171</td><td>0.23617212</td><td>10×AGA</td><td>E</td></tr>\n",
       "\t<tr><td>schp685</td><td>7379</td><td>22956</td><td>0.32144102</td><td>10×AGA</td><td>F</td></tr>\n",
       "\t<tr><td>schp683</td><td>9365</td><td>23866</td><td>0.39239923</td><td>10×AGA</td><td>G</td></tr>\n",
       "\t<tr><td>schp689</td><td>8693</td><td>22649</td><td>0.38381385</td><td>10×AGA</td><td>H</td></tr>\n",
       "\t<tr><td>schp679</td><td>2528</td><td>19906</td><td>0.12699689</td><td>10×AAG</td><td>A</td></tr>\n",
       "\t<tr><td>schp675</td><td>3687</td><td>20438</td><td>0.18039926</td><td>10×AAG</td><td>B</td></tr>\n",
       "\t<tr><td>schp681</td><td>3705</td><td>20227</td><td>0.18317101</td><td>10×AAG</td><td>C</td></tr>\n",
       "\t<tr><td>schp678</td><td>4378</td><td>20630</td><td>0.21221522</td><td>10×AAG</td><td>D</td></tr>\n",
       "\t<tr><td>schp677</td><td>3967</td><td>20604</td><td>0.19253543</td><td>10×AAG</td><td>E</td></tr>\n",
       "\t<tr><td>schp676</td><td>2657</td><td>20223</td><td>0.13138506</td><td>10×AAG</td><td>F</td></tr>\n",
       "\t<tr><td>schp674</td><td>1270</td><td>20316</td><td>0.06251231</td><td>10×AAG</td><td>G</td></tr>\n",
       "\t<tr><td>schp680</td><td>1117</td><td>19377</td><td>0.05764566</td><td>10×AAG</td><td>H</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 16 × 6\n",
       "\\begin{tabular}{llllll}\n",
       " strain & mean\\_yfp & mean\\_rfp & mean\\_ratio & insert\\_sequence & kozak\\_region\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t schp688 & 1748 & 20754 & 0.08422473 & 10×AGA & A\\\\\n",
       "\t schp684 & 3294 & 20585 & 0.16001943 & 10×AGA & B\\\\\n",
       "\t schp690 & 3535 & 20593 & 0.17166027 & 10×AGA & C\\\\\n",
       "\t schp687 & 4658 & 20860 & 0.22329818 & 10×AGA & D\\\\\n",
       "\t schp686 & 5000 & 21171 & 0.23617212 & 10×AGA & E\\\\\n",
       "\t schp685 & 7379 & 22956 & 0.32144102 & 10×AGA & F\\\\\n",
       "\t schp683 & 9365 & 23866 & 0.39239923 & 10×AGA & G\\\\\n",
       "\t schp689 & 8693 & 22649 & 0.38381385 & 10×AGA & H\\\\\n",
       "\t schp679 & 2528 & 19906 & 0.12699689 & 10×AAG & A\\\\\n",
       "\t schp675 & 3687 & 20438 & 0.18039926 & 10×AAG & B\\\\\n",
       "\t schp681 & 3705 & 20227 & 0.18317101 & 10×AAG & C\\\\\n",
       "\t schp678 & 4378 & 20630 & 0.21221522 & 10×AAG & D\\\\\n",
       "\t schp677 & 3967 & 20604 & 0.19253543 & 10×AAG & E\\\\\n",
       "\t schp676 & 2657 & 20223 & 0.13138506 & 10×AAG & F\\\\\n",
       "\t schp674 & 1270 & 20316 & 0.06251231 & 10×AAG & G\\\\\n",
       "\t schp680 & 1117 & 19377 & 0.05764566 & 10×AAG & H\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 16 × 6\n",
       "\n",
       "| strain &lt;chr&gt; | mean_yfp &lt;dbl&gt; | mean_rfp &lt;dbl&gt; | mean_ratio &lt;dbl&gt; | insert_sequence &lt;chr&gt; | kozak_region &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| schp688 | 1748 | 20754 | 0.08422473 | 10×AGA | A |\n",
       "| schp684 | 3294 | 20585 | 0.16001943 | 10×AGA | B |\n",
       "| schp690 | 3535 | 20593 | 0.17166027 | 10×AGA | C |\n",
       "| schp687 | 4658 | 20860 | 0.22329818 | 10×AGA | D |\n",
       "| schp686 | 5000 | 21171 | 0.23617212 | 10×AGA | E |\n",
       "| schp685 | 7379 | 22956 | 0.32144102 | 10×AGA | F |\n",
       "| schp683 | 9365 | 23866 | 0.39239923 | 10×AGA | G |\n",
       "| schp689 | 8693 | 22649 | 0.38381385 | 10×AGA | H |\n",
       "| schp679 | 2528 | 19906 | 0.12699689 | 10×AAG | A |\n",
       "| schp675 | 3687 | 20438 | 0.18039926 | 10×AAG | B |\n",
       "| schp681 | 3705 | 20227 | 0.18317101 | 10×AAG | C |\n",
       "| schp678 | 4378 | 20630 | 0.21221522 | 10×AAG | D |\n",
       "| schp677 | 3967 | 20604 | 0.19253543 | 10×AAG | E |\n",
       "| schp676 | 2657 | 20223 | 0.13138506 | 10×AAG | F |\n",
       "| schp674 | 1270 | 20316 | 0.06251231 | 10×AAG | G |\n",
       "| schp680 | 1117 | 19377 | 0.05764566 | 10×AAG | H |\n",
       "\n"
      ],
      "text/plain": [
       "   strain  mean_yfp mean_rfp mean_ratio insert_sequence kozak_region\n",
       "1  schp688 1748     20754    0.08422473 10×AGA          A           \n",
       "2  schp684 3294     20585    0.16001943 10×AGA          B           \n",
       "3  schp690 3535     20593    0.17166027 10×AGA          C           \n",
       "4  schp687 4658     20860    0.22329818 10×AGA          D           \n",
       "5  schp686 5000     21171    0.23617212 10×AGA          E           \n",
       "6  schp685 7379     22956    0.32144102 10×AGA          F           \n",
       "7  schp683 9365     23866    0.39239923 10×AGA          G           \n",
       "8  schp689 8693     22649    0.38381385 10×AGA          H           \n",
       "9  schp679 2528     19906    0.12699689 10×AAG          A           \n",
       "10 schp675 3687     20438    0.18039926 10×AAG          B           \n",
       "11 schp681 3705     20227    0.18317101 10×AAG          C           \n",
       "12 schp678 4378     20630    0.21221522 10×AAG          D           \n",
       "13 schp677 3967     20604    0.19253543 10×AAG          E           \n",
       "14 schp676 2657     20223    0.13138506 10×AAG          F           \n",
       "15 schp674 1270     20316    0.06251231 10×AAG          G           \n",
       "16 schp680 1117     19377    0.05764566 10×AAG          H           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data %>% \n",
    "  inner_join(annotations, by = \"strain\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 16 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>strain</th><th scope=col>mean_yfp</th><th scope=col>mean_rfp</th><th scope=col>mean_ratio</th><th scope=col>insert_sequence</th><th scope=col>kozak_region</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>schp688</td><td>1748</td><td>20754</td><td>0.08422473</td><td>10×AGA</td><td>A</td></tr>\n",
       "\t<tr><td>schp684</td><td>3294</td><td>20585</td><td>0.16001943</td><td>10×AGA</td><td>B</td></tr>\n",
       "\t<tr><td>schp690</td><td>3535</td><td>20593</td><td>0.17166027</td><td>10×AGA</td><td>C</td></tr>\n",
       "\t<tr><td>schp687</td><td>4658</td><td>20860</td><td>0.22329818</td><td>10×AGA</td><td>D</td></tr>\n",
       "\t<tr><td>schp686</td><td>5000</td><td>21171</td><td>0.23617212</td><td>10×AGA</td><td>E</td></tr>\n",
       "\t<tr><td>schp685</td><td>7379</td><td>22956</td><td>0.32144102</td><td>10×AGA</td><td>F</td></tr>\n",
       "\t<tr><td>schp683</td><td>9365</td><td>23866</td><td>0.39239923</td><td>10×AGA</td><td>G</td></tr>\n",
       "\t<tr><td>schp689</td><td>8693</td><td>22649</td><td>0.38381385</td><td>10×AGA</td><td>H</td></tr>\n",
       "\t<tr><td>schp679</td><td>2528</td><td>19906</td><td>0.12699689</td><td>10×AAG</td><td>A</td></tr>\n",
       "\t<tr><td>schp675</td><td>3687</td><td>20438</td><td>0.18039926</td><td>10×AAG</td><td>B</td></tr>\n",
       "\t<tr><td>schp681</td><td>3705</td><td>20227</td><td>0.18317101</td><td>10×AAG</td><td>C</td></tr>\n",
       "\t<tr><td>schp678</td><td>4378</td><td>20630</td><td>0.21221522</td><td>10×AAG</td><td>D</td></tr>\n",
       "\t<tr><td>schp677</td><td>3967</td><td>20604</td><td>0.19253543</td><td>10×AAG</td><td>E</td></tr>\n",
       "\t<tr><td>schp676</td><td>2657</td><td>20223</td><td>0.13138506</td><td>10×AAG</td><td>F</td></tr>\n",
       "\t<tr><td>schp674</td><td>1270</td><td>20316</td><td>0.06251231</td><td>10×AAG</td><td>G</td></tr>\n",
       "\t<tr><td>schp680</td><td>1117</td><td>19377</td><td>0.05764566</td><td>10×AAG</td><td>H</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 16 × 6\n",
       "\\begin{tabular}{llllll}\n",
       " strain & mean\\_yfp & mean\\_rfp & mean\\_ratio & insert\\_sequence & kozak\\_region\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t schp688 & 1748 & 20754 & 0.08422473 & 10×AGA & A\\\\\n",
       "\t schp684 & 3294 & 20585 & 0.16001943 & 10×AGA & B\\\\\n",
       "\t schp690 & 3535 & 20593 & 0.17166027 & 10×AGA & C\\\\\n",
       "\t schp687 & 4658 & 20860 & 0.22329818 & 10×AGA & D\\\\\n",
       "\t schp686 & 5000 & 21171 & 0.23617212 & 10×AGA & E\\\\\n",
       "\t schp685 & 7379 & 22956 & 0.32144102 & 10×AGA & F\\\\\n",
       "\t schp683 & 9365 & 23866 & 0.39239923 & 10×AGA & G\\\\\n",
       "\t schp689 & 8693 & 22649 & 0.38381385 & 10×AGA & H\\\\\n",
       "\t schp679 & 2528 & 19906 & 0.12699689 & 10×AAG & A\\\\\n",
       "\t schp675 & 3687 & 20438 & 0.18039926 & 10×AAG & B\\\\\n",
       "\t schp681 & 3705 & 20227 & 0.18317101 & 10×AAG & C\\\\\n",
       "\t schp678 & 4378 & 20630 & 0.21221522 & 10×AAG & D\\\\\n",
       "\t schp677 & 3967 & 20604 & 0.19253543 & 10×AAG & E\\\\\n",
       "\t schp676 & 2657 & 20223 & 0.13138506 & 10×AAG & F\\\\\n",
       "\t schp674 & 1270 & 20316 & 0.06251231 & 10×AAG & G\\\\\n",
       "\t schp680 & 1117 & 19377 & 0.05764566 & 10×AAG & H\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 16 × 6\n",
       "\n",
       "| strain &lt;chr&gt; | mean_yfp &lt;dbl&gt; | mean_rfp &lt;dbl&gt; | mean_ratio &lt;dbl&gt; | insert_sequence &lt;chr&gt; | kozak_region &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| schp688 | 1748 | 20754 | 0.08422473 | 10×AGA | A |\n",
       "| schp684 | 3294 | 20585 | 0.16001943 | 10×AGA | B |\n",
       "| schp690 | 3535 | 20593 | 0.17166027 | 10×AGA | C |\n",
       "| schp687 | 4658 | 20860 | 0.22329818 | 10×AGA | D |\n",
       "| schp686 | 5000 | 21171 | 0.23617212 | 10×AGA | E |\n",
       "| schp685 | 7379 | 22956 | 0.32144102 | 10×AGA | F |\n",
       "| schp683 | 9365 | 23866 | 0.39239923 | 10×AGA | G |\n",
       "| schp689 | 8693 | 22649 | 0.38381385 | 10×AGA | H |\n",
       "| schp679 | 2528 | 19906 | 0.12699689 | 10×AAG | A |\n",
       "| schp675 | 3687 | 20438 | 0.18039926 | 10×AAG | B |\n",
       "| schp681 | 3705 | 20227 | 0.18317101 | 10×AAG | C |\n",
       "| schp678 | 4378 | 20630 | 0.21221522 | 10×AAG | D |\n",
       "| schp677 | 3967 | 20604 | 0.19253543 | 10×AAG | E |\n",
       "| schp676 | 2657 | 20223 | 0.13138506 | 10×AAG | F |\n",
       "| schp674 | 1270 | 20316 | 0.06251231 | 10×AAG | G |\n",
       "| schp680 | 1117 | 19377 | 0.05764566 | 10×AAG | H |\n",
       "\n"
      ],
      "text/plain": [
       "   strain  mean_yfp mean_rfp mean_ratio insert_sequence kozak_region\n",
       "1  schp688 1748     20754    0.08422473 10×AGA          A           \n",
       "2  schp684 3294     20585    0.16001943 10×AGA          B           \n",
       "3  schp690 3535     20593    0.17166027 10×AGA          C           \n",
       "4  schp687 4658     20860    0.22329818 10×AGA          D           \n",
       "5  schp686 5000     21171    0.23617212 10×AGA          E           \n",
       "6  schp685 7379     22956    0.32144102 10×AGA          F           \n",
       "7  schp683 9365     23866    0.39239923 10×AGA          G           \n",
       "8  schp689 8693     22649    0.38381385 10×AGA          H           \n",
       "9  schp679 2528     19906    0.12699689 10×AAG          A           \n",
       "10 schp675 3687     20438    0.18039926 10×AAG          B           \n",
       "11 schp681 3705     20227    0.18317101 10×AAG          C           \n",
       "12 schp678 4378     20630    0.21221522 10×AAG          D           \n",
       "13 schp677 3967     20604    0.19253543 10×AAG          E           \n",
       "14 schp676 2657     20223    0.13138506 10×AAG          F           \n",
       "15 schp674 1270     20316    0.06251231 10×AAG          G           \n",
       "16 schp680 1117     19377    0.05764566 10×AAG          H           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data %>% \n",
    "  left_join(annotations, by = \"strain\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 17 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>strain</th><th scope=col>mean_yfp</th><th scope=col>mean_rfp</th><th scope=col>mean_ratio</th><th scope=col>insert_sequence</th><th scope=col>kozak_region</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>schp688</td><td>1748</td><td>20754</td><td>0.08422473</td><td>10×AGA</td><td>A </td></tr>\n",
       "\t<tr><td>schp684</td><td>3294</td><td>20585</td><td>0.16001943</td><td>10×AGA</td><td>B </td></tr>\n",
       "\t<tr><td>schp690</td><td>3535</td><td>20593</td><td>0.17166027</td><td>10×AGA</td><td>C </td></tr>\n",
       "\t<tr><td>schp687</td><td>4658</td><td>20860</td><td>0.22329818</td><td>10×AGA</td><td>D </td></tr>\n",
       "\t<tr><td>schp686</td><td>5000</td><td>21171</td><td>0.23617212</td><td>10×AGA</td><td>E </td></tr>\n",
       "\t<tr><td>schp685</td><td>7379</td><td>22956</td><td>0.32144102</td><td>10×AGA</td><td>F </td></tr>\n",
       "\t<tr><td>schp683</td><td>9365</td><td>23866</td><td>0.39239923</td><td>10×AGA</td><td>G </td></tr>\n",
       "\t<tr><td>schp689</td><td>8693</td><td>22649</td><td>0.38381385</td><td>10×AGA</td><td>H </td></tr>\n",
       "\t<tr><td>schp679</td><td>2528</td><td>19906</td><td>0.12699689</td><td>10×AAG</td><td>A </td></tr>\n",
       "\t<tr><td>schp675</td><td>3687</td><td>20438</td><td>0.18039926</td><td>10×AAG</td><td>B </td></tr>\n",
       "\t<tr><td>schp681</td><td>3705</td><td>20227</td><td>0.18317101</td><td>10×AAG</td><td>C </td></tr>\n",
       "\t<tr><td>schp678</td><td>4378</td><td>20630</td><td>0.21221522</td><td>10×AAG</td><td>D </td></tr>\n",
       "\t<tr><td>schp677</td><td>3967</td><td>20604</td><td>0.19253543</td><td>10×AAG</td><td>E </td></tr>\n",
       "\t<tr><td>schp676</td><td>2657</td><td>20223</td><td>0.13138506</td><td>10×AAG</td><td>F </td></tr>\n",
       "\t<tr><td>schp674</td><td>1270</td><td>20316</td><td>0.06251231</td><td>10×AAG</td><td>G </td></tr>\n",
       "\t<tr><td>schp680</td><td>1117</td><td>19377</td><td>0.05764566</td><td>10×AAG</td><td>H </td></tr>\n",
       "\t<tr><td>control</td><td>  NA</td><td>   NA</td><td>        NA</td><td>NA    </td><td>NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 17 × 6\n",
       "\\begin{tabular}{llllll}\n",
       " strain & mean\\_yfp & mean\\_rfp & mean\\_ratio & insert\\_sequence & kozak\\_region\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t schp688 & 1748 & 20754 & 0.08422473 & 10×AGA & A \\\\\n",
       "\t schp684 & 3294 & 20585 & 0.16001943 & 10×AGA & B \\\\\n",
       "\t schp690 & 3535 & 20593 & 0.17166027 & 10×AGA & C \\\\\n",
       "\t schp687 & 4658 & 20860 & 0.22329818 & 10×AGA & D \\\\\n",
       "\t schp686 & 5000 & 21171 & 0.23617212 & 10×AGA & E \\\\\n",
       "\t schp685 & 7379 & 22956 & 0.32144102 & 10×AGA & F \\\\\n",
       "\t schp683 & 9365 & 23866 & 0.39239923 & 10×AGA & G \\\\\n",
       "\t schp689 & 8693 & 22649 & 0.38381385 & 10×AGA & H \\\\\n",
       "\t schp679 & 2528 & 19906 & 0.12699689 & 10×AAG & A \\\\\n",
       "\t schp675 & 3687 & 20438 & 0.18039926 & 10×AAG & B \\\\\n",
       "\t schp681 & 3705 & 20227 & 0.18317101 & 10×AAG & C \\\\\n",
       "\t schp678 & 4378 & 20630 & 0.21221522 & 10×AAG & D \\\\\n",
       "\t schp677 & 3967 & 20604 & 0.19253543 & 10×AAG & E \\\\\n",
       "\t schp676 & 2657 & 20223 & 0.13138506 & 10×AAG & F \\\\\n",
       "\t schp674 & 1270 & 20316 & 0.06251231 & 10×AAG & G \\\\\n",
       "\t schp680 & 1117 & 19377 & 0.05764566 & 10×AAG & H \\\\\n",
       "\t control &   NA &    NA &         NA & NA     & NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 17 × 6\n",
       "\n",
       "| strain &lt;chr&gt; | mean_yfp &lt;dbl&gt; | mean_rfp &lt;dbl&gt; | mean_ratio &lt;dbl&gt; | insert_sequence &lt;chr&gt; | kozak_region &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| schp688 | 1748 | 20754 | 0.08422473 | 10×AGA | A  |\n",
       "| schp684 | 3294 | 20585 | 0.16001943 | 10×AGA | B  |\n",
       "| schp690 | 3535 | 20593 | 0.17166027 | 10×AGA | C  |\n",
       "| schp687 | 4658 | 20860 | 0.22329818 | 10×AGA | D  |\n",
       "| schp686 | 5000 | 21171 | 0.23617212 | 10×AGA | E  |\n",
       "| schp685 | 7379 | 22956 | 0.32144102 | 10×AGA | F  |\n",
       "| schp683 | 9365 | 23866 | 0.39239923 | 10×AGA | G  |\n",
       "| schp689 | 8693 | 22649 | 0.38381385 | 10×AGA | H  |\n",
       "| schp679 | 2528 | 19906 | 0.12699689 | 10×AAG | A  |\n",
       "| schp675 | 3687 | 20438 | 0.18039926 | 10×AAG | B  |\n",
       "| schp681 | 3705 | 20227 | 0.18317101 | 10×AAG | C  |\n",
       "| schp678 | 4378 | 20630 | 0.21221522 | 10×AAG | D  |\n",
       "| schp677 | 3967 | 20604 | 0.19253543 | 10×AAG | E  |\n",
       "| schp676 | 2657 | 20223 | 0.13138506 | 10×AAG | F  |\n",
       "| schp674 | 1270 | 20316 | 0.06251231 | 10×AAG | G  |\n",
       "| schp680 | 1117 | 19377 | 0.05764566 | 10×AAG | H  |\n",
       "| control |   NA |    NA |         NA | NA     | NA |\n",
       "\n"
      ],
      "text/plain": [
       "   strain  mean_yfp mean_rfp mean_ratio insert_sequence kozak_region\n",
       "1  schp688 1748     20754    0.08422473 10×AGA          A           \n",
       "2  schp684 3294     20585    0.16001943 10×AGA          B           \n",
       "3  schp690 3535     20593    0.17166027 10×AGA          C           \n",
       "4  schp687 4658     20860    0.22329818 10×AGA          D           \n",
       "5  schp686 5000     21171    0.23617212 10×AGA          E           \n",
       "6  schp685 7379     22956    0.32144102 10×AGA          F           \n",
       "7  schp683 9365     23866    0.39239923 10×AGA          G           \n",
       "8  schp689 8693     22649    0.38381385 10×AGA          H           \n",
       "9  schp679 2528     19906    0.12699689 10×AAG          A           \n",
       "10 schp675 3687     20438    0.18039926 10×AAG          B           \n",
       "11 schp681 3705     20227    0.18317101 10×AAG          C           \n",
       "12 schp678 4378     20630    0.21221522 10×AAG          D           \n",
       "13 schp677 3967     20604    0.19253543 10×AAG          E           \n",
       "14 schp676 2657     20223    0.13138506 10×AAG          F           \n",
       "15 schp674 1270     20316    0.06251231 10×AAG          G           \n",
       "16 schp680 1117     19377    0.05764566 10×AAG          H           \n",
       "17 control   NA        NA            NA NA              NA          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data %>% \n",
    "  right_join(annotations, by = \"strain\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## You can combine `dplyr` and `ggplot2` functions\n",
    "\n",
    "But remember to use ` %>% ` in `dplyr` vs ` +` in `ggplot2`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YkV0iiIuD/J9SyJwdDgDARUEJBwCg/jBCb2blpugNr3h5TPKCcb0AAA/l8BKOCxcubNy4MSsry93dvV+/fuPGjSOIWup3f/vtt3379mVnZxsMBm9v72eeeWbs2LEMlM0C4Nr+r6Dox4rKbnJ+YYCfs2MBALg0xyYcqampCxcuHDRo0DvvvJOWlrZixQpJkiZOnFhzT4qi+vXrFxQUxLLsnTt31q9fX1FRMWvWLIeGBwBoiAMVmsUFxWqG2RAazEJDUQBAnRybcCQmJqrV6hkzZiCEwsLCcnNz9+7dGx8fz3FctT2ffvppy+u2bdtmZGRcuXLFobEBABrilsH4RnYeSxDrQoN8aMrZ4QAAXJ1jE46UlJS4uDjLYufOnbdv33737t3o6OiHHSJJUnp6+uXLl5988knr9YIgaLVa691qrZqxu8a5SsNBnPYFcdatTBQnZmRpRGl5cGBneV0NRc0REgTRJD5SiBMAx3FgwoExLisr8/T8a8BB8+uSkpJa9zeZTPHx8RhjjHH//v1fe+01662//fbbnDlzLIsrVqzo3r27YwL/C03T3t7ejr5Kw1EU1STiRAhBnPbl5eXV+BcVMX75aso9o2lOiPrNVuG2HGJ9H3Bl7u7uzg7BJm5ubs4OAYDH5kLdYmmaXrZsmclkun379qZNm9zc3CZNmmTZ6uXlZZ1hyOVyk8nk0HgYhsEYC4Lg0Ks0XFOJk6ZpgiAc/a/WcBDnI72ffv9gSWk/D/cFIUGPDICiKJIkBUHAGDdOePUDcVYDbfaB3Tkw4SAIwsPDo7S01LLG/Pphv8kIgggLC0MIRUVFkSS5YsWKkSNHKpVK89bY2NgVK1ZYdi4vLy8vL3dc8AghHx8fURQdfZWG8/b2liTJ9eP09PQkCML14/Tw8KBp2vXjdHd3ZximoqKikR+Qu8oqlmbnhTLMfwN9KysqHrm/SqXiOK6iosLFZ4tVKpU8z1dWVrp47q5QKGQyWVVVlaNzTR8fH4eeH7RAjh2HIzo6+uLFi5bFixcv8jwfGRn5yAPN+buLf/MBaGmu6g3v5OQpSHJTmNqLgoaiAIDH4NiEY+TIkdnZ2atWrcrIyDh+/PiePXuGDh1q7qKSlJT0wQcfWNqBfvvttydOnEhJSbl+/fqePXs2bNjQtWtXDw8YRwgAV1EoiC9nZOslvDw4IJqv3tEMAADq5tg2HG3btv3oo482bdp0+PBhd3f3ESNGjB8/3rypuLg4JSXFUobB8/zOnTsLCgpIkvTz84uPjx8yZIhDYwMA2M6E8bTMnGyT6X0/n6FuKmeHAwBoeggXbyH1MOXl5Y1QhSkIQllZmUOv0nDmNhzWbWVck7kNx8P6KLkOcxuOoqIiZwfyCOY2HMXFxY3zFX4vJ399SdkgN+X3IerHGuLL3IajpKSkSbThKCsrc/GaXHMbjsa5ATr0/KAFgrlUAACP8H1J2fqSstYc+7U6EAYUBQDUDyQcAIC6/K7Vf5RX4EFRm8OC3Si4YwAA6gluHwCAh8ozCVMzswUJrwwJjGBhYAYAQP09XqPRioqK9PR0hFB4eDgMdQdA82bAeNL97DyT8GmA7/NKhbPDAQA0bbaWcNy8eXPAgAGenp6xsbGxsbGenp4DBw5MTU11aHAAACeak513Sacf7eH2dx8nDKAOAGhmbCrhuHPnztNPP11aWvrUU0/FxMQghK5du3b48OGnnnrq/PnzUVFRDg4SANDY/ltUsrWsoiPPLQkKcHYsAIDmwKaE45NPPtFqtYcPH+7fv79l5ZEjR4YOHZqQkLB582aHhQcAcIKTlVUL84t8aWpTmFoG/VIAAPZgU5XKsWPH/v73v1tnGwih/v37v/7668eOHXNMYAAA58g0Ca9l5hIIrQkJUsMMXgAAO7GphKOsrKx169Y117du3dr1x8UCANiuSpImZGSViOIXQf5PK+TODgcA0HzYVMIRFBR0+vTpmutPnz4dFBRk75AAAM6BEfpHVl6K3jDWw22KF8xkBACwJ5sSjpEjR27atOnf//63Xq83r9Hr9YsWLdq8efPIkSMdGR4AoPF8WVC8r0LTTc4vVkNDUQCAndnaaPTo0aNz58797LPPoqKiMMZpaWmVlZUxMTHz5s1zdIgAgEZwsKLyy4Iif5peG6JmCWgoCgCwM5tKODw8PM6ePTt//vzIyMjbt2+npaVFRkZ++umnZ86cgRnkAWgGbhuMb2TnMgSxIVQdwDh2EmkAQMtk651FoVAkJCQkJCQ4NBoAQOMrE8WJGdkVorRMHdBZzjs7HABA8wRzqQDQokkYvZ6Ve9dofN3Hc7ynu7PDAc3ZsWPHCIL4/vvvnR0IcI6HlnCY/yZefvlliqLq/vuYMmWKnYMCADSWBfmFxzRVfZTyT/x9nR0LAI3k1q1bW7ZsGTly5BNPPOHsWFqQhyYcr7zyCkJo7NixFEWZXz8MJBwANFG7yyr+W1QSwtCrQ4JoaCgKHOy5557T6XSMC4wmd+vWrU8//TQqKgoSjsb00ITj6NGjCCGWZS2vAQDNyTW9YXZOnoIkN4eHeFGUs8MBzR9Jkjzv5EZCWq1WLocR7ZzjoW04+vXr169fP5IkLa8fphGjBQDYR4kgTs7I1kt4mTogmmOdHQ5oEaq14di1axdBELt37/7888/btGnDcVxoaOhnn32GMbYcIgjC//3f/8XExKhUKpVK1bp16ylTpmg0GusdlixZ0qlTJ5lMplKp+vbte+TIEctW8yV27Njx6aeftm7dmmXZBQsWzJ8/f8iQIQihl19+mSAIgiD69u1bd+QNDAMhlJ+fP3nyZC8vL4VCERcXd/r06dGjR1unX0uXLiUI4sKFC9ZHDR8+XKlUVovkke+37o/UfJKlS5d26dJFoVCoVKonnnjCukfII99LvdnUS6Vv375Lly7t1KlTtfW//PLLggULTpw4YZdQAACNw4Tx1Myc+ybTe37ew9xVzg4HtGjvv/9+mzZtli9f7uHhsWbNmo8//tjb23vmzJnmrXPnzv3yyy/Hjx//j3/8gyTJjIyM/fv3V1RUqFQqhJAoikOHDj18+HB8fPy0adP0ev2mTZsGDhy4efPmcePGWS7xwQcfqNXqRYsWBQQEMAwTEBDAcdw///nPf/7zny+88AJC6JHjOzQwjMrKyri4uNu3b0+fPr1Lly6XLl3q379/aGjo435WNr7fuj9SQRAGDx58+PDhuLi4Tz75xM3N7ebNmzt37vz0009tv0T92JRwnDx5stY5UwoKCk6ePNnACAAAjeyj3IKkKu1AN+UcXx9nxwJaOi8vrwMHDhAEgRDq3r37r7/+unz5csvTcffu3c8++6z1nOQLFiywvF61atXBgwfXrl1raWj41ltv9ezZc/bs2fHx8TT95wOOZdkTJ05YFhFCMTExCKHo6OhHlm3YJYwlS5akpqZ+8803lvfVuXPn6dOncxxn24f0eO+37o/066+/Pnz48Jtvvrls2TLify23JEl6rEvUT4O6xZaVlTm9Qg4A8Fi2lVWsKymL4tj/qgNh5nngdOZ6DfNrkiS7du2alpZmef55eHikpKT8/vvvtR67YcMGPz+/cePG6f9HFMVx48bl5+cnJydbdnvllVca+KRsYBi7d+/29vaeNm2a5ZCpU6eq1erHDcPG91v3R7pp0yaZTLZo0SLCqp24ufmE7Zeon7r+Da5cuXLlyhXz66NHj2ZlZVlvLSkp+eqrr6KjoxsYAQCg0fyu1b2bnaeiyPWhajcKhuEBzhcSEmK96ObmZjQaNRqNu7s7QujLL78cM2ZM9+7dQ0NDe/fu3a9fv5deesnS6jMlJaWiokImk9U8bUFBgeV1REREA4NsYBhpaWkxMTHWSQ9Jku3atTt16tRjhWHj+637I71161ZUVFS1piGPe4n6qSvhSExMNFfqIIQWLVpUcweZTLZt27YGRgAAaBz5gjA1M0fA+PtgdRtoKApcA1Fbf2xLI8fnnnvu3r17hw4dOn78+MmTJ7ds2ZKQkHDmzBlz8YAkSa1bt96wYUPNM7Rr187y+nFrLmpqeBg132a1hpy1fg6CIFgv2vh+6/5IMca17vBYl6ifuhKO8ePHd+3aFSE0ZMiQRYsWmSu9zAiCUKlUnTp1cnNza2AEAIBGYMB40v3sPJPwib/vCyqFs8MBwFYqlSo+Pj4+Ph4htG3btnHjxi1fvvzzzz9HCLVp0+batWsdO3Z82O/1h6njieuIMFq1anX79m1BECyFHJIkpaamWu/j5eWFECopKbFeeefOHevFer9fa23btr1x40ZlZWWtJ7HLJR6mrjLVNm3aDB48ePDgwQkJCePGjRts5cUXX+zTpw9kGwA0Fe/n5F/U6ge7Kd/w9XJ2LADYqtoDuGfPntYrJ02aZDQa33vvvWqlBTk5OXWf1ty7pNrJHRfGyJEji4qK1q5da9m0fv367Oxs653btm2LEDp06JBlTWJiYrWkpN7v19rEiRN1Ol21md4tJ7TLJR7GpnY08+fPb/iVAADOsrK4dEtpeUeeWxEcBO1EQRMSFBQ0ePDgLl26qNXqgoKCNWvWUBT18ssvm7fOmjXr2LFjq1atunTp0rBhw3x9fTMzM8+cOZOcnFx3g4PY2Fie57/66iuWZT08PPz8/J577jnHhfHOO+9s3rz59ddfv3z58pNPPpmcnLx+/fro6Oi7d+9aLtG9e/eePXsuW7ZMo9F07NjxypUre/fujYmJsd6n3u/X2qxZs/bv37906dLLly8PGjTIzc3t9u3bhw8fvnbtmr0u8TC2NtzFGB87duzcuXMlJSWWxq5mS5cubWAQAADH+bWy6tO8Qi+aWh+mlkG/FNCkvPvuuydOnFiyZEl5ebmfn1+3bt3WrVv31FNPmbfSNL13797Vq1d///33//rXvwRBCAgI6NSp05IlS+o+rbu7+5YtWz799NO3337bYDDExcXVnXA0MAyVSvXrr7/OmTNn69at69ev79Kly+HDh5csWWKdTCCEduzY8Y9//GPbtm0Y46effvr48ePz5s2z3qfe79cawzAHDx5cunTpxo0bExISGIaJiIgwVxXZ6xIPQ1QrNqmVRqMZNGhQUlJSrVttOYPdlZeXm0wmh17Cx8dHEIRaByBxKd7e3pIklZaWOjuQR/D09CQIwvYyTGfx8PCgabqoqMjZgTyCu7s7wzDFxcV1fwEzTUK/O+kaSdoZHtxL4YQRnVUqFcdxNX+ouBqlUsnzfFlZWbVmeq5GoVDIZLLGuQE69Pxg9OjR+/fv1+v1zg6k8djUL87cHHfRokU3btxACO3fv//kyZP9+/fv1q1benq6YwMEANRXlSRNSM8sEcWFAb5OyTYAAMDCpoRjz549Y8aMmTt3rrk3s7e3d58+fQ4cOIAx/vrrrx0cIQCgPjBCb2XnpRiMYz3dp3p7OjscAFwXxlj/cC5eONeE2JRwZGdnP/PMM+h/g5GZi/Ioiho7duzOnTsdGh8AoH6WFBbvLdd0k8sWB/k7OxYAXNr169dlD7djxw5nB9hM2NRoVKFQmJMMlmV5nrd0j3Fzc8vLy3NgdACAevmlsuqLgmJ/mv4uJIh9/CEHAGhRoqKiHjZsOUKoVatWjrjorl27HHFaV2ZTwhEZGWnpDRwbG7tt27YxY8aIorh9+/bg4GBHhgcAeGx3DMbpmTk0QhtC1YFMg6aQAKAl4HnePMolcCibeqnMnTt37dq1WVlZDMOsWbNm+vTpkZGRkiSlp6cvXLjwo48+aoRAqxEEgaIoh17CPBSdU/rgPBaI076aepwVgvj05as3qrSr27R6NdD5lSlN/fN0NY0WZz3G4gSgbjYlHOXl5ZmZma1btzaPSL948eK1a9eSJDl69OiPP/7Y0Q/+h4UE3WLNoFusfTXpbrESRhPvZx3VVM3w9lwY6OfE8CygW6x9QbdY0HTZVNzq7u5unmXO7N1333333XcdFhIAoJ4+Kyg6qqnqo5TPD/B1diwAAPCAR/dS0Wq1H3744fnz5xshGgBAve2vqPyqsDiEZb4NCdAKbCoAACAASURBVKKhPBwA4GIenXDIZLIlS5Y4uvgOANAQ1/WGWVm5HEF8HxLk7YxaTgAAqNujq1QIgggNDc3NzW2EaAAA9VAiiJPvZ+sk6duQoCdkvLPDAaAWGo3GEac1z/sKmgSbBv56+eWXly5d6uJtqQBomQSMX83MyTCaZvt6D3eHmy8AwEXZ1Gg0Ojr6+++/79ChwyuvvBIREWHuq2IxfPhwx8QGAHgonSSVm0wEQh/nFZ6q0j6rlL/v5+3soAAA4KFsSjheeukl84u5c+fW3Or6PdcBaE5uGYwf5uYnVWolhLwpulgUojh2TYiagoaiAAAXZlPCAROmAOAiSgQxPj0zx/Rn/WaxKCCEXvf2dKNsqh4FAABnsSnhGD169CP3kSRpx44dAwYM8PSEeSkBcJTvSspyTALCCFkVZ6wrKZvk5eG8oAAA4NHs9qvIaDSOGzcuLS3NXicEAFhghNKNpv0VlXvLKxB6INtACN0yGp0SFQAA2A4mdgLAFRkwTtEbrpn/0+lvGAwa8aFDg7uTUJ8CAHB1kHAA4BLKRfGmwZis0yfrDKkGQ4reYLRqju1P00+p5LEyjkTE5wXV53mJ93BHAADg2iDhAMA58kxCsl5vyTAyjH8N5ssQRCTHxvJ8rIxry7ExPO9F/zV4KE0QXxQWGaU/05FnlPJ/+sM8WwDU4ty5c59//vmlS5fS09NfffXVNWvWWG/96aefPvroo5s3b/r6+k6dOjUhIYF8zMJCjUajVqsFQcjMzPT2rt4vvY6tWq12+fLlO3fuvHXrFkIoLCysd+/es2bNiomJqdcbbRog4QCgMRglfNdoTNbpk/X6ZJ3hut5QZTV7qgdFdZfLYmVcLM+347lonmMf3sf1bV+vQSrFaUHSE6gtlp5VKqA7LGgmMCZvXifycxHHS1FtsW9DZzyuqqqKiooaPXp0QkJCtU1nz54dNmzYzJkzN27ceOnSpZkzZ4qiuHDhQvPWixcvhoeHe3l5WYWGf/nll+eff976JJs2bYqNjeU4bt26de+99161Szxsa2lpad++fTMyMt5///1u3bp5e3unp6efOXNm0qRJly5dauBbdmU2TU9vC71eL5PJfv/9965du9rlhHWD6ektYHp6+7LX9PRlopj6vyqSZL3+jsEoPlhFEivjY2VcrIxvx3FhLPO45691enoXBNPT21fTnZ7+kUObEwYDtek7Mjf7z2WaFvu+IPbsXfdRNg5t3qlTp65du1qXcIwaNSolJeXGjRvmxXnz5v3nP/8pKCiQy+UIoSFDhmRmZh47dsz8OWCMZ86cmZiYePPmTeuyitjY2LfeeovjuPnz59+6dYt48HfCw7ZOmzZty5YtV65ciYqKst5fFEWqWU+EBCUcANiBiHGWSbhpMPyZYej0+VbPLZYkov5XRRIr42N4Tg7NPAF4EHX0wF/ZBkJIMFHHDkoh4Vgd7IjLJSUljR8/3rI4cODAhQsXXrp0qVevXgihbdu2DR48uG/fvj///LO5wuXgwYPHjx+3zjaSkpLS0tLGjBlDUdSsWbOOHTv2wgsvPHKr0Wjctm3blClTqmUbCKHmnW0gSDgAsHaoovJMcZkJoRiKjHdX1THJe6UkpRmMN/UGcxXJVb1eJ/1V0mBdRRIr41tzLAwDCoAZ9fsZojC/+lqMySsXH1xFIITonxJxcGjNk4idu+OAoHrHIElSfn5+QECAZY35dU5OjnlRoVAcOHBg6NChcXFxMTExp06dOnHiRHR0tPVJvvnmm/j4eKVSiRAaO3bsypUrrROOh23Nysqqqqrq2LGjZc9XX311/fr15tfJyckdOnSo9/tycZBwAIAQQhihGZk5e8r/KvVdV1y6NyJURv6ZKOSZhFSDIdVgNFeR3NYbLDUEFEEEM3RbjjMXYDwp4/1o+GYBUDviTiqZdtvWnQvyiYIa2QlCUmTrhiQcD72c1Q8DmUyWmJgYERGxa9euI0eOVMs2ioqKdu3adfToUfPi1KlTe/XqlZOTExQUVPfWmnWgCxYsmD179tmzZ6dPn+7iNaQNZLfbIsdxV69erVlGBECTsKO03DrbQAhd0umnZ+U8wXPJOsMlna5QEC2blCQZI+MtGcYTPG/JSwAAdROGjCJqayhDb1pLlFVv4yX27it1qq1doELZkBhIkvT398/Ly7OsMb8ODAy0rDEajZMmTXJzc+vRo8f06dOPHz8eERFh2bpu3TqDwfDss8/+Faoorlmz5pNPPql7a0hIiFwuv3btmmWTWq1Wq9XWwTRXj5dwSJKk0WiqpWAeHh4IIYIgrMuIAGhaDmqqaq48XFF5uKISIaRmmAEqWQee6yjjY3gujGUgvwCgnpSqWn/Fi/3/Ru/YZL0Ge3lLveIwwzoiil69eh06dGjJkiXmxUOHDikUiieffNK8aDAYRo0alZqaevLkyYCAgAkTJvTp0+f48ePmH9UY41WrVs2ePXvq1KmWE27dunX16tUfffQRSZJ1bGVZduzYsd9///0777zT0n6i25RwSJK0atWq5cuX371711hjEOXmXQQEWgitJNZcqSSp9aGBMTzvSTfzxlwAOJ3UJloY8RJ14ihRWoIoSmrVRuz/YgOzDZ1Ol5qaan5RUlJy+fJlgiBiY2MRQnPmzOndu/cbb7wxY8aMy5cvL168+J133jF3UUEIjRs37s6dOydPnjRXkWzdunXy5MlxcXFXrlzx9vY+cuRIWlrajBkz2rZta7nWzJkz//Wvf+3fv5/n+Tq2Dhs27Isvvjh37ly3bt3M3WJ9fHwyMzNXr15NEATLOiS7chE2dYtdsGBBQkJC69atu3Tp4u5efUzDlStXOia2ukC3WAvoFmsXMzNzd5dXVJsU7QWVYkuYQ1rINxx0i7Uv6BZbTeN3i7Ug9HrMMMi2Lht1d4u9fPmypdDCjKIoyz/x/v37P/7445SUFHM/lPnz51sG/jp9+nSrVq38/f0tB0qS9OOPPw4bNgwhNHz48IyMjJpjZvTu3dv8B1/H1oMHDyKEqqqqli1btmvXrtTUVEEQgoKC4uLiZs2a1a1bN1vedRNlU8IREhIyYMCAb7/99nFHYXMcSDgsIOFoIIzQV4XFiwqKRYQRNq8gEEJykjwSGdqW55wdYO0g4bAvSDiqcWLC8VhsHIcDuAKbqlTy8/OnT5/uOtkGAPZSLIqvZ+Ycr9T60tT/C/A7Wll1okprlHBXOf+xn4/LZhsAANDk2JRwhIaGlpeXOzoUABpZUpV2ZlZunknoo5R/ExzoR9OjPNzsNdIoAAAAazYlHFOnTl2+fPkLL7xAwOBFoFmwVKMQCM3x837P18fcrZVOuyXl5QiiSHv5CO1jEJTqAQCAndiUcLRp0+a7777r2bPnhAkTQkJCqqUdw4cPd0xsADiEpRoliKFXhQT1lMvM6/n9e5iUqxghESEZQuLlC7qxkzEM4QUAAPZg0800Pj4eIXT37t3z58/X3OribdYAsJZUpZ2RmZsvCH2U8pXBQb7/6+9KX7/CpFy13pPKzWZPHTf0faG20wAAAHg8NiUcO3fudHQcADjaw6pRzJg7qTUPoW/fhIQDAADswqaEY/To0Y6OAwCHKhLEv2fnHtdUBTH0tyFBPf5XjfInjImKWppFk5oK7uwpIaKV6BeAoAETAAA0ANRPg+bvVJV2ZmZuviDEKRXfBAf6Wg0bShgMzPUrzKXfyZLauqWIIvvbL+xvv2C5QoyMEiKihLBILJPVsicAAIA62ZpwYIyPHTt27ty5mgP4LF261AGBAWAHIsaLC4uXFJbUrEYhS4uZq5eZ5D8IvR6RpBjRiszPI7R/zaiCaUY3ZiJZqaEy7tJ3btHXkulryYgkRV9/MTxSaNVGDAqGYg8AALCRTQmHRqMZNGhQUlJSrVsh4QCuqUgQX8/KPVFZFcTQq0OCupurUTCmMu6xF8/Td28jjLFcYezRy9ipK3ZzJyvK2ZPHmPv3kCCKQWpDn+dF/0ARIVPb9ugFTBXkUel36bRbVE4WlZ/LnkvCMpkQGiGGRQpRbXDD5q4EAIBmz6aEIyEh4cyZM4sWLRo+fHj79u3379+vUqk+++yz0tJSaE8KXJOlGqW/SvmVOsCLpghtFXP1Mpv8B1FehhAS/QNNsV2Ejk9g6s9vgeTmrh8yiq914C+CEP0DRf9AY49ehE5H379nLvZgUm8wqTfQUUL0C4BiDwAAqINNCceePXvGjBkzd+5cvV6PEPL29u7Zs2evXr169uz59ddff/HFF3Uce+HChY0bN2ZlZbm7u/fr12/cuHG1jh527NixkydPpqenGwyGoKCgF1988YUXoHcAqA9zNcrigmKSIMzVKExBLpP8B339CiEImKKEDk8Yuz4l+vk/+ly1wTKZqW37B4o97t+j7qdDsQcAANTBpoQjOzv7mWeeQQiZp1MxTxpEUdTYsWO/+uqrOhKO1NTUhQsXDho06J133klLS1uxYoUkSRMnTqy55y+//NKhQ4dhw4bJ5fLTp09/9dVXgiAMGjSonm8LtFSWahQ1w3wb5Ncr5z5z7EcqOxMhJLl7GGO7GJ94Esnk9rmYVbEH0mmZ++lQ7AEAAA9jU8KhUCjMSQbLsjzP5+TkmNe7ubnl5eXVcWBiYqJarZ4xYwZCKCwsLDc3d+/evfHx8RxXfU6sRYsWWV63b9/+3r17SUlJkHCAx/JbpXZmVm6BIAyQ8avy7/sfTSR0WkQQYnik6YnOptbtHDhUuUwOxR4AAFAHmxKOyMjI1NQ/h0WKjY3dtm3bmDFjRFHcvn17cHBwHQempKTExcVZFjt37rx9+/a7d+9GR0fXfUWj0ejn52dLbAAg62oUhOaW5s87/BspiZjjTLGdjZ17SD6+jRdKrcUeaberF3uERoih4TBXCwCg5bAp4ejfv//atWuXLVvGMMy0adOmT58eFRUlSVJ6evrChQsfdhTGuKyszNPT07LG/LqkpKTuyx07duzOnTuvvfaa9coLFy4sW7bMsjhnzpz27dvbEnxDUBTl4eHh6Ks0EEEQJEm6fpwkSRIE4Yg4sw3GiSm3TlVWBRsNm/441bOsCPn4kT2eJrr2pFmWf8yzURSFELJPnB4eKDAI9XgaYYxys/GdWzjtFnX3jrnYg1AoUGRrolUb1K49oXJ73HOb43R3d7dDnI5kjtPN7bHfYCMz1xerVCoXn6vBHKdSqXTxOAGoyaaE48MPP5wwYYJ5+I1p06aVl5evXbuWJMn58+d/+OGH9g3ot99+W7ly5ezZs1u3bm29XqPRpKSkWBb1ej3t+Fm1CIJohKs0XFOJEyFk9ziPpae/fC8rjyBeLMhZfe13n8goavRLZFSbBraZsP/nGRqOQsPRc/2xtgrfuSXdSZVSruOrl/HVy4ggiKBgsnVbMqot2ar1YxV7tNh/dwcxp0eur6nECYA1wqFp8qRJk+Li4l599VXzYkpKygcffPD5558/rErl4MGD33333XvvvdezZ8+6z1xeXm5uVuI4Pj4+giCUlZU59CoN5+3tLUlSaWmpswN5BE9PT4IgHlm+ZSuMcca9pRn3/+0VQCL0wf3bc1QKoVMX7NbQX/wetXaLdQyyqIBOu03fv0dlZiBRRAghmdwUGi6GRQqtWmOlqo5j3d3dGYYpLi528V+6KpWK47iaAwa6GqVSyfN8WVmZIAjOjqUuCoVCJpM1zg3QvifUaDT2PaGZSlXX1wS4lMf4LSUIwh9//HHw4EHbn8HR0dEXL160LF68eJHn+cjIyFp33rZt27p16+bNm/fIbAO0ZIS2ij2XVL5+9Yj7OYu8A9WC8SfJMHvgIFOf5xqebTQyycfP2KOXNn6i5vXZ+qGjTbGdMUUxqTf4I/uVK5fKN6zmfv2ZSr+LajyqqXtp+OA+YfdWJvmPPzMVAEAN586dGzlyZEREBEEQ06ZNq7b1p59+6tSpE8/zISEhCQkJ9ciJNRqNm5ubXC4vLi6utkmr1f773//u0qWLSqVSqVQdO3acOXPm1atXbTy8WbK1nHPr1q2zZ8/Oz89HCJ05c6Znz545OTmdOnVasmRJrd1czUaOHPnBBx+sWrVq4MCBd+/e3bNnz/Dhw81dVJKSkvbt25eQkCCXyxFCq1evPnDgwGuvvaZSqe7evYsQYhgmJCTEDm8RNBdU/p/DaZxw95rc6akClh/EMcvaRXnSlEv/xreFpZPLg8Ue5tYe1Yo9+GMHmUu/Y4REhDiEmD/Oa8dNwfzjNlYBwOUUC8LX+UVXtDo3mnrRw22Up0cDe5NXVVVFRUWNHj06ISGh2qazZ88OGzZs5syZGzduvHTp0syZM0VRtLRKvHjxYnh4uJeXl2V/jPEvv/zy/PPPW59k06ZNsbGxHMetW7fuvffes6wvLS3t27dvRkbG+++/361bN29v7/T09DNnzkyaNOnSpUuPPLy5sqlK5fDhw4MGDercufPYsWPnzJljTjgQQv369VOpVHv27Knj2N9//33Tpk2ZmZnmgb/Gjx9vHvhr3759a9as2bRpk7k12YQJE6oVuAUEBHz77bcPOy1UqVg0+yoVQhTpO6nMH+eo7EyBIP7VofO/gluRJDnXz/tNX2+7j27RmFUqdftrSNO7twnzt4MgJHdPsqz6Z2iK6aQfONQJIdoAqlTsqxlXqdw3muJSbhdbff5jvTy/jXjEz04bq1Q6derUtWvXNWvWWNaMGjUqJSXlxo0b5sV58+b95z//KSgoMP8GHjJkSGZm5rFjx8yfA8Z45syZiYmJN2/e9Pb2tpwkNjb2rbfe4jhu/vz5t27dsgxrOW3atC1btly5ciUqKso6DFEUrdvfPOzw5sqmhCMuLk6j0Zw/f14QBJlMZkk45s2bt3nzZnOBRCODhMOiGSccRHkZm/wHc+UiodMhgsiIip7c5omzEg5mmW+DA7vJHTJrq+skHH/BmMrPpe+l0ffukDlZqMZ3FstklW/McUpojwQJh30144Rj1O17Ryuq77O1VfiLHnV1cap3whEQEDB+/PglS5aYF5OSknr37n3q1KlevXohhKqqqgYPHlxYWPjzzz/7+vpOnTr14MGDP//8c8eOHS1nSEpKGjBgQF5eHkVRgYGBO3fuNA+QbTQavby8Jk2atGLFijpCetjhzZhNVSp//PHHggULaJqu9lUMDQ3Nzc11TGCgBcOYyrjLXrpA372NJAnzvLFrz2Ptn5hRWlkoiIPclMvVAR4tqpU+QYgBQWJAkOGpZ/gfdjC3b1bfwWh0RlgA1Md797PPV2mrr8UoWaerufObGZmf57I11y8MDuyjqv8YepIk5efnBwQEWNaYX1uGtVQoFAcOHBg6dGhcXFxMTMypU6dOnDhRrbvDN998Ex8fr1QqEUJjx45duXKlOWPIysqqqqqyTk1effXV9evXm18nJyd36NChjsObMZsSDlEUa44NihAqKChgGMbeIYGWizAYmOtXmEvnyZJihJDo62fq3F3fruPisgrz3Cjz/H0cUY3ShEhBalQj4cA+MEoeaDIKBCHdUEuKXGthe4Uo1bqz1jEFZtaVGjKZLDExMSIiYteuXUeOHKmWbRQVFe3atevo0aPmxalTp/bq1SsnJycoKKhmvcGCBQtmz5599uzZ6dOnm7fWcbgj3peLsCnhaNOmzalTp2bNmmW9EmO8b98+6yQOgHojS4uZq5eZ5D8IvR6RpNCqjbFzdzEsIkcQp2fmnNfqgllmdXBgV8dUozQhps7dmavJZMkDNT5kWSmVkyUG1TXsLwAuYkNkWK3r+928U7PkY3lY8Hhvz1r3bwiSJP39/a2n5jC/DgwMtKwxGo2TJk1yc3Pr0aPH9OnTjx8/HhERYdm6bt06g8Hw7LPPWtaIorhmzZpPPvkkJCRELpdfu3bNskmtVqvVauvL1XG4vd+rC7GpW+zkyZN37Nixbt06y5rKysrXX3/9/PnzU6ZMcVRooJnBuGb3ToQxlX5XlrhN8d0K9lwSIiljj16V09/UjRwrhkceqax6Ni39vFY3yE15vFUYZBsIIUwz2rGTTB07EUoVYlgxJMzYpQcyGmQ7NtJpt5wdHQD1tzhUzT846t0zKuVYL/tnG2a9evU6dOiQZfHQoUMKheLJJ580LxoMhpEjR167du3kyZM//PBD9+7d+/Tpc+fOHfNWjPGqVatmz5592co///nP1atXi6LIsuzYsWO///57y/7V1H24g96vK7Cp0aggCEOHDj148KCfn19BQUHr1q0zMjKMRuOQIUN++OEH0hnzQUCjUQvXbzRKFhZwJ47S2fcRRqI62ND3BdEvgNBWMVcvs8l/EOVlCCHRP9AU20Xo+ASmaISQgPGSwuLFBcU0QSQE+E739my0ahRXbDRaG+uBv+hbN/mfEglJ0j8/0NSpq7NDewA0GrWvZtxoFCGUotN/nldwqUrrTlEverj9w99X9qjnS92NRnU6nXkisJdeeqlDhw6ffPIJQRCxsbEIobNnz/bu3XvmzJkzZsy4fPnyjBkz3nnnHUu32JEjR964ceOXX34x13GIojh58uTjx49fuXLF29v78OHDAwcOvHnzZtu2bS3XyszMDAsL27Nnz7Bhw0pKSvr06ZOdnW3uFuvj45OZmbl69er9+/ffvHnz3r17dR/+yA+qibJ1pFFRFFetWrVhw4aUlBRJktq0aTNp0qQ33njDWSPsQsJh4eIJB1FRrtjwLWHVHAwzjBjVhrqdSggCpiixXQdj16dEP3/LDtkm02uZuee1uhCW+bbRq1GaYsKBECKzM+V7thE6nbFHL8MzzzVwZHc7goTDvpp3wlEPdSccly9fthRamFEUZfkn3r9//8cff5ySkmLuhzJ//nzL7+fTp0+3atXK3/+v+5IkST/++KM5Gxg+fHhGRob1iBpmvXv3VqlUBw8eRAhVVVUtW7Zs165dqampgiAEBQXFxcXNmjWrW7duthzeLDl2aHPHgYQDIUQYjeyFM3xBPiYIfVCwqXM3c/GAS+EP/8hceeB7hREiEJI8PE2duppiOmH+gXzisKbyzey8UkH8m5tqmdq/8XujNNGEAyFEFhXKd28hKspNMU/q+7/oIlPRQsJhX5BwVANDmzchLvd8AjYiDHr5hjVkWYn5Ls7dSqFvXteNfwXb7wlNiAIymZDBQEgSYTQgQUAmE2E0EJKEDAZCMCFBIIwGJIqE0UAIAhYE0mhAgoiMVlv1+uqnRUhSuVVNe6Par3DrapTPAv1ec0BLseZN8vGtmjBVtmsLc/USUanRDxuNmVr6EwIAgFM8RsJRWFiYlpZWc7KowYMH2zsq8GjsqePVRpyk8nLY388Yuj1FmIzIaCQkiTDokSgio5EwGQlJQno9IYrIZERGg3krIYrYnEOIEjL8uZUwGpCECUP1ROExkCRmOUTTmOUIQUSm6r3asFJVLdvINpmmZ+b8rtWHsMzq4MAu0D60XrBSpRs3RbZ3B33vjmzbBt2ocViucHZQAACAkI0JR2lp6axZs7Zv315roWgTrZRp6uj76TVXsr/9wv72Sz3PSBCY4xFFYZbDLIsoL8TzmKQwwyCWRRSNOQ5TNKJpzLKIohHHYZrGFI047n9bKXOSYV2Yz1z6nT9WvUpSaPNAj3ZLNcqLbqplan/3FjWol71hjtOOGi879CN944p803e6+AmSp/ejDwMAAAezKeF4/fXXt2/fPmLEiL59+1pPZgOcqdYacZYVA9WYpDDLIoZBFIV5GSZJxLCYYRBFI57HFIVpBrMs8ddWBjMscsxj3tSpK3U/nbmVYlkjREYZu/45ITBUozgERen+NoxTqdhzSfLNa7Ujx0kwRAcAwNlsSjh++umniRMnbty40dHRANthhRKVVJ/R2NC5u/GZ55wSz0MRhH5YvHD3tqIwH0lSla+/0KqNuT4ly2h6LSvnd60+hKFXhwRBNYo9EYShz/OSpxd/5Cf5tvWGF0eYp6IFAABnsSnhoCiqa1fX6tzfkhGiwB07SGVm/Lls7vWBkOTlberZ24mB1UGIbE126U4QhPC/ydsOVVT+IweqURzLFPMklsn5HxP5/YlIW2V6spuzIwIAtFw2JRx9+/a9ePGio0MBtiDKy2T7dlF5OaKfv6H/YObGVTYvBxOEMTjU2L2Xa/ZKkDDaWVZ+sahUwvhJmhrlrvq/wpKvCosZqEZxPCGqrfalSfLErfyxg2RJseG5Aa4zRAcAoEWxKeH48ssve/fuvXLlytdee80p44oCMzrtFn9gL6HXCR2e0Pd/EdOMGKhWeHtLkqRx1YG/BIxfysj6tfLPKRK+R+ijvIJKUYpk2e9CgzrytUwKCOxLClJrJ74q27WZvXieNOh1A4Y4qL0OAADUwaaEIyoq6r///W98fPycOXPCwsJo+oGjLl++7JjYgBVJYs/8xp35FZOk4bkBxi49nB2Qrb4tLrVkG2aVotSGZw9EhEI1SqORPDy141+R7dpCX78i12h0w+Mxxzs7KNCywAhdwKbiih07dowZMwZjLJPJBEHQP8jRIQJCp5Pv3sqdPolVbtqxU5pQtiFg/EN5Zc31PEFCttHIsFyhGzdFiGxN3b8n3/o9oalwdkQAgJbFphKOhISEkJCQn376qUOHDo4OCFRD5eXI9u0iysuEyCj930Zgmet25RAxzjCZUvSGWwZTil6fajDeMRqNUi3DtBhg7BZnwAyjG/ESf+wAk3xRsfk73agJoq+fs4MCALQUNiUc9+7dmz9/PmQbjY9Jvsj/fBBJkqvNyGWWZxJSDYZUgzHVYLypN1zTG7RWo4MwBBHJsUYJ3zNWH2n0SRmU5zsJSer7D5bcPblff5ZtW68bMUYMDnN2TACAFsGmhCM0NNRY45kBHIowGblDPzI3ryOZXDd4hBDeytkRVU8vrusNVQ+mF0EM3ZaTx8q4djzXhmVbcyxFEEWCGHcnvcBqQiwvmprrZ+d5ocBjMfbohRVK/sh+2Y5Nhr8NN7WD3xIAAIezKeF48803ly1bI0+vAAAAIABJREFU9s477yiVSkcHBBBCZEmxbN9OsrBA9A/UD4uX3D0aP4YyUUw1GFP1hpsGQ7LOcENvqLRKL2iCUDN0L07ejufasEw7novmOba2AhgfmjrUKuyz/MLTWr2EcA+e/zjAN4iBWQOdzNQxVlIqZXt38vsTiYoyY/dezo4IANDM2XTfDwkJ8ff3j4mJmTlzZqtWrar1Uhk+fLhjYmuh6Ns3+YP7CIPeFNtZ//ygxunBaJ1e3DKYruv1RYL4V0gEoWbop63Si7Y8x9tcvxPC0CuDAz09PQmCKCkpefQBoFGI4a10L02S7d7KnfyZ0GhgiA4AgEPZlHCMGDHC/OLDDz+suRUmb7MbSeJOHWfPn8YkpR841BTT6ZFHmDC+XqWlMPbCmLL5aVEtvbih1xdapRcIIX+ajlMq2nBMO45ry3NP8LyMhEdRMyQGBFmG6CAqNYbBIzAFhU8AAIew6eayc+dOR8cBCI1G9uMuKjtT8vTSDxtjS/eBXWUV8/IKigQRYRTMMf8X6P+Cqpa5yMtFMd1ouqk33DKabuoNyTp9vlWLClQjvYjhOTkM79ZiSO4e2vFTZXu2MbdSyJ063fAxmIcmvQAA+7Mp4Rg9erSj42jhqKwM2b7dRFWlENVWP2iYLXf8U1Xa17Ny/1wgUJbRNPV+9qFWYSEMc89otKQXqQbDfaPJugyqWnrRkecUkF60bFgm042ZyO/bTafdkm9dpx01Hru5OzsoAEBzA8WnzoYxe/E8d/IYwtjwdJzx6T421qMvK6zeGEKP8aC7GboHx70IZOg4paIdx7bluXYc25bjVBSkF6A6TDO64WP4nw8xly8oNn2nGz1B9PN3dlAAgGYFEg5nIgwG7tA+5lYKlsl1g0eK4ZG2H1tzcAuEkIBRH6W8DctG81xbnmvLsR4woCewEUnqX/ib5OXNHT8i27pOPyzeFTpjAwCaDUg4nIYsLpLt3UEWF4nqEN3Q0Vj5eBMN+NF0htFUbeULKuX60CD7xQhaHGOXHhLHy47slyVu0w8aaoqOcXZEAIBmAhIO56BvXOEP/0QIpnr3fR3u7va7Vldt5cueUPUOGkroGKtVucn27uB/+oEoKTH2inN2RACA5gCq8xsbIYrcz4dkP/2ASEI/dLS+/+B6ZBsaUdpWWma9hiWIj/19+tXWSwWAxyWGRejGv4KVKu70Se7ngwi6vgMAGgxKOBoVUVEu/3E3mZMlefnoho2WfOozdZZRwq9k5lzVG8Z4uM/29UohKRqhjlgKYRm7BwxaLNHHTzv+FdnOzezF30mNRj94JKbhdgEAqD+4gzQe6v492Y+JhLbKFN3RMGAIZuqTH4gYz8zKOVlZNUClXKb2pwmih7e3JEmlpaV2Dxi0cJKbu3bCK7I92+nbN+XbN2hHjnPlyYoBAC4OqlQaBcbsuST5jk2EXmfo87x+8Mj6ZRsYoTk5+T9WVHaVy1aHBNEwEDVwMMzLdGNeNrVtT+ZkyTd9R5bByPQAgHqChMPxdFrZ7i3crz9jpVI7boqxR/1nyfpXfuHG0vJontsapoaxxkHjwBSlHzLK2O0psqxEvuV7Kj/30ccAAEANRBOdCcVgMDj6EhzHSZJkMlXvevp4crLQ9o2otASFt0JjJqDH7Ptq7dv8wn/czQhm2RMx7YJZ1jpOjLGxtmE5XArLsgihJhEnQRCN8AfWQAzDkCTZqHGe+Q0d+hExLHppImrdzsaDzHEajUYXv9XQNE1RVFOJ02QySVazNzsCx3EOPT9ogZpqGw6j0SiK4qP3awBzwqHTVe96ajvq6mXm0D5CFIWnnjH1eR6RJKrv2RLLKt5Oz/Sm6cTIUG9RtI6KZVmMcUPibBwMwxAE4fpxmm/orh8nRVEkSer1+sZ7QHbqSvEyZt9uYvM644DBYqeuthxEkqQ5Tkc/IBtIJpNRFGUwGBx9Y2kgnufNcQoPzohkd5BwALtrqgmHJEmO/r6Z1e8qhChwRw8yVy9hjtO9OEJoE40kCdX3hvtbpfb1jCyOJDaGBkXQVM2QMMaN82k0hPm5CHHaiyXOxvxFLkS1FUaPl/2wgz30o6G83JYhOsx5hiAILp5wmD9GURRd/J++qcQJQE3QhsP+yLIS2cY1zNVLop+/dtJrQpvohpztsk4/6X42Rmh9aHA3OfQRAE4mhoZrx0/BKjfu9En+0L56p9EAgJYGEg47o9NuyTd+RxUWCB2e0E2YKnl4NuRs94ym8RlZWklaERz4rFJuryABaAjJx69qwlTR15+5elm2dychNKydEwCgZYCEw34kiU06KduzHZmMhucG6P42HNMNGokrzySMune/UBAXBPoNd69/a1MA7A4rVbqxk8WQMPpOqmzbBkJb5eyIAACuDhIO+yB0Wvnurdzpk1jlphs3xdilRwNPWCFK4zKyMk3Cu77eM7wbVEwCgCNgntfGTzRFd6Rys+WbviNLYYgOAEBdIOGwAyovR7FxDZWeJkRGVU16TQxUN/CEeownZGRd0xsmeXl86O9jlyABsD+K0r84wvB0HFlepti8lsrJcnZAAADXBQlHQzHJF+Vb1hEV5cYevXT2GPtZxPj1rNyzWt1AN+XngfWZbAWAxkMQxl5xhucHIr1OtmMjnXbL2QEBAFwUJBz1R5iM/I+7+SP7EcvpRo839HkeNXiscYzQuzn5+8s1Tyvka2DwctBEGDt31w2NRxjLftjBXL7g7HAAAK6oqY7D4XRkSbFs306ysED0D9QPHyO5udvltJ/lF20uLY/m2PWhQRxkG6DpENq00ypelu/Zxh89QFaUG555ruH5NwCgOYESjvqgb9+Ub/qOLCwwxXbWTphqr2xjbXHpssLiMJbZGRHiQVF2OScAjUZSh2jHTsHuHuy5JNmhfci1h+wEADQyKOF4TJLEnTrOnkvCFK0fONQU08leJ04sr5ibW+BNUdvCgv1p+HcBTZLk41s1Yaps1xb6WrKiQiP4+qL0u0aDnvf11z/1jKQOcXaAAACngQfbYyA0GtmPu6jsTMnTSz98jOhjtxadv1ZWvZmVJyfJHeHBURz76AMAcFVYodSNn8L/sINOv8vev4sQwghRlRrFvTvacVPE4FBnBwgAcA6oUrEVlZmh2Liays4UotpqJ06zY7ZxSaefdD8HEWhtSNATMt5epwXAWTDDmtp2qLmeO3qg8YMBALgIKOGoHVleJuVkYpIk3L0wz7PnkrikEwghQ98XjF172rE1nHnwcp0krQ4NelalsNdpAXAuuiCv5kqqqIAQTA0cgRcA0ERBwlEDxtzxI+wf58zzQyhYVvLwogrysEKpGzpKDA6z46XMg5cXCeKiQP+hbjB4OWg+MEVhhKon5iSJCShVBaCFgi9/dczlC+wf5yyLhNFIFeRJ/v+/vTsPj6LK+wV+qnqr7iydpclGAiQhCQQSAhdFBCODzIQMELbAEAMzOKj4yjPvoPIOD4LvFYU48+iNy8yoIAMoBERCkMEYCRERCLlXfcmgSAxgEsjCkoXsXd3V1XX/aKdtOp2F0JWqTr6fhz+6T1dXfbvo5ZdTp06FdPz2CfdWG628ddnVmmrO8qcgwxOBfm5cM4DkLNExXbsB+dDhBKdfAQxVKDicqV1NW8QPCxG83dkDwQrCo1drvmdNvwvw+6+gQDeuGUAO+BGR5kn3OzUqGusVt25KkgcAJIdDKs6o9nYXjR0uGvuNF4Snqq//v05jKiYvh8HL9MhsPmq0tupH2mQyBRisSqXmxDHtgfeNS5bzIWFSpwOAgYaCw5nVV69gjU6Ngp/brtcqEPJs3c381rZpXrr3IsIUmI0RBi9L5GiSOFGl0bQ1NVmtVkGhYI5/qsvd17l0OR8UInU6ABhQOKTijJsyzalFUKnMk+5z1/pfulG/73ZLPKPB5OUw1HAT/hebMpewRu2BDxQ36qSOAwADCgWHM27MONMvfiWofpp9S/DVs/OXWAPcc434HY23/9bQNEqtOjgqXI/RczD0cAkT2ZS5lMmkPbgXNQfAkIJDKi6YJz/AJU7yt5itFN2s1rhrXP2h5taN/568PAiTl8NQxSVMJIQwxz7RHtyL8RwAQwd6OFwT1Gp6xCgSGuauauPL9o7/rLvhpaAPRkZEY/JyGNrQzwEwBKHgGAjnOtnfXasjhOyMCEtgNFLHAZAeag6AoQYFh+gqzObMazWsILwbHjbDG5OXA/yES5jIpsxDzQEwRKDgENd1zpJeVdNg4beEBs3z9ZY6DoC8cAlJqDkAhggUHCJqsvBLrtZUm7kNwYbHAzB5OYALd9Qc12uljgMAYkHBIRZWEFZcqy1nTY8F+D07DJOXA3Tr55ojNwc1B8BghYJDFJwgPHat9qtO4699fV7B5OUAvUHNATDooeBwP4GQ52pvFLV1TPfSvRcRisnLAfoCNQfA4IaCw/1evFG/v7l1HKN5f8RwNaoNgD5zGM+BmgNgsEHB4WZ/rW96+6fJyyN8Fdi9AHeHS0hiZ8+jzKg5AAYb/CK608Hm1pdv1oeolIciI4YpcakUgP7gxqPmABiEUHC4zfG2jv+sveGtoPePDB+hUkkdB8CDOdYcdB1qDoDBQPRLiH3zzTd79uypqanR6/WzZs3KyMigXA1ruHTp0qFDh3788cdbt2798pe//MMf/iB2MPf6n07j49V1CkJyRoSPx+TlAPeMG59ECGE+O6rLzelMz7SGDZc6EQDcE3F7OMrLy7ds2RIfH5+dnb18+fK8vLycnByXS7IsGxoaumLFitDQUFEjieEH1pRxrdYkCO9GhE310kodB2CQsPdz6HLRzwHg8cTt4cjLyxs+fPjq1asJISNHjrx+/fqRI0eWLFmi0Tj3ASQmJiYmJtqeImokt6vjLBnXapstfPbwkLmYvBzArbjxSYRQzGf/RD8HgKcTt4ejrKxs0qRJ9ruTJk1iWbaiokLUjQ6kJgu/pKq6xsw9H2xY7q+XOg7AIMSNn8DOTkM/B4CnE7GHQxCE5uZmf39/e4vtdlNTUz/WVlFRkZ+fb7/761//Oiws7N5D9oymaS+vbq/v2slbf1d++ZLJ/GTwsI2jIsQO04Oec8oETdOEEOR0F4VCQQjR6XRSB+mFUqkkhOh0OkEQ+r+WKQ8KXl5U3oe63BxhxeMk3P0fN1tOrVZrtVrdvnI3UqlUhBCGYdRqtdRZAO6O6ING3eXq1avvv/++/e6UKVOio6PF3ihN01qt6zEZnCCs+K7s/7Z1LAsyvDMmlpZ6fq/ucsoNcrqXp+RkGOZeV3HfA1a1mvvwA2rvP1SrnqZHjHRHLmddj/bKk6fkBHAkYsFBUZSfn9/t27ftLbbbAQEB/VjbhAkT3n77bfvdiIiIlpaWew/ZA71ez/N8e3t714cEQp6+WlPQ1Jzs4/VmaFBbq7hJeubr6ysIQltbm4QZ+sLHx4eiqNbWVqmD9MLb21uhUIj97rp3Xl5eSqWytbX1nnoOxKfT6VQqVVtbmxt6DkZFK9LSVf/MNe/4u3nZ74Th4e4I+BOtVqtWq9vb23med+Nq3Y5hGI1G09HRYbFYRN2QXo9jxOBm4vZwjB079ty5c6tWrbLdPXfuHMMwUVFR/VhVQEDA/fffb7/b0tLCcZx7UnZPEASXW/nvG7f2NzVP1DIfRITRPM9J/Q3VXU5Zsf0uIqe72HPKvOCw1Rkcx7nlUAUXM4afs5DJP6z+8P3O9EetYW6rOWx9BhaLRewf8ntkO5JisVjk/xYFcCLuoNFFixbV1tZu27bt6tWrX3zxxeHDh9PS0mwf7OLi4vXr13d2dtqWNJvNFRUVFRUVZrO5vb29oqKisrJS1Gz99kZ94zsNtyPVqpyR4V40Zk4DGFDcmHHsnIUUZ9bl7qPraqSOAwB9JW4PR1xc3MaNG/fu3Xvs2DG9Xr9w4cJHH33U9lBjY2NZWZn9j4mampq1a9fabtfW1paUlNA0/fHHH4sarx8+am7JutkQolIeihyBycsBJMGNGUcIYfIP63L3ubefAwDEQ8m8P7Y7A3BIxWAwWCyW5uZme8uxtvaV1+p0NPXPyBHjZDOdaGBgoNVqdRwrI0/+/v4URfXvHKWB5Ofnp1QqGxoapA7SC71er1KpGhsbZf4R9vHx0Wg0TU1Nbj/7Q/XD90z+YUGl6kzPvPeaw9vbm2GY5uZmmR9S8fLy0mq1A/MFKOr6YQjCEYG++qbT+GT1dSUhOSPD5VNtAAxZ/z62wulyc3BsBUD+UHD0SRlryrhaaxKEd8JDH9B5xomIAIMeag4AD4KCo3e1HJdxtbaF5/9PWPBcvY/UcQDgZ6g5ADyFx0z8NZB4Qfi4pe1ic6uGUAkK6uUb9bUc99/BwzIxeTmA/Pw8hvSjPcb0R/lwUeYEA4B7hILDmdEqLKyq/p9Oo2PjqkD/Pwzrz3xlADAA7DWHNncfag4AecIhFWdZN+udqg1CyFTZX7ECYIjjxoxj5y6ieF6bu09RfVXqOADgDAWHs/w2F3OZ58t+Qm4A4OLif6o5DqHmAJAdFBzOOlzNFtBhlfVUBwBgg5oDQLZQcDhznmNDIISQ8Zh4A8BDoOYAkCcUHM7+d/AwDeVwsXmKhKtV/2Hwly4RANwd1BwAMoSCw9kELXMoMuJ+nVZNUz4KxVxf7yOREX4KXDYFwJPcWXNUSR0HAHBarCtTdNr8qBF+gYECz7c4XEsFADwIFxdPCGE+ydMe2m9cnMFHjJI6EcCQhh6ObikpxyMrAOB5HPo59qOfA0BaKDgAYDBDzQEgEyg4AGCQQ80BIAcoOABg8EPNASA5FBwAMCSg5gCQFgoOABgquLh4dt5i1BwAkkDBAQBDCBc79uea41qV1HEAhhAUHAAwtKDmAJAECg4AGHJ+qjmsqDkABg5mGgWAoYiLHUvmLbbNQ2qJiaNuXjd1dmgMQWTqQ5ZR0VKnAxiE0MMBAEMUFzuWnbOQ4i2qsgukqZGwLF1zTXswR3mlXOpoAIMQCg4AGLoEmiaC4NTIFH3atREA7hEOqQDA0EXfvN61kWpr0+173+rtRbQ6QasVGNs/RtBqBY1W0GoJoxWU+PIEuDv4zADA0EUpVS7bFXXXFD0+UVAqiVZr1WiJVmsvRASGEbQ6wmitjJZotVYNQ7Q6QeV6E3cdlePUJafpsu9M7W3qAIMw5UFLfKJb1gwwMFBwAMDQZYkarT59wqmRHx5hXLqCsEaKNVImE8UaidFIm1jCshT78w3KxNLGTqrhVq9bERRKwdtb8PYRGC1hGKuGsd8gth4UjUZgtILOi9DdHuZm8g8rL/9gu0033NLmf8yazVzS5Ht5+QADCQUHAAxdfFCI+aGZ6tMniEAIRQghglbHps4XlEri7SN4+/S6Boq3EKORMhopk5EyGilbLcIaKaORsEb638UKZeykW5p7WRdNOxy+0RGGsTJawmitDEN3tNurDTvNl0WWhCRBga9x8Ax4pwLAkGZ6YLolYqSu8gpt7DT7BbAJSQKj7fvTBUVfSxNCCGWx/NRx0t5Otbe57DWhjEa6uYlYrb2vzWymbzfxhqC+pwWQEAoOABjq+OERQtxYFcN0NDcLFot4G/q546TnKkEQKJal2E5iNNIsS9hO1Y+XlD9cdLGgSi1WVgB3Q8EBACAzFCVotYJWS/yJraPDGj5SceUyZeEcl+KDQqx6P0kCAvQD5uEAAJA7q6/eNGs2Ufx86oyg1bFzFkgYCeBuoYcDAMADcAkT+dBwXcUlZWeHydePjU8UGEbqUAB3AQUHAIBnsBqGCSNHKbXajpYWgeN6fwKAnHhqwaFSqRSKnifmcQOKohhP+BvCI3JSFOUROWmaJoR4UE5B3pNw2z6nGo3GI3Kq1WqlvKcQteccgC9AAPeS9UerZxRFDcAmBmArboGc7oWc7iX/nLaE8v/Ie0pOgK48teDgOI4TuUfRy8vLarUajUZRt3LvdDqdIAjyz2nrM5B/To1GQ9O0/HPa/sY1Go0y7zlQKpVKpZJlWWsfJpaQkEKhUCqVJpPJIuZpsfeOpmmVSmUymQbgC1DU9cMQhLNUAAAAQHQoOAAAAEB0KDgAAABAdCg4AAAAQHQoOAAAAEB0lMyHuEsoKysrLCxs5cqVUgfpRXZ2tk6ne+qpp6QO0ou3337bbDavXbtW6iC92Llz582bNzds2CB1kF7s27evqqpq3bp1arWsL991+PDhsrKyNWvW6PV6qbP0pKCgoLS0dNWqVcHBwVJn6ckXX3xRUlKSmZk5cuRIqbMA3B30cHQrLy/vyy+/lDpF7z755JPCwkKpU/SusLAwPz9f6hS9O3ny5Mcffyx1it4VFxfn5eXJ/BxOQsjXX3+dl5fX2dkpdZBe/Otf/8rLy2tpaZE6SC8uXryYl5dXX18vdRCAu4aCAwAAAESHggMAAABEh4IDAAAARIdBowAAACA69HAAAACA6FBwAAAAgOhQcAAAAIDoPPXy9GIzGo0rV67keX7Xrl0+Pj5Sx3HhvffeO3r0qO22RqMJDg5OTU2dM2eOtKlcMplMR44cKS4urqurUyqVoaGh9913X1pamqyuf23fnxRFabXa0NDQxMTEOXPmBAUFSR3NmeN/vV12dvbo0aMlydOdrjlnz5799NNPS5WnOyaT6ejRo8XFxbW1tYSQoKCg+Pj4OXPmyGpmrffee+/s2bO7du1ybHzyySeTkpJkuEsBXELB4drJkycjIyNVKlVRUdHChQuljuOan59fVlYWIYRl2bNnz27bts3Pz2/atGlS57pDR0fHhg0b6uvrlyxZEhMT4+XlVV1dXVhYyDCM3HasbX8KgmA0GisrKz/99NOCgoJNmzYlJiZKHc2ZXq/fvHmzY0t4eLhUYXrglNPX11fCMC61t7c///zzt27dWrx4cUxMjI+Pz61bt3744YfXX3/9jTfekDodwKCCgsO1goKCtLQ0pVK5f//+BQsWUBQldSIXFAqF/Wdm9OjRx44du3LlitwKjl27dtXV1f31r38NDQ21tURFRT388MMynNLRcX/GxsY+8sgjzz///Ouvv759+3aVSiVtNidKpTIqKkrqFL2Tf86u78/o6OipU6darVZpgwEMPhjD4UJZWdmNGzemT5/+4IMPtrS0nD9/XupEveB5/syZM+3t7WPHjpU6yx14nj916tQjjzxi/za3k/mVNQghSqUyPT29sbHx4sWLUmcBUVgsltOnT7t8f9I0vhsB3Aw9HC4UFBRMmzaNYRhCSHJyckFBQVJSktShXGhsbFy8eDEhxGKx0DS9YsWK+++/X+pQd6ivr2dZVlbHwu/KqFGjCCE3btyYMGGC1Fnu0NjYmJaWZr9L07Q8r//ilHPHjh2yGhPT0NDg9P586623Tpw4Yb89YsQIiaK54LQzATwOCg5nra2txcXFL730ku3urFmz1q9f39TUFBAQIG2wruxjOMxmc3l5+c6dO728vFJTU6XONXjIdlo8p7ER8jzkR7rklOGHyElmZub8+fPLy8v/9re/ye1/v+vAnS1btkgVBqAfUHA4Kyoq4jhu48aN9har1VpYWLhs2TIJU7nkOOYgKiqqsrIyJydHVgWHwWBgGObq1atSB+mnqqoqQkjX/nbJyX9shI3McxoMBo1G4/j+DAwMDAwMbG5uljBVd7ruTKUSX+DgSfB+vYMgCJ999tn8+fNnzZplbzx16lRhYeHSpUvlf1jXbDYLgiCfv3eVSuVDDz30+eefL1iwwOlnu6WlRebDOCwWy6FDhwwGQ3x8vNRZQBQ9vD8BwO1QcNyhtLT0xo0bs2fPHj58uL0xNTU1Nzf366+/njJlioTZuuJ5vqamhvz7kMqJEyemT58un2rDZuXKleXl5c8+++ySJUtiY2N1Op3ttNjJkyfL7bRY+/60nRabn59//fr1TZs2yfDvSIvFUlFR4dgSEhKi0+mkyuO5HnvssUuXLj377LP202IbGhoKCwspipLh/zuAR8Mn6g4FBQVRUVGO1QYhxGAwjBkzpqCgQG4FR3Nzs23OH5VKZTAY0tLSfvOb30gdypmPj89rr7125MiRkydP7tu3T6VShYWFJScnp6SkSB3NmW1/2ib+CgkJmTBhwsaNG2U1yNGupaVl7dq1ji3r16+X2xnRHsH2/jx69OiZM2cOHDjA83xAQMD48eNfe+01p+8BALhHuFosAAAAiE7ugxIAAABgEEDBAQAAAKJDwQEAAACiQ8EBAAAAokPBAQAAAKJDwQEAAACiQ8EBHik3N5eiKKmuWCbt1rtTVFREUdTu3bulDgIA4AIKDgAAABAdZhoFGCRmzpxpNBpVKpXUQQAAXEAPB4DEOjs73bIemqYZhlEoFG5ZGwCAe6HggEFi7969Go0mJSWlra2NENLc3Pzcc89FRkZqNJrg4ODMzMwrV67YF1YqlVQX48ePtz3a0tKyadOmKVOm2C5fHhUVtW7duvb29r5vvQe28R8fffTR5s2bY2Ji1Gr1Sy+9RAixWCzZ2dlJSUlardbHx2fGjBmFhYWOT6yrq1u+fLm/v7+3t/eMGTNKSkrS09MZhrEv0HUMR887wZbk0KFDf/nLX2JjYzUazYgRI7Zu3YrLHQCAGHBIBQaDrKysjRs3/v73v9+2bZtSqezo6EhOTv7uu+8yMzMffPDBy5cvv/POOwUFBSUlJXFxcYSQTz/91Gq12p9eXFy8ZcuWUaNG2e5WV1dv3749PT09IyNDrVafOnUqOzv7q6+++vLLL11ejNdp630JvH79+uHDh2dlZYWEhKhUKp7n09LSjh07tmTJkscff5xl2b17986ePTsnJycjI4MQ0trampycXFlZuXr16okTJ3777bcpKSkRERE9bKLXnWDzpz/9KTY29q233vLz89uxY8emTZsCAwOfeuqpvrwKAIC7IAB4oIMHDxJCDh8+zHHcE088QQh58cUX7Y9u3ryZEGL7Y93m2LFjhJCUlJSuq7py5YrBYIiKiqqvr7e1sCxrNpsdl9m6dSsh5Pjx433DLvu8AAAFLklEQVTZel+Sx8bGchxnb/z73/9OCNm5c6e9xWw2T5o0KTg42LbYCy+8QAjZvn27fYEPPviAEKLRaOwtx48fJ4Ts2rWrjzvBlmTy5MlWq9XWwvN8TEzM2LFj+/haAAD6DgUHeCTbj+WePXtSU1NVKtXu3bsdH01MTPT29jYajY6NU6dOpWm6paXFsbGpqSkuLs7Pz6+srMzlhsxms9FovHjxIiHk5Zdf7svW+5L8lVdecWycMmVKUFCQ8U6vvvoqIeSbb74RBCEhIcFgMFgsFvtTrFZreHh4DwVHrzvBluTNN990XMDWqcPzfN9fEQBAX+CQCniwNWvWtLa2Hjx4MD093bG9oqIiOjracXwDISQhIaGkpKSqqioxMdHWwnHcokWLKioqPvvsszFjxjguvHv37u3bt58/f95xRGdTU1Nftt4XkZGRjnfLyspaW1u1Wm3XJW/dumV7RQkJCY4DQimKiouLq6+v724TfdwJTsdlfH19zWZzW1ubXq+/2xcFANADFBzgwdLT03Nycv785z//4he/CAwMtLcLguBysIWTJ5988uTJkzt27Jg5c6Zje3Z29nPPPTdv3rwdO3aEhYVpNJrGxsa5c+c6DvvoYet9odFoHO9ardaYmBjbURIn9kqoL6/IUR93gstlBIwbBQB3Q8EBHmzevHlLly5duHDhjBkzioqKgoODbe3R0dFXrlxhWdbx7/sLFy7QNG0fGbp169bdu3evX79+1apVTqv9xz/+ERkZeeTIEfuP8enTp/u+9X6IjY29cOHC+PHjvb29XS4QFRV1+fJlnuftnRyCIFy6dKmHdfZlJwAADBicFgueLSUlpaCgoKqqKjk5uaamxta4aNGi9vb2N954w75YUVHR2bNnZ82a5evrSwg5cODACy+8sHjx4ldeeaXrOmmaFgSB53nbXZ7ns7Ky+r71fvjtb39rNpvXrVvn1LVQV1dnuzF//vyGhgbHU1737dtXXV3dwzp73QkAAAMJPRzg8R5++OHjx4+npqYmJyd//vnnkZGR69aty83N3bBhw/fff28/I9Tf3//NN98khFRWVq5cuTIgICAzM9N24oaNj4/PtGnTCCHp6ekvvvhiamrq0qVL29raPvzwwx4OMXTdej9ewpo1a4qKirZt21ZaWjp//vxhw4ZVV1eXlJScP3/eNoZj3bp1OTk5q1evLi0tTUpK+vbbb3fv3h0fH19RUdHdOnveCQAAA03CAasA/WY/MdXecu7cOYPBEB4eXl5eLgjC7du3n3nmmZEjR6pUqmHDhmVkZFy+fNm2ZElJicvPwrhx42wLcBy3ZcuW6OhotVodERHxzDPPVFZWEkL++Mc/9nHrd5Xchuf5d99994EHHvD29mYYZtSoUQsWLNizZ499gZqamoyMDL1er9Pppk+ffubMmV/96lf+/v72BZzOUul5J3SXZPXq1YSQ27dv9/wqAADuFiVgdBiAZ4qKitLr9aWlpVIHAQDoHcZwAHgGlmUd7x44cKCysjIlJUWqPAAAdwU9HADuJAiCyWTq7lG1Wk3T/azyZ86cGRkZOXnyZJVK9dVXX+3cuTMkJKS0tHTYsGH9DQsAMHBQcAC404ULFxISErp7dP/+/cuWLevfml999dWcnJyqqqqOjo7g4OCUlJTNmzeHh4f3NykAwIBCwQHgTizLXrhwobtHo6Oj/f39BzIPAIBMoOAAAAAA0WHQKAAAAIgOBQcAAACIDgUHAAAAiA4FBwAAAIgOBQcAAACIDgUHAAAAiO7/A90XPye5B/VaAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 180,
       "width": 360
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data %>% \n",
    "  mutate(mean_ratio = mean_yfp / mean_rfp) %>%\n",
    "  left_join(annotations, by = \"strain\") %>%\n",
    "  ggplot(aes(x = kozak_region, y = mean_ratio, \n",
    "             color = insert_sequence, group = insert_sequence)) +\n",
    "  geom_line() +\n",
    "  geom_point()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use `stringr` functions to manipulate string columns\n",
    "\n",
    "All functions are named nicely and begin with `str_`. I find them easier to use than the equivalent `Python` regular expression functions.\n",
    "\n",
    "See [https://stringr.tidyverse.org/reference/index.html](https://stringr.tidyverse.org/reference/index.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 16 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>strain</th><th scope=col>mean_yfp</th><th scope=col>mean_rfp</th><th scope=col>mean_ratio</th><th scope=col>insert_sequence</th><th scope=col>kozak_region</th><th scope=col>codon</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>schp688</td><td>1748</td><td>20754</td><td>0.08422473</td><td>10×AGA</td><td>A</td><td>AGA</td></tr>\n",
       "\t<tr><td>schp684</td><td>3294</td><td>20585</td><td>0.16001943</td><td>10×AGA</td><td>B</td><td>AGA</td></tr>\n",
       "\t<tr><td>schp690</td><td>3535</td><td>20593</td><td>0.17166027</td><td>10×AGA</td><td>C</td><td>AGA</td></tr>\n",
       "\t<tr><td>schp687</td><td>4658</td><td>20860</td><td>0.22329818</td><td>10×AGA</td><td>D</td><td>AGA</td></tr>\n",
       "\t<tr><td>schp686</td><td>5000</td><td>21171</td><td>0.23617212</td><td>10×AGA</td><td>E</td><td>AGA</td></tr>\n",
       "\t<tr><td>schp685</td><td>7379</td><td>22956</td><td>0.32144102</td><td>10×AGA</td><td>F</td><td>AGA</td></tr>\n",
       "\t<tr><td>schp683</td><td>9365</td><td>23866</td><td>0.39239923</td><td>10×AGA</td><td>G</td><td>AGA</td></tr>\n",
       "\t<tr><td>schp689</td><td>8693</td><td>22649</td><td>0.38381385</td><td>10×AGA</td><td>H</td><td>AGA</td></tr>\n",
       "\t<tr><td>schp679</td><td>2528</td><td>19906</td><td>0.12699689</td><td>10×AAG</td><td>A</td><td>AAG</td></tr>\n",
       "\t<tr><td>schp675</td><td>3687</td><td>20438</td><td>0.18039926</td><td>10×AAG</td><td>B</td><td>AAG</td></tr>\n",
       "\t<tr><td>schp681</td><td>3705</td><td>20227</td><td>0.18317101</td><td>10×AAG</td><td>C</td><td>AAG</td></tr>\n",
       "\t<tr><td>schp678</td><td>4378</td><td>20630</td><td>0.21221522</td><td>10×AAG</td><td>D</td><td>AAG</td></tr>\n",
       "\t<tr><td>schp677</td><td>3967</td><td>20604</td><td>0.19253543</td><td>10×AAG</td><td>E</td><td>AAG</td></tr>\n",
       "\t<tr><td>schp676</td><td>2657</td><td>20223</td><td>0.13138506</td><td>10×AAG</td><td>F</td><td>AAG</td></tr>\n",
       "\t<tr><td>schp674</td><td>1270</td><td>20316</td><td>0.06251231</td><td>10×AAG</td><td>G</td><td>AAG</td></tr>\n",
       "\t<tr><td>schp680</td><td>1117</td><td>19377</td><td>0.05764566</td><td>10×AAG</td><td>H</td><td>AAG</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 16 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " strain & mean\\_yfp & mean\\_rfp & mean\\_ratio & insert\\_sequence & kozak\\_region & codon\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t schp688 & 1748 & 20754 & 0.08422473 & 10×AGA & A & AGA\\\\\n",
       "\t schp684 & 3294 & 20585 & 0.16001943 & 10×AGA & B & AGA\\\\\n",
       "\t schp690 & 3535 & 20593 & 0.17166027 & 10×AGA & C & AGA\\\\\n",
       "\t schp687 & 4658 & 20860 & 0.22329818 & 10×AGA & D & AGA\\\\\n",
       "\t schp686 & 5000 & 21171 & 0.23617212 & 10×AGA & E & AGA\\\\\n",
       "\t schp685 & 7379 & 22956 & 0.32144102 & 10×AGA & F & AGA\\\\\n",
       "\t schp683 & 9365 & 23866 & 0.39239923 & 10×AGA & G & AGA\\\\\n",
       "\t schp689 & 8693 & 22649 & 0.38381385 & 10×AGA & H & AGA\\\\\n",
       "\t schp679 & 2528 & 19906 & 0.12699689 & 10×AAG & A & AAG\\\\\n",
       "\t schp675 & 3687 & 20438 & 0.18039926 & 10×AAG & B & AAG\\\\\n",
       "\t schp681 & 3705 & 20227 & 0.18317101 & 10×AAG & C & AAG\\\\\n",
       "\t schp678 & 4378 & 20630 & 0.21221522 & 10×AAG & D & AAG\\\\\n",
       "\t schp677 & 3967 & 20604 & 0.19253543 & 10×AAG & E & AAG\\\\\n",
       "\t schp676 & 2657 & 20223 & 0.13138506 & 10×AAG & F & AAG\\\\\n",
       "\t schp674 & 1270 & 20316 & 0.06251231 & 10×AAG & G & AAG\\\\\n",
       "\t schp680 & 1117 & 19377 & 0.05764566 & 10×AAG & H & AAG\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 16 × 7\n",
       "\n",
       "| strain &lt;chr&gt; | mean_yfp &lt;dbl&gt; | mean_rfp &lt;dbl&gt; | mean_ratio &lt;dbl&gt; | insert_sequence &lt;chr&gt; | kozak_region &lt;chr&gt; | codon &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| schp688 | 1748 | 20754 | 0.08422473 | 10×AGA | A | AGA |\n",
       "| schp684 | 3294 | 20585 | 0.16001943 | 10×AGA | B | AGA |\n",
       "| schp690 | 3535 | 20593 | 0.17166027 | 10×AGA | C | AGA |\n",
       "| schp687 | 4658 | 20860 | 0.22329818 | 10×AGA | D | AGA |\n",
       "| schp686 | 5000 | 21171 | 0.23617212 | 10×AGA | E | AGA |\n",
       "| schp685 | 7379 | 22956 | 0.32144102 | 10×AGA | F | AGA |\n",
       "| schp683 | 9365 | 23866 | 0.39239923 | 10×AGA | G | AGA |\n",
       "| schp689 | 8693 | 22649 | 0.38381385 | 10×AGA | H | AGA |\n",
       "| schp679 | 2528 | 19906 | 0.12699689 | 10×AAG | A | AAG |\n",
       "| schp675 | 3687 | 20438 | 0.18039926 | 10×AAG | B | AAG |\n",
       "| schp681 | 3705 | 20227 | 0.18317101 | 10×AAG | C | AAG |\n",
       "| schp678 | 4378 | 20630 | 0.21221522 | 10×AAG | D | AAG |\n",
       "| schp677 | 3967 | 20604 | 0.19253543 | 10×AAG | E | AAG |\n",
       "| schp676 | 2657 | 20223 | 0.13138506 | 10×AAG | F | AAG |\n",
       "| schp674 | 1270 | 20316 | 0.06251231 | 10×AAG | G | AAG |\n",
       "| schp680 | 1117 | 19377 | 0.05764566 | 10×AAG | H | AAG |\n",
       "\n"
      ],
      "text/plain": [
       "   strain  mean_yfp mean_rfp mean_ratio insert_sequence kozak_region codon\n",
       "1  schp688 1748     20754    0.08422473 10×AGA          A            AGA  \n",
       "2  schp684 3294     20585    0.16001943 10×AGA          B            AGA  \n",
       "3  schp690 3535     20593    0.17166027 10×AGA          C            AGA  \n",
       "4  schp687 4658     20860    0.22329818 10×AGA          D            AGA  \n",
       "5  schp686 5000     21171    0.23617212 10×AGA          E            AGA  \n",
       "6  schp685 7379     22956    0.32144102 10×AGA          F            AGA  \n",
       "7  schp683 9365     23866    0.39239923 10×AGA          G            AGA  \n",
       "8  schp689 8693     22649    0.38381385 10×AGA          H            AGA  \n",
       "9  schp679 2528     19906    0.12699689 10×AAG          A            AAG  \n",
       "10 schp675 3687     20438    0.18039926 10×AAG          B            AAG  \n",
       "11 schp681 3705     20227    0.18317101 10×AAG          C            AAG  \n",
       "12 schp678 4378     20630    0.21221522 10×AAG          D            AAG  \n",
       "13 schp677 3967     20604    0.19253543 10×AAG          E            AAG  \n",
       "14 schp676 2657     20223    0.13138506 10×AAG          F            AAG  \n",
       "15 schp674 1270     20316    0.06251231 10×AAG          G            AAG  \n",
       "16 schp680 1117     19377    0.05764566 10×AAG          H            AAG  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data %>% \n",
    "  mutate(mean_ratio = mean_yfp / mean_rfp) %>%\n",
    "  left_join(annotations, by = \"strain\") %>% \n",
    "  mutate(codon = str_extract(insert_sequence, \"[A-Z]{3}$\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In-class data transformation exercises\n",
    "\n",
    "(20 min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Create log2-transformed YFP/RFP ratio as a new column\n",
    "\n",
    "Google for `log2 R` to find the appropriate function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m16\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m3\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \"\\t\"\n",
      "\u001b[31mchr\u001b[39m (1): strain\n",
      "\u001b[32mdbl\u001b[39m (2): mean_yfp, mean_rfp\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[90m# A tibble: 16 × 4\u001b[39m\n",
      "   strain  mean_yfp mean_rfp log2_yfp_rfp_ratio\n",
      "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m      \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
      "\u001b[90m 1\u001b[39m schp688     \u001b[4m1\u001b[24m748    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m754              -\u001b[31m3\u001b[39m\u001b[31m.\u001b[39m\u001b[31m57\u001b[39m\n",
      "\u001b[90m 2\u001b[39m schp684     \u001b[4m3\u001b[24m294    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m585              -\u001b[31m2\u001b[39m\u001b[31m.\u001b[39m\u001b[31m64\u001b[39m\n",
      "\u001b[90m 3\u001b[39m schp690     \u001b[4m3\u001b[24m535    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m593              -\u001b[31m2\u001b[39m\u001b[31m.\u001b[39m\u001b[31m54\u001b[39m\n",
      "\u001b[90m 4\u001b[39m schp687     \u001b[4m4\u001b[24m658    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m860              -\u001b[31m2\u001b[39m\u001b[31m.\u001b[39m\u001b[31m16\u001b[39m\n",
      "\u001b[90m 5\u001b[39m schp686     \u001b[4m5\u001b[24m000    \u001b[4m2\u001b[24m\u001b[4m1\u001b[24m171              -\u001b[31m2\u001b[39m\u001b[31m.\u001b[39m\u001b[31m0\u001b[39m\u001b[31m8\u001b[39m\n",
      "\u001b[90m 6\u001b[39m schp685     \u001b[4m7\u001b[24m379    \u001b[4m2\u001b[24m\u001b[4m2\u001b[24m956              -\u001b[31m1\u001b[39m\u001b[31m.\u001b[39m\u001b[31m64\u001b[39m\n",
      "\u001b[90m 7\u001b[39m schp683     \u001b[4m9\u001b[24m365    \u001b[4m2\u001b[24m\u001b[4m3\u001b[24m866              -\u001b[31m1\u001b[39m\u001b[31m.\u001b[39m\u001b[31m35\u001b[39m\n",
      "\u001b[90m 8\u001b[39m schp689     \u001b[4m8\u001b[24m693    \u001b[4m2\u001b[24m\u001b[4m2\u001b[24m649              -\u001b[31m1\u001b[39m\u001b[31m.\u001b[39m\u001b[31m38\u001b[39m\n",
      "\u001b[90m 9\u001b[39m schp679     \u001b[4m2\u001b[24m528    \u001b[4m1\u001b[24m\u001b[4m9\u001b[24m906              -\u001b[31m2\u001b[39m\u001b[31m.\u001b[39m\u001b[31m98\u001b[39m\n",
      "\u001b[90m10\u001b[39m schp675     \u001b[4m3\u001b[24m687    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m438              -\u001b[31m2\u001b[39m\u001b[31m.\u001b[39m\u001b[31m47\u001b[39m\n",
      "\u001b[90m11\u001b[39m schp681     \u001b[4m3\u001b[24m705    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m227              -\u001b[31m2\u001b[39m\u001b[31m.\u001b[39m\u001b[31m45\u001b[39m\n",
      "\u001b[90m12\u001b[39m schp678     \u001b[4m4\u001b[24m378    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m630              -\u001b[31m2\u001b[39m\u001b[31m.\u001b[39m\u001b[31m24\u001b[39m\n",
      "\u001b[90m13\u001b[39m schp677     \u001b[4m3\u001b[24m967    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m604              -\u001b[31m2\u001b[39m\u001b[31m.\u001b[39m\u001b[31m38\u001b[39m\n",
      "\u001b[90m14\u001b[39m schp676     \u001b[4m2\u001b[24m657    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m223              -\u001b[31m2\u001b[39m\u001b[31m.\u001b[39m\u001b[31m93\u001b[39m\n",
      "\u001b[90m15\u001b[39m schp674     \u001b[4m1\u001b[24m270    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m316              -\u001b[31m4\u001b[39m\u001b[31m.\u001b[39m\u001b[31m00\u001b[39m\n",
      "\u001b[90m16\u001b[39m schp680     \u001b[4m1\u001b[24m117    \u001b[4m1\u001b[24m\u001b[4m9\u001b[24m377              -\u001b[31m4\u001b[39m\u001b[31m.\u001b[39m\u001b[31m12\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "data <- read_tsv(\"data/example_dataset_2.tsv\") %>%\n",
    "  mutate(log2_yfp_rfp_ratio = log2(mean_yfp/mean_rfp)) %>%\n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Extract strain number from the `strain` column into a new column and sort numerically by strain number\n",
    "\n",
    "Extract the strain number using a `stringr` function.\n",
    "\n",
    "Google for `character to integer R` to find appropriate function to use in `mutate`.\n",
    "\n",
    "Then sort."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m17\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m3\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \"\\t\"\n",
      "\u001b[31mchr\u001b[39m (3): strain, insert_sequence, kozak_region\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A spec_tbl_df: 17 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>strain</th><th scope=col>insert_sequence</th><th scope=col>kozak_region</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>schp674</td><td>10×AAG</td><td>G </td></tr>\n",
       "\t<tr><td>schp675</td><td>10×AAG</td><td>B </td></tr>\n",
       "\t<tr><td>schp676</td><td>10×AAG</td><td>F </td></tr>\n",
       "\t<tr><td>schp677</td><td>10×AAG</td><td>E </td></tr>\n",
       "\t<tr><td>schp678</td><td>10×AAG</td><td>D </td></tr>\n",
       "\t<tr><td>schp679</td><td>10×AAG</td><td>A </td></tr>\n",
       "\t<tr><td>schp680</td><td>10×AAG</td><td>H </td></tr>\n",
       "\t<tr><td>schp681</td><td>10×AAG</td><td>C </td></tr>\n",
       "\t<tr><td>schp683</td><td>10×AGA</td><td>G </td></tr>\n",
       "\t<tr><td>schp684</td><td>10×AGA</td><td>B </td></tr>\n",
       "\t<tr><td>schp685</td><td>10×AGA</td><td>F </td></tr>\n",
       "\t<tr><td>schp686</td><td>10×AGA</td><td>E </td></tr>\n",
       "\t<tr><td>schp687</td><td>10×AGA</td><td>D </td></tr>\n",
       "\t<tr><td>schp688</td><td>10×AGA</td><td>A </td></tr>\n",
       "\t<tr><td>schp689</td><td>10×AGA</td><td>H </td></tr>\n",
       "\t<tr><td>schp690</td><td>10×AGA</td><td>C </td></tr>\n",
       "\t<tr><td>control</td><td>NA    </td><td>NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A spec\\_tbl\\_df: 17 × 3\n",
       "\\begin{tabular}{lll}\n",
       " strain & insert\\_sequence & kozak\\_region\\\\\n",
       " <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t schp674 & 10×AAG & G \\\\\n",
       "\t schp675 & 10×AAG & B \\\\\n",
       "\t schp676 & 10×AAG & F \\\\\n",
       "\t schp677 & 10×AAG & E \\\\\n",
       "\t schp678 & 10×AAG & D \\\\\n",
       "\t schp679 & 10×AAG & A \\\\\n",
       "\t schp680 & 10×AAG & H \\\\\n",
       "\t schp681 & 10×AAG & C \\\\\n",
       "\t schp683 & 10×AGA & G \\\\\n",
       "\t schp684 & 10×AGA & B \\\\\n",
       "\t schp685 & 10×AGA & F \\\\\n",
       "\t schp686 & 10×AGA & E \\\\\n",
       "\t schp687 & 10×AGA & D \\\\\n",
       "\t schp688 & 10×AGA & A \\\\\n",
       "\t schp689 & 10×AGA & H \\\\\n",
       "\t schp690 & 10×AGA & C \\\\\n",
       "\t control & NA     & NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A spec_tbl_df: 17 × 3\n",
       "\n",
       "| strain &lt;chr&gt; | insert_sequence &lt;chr&gt; | kozak_region &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| schp674 | 10×AAG | G  |\n",
       "| schp675 | 10×AAG | B  |\n",
       "| schp676 | 10×AAG | F  |\n",
       "| schp677 | 10×AAG | E  |\n",
       "| schp678 | 10×AAG | D  |\n",
       "| schp679 | 10×AAG | A  |\n",
       "| schp680 | 10×AAG | H  |\n",
       "| schp681 | 10×AAG | C  |\n",
       "| schp683 | 10×AGA | G  |\n",
       "| schp684 | 10×AGA | B  |\n",
       "| schp685 | 10×AGA | F  |\n",
       "| schp686 | 10×AGA | E  |\n",
       "| schp687 | 10×AGA | D  |\n",
       "| schp688 | 10×AGA | A  |\n",
       "| schp689 | 10×AGA | H  |\n",
       "| schp690 | 10×AGA | C  |\n",
       "| control | NA     | NA |\n",
       "\n"
      ],
      "text/plain": [
       "   strain  insert_sequence kozak_region\n",
       "1  schp674 10×AAG          G           \n",
       "2  schp675 10×AAG          B           \n",
       "3  schp676 10×AAG          F           \n",
       "4  schp677 10×AAG          E           \n",
       "5  schp678 10×AAG          D           \n",
       "6  schp679 10×AAG          A           \n",
       "7  schp680 10×AAG          H           \n",
       "8  schp681 10×AAG          C           \n",
       "9  schp683 10×AGA          G           \n",
       "10 schp684 10×AGA          B           \n",
       "11 schp685 10×AGA          F           \n",
       "12 schp686 10×AGA          E           \n",
       "13 schp687 10×AGA          D           \n",
       "14 schp688 10×AGA          A           \n",
       "15 schp689 10×AGA          H           \n",
       "16 schp690 10×AGA          C           \n",
       "17 control NA              NA          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "annotations <- read_tsv(\"data/example_dataset_3.tsv\")\n",
    "\n",
    "annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[90m# A tibble: 17 × 4\u001b[39m\n",
      "   strain  insert_sequence kozak_region strain_number\n",
      "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m           \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m                \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
      "\u001b[90m 1\u001b[39m schp674 10×AAG          G                      674\n",
      "\u001b[90m 2\u001b[39m schp675 10×AAG          B                      675\n",
      "\u001b[90m 3\u001b[39m schp676 10×AAG          F                      676\n",
      "\u001b[90m 4\u001b[39m schp677 10×AAG          E                      677\n",
      "\u001b[90m 5\u001b[39m schp678 10×AAG          D                      678\n",
      "\u001b[90m 6\u001b[39m schp679 10×AAG          A                      679\n",
      "\u001b[90m 7\u001b[39m schp680 10×AAG          H                      680\n",
      "\u001b[90m 8\u001b[39m schp681 10×AAG          C                      681\n",
      "\u001b[90m 9\u001b[39m schp683 10×AGA          G                      683\n",
      "\u001b[90m10\u001b[39m schp684 10×AGA          B                      684\n",
      "\u001b[90m11\u001b[39m schp685 10×AGA          F                      685\n",
      "\u001b[90m12\u001b[39m schp686 10×AGA          E                      686\n",
      "\u001b[90m13\u001b[39m schp687 10×AGA          D                      687\n",
      "\u001b[90m14\u001b[39m schp688 10×AGA          A                      688\n",
      "\u001b[90m15\u001b[39m schp689 10×AGA          H                      689\n",
      "\u001b[90m16\u001b[39m schp690 10×AGA          C                      690\n",
      "\u001b[90m17\u001b[39m control \u001b[31mNA\u001b[39m              \u001b[31mNA\u001b[39m                      \u001b[31mNA\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "annotations <- annotations %>%\n",
    "  mutate(strain_number = as.numeric(str_extract(strain, \"\\\\d+\"))) %>%\n",
    "  arrange(strain_number) %>%\n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Plot with the X axis as `kozak_region` but sorted by strain number\n",
    "\n",
    "This requires a bit more reading and discussion, but it is a good example of how to learn new `tidyverse` functions on your own!\n",
    "\n",
    "Use [fct_reorder](https://forcats.tidyverse.org/reference/fct_reorder.html) function from the [`forcats`](https://forcats.tidyverse.org/reference/index.html) package to sort `kozak_region` by strain number you created above in a `mutate` step and then feed it into `ggplot`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6RdODW+VK10cC/Fad1XrTZBoiIvnt37OtJalSKYPQPuhVAcBhkHA4TT9SGMXnH9HqTLDkaAPNzjFVW+B7IXCd01o967z5FJspMhqDMg4A2gMSDqfBEEqhKbWV+U0HS47+pVtzXYjdO92aCIDjTun0CKFRnVtjtJFoPu8eEXlCq6+Fsi0AHAMJhzMl0xIEzbF3+0dIkLDJOPazwYFuCQb4pzO2Pds6t8ZoU6lSiZVlD8IgBwCOgYTDmcZKxCIcy4I3oAYGicgXQoPsV0kcQwhtkSth2gm4hollL+oNfYQCGUE495FTZTSCXhUAHAYJhzORGDaOoopNZthnoaHLBiNCaEufuItDEwr6xo0Si/ap1Gtr5O6OC/iFC3qDie3snm3NihUI4oWCX7W6ephVAcABkHA4WTL0qtyt1GTOVmvjSeHjYSEDKbEYx7+KiYzk8/5TVQOvEnCBO3u2OWON0aZSZVIzy8JvMgCOgITDySbSEgyhQ1DG8aev5Aoryy5qULQRxuNti4kSYNizpRVXYfcZwLEzegNCaCTFScIxRSpBCGVCwgGAAyDhcLIIPm8AKfxNp1daYZQVGVj2W4UygCD+JpM2PH6PiPw4KlzDMPNKy+GFAtxhETqj1YfxeDF8Thqj4klhL6HgZ7VWbWW4eHwAfAkkHM6XQkssLJurgeZY9H29Um6xPhkUIMIbN6rMDJAtCAooMpoW3q6wslBCCjhRZDTVWa0jnboCRyMPSSUmls3RwKAmAG2AhMP5bGUcMKuCEPpKriAw7MmggGbP/rtL2BhKnKvWvl9d5+LAgJ+4s2cbBxWjdqlSW68KrNYPQBsg4XC+wSJRGI93SKPx8y/ux7S6PwzGyTQVzec1ewMehm2Kjuwu4K+tqdsFvYWAA7YlvziqGLW5R0R2F/BzNFodA7MqALQGEg7nwzH0gERcb7Ge0xvcHYs7baxTIIQWtrrGVyCP2BoTJcLxf96uyPPvlwtw4bROT2JYf6Fz9mxryUNSWs+wuTCrAkCrIOHgRIqURv49q3LbZM5Sa/qSwnvbGs3uRwr/LyrCyLJPlZbXQQEpcB651VpkNA0ViwRNSoic606vigp6VQBoTfNj3U509uzZbdu23b59WyaTJSUlzZ49G2tuw8Zff/11z549ZWVlRqMxODh47Nixs2bN4nNTWO4CEyRiAYZlqdRvhYe4Oxb32FyvtLDsM8GBjrzTT5HRLxiC19TUPVlSltE9muuPB+AnbHu2jeCyYtRmqFgUxecfVGmMLNt0IX8AgA23CUdBQcHKlSsnT5780ksvFRUVffbZZwzDPP74401vSRBEUlJSZGSkQCAoLCzcunWrSqVavHgxp+Fxh8Lx+yjxEY221GxpqYLBhxlYNr1eEUAQj8hoB+/yRljIHwZjtlqzvKp6VZdwTsMDfuK0nvOKURsMoQel1IY6xS8ana1mHADQFLefhRkZGVFRUc8++yxCqFu3bhUVFbt3754xY4awyZTqfffdZ7/cp0+fkpKSS5cucRob15Jp6ohGm61SP+1/G5XtVKjkFuvzoUFi3NE5OxxD66O7TLpxa0Odoh9JPh4o4zRC4A9OafUYQkNIzkc4EEKpUumGOsVepQoSDgBawm3CkZ+fn5iYaL86ZMiQ77777saNG/Hx8S3dhWGY4uLiixcvDh48uOFxi8Wia7DtO8MwzU7NOF2Hn2WSlH6rojpHo1sQEtT2rTvNNa+GgzbW1RMYNi8ooGlUrcRJE8TWmKiJRSWvllf1IoWjuOwsaJNHvZ6t8Pw4bRFiGObiUE0sm6c39CGFQe0ZYuxwnKMocTiPd0CtNbPIBXOCrn89Aeg8DhMOlmUVCkVg4F/f722X5fLmd+0ym80zZsxgWZZl2ZSUlGeeeabh2V9//XXJkiX2q5999tmIESO4CfwvPB4vODi4Y/cNRii+tPxXrU4YECBx9jaVjRAE0eE4ne4Xhep3g3FaSPCQLl2anm09zmCEviNFD13+Y0FpxdmhCVFCAWdhtsFzXs/WBQW5Ip3tvIbvA65xQqk2suy4oMB2/VfKZB0fWpsWplhfXnmFx0sObH7hGSeSSqVt3wgAD+NB5QU8Hm/t2rVms/n69evp6elSqXTu3Ln2s0FBQQ0zDLFYbDabOY2Hz+ezLGuxWDr8CA8GBnxcVpFdK09tYeUrp+h8nM61tvQ2Qui58NBG/0E8Hg/DsDb/1+6nqaXRUctu3X7kSn7OgL5ChydlnMXBON3OW+IkCALHcYvFwrp2WZpf6usRQqMoR98oOh/nw4Gy9eWVP1TVjJdwOKvistfTe2v2gcfiMOHAMCwgIKC+vt5+xHa5pe9kGIZ169YNIRQXF4fj+GeffZaWliaRSGxnExISPvvsM/uNlUqlUqnkLniEUEhIiNVq7cyzjBPwPkbop8qqcQSHg5/BwcEMw3D9ajiowmzZK1f0IYWDMbZRSIGBgRiGORLnc1Lqgoz+SalecPX6p1ERnAXbvICAAB6P5yGvZytkMhmfz1epVC7+IG8vmqaFQqFKpWJcuy7WL/J6hNBADDn4XymRSEiS1Gg0Hc7dByM2iCB+qq37d3AAwdl8B0VRIpFIq9VynWuGhPhphx3gDrdfH+Pj48+fP2+/ev78eZIkY2Nj27yjLX/3nG/tHTNSLAriEdlqrUd/IDjVJrnCzLILgwI683aLIfRJ1y4JIvK7euXGuvq27wDA3ViEzuj0oTyiu8B1X9N5GDaRpmosVtvypgCARrhNONLS0srKytavX19SUpKbm7tr166pU6faWlSOHz/+2muv2etAv/zyyyNHjuTn5//++++7du36+uuvhw0bFhDA+VQopwgMG09RVRbLJf9YQ9PEsun1ChlBzAjo7AQziWGboyODCeKdyppjWtgGD7TPDaOp1mJ1QUNsI6kyKUJoL6zTD0BzuK3h6NOnz1tvvZWenp6VlSWTyf72t7/NmTPHdqquri4/P98+hkGS5A8//FBdXY3jeFhY2IwZM1JTUzmNzTWSaSpDqcpWaxNErujNc68flepai/XvIe3ohm1FtIC/pVtU2s3Sp0vLs2O7dXPhV1Xg7U7rDQihESJXNzolUmIZQexTa1ahcOghAaARzotGhw8fPnz48KbHp06dOnXqVPvVuXPnNiwR9RkPSCgehh1Sa5aEeUfXQ2dsqqvHEZrvvArZUWLR8ojQtyqqn7xVtj82xil5DPAHp7Q65JI1RhsR4FgyTe1UqM7p9MPc2tcNgAeCd3BuBfKIYSLyot5QafbuepQ2ndLpL+oNKVKJc4cingkOfCxQ9rvBuLis0n9KYUAnndHpSQwb6JIlvxqBfVUAaAkkHJxLpikWocO+vpPkhrp6hNACDhqAP4gMHyoWZSrV62rqnP7gwPcorNZCo2mwmHTLpjwP0BIxju9RqiA/BqARSDg4l0JLkK/vHFtptuxXaXoLBeM4WIFAiGFbYyK78Hmrqmqz1fDFEbThtE7PIDRS7OqKURsSwx6QiEvNlsv+USoOgOPal3CoVKpLly5dunRJpVJxFJDv6UsKuwv4uRqt0bPXS+iMTfJ6M8sucGxv2A4I5/G+io7kYdizpRUFBiM3TwJ8xGmdASE0wn0lFHd6VVTQqwLAXRxNOK5evTpx4sTAwMCEhISEhITAwMBJkyYVFBRwGpzPSJJQOoY5ofXN7nwTy6bXK2kCny7jcLnl4WLRx5HhGoaZV1qusrp0CSngXU5pdRhCw9zXF5ZCUySG7YUyDgDu5lDCUVhYeN9992VnZ48cOfKZZ5555plnRo0alZWVde+99xYWFnIdog9IkdIIoWwf/caToVDVWKyPBchogtsZulmBsqeDAwuNpgWlZVbfHS4CnWFm2TyDoTcpDORxu4FRKygcH09TRUZTPozGAdCAQ58QS5cu1el0WVlZJ06cWL9+/fr1648fP56VlaXT6ZYtW8Z1iD5gNCWS4HiWj9aNbpIrMITmcblfjN3KiNAxlDhXo/tvNRSQgmbk6Q16hh3h7mVvptDQqwJAYw4lHDk5OX//+99TUlIaHkxJSXnuuedycnK4CcynCDAsUUKVmsxXfe4bzxmd4YLekExLerpkZ1cehn0VHdlNwP9fTd1uWM8RNHFap0duLeCwmSSVCHBsrxJq3QD4i0MJh0Kh6NWrV9PjvXr1UigUzg7JNyXTFELI95osNtTJETfdsC0J4hFbY6JEOP5CWWW+0eSy5wVewVYx6vpFzRuREcQYsTjfaCqEX1EA/uRQwhEZGXnixImmx0+cOBEZGenskHzTRFqC+1xzbJXFsk+tiRUIErncj7up/qTw/7p20THMY8WldVarK58aeLgzOn0Q4dI921qSKqMRQpk+WrkFQAc4lHCkpaWlp6e///77BsOdznKDwbBq1art27enpaVxGZ7vCOERCSLyjN4gt/jOB+TmOoWJYZ8JDnD9AktTpJJ/hAaVmi3PlJZboIAUIIQQumkyV1ssoyixJ+xj8qBUwsMwKOMAwM7RotEBAwa88cYboaGhgwcPHjRoUGho6FtvvTVw4MB33nmH6xB9RjItsbLsz75SOmpi2G31SgmOzwyQuSWAt8NCk2nqF41ueWWNWwIAnsZdW6g0K4gg7qVEeXpDicns7lgA8AgOJRwBAQG//fbb8uXLY2Njr1+/XlRUFBsb++677548edLbd5B3pRSaQj40q/KTSlVtscwJ5LwbtiU4htZ3jewtFKyvq99er3RLDMCjeEjFqJ2tV2U/DHIAgBByfOEviqKWLVuWl5en0WjUanVeXt7SpUspyqUz997uHhHZhc/L0WjMPjEF8JVcgSH0lAvLRZuiCXxrTJSUwF8rrzqvg5Wk/d1pvUGAYQnu7om1myKjcYT2wrrMACCEYC8VV8IQSpJQKitzRuf1S46e1enP6wwP0FScS7phWxEnFPxfVBczy869VVbh61vyglYorNbrBuNgESnAPKGEAyGEwni8YWLRWZ2hzAyzKgghlJOTg2HYli1b3B0IcA9eSydsvxNPPPEEQRCt/37MmzfPyUH5rmRasq1eeUitvc/dbXudtFGuQAgtDA50dyAIITRJKnk1POT9qtqnS8t/6hHtOZ83wJXO6AyMBzTENpIqo0/r9AdUmgWe8ccCbK5du/bNN9+kpaXdc8897o7Fj7SYcDz11FMIoVmzZhEEYbvcEkg4HJcoEQsxLFutWRYR6u5YOq7KYtmrUscKBOM9Zk7tpdDgfINxt1L9cnnVJ1ER7g4HuMGfBRyeMp9iM1VKL62ozoSEAyGE0P3336/X6/l89zctX7t27d13342Li4OEw5VaTDgOHTqEEBIIBPbLoPPEOD6GEh/WaG+azD08YKmAjtkqV5gYdmFIoOu7YVuCIbQ2KuKawbijXjlERLq3sgS4xSmdDkNomMdUjNpE8nmDRORJra7aYgnjtfh+6ydwHCdJN2eEOp1OLPasYTD/0WINR1JSUlJSEo7j9sstcWG0viD5Tq+Ktxaum1k23dYNy+XesB1A4fj27tFBBPFWRfVxrc7d4QCXMrPsRb0hTigIJty2Z1tLpkglDEIHoVelSQ3Hzp07MQz78ccfP/jgg969ewuFwpiYmPfee49tUFZvsVj++9//Dhw4kKZpmqZ79eo1b948tVrd8AarV68eNGiQSCSiaXr8+PHZ2dn2s7an+P777999991evXoJBIIVK1YsX748NTUVIfTEE09gGIZh2Pjx41uPvJNhIISqqqqefPLJoKAgiqISExNPnDgxffr0hunXmjVrMAw7e/Zsw3tNmzZNIpE0iqTNn7f1l9T2IGvWrBk6dChFUTRN33PPPQ23RWvzZ+kwhzLu8ePHr1mzZtCgQY2O//zzzytWrDhy5IhTQvETE2nJ6xXVh9TaZ7xziHW3Ul1htiwICpC6qRu2FdF83oboLo+WlM0vLT/Us1uMB4zcAte4rDfqGdZzGmIbelgm/XdVbaZaMxcG3prz6quv9u7de926dQEBARs3bnz77beDg4MXLVpkO/vGG2989NFHc+bM+d647JcAACAASURBVOc//4njeElJSWZmpkqlomkaIWS1WqdOnZqVlTVjxowFCxYYDIb09PRJkyZt37599uzZ9qd47bXXoqKiVq1aFRERwefzIyIihELhm2+++eabbyYnJyOE2lzfoZNhaDSaxMTE69evL1y4cOjQoRcuXEhJSYmJiWnva+Xgz9v6S2qxWKZMmZKVlZWYmLh06VKpVHr16tUffvjh3XffdfwpOsahhOPo0aPN7plSXV199OjRTkbgb7oK+PFCwXGtTmVlPPAzu00b5QoMofmemi2Nk1BLw0OXVlY/WVK2P7abyHNmfQCXTuv1yPMqRm26Cfj9SeExja7eYg3kedwAjNsFBQXt378fwzCE0IgRI3755Zd169bZPx1//PHHCRMmbN++3X77FStW2C+vX7/+wIEDmzZtshcavvDCC6NGjXrxxRdnzJjB+3MOSyAQHDlyhNdgSmvgwIEIofj4+DbHNpwSxurVqwsKCj7//HP7zzVkyJCFCxcKhULHXqT2/bytv6SffvppVlbW888/v3btWuzPEnuGYdr1FB3TqQ88hULh9gk5bzRRSptZ9hcvHPbP0xvO6fQTJFQvd3fDtuK5kMA5gbIrBuPfb5f7woInwAF31hj1mBU4GkmV0WaWzfKVVYadyzavYbuM4/iwYcOKiorsn38BAQH5+flnzpxp9r5ff/11WFjY7NmzDX+yWq2zZ8+uqqrKy8uz3+ypp57q5CdlJ8P48ccfg4ODFyxYYL/L/Pnzo6Ki2huGgz9v6y9penq6SCRatWoV1qChz1Y+4fhTdExr/weXLl26dOmS7fKhQ4du377d8KxcLv/kk0/i4+M7GYEfSqapNTV12Sr1FKmk7Vt7kvV19chjumFb8d/I8KtGY6ZK83+18n+EBLk7HMC5MzpDEEHEemoePIWWvF9Vu1epnhXgWZVPniA6OrrhValUajKZ1Gq1TCZDCH300UczZ84cMWJETEzMmDFjkpKSHn30UXvVZ35+vkqlEomamUqrrq62X+7Ro0cng+xkGEVFRQMHDmyY9OA43rdv32PHjrUrDAd/3tZf0mvXrsXFxTUqDWnvU3RMawlHRkaGbVIHIbRq1aqmNxCJRDt27OhkBH5oqIgMIohDGi3DIi8a8q+zWvco1d0F/PtduzdsBwgx7OuYqKSikn9X1vQVCpNoTw8YdEaJyVxlsUyWSjz2j6kPKewtFBzRaNVWxl1bAXgsrLmFc+xFjvfff//NmzcPHjyYm5t79OjRb775ZtmyZSdPnrQNDzAM06tXr6+//rrpI/Tt29d+ub0zF011PoymP2ajQs5mXweL5a7FDB38eVt/SVmWbfYG7XqKjmkt4ZgzZ86wYcMQQqmpqatWrbJNetlgGEbT9KBBg6RSSNjbjcCwJFryvUJ5Qa8f6pFlbs3aXKcwsuyCYA/qhm1FOI+3KTpy2s3S525XZPWMiRV46Hdf0HmndLY92zz6T2mKlF5dU3dIo0nzsPYuz0fT9IwZM2bMmIEQ2rFjx+zZs9etW/fBBx8ghHr37n3lypUBAwa09H29Ja184nIRRs+ePa9fv26xWOyDHAzDFBQUNLxNUFAQQkgulzc8WFhY2PBqh3/ehvr06fPHH39oNJpmH8QpT9GS1nLt3r17T5kyZcqUKcuWLZs9e/aUBh566KFx48ZBttFhyd62kZuZZb+uV1A4PttNe8N2wHCx6OOoCIXV+nhJmcrKuDscwJXTOgNCaKRnL66QKqMRQnuV6jZvCRpq9AE8atSohgfnzp1rMpleeeWVRqMF5eXlrT+srbuk0YNzF0ZaWlptbe2mTZvsp7Zu3VpWVtbwxn369EEIHTx40H4kIyOjUVLS4Z+3occff1yv1zfa6d3+gE55ipY4VEezfPnyzj8TaOh+CcXHsGy15vXwEHfH4pC9KnWF2fJ0cKB3ddbMCpCe0+m3yBX/KKvYEh3lFWMzoL1O6/QCDLtH1Nlhc04NIIXdBfzDGq2OYcS4N/0RuVdkZOSUKVOGDh0aFRVVXV29ceNGgiCeeOIJ29nFixfn5OSsX7/+woULDz/8cGhoaGlp6cmTJ/Py8lovOEhISCBJ8pNPPhEIBAEBAWFhYffffz93Ybz00kvbt29/7rnnLl68OHjw4Ly8vK1bt8bHx9+4ccP+FCNGjBg1atTatWvVavWAAQMuXbq0e/fugQMHNrxNh3/ehhYvXpyZmblmzZqLFy9OnjxZKpVev349KyvrypUrznqKljhauMuybE5OzqlTp+Ryub3Y1WbNmjWdDMIPSQl8pFh0TKu7bTJ39YYlRzfWKRBCTwZ6zfCG3aouYdeMpgMqzUc1ta+GeUd6BxyntFoLDMZhYpHQ4/fQmSKlP62V/6zReV21uBu9/PLLR44cWb16tVKpDAsLGz58+ObNm++9917bWR6Pt3v37g0bNmzZsuU///mPxWKJiIgYNGjQ6tWrW39YmUz2zTffvPvuu//617+MRmNiYmLrCUcnw6Bp+pdfflmyZMm33367devWoUOHZmVlrV69umEygRD6/vvv//nPf+7YsYNl2fvuuy83N/edd95peJsO/7wN8fn8AwcOrFmzZtu2bcuWLePz+T169LBNFTnrKVqCsQ5sla5WqydPnnz8+PFmzzryCE6nVCrNHG/AGBISYrFYml2AxCk+q5Uvq6z5KDL8yc4tBxQcHMwwTH19vbMCa+qywXh/YfF4CfVD964dfpDAwEAMwxwfw3QiucWaXFRSajZviI58WEa3fuOAgAAej1dbW+ua2DpMJpPx+fy6ujq3/AE6jqZpoVDY9IuKsxzWaGcV334+NGhpeKf2J5JIJCRJKhSKRmV6TnROp59041aaTLo+ukuHH4SiKJFI5Jo3QE4fH0yfPj0zM9NgMLg7ENdxaGTPVo67atWqP/74AyGUmZl59OjRlJSU4cOHFxcXcxug70qhJchLyji+rKtHCC0I9tZ1EoN4xNZuUSSOvVBWmW80uTsc4Ey2FTiGN9fF52mGiEVdBfxstcbo2TkiABxxKOHYtWvXzJkz33jjDVs3c3Bw8Lhx4/bv38+y7KeffspxhD4rTiiIFQiOarR6xqPffeqs1l0KVTSfl+Tx3bCtGEAKV0dGaBnmseJSudXq7nCA05zWGTCEhnt2i4oNhtCDtETDMEdgBTAPw7KsoWUcDc75IYcSjrKysrFjx6I/FyOzDeURBDFr1qwffviB0/h8WwpNGVj2mGcvObpVrjCy7MLgIMLj58hbNz1AujgkqNRseaa0wgJfMX2ChWUv6g2xQkGIlywZbutVyYSN3DzM77//LmrZ999/7+4AfYRDRaMURdmSDIFAQJKkvT1GKpVWVlZyGJ2vS6apL+rqD6k1yZ66MpWFZbfKFSIc840VEpeGhxYYjTlq7b+rat+N6NSUP/AElw1GLcN47IrmTY0QicJ5vAMqjakLK4CmKY8RFxfX0rLlCKGePXty8aQ7d+7k4mE9mUMJR2xsrL0bOCEhYceOHTNnzrRard99913Xrh2vIgT3UmIZQWSrtR8g5JnvPftUmnKzZV5QgG9sOoVj6POuXVKKSj6rlfclhbN9IovyZ6d1nrtnW7NwDD0olWyWK45rdRM89WuGHyJJ0rbKJeCUQwlHSkrKpk2b1q5dy+fzFyxYsHDhwri4OIZhiouLV65cyXWIzaIoiiA4/wjk8XjBwcGcPkVKkPyHmtoyIZnQ0QoJDMMIguAozi2lFQihF2O7B3f6Pd22tB/Xr2ebghHaJ6HvvXDp1fKqkWGhw+nGDYoeEmebbHHaVif0ZLY4AwM52X/nYlUtQiglsktwp2s4bHHaNpvg1Byc2CxX5Jgt0zv0O2aLExZdBN7IobZYpVJZWlraq1cv24r0H3/88aZNm3Acnz59+ttvv+2CD/5mQ/L2tlibHfXK58sq3wwPfTG0g58c3LXFXjEYJxQWj5OIf+we3fat2+LGttimDqg0826VhfF5h2K7RfDvSruhLda5OG2LvaegyMiyV/vGdX6A0AVtsTZWlu1/tYhF6Pe+PXntr4uCtljgvRwqGpXJZAMGDLDvf/Pyyy///vvvly9fXrZsmVuyDV+SLJUQGHZI7YlFZF/WyhFCC4I8fW/YDpgslbwcFlxptswvLTN59gc2aMkts7nCbBkuEnnmdGRLCAybJJXIrdZTOr27YwHApdpOOHQ63euvv3769GkXROOHggliiIg8p9PXWDyrV1NutWYo1dECfoqPzjQvCQuZKqXP6Awvl0Hhs1c6pdUjhEaIvaZi1A56VYB/ajvhEIlEq1ev5nr4zp8l0xIGocMeNsjxtVxhZNmngwK8vRu2JRhC67pGxJPCHQrVVjm3E2eAC2e8rWLUbhwlDiCIPUq1Zy/BA4CTtV00imFYTExMRUWFC6LxTyk0taqq5pBaO8tjdiqxsOwWuVKEY160N2wHUDie3i0qubDkjYrqXkLBfV740eXPTun0AhxL8J6eWDs+hiXT1A8K1Tm9YbgXjtB0jFrNyU65tn1fgVdwqIbjiSeeWLNmDde1VH6rPymMFvBztVqTx3zfOaDWlpnNMwJkQT7RDduKGD7/y+guLEILSsvLYBjPe6itTIHRlECSpHeOwNn2b9urUrk7EABcx6G22Pj4+C1btvTv3/+pp57q0aOHvXrUZtq0adzE5keSJNRmueI3nW6cZywfvqFOjhB6unO7ynmLRAn1dnjI8sqaJ0rK9sV241utsI6x5zurN1hZdoQ3rGjerAdoiQTHM5XqdyPCvDJjAqD9HEo4Hn30UduFN954o+lZD+/K8wrJtGSzXHFIrfWEhCPfYDyp1Y+hxP1IYdu39gmLQ4IKDMZvFarB14rkFiuLUC+hYEVEWJKPFsz6ANuebd5YMWojxLAHaGq3Up2nNwzywlkhADrAoYQDNkzh2liJWIRjB9Waf3cJc3csaL2X7w3bMS+HhfygVNX92St03Wiad6tsV48Y/5li9y6n9XqE0DCvHeFACKVK6d1KdaZKAwkH8BMOJRzTp09v8zYMw3z//fcTJ07kaElB30Zi2FiKylZrCo2mOKHAjZHILdYfFaquAv6kJktw+rYv6+otdw/VGVl2VVXNrh5OWPQMOJeFZc/rDD0E/DCeQ+9gnimJpkgM26tUvx0OS2wBv+BQ0agjTCbT7Nmzi4qKnPWA/sa23EW2u5tjt9crDSz7VKDMV7thW3LdZGp68Jxev6NeecVgNMO8oSf5w2DUMow3NsQ2ROH4/bTkhsmUb2zmdw8A3+PF3w98TDItwVBVtlr79xC37Y5hZdnN9QoSwx4P9K/5FISQDG8m+dYz7PNllQghAY71FggGkMIBIrI/KRxACgNgjV33Oa03IISGe/9MxBSpZL9KvVepig+DQQ7g+yDh8BSRfN4AUnhKp1darTI3fZgdUGlKTebHA32/G7apR2TSn5SN1wl4IjBgkEh41WjM0xsv6Q1XDEakuNPHGM7jJYjIvqSwt4CfICJ7C4Ww2bjL/KbVIYRGenMBh81EWiLAsb0qzauQcAA/4LQpFdB5KbTEwrK5Gp27AtgoVyCEFgT7YxXOJKnk+bv3z5sslfw3MmxuUMCqLuH7YmNu9uv1a1z3r2Iil4QFp9ASK2Kz1Zp1NXX/KKscW1jcM//6QzduvVxe9WVd/SmdXu8xS6r4pDM6fQBB9BJ6fReVlMDHisVXDcbrMKvCjVOnTqWlpfXo0QPDsAULFjQ6u2/fvkGDBpEkGR0dvWzZsg7sL6hWq6VSqVgsrqura9dZnU73/vvvDx06lKZpmqYHDBiwaNGiy5cvtzcA7wIjHB4kmaY+rqk7pNZOk7lh7byrBuMJre4+Stzfb7phG1kaHpomk56yMhaE9WOZsZK7SgR4GNaXFPYlhVOld/53Ks2WAqOxwGjK0xvzDIazOv3pP7fj4mFYT6Ggj0DQhxQkiMjBItKryxs9SqnJXG62JNOUbwwppcrowxrtPpXmXx3dL9qnsCx+9XesqgIJSSauDxva2a49rVYbFxc3ffr0ZcuWNTr122+/Pfzww4sWLdq2bduFCxcWLVpktVpXrlxpO3v+/Pnu3bsHBf31n8Ky7M8///zAAw80fJD09PSEhAShULh58+ZXXnml0VO0dLa+vn78+PElJSWvvvrq8OHDg4ODi4uLT548OXfu3AsXLnTyR/ZkDm1P7wiDwSASic6cOTNs2DCnPGDrfGZ7+oYYFg0sKDIjNr9PT8drNp21Pf1LZZXb6pWbY6JsayA6nUdtT9+KDm9Pr2GYIqPpqsGYZzDk6Y2XDYaG4xzhPF5fUthbyE8gSadMwfjt9vQ/KlSLble8FR7yr9Bgpzygjcu2p29EbrH2LyjqRwoP9+zmyO29d3v6Npc2x4xGIv0rvKLsznUezzo+2TpqTOv3cnBp80GDBg0bNmzjxo32I4888kh+fv4ff/xhu/rOO+/873//q66uFovFCKHU1NTS0tKcnBzb68Cy7KJFizIyMq5evRoc/NcvXkJCwgsvvCAUCpcvX37t2jXs7vftls4uWLDgm2++uXTpUlxcXMPbW61W396AHaZUPAiOoQck4nqL9Zze4OKnVlitPyrVXfi8ibDUVUdJcDxBRD4aKLNNwdyI73W2d2x6tyjbFAyL0FGNdkOdwjYFE5t/PamoZPHtStsUjM5JH8b+wFYxOlLs3S0qdkE84j5KdElvKDb5+8r6xKH9f2UbCCGLmcg5gJXd5ujpjh8/PmnSJPvVSZMmabVa+wDDjh07AgMDx48fX1VVxTDMU0899dNPP+Xm5jbMNo4fP15UVDRz5sy0tLSampqcnJxGj9/sWZPJtGPHjnnz5jXKNhBCvp1tIJhS8TQpUvpbheqQWuviNZvT65U6hnkpNIjvZ92w3OFhWDcBv5uAP/HPFU0UVmuB0ZSnN9imYC7rDXl6w/cKhBAiMKwrn9dHKEwQCRNE5CARGd7qFEyuWntMrtSybDyOzZZJBb4xu+CYU1odH8N8abGsKVL6F41un0q92H0daq5EnDmJ1VQ1Psqy+KXzdx/CEEK8fRls15imD2IdMoKNiOxwDAzDVFVVRURE2I/YLpeXl9uuUhS1f//+qVOnJiYmDhw48NixY0eOHImPj2/4IJ9//vmMGTMkEglCaNasWV988UVycnKbZ2/fvq3VagcMGGC/5dNPP71161bb5by8vP79+3f45/JwkHB4lgkSsQDDslTqt1y4FpCVZTfXKQQY9pj/dcO6UgBBjBSL7L0VJoa9YTLl6Q22KZgrBmOJSWNfiCWAIHoLBQkioW0KppdQYJ9le7W8arP8r5m+DXX1+3pEu6uzycXUVuaq0TRIRIp8KMeaIqXfqKjOVGn8JOHACgvwouuO3ri6Cqtukp0gxMT26kzC0eLTNfjGJRKJMjIyevTosXPnzuzs7EbZRm1t7c6dOw8dOmS7On/+/NGjR5eXl0dGRrZ+tukc6IoVK1588cXffvtt4cKFHj5D2klOSziEQuHly5ebjhGBdqFw/D5KfESjLTVbovkuSgez1JpbZvOcQFmI/3XDupEAv1OF+iiS2Y5Umi15BoNtCKTAaDzdoAqVj2GxQkECSQpxtE2ubPg4BQbj0sqatVERjZ/AF53V6a0s671bqDQrlEcMF5GndPoyszmKz3d3OJyzpD6CNVcow0vfhCka13hZx4xnBjVXF0h1qtQMx/Hw8PDKykr7EdvlLl262I+YTKa5c+dKpdKRI0cuXLgwNze3R48e9rObN282Go0TJkz4K1SrdePGjUuXLm39bHR0tFgsvnLliv1UVFRUVFRUw2B8Vfs+0hiGUavVjVKwgIAAhBCGYQ3HiECHJdPUEY02W6V+2lXtqV/5cTesR4ng8yL4EvsUTI3FesVgvKzXXzEYfzcYC42mAoOx2TseUGvWujBONzqj1yOERvhKAYddqoz+Taffr9Iu9Ic9jCR0s9/irSkP8r5Pb3iEDQpmRieyfE52exg9evTBgwdXr15tu3rw4EGKogYPHmy7ajQaH3nkkYKCgqNHj0ZERDz22GPjxo3Lzc21falmWXb9+vUvvvji/Pnz7Q/47bffbtiw4a233sJxvJWzAoFg1qxZW7Zseemll/ztK7pDRaMMw3z++efx8fEikSggICDwblyH6G9SaAlCKFutdc3TXTUYf9XoRopFA/21G9ZjhfKICRLxP0ODv4yOPN6rR3G/Xlmx3QY3V7ugs/pLzekZnQEh5Hs76j0klWAI7VWp3B2IOzG94y1/e5QNDEIIIYJgesdb5jzVyWxDr9dfvHjx4sWLer1eLpdfvHgxLy/PdmrJkiXXrl37xz/+cfny5W3btn388cf/+te/xH/msrNnzy4sLDx69Gh0dDSfz//2228TExMTExNtK2pkZ2cXFRU9++yzAxpYtGhRWVlZZmZm62cRQh9++GFsbOzw4cP/85//5OTkXLx4ce/evevWrcMwTCBw515aXHOoLXbFihXLli3r1avX0KFDZTJZo7NffPEFN7G1xifbYu1GX79ZYjJfi48TN7fediOdbItdUl61Ra7YGB35MMeLf/h8W6xrfForf7eyptHB4WJyf6xDTZWu58S2WCvLxuUXBvOIs71jnRJbQ+5qi7WbdOPWRb3hcp+eoa3ObPpwW6wdZjCwfD5yrCyp9bbYixcv2gctbAiCsP8XZ2Zmvv322/n5+aGhofPnz1++fDn+51vuiRMnevbsGR4ebr8jwzB79+59+OGHEULTpk0rKSlpumbGmDFjbL/wrZw9cOAAQkir1a5du3bnzp0FBQUWiyUyMjIxMXHx4sXDhw935Kf2Ug4lHNHR0RMnTvzyyy9xBz7/XMO3E453K2s+rZV/HRM12YElMTqTcCit1nsKbkgJ/HzvWK77UyDhcAo9wyYVFV9rsDAlH8Mye8QM8dQv/U5MOC4bjPcXFj8aKPuUg4IVtycctlTyo8jwJ4Nam1Xxh4SjXRxchwN4AocSiKqqqoULF3pOtuHzkmkKIXSI+51jt9crdQwzPygQumG9hQjHdvWInhMoCxfwRTiBEOomEAz21GzDuU5pdQihET7UENvQVBmNEMpUuXm/aAC441AOERMTo1Qq274dcJKRYlEQj8hWazltkGJYtEmuEGDY44GNp8mAJwvj8dZGRZQOH6wbN2qyVFJoNOa4quLHvWw9Oy5eosZlYvj8AaTwmFYnt1rdHQsAnHAo4Zg/f/66det8uz/YoxAYNp6iqiyWS1wuOZqt1pSYzGkyuvU5Y+CBiOIi9sAey487PqotE7LMB9W1/vDHeVpnkBFEb+/fs60lqTLawrJZ/pE+Aj/kUFts7969v/rqq1GjRj322GPR0dGNloufNm0aN7H5tWSaylCqstXaBM4GkG17w7qs+RY4C5lzgH/hDMsiK4Z6InRZGjh85PifNdoHJL68LH2F2VJmNif5yp5tzXpYSv+nqnavUjU7QOruWABwPocSjhkzZiCEbty4cfr06aZnYeSDCw9IKB6GHVJrloQ5c4cqu2tG0y8a7XAx6UtLRPsDXtE1/oUzCNnWfUYIoRhV/Qf5ef+hpfdLKN/9LEa/6fTIh7ZQaVZPoaAPKTyq1Sms1gD/WDoW+BWHEo4ffviB6zhAI4E8YpiIPKXTV5otERwsObqxrp5FaGGwXyyl7Et41wuaHnykumyR3nBEo5sg8dnPY1sBh++twNHIFFrycU1djlo7HQY5gM9x6JNs+vTpXMcBmkqmqd90+sMa7WPOLurUMMxOpSqcx+NoJ3rABVylJG5cJ24WNj1FWS0YQv+trp0gaWabK99g27NtiMg3K0btUmX0xzV1mSoNJBzA98DmbZ4rhZb8u6r2kNr5CUd6vVJtZRaHw96wng6zWvHSYt7NIt6NQlze4tIgbHiX+yXUYY32iEY73hcrObQMk280DSSFvrRnW7P6k8I4oeCwWqNjGEfW/QPAiziacLAsm5OTc+rUqaYL+KxZs4aDwADqSwq7C/i5Gq2RZYXOywxYhLbIFQIcmwt7w3oqXKkgim8QJTd4xUWY0YgQYgmetXusJaYH0zVGeHDv3ckHZr5nyJKw4MMa7X+r63wy4Tir01tY1lcbYht5UEqvq6nL0WinSmFJK+BTHEo41Gr15MmTjx8/3uxZSDi4kyShNsoVJ7R6J87NH1JrioymmQEy6Ib1LAxDVJTxiq4RxTeIqoo7x2QBlr79rd1iLT3i2D83WbDOmiv85bDgZiFrMjGyQFxeK/z152Hde06QiHM1ul802nE+l3Oc1hkQQiMpn61QaWiKVLKupm6vUg0JB/AxDiUcy5YtO3ny5KpVq6ZNm9avX7/MzEyapt977736+nqoJ+VUipTeKFdkq9ROTDjudMO2unwycJlWBjOs3WOt4V2a3oWlJIbJDwtlMgGfX1dXJzhxVHDsiHjvztemzszV6N6vrvXFhEOPEBrmHx1Vg0RktIB/SK01sCwJk57AhziUcOzatWvmzJlvvPGGwWBACAUHB48aNWr06NGjRo369NNPP/zww1bue/bs2W3btt2+fVsmkyUlJc2ePRtr7k8oJyfn6NGjxcXFRqMxMjLyoYceSk5O7tiP5EtGUyIJjmdptP9x0gPeMJmOqrXDxCKP3XrDH2BWC156i3frZpuDGY4wjhqL1VTzC/4Y82vO+PihRzTaY1rdGB8aDLCy7Hm9PobP78JBu5YHwhB6iJZ8UVd/RK2dBGXdwIc49AdcVlY2duxYhJBtOxXbpkEEQcyaNeuTTz5pJeEoKChYuXLl5MmTX3rppaKios8++4xhmMcff7zpLX/++ef+/fs//PDDYrH4xIkTn3zyicVimTx5cgd/LF8hwLBECbVPpb5qMPZ1xvbxG+oUDEILYHjDHf4azLhZiJlMyIHBDIdgmHHyVLyulncl780u0Ud41H+qavfF+k67Sr7RpLIyE2k/+uhNldFf1NVnqjWQcABf4lDCQVGULckQVZKTTgAAIABJREFUCAQkSZaXl9uOS6XSysrKVu6YkZERFRX17LPPIoS6detWUVGxe/fuGTNmCJssTrxq1Sr75X79+t28efP48eOQcCCEkmlqn0qdrdZ0PuHQMMx3CmU4j5cKc8Ou0uJgRvyADgxmtITlCwyPzBZ//eW4nw8kPjjjqE5/Qqu7z1cGOe7s2eYfFaM2w0SiCD7voEpj6sIKfL0xB/gPh9quYmNjCwruLDeUkJCwY8cOlmUtFst3333XtWvXVu6Yn58/ZMgQ+9UhQ4YYDIYbN260+Ywmk0kmgx3FEEJoIi3BETrkjO0VvqlXqq3Mk0EB8BbGNVyp4OedJ/fspD79SPxDuuDUcbyuxto91jjuAd3chdpn/mlImWLu088p2YYNI5XpU6cjln339DGE0IfVdc56ZLc7o/ejilEbHEMPSmml1XpMp3N3LAA4jUMjHCkpKZs2bVq7di2fz1+wYMHChQvj4uIYhikuLl65cmVL92JZVqFQBAb+tVWH7bJcLm/96XJycgoLC5955pmGB8+ePbt27Vr71SVLlvTr18+R4DuDIIiAADfPPgQgNERScUars1KS4ObmsDEMw3G8zThZhLYWlfAx7O/dogOETvuccxyO4xiGuf31bBNBEAihjsRpMbPFN9mia2zhNVR++87BwCAsoTfWszfWOx4Jnfm62+K8Ky8PGMxqVKP2705UK44idBkjxsrcP5Rli1Mq7fgyVmcLi6U8YkRYKMFlBaVtvpimaQ/Zq2EWwjfV1WfpjWldoxoet8UpkUg8JE4AHOdQwvH6668/9thjtuU3FixYoFQqN23ahOP48uXLX3/9decG9Ouvv37xxRcvvvhir169Gh5Xq9X5+fn2qwaDgcfjvIIMwzAXPEubpoQEn9Voc1Tqx8JDm72BI3EekNdf0xseCw/t5tZvip7wejrC8ThZeR1zvYApLGAK/kBGI0II8fl4rz54XB88fgAWHsFhlE3jTHzAUlO9/PdzE0Y98N7tspzgAZw+u+M6/P9ebjSVGIyTggKFfL5zQ2oW4TE7mNwfHBgm4O+RK9YTBK9JpuU5cQLgOIfeBWQyWcMvUi+//PLLL7/c5r1sX2fr6+vtR2yXg4Ja3L/jwIEDX3311SuvvDJq1KhGpyZMmHD27Fn7VaVSWVvb4sKLThESEmKxWBQKBafP4ogxBIYQyiivnEg08w0vODiYYZiGr3OzPi6+jRB6ghJx/bq1JDAwEMOwNse33C4gIIDH47X+KmEWC367mcoMq63NJDaO5f85lsHZqy2Tyfh8fl1dXaNvutjYCSNu3xorrzmMUOat0lHuLn2gaVooFDZdMNBBB5RqhNBgPsH1761EIiFJUqFQWCwWTp/IcSmUOL1eubekdGyDrniKokQikVKptNXVcSckJITTxwd+qB1L51oslnPnzh04cMDxz+D4+Pjz58/br54/f54kydjY2GZvvGPHjs2bN7/zzjtNsw0/d4+I7MLn5Wg05o4Oot40mXM12gQROdTdHz8ejld0jcnMsGTs4F/JQ00+IP+qzPi/xpUZ2qee+6syg++GGSs7luDp//boW+U3EEKri0vdGIlTnNL5XcWoXaqMRghlqjXuDsSLnTp1Ki0trUePHhiGLViwoNHZffv2DRo0iCTJ6OjoZcuWdSAnVqvVUqlULBbX1TWumtLpdO+///7QoUNpmqZpesCAAYsWLbp8+bKDd/dJjiYc3377bdeuXYcNG/bggw9evXoVIVReXh4WFpaent7KvdLS0srKytavX19SUpKbm7tr166pU6faWlSOHz/+2muv6f4sidqwYcN333331FNP0TR948aNGzdulJZ6/Xuls2AIJUkolZU5o9N37BE21MkZhJ4NDmz7pn6LZcnMDFHGDvbkMeupE+SB3eLtmzCLBbNYiOIbwl8OU5s/p75cR2Zn8gv+YEVic8IQw9Tp2n+8opvxuGnkaCak+dkut2ApyfCxE8bU1+Qy7OnyCneH0ymndQYehg32jyW/GhlLiQN5RKZSzfhNtUadxfJuWeUj128+dfPWznpF539urVYbFxf33nvvxcXFNTr122+/Pfzww2PGjDlz5sx777334YcfLl261H72/PnzjYZjWZY9fPhwowdJT09PSEi47777Nm/e3PB4fX39vffe+/777z/yyCMZGRlHjx5dsWIFTdNz58515O6+CnOk8igrK2vy5MlDhgyZNWvWkiVLTp48aRuESEpKoml6165drdz3zJkz6enppaWltoW/5syZY1v4a8+ePRs3bkxPT7dVkz322GNqtbrhHSMiIr788suWHtY1I4oeMqWCEDqg0sy9VfaPkKBlEY0/2NqcUtEwTEJBkQDDLvbp6cQ9WdrLw6dUeFfyRAd2NzrIBAVjajVmNiGEWB6P6RpjielhievDBLt/tLmlKRW741cuT0OCJGXdjmFDWKHbPrA7M6WiY5ie+YUDSOGhnt24iK0hD5xSQQj9o6zyu3rlvtgY+xiP906pNHqHb+qWyZyYf72uwes/Kyjwyx7Rrd+Lph2qjB40aNCwYcM2btxoP/LII4/k5+f/8ccftqvvvPPO//73v+rqarFYjBBKTU0tLS3NycmxvQ4syy5atCgjI+Pq1avBwcH2B0lISHjhhReEQuHy5cuvXbtmX9ZywYIF33zzzaVLlxolOlartWH9TUt391UO1XCsWrVq0KBBv/32m8ViWbJkif34vffeu3379tbvO3z48OHDhzc9PnXq1KlTp9qvtvk4fi5RIhZiWLZa0zThaNOOeqXKyrwSFuzGbMPz8YsKmh7E5XVMULC5R5ylRxwT040lvKPi1Wb0gIH3nc/LkQVf/DknYdJDyAv/98/pDP6zZ1uzptCS7+qVe5Vqf3gRXiy5XXd3trdDXv9woOyhgI63OLXi+PHjc+bMsV+dNGnSypUrL1y4MHr0aITQjh07pkyZMn78+MOHD4eGhs6fP//AgQO5ubkNs43jx48XFRXNnDmTIIjFixfn5OTYFsg2mUw7duyYN29e02GVhtlGS3f3YQ69gZ47d27FihU8Hq9R7h8TE1NR4d0Dtt5CjONjKPFhjfamydxD0I5yfRahTXIFH4O9Ydtiaub7IiumtE8vdn0szvJiXOyJW+UfkpLvjx8xjZng7nDa7bRejxAaSfn+Z21L7qcpmsD3qNQruoR5X8LYgldulZ3WNllfhEV5+mamjJ8vKf2gopmiqJVdu4zrxOKzDMNUVVVFRPzVRGa7bF/WkqKo/fv3T506NTExceDAgceOHTty5Eh8fHzDB/n8889nzJghkUgQQrNmzfriiy9sGcPt27e1Wu2AAX/1iD399NNbt261Xc7Ly+vfv38rd/dhDtVwWK3WpmuDIoSqq6v5LulVAwihZJpCCB1qZwXZEY3uutGUKqX9ZB+KDmPCwpoetEZEuj4SJxovpUeRwqzQLpfy8/nX8tu+g4exrTE6TOS/CYcAw5IkVLnZclFvcHcsTlNtsRQbTY3/mUzNTg2qrEwzNzaadB1qempTw0kNkUiUkZFRW1u7c+fOr7/+ulG2YTs+f/5829X58+fv2bPHlq80neVcsWLFxYsXv/jiC6vVajvbyt19mEMfQr179z527NjixXd91WNZds+ePQ2TOMCpibTk9YrqQ2rtM+2p/dxQJ0cILQiG4Y3WYHo9r/RWo4Msj29MTHJLPE70UkTYzOLSVb36Z+zfzQQFW0Oayas8E8Oic3pDNJ8X6d+5cqpMukupzlRpfKZy9uvY5itykq4WNh35WNet6xwOqt1xHA8PD2+4NYftcpcuf21pZDKZ5s6dK5VKR44cuXDhwtzc3B49etjPbt682Wg0Tpjw18Ch1WrduHHj0qVLo6OjxWLxlStX7KeioqKioqIaPl0rd3f2z+pBHBrhePLJJ7///vuGZbQajea55547ffr0vHnzuAoN3K2rgB8vFBzX6lRWR1P7YpP5sFp7j4gc7gcTwB2GKxXibzbhFWXmHnHmvv0xMYUEQmv3nvo58zyq96RjJkjEI8SiAyER5yia/PFbTO81S2VfNRpVVmaEP61o3qwkCSXG8d1K1V+HrFb3hcOhj2OiSPyuj6SxtGRWEFe9daNHjz548KD96sGDBymKGjx4sO2q0WhMS0u7cuXK0aNHf/rppxEjRowbN66wsNB2lmXZ9evXv/jiixcbePPNNzds2GC1WgUCwaxZs7Zs2WK/fSOt352jn9cTONSlYrFYpk6deuDAgbCwsOrq6l69epWUlJhMptTU1J9++gnH27GYh7P4W5eKzXtVtWtq6jbHRE1psIdkK10qb1dUr6+r/zQq4tFA929M45ldKkRluejHbzGd1jRkhPH+iQjDHFn4yxO02aVid1ijnVV8e7LJsOvwbmu3HrrpjyEX/s12uEtls1zxannVB13C5rukndszu1Rs5t0q36dS/xoTmXDupOCPy0ivYwODjCNGmwckcFcL7PouFfT/7d15QBN33j/w79yTBAhHALk8wPuuFbEeaL2tFsXifdS222rX7W7b7bM+3Xafp92n7e7z2z722h72UKu1ra1HPfFABRVQsOKNeIAWvLjPzGQyM9/fH6kUMULAJJPA5/UXmUxm3gSSfPKd74FQriD+763inDqzkaKm+Pv9MTRY19z/atOjVARBsC0ENnv27D59+vzXf/0XQRADBgxACB09enTEiBFLly5dsmTJyZMnlyxZ8vLLL9cv1jFjxozz588fOHAgPDwcIaQoypNPPnnw4MHTp08HBQXt2bNn0qRJFy5c6NGjR/25CgsLO3XqtGXLlmnTppWXl8fHx1+/fv0vf/lLbGysyWQqLCz84osvduzYceHChYKCgqYf3uwT5aUcKjgQQoqirFy5cu3atbm5uaqqdu/efdGiRX/4wx+0mmG3fRYcWWZhSv4vc/39Poz8rd3vfgVHnar2z7vCIOJUTy1Hw9bzwIKDunpFt/VHwmq1xI+Rhgy3bWx7BQdCaGL+tRNmMe1aXtz5k9LDcZYxE92Q0KbVBcfzRTc3Vlandu3c54HXSXaEJxccGyurf194I+dsVs+iqw23i2MnWQcNcdFJNSk4WqHpguPkyZP1jRY2FEXV/4l37Njx+uuv5+bm2sahvPHGG/XfnzMyMmJiYkJDQ+sfqKrq9u3bbdXA9OnTr127lpOT0+h0I0aM8PX1TU5ORgjV1dV98MEHGzduzMvLk2U5PDx81KhRy5Yti42NdeThbZKjBYenaZ8Fh4Jx7wtXSAKd69GVJBAhSezxTL74NiYIMTzSOii24bjNVWUVy28Wvxwc9Gqo9pNGIM8rOJizp/i9OzBClsemW3v2qd/eJguOfTV1864VTfbRbzq4gywtESc9bu33ULOPcopWFxyD8q5Uquqlnl1dumZbPU8uOGpV9fnDR37KSm20HTNM3R/+A7tmfaK2UXAAj6LB1RDQahRBjPP1KZWVHEEgLKL+68/Z9DT10gV8MZdL3af7dg3R4PrfmooqmiCeDITuovfAmE1P45O3YoYVZi1oWG20VeN9DQ/p+N215szJiZjjuH3J1M3rWodqyi2rXGiVB+t17qk2PJwPSU6z2Ol8Q1itZKWnVPAANKsFpXFJScmVK1fu/UY1depUZ6cC9zXe1/BDZdW+mrphZ7MbvddQt26w2ZmWoSMQQqm1dbmiZbrRt5338LdDVfm9O5gzJ7Gf0fzEvDbQLdRBfw4JWnDt+gpJHfTYdN1PP+i2/lC38FlsaP1MBi5lm4FjSDseENtIVz/73+Mx647rTQA4hUOfRhUVFcuWLduwYYPdRlEvvSjjpcb4GBiC2FtT+z+/XL33XurqFTR0BELoy7JKhNCzsHjK3QirxG/dSBdcVoJDhaR52KcdNcZO9PV5SMfvqq451bXzoOGj2COpup9+MM95EnnkQufH6gSE0BB9GxkI+uBievauy0g13D2EQekQrvpp3x8cAAc5VHA8//zzGzZsSExMHD16dBOLywM38KPIOL0uvc4sK4qd6fcwRggVWuWU2rp+PNcepkN2HGGu0236jrp1Q+kcLSTMxPbmsmvbXgoOWvTL9XeLS1cPHUkU32Yu5vIpu8SJj2udy44ss0ARxCD4B77D1z/gkwFxfzx5lLvzrQ8bfMQp07VNBUCLOFRw7Ny5c8GCBevWrXN1GuCI8b6GI3XmyyFhvSvuuXyrqkhRvigrVzB+zgSl4W/IijLdj+vJqkq5d39h0uOe+bXe1Sb5+QzU8Tura89bpD6PTSPLy5jTOUpIB+tDdlY70pCI8TnR0ofnfLQYcu+x/Ag0eOSkz24Xxfv7ib5GS6++7bBoBl7NodczRVGDBw92dRTgoAm+Pgihf/QciBgWIYTqr2iRJHWjCH2z6vvyykCKSjS2o+sFTaNuXtevX01WVUqDhgiPTWuf1QZCiEDoxeBAjND/FZdhhhWfmIt1Ov7AHsre5TkN/WwWrBgPaSsTazoFVXQt8cyJfL3v8n4P04mzlNihUG0Ar+NQwTF69OgTJ064OgpwUFeOjWbZ7VbFTJCIZoiwcBQRJcUNr3v2j3Lv/t/zhgoVPyXVwbuRDX05T/f9WsIiiuMmW8ZO8sZFU53oMT/fPjy3vbom1yKpfkbh8SSEkH7bRrLKg4Z/Z5lFhFBcu59j9DcY84cOhEhiH5o8Xmuedib3nzeLS+S2PCUlaJMcKjjefffdPXv2fPbZZy0dSQ9cZIKvQcQ4zRgojn+MeXE5vexlS/xY1c9PmDL9076DaIyfP7xPt/Fboq5lK721PcyZHN3WHxFWxakzPO3CgSYIhF4JMWGEVhSXIoSUTl0so8Yhwcxv2UC4eGIbx9nWbIuFHqN3MHnnyeuFHw8ecVpWEULbysr/eat46KX8M6JF62gAtIBDfTi6du368ccfz5w58z/+4z86depE3z3PzMmTJ12TDdzXRKHmM4R2dYoZ0ad/w+2HauvOY2Kang8LMlFXrxjWrBQnJ8jR3bTKqSWM2YxDXEYa1umExDlKRJTWgTzFFD+f3jy3rarm5WBLL56TBg8lS0uYMzncrp/EhCTNW4AwQidEMZJlImAlahtFYQ8fvOLj99eQSNRgSGC1oj5fdPNw187e0mQHM3QBh1o4fvjhh1mzZmGMdTqdLMvi3VwdETSmqqMP7fdTrLtCI/HdHw9flVchhJ7pEGKevcgSP5awiLrN3/N7dxCyp3x5dRNV5ffu4DLSVKO/ed5TUG00RCD055AgFaH3S37tdGwZP1kJi2Au5rJZGdpmQwjliZYKWYmFDhx3sDnZZGX5ngGxlnsmIMgTLdekdvbSBt7MoRaO//7v/46Kitq5c2efPm1/TkbPx+ZkcyW3xljEnyjmvGiJv7O90Crvqantw3OPGHQIISluuNKxM79zC3PqBHW9UJw6QwkObeKwbUaDyTZChCfmY/hedY+pvr49eW5rdc0rlqBuHIspWkicbVj3BXf4gBocom2TWJbZNgMHDIhFCCFCFLmjRzDD1naOQeVVje/GSIDL3MB7ONTCUVBQsHTpUqg2PIJg5jIPY5Yd16kjQmhvTV39PV+VVSgYP9dgsi8lLMK86DnrgEFkaYlu3VfssXTU1mdpI+pqdd99TRdcVjpHC3OfgmrDLpJAfw4OUjBeUVJm24INPubHkzBJ8js2k6UlGmY7ZhYRQkOhxyhCCCH22BEkmK1xwwca7UzwZaSpGM7OdDwAeCaHCo6OHTtKkuTqKMAR/KEDSDBLw0aNMwVRBLGv5tduoSLG31VW+VNUotGv4f6YZcUJU8VpMxHLcIf26zeuJ2pdsoSSJyDLSg3rV1G3b8p9B5hnzIVxg01I8PPtxXNbqmouW359aasRUeKEqYTFotv6A2HR7FLpMbPZQJI94XMUIaKmmjmRhX18pcFDHzHo7h3o/laHYFbrPjcAOM6hguOFF15Yu3ZtbW17H/KgOar4FnP2pOofaB00JIiiBun4n83CbcmKEPqhoqpcVp4M9NeRdt6ArN171S16TunYmbqab1izkr6c5/bsLkfeKNJ/t5qoqpTihguTEtrtZBsOIgn0J1OggvF7Jb9NHyf3HWB9KJYsL+O3b0JatNWXyMo1yRqr19HwOYoQf+gAIcuWEaMxwyCEPooMey00uIeO96OpWIP+647hcwJgXnPgTRzqwxEVFRUaGtqvX7+lS5fGxMQ0GqUyfTpMr+sWGHP7dyNVtYybhCkKITTe1yfbLOwur1gYYvqqvJJqcm1Y7Gc0z1rInsji0lJ0WzbIffqL46fgtjIQgL54gd+5mVBVcfxj1oEwSZ1Dpht9/6+kbFNV9UvBgV3vtCiIj04gS0vogitsRpo04lE3RzpmFhAMiEUIIUSV3KZzz6hBJmufAbYtHEG8GBz4WuconU5XVVVl9ZhhzAA4yKGCIzEx0fbDf/7nf957Lyze5h507hmq6Be5aw+5S1fblgm+hndul3xfXEYgdF60TPXziWp6bViCkB6OUzp14bZvps+d1t+8IU5NVELD3JHeldgTWdyBPZimhemz5JjuWsfxGhRBvBQc9Puimx+Uln8U0eHOVso8Lcmw7kvu6BEcHGrt0dudkWwzcECPUYQQl7oPYWwZPQHB/O6grXCo4Pjxxx9dnQM0jbBK/KH9iKIso8fVbzwvWkgC7amo2FNegQg00LG1vBVTiLDwd2xaCpuTrV+/yhI3QhoWr/nsC610Z7INpNMLM+Yo4ZFaB/IyM4y+/1dStrGy+uXgoC7sneYunV5MnK1bv4pL3qYGmRRTiNvyZAsiRRAPt/tV6emCy9TVfCWqsxzdVessADiNQwVHUlKSq3OAprGZR4iaGmnoCDUgyLblSJ3590U3f72bQAihd4tLx/ka+vDN95TENG0ZO0mJ7sYnb+Uy0uiiX4THpmFfv2Yf6FkUhU/exuSeUY3+wsz59c8McBxFEC+ZAv9w/dZ7xaUfRv7W1qUEh1oem85v28hv+s686Fmsc8eYERHjM6LYi2N9qfb9nV5V2dQURBCWR8drHQUAZ2rfL2wvQVZWMMczscFHGjK8fuMHJY2XihUx/nfpPevH3p/cJaZu8RI5uhv1S4Fh9WdM7lnnxHULQpL0W75ncs8oHcLNC56BaqPVkvz9Yjh2Y1VNoymkrN17SbHDyOoqnbs6kJ4wC5KKYQkV5twpqrRY7tWvDVzuBKAhKDi8AHdgD6Eo4ujxDcd5FtgbqHy1hdMOYr1BmDFHnDAVKQq/Y7Nu10+E1QvGPxO1NbrvVlMFV5QuMcLsRVhv0DqRF6MI4kVToBXj9+7MyVHPEj9Gju5GXSvgUve5IYltyq92PscoIctcxiFEUZYRo7XOAoCTQcHh6airV+grF5XwSLlX34bbQ2g7l8PsbmwGQVgHDDIvfEYJDqXPndav+pS6XtjqtG5AlhYb1q+iim9b+w00z5iLWZiw4UEl+ft1ZpkfKqt/aTTwgSDEqTNUUzD78zHmTI6rY2SbRdTue4yy2ZlEdZX0cJxqvO+IMwC8FBQcnk1R+P27EUHcu676QnsjYBe2dly+agoRFj4jxQ0na6r133/NpqdpMg1Ds6hfruq/W0PUVFuGjRInJUAHfqegCeKl4CArxu8XN27kwBwnTJuFOZ7bl0zeuO66DBihbEEIY+goto0M1W4FQjAz2RmY1zW8eApAmwHv1x6N/fkYWV5m7T9I6RDe6K65/n7Pm36bxZwliNdDTeN8W39xAVO0JX6sOWke1hu4jDTDd2vIyopWH80VmIu5uo3fEpIkTpgiDR+ldZw2Zaa/XyeW+f7eRg6E1MAg8fEnCKzqf9pA1Lhqmlrbmm1x7bt5g0tPIywW6ZGRuN2P0wFtEhQcnosw17FHj2BeZxlpf/6lv3cIOdqty6oeXdf26Hq0W5c/BTuh46TSOaZu8RK5aw/yRpF+7ef0udMPfkynYH8+xm/biEhSSJxj7T9I6zhtDUMQLwYHWTH+8J7OyAghuUuMNCyeqKvVb99IKIorAmQLIkKoPfcYJSvKmNMnVKO/9aFYrbMA4BJQcHguPi2FsIjS8FFNDEqM4djFHULmhZic2BCNdXohcbYweRpSVd2un/htGwlRcNbBWxUIc2kp3IE9WG8Q5j4JMxO4yGx/v44M821FVaFVvvdey9CR1h69yeuF/N4drjj7r4vEtuMeo1zafqQoUvxYDLPygzYKCg4PRd26QZ87rZqCpQEPaxJA7jvAvOhZJTSMyTtvWPsFVXRNkxiEovA7NrNZGap/oHneYhgo6DoMQfwpONCK8Uf3DFdBCCGCsExOUEwh9NlTTE62089+rM6sJ8neDswi0yaRN4roy3lKh3A3T+0KgDtBweGRMOZSkhHG4ugJGi5CpgaazPOftgwbRVRX6Tes4w7tR65pTr8fQhR0P6xjLpxTwyPMC55W/QPdefZ2aG6AMYqh11dWFdobX40ZVnxiLtbp+AN7qF+uOvG8pbJSIFkH6/l2u2Ybb5vIfNRYb53zFwAHtHwUpWfgeZ7nXd76SpKkr2/jJaHd4UQ2unkd9emv7z+w2X0JgnBtzklTUY+eaNP37LF0tugXlDQPBZlacRiSJBFCLchZWYG+/xqVFKNefcmkeT7uWmeOoijUopwaseX08fFx7mH/EhXxQv61z6tr3+/S0c7dvr5o9iK09gv9to1o6Z9QQPMlIMMwtpxNLLp0oLwCITQywF/Dp922JqVer9dgcaizp9D1QtSzj75P/2b3rc+peuQ4MgCa4K0FhyRJiou/bXMcp6qqILi7+wIhSdy+XYimLaPGYQfOzrIsxti1OTtEEL9bRu/eTp8/gz99zzpmkvJQi1dkZRiGIAgHc5Ilt9kN64iaavnhOOu4yUiWkWynY4Er0DRNUZT7/+4tRVEUSZKiKDr3A3K2r+H/sczq4tI/BgWE2V0LMCyCHjORSUlW16+WFj3b7ILDJEnacjbxAXm4ogohNIhhNHzadTodRVEWi8XVbyyRq4F7AAAgAElEQVSNKQqfkkyQpGXko6oDvz7P87acsotfERzXTi9vAdfx1oJDVVVXv95s3HOWhri0FKKu1jJslNXg4+CnLMbY5Tkp2jolke4Uzacks7u3yfmXhAlTUEuW2LB9LjqSk7pWwG79gZAky7BR0vBRSFXdOSmI4zm1VZ/TuQUHgdAfggKW3yx+79btd8JC7e4jPxSLbt9izuRQ2zaKCUlNXwWw1RmyLDdRcBytrSMRGsixGj7ttqdRURQ3Z2BPZBHlZdYBD0v+gY683rXKCcCDgz4cnoUsK2VzsrGf0TpkmNZZ7JD7DjA/tVSJiKIv5vqs/owuuOL0U9DnTus3fUvIsvDYdJhsQxMLAvyjWGZtedVNe8NVbCzjJythEczFXDYr4wFPJ2J8WhR78Zxf+1uzjZAkNvMwZlgL/KuDdqDdvcI9HHdwD1IUy+jxzbZUa0U1+pvnPGkZNooQzLpN33L7dzuxJyn78zFd8lZMUsKMOXLvfs46LGgRliR+HxRgwfjj+68FiClaSJyNfX25wwfo/EsPcrqTgthu12xjjx4hzHXWIY9gg5P74gDggaDg8CD05Ty64IoSEWXt3kvrLE0iSWn4qLq5i1WjP3siy7DuC6q0+EGPqap8SvKvk23MWyx3jnFGUNBKiwL9wxh6TXnlrfs3cmCDj/nxJEyS/I7NZGlJq891rM6M2uWabURNDfPzMaw3SIMf0ToLAO4ABYfHUBQudR8iScv4KV4xNE4NjzQvek7u058sKdat+5L9+RhqbWcCQpH57ZuYnGzVFFy34BklpINzo4KWYgniBVOQBeNPyu7byIEQUiOixAlTCYtFt/UHwiK27lxZ5nY6xyh/+AAhWy0jH4UFCEE7AQWHp+CyM8mKcuvAwUpwiNZZHIU5TnhsupiQhGiGO7BHt/Fboq62pQchBEG3YR1zMVeNiKqb8yT2a+X6c8C5FgUawxh6VVnl7SY7J8p9B1gfiiXLy/htm1rRtxcjdNwsdGDoKLsjYtouqqSYPn9aDTRZ+zY/9B2AtgEKDo9A1NQwR49gnU4cFq91lhaz9uhdt+hZJbIjffWKYc3KFl3RJ6sq9d+uoq4Xyt17mWctbNGwF+BSHEEsMwVaMP60tJk1/MRHJygdO9NXr7AZaS09yyWLVK4oQ9rfWmVc2j6EsWX0OFjxGLQf8L/uEbi0fYRVsox41Es/cbHR3zx7kSV+LGERdZu/5/fuIGQ7U1U2Qt26of/mK7K8TBo0REhIwnT7+o7r+RYH+ndg6NXllaVyk/2CKcqckKQa/bmjR5i88y06hW0JlThD+yo46KtXqIIrSlQnOaa71lkAcB8oOLRH3ShiLpxTQkK9exFUkpTihpvnPaX6BzCnTujXfUmV3G5id+rqFd2GtYRgtsSPtYyd5BXdVtobjiB+HxRoVtWme3IghJBOLybOxjTNJW+jSlrQg9jWY3RIu1qVHmM2bT8iCMvo8VpHAcCtoODQGsbc/t0IY8uYSW2gcVXpEG5e9Jx1wCCytES37iv2WLrdnqTM2VP6zd8jRRGnzpDihrs/J3DQ00H+oTT9VVlzjRwIKcGhlsemE7JVt+V7QjA7ePwss6gnyT7tac025txpqviWtWdfpUO41lkAcCtoxNYYc+pn6tYNa6++SlQnrbM4B2ZZccJUpXMMt3cHd2g//UuBOHIMdyJLuVGEMObDIrCvH5uVgXmdMH1Wm/mt2yqOIJ43Bbxxq+SzsorXQ5tZQ8favRcZO4zNStdt32ROmt9sAV2uKAWSNNygZ9pN+xYhy1x6KqIoacRorbMA4G5QcGiJEAXuyEFMM5aRY7TO4mTW7r3kiCg+eStdcMVwrQDdmX+bqaxACGGDjzBzvhJsf+Zs4FGeCvT/d2n5F2UVz5sCgppbu9gSP4YsLabzL3Gp+yxjJja989E6M25n11PY40eJ6iop9hHVP0DrLAC4m9e34Xs1Nj2VEATr0BHY6K91FufDBh/hiXlKROS9V1WUyE5QbXgLPUnaenJ8dv+JR39DEOKURDUgiP35GH32VNP7Zv86A0d7KTgIwcxkZWBeJ8WN0DoLABqAgkMzZEkxe/Jn1egvDWm78wwSBGGvDwdZ9sAzkwI3eibI30RTX5RVljswjT3meSFxNuY4fu9O+pcCdDlPPfmz3alIj5nNJEKD2s0co2xGGmERpaEjcPsbBgwAgksqGuIP7EGqanl0Aqba8l8Bk3Ya4dv2r9z26ElyaVDAW7dLV5ZWvNpcTw6EkBpkEh+brvtpg+6H9QirVoR0CFl79xMnJaA7F2UkjE8Llh4c69/cZZq2gawsZ0+dUP2M1kGxWmcBQBvQwqENJu889UuB0qmL3K2n1llcS47p5uBG4MmeDQoIoqgvyisqmhuuYqMGh2KSQvi3uUeZ82e49N9mBjspiBbcjtZsY9P2I0WR4sdAtQ3aLSg4NEDIMpeWgihKHDNJ6ywuJw1+pNFQFDU8wjp0pFZ5QOvoSXKpKbBGUT8vb2biURvm3GninusvzMns+g49x+oE1G56jJLXC5lLF5TQMGvPvlpnAUAzUGtrgDmWTlRVSoOHqqZgrbO4HkmaZy1kzp/W37qBVSyEhln7DWwDM460Q88G+X9aWv55WcWSoIBmr4PYXVWHsFgIq9W2VlmW0I7mGOXTUhDGltHjYYI70J7B+767EVWVbFY61hskL1w2pZVI0tp3IDlzPj1noXXAIKg2vJSBJJeYAqoV9YuyymZ3Vu0twod1eswwCCGMUHadEELTHRnG+UE9DH0xl7peKEd3Uzp21joLAFqCt35341L3EbJsiR+LufbSOR+0Gc8GBgTQ1Gdl5VXNDVex9huI9YZGG1VTsO0r/hWLVKYocfp28BJQVe7IQUSSlvixWkcBQGNQcLgV9UsBczFXCQ2z9h2gdRYAWsyXIp8LDKhW1C/Lm2nkwHqDOXG2GnRnSAtJYp6nCq9xqfvQb2u2tf0eo8zJ42RZqbXvQDU4ROssAGgM+nC4kapyB/YggoC1yoD3ei4oYGVZxaelFb8L9Dc22ZNDDY+sW7zU1yIwilLFcqoo6r//ms3ORARxrGtfhFBsW5+OgpAkLvMwpmlL+7l+CsD9QQuH+7Anj1MlxXKf/kpElNZZAGglP4p8Nsi/SlFWlVc1vzdJopAOZOdozOuwn9E850nV6M9mZWSXlvEE0betr9nGHDtCmOukIcOwr5/WWQDQHhQcbkIIApdxCLOs2OaWTQHtzdKgQCNFfVpWXquqze/dgK3mKDEFXyapwZKFJdtyOx9RU8MeP4b1BuvgtjuVMAAtAQWHm3CH9yPBLD0Sj318tc4CwAPxo8jfBfpXyMqXZQ7NydEQ9jOmTX4CE8TwXy6zDeYBa3u4IwcJ2WoZPgpzbbwhBwAHQcHhDlTxLebMSdU/0PpwnNZZAHCC500BRor6tKyipY0cCKFjBIEQGmoRuYw0LqNt1hxUaTFz/rQaGGTt95DWWQDwFFBwuB7G3P7dSFUtYyfh9rFsBGjzjBT1dKCxXFZWtbyR41idQCDUd9wk1c/IpqdxGYdckVBb7MF9SFUto8YheMkDcAcUHC5H556lin6RY7rL0V21zgKA0zwfFOhDkh+3sJFDwviUIPbgOf/AAPOcJ1U/I5ueymW2qZqD+uUqffWKEtlR7tpD6ywAeBAoOFyLkK384QOIoiyPjtc6CwDOFEBTvwsKKJeVNc3NydHQKcFiwdi2hAo2+v9acxxpQzUHxlzqPkQQllHjtI4CgGeBgsO12MzDRHWVNPgRNSBI6ywAONnzQQE+JPlRSXmdw40cx8xmhNAQ3a9zjGKjvzB7Ifb1ZY+kcpmHXRXUjehzp6nbN609eivhkVpnAcCzQMHhQmRVJZN9FBt8pLjhWmcBwPkCaerpoIByRfna4UaObLOI7p5jVPUPNM9+Evv6skcOckePuCSouxCKzKenIoqSRj6qdRYAPA4UHC7EHdhDKLI4ehyMiwNt1bKgAB+S/HdpuaDiZnfGCGWZzcE01Zm9a802NeBOzXH4gFfXHMzxY0R1lfRQrOofqHUWADwOFByuQl3Npy/nqRFRcq9+WmcBwFUCaWpxoH+JrHxd0XwjR4EklcqK3SVUfq05fLy45iBEgc3OwDxvGTpC6ywAeCIoOFxDUfj9uxFBiI9OgGVTQNv2QnCggSQ/Kmm+kSPLLCKEhtxnCRU1INA8Z5Gt5mC9sOZgMw4RgiDFjUC6tr8oHQCt4PLF244fP75u3bqioiKj0Thu3Li5c+cS9j6AL168uGnTpitXrhQXF48fP/6FF15wdTCXYk9kkeWl1v6DlLAIrbMA4FqBFPVkoP8npeXrKiqfCwpoYs9jdWaE0JD7r0qvBgSZ5yzSf7+WO3wAISR5T1MBWVXJnDyOff2sg4ZonQUAD+XaFo68vLy33nqrd+/eK1asWLBgwebNm9evX293T1EUw8LCFi5cGBYW5tJIbkCY69jMw5jjLCNGa50FAHf4gylQRxIflJSLuKlGjiyzwBNEP/6+BQey1RyzF2EfX+7wAfaY17RzsGkphKKI8WMwDUtwA2CfawuOzZs3R0RELFmypFOnTmPGjElMTNy2bZvFYrl3z/79+y9evHjUqFF8k29GXoFL209YRGn4aGzw0ToLAO4QTFNPBvgXy/I3FfddQrZSUS5bpIf0fLNrtqmBQebZi7DBhzt0gD2W7uywzkfdvM5czFWCQ6HDFgBNcG3BkZubO2jQoPqbgwYNEkUxPz/fpSfVFnX7JnPulBpkkgYO1joLAO7zQnCgjiTeLy67XyNHlllQEYrTO9S/QQ0MMs95Eht8uEP7Pb/m4A7uRRhbRo+HDlsANMGFrX8Y48rKyoCA367p2n4uLy9vxdHy8/N37txZf/Oxxx4LDw9/8JBNI0nSYDC04AEYkwd2I4yJx6YZ/PxclquxFufUAkmSCCHI6SwURSGE9I59frtBF4SeDgn++FbxxjphSWhw/XaaphFCer0+p7QCIRQfGODoc2sw4KeWEGs+5w4fYA0G7OLJbGw5dTqd2sLl6IgL54jrhbhrD76PO5o3GIZBCPE8z7KsG04HgBN5zeXGa9euff311/U34+LiYmJiXH1SkiR19+lRb5dy/KhcVEj2HcD0HeC6VHa1KKeGIKdzeVTO16M7rSou/X83bi/tGMmTd7We8jx/rM5MIDTKFKRjHH7bieqEl/7RuvIjtHs7w7DU8Hjnh74b19Ipc1RVOrgXkyT3eCLhxr9Fi3MC4AFcWHAQBOHv719R8dtikrafAwNbMyXOgAEDPvnkk/qbUVFRVVX3vVrsFEajUVGU2tpaB/cnJIlL3o5oRogfa3Zxtob8/PwwxjU1NW47Y+v4+voSBFFdXa11kGb4+PhQFOXq/64HZzAYaJqurq7GTfbTdCcdQouCAlaWlH1ScO0Z068vc71ezzBMeXX18Zqa7jxHmeta9syyPDFvMbd+tbx9kyAKyuChLgiOEEI6nY5l2draWkVRHH8U9fMx5vYtZcDD1ToDcsv/DM/zHMfV1dXJsuzSExmNRpceH7RDrm3h6NWr14kTJ5555hnbzRMnTvA8Hx0d3YpDBQYGDhny23izqqoqq9XqnJT3hzF2/CxcWgqqrbEMi5cMPsj12RpqUU6t2D4XIaez1Of0nIIDIfRCkP/XpeUrbpXM8fNhCQIhZLtCcby6RlBxrI5vzRPr5y/PWqjfsJbZt0tRFBeNO7W1Gciy7PgHOWGVuCOpmKaFoSOwu/5hbFdSZFn2/H9RABpxbafRGTNmXL9+feXKldeuXTt48OCWLVsSEhJsL+z09PTly5ebzWbbnpIk5efn5+fnS5JUW1ubn59fUFDg0mzORVaWMyeysK+fdQgsmwLar1Canh/of91q/e7u4Sq2GTjszjHqCDXIJMxcgHmeP7CHycl2QlBnYI6lE3W1Uuwj2A8aAwBonmtbOHr06PHaa6998803e/bsMRqNiYmJ8+bNs91VVlaWm5tb/2WiqKjoxRdftP18/fr1zMxMkiR/+uknl8ZzIi5lN6Eo4ujxmGGa3xuAtuuPpsB15ZXvl5bP9TfWj4A9ViegJqf8apYSHCLMXqTbsJbfvxshZH0o1ilpW42orWGPH8U6vTV2mLZJAPAWLu80GhsbGxtr560hISEhISGh/mZ0dPS2bdtcHcZF6Mt5dMFlJSLK2qO31lkA0Fg4Q88PMK4ur9xQVb0w4Nev/tlmIZCiujzYwAolONRzag7uSCphtYqjYGlGABwFa6k8MEXhUlMQSVrGPwaj8AFACP0pOJAliBUlZZKKEUKXBbFYloca9A/+8rDVHIjX8ft3a3hthSwrZc6dUgOCrP0HNb83AAAhBAXHg2OzM8mKMuuAh5XgUK2zAOARIhhmboCxSLL+WFWNEEqvqkYPdj2lISU41Dxr4a81x8njTjlmS3Gpe5GqWuLHIorSJAAA3ggKjgdC1NWyWemY11mGj9Y6CwAe5EVTIEsS/1dSJqn4TsHhtGkqlJA7NUdKsvtrDqrwKp1/WQ2PlLv1cPOpAfBqUHA8EC51H2GxWEaMxp40/xIAmotkmdlGv0LJOufi5U2lZTRB9OKd2dfBVnNgnnd3zYExd3AfQkiEicwBaCEoOFqPulHE5J5VTSHWAQ9rnQUAj+NDUQihXRVV5VZZxnj8lWtlLZlTq1lKSKhQX3Oc+tmJR24CnXuGun3T2qO3EhHlnjMC0GZAwdFaGHP7dyOMxXGTEAlPIwB3yTYLn5betWrSZYv0l+u3nXsWJaTDrzXHvl302VPOPbi98ynckVREUdLIMS4/FwBtDnxSthJzOoe6dcPas48S1VnrLAB4nOQaO2sCJNfUys6eF1UJ6WCbE0y3e5uraw7252NkVaU04GE1oDXrMwDQzkHB0RqEKHCHD2CatsSP1ToLAJ6oTrVTWFgxllwwEbsSGibMXIA5Xrdnu+tqDkIU2Kx0zLLSIyNddAoA2jYoOFqDTU8jBLN16Ehs9Nc6CwCeqA9nZ46vaJbVu+b6oxIaJsxagFlOt2c7fe60K07BZh4mBEEaOgLrDa44PgBtHhQcLUaWlrCnfsZ+RinWVQtXAuDt5gQY+90zLOXtsBDXnVEJDTPPmIMZRrd7G33eyTUHWVXJ5GRjX1/rw3HOPTIA7QcUHC3Gp+5FiiI+OhHTsGwKAPaxBLGhc9Rcf78AmqYJor+OX98pYpyva9sG1Igo8xPzMMPokp1cc7CH9hOKIo4YA696AFrN5WuptDHMxVyq4IrSqYvcvafWWQDwaME09WFkmK+vL8myNRUVtnXqXc1Wc+g3rtclbxMQknv3f/BjUrduMHnnleAQuY8TjgZAuwUtHC1AyDKbug+RpGXMRK2zAOA1GPdOkKVGRAkzF2Ca1iVvo8+fefADcmn7EcaWUTDTFwAPBAqOFmCz0smqSumhWMXkwkvRAIAHpIRH3qk5tjK5D1Rz0JfzqF8K5M4xSpcYZ8UDoH2CgsNRRHUVk5WB9QZp2CitswAAmqGERwpJ8zFN87seoOZQVe7wQUQQ0igYAA/Ag4KCw1Fc6j7CarWMHIN55yx6CQBwKSUiqkHNcbYVR2DO5JClxdY+/ZWQDk6PB0B7AwWHQ6jrhczFXCU0zNpvoNZZAACO+rXmoGh+10/MhXMteixhlbj0NEzTsBY0AE4BBYcDVJXbtwshZBkzEXqNAeBdlIgoYeZ8TNH8zi0tqjmYrEyirlYaPBT7GV0XD4D2AwqO5jEnj1Mlt+Xe/ZTIjlpnAQC0mBIRJSTNs7Vz0JcuOPIQwlzHHs/EOp11yDBXxwOgnYCCoxmEIPAZhzDDirBsCgBeS4nsKCTNwySl277JkZqDO3yQkCTLsFGYgz5bADgHFBzN4I4cRIJZemQk9vHVOgsAoPWUyI5C0lxMkrrtm+jLeU3sSZaXMmdPqkZ/64CH3RYPgDYPZhq1gyq+zR7ab7l9g6BoprZG9TNaB8OyKQB4PSWyk5A0T7fxW922jUJCkty1h93duNQUpKrS6PGIotycEIA2DFo4GiNLi3XrV9EFl5HZjGuqEcaEqiJV0ToXAMAJlMhOwvRZmCB02zbabeegCq/RVy6q4RHWbrB8AQDOBAVHY1zqPkK2NtxC1Naw2Ue1ygMAcC6lc4yQONt+zYExl7oPISSOngBD0gBwLig4GqNu3bh3I2lvIwDASymdY8TE2RgR/LaNdP7l+u30hXPUrRty915KRJSG8QBok6APxz1oBiGh0TZMwxMFQJsid44RE2fzWzbwWzYoMd2Im9ctgplGCJGkJX6M1ukAaIOghaMxu/3I5K7d3Z8EAOBScpcYMXEWgVX60gVUW4MUhVAUpKrU7VtaRwOgDYKCozHLyDHq3YvBWnv1lXv31yoPAMB1CKsVYdxoI3dgz70bAQAPCK4UNIY5zrzoWfpMjqGsVKUoc3iU3B06qwPQNhG3b9rZWFdL1NZgXz/35wGgDYOCww5MUdaBg2mTSZZlubJS6zgAAJdhWDsbCQIxjNujANDGwSUVAED7pXTtjqm7vndhhJSozpjXaRUJgLYKCg4AQPulmEKkRmNSfH3FyQkaxQGgLYNLKgCAdk0aPFTp2FmXf4m2iKIxwNKnP7Z7nQUA8GCg4AAAtHdKSAcU3ZXmebmyEsuy1nEAaJu8teBgGIZy/bpKBEHwvBcsTu0VOQmC8IqcJEkihLwoJ/bsAZy21ynHcV6Rk2VZ2rNn+avP6YY3QACcy6NfWk0jXL/Sge0z0tVncQrI6VyQ07k8P6ctoee/5L0lJwD38taCw2q1Wq3W5vd7AAaDQVVVQWg8zbmn0ev1GGPPz2lrM/D8nBzHkSTp+Tlt33EFQfDwlgOapmmaFkVRVVWtszSFoiiapi0Wi+zZl1RIkmQYxmKxuOEN0KXHB+0QjFIBAAAAgMtBwQEAAAAAl4OCAwAAAAAuBwUHAAAAAFwOCg4AAAAAuBzh4V3cNfTOO++Eh4cvXrxY6yDNWLFihV6vX7p0qdZBmvHJJ59IkvTiiy9qHaQZq1atun379quvvqp1kGZ8++23V69efeWVV1jWo6fF3LJlS25u7rJly4xGo9ZZmpKcnJyTk/PMM8+EhoZqnaUpBw8ezMzMnD9/fqdOnbTOAkDLQAvHfW3evDktLU3rFM3bsWPH3r17tU7RvL179+7cuVPrFM1LTU396aeftE7RvPT09M2bN3v4GE6EUHZ29ubNm81ms9ZBmnHy5MnNmzdXVVVpHaQZ58+f37x5c0lJidZBAGgxKDgAAAAA4HJQcAAAAADA5aDgAAAAAIDLQadRAAAAALgctHAAAAAAwOWg4AAAAACAy0HBAQAAAACX89bl6V3HYrFs3749PT39+vXrCKGQkJDevXtPmTLF06bZ+eKLL7Zv3277meO40NDQyZMnT5kyRdtUdjWMajNp0qTf//73WuW5n3tzIoRWrFjRtWtXTfLYVR+SIAidThcWFta/f/8pU6aEhIRoHc0OQRAWL16sKMrq1at9fX21jmPHF198kZGRsXr16oYbn3vuuYEDB3rav6jFYtm6dWt6evqNGzdomg4LC4uNjU1ISIB15IG3gILjLrW1tX/961+Li4ufeOKJbt26+fr6FhcXX7hw4b333nv//fe1TteYv7//O++8gxASRTEjI2PlypX+/v7Dhw/XOpcdRqPxzTffrL/p5+enYZgmNMqJEIqMjNQqzP3Y/u4YY0EQCgoKdu3alZyc/Prrr/fv31/raI2lpqZ26dKFYZiUlJTExESt43ixurq6V199taSkZObMmd26dTMYDIWFhXv37uV5Hp5Y4C2g4LjL6tWrb9y48dFHH4WFhdm2xMTEPPLII6qqahvMLoqi6j8Ou3btumfPnsuXL3tmwUHTdHR0tNYpmucVORv+3bt37z527Ni//vWv77333ueff84wjLbZGklOTk5ISKBp+rvvvps+fTpBEFon8lb3vjVFR0ePGjXK86dGBaAe9OH4jSzLhw8fHjt2bP1Luh5JevQTpSjKkSNHamtre/XqpXUW4G40TSclJZWVlZ0/f17rLHfJzc29devWiBEjhg0bVlVVderUKa0TeStFUQ4dOmT3rcnDV6gBoCFo4fhNaWmpKIoN+2p8+OGHBw4cqP+5Y8eOGkWzr6ys7IknnkAIybJMkuTChQuHDBmidSj7ysrKEhIS6m9++eWXntnnoFFOkiS9Yl2Vzp07I4Ru3bo1YMAArbP8Jjk5efjw4TzPI4Ti4+OTk5MHDhyodSg7Gv3RPVBJSUmjtyYAvBEUHE2ZP3/+tGnT8vLy/v3vf3vgDGn1fTgkScrLy1u1apXBYJg8ebLWuexo1DciMDBQwzBNaJTTWy4BeOA/Z3V1dXp6+t///nfbzXHjxi1fvry8vNwD//T3dtx56623tAoDQBsGBcdvTCYTx3HXrl2r3xIUFBQUFFRZWalhqiY0vJYfHR1dUFCwfv16zyw4vKJvBPKenI1cvXoVIXRve7uGUlJSrFbra6+9Vr9FVdW9e/fOmTNHw1R23ftHp2nPemM0mUw8zzd8awLAG3nW60pbNE2PHDly//7906dP96j3bsdJkoQx9pbv5cApZFnetGmTyWTq3bu31ll+hTHevXv3tGnTxo0bV7/x0KFDe/funTVrlof3iPJATbw1VVVVQTcO4C2g4LjLU089dfHixZdffrl+WGxpaenevXsJgvC0Lz0IIUVRioqK0J1LKgcOHBgxYgRUGw9CluX8/PyGWzp06KDX67XKY1f93902LHbnzp03b958/fXXPedfNCcn59atW5MmTYqIiKjfOHny5I0bN2ZnZ8fFxWmYzUstXrw4Ly/v5ZdfnjlzZvfu3fV6vW1Y7ODBg2FYLPAWnvIO5SF8fX3ffffd7du3HzlyZMOGDYqiBAYG9u3b991332341ukhKisrbXMTMQxjMpkSEhJmz56tdSjvVlVV9eKLLzbcsnz5clqZBb0AAAgXSURBVE8baWz7u9sm/urQocOAAQNee+01j+qEm5ycHB0d3eglYzKZevbsmZycDAVHK9jemrZu3Zqamvrtt98yDBMeHh4fHz9x4kStowHgKFgtFgAAAAAuBxdTAQAAAOByUHAAAAAAwOWg4AAAAACAy0HBAQAAAACXg4IDAAAAAC4HBQcAAAAAXA4KDuCVNm7cSBCEViuraXv2+0lJSSEIYs2aNVoHAQAAO6DgAAAAAIDLwUyjALQRY8aMEQSBYRitgwAAgB3QwgGAxsxms1OOQ5Ikz/MURTnlaAAA4FxQcIA24ptvvuE4buLEiTU1NQihysrKP//5z126dOE4LjQ0dP78+ZcvX67fmaZp4h59+/a13VtVVfX666/HxcWZTCaO46Kjo1955ZXa2lrHz94EW/+PH3744c033+zWrRvLsn//+98RQrIsr1ixYuDAgTqdztfXd/To0Xv37m34wBs3bixYsCAgIMDHx2f06NGZmZlJSUk8z9fvcG8fjqafBFuSTZs2/e///m/37t05juvYsePbb78Nyx0AAFwBLqmAtuCdd9557bXXnn766ZUrV9I0XVdXFx8ff+bMmfnz5w8bNuzSpUuffvppcnJyZmZmjx49EEK7du1SVbX+4enp6W+99Vbnzp1tNwsLCz///POkpKS5c+eyLHvo0KEVK1ZkZWWlpaXZXYy30dkdCbx8+fKIiIh33nmnQ4cODMMoipKQkLBnz56ZM2f+7ne/E0Xxm2++mTRp0vr16+fOnYsQqq6ujo+PLygoWLJkyUMPPXT69OmJEydGRUU1cYpmnwSbv/zlL927d//www/9/f2//PLL119/PSgoaOnSpY78FgAA0AIYAC/0448/IoS2bNlitVqfffZZhNAbb7xRf++bb76JELJ9WbfZs2cPQmjixIn3Hury5csmkyk6OrqkpMS2RRRFSZIa7vP2228jhPbt2+fI2R1J3r17d6vVWr/x448/RgitWrWqfoskSYMGDQoNDbXt9re//Q0h9Pnnn9fvsHbtWoQQx3H1W/bt24cQWr16tYNPgi3J4MGDVVW1bVEUpVu3br169XLwdwEAAMdBwQG8ku3Dct26dZMnT2YYZs2aNQ3v7d+/v4+PjyAIDTc+8sgjJElWVVU13FheXt6jRw9/f//c3Fy7J5IkSRCE8+fPI4T+53/+x5GzO5L8H//4R8ONcXFxISEhwt3+9a9/IYSOHz+OMe7Xr5/JZJJluf4hqqpGRkY2UXA0+yTYknzwwQcNd7A16iiK4vhvBAAAjoBLKsCLLVu2rLq6+scff0xKSmq4PT8/PyYmpmH/BoRQv379MjMzr1692r9/f9sWq9U6Y8aM/Pz83bt39+zZs+HOa9as+fzzz0+dOtWwR2d5ebkjZ3dEly5dGt7Mzc2trq7W6XT37llcXGz7jfr169ewQyhBED169CgpKbnfKRx8Ehpdl/Hz85Mkqaamxmg0tvSXAgCAJkDBAbxYUlLS+vXr//nPfz766KNBQUH12zHGdjtbNPLcc8+lpqZ++eWXY8aMabh9xYoVf/7znx9//PEvv/wyPDyc47iysrKpU6c27PbRxNkdwXFcw5uqqnbr1s12laSR+krIkd+oIQefBLv7YOg3CgBwNig4gBd7/PHHZ82alZiYOHr06JSUlNDQUNv2mJiYy5cvi6LY8Pv92bNnSZKs7xn69ttvr1mzZvny5c8880yjw3711VddunTZunVr/Yfx4cOHHT97K3Tv3v3s2bN9+/b18fGxu0N0dPSlS5cURalv5MAYX7x4sYljOvIkAACA28CwWODdJk6cmJycfPXq1fj4+KKiItvGGTNm1NbWvv/++/W7paSkZGRkjBs3zs/PDyG0YcOGv/3tb0888cQ//vGPe49JkiTGWFEU201FUd555x3Hz94KixYtkiTplVdeadS0cOPGDdsP06ZNKy0tbTjk9dtvvy0sLGzimM0+CQAA4E7QwgG83qhRo/bt2zd58uT4+Pj9+/d36dLllVde2bhx46uvvnru3Ln6EaEBAQEffPABQqigoGDx4sWBgYHz58+3Ddyw8fX1HT58OEIoKSnpjTfemDx58qxZs2pqar7//vsmLjHce/ZW/ArLli1LSUlZuXJlTk7OtGnTgoODCwsLMzMzT506ZevD8corr6xfv37JkiU5OTkDBw48ffr0mjVrevfunZ+ff79jNv0kAACAu2nYYRWAVqsfmFq/5cSJEyaTKTIyMi8vD2NcUVHx0ksvderUiWGY4ODguXPnXrp0ybZnZmam3ddCnz59bDtYrda33norJiaGZdmoqKiXXnqpoKAAIfSnP/3JwbO3KLmNoiifffbZ0KFDfXx8eJ7v3Lnz9OnT161bV79DUVHR3LlzjUajXq8fMWLEkSNHJkyYEBAQUL9Do1EqTT8J90uyZMkShFBFRUXTvwUAALQUgaF3GADeKTo62mg05uTkaB0EAACaB304APAOoig2vLlhw4aCgoKJEydqlQcAAFoEWjgAcCaMscViud+9LMuSZCur/DFjxnTp0mXw4MEMw2RlZa1atapDhw45OTnBwcGtDQsAAO4DBQcAznT27Nl+/frd797vvvtuzpw5rTvyv/71r/Xr11+9erWuri40NHTixIlvvvlmZGRka5MCAIBbQcEBgDOJonj27Nn73RsTExMQEODOPAAA4CGg4AAAAACAy0GnUQAAAAC4HBQcAAAAAHA5KDgAAAAA4HJQcAAAAADA5aDgAAAAAIDLQcEBAAAAAJf7/5p/FW3rJWHYAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 180,
       "width": 360
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data %>% \n",
    "  mutate(mean_ratio = mean_yfp / mean_rfp) %>%\n",
    "  left_join(annotations, by = \"strain\") %>%\n",
    "  mutate(kozak_region = fct_reorder(kozak_region, strain_number)) %>%  # Reorder kozak_region based on strain_number\n",
    "  ggplot(aes(x = kozak_region, y = mean_ratio, \n",
    "             color = insert_sequence, group = insert_sequence)) +\n",
    "  geom_line() +\n",
    "  geom_point()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grouping, summarizing, and reshaping data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use `summarize` to calculate stats across rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m16\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m3\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \"\\t\"\n",
      "\u001b[31mchr\u001b[39m (1): strain\n",
      "\u001b[32mdbl\u001b[39m (2): mean_yfp, mean_rfp\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    }
   ],
   "source": [
    "data <- read_tsv(\"data/example_dataset_2.tsv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[90m# A tibble: 1 × 2\u001b[39m\n",
      "  max_yfp max_rfp\n",
      "    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
      "\u001b[90m1\u001b[39m    \u001b[4m9\u001b[24m365   \u001b[4m2\u001b[24m\u001b[4m3\u001b[24m866\n"
     ]
    }
   ],
   "source": [
    "data %>%\n",
    "  summarize(max_yfp = max(mean_yfp),\n",
    "            max_rfp = max(mean_rfp)) %>%\n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other examples of summary functions:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- min()\t\n",
    "- mean()\n",
    "- sd()\t\n",
    "- dplyr::first()\t\n",
    "- dplyr::n()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use `group_by` to group rows by one or more columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m74\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m4\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \"\\t\"\n",
      "\u001b[31mchr\u001b[39m (1): strain\n",
      "\u001b[32mdbl\u001b[39m (3): yfp, rfp, replicate\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[90m# A tibble: 74 × 4\u001b[39m\n",
      "   strain    yfp   rfp replicate\n",
      "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
      "\u001b[90m 1\u001b[39m schp677  \u001b[4m4\u001b[24m123 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m661         1\n",
      "\u001b[90m 2\u001b[39m schp678  \u001b[4m4\u001b[24m550 \u001b[4m2\u001b[24m\u001b[4m1\u001b[24m437         1\n",
      "\u001b[90m 3\u001b[39m schp675  \u001b[4m3\u001b[24m880 \u001b[4m2\u001b[24m\u001b[4m1\u001b[24m323         1\n",
      "\u001b[90m 4\u001b[39m schp676  \u001b[4m2\u001b[24m863 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m668         1\n",
      "\u001b[90m 5\u001b[39m schp687  \u001b[4m4\u001b[24m767 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m995         1\n",
      "\u001b[90m 6\u001b[39m schp688  \u001b[4m1\u001b[24m274 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m927         1\n",
      "\u001b[90m 7\u001b[39m schp679  \u001b[4m2\u001b[24m605 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m840         1\n",
      "\u001b[90m 8\u001b[39m schp680  \u001b[4m1\u001b[24m175 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m902         1\n",
      "\u001b[90m 9\u001b[39m schp681  \u001b[4m3\u001b[24m861 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m659         1\n",
      "\u001b[90m10\u001b[39m schp683  \u001b[4m9\u001b[24m949 \u001b[4m2\u001b[24m\u001b[4m5\u001b[24m406         1\n",
      "\u001b[90m# … with 64 more rows\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "data <- read_tsv(\"data/example_dataset_4.tsv\") %>% \n",
    "  print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[90m# A tibble: 74 × 4\u001b[39m\n",
      "\u001b[90m# Groups:   strain [16]\u001b[39m\n",
      "   strain    yfp   rfp replicate\n",
      "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
      "\u001b[90m 1\u001b[39m schp677  \u001b[4m4\u001b[24m123 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m661         1\n",
      "\u001b[90m 2\u001b[39m schp678  \u001b[4m4\u001b[24m550 \u001b[4m2\u001b[24m\u001b[4m1\u001b[24m437         1\n",
      "\u001b[90m 3\u001b[39m schp675  \u001b[4m3\u001b[24m880 \u001b[4m2\u001b[24m\u001b[4m1\u001b[24m323         1\n",
      "\u001b[90m 4\u001b[39m schp676  \u001b[4m2\u001b[24m863 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m668         1\n",
      "\u001b[90m 5\u001b[39m schp687  \u001b[4m4\u001b[24m767 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m995         1\n",
      "\u001b[90m 6\u001b[39m schp688  \u001b[4m1\u001b[24m274 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m927         1\n",
      "\u001b[90m 7\u001b[39m schp679  \u001b[4m2\u001b[24m605 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m840         1\n",
      "\u001b[90m 8\u001b[39m schp680  \u001b[4m1\u001b[24m175 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m902         1\n",
      "\u001b[90m 9\u001b[39m schp681  \u001b[4m3\u001b[24m861 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m659         1\n",
      "\u001b[90m10\u001b[39m schp683  \u001b[4m9\u001b[24m949 \u001b[4m2\u001b[24m\u001b[4m5\u001b[24m406         1\n",
      "\u001b[90m# … with 64 more rows\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "data %>% \n",
    "  group_by(strain) %>%\n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use `group_by` + `summarize` for statistics by group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[90m# A tibble: 16 × 3\u001b[39m\n",
      "   strain  mean_yfp mean_rfp\n",
      "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m      \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
      "\u001b[90m 1\u001b[39m schp674    \u001b[4m1\u001b[24m270    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m316 \n",
      "\u001b[90m 2\u001b[39m schp675    \u001b[4m3\u001b[24m687.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m438.\n",
      "\u001b[90m 3\u001b[39m schp676    \u001b[4m2\u001b[24m656.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m223.\n",
      "\u001b[90m 4\u001b[39m schp677    \u001b[4m3\u001b[24m967.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m604 \n",
      "\u001b[90m 5\u001b[39m schp678    \u001b[4m4\u001b[24m378.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m630.\n",
      "\u001b[90m 6\u001b[39m schp679    \u001b[4m2\u001b[24m528    \u001b[4m1\u001b[24m\u001b[4m9\u001b[24m906 \n",
      "\u001b[90m 7\u001b[39m schp680    \u001b[4m1\u001b[24m117.   \u001b[4m1\u001b[24m\u001b[4m9\u001b[24m377.\n",
      "\u001b[90m 8\u001b[39m schp681    \u001b[4m3\u001b[24m705    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m227 \n",
      "\u001b[90m 9\u001b[39m schp683    \u001b[4m9\u001b[24m364.   \u001b[4m2\u001b[24m\u001b[4m3\u001b[24m866.\n",
      "\u001b[90m10\u001b[39m schp684    \u001b[4m3\u001b[24m294.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m585.\n",
      "\u001b[90m11\u001b[39m schp685    \u001b[4m7\u001b[24m379    \u001b[4m2\u001b[24m\u001b[4m2\u001b[24m956 \n",
      "\u001b[90m12\u001b[39m schp686    \u001b[4m5\u001b[24m000.   \u001b[4m2\u001b[24m\u001b[4m1\u001b[24m171.\n",
      "\u001b[90m13\u001b[39m schp687    \u001b[4m4\u001b[24m658.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m860.\n",
      "\u001b[90m14\u001b[39m schp688    \u001b[4m1\u001b[24m748.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m755.\n",
      "\u001b[90m15\u001b[39m schp689    \u001b[4m8\u001b[24m693.   \u001b[4m2\u001b[24m\u001b[4m2\u001b[24m650.\n",
      "\u001b[90m16\u001b[39m schp690    \u001b[4m3\u001b[24m535.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m594.\n"
     ]
    }
   ],
   "source": [
    "data %>% \n",
    "  group_by(strain) %>%\n",
    "  summarize(mean_yfp = mean(yfp), mean_rfp = mean(rfp)) %>%\n",
    "  print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[90m# A tibble: 16 × 5\u001b[39m\n",
      "   strain  mean_yfp mean_rfp se_yfp se_rfp\n",
      "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m      \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m  \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m  \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
      "\u001b[90m 1\u001b[39m schp674    \u001b[4m1\u001b[24m270    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m316    54     717 \n",
      "\u001b[90m 2\u001b[39m schp675    \u001b[4m3\u001b[24m687.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m438.   84.6   483.\n",
      "\u001b[90m 3\u001b[39m schp676    \u001b[4m2\u001b[24m656.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m223.  137.    380.\n",
      "\u001b[90m 4\u001b[39m schp677    \u001b[4m3\u001b[24m967.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m604   107.    423.\n",
      "\u001b[90m 5\u001b[39m schp678    \u001b[4m4\u001b[24m378.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m630.  111.    575.\n",
      "\u001b[90m 6\u001b[39m schp679    \u001b[4m2\u001b[24m528    \u001b[4m1\u001b[24m\u001b[4m9\u001b[24m906    33.9  \u001b[4m1\u001b[24m034.\n",
      "\u001b[90m 7\u001b[39m schp680    \u001b[4m1\u001b[24m117.   \u001b[4m1\u001b[24m\u001b[4m9\u001b[24m377.   27.7   700.\n",
      "\u001b[90m 8\u001b[39m schp681    \u001b[4m3\u001b[24m705    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m227    90.8   469.\n",
      "\u001b[90m 9\u001b[39m schp683    \u001b[4m9\u001b[24m364.   \u001b[4m2\u001b[24m\u001b[4m3\u001b[24m866.  352.    515.\n",
      "\u001b[90m10\u001b[39m schp684    \u001b[4m3\u001b[24m294.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m585.   49.6   318.\n",
      "\u001b[90m11\u001b[39m schp685    \u001b[4m7\u001b[24m379    \u001b[4m2\u001b[24m\u001b[4m2\u001b[24m956   194.    973.\n",
      "\u001b[90m12\u001b[39m schp686    \u001b[4m5\u001b[24m000.   \u001b[4m2\u001b[24m\u001b[4m1\u001b[24m171.   81.5   307.\n",
      "\u001b[90m13\u001b[39m schp687    \u001b[4m4\u001b[24m658.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m860.   80.9   199.\n",
      "\u001b[90m14\u001b[39m schp688    \u001b[4m1\u001b[24m748.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m755.  160.    203.\n",
      "\u001b[90m15\u001b[39m schp689    \u001b[4m8\u001b[24m693.   \u001b[4m2\u001b[24m\u001b[4m2\u001b[24m650.  667.   \u001b[4m1\u001b[24m045.\n",
      "\u001b[90m16\u001b[39m schp690    \u001b[4m3\u001b[24m535.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m594.   31.0   173.\n"
     ]
    }
   ],
   "source": [
    "data %>% \n",
    "  group_by(strain) %>%\n",
    "  summarize(mean_yfp = mean(yfp), mean_rfp = mean(rfp),\n",
    "            se_yfp = sd(yfp) / sqrt(n()), \n",
    "            se_rfp = sd(rfp) / sqrt(n())) %>%\n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `%>%` enables complex yet easily understandable data analysis pipelines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m17\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m3\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \"\\t\"\n",
      "\u001b[31mchr\u001b[39m (3): strain, insert_sequence, kozak_region\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A spec_tbl_df: 17 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>strain</th><th scope=col>insert_sequence</th><th scope=col>kozak_region</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>schp674</td><td>10×AAG</td><td>G </td></tr>\n",
       "\t<tr><td>schp675</td><td>10×AAG</td><td>B </td></tr>\n",
       "\t<tr><td>schp676</td><td>10×AAG</td><td>F </td></tr>\n",
       "\t<tr><td>schp677</td><td>10×AAG</td><td>E </td></tr>\n",
       "\t<tr><td>schp678</td><td>10×AAG</td><td>D </td></tr>\n",
       "\t<tr><td>schp679</td><td>10×AAG</td><td>A </td></tr>\n",
       "\t<tr><td>schp680</td><td>10×AAG</td><td>H </td></tr>\n",
       "\t<tr><td>schp681</td><td>10×AAG</td><td>C </td></tr>\n",
       "\t<tr><td>schp683</td><td>10×AGA</td><td>G </td></tr>\n",
       "\t<tr><td>schp684</td><td>10×AGA</td><td>B </td></tr>\n",
       "\t<tr><td>schp685</td><td>10×AGA</td><td>F </td></tr>\n",
       "\t<tr><td>schp686</td><td>10×AGA</td><td>E </td></tr>\n",
       "\t<tr><td>schp687</td><td>10×AGA</td><td>D </td></tr>\n",
       "\t<tr><td>schp688</td><td>10×AGA</td><td>A </td></tr>\n",
       "\t<tr><td>schp689</td><td>10×AGA</td><td>H </td></tr>\n",
       "\t<tr><td>schp690</td><td>10×AGA</td><td>C </td></tr>\n",
       "\t<tr><td>control</td><td>NA    </td><td>NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A spec\\_tbl\\_df: 17 × 3\n",
       "\\begin{tabular}{lll}\n",
       " strain & insert\\_sequence & kozak\\_region\\\\\n",
       " <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t schp674 & 10×AAG & G \\\\\n",
       "\t schp675 & 10×AAG & B \\\\\n",
       "\t schp676 & 10×AAG & F \\\\\n",
       "\t schp677 & 10×AAG & E \\\\\n",
       "\t schp678 & 10×AAG & D \\\\\n",
       "\t schp679 & 10×AAG & A \\\\\n",
       "\t schp680 & 10×AAG & H \\\\\n",
       "\t schp681 & 10×AAG & C \\\\\n",
       "\t schp683 & 10×AGA & G \\\\\n",
       "\t schp684 & 10×AGA & B \\\\\n",
       "\t schp685 & 10×AGA & F \\\\\n",
       "\t schp686 & 10×AGA & E \\\\\n",
       "\t schp687 & 10×AGA & D \\\\\n",
       "\t schp688 & 10×AGA & A \\\\\n",
       "\t schp689 & 10×AGA & H \\\\\n",
       "\t schp690 & 10×AGA & C \\\\\n",
       "\t control & NA     & NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A spec_tbl_df: 17 × 3\n",
       "\n",
       "| strain &lt;chr&gt; | insert_sequence &lt;chr&gt; | kozak_region &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| schp674 | 10×AAG | G  |\n",
       "| schp675 | 10×AAG | B  |\n",
       "| schp676 | 10×AAG | F  |\n",
       "| schp677 | 10×AAG | E  |\n",
       "| schp678 | 10×AAG | D  |\n",
       "| schp679 | 10×AAG | A  |\n",
       "| schp680 | 10×AAG | H  |\n",
       "| schp681 | 10×AAG | C  |\n",
       "| schp683 | 10×AGA | G  |\n",
       "| schp684 | 10×AGA | B  |\n",
       "| schp685 | 10×AGA | F  |\n",
       "| schp686 | 10×AGA | E  |\n",
       "| schp687 | 10×AGA | D  |\n",
       "| schp688 | 10×AGA | A  |\n",
       "| schp689 | 10×AGA | H  |\n",
       "| schp690 | 10×AGA | C  |\n",
       "| control | NA     | NA |\n",
       "\n"
      ],
      "text/plain": [
       "   strain  insert_sequence kozak_region\n",
       "1  schp674 10×AAG          G           \n",
       "2  schp675 10×AAG          B           \n",
       "3  schp676 10×AAG          F           \n",
       "4  schp677 10×AAG          E           \n",
       "5  schp678 10×AAG          D           \n",
       "6  schp679 10×AAG          A           \n",
       "7  schp680 10×AAG          H           \n",
       "8  schp681 10×AAG          C           \n",
       "9  schp683 10×AGA          G           \n",
       "10 schp684 10×AGA          B           \n",
       "11 schp685 10×AGA          F           \n",
       "12 schp686 10×AGA          E           \n",
       "13 schp687 10×AGA          D           \n",
       "14 schp688 10×AGA          A           \n",
       "15 schp689 10×AGA          H           \n",
       "16 schp690 10×AGA          C           \n",
       "17 control NA              NA          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "annotations <- read_tsv(\"data/example_dataset_3.tsv\")\n",
    "\n",
    "annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[90m# A tibble: 16 × 6\u001b[39m\n",
      "   strain  mean_yfp mean_rfp mean_ratio insert_sequence kozak_region\n",
      "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m      \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m      \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m           \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m       \n",
      "\u001b[90m 1\u001b[39m schp674    \u001b[4m1\u001b[24m270    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m316      0.062\u001b[4m5\u001b[24m 10×AAG          G           \n",
      "\u001b[90m 2\u001b[39m schp675    \u001b[4m3\u001b[24m687.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m438.     0.180  10×AAG          B           \n",
      "\u001b[90m 3\u001b[39m schp676    \u001b[4m2\u001b[24m656.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m223.     0.131  10×AAG          F           \n",
      "\u001b[90m 4\u001b[39m schp677    \u001b[4m3\u001b[24m967.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m604      0.193  10×AAG          E           \n",
      "\u001b[90m 5\u001b[39m schp678    \u001b[4m4\u001b[24m378.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m630.     0.212  10×AAG          D           \n",
      "\u001b[90m 6\u001b[39m schp679    \u001b[4m2\u001b[24m528    \u001b[4m1\u001b[24m\u001b[4m9\u001b[24m906      0.127  10×AAG          A           \n",
      "\u001b[90m 7\u001b[39m schp680    \u001b[4m1\u001b[24m117.   \u001b[4m1\u001b[24m\u001b[4m9\u001b[24m377.     0.057\u001b[4m7\u001b[24m 10×AAG          H           \n",
      "\u001b[90m 8\u001b[39m schp681    \u001b[4m3\u001b[24m705    \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m227      0.183  10×AAG          C           \n",
      "\u001b[90m 9\u001b[39m schp683    \u001b[4m9\u001b[24m364.   \u001b[4m2\u001b[24m\u001b[4m3\u001b[24m866.     0.392  10×AGA          G           \n",
      "\u001b[90m10\u001b[39m schp684    \u001b[4m3\u001b[24m294.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m585.     0.160  10×AGA          B           \n",
      "\u001b[90m11\u001b[39m schp685    \u001b[4m7\u001b[24m379    \u001b[4m2\u001b[24m\u001b[4m2\u001b[24m956      0.321  10×AGA          F           \n",
      "\u001b[90m12\u001b[39m schp686    \u001b[4m5\u001b[24m000.   \u001b[4m2\u001b[24m\u001b[4m1\u001b[24m171.     0.236  10×AGA          E           \n",
      "\u001b[90m13\u001b[39m schp687    \u001b[4m4\u001b[24m658.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m860.     0.223  10×AGA          D           \n",
      "\u001b[90m14\u001b[39m schp688    \u001b[4m1\u001b[24m748.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m755.     0.084\u001b[4m2\u001b[24m 10×AGA          A           \n",
      "\u001b[90m15\u001b[39m schp689    \u001b[4m8\u001b[24m693.   \u001b[4m2\u001b[24m\u001b[4m2\u001b[24m650.     0.384  10×AGA          H           \n",
      "\u001b[90m16\u001b[39m schp690    \u001b[4m3\u001b[24m535.   \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m594.     0.172  10×AGA          C           \n"
     ]
    }
   ],
   "source": [
    "data %>% \n",
    "  group_by(strain) %>%\n",
    "  summarize(mean_yfp = mean(yfp), mean_rfp = mean(rfp)) %>%\n",
    "  mutate(mean_ratio = mean_yfp / mean_rfp) %>%\n",
    "  left_join(annotations, by = \"strain\") %>%\n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## %>% and + if you want to plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 180,
       "width": 300
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 5, repr.plot.height = 3)\n",
    "\n",
    "data %>% \n",
    "  group_by(strain) %>%\n",
    "  summarize(mean_yfp = mean(yfp), mean_rfp = mean(rfp)) %>%\n",
    "  mutate(mean_ratio = mean_yfp / mean_rfp) %>%\n",
    "  left_join(annotations, by = \"strain\") %>%\n",
    "  ggplot(aes(x = kozak_region, y = mean_ratio, \n",
    "             color = insert_sequence, group = insert_sequence)) +\n",
    "  geom_line() + \n",
    "  geom_point() +\n",
    "  theme_classic() +\n",
    "  labs(x = \"Kozak region\", y = \"Mean YFP / RFP ratio\", color = \"Insert\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Re-shape data using `pivot_longer` and `pivot_wider`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m74\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m4\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \"\\t\"\n",
      "\u001b[31mchr\u001b[39m (1): strain\n",
      "\u001b[32mdbl\u001b[39m (3): yfp, rfp, replicate\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[90m# A tibble: 74 × 4\u001b[39m\n",
      "   strain    yfp   rfp replicate\n",
      "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
      "\u001b[90m 1\u001b[39m schp677  \u001b[4m4\u001b[24m123 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m661         1\n",
      "\u001b[90m 2\u001b[39m schp678  \u001b[4m4\u001b[24m550 \u001b[4m2\u001b[24m\u001b[4m1\u001b[24m437         1\n",
      "\u001b[90m 3\u001b[39m schp675  \u001b[4m3\u001b[24m880 \u001b[4m2\u001b[24m\u001b[4m1\u001b[24m323         1\n",
      "\u001b[90m 4\u001b[39m schp676  \u001b[4m2\u001b[24m863 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m668         1\n",
      "\u001b[90m 5\u001b[39m schp687  \u001b[4m4\u001b[24m767 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m995         1\n",
      "\u001b[90m 6\u001b[39m schp688  \u001b[4m1\u001b[24m274 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m927         1\n",
      "\u001b[90m 7\u001b[39m schp679  \u001b[4m2\u001b[24m605 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m840         1\n",
      "\u001b[90m 8\u001b[39m schp680  \u001b[4m1\u001b[24m175 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m902         1\n",
      "\u001b[90m 9\u001b[39m schp681  \u001b[4m3\u001b[24m861 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m659         1\n",
      "\u001b[90m10\u001b[39m schp683  \u001b[4m9\u001b[24m949 \u001b[4m2\u001b[24m\u001b[4m5\u001b[24m406         1\n",
      "\u001b[90m# … with 64 more rows\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "data <- read_tsv(\"data/example_dataset_4.tsv\") %>% \n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do you plot a histogram of both `yfp` and `rfp` as separate panels?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[90m# A tibble: 148 × 4\u001b[39m\n",
      "   strain  replicate channel value\n",
      "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m       \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
      "\u001b[90m 1\u001b[39m schp677         1 yfp      \u001b[4m4\u001b[24m123\n",
      "\u001b[90m 2\u001b[39m schp677         1 rfp     \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m661\n",
      "\u001b[90m 3\u001b[39m schp678         1 yfp      \u001b[4m4\u001b[24m550\n",
      "\u001b[90m 4\u001b[39m schp678         1 rfp     \u001b[4m2\u001b[24m\u001b[4m1\u001b[24m437\n",
      "\u001b[90m 5\u001b[39m schp675         1 yfp      \u001b[4m3\u001b[24m880\n",
      "\u001b[90m 6\u001b[39m schp675         1 rfp     \u001b[4m2\u001b[24m\u001b[4m1\u001b[24m323\n",
      "\u001b[90m 7\u001b[39m schp676         1 yfp      \u001b[4m2\u001b[24m863\n",
      "\u001b[90m 8\u001b[39m schp676         1 rfp     \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m668\n",
      "\u001b[90m 9\u001b[39m schp687         1 yfp      \u001b[4m4\u001b[24m767\n",
      "\u001b[90m10\u001b[39m schp687         1 rfp     \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m995\n",
      "\u001b[90m# … with 138 more rows\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "data %>%\n",
    "  pivot_longer(c(\"yfp\", \"rfp\"), names_to = \"channel\", values_to = \"value\") %>%\n",
    "  print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[90m# A tibble: 74 × 4\u001b[39m\n",
      "   strain  replicate   yfp   rfp\n",
      "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m       \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
      "\u001b[90m 1\u001b[39m schp677         1  \u001b[4m4\u001b[24m123 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m661\n",
      "\u001b[90m 2\u001b[39m schp678         1  \u001b[4m4\u001b[24m550 \u001b[4m2\u001b[24m\u001b[4m1\u001b[24m437\n",
      "\u001b[90m 3\u001b[39m schp675         1  \u001b[4m3\u001b[24m880 \u001b[4m2\u001b[24m\u001b[4m1\u001b[24m323\n",
      "\u001b[90m 4\u001b[39m schp676         1  \u001b[4m2\u001b[24m863 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m668\n",
      "\u001b[90m 5\u001b[39m schp687         1  \u001b[4m4\u001b[24m767 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m995\n",
      "\u001b[90m 6\u001b[39m schp688         1  \u001b[4m1\u001b[24m274 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m927\n",
      "\u001b[90m 7\u001b[39m schp679         1  \u001b[4m2\u001b[24m605 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m840\n",
      "\u001b[90m 8\u001b[39m schp680         1  \u001b[4m1\u001b[24m175 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m902\n",
      "\u001b[90m 9\u001b[39m schp681         1  \u001b[4m3\u001b[24m861 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m659\n",
      "\u001b[90m10\u001b[39m schp683         1  \u001b[4m9\u001b[24m949 \u001b[4m2\u001b[24m\u001b[4m5\u001b[24m406\n",
      "\u001b[90m# … with 64 more rows\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "data %>%\n",
    "  pivot_longer(c(\"yfp\", \"rfp\"), names_to = \"channel\", values_to = \"value\") %>%\n",
    "  pivot_wider(names_from = \"channel\", values_from = \"value\") %>%\n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pivot_longer` is useful for handling distinct measurements without listing them explicitly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 180,
       "width": 300
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data <- data %>%\n",
    "  pivot_longer(c(\"yfp\", \"rfp\"), names_to = \"channel\", values_to = \"value\")\n",
    "\n",
    "plot_data %>%\n",
    "  ggplot(aes(x = value)) +\n",
    "  facet_wrap(~ channel) +\n",
    "  geom_histogram(bins = 30) +\n",
    "  theme_bw()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use `purrr::map` and `purrr::unnest` to read and work with tables from many files\n",
    "\n",
    "We use `list.files` and `enframe` to go from a list of files to a `tibble` quickly. Also check out `deframe`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m74\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m3\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \"\\t\"\n",
      "\u001b[31mchr\u001b[39m (1): strain\n",
      "\u001b[32mdbl\u001b[39m (2): replicate, value\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n",
      "\u001b[1mRows: \u001b[22m\u001b[34m74\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m3\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \"\\t\"\n",
      "\u001b[31mchr\u001b[39m (1): strain\n",
      "\u001b[32mdbl\u001b[39m (2): replicate, value\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[90m# A tibble: 148 × 4\u001b[39m\n",
      "   strain  replicate value channel\n",
      "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m       \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m  \n",
      "\u001b[90m 1\u001b[39m schp677         1 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m661 rfp    \n",
      "\u001b[90m 2\u001b[39m schp678         1 \u001b[4m2\u001b[24m\u001b[4m1\u001b[24m437 rfp    \n",
      "\u001b[90m 3\u001b[39m schp675         1 \u001b[4m2\u001b[24m\u001b[4m1\u001b[24m323 rfp    \n",
      "\u001b[90m 4\u001b[39m schp676         1 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m668 rfp    \n",
      "\u001b[90m 5\u001b[39m schp687         1 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m995 rfp    \n",
      "\u001b[90m 6\u001b[39m schp688         1 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m927 rfp    \n",
      "\u001b[90m 7\u001b[39m schp679         1 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m840 rfp    \n",
      "\u001b[90m 8\u001b[39m schp680         1 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m902 rfp    \n",
      "\u001b[90m 9\u001b[39m schp681         1 \u001b[4m2\u001b[24m\u001b[4m0\u001b[24m659 rfp    \n",
      "\u001b[90m10\u001b[39m schp683         1 \u001b[4m2\u001b[24m\u001b[4m5\u001b[24m406 rfp    \n",
      "\u001b[90m# … with 138 more rows\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "data <- list.files(\"data/channel_data/\", full.names = T) %>%\n",
    "  enframe(\"number\", \"file\") %>%\n",
    "  mutate(data = map(file, read_tsv)) %>%\n",
    "  unnest(data) %>%\n",
    "  mutate(channel = str_extract(file, \"[^/]+(?=.tsv)\")) %>%\n",
    "  select(-number, -file) %>%\n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In-class exercises\n",
    "\n",
    "(20 min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Re-shape data read above from two separate files to create columns for `yfp` and `rfp`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 74 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>strain</th><th scope=col>replicate</th><th scope=col>rfp</th><th scope=col>yfp</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>schp677</td><td>1</td><td>20661</td><td>4123</td></tr>\n",
       "\t<tr><td>schp678</td><td>1</td><td>21437</td><td>4550</td></tr>\n",
       "\t<tr><td>schp675</td><td>1</td><td>21323</td><td>3880</td></tr>\n",
       "\t<tr><td>schp676</td><td>1</td><td>20668</td><td>2863</td></tr>\n",
       "\t<tr><td>schp687</td><td>1</td><td>20995</td><td>4767</td></tr>\n",
       "\t<tr><td>schp688</td><td>1</td><td>20927</td><td>1274</td></tr>\n",
       "\t<tr><td>schp679</td><td>1</td><td>20840</td><td>2605</td></tr>\n",
       "\t<tr><td>schp680</td><td>1</td><td>20902</td><td>1175</td></tr>\n",
       "\t<tr><td>schp681</td><td>1</td><td>20659</td><td>3861</td></tr>\n",
       "\t<tr><td>schp683</td><td>1</td><td>25406</td><td>9949</td></tr>\n",
       "\t<tr><td>schp684</td><td>1</td><td>21261</td><td>3332</td></tr>\n",
       "\t<tr><td>schp685</td><td>1</td><td>24049</td><td>7595</td></tr>\n",
       "\t<tr><td>schp686</td><td>1</td><td>21560</td><td>5132</td></tr>\n",
       "\t<tr><td>schp689</td><td>1</td><td>24399</td><td>9790</td></tr>\n",
       "\t<tr><td>schp690</td><td>1</td><td>20944</td><td>3640</td></tr>\n",
       "\t<tr><td>schp677</td><td>2</td><td>21796</td><td>4169</td></tr>\n",
       "\t<tr><td>schp678</td><td>2</td><td>21656</td><td>4581</td></tr>\n",
       "\t<tr><td>schp674</td><td>2</td><td>21033</td><td>1324</td></tr>\n",
       "\t<tr><td>schp675</td><td>2</td><td>21669</td><td>3733</td></tr>\n",
       "\t<tr><td>schp676</td><td>2</td><td>21219</td><td>2755</td></tr>\n",
       "\t<tr><td>schp687</td><td>2</td><td>21141</td><td>4742</td></tr>\n",
       "\t<tr><td>schp688</td><td>2</td><td>20945</td><td>1913</td></tr>\n",
       "\t<tr><td>schp679</td><td>2</td><td>22135</td><td>2549</td></tr>\n",
       "\t<tr><td>schp680</td><td>2</td><td>21091</td><td>1152</td></tr>\n",
       "\t<tr><td>schp681</td><td>2</td><td>20974</td><td>3735</td></tr>\n",
       "\t<tr><td>schp683</td><td>2</td><td>23366</td><td>8453</td></tr>\n",
       "\t<tr><td>schp684</td><td>2</td><td>21318</td><td>3315</td></tr>\n",
       "\t<tr><td>schp685</td><td>2</td><td>26104</td><td>7923</td></tr>\n",
       "\t<tr><td>schp686</td><td>2</td><td>22028</td><td>5088</td></tr>\n",
       "\t<tr><td>schp689</td><td>2</td><td>24932</td><td>9821</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>schp689</td><td>3</td><td>23007</td><td>9310</td></tr>\n",
       "\t<tr><td>schp690</td><td>3</td><td>20063</td><td>3569</td></tr>\n",
       "\t<tr><td>schp677</td><td>4</td><td>20032</td><td>3615</td></tr>\n",
       "\t<tr><td>schp678</td><td>4</td><td>19170</td><td>4128</td></tr>\n",
       "\t<tr><td>schp674</td><td>4</td><td>19599</td><td>1216</td></tr>\n",
       "\t<tr><td>schp675</td><td>4</td><td>19368</td><td>3375</td></tr>\n",
       "\t<tr><td>schp676</td><td>4</td><td>19600</td><td>2700</td></tr>\n",
       "\t<tr><td>schp687</td><td>4</td><td>20382</td><td>4598</td></tr>\n",
       "\t<tr><td>schp688</td><td>4</td><td>20783</td><td>1978</td></tr>\n",
       "\t<tr><td>schp679</td><td>4</td><td>17329</td><td>2515</td></tr>\n",
       "\t<tr><td>schp680</td><td>4</td><td>17485</td><td>1034</td></tr>\n",
       "\t<tr><td>schp681</td><td>4</td><td>21130</td><td>3813</td></tr>\n",
       "\t<tr><td>schp683</td><td>4</td><td>23256</td><td>9166</td></tr>\n",
       "\t<tr><td>schp684</td><td>4</td><td>19905</td><td>3305</td></tr>\n",
       "\t<tr><td>schp685</td><td>4</td><td>21539</td><td>7143</td></tr>\n",
       "\t<tr><td>schp686</td><td>4</td><td>20554</td><td>5009</td></tr>\n",
       "\t<tr><td>schp689</td><td>4</td><td>19075</td><td>6269</td></tr>\n",
       "\t<tr><td>schp690</td><td>4</td><td>20773</td><td>3475</td></tr>\n",
       "\t<tr><td>schp677</td><td>5</td><td>19370</td><td>3826</td></tr>\n",
       "\t<tr><td>schp675</td><td>5</td><td>19336</td><td>3767</td></tr>\n",
       "\t<tr><td>schp676</td><td>5</td><td>19118</td><td>2122</td></tr>\n",
       "\t<tr><td>schp687</td><td>5</td><td>20408</td><td>4369</td></tr>\n",
       "\t<tr><td>schp688</td><td>5</td><td>19977</td><td>1469</td></tr>\n",
       "\t<tr><td>schp680</td><td>5</td><td>18530</td><td>1069</td></tr>\n",
       "\t<tr><td>schp681</td><td>5</td><td>18603</td><td>3352</td></tr>\n",
       "\t<tr><td>schp684</td><td>5</td><td>19833</td><td>3109</td></tr>\n",
       "\t<tr><td>schp685</td><td>5</td><td>20587</td><td>6788</td></tr>\n",
       "\t<tr><td>schp686</td><td>5</td><td>20398</td><td>4684</td></tr>\n",
       "\t<tr><td>schp689</td><td>5</td><td>21835</td><td>8273</td></tr>\n",
       "\t<tr><td>schp690</td><td>5</td><td>20307</td><td>3487</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 74 × 4\n",
       "\\begin{tabular}{llll}\n",
       " strain & replicate & rfp & yfp\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t schp677 & 1 & 20661 & 4123\\\\\n",
       "\t schp678 & 1 & 21437 & 4550\\\\\n",
       "\t schp675 & 1 & 21323 & 3880\\\\\n",
       "\t schp676 & 1 & 20668 & 2863\\\\\n",
       "\t schp687 & 1 & 20995 & 4767\\\\\n",
       "\t schp688 & 1 & 20927 & 1274\\\\\n",
       "\t schp679 & 1 & 20840 & 2605\\\\\n",
       "\t schp680 & 1 & 20902 & 1175\\\\\n",
       "\t schp681 & 1 & 20659 & 3861\\\\\n",
       "\t schp683 & 1 & 25406 & 9949\\\\\n",
       "\t schp684 & 1 & 21261 & 3332\\\\\n",
       "\t schp685 & 1 & 24049 & 7595\\\\\n",
       "\t schp686 & 1 & 21560 & 5132\\\\\n",
       "\t schp689 & 1 & 24399 & 9790\\\\\n",
       "\t schp690 & 1 & 20944 & 3640\\\\\n",
       "\t schp677 & 2 & 21796 & 4169\\\\\n",
       "\t schp678 & 2 & 21656 & 4581\\\\\n",
       "\t schp674 & 2 & 21033 & 1324\\\\\n",
       "\t schp675 & 2 & 21669 & 3733\\\\\n",
       "\t schp676 & 2 & 21219 & 2755\\\\\n",
       "\t schp687 & 2 & 21141 & 4742\\\\\n",
       "\t schp688 & 2 & 20945 & 1913\\\\\n",
       "\t schp679 & 2 & 22135 & 2549\\\\\n",
       "\t schp680 & 2 & 21091 & 1152\\\\\n",
       "\t schp681 & 2 & 20974 & 3735\\\\\n",
       "\t schp683 & 2 & 23366 & 8453\\\\\n",
       "\t schp684 & 2 & 21318 & 3315\\\\\n",
       "\t schp685 & 2 & 26104 & 7923\\\\\n",
       "\t schp686 & 2 & 22028 & 5088\\\\\n",
       "\t schp689 & 2 & 24932 & 9821\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t schp689 & 3 & 23007 & 9310\\\\\n",
       "\t schp690 & 3 & 20063 & 3569\\\\\n",
       "\t schp677 & 4 & 20032 & 3615\\\\\n",
       "\t schp678 & 4 & 19170 & 4128\\\\\n",
       "\t schp674 & 4 & 19599 & 1216\\\\\n",
       "\t schp675 & 4 & 19368 & 3375\\\\\n",
       "\t schp676 & 4 & 19600 & 2700\\\\\n",
       "\t schp687 & 4 & 20382 & 4598\\\\\n",
       "\t schp688 & 4 & 20783 & 1978\\\\\n",
       "\t schp679 & 4 & 17329 & 2515\\\\\n",
       "\t schp680 & 4 & 17485 & 1034\\\\\n",
       "\t schp681 & 4 & 21130 & 3813\\\\\n",
       "\t schp683 & 4 & 23256 & 9166\\\\\n",
       "\t schp684 & 4 & 19905 & 3305\\\\\n",
       "\t schp685 & 4 & 21539 & 7143\\\\\n",
       "\t schp686 & 4 & 20554 & 5009\\\\\n",
       "\t schp689 & 4 & 19075 & 6269\\\\\n",
       "\t schp690 & 4 & 20773 & 3475\\\\\n",
       "\t schp677 & 5 & 19370 & 3826\\\\\n",
       "\t schp675 & 5 & 19336 & 3767\\\\\n",
       "\t schp676 & 5 & 19118 & 2122\\\\\n",
       "\t schp687 & 5 & 20408 & 4369\\\\\n",
       "\t schp688 & 5 & 19977 & 1469\\\\\n",
       "\t schp680 & 5 & 18530 & 1069\\\\\n",
       "\t schp681 & 5 & 18603 & 3352\\\\\n",
       "\t schp684 & 5 & 19833 & 3109\\\\\n",
       "\t schp685 & 5 & 20587 & 6788\\\\\n",
       "\t schp686 & 5 & 20398 & 4684\\\\\n",
       "\t schp689 & 5 & 21835 & 8273\\\\\n",
       "\t schp690 & 5 & 20307 & 3487\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 74 × 4\n",
       "\n",
       "| strain &lt;chr&gt; | replicate &lt;dbl&gt; | rfp &lt;dbl&gt; | yfp &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| schp677 | 1 | 20661 | 4123 |\n",
       "| schp678 | 1 | 21437 | 4550 |\n",
       "| schp675 | 1 | 21323 | 3880 |\n",
       "| schp676 | 1 | 20668 | 2863 |\n",
       "| schp687 | 1 | 20995 | 4767 |\n",
       "| schp688 | 1 | 20927 | 1274 |\n",
       "| schp679 | 1 | 20840 | 2605 |\n",
       "| schp680 | 1 | 20902 | 1175 |\n",
       "| schp681 | 1 | 20659 | 3861 |\n",
       "| schp683 | 1 | 25406 | 9949 |\n",
       "| schp684 | 1 | 21261 | 3332 |\n",
       "| schp685 | 1 | 24049 | 7595 |\n",
       "| schp686 | 1 | 21560 | 5132 |\n",
       "| schp689 | 1 | 24399 | 9790 |\n",
       "| schp690 | 1 | 20944 | 3640 |\n",
       "| schp677 | 2 | 21796 | 4169 |\n",
       "| schp678 | 2 | 21656 | 4581 |\n",
       "| schp674 | 2 | 21033 | 1324 |\n",
       "| schp675 | 2 | 21669 | 3733 |\n",
       "| schp676 | 2 | 21219 | 2755 |\n",
       "| schp687 | 2 | 21141 | 4742 |\n",
       "| schp688 | 2 | 20945 | 1913 |\n",
       "| schp679 | 2 | 22135 | 2549 |\n",
       "| schp680 | 2 | 21091 | 1152 |\n",
       "| schp681 | 2 | 20974 | 3735 |\n",
       "| schp683 | 2 | 23366 | 8453 |\n",
       "| schp684 | 2 | 21318 | 3315 |\n",
       "| schp685 | 2 | 26104 | 7923 |\n",
       "| schp686 | 2 | 22028 | 5088 |\n",
       "| schp689 | 2 | 24932 | 9821 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| schp689 | 3 | 23007 | 9310 |\n",
       "| schp690 | 3 | 20063 | 3569 |\n",
       "| schp677 | 4 | 20032 | 3615 |\n",
       "| schp678 | 4 | 19170 | 4128 |\n",
       "| schp674 | 4 | 19599 | 1216 |\n",
       "| schp675 | 4 | 19368 | 3375 |\n",
       "| schp676 | 4 | 19600 | 2700 |\n",
       "| schp687 | 4 | 20382 | 4598 |\n",
       "| schp688 | 4 | 20783 | 1978 |\n",
       "| schp679 | 4 | 17329 | 2515 |\n",
       "| schp680 | 4 | 17485 | 1034 |\n",
       "| schp681 | 4 | 21130 | 3813 |\n",
       "| schp683 | 4 | 23256 | 9166 |\n",
       "| schp684 | 4 | 19905 | 3305 |\n",
       "| schp685 | 4 | 21539 | 7143 |\n",
       "| schp686 | 4 | 20554 | 5009 |\n",
       "| schp689 | 4 | 19075 | 6269 |\n",
       "| schp690 | 4 | 20773 | 3475 |\n",
       "| schp677 | 5 | 19370 | 3826 |\n",
       "| schp675 | 5 | 19336 | 3767 |\n",
       "| schp676 | 5 | 19118 | 2122 |\n",
       "| schp687 | 5 | 20408 | 4369 |\n",
       "| schp688 | 5 | 19977 | 1469 |\n",
       "| schp680 | 5 | 18530 | 1069 |\n",
       "| schp681 | 5 | 18603 | 3352 |\n",
       "| schp684 | 5 | 19833 | 3109 |\n",
       "| schp685 | 5 | 20587 | 6788 |\n",
       "| schp686 | 5 | 20398 | 4684 |\n",
       "| schp689 | 5 | 21835 | 8273 |\n",
       "| schp690 | 5 | 20307 | 3487 |\n",
       "\n"
      ],
      "text/plain": [
       "   strain  replicate rfp   yfp \n",
       "1  schp677 1         20661 4123\n",
       "2  schp678 1         21437 4550\n",
       "3  schp675 1         21323 3880\n",
       "4  schp676 1         20668 2863\n",
       "5  schp687 1         20995 4767\n",
       "6  schp688 1         20927 1274\n",
       "7  schp679 1         20840 2605\n",
       "8  schp680 1         20902 1175\n",
       "9  schp681 1         20659 3861\n",
       "10 schp683 1         25406 9949\n",
       "11 schp684 1         21261 3332\n",
       "12 schp685 1         24049 7595\n",
       "13 schp686 1         21560 5132\n",
       "14 schp689 1         24399 9790\n",
       "15 schp690 1         20944 3640\n",
       "16 schp677 2         21796 4169\n",
       "17 schp678 2         21656 4581\n",
       "18 schp674 2         21033 1324\n",
       "19 schp675 2         21669 3733\n",
       "20 schp676 2         21219 2755\n",
       "21 schp687 2         21141 4742\n",
       "22 schp688 2         20945 1913\n",
       "23 schp679 2         22135 2549\n",
       "24 schp680 2         21091 1152\n",
       "25 schp681 2         20974 3735\n",
       "26 schp683 2         23366 8453\n",
       "27 schp684 2         21318 3315\n",
       "28 schp685 2         26104 7923\n",
       "29 schp686 2         22028 5088\n",
       "30 schp689 2         24932 9821\n",
       "⋮  ⋮       ⋮         ⋮     ⋮   \n",
       "45 schp689 3         23007 9310\n",
       "46 schp690 3         20063 3569\n",
       "47 schp677 4         20032 3615\n",
       "48 schp678 4         19170 4128\n",
       "49 schp674 4         19599 1216\n",
       "50 schp675 4         19368 3375\n",
       "51 schp676 4         19600 2700\n",
       "52 schp687 4         20382 4598\n",
       "53 schp688 4         20783 1978\n",
       "54 schp679 4         17329 2515\n",
       "55 schp680 4         17485 1034\n",
       "56 schp681 4         21130 3813\n",
       "57 schp683 4         23256 9166\n",
       "58 schp684 4         19905 3305\n",
       "59 schp685 4         21539 7143\n",
       "60 schp686 4         20554 5009\n",
       "61 schp689 4         19075 6269\n",
       "62 schp690 4         20773 3475\n",
       "63 schp677 5         19370 3826\n",
       "64 schp675 5         19336 3767\n",
       "65 schp676 5         19118 2122\n",
       "66 schp687 5         20408 4369\n",
       "67 schp688 5         19977 1469\n",
       "68 schp680 5         18530 1069\n",
       "69 schp681 5         18603 3352\n",
       "70 schp684 5         19833 3109\n",
       "71 schp685 5         20587 6788\n",
       "72 schp686 5         20398 4684\n",
       "73 schp689 5         21835 8273\n",
       "74 schp690 5         20307 3487"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data %>% pivot_wider(names_from = \"channel\", values_from = \"value\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Calculate ratio of YFP to RFP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 74 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>strain</th><th scope=col>replicate</th><th scope=col>rfp</th><th scope=col>yfp</th><th scope=col>ratio</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>schp677</td><td>1</td><td>20661</td><td>4123</td><td>0.19955472</td></tr>\n",
       "\t<tr><td>schp678</td><td>1</td><td>21437</td><td>4550</td><td>0.21224985</td></tr>\n",
       "\t<tr><td>schp675</td><td>1</td><td>21323</td><td>3880</td><td>0.18196314</td></tr>\n",
       "\t<tr><td>schp676</td><td>1</td><td>20668</td><td>2863</td><td>0.13852332</td></tr>\n",
       "\t<tr><td>schp687</td><td>1</td><td>20995</td><td>4767</td><td>0.22705406</td></tr>\n",
       "\t<tr><td>schp688</td><td>1</td><td>20927</td><td>1274</td><td>0.06087829</td></tr>\n",
       "\t<tr><td>schp679</td><td>1</td><td>20840</td><td>2605</td><td>0.12500000</td></tr>\n",
       "\t<tr><td>schp680</td><td>1</td><td>20902</td><td>1175</td><td>0.05621472</td></tr>\n",
       "\t<tr><td>schp681</td><td>1</td><td>20659</td><td>3861</td><td>0.18689191</td></tr>\n",
       "\t<tr><td>schp683</td><td>1</td><td>25406</td><td>9949</td><td>0.39160041</td></tr>\n",
       "\t<tr><td>schp684</td><td>1</td><td>21261</td><td>3332</td><td>0.15671887</td></tr>\n",
       "\t<tr><td>schp685</td><td>1</td><td>24049</td><td>7595</td><td>0.31581355</td></tr>\n",
       "\t<tr><td>schp686</td><td>1</td><td>21560</td><td>5132</td><td>0.23803340</td></tr>\n",
       "\t<tr><td>schp689</td><td>1</td><td>24399</td><td>9790</td><td>0.40124595</td></tr>\n",
       "\t<tr><td>schp690</td><td>1</td><td>20944</td><td>3640</td><td>0.17379679</td></tr>\n",
       "\t<tr><td>schp677</td><td>2</td><td>21796</td><td>4169</td><td>0.19127363</td></tr>\n",
       "\t<tr><td>schp678</td><td>2</td><td>21656</td><td>4581</td><td>0.21153491</td></tr>\n",
       "\t<tr><td>schp674</td><td>2</td><td>21033</td><td>1324</td><td>0.06294870</td></tr>\n",
       "\t<tr><td>schp675</td><td>2</td><td>21669</td><td>3733</td><td>0.17227376</td></tr>\n",
       "\t<tr><td>schp676</td><td>2</td><td>21219</td><td>2755</td><td>0.12983647</td></tr>\n",
       "\t<tr><td>schp687</td><td>2</td><td>21141</td><td>4742</td><td>0.22430349</td></tr>\n",
       "\t<tr><td>schp688</td><td>2</td><td>20945</td><td>1913</td><td>0.09133445</td></tr>\n",
       "\t<tr><td>schp679</td><td>2</td><td>22135</td><td>2549</td><td>0.11515699</td></tr>\n",
       "\t<tr><td>schp680</td><td>2</td><td>21091</td><td>1152</td><td>0.05462045</td></tr>\n",
       "\t<tr><td>schp681</td><td>2</td><td>20974</td><td>3735</td><td>0.17807762</td></tr>\n",
       "\t<tr><td>schp683</td><td>2</td><td>23366</td><td>8453</td><td>0.36176496</td></tr>\n",
       "\t<tr><td>schp684</td><td>2</td><td>21318</td><td>3315</td><td>0.15550239</td></tr>\n",
       "\t<tr><td>schp685</td><td>2</td><td>26104</td><td>7923</td><td>0.30351670</td></tr>\n",
       "\t<tr><td>schp686</td><td>2</td><td>22028</td><td>5088</td><td>0.23097875</td></tr>\n",
       "\t<tr><td>schp689</td><td>2</td><td>24932</td><td>9821</td><td>0.39391144</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>schp689</td><td>3</td><td>23007</td><td>9310</td><td>0.40465945</td></tr>\n",
       "\t<tr><td>schp690</td><td>3</td><td>20063</td><td>3569</td><td>0.17788965</td></tr>\n",
       "\t<tr><td>schp677</td><td>4</td><td>20032</td><td>3615</td><td>0.18046126</td></tr>\n",
       "\t<tr><td>schp678</td><td>4</td><td>19170</td><td>4128</td><td>0.21533646</td></tr>\n",
       "\t<tr><td>schp674</td><td>4</td><td>19599</td><td>1216</td><td>0.06204398</td></tr>\n",
       "\t<tr><td>schp675</td><td>4</td><td>19368</td><td>3375</td><td>0.17425651</td></tr>\n",
       "\t<tr><td>schp676</td><td>4</td><td>19600</td><td>2700</td><td>0.13775510</td></tr>\n",
       "\t<tr><td>schp687</td><td>4</td><td>20382</td><td>4598</td><td>0.22559121</td></tr>\n",
       "\t<tr><td>schp688</td><td>4</td><td>20783</td><td>1978</td><td>0.09517394</td></tr>\n",
       "\t<tr><td>schp679</td><td>4</td><td>17329</td><td>2515</td><td>0.14513244</td></tr>\n",
       "\t<tr><td>schp680</td><td>4</td><td>17485</td><td>1034</td><td>0.05913640</td></tr>\n",
       "\t<tr><td>schp681</td><td>4</td><td>21130</td><td>3813</td><td>0.18045433</td></tr>\n",
       "\t<tr><td>schp683</td><td>4</td><td>23256</td><td>9166</td><td>0.39413485</td></tr>\n",
       "\t<tr><td>schp684</td><td>4</td><td>19905</td><td>3305</td><td>0.16603868</td></tr>\n",
       "\t<tr><td>schp685</td><td>4</td><td>21539</td><td>7143</td><td>0.33163099</td></tr>\n",
       "\t<tr><td>schp686</td><td>4</td><td>20554</td><td>5009</td><td>0.24369952</td></tr>\n",
       "\t<tr><td>schp689</td><td>4</td><td>19075</td><td>6269</td><td>0.32865007</td></tr>\n",
       "\t<tr><td>schp690</td><td>4</td><td>20773</td><td>3475</td><td>0.16728446</td></tr>\n",
       "\t<tr><td>schp677</td><td>5</td><td>19370</td><td>3826</td><td>0.19752194</td></tr>\n",
       "\t<tr><td>schp675</td><td>5</td><td>19336</td><td>3767</td><td>0.19481796</td></tr>\n",
       "\t<tr><td>schp676</td><td>5</td><td>19118</td><td>2122</td><td>0.11099487</td></tr>\n",
       "\t<tr><td>schp687</td><td>5</td><td>20408</td><td>4369</td><td>0.21408271</td></tr>\n",
       "\t<tr><td>schp688</td><td>5</td><td>19977</td><td>1469</td><td>0.07353456</td></tr>\n",
       "\t<tr><td>schp680</td><td>5</td><td>18530</td><td>1069</td><td>0.05769023</td></tr>\n",
       "\t<tr><td>schp681</td><td>5</td><td>18603</td><td>3352</td><td>0.18018599</td></tr>\n",
       "\t<tr><td>schp684</td><td>5</td><td>19833</td><td>3109</td><td>0.15675894</td></tr>\n",
       "\t<tr><td>schp685</td><td>5</td><td>20587</td><td>6788</td><td>0.32972264</td></tr>\n",
       "\t<tr><td>schp686</td><td>5</td><td>20398</td><td>4684</td><td>0.22963036</td></tr>\n",
       "\t<tr><td>schp689</td><td>5</td><td>21835</td><td>8273</td><td>0.37888711</td></tr>\n",
       "\t<tr><td>schp690</td><td>5</td><td>20307</td><td>3487</td><td>0.17171419</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 74 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " strain & replicate & rfp & yfp & ratio\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t schp677 & 1 & 20661 & 4123 & 0.19955472\\\\\n",
       "\t schp678 & 1 & 21437 & 4550 & 0.21224985\\\\\n",
       "\t schp675 & 1 & 21323 & 3880 & 0.18196314\\\\\n",
       "\t schp676 & 1 & 20668 & 2863 & 0.13852332\\\\\n",
       "\t schp687 & 1 & 20995 & 4767 & 0.22705406\\\\\n",
       "\t schp688 & 1 & 20927 & 1274 & 0.06087829\\\\\n",
       "\t schp679 & 1 & 20840 & 2605 & 0.12500000\\\\\n",
       "\t schp680 & 1 & 20902 & 1175 & 0.05621472\\\\\n",
       "\t schp681 & 1 & 20659 & 3861 & 0.18689191\\\\\n",
       "\t schp683 & 1 & 25406 & 9949 & 0.39160041\\\\\n",
       "\t schp684 & 1 & 21261 & 3332 & 0.15671887\\\\\n",
       "\t schp685 & 1 & 24049 & 7595 & 0.31581355\\\\\n",
       "\t schp686 & 1 & 21560 & 5132 & 0.23803340\\\\\n",
       "\t schp689 & 1 & 24399 & 9790 & 0.40124595\\\\\n",
       "\t schp690 & 1 & 20944 & 3640 & 0.17379679\\\\\n",
       "\t schp677 & 2 & 21796 & 4169 & 0.19127363\\\\\n",
       "\t schp678 & 2 & 21656 & 4581 & 0.21153491\\\\\n",
       "\t schp674 & 2 & 21033 & 1324 & 0.06294870\\\\\n",
       "\t schp675 & 2 & 21669 & 3733 & 0.17227376\\\\\n",
       "\t schp676 & 2 & 21219 & 2755 & 0.12983647\\\\\n",
       "\t schp687 & 2 & 21141 & 4742 & 0.22430349\\\\\n",
       "\t schp688 & 2 & 20945 & 1913 & 0.09133445\\\\\n",
       "\t schp679 & 2 & 22135 & 2549 & 0.11515699\\\\\n",
       "\t schp680 & 2 & 21091 & 1152 & 0.05462045\\\\\n",
       "\t schp681 & 2 & 20974 & 3735 & 0.17807762\\\\\n",
       "\t schp683 & 2 & 23366 & 8453 & 0.36176496\\\\\n",
       "\t schp684 & 2 & 21318 & 3315 & 0.15550239\\\\\n",
       "\t schp685 & 2 & 26104 & 7923 & 0.30351670\\\\\n",
       "\t schp686 & 2 & 22028 & 5088 & 0.23097875\\\\\n",
       "\t schp689 & 2 & 24932 & 9821 & 0.39391144\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t schp689 & 3 & 23007 & 9310 & 0.40465945\\\\\n",
       "\t schp690 & 3 & 20063 & 3569 & 0.17788965\\\\\n",
       "\t schp677 & 4 & 20032 & 3615 & 0.18046126\\\\\n",
       "\t schp678 & 4 & 19170 & 4128 & 0.21533646\\\\\n",
       "\t schp674 & 4 & 19599 & 1216 & 0.06204398\\\\\n",
       "\t schp675 & 4 & 19368 & 3375 & 0.17425651\\\\\n",
       "\t schp676 & 4 & 19600 & 2700 & 0.13775510\\\\\n",
       "\t schp687 & 4 & 20382 & 4598 & 0.22559121\\\\\n",
       "\t schp688 & 4 & 20783 & 1978 & 0.09517394\\\\\n",
       "\t schp679 & 4 & 17329 & 2515 & 0.14513244\\\\\n",
       "\t schp680 & 4 & 17485 & 1034 & 0.05913640\\\\\n",
       "\t schp681 & 4 & 21130 & 3813 & 0.18045433\\\\\n",
       "\t schp683 & 4 & 23256 & 9166 & 0.39413485\\\\\n",
       "\t schp684 & 4 & 19905 & 3305 & 0.16603868\\\\\n",
       "\t schp685 & 4 & 21539 & 7143 & 0.33163099\\\\\n",
       "\t schp686 & 4 & 20554 & 5009 & 0.24369952\\\\\n",
       "\t schp689 & 4 & 19075 & 6269 & 0.32865007\\\\\n",
       "\t schp690 & 4 & 20773 & 3475 & 0.16728446\\\\\n",
       "\t schp677 & 5 & 19370 & 3826 & 0.19752194\\\\\n",
       "\t schp675 & 5 & 19336 & 3767 & 0.19481796\\\\\n",
       "\t schp676 & 5 & 19118 & 2122 & 0.11099487\\\\\n",
       "\t schp687 & 5 & 20408 & 4369 & 0.21408271\\\\\n",
       "\t schp688 & 5 & 19977 & 1469 & 0.07353456\\\\\n",
       "\t schp680 & 5 & 18530 & 1069 & 0.05769023\\\\\n",
       "\t schp681 & 5 & 18603 & 3352 & 0.18018599\\\\\n",
       "\t schp684 & 5 & 19833 & 3109 & 0.15675894\\\\\n",
       "\t schp685 & 5 & 20587 & 6788 & 0.32972264\\\\\n",
       "\t schp686 & 5 & 20398 & 4684 & 0.22963036\\\\\n",
       "\t schp689 & 5 & 21835 & 8273 & 0.37888711\\\\\n",
       "\t schp690 & 5 & 20307 & 3487 & 0.17171419\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 74 × 5\n",
       "\n",
       "| strain &lt;chr&gt; | replicate &lt;dbl&gt; | rfp &lt;dbl&gt; | yfp &lt;dbl&gt; | ratio &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| schp677 | 1 | 20661 | 4123 | 0.19955472 |\n",
       "| schp678 | 1 | 21437 | 4550 | 0.21224985 |\n",
       "| schp675 | 1 | 21323 | 3880 | 0.18196314 |\n",
       "| schp676 | 1 | 20668 | 2863 | 0.13852332 |\n",
       "| schp687 | 1 | 20995 | 4767 | 0.22705406 |\n",
       "| schp688 | 1 | 20927 | 1274 | 0.06087829 |\n",
       "| schp679 | 1 | 20840 | 2605 | 0.12500000 |\n",
       "| schp680 | 1 | 20902 | 1175 | 0.05621472 |\n",
       "| schp681 | 1 | 20659 | 3861 | 0.18689191 |\n",
       "| schp683 | 1 | 25406 | 9949 | 0.39160041 |\n",
       "| schp684 | 1 | 21261 | 3332 | 0.15671887 |\n",
       "| schp685 | 1 | 24049 | 7595 | 0.31581355 |\n",
       "| schp686 | 1 | 21560 | 5132 | 0.23803340 |\n",
       "| schp689 | 1 | 24399 | 9790 | 0.40124595 |\n",
       "| schp690 | 1 | 20944 | 3640 | 0.17379679 |\n",
       "| schp677 | 2 | 21796 | 4169 | 0.19127363 |\n",
       "| schp678 | 2 | 21656 | 4581 | 0.21153491 |\n",
       "| schp674 | 2 | 21033 | 1324 | 0.06294870 |\n",
       "| schp675 | 2 | 21669 | 3733 | 0.17227376 |\n",
       "| schp676 | 2 | 21219 | 2755 | 0.12983647 |\n",
       "| schp687 | 2 | 21141 | 4742 | 0.22430349 |\n",
       "| schp688 | 2 | 20945 | 1913 | 0.09133445 |\n",
       "| schp679 | 2 | 22135 | 2549 | 0.11515699 |\n",
       "| schp680 | 2 | 21091 | 1152 | 0.05462045 |\n",
       "| schp681 | 2 | 20974 | 3735 | 0.17807762 |\n",
       "| schp683 | 2 | 23366 | 8453 | 0.36176496 |\n",
       "| schp684 | 2 | 21318 | 3315 | 0.15550239 |\n",
       "| schp685 | 2 | 26104 | 7923 | 0.30351670 |\n",
       "| schp686 | 2 | 22028 | 5088 | 0.23097875 |\n",
       "| schp689 | 2 | 24932 | 9821 | 0.39391144 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| schp689 | 3 | 23007 | 9310 | 0.40465945 |\n",
       "| schp690 | 3 | 20063 | 3569 | 0.17788965 |\n",
       "| schp677 | 4 | 20032 | 3615 | 0.18046126 |\n",
       "| schp678 | 4 | 19170 | 4128 | 0.21533646 |\n",
       "| schp674 | 4 | 19599 | 1216 | 0.06204398 |\n",
       "| schp675 | 4 | 19368 | 3375 | 0.17425651 |\n",
       "| schp676 | 4 | 19600 | 2700 | 0.13775510 |\n",
       "| schp687 | 4 | 20382 | 4598 | 0.22559121 |\n",
       "| schp688 | 4 | 20783 | 1978 | 0.09517394 |\n",
       "| schp679 | 4 | 17329 | 2515 | 0.14513244 |\n",
       "| schp680 | 4 | 17485 | 1034 | 0.05913640 |\n",
       "| schp681 | 4 | 21130 | 3813 | 0.18045433 |\n",
       "| schp683 | 4 | 23256 | 9166 | 0.39413485 |\n",
       "| schp684 | 4 | 19905 | 3305 | 0.16603868 |\n",
       "| schp685 | 4 | 21539 | 7143 | 0.33163099 |\n",
       "| schp686 | 4 | 20554 | 5009 | 0.24369952 |\n",
       "| schp689 | 4 | 19075 | 6269 | 0.32865007 |\n",
       "| schp690 | 4 | 20773 | 3475 | 0.16728446 |\n",
       "| schp677 | 5 | 19370 | 3826 | 0.19752194 |\n",
       "| schp675 | 5 | 19336 | 3767 | 0.19481796 |\n",
       "| schp676 | 5 | 19118 | 2122 | 0.11099487 |\n",
       "| schp687 | 5 | 20408 | 4369 | 0.21408271 |\n",
       "| schp688 | 5 | 19977 | 1469 | 0.07353456 |\n",
       "| schp680 | 5 | 18530 | 1069 | 0.05769023 |\n",
       "| schp681 | 5 | 18603 | 3352 | 0.18018599 |\n",
       "| schp684 | 5 | 19833 | 3109 | 0.15675894 |\n",
       "| schp685 | 5 | 20587 | 6788 | 0.32972264 |\n",
       "| schp686 | 5 | 20398 | 4684 | 0.22963036 |\n",
       "| schp689 | 5 | 21835 | 8273 | 0.37888711 |\n",
       "| schp690 | 5 | 20307 | 3487 | 0.17171419 |\n",
       "\n"
      ],
      "text/plain": [
       "   strain  replicate rfp   yfp  ratio     \n",
       "1  schp677 1         20661 4123 0.19955472\n",
       "2  schp678 1         21437 4550 0.21224985\n",
       "3  schp675 1         21323 3880 0.18196314\n",
       "4  schp676 1         20668 2863 0.13852332\n",
       "5  schp687 1         20995 4767 0.22705406\n",
       "6  schp688 1         20927 1274 0.06087829\n",
       "7  schp679 1         20840 2605 0.12500000\n",
       "8  schp680 1         20902 1175 0.05621472\n",
       "9  schp681 1         20659 3861 0.18689191\n",
       "10 schp683 1         25406 9949 0.39160041\n",
       "11 schp684 1         21261 3332 0.15671887\n",
       "12 schp685 1         24049 7595 0.31581355\n",
       "13 schp686 1         21560 5132 0.23803340\n",
       "14 schp689 1         24399 9790 0.40124595\n",
       "15 schp690 1         20944 3640 0.17379679\n",
       "16 schp677 2         21796 4169 0.19127363\n",
       "17 schp678 2         21656 4581 0.21153491\n",
       "18 schp674 2         21033 1324 0.06294870\n",
       "19 schp675 2         21669 3733 0.17227376\n",
       "20 schp676 2         21219 2755 0.12983647\n",
       "21 schp687 2         21141 4742 0.22430349\n",
       "22 schp688 2         20945 1913 0.09133445\n",
       "23 schp679 2         22135 2549 0.11515699\n",
       "24 schp680 2         21091 1152 0.05462045\n",
       "25 schp681 2         20974 3735 0.17807762\n",
       "26 schp683 2         23366 8453 0.36176496\n",
       "27 schp684 2         21318 3315 0.15550239\n",
       "28 schp685 2         26104 7923 0.30351670\n",
       "29 schp686 2         22028 5088 0.23097875\n",
       "30 schp689 2         24932 9821 0.39391144\n",
       "⋮  ⋮       ⋮         ⋮     ⋮    ⋮         \n",
       "45 schp689 3         23007 9310 0.40465945\n",
       "46 schp690 3         20063 3569 0.17788965\n",
       "47 schp677 4         20032 3615 0.18046126\n",
       "48 schp678 4         19170 4128 0.21533646\n",
       "49 schp674 4         19599 1216 0.06204398\n",
       "50 schp675 4         19368 3375 0.17425651\n",
       "51 schp676 4         19600 2700 0.13775510\n",
       "52 schp687 4         20382 4598 0.22559121\n",
       "53 schp688 4         20783 1978 0.09517394\n",
       "54 schp679 4         17329 2515 0.14513244\n",
       "55 schp680 4         17485 1034 0.05913640\n",
       "56 schp681 4         21130 3813 0.18045433\n",
       "57 schp683 4         23256 9166 0.39413485\n",
       "58 schp684 4         19905 3305 0.16603868\n",
       "59 schp685 4         21539 7143 0.33163099\n",
       "60 schp686 4         20554 5009 0.24369952\n",
       "61 schp689 4         19075 6269 0.32865007\n",
       "62 schp690 4         20773 3475 0.16728446\n",
       "63 schp677 5         19370 3826 0.19752194\n",
       "64 schp675 5         19336 3767 0.19481796\n",
       "65 schp676 5         19118 2122 0.11099487\n",
       "66 schp687 5         20408 4369 0.21408271\n",
       "67 schp688 5         19977 1469 0.07353456\n",
       "68 schp680 5         18530 1069 0.05769023\n",
       "69 schp681 5         18603 3352 0.18018599\n",
       "70 schp684 5         19833 3109 0.15675894\n",
       "71 schp685 5         20587 6788 0.32972264\n",
       "72 schp686 5         20398 4684 0.22963036\n",
       "73 schp689 5         21835 8273 0.37888711\n",
       "74 schp690 5         20307 3487 0.17171419"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data %>% \n",
    "    pivot_wider(names_from = \"channel\", values_from = \"value\") %>%\n",
    "    mutate(ratio = yfp/rfp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Calculate the `mean` and `standard error` of the ratio between replicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 16 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>strain</th><th scope=col>mean_ratio</th><th scope=col>se_ratio</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>schp674</td><td>0.06249634</td><td>0.0004523589</td></tr>\n",
       "\t<tr><td>schp675</td><td>0.18058849</td><td>0.0039655570</td></tr>\n",
       "\t<tr><td>schp676</td><td>0.13113391</td><td>0.0052955241</td></tr>\n",
       "\t<tr><td>schp677</td><td>0.19254120</td><td>0.0033418563</td></tr>\n",
       "\t<tr><td>schp678</td><td>0.21229042</td><td>0.0011147893</td></tr>\n",
       "\t<tr><td>schp679</td><td>0.12793468</td><td>0.0062573354</td></tr>\n",
       "\t<tr><td>schp680</td><td>0.05778072</td><td>0.0011459648</td></tr>\n",
       "\t<tr><td>schp681</td><td>0.18320179</td><td>0.0023272205</td></tr>\n",
       "\t<tr><td>schp683</td><td>0.39236614</td><td>0.0123024387</td></tr>\n",
       "\t<tr><td>schp684</td><td>0.16007992</td><td>0.0023117569</td></tr>\n",
       "\t<tr><td>schp685</td><td>0.32232050</td><td>0.0055286888</td></tr>\n",
       "\t<tr><td>schp686</td><td>0.23621167</td><td>0.0026111021</td></tr>\n",
       "\t<tr><td>schp687</td><td>0.22327250</td><td>0.0023391158</td></tr>\n",
       "\t<tr><td>schp688</td><td>0.08411708</td><td>0.0073078286</td></tr>\n",
       "\t<tr><td>schp689</td><td>0.38147080</td><td>0.0139288283</td></tr>\n",
       "\t<tr><td>schp690</td><td>0.17167947</td><td>0.0019761750</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 16 × 3\n",
       "\\begin{tabular}{lll}\n",
       " strain & mean\\_ratio & se\\_ratio\\\\\n",
       " <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t schp674 & 0.06249634 & 0.0004523589\\\\\n",
       "\t schp675 & 0.18058849 & 0.0039655570\\\\\n",
       "\t schp676 & 0.13113391 & 0.0052955241\\\\\n",
       "\t schp677 & 0.19254120 & 0.0033418563\\\\\n",
       "\t schp678 & 0.21229042 & 0.0011147893\\\\\n",
       "\t schp679 & 0.12793468 & 0.0062573354\\\\\n",
       "\t schp680 & 0.05778072 & 0.0011459648\\\\\n",
       "\t schp681 & 0.18320179 & 0.0023272205\\\\\n",
       "\t schp683 & 0.39236614 & 0.0123024387\\\\\n",
       "\t schp684 & 0.16007992 & 0.0023117569\\\\\n",
       "\t schp685 & 0.32232050 & 0.0055286888\\\\\n",
       "\t schp686 & 0.23621167 & 0.0026111021\\\\\n",
       "\t schp687 & 0.22327250 & 0.0023391158\\\\\n",
       "\t schp688 & 0.08411708 & 0.0073078286\\\\\n",
       "\t schp689 & 0.38147080 & 0.0139288283\\\\\n",
       "\t schp690 & 0.17167947 & 0.0019761750\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 16 × 3\n",
       "\n",
       "| strain &lt;chr&gt; | mean_ratio &lt;dbl&gt; | se_ratio &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| schp674 | 0.06249634 | 0.0004523589 |\n",
       "| schp675 | 0.18058849 | 0.0039655570 |\n",
       "| schp676 | 0.13113391 | 0.0052955241 |\n",
       "| schp677 | 0.19254120 | 0.0033418563 |\n",
       "| schp678 | 0.21229042 | 0.0011147893 |\n",
       "| schp679 | 0.12793468 | 0.0062573354 |\n",
       "| schp680 | 0.05778072 | 0.0011459648 |\n",
       "| schp681 | 0.18320179 | 0.0023272205 |\n",
       "| schp683 | 0.39236614 | 0.0123024387 |\n",
       "| schp684 | 0.16007992 | 0.0023117569 |\n",
       "| schp685 | 0.32232050 | 0.0055286888 |\n",
       "| schp686 | 0.23621167 | 0.0026111021 |\n",
       "| schp687 | 0.22327250 | 0.0023391158 |\n",
       "| schp688 | 0.08411708 | 0.0073078286 |\n",
       "| schp689 | 0.38147080 | 0.0139288283 |\n",
       "| schp690 | 0.17167947 | 0.0019761750 |\n",
       "\n"
      ],
      "text/plain": [
       "   strain  mean_ratio se_ratio    \n",
       "1  schp674 0.06249634 0.0004523589\n",
       "2  schp675 0.18058849 0.0039655570\n",
       "3  schp676 0.13113391 0.0052955241\n",
       "4  schp677 0.19254120 0.0033418563\n",
       "5  schp678 0.21229042 0.0011147893\n",
       "6  schp679 0.12793468 0.0062573354\n",
       "7  schp680 0.05778072 0.0011459648\n",
       "8  schp681 0.18320179 0.0023272205\n",
       "9  schp683 0.39236614 0.0123024387\n",
       "10 schp684 0.16007992 0.0023117569\n",
       "11 schp685 0.32232050 0.0055286888\n",
       "12 schp686 0.23621167 0.0026111021\n",
       "13 schp687 0.22327250 0.0023391158\n",
       "14 schp688 0.08411708 0.0073078286\n",
       "15 schp689 0.38147080 0.0139288283\n",
       "16 schp690 0.17167947 0.0019761750"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data %>% \n",
    "    pivot_wider(names_from = \"channel\", values_from = \"value\") %>%\n",
    "    mutate(ratio = yfp/rfp) %>%\n",
    "    group_by(strain) %>%\n",
    "    summarize(mean_ratio = mean(ratio),\n",
    "              se_ratio = sd(ratio)/sqrt(n()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Combine data with annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 16 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>strain</th><th scope=col>mean_ratio</th><th scope=col>se_ratio</th><th scope=col>insert_sequence</th><th scope=col>kozak_region</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>schp674</td><td>0.06249634</td><td>0.0004523589</td><td>10×AAG</td><td>G</td></tr>\n",
       "\t<tr><td>schp675</td><td>0.18058849</td><td>0.0039655570</td><td>10×AAG</td><td>B</td></tr>\n",
       "\t<tr><td>schp676</td><td>0.13113391</td><td>0.0052955241</td><td>10×AAG</td><td>F</td></tr>\n",
       "\t<tr><td>schp677</td><td>0.19254120</td><td>0.0033418563</td><td>10×AAG</td><td>E</td></tr>\n",
       "\t<tr><td>schp678</td><td>0.21229042</td><td>0.0011147893</td><td>10×AAG</td><td>D</td></tr>\n",
       "\t<tr><td>schp679</td><td>0.12793468</td><td>0.0062573354</td><td>10×AAG</td><td>A</td></tr>\n",
       "\t<tr><td>schp680</td><td>0.05778072</td><td>0.0011459648</td><td>10×AAG</td><td>H</td></tr>\n",
       "\t<tr><td>schp681</td><td>0.18320179</td><td>0.0023272205</td><td>10×AAG</td><td>C</td></tr>\n",
       "\t<tr><td>schp683</td><td>0.39236614</td><td>0.0123024387</td><td>10×AGA</td><td>G</td></tr>\n",
       "\t<tr><td>schp684</td><td>0.16007992</td><td>0.0023117569</td><td>10×AGA</td><td>B</td></tr>\n",
       "\t<tr><td>schp685</td><td>0.32232050</td><td>0.0055286888</td><td>10×AGA</td><td>F</td></tr>\n",
       "\t<tr><td>schp686</td><td>0.23621167</td><td>0.0026111021</td><td>10×AGA</td><td>E</td></tr>\n",
       "\t<tr><td>schp687</td><td>0.22327250</td><td>0.0023391158</td><td>10×AGA</td><td>D</td></tr>\n",
       "\t<tr><td>schp688</td><td>0.08411708</td><td>0.0073078286</td><td>10×AGA</td><td>A</td></tr>\n",
       "\t<tr><td>schp689</td><td>0.38147080</td><td>0.0139288283</td><td>10×AGA</td><td>H</td></tr>\n",
       "\t<tr><td>schp690</td><td>0.17167947</td><td>0.0019761750</td><td>10×AGA</td><td>C</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 16 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " strain & mean\\_ratio & se\\_ratio & insert\\_sequence & kozak\\_region\\\\\n",
       " <chr> & <dbl> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t schp674 & 0.06249634 & 0.0004523589 & 10×AAG & G\\\\\n",
       "\t schp675 & 0.18058849 & 0.0039655570 & 10×AAG & B\\\\\n",
       "\t schp676 & 0.13113391 & 0.0052955241 & 10×AAG & F\\\\\n",
       "\t schp677 & 0.19254120 & 0.0033418563 & 10×AAG & E\\\\\n",
       "\t schp678 & 0.21229042 & 0.0011147893 & 10×AAG & D\\\\\n",
       "\t schp679 & 0.12793468 & 0.0062573354 & 10×AAG & A\\\\\n",
       "\t schp680 & 0.05778072 & 0.0011459648 & 10×AAG & H\\\\\n",
       "\t schp681 & 0.18320179 & 0.0023272205 & 10×AAG & C\\\\\n",
       "\t schp683 & 0.39236614 & 0.0123024387 & 10×AGA & G\\\\\n",
       "\t schp684 & 0.16007992 & 0.0023117569 & 10×AGA & B\\\\\n",
       "\t schp685 & 0.32232050 & 0.0055286888 & 10×AGA & F\\\\\n",
       "\t schp686 & 0.23621167 & 0.0026111021 & 10×AGA & E\\\\\n",
       "\t schp687 & 0.22327250 & 0.0023391158 & 10×AGA & D\\\\\n",
       "\t schp688 & 0.08411708 & 0.0073078286 & 10×AGA & A\\\\\n",
       "\t schp689 & 0.38147080 & 0.0139288283 & 10×AGA & H\\\\\n",
       "\t schp690 & 0.17167947 & 0.0019761750 & 10×AGA & C\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 16 × 5\n",
       "\n",
       "| strain &lt;chr&gt; | mean_ratio &lt;dbl&gt; | se_ratio &lt;dbl&gt; | insert_sequence &lt;chr&gt; | kozak_region &lt;chr&gt; |\n",
       "|---|---|---|---|---|\n",
       "| schp674 | 0.06249634 | 0.0004523589 | 10×AAG | G |\n",
       "| schp675 | 0.18058849 | 0.0039655570 | 10×AAG | B |\n",
       "| schp676 | 0.13113391 | 0.0052955241 | 10×AAG | F |\n",
       "| schp677 | 0.19254120 | 0.0033418563 | 10×AAG | E |\n",
       "| schp678 | 0.21229042 | 0.0011147893 | 10×AAG | D |\n",
       "| schp679 | 0.12793468 | 0.0062573354 | 10×AAG | A |\n",
       "| schp680 | 0.05778072 | 0.0011459648 | 10×AAG | H |\n",
       "| schp681 | 0.18320179 | 0.0023272205 | 10×AAG | C |\n",
       "| schp683 | 0.39236614 | 0.0123024387 | 10×AGA | G |\n",
       "| schp684 | 0.16007992 | 0.0023117569 | 10×AGA | B |\n",
       "| schp685 | 0.32232050 | 0.0055286888 | 10×AGA | F |\n",
       "| schp686 | 0.23621167 | 0.0026111021 | 10×AGA | E |\n",
       "| schp687 | 0.22327250 | 0.0023391158 | 10×AGA | D |\n",
       "| schp688 | 0.08411708 | 0.0073078286 | 10×AGA | A |\n",
       "| schp689 | 0.38147080 | 0.0139288283 | 10×AGA | H |\n",
       "| schp690 | 0.17167947 | 0.0019761750 | 10×AGA | C |\n",
       "\n"
      ],
      "text/plain": [
       "   strain  mean_ratio se_ratio     insert_sequence kozak_region\n",
       "1  schp674 0.06249634 0.0004523589 10×AAG          G           \n",
       "2  schp675 0.18058849 0.0039655570 10×AAG          B           \n",
       "3  schp676 0.13113391 0.0052955241 10×AAG          F           \n",
       "4  schp677 0.19254120 0.0033418563 10×AAG          E           \n",
       "5  schp678 0.21229042 0.0011147893 10×AAG          D           \n",
       "6  schp679 0.12793468 0.0062573354 10×AAG          A           \n",
       "7  schp680 0.05778072 0.0011459648 10×AAG          H           \n",
       "8  schp681 0.18320179 0.0023272205 10×AAG          C           \n",
       "9  schp683 0.39236614 0.0123024387 10×AGA          G           \n",
       "10 schp684 0.16007992 0.0023117569 10×AGA          B           \n",
       "11 schp685 0.32232050 0.0055286888 10×AGA          F           \n",
       "12 schp686 0.23621167 0.0026111021 10×AGA          E           \n",
       "13 schp687 0.22327250 0.0023391158 10×AGA          D           \n",
       "14 schp688 0.08411708 0.0073078286 10×AGA          A           \n",
       "15 schp689 0.38147080 0.0139288283 10×AGA          H           \n",
       "16 schp690 0.17167947 0.0019761750 10×AGA          C           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data %>% \n",
    "    pivot_wider(names_from = \"channel\", values_from = \"value\") %>%\n",
    "    mutate(ratio = yfp/rfp) %>%\n",
    "    group_by(strain) %>%\n",
    "    summarize(mean_ratio = mean(ratio),\n",
    "              se_ratio = sd(ratio)/sqrt(n())) %>%\n",
    "    left_join(annotations, by=\"strain\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Plot the mean and standard deviation of the YFP-RFP ratio as a function of the Kozak region.\n",
    "\n",
    "Hint: You have to use `geom_errorbar()` in addition to `geom_point()` and `geom_line()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 180,
       "width": 300
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data %>% \n",
    "    pivot_wider(names_from = \"channel\", values_from = \"value\") %>%\n",
    "    mutate(ratio = yfp/rfp) %>%\n",
    "    group_by(strain) %>%\n",
    "    summarize(mean_ratio = mean(ratio),\n",
    "              se_ratio = sd(ratio)/sqrt(n()), \n",
    "              sd_ratio = sd(ratio)) %>%\n",
    "    left_join(annotations, by=\"strain\") %>%\n",
    "    ggplot(aes(kozak_region, mean_ratio, color=insert_sequence, group = insert_sequence)) + \n",
    "    geom_point() +\n",
    "    geom_line() +\n",
    "    geom_errorbar(aes(ymin=mean_ratio-sd_ratio, ymax=mean_ratio+sd_ratio))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.1.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}