input %>%
diff --git a/docs/blog.html b/docs/blog.html
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--- a/docs/blog.html
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@@ -1,3576 +1,3576 @@
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-Blog Posts
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-`ave()` for the average {dplyr} user
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-tidyverse 🤝 base R
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-
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-
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-args(args(args)(args))
-
-The unexpected sequal to "R is a language optimized for meme-ing"
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-HelloWorld("print")
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-R is a language optimized for meme-ing
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-2023 Year in Review
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-Reflections and updates on what I've been up to in 2023
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-
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-
-
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-The many ways to (un)tidy-select
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-Deconstructing {tidyselect} and building it back up
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-![]()
-
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-Fumbling my way through an XY problem
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-Some lessons learned from a (personal) case study
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-![]()
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-Row relational operations with slice()
-
-A love letter to dplyr::slice() and a gallery of usecases
-
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-
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-![]()
-
-
-First impressions of DataFrames.jl and accessories
-
-Perspectives from a {dplyr} and {data.table} useR
-
-
-
-
-
-
-![]()
-
-
-Reflections on useR! 2022
-
-Notes from attending and speaking at my first R conference
-
-
-
-
-
-
-![]()
-
-
-Demystifying delayed aesthetic evaluation: Part 2
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-Exposing the `Stat` ggproto in functional programming terms
-
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-![]()
-
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-Demystifying delayed aesthetic evaluation: Part 1
-
-Exploring the logic of `after_stat()` to peek inside ggplot internals
-
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-
-
-
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-![]()
-
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-Setting up and debugging custom fonts
-
-A practical introduction to all (new) things font in R
-
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-![]()
-
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-Random Sampling: A table animation
-
-Plus a convenient way of rendering LaTeX expressions as images
-
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-
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-![]()
-
-
-Collapse repetitive piping with reduce()
-
-Featuring accumulate()
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-![]()
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-Plot Makeover #2
-
-Making a dodged-stacked hybrid bar plot in {ggplot2}
-
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-![]()
-
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-TidyTuesday 2020 week 45
-
-Waffle chart of IKEA furnitures in stock
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-![]()
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-TidyTuesday 2020 week 44
-
-Patched animation of the location and cumulative capacity of wind turbines in Canada
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-![]()
-
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-Analysis of @everycolorbot's tweets
-
-And why you should avoid neon colors
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-![]()
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-Designing guiding aesthetics
-
-The fine line between creativity and noise
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-![]()
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-Demystifying stat_ layers in {ggplot2}
-
-The motivation behind stat, the distinction between stat and geom, and a case study of stat_summary()
-
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-
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-
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-![]()
-
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-TidyTuesday 2020 week 39
-
-Stacked area plot of the heights of Himalayan peaks attempted over the last century
-
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-![]()
-
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-Plot Makeover #1
-
-Flattening a faceted grid for strictly horizontal comparisons
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-![]()
-
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-TidyTuesday 2020 week 38
-
-Visualizing two decades of primary and secondary education spending with {gt}
-
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-![]()
-
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-Embedding videos in {reactable} tables
-
-Pushing the limits of expandable row details
-
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-![]()
-
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-Fonts for graphs
-
-A small collection of my favorite fonts for data visualization
-
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-![]()
-
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-TidyTuesday 2020 Week 33
-
-An animation of the main characters in Avatar
-
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-![]()
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-Saving a line of piping
-
-Some notes on lesser known functions/functionalities that combine common chain of {dplyr} verbs.
-
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-![]()
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-TidyTuesday 2020 Week 32
-
-A dumbbell chart visualization of energy production trends among European countries
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-![]()
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-Six years of my Spotify playlists
-
-An analysis of acoustic features with {spotifyr}
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-![]()
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-Shiny tips - the first set
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-%||%, imap() + {shinybusy}, and user inputs in modalDialog()
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-![]()
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-geom_paired_raincloud()
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-A {ggplot2} geom for visualizing change in distribution between two conditions.
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-![]()
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-Plotting treemaps with {treemap} and {ggplot2}
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-Using underlying plot data for maximum customization
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-![]()
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-Indexing tip for {spacyr}
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-Speeding up the analysis of dependency relations.
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-The Correlation Parameter in Mixed Effects Models
-
-Notes on the Corr term in {lme4} output
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+ June Choe: Blog Posts
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+Blog Posts
+
+
+
+
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+
+
+`ave()` for the average {dplyr} user
+
+tidyverse 🤝 base R
+
+
+
+
+
+
+
+
+
+args(args(args)(args))
+
+The unexpected sequal to "R is a language optimized for meme-ing"
+
+
+
+
+
+
+
+
+
+HelloWorld("print")
+
+R is a language optimized for meme-ing
+
+
+
+
+
+
+
+
+
+2023 Year in Review
+
+Reflections and updates on what I've been up to in 2023
+
+
+
+
+
+
+
+
+
+The many ways to (un)tidy-select
+
+Deconstructing {tidyselect} and building it back up
+
+
+
+
+
+
+![]()
+
+
+Fumbling my way through an XY problem
+
+Some lessons learned from a (personal) case study
+
+
+
+
+
+
+![]()
+
+
+Row relational operations with slice()
+
+A love letter to dplyr::slice() and a gallery of usecases
+
+
+
+
+
+
+![]()
+
+
+First impressions of DataFrames.jl and accessories
+
+Perspectives from a {dplyr} and {data.table} useR
+
+
+
+
+
+
+![]()
+
+
+Reflections on useR! 2022
+
+Notes from attending and speaking at my first R conference
+
+
+
+
+
+
+![]()
+
+
+Demystifying delayed aesthetic evaluation: Part 2
+
+Exposing the `Stat` ggproto in functional programming terms
+
+
+
+
+
+
+![]()
+
+
+Demystifying delayed aesthetic evaluation: Part 1
+
+Exploring the logic of `after_stat()` to peek inside ggplot internals
+
+
+
+
+
+
+![]()
+
+
+Setting up and debugging custom fonts
+
+A practical introduction to all (new) things font in R
+
+
+
+
+
+
+![]()
+
+
+Random Sampling: A table animation
+
+Plus a convenient way of rendering LaTeX expressions as images
+
+
+
+
+
+
+![]()
+
+
+Collapse repetitive piping with reduce()
+
+Featuring accumulate()
+
+
+
+
+
+
+![]()
+
+
+Plot Makeover #2
+
+Making a dodged-stacked hybrid bar plot in {ggplot2}
+
+
+
+
+
+
+![]()
+
+
+TidyTuesday 2020 week 45
+
+Waffle chart of IKEA furnitures in stock
+
+
+
+
+
+
+![]()
+
+
+TidyTuesday 2020 week 44
+
+Patched animation of the location and cumulative capacity of wind turbines in Canada
+
+
+
+
+
+
+![]()
+
+
+Analysis of @everycolorbot's tweets
+
+And why you should avoid neon colors
+
+
+
+
+
+
+![]()
+
+
+Designing guiding aesthetics
+
+The fine line between creativity and noise
+
+
+
+
+
+
+![]()
+
+
+Demystifying stat_ layers in {ggplot2}
+
+The motivation behind stat, the distinction between stat and geom, and a case study of stat_summary()
+
+
+
+
+
+
+![]()
+
+
+TidyTuesday 2020 week 39
+
+Stacked area plot of the heights of Himalayan peaks attempted over the last century
+
+
+
+
+
+
+![]()
+
+
+Plot Makeover #1
+
+Flattening a faceted grid for strictly horizontal comparisons
+
+
+
+
+
+
+![]()
+
+
+TidyTuesday 2020 week 38
+
+Visualizing two decades of primary and secondary education spending with {gt}
+
+
+
+
+
+
+![]()
+
+
+Embedding videos in {reactable} tables
+
+Pushing the limits of expandable row details
+
+
+
+
+
+
+![]()
+
+
+Fonts for graphs
+
+A small collection of my favorite fonts for data visualization
+
+
+
+
+
+
+![]()
+
+
+TidyTuesday 2020 Week 33
+
+An animation of the main characters in Avatar
+
+
+
+
+
+
+![]()
+
+
+Saving a line of piping
+
+Some notes on lesser known functions/functionalities that combine common chain of {dplyr} verbs.
+
+
+
+
+
+
+![]()
+
+
+TidyTuesday 2020 Week 32
+
+A dumbbell chart visualization of energy production trends among European countries
+
+
+
+
+
+
+![]()
+
+
+Six years of my Spotify playlists
+
+An analysis of acoustic features with {spotifyr}
+
+
+
+
+
+
+![]()
+
+
+Shiny tips - the first set
+
+%||%, imap() + {shinybusy}, and user inputs in modalDialog()
+
+
+
+
+
+
+![]()
+
+
+geom_paired_raincloud()
+
+A {ggplot2} geom for visualizing change in distribution between two conditions.
+
+
+
+
+
+
+![]()
+
+
+Plotting treemaps with {treemap} and {ggplot2}
+
+Using underlying plot data for maximum customization
+
+
+
+
+
+
+![]()
+
+
+Indexing tip for {spacyr}
+
+Speeding up the analysis of dependency relations.
+
+
+
+
+
+
+![]()
+
+
+The Correlation Parameter in Mixed Effects Models
+
+Notes on the Corr term in {lme4} output
+
+
+
+
+
+
+
+
+
+Blog Posts
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
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diff --git a/docs/blog.xml b/docs/blog.xml
index c1750e8..de5efd8 100644
--- a/docs/blog.xml
+++ b/docs/blog.xml
@@ -12,15 +12,278 @@
https://yjunechoe.github.io
Distill
- Sun, 09 Jun 2024 00:00:00 +0000
+ Sat, 08 Jun 2024 00:00:00 +0000
-
`ave()` for the average {dplyr} user
June Choe
https://yjunechoe.github.io/posts/2024-06-09-ave-for-the-average
- tidyverse 🤝 base R
+
+
+
+<p>I think it’s safe to say that the average <code>{dplyr}</code> user
+does not know the <code>ave()</code> function. For that audience, this
+is a short appreciation post on <code>ave()</code>, a case of tidyverse
+<em>and</em> base R.</p>
+<h2 id="ave"><code>ave()</code></h2>
+<p><code>ave()</code> is a split-apply-combine function in base R
+(specifically, <code>{stats}</code>). It’s a pretty short function -
+maybe you can make out what it does from just reading the code<a
+href="#fn1" class="footnote-ref" id="fnref1"><sup>1</sup></a></p>
+<pre class="r"><code>ave</code></pre>
+<pre><code> function (x, ..., FUN = mean)
+ {
+ if (missing(...))
+ x[] <- FUN(x)
+ else {
+ g <- interaction(...)
+ split(x, g) <- lapply(split(x, g), FUN)
+ }
+ x
+ }
+ <bytecode: 0x0000020fc12974b8>
+ <environment: namespace:stats></code></pre>
+<p>Despite its (rather generic and uninformative) name, I like to think
+of <code>ave()</code> as actually belonging to the <code>*apply()</code>
+family of functions, having particularly close ties to
+<code>tapply()</code>.</p>
+<p>A unique feature of <code>ave()</code> is the invariant that it
+<strong>returns a vector of the same length as the input</strong>. And
+if you use an aggregating function like <code>sum()</code> or
+<code>mean()</code>, it simply repeats those values over the
+observations on the basis of their grouping.</p>
+<p>For example, whereas <code>tapply()</code> can be used to summarize
+the average <code>mpg</code> by <code>cyl</code>:</p>
+<pre class="r"><code>tapply(mtcars$mpg, mtcars$cyl, FUN = mean)</code></pre>
+<pre><code> 4 6 8
+ 26.66364 19.74286 15.10000</code></pre>
+<p>The same syntax with <code>ave()</code> will repeat those values over
+each element of the input vector:</p>
+<pre class="r"><code>ave(mtcars$mpg, mtcars$cyl, FUN = mean)</code></pre>
+<pre><code> [1] 19.74286 19.74286 26.66364 19.74286 15.10000 19.74286 15.10000 26.66364
+ [9] 26.66364 19.74286 19.74286 15.10000 15.10000 15.10000 15.10000 15.10000
+ [17] 15.10000 26.66364 26.66364 26.66364 26.66364 15.10000 15.10000 15.10000
+ [25] 15.10000 26.66364 26.66364 26.66364 15.10000 19.74286 15.10000 26.66364</code></pre>
+<p>You can also get to this output from <code>tapply()</code> with an
+extra step of vectorized indexing:</p>
+<pre class="r"><code>tapply(mtcars$mpg, mtcars$cyl, FUN = mean)[as.character(mtcars$cyl)]</code></pre>
+<pre><code> 6 6 4 6 8 6 8 4
+ 19.74286 19.74286 26.66364 19.74286 15.10000 19.74286 15.10000 26.66364
+ 4 6 6 8 8 8 8 8
+ 26.66364 19.74286 19.74286 15.10000 15.10000 15.10000 15.10000 15.10000
+ 8 4 4 4 4 8 8 8
+ 15.10000 26.66364 26.66364 26.66364 26.66364 15.10000 15.10000 15.10000
+ 8 4 4 4 8 6 8 4
+ 15.10000 26.66364 26.66364 26.66364 15.10000 19.74286 15.10000 26.66364</code></pre>
+<h2 id="the-problem">The problem</h2>
+<p>Nothing sparks more joy than when a base R function helps you write
+more “tidy” code. I’ve talked about this in length before with
+<code>outer()</code> in a <a
+href="https://yjunechoe.github.io/posts/2023-06-11-row-relational-operations/">prior
+blog post on <code>dplyr::slice()</code></a>, and here I want to show a
+cool <code>ave()</code> + <code>dplyr::mutate()</code> combo.</p>
+<p>This example is adapted from a reprex by <a
+href="https://cedricscherer.netlify.app/">Cédric Scherer</a><a
+href="#fn2" class="footnote-ref" id="fnref2"><sup>2</sup></a> on the
+DSLC (previously R4DS) slack.</p>
+<p>Given an input of multiple discrete columns and the frequencies of
+these values:</p>
+<pre class="r"><code>input <- data.frame(
+ a = c("A", "A", "A", "B"),
+ b = c("X", "Y", "Y", "Z"),
+ c = c("M", "N", "O", "O"),
+ freq = c(5, 12, 3, 7)
+)
+input</code></pre>
+<pre><code> a b c freq
+ 1 A X M 5
+ 2 A Y N 12
+ 3 A Y O 3
+ 4 B Z O 7</code></pre>
+<p>The task is to add new columns named <code>freq_*</code> that show
+the total frequency of the values in each column:</p>
+<pre class="r"><code>output <- data.frame(
+ a = c("A", "A", "A", "B"),
+ freq_a = c(20, 20, 20, 7),
+ b = c("X", "Y", "Y", "Z"),
+ freq_b = c(5, 15, 15, 7),
+ c = c("M", "N", "O", "O"),
+ freq_c = c(5, 12, 10, 10),
+ freq = c(5, 12, 3, 7)
+)
+output</code></pre>
+<pre><code> a freq_a b freq_b c freq_c freq
+ 1 A 20 X 5 M 5 5
+ 2 A 20 Y 15 N 12 12
+ 3 A 20 Y 15 O 10 3
+ 4 B 7 Z 7 O 10 7</code></pre>
+<p>So for example, in column <code>a</code> the value <code>"A"</code>
+is associated with values <code>5</code>, <code>12</code>, and
+<code>3</code> in the <code>freq</code> column, so a new
+<code>freq_a</code> column should be created to track their total
+frequencies <code>5 + 12 + 3</code> and associate that value
+(<code>20</code>) for all occurrences of <code>"A"</code> in the
+<code>a</code> column.</p>
+<h2 id="some-tidyverse-solutions">Some <code>{tidyverse}</code>
+solutions</h2>
+<p>The gut feeling is that this seems to lack a straightforwardly “tidy”
+solution. I mean, the input isn’t even <strong>tidy</strong><a
+href="#fn3" class="footnote-ref" id="fnref3"><sup>3</sup></a> in the
+first place!</p>
+<p>So maybe we’d be better off starting with a pivoted tidy data for
+constructing a tidy solution:</p>
+<pre class="r"><code>library(tidyverse)
+input %>%
+ pivot_longer(-freq)</code></pre>
+<pre><code> # A tibble: 12 × 3
+ freq name value
+ <dbl> <chr> <chr>
+ 1 5 a A
+ 2 5 b X
+ 3 5 c M
+ 4 12 a A
+ 5 12 b Y
+ 6 12 c N
+ 7 3 a A
+ 8 3 b Y
+ 9 3 c O
+ 10 7 a B
+ 11 7 b Z
+ 12 7 c O</code></pre>
+<p>But recall that the desired output is of a wide form like the input,
+so it looks like our tidy solution will require some indirection,
+involving something like:</p>
+<pre class="r"><code>input %>%
+ pivot_longer(-freq) %>%
+ ... %>%
+ pivot_wider(...)</code></pre>
+<p>Or maybe you’d rather tackle this with some
+<code>left_join()</code>s, like:</p>
+<pre class="r"><code>input %>%
+ left_join(summarize(input, freq_a = sum(freq), .by = a)) %>%
+ ...</code></pre>
+<p>I’ll note that there’s actually also an idiomatic
+<code>{dplyr}</code>-solution to this using the lesser-known function
+<code>add_count()</code>, but you can’t avoid the repetitiveness problem
+because it doesn’t vectorize over the first argument:</p>
+<pre class="r"><code>input %>%
+ add_count(a, wt = freq, name = "freq_a") %>%
+ add_count(b, wt = freq, name = "freq_b") %>%
+ add_count(c, wt = freq, name = "freq_c")</code></pre>
+<pre><code> a b c freq freq_a freq_b freq_c
+ 1 A X M 5 20 5 5
+ 2 A Y N 12 20 15 12
+ 3 A Y O 3 20 15 10
+ 4 B Z O 7 7 7 10</code></pre>
+<p>You could try to scale this <code>add_count()</code> solution with
+<code>reduce()</code> (see my previous blog post on <a
+href="https://yjunechoe.github.io/posts/2020-12-13-collapse-repetitive-piping-with-reduce/">collapsing
+repetitive piping</a>), but now we’re straying very far from the “tidy”
+territory:</p>
+<pre class="r"><code>input %>%
+ purrr::reduce(
+ c("a", "b", "c"),
+ ~ .x %>%
+ add_count(.data[[.y]], wt = freq, name = paste0("freq_", .y)),
+ .init = .
+ )</code></pre>
+<pre><code> a b c freq freq_a freq_b freq_c
+ 1 A X M 5 20 5 5
+ 2 A Y N 12 20 15 12
+ 3 A Y O 3 20 15 10
+ 4 B Z O 7 7 7 10</code></pre>
+<p>IMO this problem is actually a really good thinking exercise for the
+“average {dplyr} user”, so I encourage you to take a stab at this
+yourself before proceeding if you’ve read this far!</p>
+<h2 id="an-ave-dplyr-solution">An <code>ave()</code> +
+<code>{dplyr}</code> solution</h2>
+<p>The crucial piece of the puzzle here is to think a little outside the
+box, beyond “data(frame) wrangling”.</p>
+<p>It helps to simplify the problem once we think about the problem in
+terms of “(column) vector wrangling” first, and that’s where
+<code>ave()</code> comes in!</p>
+<p>I’ll start with the cake first - this is the one-liner
+<code>ave()</code> solution I advocated for:</p>
+<pre class="r"><code>input %>%
+ mutate(across(a:c, ~ ave(freq, .x, FUN = sum), .names = "freq_{.col}"))</code></pre>
+<pre><code> a b c freq freq_a freq_b freq_c
+ 1 A X M 5 20 5 5
+ 2 A Y N 12 20 15 12
+ 3 A Y O 3 20 15 10
+ 4 B Z O 7 7 7 10</code></pre>
+<p>Taking column <code>freq_a</code> as an example, the
+<code>ave()</code> part of the solution essential creates this vector of
+summed-up <code>freq</code> values by the categories of
+<code>a</code>:</p>
+<pre class="r"><code>ave(input$freq, input$a, FUN = sum)</code></pre>
+<pre><code> [1] 20 20 20 7</code></pre>
+<p>From there, <code>across()</code> handles the iteration over columns
+and, as an added bonus, the naming of the new columns in convenient
+<code>{glue}</code> syntax (<code>"freq_{.col}"</code>).</p>
+<p>It’s the perfect mashup of base R + tidyverse. Base R takes care of
+the problem at the vector level with a split-apply-combine that’s
+concisely expressed with <code>ave()</code>, and tidyverse scales that
+solution up to the dataframe level with <code>mutate()</code> and
+<code>across()</code>.</p>
+<p>tidyverse 🤝 base R</p>
+<h2 id="sessioninfo">sessionInfo()</h2>
+<pre class="r"><code>sessionInfo()</code></pre>
+<pre><code> R version 4.3.3 (2024-02-29 ucrt)
+ Platform: x86_64-w64-mingw32/x64 (64-bit)
+ Running under: Windows 11 x64 (build 22631)
+
+ Matrix products: default
+
+
+ locale:
+ [1] LC_COLLATE=English_United States.utf8
+ [2] LC_CTYPE=English_United States.utf8
+ [3] LC_MONETARY=English_United States.utf8
+ [4] LC_NUMERIC=C
+ [5] LC_TIME=English_United States.utf8
+
+ time zone: Asia/Seoul
+ tzcode source: internal
+
+ attached base packages:
+ [1] stats graphics grDevices utils datasets methods base
+
+ other attached packages:
+ [1] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
+ [5] purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
+ [9] tidyverse_2.0.0 ggplot2_3.5.1
+
+ loaded via a namespace (and not attached):
+ [1] sass_0.4.9 utf8_1.2.4 generics_0.1.3 xml2_1.3.6
+ [5] stringi_1.8.4 distill_1.6 hms_1.1.3 digest_0.6.35
+ [9] magrittr_2.0.3 evaluate_0.23 grid_4.3.3 timechange_0.2.0
+ [13] bookdown_0.38 fastmap_1.1.1 rprojroot_2.0.4 jsonlite_1.8.8
+ [17] fansi_1.0.6 scales_1.3.0 jquerylib_0.1.4 cli_3.6.2
+ [21] rlang_1.1.4 munsell_0.5.0 withr_3.0.0 cachem_1.0.8
+ [25] yaml_2.3.8 tools_4.3.3 tzdb_0.4.0 memoise_2.0.1
+ [29] colorspace_2.1-0 vctrs_0.6.5 R6_2.5.1 mime_0.12
+ [33] png_0.1-8 lifecycle_1.0.4 fontawesome_0.5.2 pkgconfig_2.0.3
+ [37] pillar_1.9.0 bslib_0.7.0 gtable_0.3.5 glue_1.7.0
+ [41] xfun_0.44 tidyselect_1.2.1 rstudioapi_0.16.0 knitr_1.47
+ [45] htmltools_0.5.8.1 rmarkdown_2.27 compiler_4.3.3 askpass_1.2.0
+ [49] downlit_0.4.3 openssl_2.1.1</code></pre>
+<pre class="r distill-force-highlighting-css"><code></code></pre>
+<div class="footnotes footnotes-end-of-document">
+<hr />
+<ol>
+<li id="fn1"><p>And check out the elusive <code>split<-</code>
+function!<a href="#fnref1" class="footnote-back">↩︎</a></p></li>
+<li id="fn2"><p>Who I can only assume was needing this for a fancy data
+viz thing 😆<a href="#fnref2" class="footnote-back">↩︎</a></p></li>
+<li id="fn3"><p>I mean that in the technical sense here. In this
+problem, the unit of observation is the “cells” of the input columns
+(the values “A”, “B”, “X”, “Y”, etc.).<a href="#fnref3"
+class="footnote-back">↩︎</a></p></li>
+</ol>
+</div>
+ 478d5b66974022ae5d6b9c8ba58783a7
dplyr
https://yjunechoe.github.io/posts/2024-06-09-ave-for-the-average
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-I think it’s safe to say that the average {dplyr} user does not know the ave() function. For that audience, this is a short appreciation post on ave(), a case of tidyverse and base R.
ave()
-ave() is a split-apply-function in base R (specifically, {stats}). It’s a pretty short function - maybe you can make out what it does from just reading the code1
ave() is a split-apply-combine function in base R (specifically, {stats}). It’s a pretty short function - maybe you can make out what it does from just reading the code1
aveave()
}
x
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<environment: namespace:stats>
Despite its (rather generic and uninformative) name, I like to think of ave() as actually belonging to the *apply() family of functions, having particularly close ties to tapply().