Merge pull request #5 from Layalchristine24/f-nest-pack

Write post about nest pack

_freeze/posts/2024-05-30_pack-nest/index/execute-results/html.json

@@ -0,0 +1,15 @@
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+    "markdown": "---\ntitle: What is the difference between (un)packing and (un)nesting a tibble?\nauthor:\n  - name:\n      given: Layal Christine\n      family: Lettry\n      orcid: 0009-0008-6396-0523\n    affiliations:\n      - id: cynkra\n      - name: cynkra GmbH\n        city: Zurich\n        state: CH\n      - id: unifr\n      - name: University of Fribourg, Dept. of Informatics, ASAM Group\n        city: Fribourg\n        state: CH\ndate: 2024-05-30\ncategories: [nest, unnest, pack, unpack, tidyr, constructive]\nimage: image.jpg\ncitation: \n  url: https://rdiscovery.netlify.app/posts/2024-05-30_pack-nest/\nformat:\n  html:\n    toc: true\n    toc-depth: 6\n    toc-title: Contents\n    toc-location: right\n    number-sections: false\neditor_options: \n  chunk_output_type: console\n---\n\n\n*Does a nested tibble have the same structure as a packed tibble?*\n\n# Initial object\n\nLet's assume that we have the object `my_tib` which is a nested tibble containing a list, namely `my_values`, with another tibble where the variables are `my_ints` and `my_chars`. \n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_tib <-\n  tibble::tibble(\n    my_values = list(tibble::tibble(\n      my_ints = 1L:5L,\n      my_chars = LETTERS[my_ints]\n    ))\n  )\nconstructive::construct(my_tib)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\ntibble::tibble(\n  my_values = list(tibble::tibble(my_ints = 1:5, my_chars = c(\"A\", \"B\", \"C\", \"D\", \"E\"))),\n)\n```\n\n\n:::\n:::\n\n\nWe could also use `tidyr::nest()` to create `my_tib` (please refer to [this article](https://tidyr.tidyverse.org/articles/nest.html) for more info).\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_nested_tib <-\n  tibble::tribble(\n    ~my_ints, ~my_chars,\n    1L, \"A\",\n    2L, \"B\",\n    3L, \"C\",\n    4L, \"D\",\n    5L, \"E\"\n  ) |>\n  tidyr::nest(my_values = c(my_ints, my_chars))\n\nconstructive::construct(my_nested_tib)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\ntibble::tibble(\n  my_values = list(tibble::tibble(my_ints = 1:5, my_chars = c(\"A\", \"B\", \"C\", \"D\", \"E\"))),\n)\n```\n\n\n:::\n:::\n\n\nAs you can see, there is no difference between `my_tib` and `my_nested_tib`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwaldo::compare(my_tib, my_nested_tib)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n✔ No differences\n```\n\n\n:::\n:::\n\n\n# What is the difference between a nested and a packed tibble?\nTo obtain a packed tibble, we should pack the variables `my_ints` and `my_chars` together so that we have a tibble in another tibble instead of a list with an element that is a tibble.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_packed_tib <-\n  tibble::tribble(\n    ~my_ints, ~my_chars,\n    1L, \"A\",\n    2L, \"B\",\n    3L, \"C\",\n    4L, \"D\",\n    5L, \"E\"\n  ) |>\n  tidyr::pack(my_values = c(my_ints, my_chars))\nconstructive::construct(my_packed_tib)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\ntibble::tibble(\n  my_values = tibble::tibble(my_ints = 1:5, my_chars = c(\"A\", \"B\", \"C\", \"D\", \"E\")),\n)\n```\n\n\n:::\n:::\n\n\nWe can assess the difference between `my_nested_tib` and `my_packed_tib` with `waldo::compare()`. \n\n::: {.cell}\n\n```{.r .cell-code}\nwaldo::compare(my_nested_tib, my_packed_tib)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n`attr(old, 'row.names')`: 1        \n`attr(new, 'row.names')`: 1 2 3 4 5\n\n`old$my_values` is a list\n`new$my_values` is an S3 object of class <tbl_df/tbl/data.frame>, a list\n```\n\n\n:::\n:::\n\n\nThis tells us that `my_nested_tib` has only one row and contains the variable `my_values` that is a list, whereas `my_packed_tib` has 5 rows and is constituted by the variable `my_values` that has, in this case, the class `data.frame`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nclass(my_packed_tib$my_values)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] \"tbl_df\"     \"tbl\"        \"data.frame\"\n```\n\n\n:::\n:::\n\n\nFor the record, a data frame is a special list where every element has the same length.\n\n::: {.cell}\n\n```{.r .cell-code}\ntypeof(my_packed_tib$my_values)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] \"list\"\n```\n\n\n:::\n:::\n\n\n\n# How to unnest or unpack a tibble?\n\nTo get a tibble without any variable that is a list or a tibble, we should unnest and, respectively, unpack our nested/packed tibble.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_unnested_tib <-\n  my_nested_tib |>\n  tidyr::unnest(my_values)\n\nconstructive::construct(my_unnested_tib)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\ntibble::tibble(my_ints = 1:5, my_chars = c(\"A\", \"B\", \"C\", \"D\", \"E\"))\n```\n\n\n:::\n:::\n\n\nNow, we have a simple tibble with two variables instead of one single variable that is a list.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_unpacked_tib <-\n  my_packed_tib |>\n  tidyr::unpack(my_values)\n\nconstructive::construct(my_unpacked_tib)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\ntibble::tibble(my_ints = 1:5, my_chars = c(\"A\", \"B\", \"C\", \"D\", \"E\"))\n```\n\n\n:::\n:::\n\n\nHere again, we obtain a simple tibble with two variables instead of one single variable that has the class `data.frame`.\n",
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+}

posts/2024-05-30_pack-nest/index.qmd

@@ -0,0 +1,129 @@
+---
+title: What is the difference between (un)packing and (un)nesting a tibble?
+author:
+  - name:
+      given: Layal Christine
+      family: Lettry
+      orcid: 0009-0008-6396-0523
+    affiliations:
+      - id: cynkra
+      - name: cynkra GmbH
+        city: Zurich
+        state: CH
+      - id: unifr
+      - name: University of Fribourg, Dept. of Informatics, ASAM Group
+        city: Fribourg
+        state: CH
+date: 2024-05-30
+categories: [nest, unnest, pack, unpack, tidyr, constructive]
+image: image.jpg
+citation: 
+  url: https://rdiscovery.netlify.app/posts/2024-05-30_pack-nest/
+format:
+  html:
+    toc: true
+    toc-depth: 6
+    toc-title: Contents
+    toc-location: right
+    number-sections: false
+editor_options: 
+  chunk_output_type: console
+---
+
+*Does a nested tibble have the same structure as a packed tibble?*
+
+# Initial object
+
+Let's assume that we have the object `my_tib` which is a nested tibble containing a list, namely `my_values`, with another tibble where the variables are `my_ints` and `my_chars`. 
+
+```{r}
+my_tib <-
+  tibble::tibble(
+    my_values = list(tibble::tibble(
+      my_ints = 1L:5L,
+      my_chars = LETTERS[my_ints]
+    ))
+  )
+constructive::construct(my_tib)
+```
+
+We could also use `tidyr::nest()` to create `my_tib` (please refer to [this article](https://tidyr.tidyverse.org/articles/nest.html) for more info).
+
+```{r}
+my_nested_tib <-
+  tibble::tribble(
+    ~my_ints, ~my_chars,
+    1L, "A",
+    2L, "B",
+    3L, "C",
+    4L, "D",
+    5L, "E"
+  ) |>
+  tidyr::nest(my_values = c(my_ints, my_chars))
+
+constructive::construct(my_nested_tib)
+```
+
+As you can see, there is no difference between `my_tib` and `my_nested_tib`.
+
+```{r}
+waldo::compare(my_tib, my_nested_tib)
+```
+
+# What is the difference between a nested and a packed tibble?
+To obtain a packed tibble, we should pack the variables `my_ints` and `my_chars` together so that we have a tibble in another tibble instead of a list with an element that is a tibble.
+
+```{r}
+my_packed_tib <-
+  tibble::tribble(
+    ~my_ints, ~my_chars,
+    1L, "A",
+    2L, "B",
+    3L, "C",
+    4L, "D",
+    5L, "E"
+  ) |>
+  tidyr::pack(my_values = c(my_ints, my_chars))
+constructive::construct(my_packed_tib)
+```
+
+We can assess the difference between `my_nested_tib` and `my_packed_tib` with `waldo::compare()`. 
+```{r}
+waldo::compare(my_nested_tib, my_packed_tib)
+```
+
+This tells us that `my_nested_tib` has only one row and contains the variable `my_values` that is a list, whereas `my_packed_tib` has 5 rows and is constituted by the variable `my_values` that has, in this case, the class `data.frame`.
+
+```{r}
+class(my_packed_tib$my_values)
+```
+
+For the record, a data frame is a special list where every element has the same length.
+```{r}
+typeof(my_packed_tib$my_values)
+```
+
+
+# How to unnest or unpack a tibble?
+
+To get a tibble without any variable that is a list or a tibble, we should unnest and, respectively, unpack our nested/packed tibble.
+
+```{r}
+my_unnested_tib <-
+  my_nested_tib |>
+  tidyr::unnest(my_values)
+
+constructive::construct(my_unnested_tib)
+```
+
+Now, we have a simple tibble with two variables instead of one single variable that is a list.
+
+```{r}
+my_unpacked_tib <-
+  my_packed_tib |>
+  tidyr::unpack(my_values)
+
+constructive::construct(my_unpacked_tib)
+```
+
+Here again, we obtain a simple tibble with two variables instead of one single variable that has the class `data.frame`.