Column-wise operations with

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# Column-wise operations with `dplyr`:
# Old and New

## Brendan Cullen
### [RStudio Certified Trainer](https://education.rstudio.com/trainers/people/cullen+brendan/)
### [](https://bcullen.rbind.io/) [bcullen.rbind.io](https://bcullen.rbind.io/) | [](https://twitter.com/_bcullen) [_bcullen](https://twitter.com/_bcullen) | [](https://github.com/brendanhcullen) [brendanhcullen](https://github.com/brendanhcullen)

### Slides: [](https://columnwise-operations-dplyr.netlify.app/) [columnwise-operations-dplyr.netlify.app](https://columnwise-operations-dplyr.netlify.app/)

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# Concept map

![](img/columnwise_concept_map.png)

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# Learning objectives

- ***Review*** how to apply `dplyr` functions, e.g. `mutate()`, `summarize()`, to single columns

--

- ***Learn*** how to perform column-wise operations two ways:

- scoped verbs, e.g. `summarize_at()`, `summarize_if()`, etc...

- `across()` from `dplyr` 1.0.0

- ***Apply*** `across()` to summarize multiple columns of data

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# Column-wise operations

**Column-wise operations** refers to applying the same `dplyr` verbs (or other data transformation functions) to multiple columns simultaneously.

e.g. Create multiple new columns with `mutate()`

e.g. Summarize multiple columns with `summarize()`

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background-image: url(img/penguins_hex.png)
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# Palmer Penguins

```r
library(palmerpenguins)
library(tidyverse)
```

```r
glimpse(penguins)
```

```
## Rows: 344
## Columns: 8
## $ species <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adeli…
## $ island <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
## $ bill_length_mm <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, 42.0, 37.8, 37.8, 41.1, 38.6, 34.…
## $ bill_depth_mm <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, 20.2, 17.1, 17.3, 17.6, 21.2, 21.…
## $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186, 180, 182, 191, 198, 185, 195, 1…
## $ body_mass_g <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, 4250, 3300, 3700, 3200, 3800, 440…
## $ sex <fct> male, female, female, NA, female, male, female, male, NA, NA, NA, NA, female, male, m…
## $ year <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2…
```

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# Column-wise operations

***Let's review...***

Apply `summarize()` to a **single** column.

.panelset[

.panel[.panel-name[Example]
.pull-left[
🤔 Use `summarize()` to calculate the mean **bill length** for each species in `penguins`

❓ What function do you need to include before `summarize()` in order to calculate means for each species?
]

.pull-right[
<img src="img/penguins.png" width="75%" />

Artwork by @allison_horst

]
]

.panel[.panel-name[Code]

```r
penguins %>% 
  group_by(species) %>% 
  summarize(bill_length_mm = mean(bill_length_mm, na.rm = TRUE))
```
]

.panel[.panel-name[Output]

```
## # A tibble: 3 x 2
## species bill_length_mm
## <fct> <dbl>
## 1 Adelie 38.8
## 2 Chinstrap 48.8
## 3 Gentoo 47.5
```

]
]
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# Column-wise operations

Apply `summarize()` to **multiple columns** at once

.panelset[

.panel[.panel-name[Example #1]
.pull-left[
🤔 Calculate the mean **bill length** and **bill depth** for each species

🚫 Avoid copying and pasting or repeating `mean()` more than once in your solution
]

.pull-right[
<img src="img/culmen_depth.png" width="100%" />
]

.pull-right[Artwork by @allison_horst]

]

.panel[.panel-name[Code]

<code class ='r hljs remark-code'>penguins %>% &nbsp;&nbsp;group_by(species) %>% &nbsp;&nbsp;summarize_at(vars(c(bill_length_mm, bill_depth_mm)), &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;mean, na.rm = TRUE)</code>

🔍 `summarize_at()` is an example of a **scoped verb**. It is a special variant of `summarize()` that applies a summary function to a specific set of columns.

⚠️ When using scoped verbs ending in `_at()` you must use `vars()` to select columns.

❓ How many columns will be in our output? 
]

.panel[.panel-name[Output]

```
## # A tibble: 3 x 3
## species bill_length_mm bill_depth_mm
## <fct> <dbl> <dbl>
## 1 Adelie 38.8 18.3
## 2 Chinstrap 48.8 18.4
## 3 Gentoo 47.5 15.0
```
]
]

---
template: multiple columns

.panelset[

.panel[.panel-name[Example #2]
🤔 Calculate the mean of **all numeric** variables for each species.

🚫 Again, no copying and pasting or repeating `mean()` more than once!
]

.panel[.panel-name[Code]

<code class ='r hljs remark-code'>penguins %>% &nbsp;&nbsp;group_by(species) %>% &nbsp;&nbsp;summarize_if(is.numeric, mean, na.rm = TRUE)</code>

🔍 `summarize_if()` is another example of a **scoped verb**. It is a special variant of `summarize()` that applies a summary function to a set of columns that all satisfy some logical criteria.

🔍 That logical criteria is specified using a **predicate function**, e.g. `is.numeric()`, which will return `TRUE` or `FALSE`.

]

.panel[.panel-name[Output]

```
## # A tibble: 3 x 6
## species bill_length_mm bill_depth_mm flipper_length_mm body_mass_g year
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Adelie 38.8 18.3 190. 3701. 2008.
## 2 Chinstrap 48.8 18.4 196. 3733. 2008.
## 3 Gentoo 47.5 15.0 217. 5076. 2008.
```
]
]
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# Your Turn #1

### Click [here](https://brendancullen.shinyapps.io/columnwise_operations_formative_assessment/#section-your-turn-1).

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name: better way

# A better way?

.pull-left[
.center[
## `dplyr` < 1.0.0
<img src="img/dplyr_hex_old.png" width="50%" />

`summarize_at()`, `summarize_if()`, `summarize_all()`, `mutate_if()`, `mutate_at()`, `mutate_all()`, ...
##😓
]
]

.pull-right[
.center[
## `dplyr` >= 1.0.0
<img src="img/dplyr_hex_new.png" width="50%" />

`across()`
 
##😎
]
]

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# `dplyr::across()`

![](img/dplyr_across_blank.png)
.pull-right[Artwork by @allison_horst]

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# `dplyr::across()`

![](img/dplyr_across.png)
.pull-right[Artwork by @allison_horst]

---
# `dplyr::across()`

.footnote[Source: [rstudio/concept-maps](https://github.com/rstudio/concept-maps/#across), courtesy of [Emma Vestesson](https://emmavestesson.netlify.app/)]
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# `dplyr::across()`

<code class ='r hljs remark-code'>across(.cols, .fns, ..., .names)</code>

`.cols` = the columns you want to transform

--

`.fns` = the function(s) you want to apply to each of the selected columns

--

`...` = additional arguments for the function(s) specified in `.fns` (e.g. `na.rm = TRUE`)

--

`.names` = how you want to name the output columns. Here, `"{col}"` is a special placeholder for the input column name, and you can add any suffix you want to it

- e.g. When calculating the mean of the `year` column, specifying `.names = "{col}_mean"` would result in an output column named `"year_mean"`

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# `dplyr::across()`

.panelset[

.panel[.panel-name[Example]
🤔 Use `across()` to calculate the mean of **all numeric** columns for each species in `penguins`

📣 Remember, `across()` goes *inside* of the `dplyr` function that you want to apply to multiple columns.
]

.panel[.panel-name[Code 1.0]

<code class ='r hljs remark-code'>penguins %>% &nbsp;&nbsp;group_by(species) %>% &nbsp;&nbsp;summarize(across(where(is.numeric), &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;mean, &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;na.rm = TRUE, &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;.names = '{col}_mean'))</code>

***

🔍 `where()` is an example of a `tidyselect` helper function, like `starts_with()` or `contains()`.

It selects the variables for which some **predicate function** , such as `is.numeric()` , returns `TRUE`. 
]

.panel[.panel-name[Output 1.0]

```
## # A tibble: 3 x 6
## species bill_length_mm_mean bill_depth_mm_mean flipper_length_mm_mean body_mass_g_mean year_mean
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Adelie 38.8 18.3 190. 3701. 2008.
## 2 Chinstrap 48.8 18.4 196. 3733. 2008.
## 3 Gentoo 47.5 15.0 217. 5076. 2008.
```

👀 Notice that all of the output variables have the suffix `"_mean"`.

]

.panel[.panel-name[Code 2.0]

<code class ='r hljs remark-code'>penguins %>% &nbsp;&nbsp;group_by(species) %>% &nbsp;&nbsp;summarize(across(where(is.numeric), &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;list(mean = mean), &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;na.rm = TRUE))</code>

***

🔍 If you specify `.funs` as a named list, e.g. `list(mean = mean)`, then `across()` will automatically append the name of the applied function(s) to the names of the output columns.

This way you don't have to manually specify a `.names` argument at all! 
]

.panel[.panel-name[Output 2.0]

👀 Same exact output as before!

]
]

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# Your Turn #2

### Click [here](https://brendancullen.shinyapps.io/columnwise_operations_formative_assessment/#section-your-turn-2).

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# Concept map

![](img/columnwise_concept_map.png)

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# More resources

- [Blog post](https://www.tidyverse.org/blog/2020/04/dplyr-1-0-0-colwise/) by Hadley Wickham highlighting `across()` as a key component of the `dplyr` 1.0.0 update.

- [This vignette](https://dplyr.tidyverse.org/articles/colwise.html) on tidyverse.org about column-wise operations.

- Two blog posts by Rebecca Barter on [scoped verbs](http://www.rebeccabarter.com/blog/2019-01-23_scoped-verbs/) and [`across()`](http://www.rebeccabarter.com/blog/2020-07-09-across/).

- [Interactive tutorial](https://tladeras.shinyapps.io/learning_tidyselect/) by Ted Laderas on `tidyselect` and a [full list](https://dplyr.tidyverse.org/reference/select.html) of selection helpers.