Using R more “wisely”

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# Using R more "wisely"
### Column-wise & row-wise operations with dplyr

## Brendan Cullen

### [dplyr-wisely.netlify.app](https://dplyr-wisely.netlify.app)

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# About me

## Brendan Cullen

#### NSF Graduate Research Fellow, University of Oregon RStudio Certified Instructor

.fade[Portland, OR, USA]

[](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)

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# About you

#

#### ✅ Familiar with the tidyverse

#### ✅ Comfortable with common dplyr verbs, e.g. `mutate()` and `summarize()`

#### ✅ Want to more efficiently wrangle and summarize data with dplyr

---
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# 1️⃣
# Column-wise operations

---
# 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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# Palmer Penguins

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

```r
glimpse(penguins)
```

```
## Rows: 344
## Columns: 8
## $ species <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Ade…
## $ island <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgers…
## $ bill_length_mm <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1,…
## $ bill_depth_mm <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1,…
## $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 18…
## $ body_mass_g <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475,…
## $ sex <fct> male, female, female, NA, female, male, female, mal…
## $ year <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 200…
```

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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. 
]

.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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# 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)
.footnote[Artwork by @allison_horst]

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# `dplyr::across()`

![](img/dplyr_across.png)
.footnote[Artwork by @allison_horst]

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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 `penguins$year`, 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_… bill_depth_mm_m… flipper_length_… body_mass_g_mean
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 Adelie 38.8 18.3 190. 3701.
## 2 Chinst… 48.8 18.4 196. 3733.
## 3 Gentoo 47.5 15.0 217. 5076.
## # … with 1 more variable: year_mean <dbl>
```

👀 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 .fns 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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# `dplyr::across()`

Multiple summaries with `across()` -- highly flexible!

```r
penguins %>%
  group_by(island) %>% 
  summarize(
    n = n(),
    across(where(is.factor), n_distinct),
    across(where(is.numeric), mean, na.rm = TRUE)
  )
```

```
## # A tibble: 3 x 9
## island n species sex bill_length_mm bill_depth_mm flipper_length_…
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Biscoe 168 2 3 45.3 15.9 210.
## 2 Dream 124 2 3 44.2 18.3 193.
## 3 Torge… 52 1 3 39.0 18.4 191.
## # … with 2 more variables: body_mass_g <dbl>, year <dbl>
```

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# `dplyr::across()`

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

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# Recap

.footnote[Source: [rstudio/concept-maps](https://github.com/rstudio/concept-maps/#column-wise-operations)]

---
# More resources

### Further reading 📖

- [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/).

### Practice 💻

- Small [collection of exercises](https://brendancullen.shinyapps.io/columnwise_operations_formative_assessment/) I put together to accompany these slides

- [Interactive tutorial](https://github.com/laderast/tidyowl#learning_tidyselect) by Ted Laderas on `tidyselect` and a [full list](https://dplyr.tidyverse.org/reference/select.html) of selection helpers.

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# Q & A
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# 2️⃣
# Row-wise operations

---
# Learning objectives

- Learn and apply `rowwise()` to produce row-by-row summaries

- Use `nest_by()` to create list-columns

- Run multiple statistical models on a list-column using `rowwise()`

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# Row-wise operations

`rowwise()` lets you run operations one row at a time on the data. It’s a lot like `group_by()`, but it groups the data by row, rather than group.

--
***

What happens when you use `rowwise()` to create a rowwise data frame? 👀

```r
penguins %>% 
  rowwise()
```

```
## # A tibble: 344 x 8
## # Rowwise: 
## species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Adelie Torge… 39.1 18.7 181 3750
## 2 Adelie Torge… 39.5 17.4 186 3800
## 3 Adelie Torge… 40.3 18 195 3250
## 4 Adelie Torge… NA NA NA NA
## 5 Adelie Torge… 36.7 19.3 193 3450
## 6 Adelie Torge… 39.3 20.6 190 3650
## 7 Adelie Torge… 38.9 17.8 181 3625
## 8 Adelie Torge… 39.2 19.6 195 4675
## 9 Adelie Torge… 34.1 18.1 193 3475
## 10 Adelie Torge… 42 20.2 190 4250
## # … with 334 more rows, and 2 more variables: sex <fct>, year <int>
```

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# Row-wise operations

Let's say we want to compute the mean of `bill_length_mm` and `bill_depth_mm` for each penguin.

--
***

❗ Using `mutate()` and `mean()` won't give us what we want.

.panelset[

.panel[.panel-name[Code]

```r
penguins %>%
  mutate(avg_bill = mean(c(bill_length_mm, bill_depth_mm), na.rm = TRUE)) %>%
  select(species, island, avg_bill)
```
]

.panel[.panel-name[Output]

```
## # A tibble: 344 x 3
## species island avg_bill
## <fct> <fct> <dbl>
## 1 Adelie Torgersen 30.5
## 2 Adelie Torgersen 30.5
## 3 Adelie Torgersen 30.5
## 4 Adelie Torgersen 30.5
## 5 Adelie Torgersen 30.5
## 6 Adelie Torgersen 30.5
## 7 Adelie Torgersen 30.5
## 8 Adelie Torgersen 30.5
## 9 Adelie Torgersen 30.5
## 10 Adelie Torgersen 30.5
## # … with 334 more rows
```

]
]
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# Row-wise operations

If we first use `rowwise()` to create a rowwise data frame, then this calculation will be carried out row-by-row (i.e. for each penguin)

.panelset[

.panel[.panel-name[Code]

```r
penguins %>%
  rowwise() %>%
  mutate(avg_bill = mean(c(bill_length_mm, bill_depth_mm), na.rm = TRUE)) %>%
  select(species, island, avg_bill)
```
]

.panel[.panel-name[Output]

```
## # A tibble: 344 x 3
## # Rowwise: 
## species island avg_bill
## <fct> <fct> <dbl>
## 1 Adelie Torgersen 28.9
## 2 Adelie Torgersen 28.4
## 3 Adelie Torgersen 29.2
## 4 Adelie Torgersen NaN 
## 5 Adelie Torgersen 28 
## 6 Adelie Torgersen 30.0
## 7 Adelie Torgersen 28.4
## 8 Adelie Torgersen 29.4
## 9 Adelie Torgersen 26.1
## 10 Adelie Torgersen 31.1
## # … with 334 more rows
```

]
]

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# Row-wise operations

`c_across()` uses tidy selection syntax so you can succinctly select many variables at once

.panelset[

.panel[.panel-name[Code]

```r
penguins %>%
  rowwise() %>%
  mutate(avg_bill = mean(c_across(contains("bill")), na.rm = TRUE)) %>%
  select(species, island, avg_bill)
```

]

.panel[.panel-name[Output]

]
]

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# List columns

Data frames are not limited to atomic vectors. They can also contain recursive vectors, i.e. lists in the form of a **list column**.

A list column can be a list of data frames.

--
***

You can create a list column containing data frames using `nest_by()`.

.pull-left[

```r
nested_penguins <- 
 penguins %>% 
 nest_by(species)

nested_penguins
```

```
## # A tibble: 3 x 2
## # Rowwise: species
## species data
## <fct> <list<tbl_df[,7]>>
## 1 Adelie [152 × 7]
## 2 Chinstrap [68 × 7]
## 3 Gentoo [124 × 7]
```
]

.pull-right[
#### Notice the following 👀

- Similar to `group_by()` but it visually changes the structure of the data

- Returns a rowwise data frame

- Default output column name is `data` (can override with `.key` argument)
]

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# List columns

How would you extract the data frame containing the data for Chinstrap penguins out of `nested_penguins`?

Notice it does not contain the `species` variable 👀. By default, `.keep = FALSE`.

```r
nested_penguins$data[[2]]
```

```
## # A tibble: 68 x 7
## island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year
## <fct> <dbl> <dbl> <int> <int> <fct> <int>
## 1 Dream 46.5 17.9 192 3500 fema… 2007
## 2 Dream 50 19.5 196 3900 male 2007
## 3 Dream 51.3 19.2 193 3650 male 2007
## 4 Dream 45.4 18.7 188 3525 fema… 2007
## 5 Dream 52.7 19.8 197 3725 male 2007
## 6 Dream 45.2 17.8 198 3950 fema… 2007
## 7 Dream 46.1 18.2 178 3250 fema… 2007
## 8 Dream 51.3 18.2 197 3750 male 2007
## 9 Dream 46 18.9 195 4150 fema… 2007
## 10 Dream 51.3 19.9 198 3700 male 2007
## # … with 58 more rows
```

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# Many models

You can use a rowwise workflow to run a separate model on each data set in a list column.

```r
penguins_models <- 
 nested_penguins %>%
 mutate(model = list(lm(bill_length_mm ~ flipper_length_mm, data = data)))

penguins_models
```

```
## # A tibble: 3 x 3
## # Rowwise: species
## species data model 
## <fct> <list<tbl_df[,7]>> <list>
## 1 Adelie [152 × 7] <lm> 
## 2 Chinstrap [68 × 7] <lm> 
## 3 Gentoo [124 × 7] <lm>
```

❗ Notice that you must wrap your call to `lm()` inside `list()` in order for the model output to fit into a list column.

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# Many models

However, if we access the model column, you’ll notice that it just prints the output of each fit.

```r
penguins_models$model[[1]]
```

```
## 
## Call:
## lm(formula = bill_length_mm ~ flipper_length_mm, data = data)
## 
## Coefficients:
##       (Intercept)  flipper_length_mm  
##           13.5871             0.1327
```

That output is not super helpful. How can we get more useful information? 🤔

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# Many models

We can use functions from the **broom** package to extract summary information about each model in a tidy data frame.

.panelset[

.panel[.panel-name[`broom::tidy()`]

`broom::tidy()` returns a row for each coefficient in the model. Each column gives information about the estimate or its variability.

```r
nested_penguins %>%
  mutate(model = list(lm(bill_length_mm ~ flipper_length_mm, data = data))) %>%
  summarize(broom::tidy(model))
```

]

.panel[.panel-name[Output]

```
## # A tibble: 6 x 6
## # Groups: species [3]
## species term estimate std.error statistic p.value
## <fct> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Adelie (Intercept) 13.6 6.00 2.27 2.49e- 2
## 2 Adelie flipper_length_mm 0.133 0.0315 4.21 4.47e- 5
## 3 Chinstrap (Intercept) 5.59 9.96 0.562 5.76e- 1
## 4 Chinstrap flipper_length_mm 0.221 0.0508 4.34 4.92e- 5
## 5 Gentoo (Intercept) -20.7 7.04 -2.94 3.88e- 3
## 6 Gentoo flipper_length_mm 0.314 0.0324 9.69 8.60e-17
```
]

.panel[.panel-name[`broom:glance()`]

`broom::glance()` returns a row for each model. Each column gives a model summary: either a measure of model quality, or complexity, or a combination of the two.

```r
nested_penguins %>%
  mutate(model = list(lm(bill_length_mm ~ flipper_length_mm, data = data))) %>%
  summarize(broom::glance(model))
```

]

.panel[.panel-name[Output]

```
## # A tibble: 3 x 13
## # Groups: species [3]
## species r.squared adj.r.squared sigma statistic p.value df logLik AIC
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Adelie 0.106 0.100 2.53 17.7 4.47e- 5 1 -353. 712.
## 2 Chinst… 0.222 0.211 2.97 18.9 4.92e- 5 1 -169. 345.
## 3 Gentoo 0.437 0.432 2.32 94.0 8.60e-17 1 -277. 560.
## # … with 4 more variables: BIC <dbl>, deviance <dbl>, df.residual <int>,
## # nobs <int>
```
]
]

---
# More resources

### Further reading 📖

- [Blog post](https://www.tidyverse.org/blog/2020/04/dplyr-1-0-0-rowwise/) by Hadley Wickham demonstrating helpful use cases for `rowwise()`, e.g. simulations and modeling

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

- [Chapter 25](https://r4ds.had.co.nz/many-models.html) from r4ds on "Many models"

### Practice 💻

- [Interactive tutorial](https://github.com/laderast/tidyowl#learning_rowwise-in-development) by Ted Laderas on learning `rowwise()`

---
# Acknowledgements

Much of this material was based on blog posts written by [Hadley Wickham](http://hadley.nz/) and [Rebecca Barter](http://www.rebeccabarter.com/) along with tutorials developed by [Ted Laderas](https://laderast.github.io/), which you can access through his [tidyowl](https://github.com/laderast/tidyowl) package.

Thanks also to [Allison Horst](https://twitter.com/allison_horst) for the great artwork!

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# Thank you! 
# 🙂

### Materials at [github.com/brendanhcullen/wisely](https://github.com/brendanhcullen/wisely)