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wibeasley committed Oct 22, 2023
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100 changes: 100 additions & 0 deletions ch-snippets.Rmd
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Expand Up @@ -110,6 +110,106 @@ ds <- vroom::vroom(
rm(col_types)
```


Row Operations {#snippets-row}
------------------------------------

We frequently have to find the mean or sum across columns (within a row).
If
Finding mean across a lot of columns

Here are several approaches for finding the mean across columns, without naming each column. Some remarks:

* `m1` & `m2` are sanity checks for this example.
`m1` would be clumsy if you have 10+ items.
`m2` is discouraged because it's brittle.
A change in the column order could alter the calculation.
We prefer to use `grep()` to specify a sequence of items.
* Especially for large datasets,
I’d lean towards `m3` if the items are reasonably complete and
`m4` if some participants are missing enough items that their summary score is fishy.
In the approaches below, `m4` and `m6` return the mean only if the participant completed 2 or more items.
* `dplyr::rowwise()` is convenient, but slow for large datasets.
* If you need a more complex function that’s too clumsy to include directly in a `mutate()` statement,
see how the calculation for `m6` is delegated to the external function, `f6`.
* The technique behind `nonmissing` is pretty cool,
because you can apply an arbitrary function on each cell before they’re summed/averaged.
* This is in contrast to `f6()`, which applies to an entire (row-wise) data.frame.

```r
# Isolate the columns to average. Remember the `grep()` approach w/ `colnames()`
columns_to_average <- c("hp", "drat", "wt")

f6 <- function(x) {
# browser()
s <- sum(x, na.rm = TRUE)
n <- sum(!is.na(x))

dplyr::if_else(
2L <= n,
s / n,
NA_real_
)
}

mtcars |>
dplyr::mutate(
m1 = (hp + drat + wt) / 3,
m2 =
rowMeans(
dplyr::across(hp:wt), # All columns between hp & wt.
na.rm = TRUE
),
m3 =
rowMeans(
dplyr::across(!!columns_to_average),
na.rm = TRUE
),
s4 = # Finding the sum (used by m4)
rowSums(
dplyr::across(!!columns_to_average),
na.rm = TRUE
),
nonmissing =
rowSums(
dplyr::across(
!!columns_to_average,
.fns = \(x) { !is.na(x) }
)
),
m4 =
dplyr::if_else(
2 <= nonmissing,
s4 / nonmissing,
NA_real_
)
) |>
dplyr::rowwise() |> # Required for `m5`
dplyr::mutate(
m5 = mean(dplyr::c_across(dplyr::all_of(columns_to_average))),
) |>
dplyr::ungroup() |> # Clean up after rowwise()
dplyr::rowwise() |> # Required for `m6`
dplyr::mutate(
m6 = f6(dplyr::across(!!columns_to_average))
) |>
dplyr::ungroup() |> # Clean up after rowwise()
dplyr::select(
hp,
drat,
wt,
m1,
m2,
m3,
s4,
nonmissing,
m4,
m5,
m6,
)
```


Grooming {#snippets-grooming}
------------------------------------

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100 changes: 100 additions & 0 deletions docs/ch-snippets.md
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Original file line number Diff line number Diff line change
Expand Up @@ -110,6 +110,106 @@ ds <- vroom::vroom(
rm(col_types)
```


Row Operations {#snippets-row}
------------------------------------

We frequently have to find the mean or sum across columns (within a row).
If
Finding mean across a lot of columns

Here are several approaches for finding the mean across columns, without naming each column. Some remarks:

* `m1` & `m2` are sanity checks for this example.
`m1` would be clumsy if you have 10+ items.
`m2` is discouraged because it's brittle.
A change in the column order could alter the calculation.
We prefer to use `grep()` to specify a sequence of items.
* Especially for large datasets,
I’d lean towards `m3` if the items are reasonably complete and
`m4` if some participants are missing enough items that their summary score is fishy.
In the approaches below, `m4` and `m6` return the mean only if the participant completed 2 or more items.
* `dplyr::rowwise()` is convenient, but slow for large datasets.
* If you need a more complex function that’s too clumsy to include directly in a `mutate()` statement,
see how the calculation for `m6` is delegated to the external function, `f6`.
* The technique behind `nonmissing` is pretty cool,
because you can apply an arbitrary function on each cell before they’re summed/averaged.
* This is in contrast to `f6()`, which applies to an entire (row-wise) data.frame.

```r
# Isolate the columns to average. Remember the `grep()` approach w/ `colnames()`
columns_to_average <- c("hp", "drat", "wt")

f6 <- function(x) {
# browser()
s <- sum(x, na.rm = TRUE)
n <- sum(!is.na(x))

dplyr::if_else(
2L <= n,
s / n,
NA_real_
)
}

mtcars |>
dplyr::mutate(
m1 = (hp + drat + wt) / 3,
m2 =
rowMeans(
dplyr::across(hp:wt), # All columns between hp & wt.
na.rm = TRUE
),
m3 =
rowMeans(
dplyr::across(!!columns_to_average),
na.rm = TRUE
),
s4 = # Finding the sum (used by m4)
rowSums(
dplyr::across(!!columns_to_average),
na.rm = TRUE
),
nonmissing =
rowSums(
dplyr::across(
!!columns_to_average,
.fns = \(x) { !is.na(x) }
)
),
m4 =
dplyr::if_else(
2 <= nonmissing,
s4 / nonmissing,
NA_real_
)
) |>
dplyr::rowwise() |> # Required for `m5`
dplyr::mutate(
m5 = mean(dplyr::c_across(dplyr::all_of(columns_to_average))),
) |>
dplyr::ungroup() |> # Clean up after rowwise()
dplyr::rowwise() |> # Required for `m6`
dplyr::mutate(
m6 = f6(dplyr::across(!!columns_to_average))
) |>
dplyr::ungroup() |> # Clean up after rowwise()
dplyr::select(
hp,
drat,
wt,
m1,
m2,
m3,
s4,
nonmissing,
m4,
m5,
m6,
)
```


Grooming {#snippets-grooming}
------------------------------------

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4 changes: 2 additions & 2 deletions docs/example-chapter.html
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Expand Up @@ -113,7 +113,7 @@ <h1>
<p><em>This intro was copied from the 1st chapter of the example bookdown repo. I’m keeping it temporarily for reference.</em></p>
<p>You can label chapter and section titles using <code>{#label}</code> after them, e.g., we can reference the <a href="index.html#intro">Intro</a> Chapter. If you do not manually label them, there will be automatic labels anyway</p>
<p>Figures and tables with captions will be placed in <code>figure</code> and <code>table</code> environments, respectively.</p>
<div class="sourceCode" id="cb101"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb102"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span>, <span class="fl">.1</span>, <span class="fl">.1</span><span class="op">)</span><span class="op">)</span></span>
<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="va">pressure</span>, type <span class="op">=</span> <span class="st">'b'</span>, pch <span class="op">=</span> <span class="fl">19</span><span class="op">)</span></span></code></pre></div>
<div class="figure" style="text-align: center">
Expand All @@ -123,7 +123,7 @@ <h1>
</p>
</div>
<p>Reference a figure by its code chunk label with the <code>fig:</code> prefix, e.g., see Figure <a href="example-chapter.html#fig:nice-fig">G.1</a>. Similarly, you can reference tables generated from <code><a href="https://rdrr.io/pkg/knitr/man/kable.html">knitr::kable()</a></code>, e.g., see Table <a href="example-chapter.html#tab:nice-tab">G.1</a>.</p>
<div class="sourceCode" id="cb102"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb103"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu">knitr</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html">kable</a></span><span class="op">(</span></span>
<span> <span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span><span class="op">(</span><span class="va">iris</span>, <span class="fl">20</span><span class="op">)</span>, caption <span class="op">=</span> <span class="st">'Here is a nice table!'</span>,</span>
<span> booktabs <span class="op">=</span> <span class="cn">TRUE</span></span>
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1 change: 1 addition & 0 deletions docs/reference-keys.txt
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Expand Up @@ -275,6 +275,7 @@ snippets-reading
snippets-reading-excel
snippets-reading-trailing-comma
snippets-reading-vroom
snippets-row
snippets-grooming
snippets-grooming-two-year
snippets-identification
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2 changes: 1 addition & 1 deletion docs/search.json
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