From 8224639570220fdb7ea2004683848c9b17dbbad7 Mon Sep 17 00:00:00 2001 From: Layal Christine Lettry Date: Thu, 30 May 2024 11:24:36 +0200 Subject: [PATCH 1/3] feat: write first part nest-pack post --- .../index/execute-results/html.json | 15 +++ posts/2024-05-30_pack-nest/image.jpg | Bin 0 -> 573048 bytes posts/2024-05-30_pack-nest/index.qmd | 93 ++++++++++++++++++ 3 files changed, 108 insertions(+) create mode 100644 _freeze/posts/2024-05-30_pack-nest/index/execute-results/html.json create mode 100644 posts/2024-05-30_pack-nest/image.jpg create mode 100644 posts/2024-05-30_pack-nest/index.qmd diff --git a/_freeze/posts/2024-05-30_pack-nest/index/execute-results/html.json b/_freeze/posts/2024-05-30_pack-nest/index/execute-results/html.json new file mode 100644 index 0000000..318e0c3 --- /dev/null +++ b/_freeze/posts/2024-05-30_pack-nest/index/execute-results/html.json @@ -0,0 +1,15 @@ +{ + "hash": "ed63d35bcc26dd1381133b4c2095369e", + "result": { + "engine": "knitr", + "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*What is the difference between (un)packing and (un)nesting a tibble?*\n\n# Initial object\n\nLet's assume that `my_tib` is a nested tibble containing a list, namely `my_values`, with another tibble whose 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 if we do not want any nested tibble?\nIn this case, we should pack the variables `my_ints` and `my_chars` so that we have a tibble in a tibble instead of having a list with a tibble element.\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 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 , a list\n```\n\n\n:::\n:::\n", + "supporting": [], + "filters": [ + "rmarkdown/pagebreak.lua" + ], + "includes": {}, + "engineDependencies": {}, + "preserve": {}, + "postProcess": true + } +} \ No newline at end of file diff --git a/posts/2024-05-30_pack-nest/image.jpg b/posts/2024-05-30_pack-nest/image.jpg new file mode 100644 index 0000000000000000000000000000000000000000..7751aaf33a8be5d6b6262b31353cac5731b11436 GIT binary patch literal 573048 zcmdSgcRZWl|2X{EMN92bq%~@!s;c%#7mBKulA39Y+O=CF5?k$}Xi8QuZ_43xiUuU)?`n?UuCJQXz!;M2dp zm%VoF6V=~q{6zh)HJ$~0qWL@jnf{;4P*(O=qNk$y=Qs5KS#BzpzspU<{a?yW^{=&2 z2~q!hE7~sqT;G2w_vzT_Bp{U1ZhsOr6f4{-25AuXN^5 zV*Gdd>%Xa~X(?|;dOG@lfB%1+Ii04Ind!6#^ulxoxc zRlq;Z{a2!tlTt8-GmK2k=K<7IG&Iz-G<1~mQkD$()9AEpbnKU8Z_#r+dd48^&Urm3 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`my_ints` and `my_chars` so that we have a tibble in a tibble instead of having a list with a tibble element. + +```{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 with `waldo::compare()`. +```{r} +waldo::compare(my_nested_tib, my_packed_tib) +``` From a6f118367990dc76b8b73cfa366cd3c955ccb874 Mon Sep 17 00:00:00 2001 From: Layal Christine Lettry Date: Thu, 30 May 2024 20:02:55 +0200 Subject: [PATCH 2/3] feat: complete section pack and nest --- .../index/execute-results/html.json | 4 ++-- posts/2024-05-30_pack-nest/index.qmd | 13 ++++++++++--- 2 files changed, 12 insertions(+), 5 deletions(-) diff --git a/_freeze/posts/2024-05-30_pack-nest/index/execute-results/html.json b/_freeze/posts/2024-05-30_pack-nest/index/execute-results/html.json index 318e0c3..4d86b02 100644 --- a/_freeze/posts/2024-05-30_pack-nest/index/execute-results/html.json +++ b/_freeze/posts/2024-05-30_pack-nest/index/execute-results/html.json @@ -1,8 +1,8 @@ { - "hash": "ed63d35bcc26dd1381133b4c2095369e", + "hash": "1d5ca2e36ca4bff2ff5586284c49d806", "result": { "engine": "knitr", - "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*What is the difference between (un)packing and (un)nesting a tibble?*\n\n# Initial object\n\nLet's assume that `my_tib` is a nested tibble containing a list, namely `my_values`, with another tibble whose 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 if we do not want any nested tibble?\nIn this case, we should pack the variables `my_ints` and `my_chars` so that we have a tibble in a tibble instead of having a list with a tibble element.\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 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 , a list\n```\n\n\n:::\n:::\n", + "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*What is the difference between (un)packing and (un)nesting a tibble?*\n\n# Initial object\n\nLet's assume that `my_tib` 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 if we do not want any nested tibble?\nIn this case, 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 , 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 is in this case a data frame. \nFor the record, a data frame is a special list where every element has the same lenght.\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/posts/2024-05-30_pack-nest/index.qmd b/posts/2024-05-30_pack-nest/index.qmd index 00809c2..15ad87a 100644 --- a/posts/2024-05-30_pack-nest/index.qmd +++ b/posts/2024-05-30_pack-nest/index.qmd @@ -34,7 +34,7 @@ editor_options: # Initial object -Let's assume that `my_tib` is a nested tibble containing a list, namely `my_values`, with another tibble whose variables are `my_ints` and `my_chars`. +Let's assume that `my_tib` 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 <- @@ -71,7 +71,7 @@ waldo::compare(my_tib, my_nested_tib) ``` # What if we do not want any nested tibble? -In this case, we should pack the variables `my_ints` and `my_chars` so that we have a tibble in a tibble instead of having a list with a tibble element. +In this case, 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 <- @@ -87,7 +87,14 @@ my_packed_tib <- constructive::construct(my_packed_tib) ``` -We can assess the difference with `waldo::compare()`. +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 is, in this case, a data frame. +For the record, a data frame is a special list where every element has the same length. + +```{r} +typeof(my_packed_tib) +``` From 41828ebf6b471e5f5c09b4b9f2b16b15a5b22db2 Mon Sep 17 00:00:00 2001 From: Layal Christine Lettry Date: Thu, 30 May 2024 20:23:25 +0200 Subject: [PATCH 3/3] feat: complete blog post --- .../index/execute-results/html.json | 4 +- posts/2024-05-30_pack-nest/index.qmd | 41 ++++++++++++++++--- 2 files changed, 37 insertions(+), 8 deletions(-) diff --git a/_freeze/posts/2024-05-30_pack-nest/index/execute-results/html.json b/_freeze/posts/2024-05-30_pack-nest/index/execute-results/html.json index 4d86b02..b49d7b0 100644 --- a/_freeze/posts/2024-05-30_pack-nest/index/execute-results/html.json +++ b/_freeze/posts/2024-05-30_pack-nest/index/execute-results/html.json @@ -1,8 +1,8 @@ { - "hash": "1d5ca2e36ca4bff2ff5586284c49d806", + "hash": "6d6be9bd60dc0bed592932a4063b2569", "result": { "engine": "knitr", - "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*What is the difference between (un)packing and (un)nesting a tibble?*\n\n# Initial object\n\nLet's assume that `my_tib` 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 if we do not want any nested tibble?\nIn this case, 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 , 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 is in this case a data frame. \nFor the record, a data frame is a special list where every element has the same lenght.\n", + "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 , 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", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/posts/2024-05-30_pack-nest/index.qmd b/posts/2024-05-30_pack-nest/index.qmd index 15ad87a..f0a7baa 100644 --- a/posts/2024-05-30_pack-nest/index.qmd +++ b/posts/2024-05-30_pack-nest/index.qmd @@ -30,11 +30,11 @@ editor_options: chunk_output_type: console --- -*What is the difference between (un)packing and (un)nesting a tibble?* +*Does a nested tibble have the same structure as a packed tibble?* # Initial object -Let's assume that `my_tib` is a nested tibble containing a list, namely `my_values`, with another tibble where the variables are `my_ints` and `my_chars`. +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 <- @@ -70,8 +70,8 @@ As you can see, there is no difference between `my_tib` and `my_nested_tib`. waldo::compare(my_tib, my_nested_tib) ``` -# What if we do not want any nested tibble? -In this case, 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. +# 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 <- @@ -92,9 +92,38 @@ We can assess the difference between `my_nested_tib` and `my_packed_tib` with `w 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 is, in this case, a data frame. +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} -typeof(my_packed_tib) +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`.