02-dplyr.Rmd

---
layout: topic
title: Aggregating and analyzing data with dplyr
author: Data Carpentry contributors
---

```{r, echo=FALSE, message = FALSE}
source("setup.R")
surveys <- read.csv("data/portal_data_joined.csv")
```

------------

# Data Manipulation using dplyr

Bracket subsetting is handy, but it can be cumbersome and difficult to read,
especially for complicated operations. Enter `dplyr`. `dplyr` is a package for
making data manipulation easier.

Packages in R are basically sets of additional functions that let you do more
stuff. The functions we've been using so far, like `str()` or `data.frame()`,
come built into R; packages give you access to more of them. Before you use a
package for the first time you need to install it on your machine, and then you
should to import it in every subsequent R session when you'll need it.

```{r, eval = FALSE}
install.packages("dplyr")
```

While we're installing stuff, let's also install the ggplot2 package,
which we'll use next.

```{r, eval = FALSE}
install.packages("ggplot2")
```


You might get asked to choose a CRAN mirror -- this is basically asking you to
choose a site to download the package from. The choice doesn't matter too much;
we recommend the RStudio mirror.

```{r, message = FALSE}
library(dplyr)    ## load the package
```

## What is `dplyr`?

The package `dplyr` provides easy tools for the most common data manipulation
tasks. It is built to work directly with data frames. The thinking behind it was
largely inspired by the package `plyr` which has been in use for some time but
suffered from being slow in some cases.` dplyr` addresses this by porting much
of the computation to C++. An additional feature is the ability to work with
data stored directly in an external database. The benefits of doing this are
that the data can be managed natively in a relational database, queries can be
conducted on that database, and only the results of the query returned.

This addresses a common problem with R in that all operations are conducted in
memory and thus the amount of data you can work with is limited by available
memory. The database connections essentially remove that limitation in that you
can have a database of many 100s GB, conduct queries on it directly, and pull
back just what you need for analysis in R.

### Selecting columns and filtering rows

We're going to learn some of the most common `dplyr` functions: `select()`,
`filter()`, `mutate()`, `group_by()`, and `summarize()`. To select columns of a
data frame, use `select()`. The first argument to this function is the data
frame (`surveys`), and the subsequent arguments are the columns to keep.

```{r, results = 'hide'}
selected_col <- select(surveys, plot_id, species_id, weight)
head(selected_col)
```

To choose rows, use `filter()`:

```{r}
surveys1995 <- filter(surveys, year == 1995)
head(surveys1995)
```

### Pipes

The _pipe_ operator (`%>%`) from the magrittr package makes it easy to
chain these actions together: the output of one function becomes the
input of the next.

```{r}
surveys %>%
  filter(weight < 5) %>%
  select(species_id, sex, weight)
```

Another cumbersome bit of typing. In RStudio, type <kbd>`Ctrl`</kbd> +
<kbd>`Shift`</kbd> + <kbd>`M`</kbd> and the `%>%` operator will be inserted.

In the above we use the pipe to send the `surveys` data set first through
`filter`, to keep rows where `wgt` was less than 5, and then through `select` to
keep the `species` and `sex` columns. When the data frame is being passed to the
`filter()` and `select()` functions through a pipe, we don't need to include it
as an argument to these functions anymore.

If we wanted to create a new object with this smaller version of the data we
could do so by assigning it a new name:

```{r}
surveys_sml <- surveys %>%
  filter(weight < 5) %>%
  select(species_id, sex, weight)
```

Note that the final data frame is the leftmost part of this expression.

### Challenge

Using pipes, subset the data to include individuals collected before 1995,
and retain the columns `year`, `sex`, and `weight.`

<!-- end challenge -->


### Mutate

Frequently you'll want to create new columns based on the values in existing
columns, for example to do unit conversions, or find the ratio of values in two
columns. For this we'll use `mutate()`.

To create a new column of weight in kg:

```{r}
surveys %>%
  mutate(weight_kg = weight / 1000)
```

If this runs off your screen and you just want to see the first few rows, you
can use a pipe to view the `head()` of the data (pipes work with non-dplyr
functions too, as long as the `dplyr` or `magrittr` packages are loaded).

```{r}
surveys %>%
  mutate(weight_kg = weight / 1000) %>%
  head
```

The first few rows are full of NAs, so if we wanted to remove those we could
insert a `filter()` in this chain:

```{r}
surveys %>%
  filter(!is.na(weight)) %>%
  mutate(weight_kg = weight / 1000) %>%
  head
```

`is.na()` is a function that determines whether something is or is not an `NA`.
The `!` symbol negates it, so we're asking for everything that is not an `NA`.

### Challenge

Create a new dataframe from the survey data that meets the following
criteria:

- contains only the `species_id` column and a column that contains
values that are the square-root of `hindfoot_length` values (e.g. a new column
`hindfoot_sqrt`).
- In this `hindfoot_sqrt` column, there are no NA values
and all values are < 3.

Hint: think about how the commands should be ordered

<!-- end challenge -->


### Split-apply-combine data analysis and the summarize() function

Many data analysis tasks can be approached using the "split-apply-combine"
paradigm: split the data into groups, apply some analysis to each group, and
then combine the results. `dplyr` makes this very easy through the use of the
`group_by()` function. `group_by()` splits the data into groups upon which some
operations can be run. For example, if we wanted to group by sex and find the
number of rows of data for each sex, we would do:

```{r}
surveys %>%
  group_by(sex) %>%
  tally()
```

Here, `tally()` is the action applied to the groups created to `group_by()` and counts the total number of records for each category. `group_by()` is often used together with `summarize()` which collapses each
group into a single-row summary of that group. So to view mean `weight` by sex:

```{r}
surveys %>%
  group_by(sex) %>%
  summarize(mean_weight = mean(weight, na.rm = TRUE))
```

You can group by multiple columns too:

```{r}
surveys %>%
  group_by(sex, species_id) %>%
  summarize(mean_weight = mean(weight, na.rm = TRUE))
```

It looks like most of these species were never weighed. We could then discard
rows where `mean_weight` is `NA` with
`filter()`:

```{r}
surveys %>%
  group_by(sex, species_id) %>%
  summarize(mean_weight = mean(weight, na.rm = TRUE)) %>%
  filter(!is.na(mean_weight))
```

Another thing we might do here is sort rows by `mean_weight`, using
`arrange()`.


```{r}
surveys %>%
  group_by(sex, species_id) %>%
  summarize(mean_weight = mean(weight, na.rm = TRUE)) %>%
    filter(!is.na(mean_weight)) %>%
    arrange(mean_weight)
```

If you want them sorted from highest to lowest, use `desc()`.

```{r}
surveys %>%
  group_by(sex, species_id) %>%
  summarize(mean_weight = mean(weight, na.rm = TRUE)) %>%
    filter(!is.na(mean_weight)) %>%
    arrange(desc(mean_weight))
```

Also note that you can include multiple summaries.

```{r}
surveys %>%
  group_by(sex, species_id) %>%
  summarize(mean_weight = mean(weight, na.rm = TRUE),
            min_weight = min(weight, na.rm = TRUE)) %>%
    filter(!is.na(mean_weight)) %>%
    arrange(desc(mean_weight))
```

### Challenge

How many times was each `plot_type` surveyed?

<!-- end challenge -->


### Challenge

Use `group_by()` and `summarize()` to find the mean, min, and max hindfoot
length for each species.

<!-- end challenge -->

### Challenge

What was the heaviest animal measured in each year? Return the columns `year`,
`genus`, `species`, and `weight`.

Hint: Use `filter()` rather than `summarize()`.

<!-- end challenge -->

### A bit of data cleaning

In preparations for the plotting, let's do a bit of data cleaning:
remove rows with missing `species_id`, `weight`, `hindfoot_length`, or
`sex`.

```{r clean_data_1}
surveys_complete <- surveys %>%
    filter(species_id != "", !is.na(weight)) %>%
    filter(!is.na(hindfoot_length), sex != "")
```

There are a lot of species with low counts. Let's remove the species
with less than 10 counts.

```{r}
# count records per species
species_counts <- surveys_complete %>%
    group_by(species_id) %>%
    tally

head(species_counts)

# get names of the species with counts >= 10
frequent_species <-  species_counts %>%
    filter(n >= 10) %>%
    select(species_id)

# filter out the less-frequent species
reduced <- surveys_complete %>%
    filter(species_id %in% frequent_species$species_id)
```

We might save this to a file:

```{r save_reduced_data_to_file, eval=FALSE}
write.csv(reduced, "CleanData/portal_data_reduced.csv")
```



[Handy dplyr cheatsheet](http://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf)