An Antarctic Tour of the Tidyverse

<img src="data:image/png;base64,#images/lter_penguins.png" width="400" />
# An Antarctic Tour of the Tidyverse

## R-Ladies Chicago

### Silvia Canelón, PhD &#8226; August 31, 2020

### <svg aria-label="GitHub" role="img" viewBox="0 0 496 512" style="height:1em;width:0.97em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><title>GitHub</title><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg> repo: [spcanelon/tour-of-the-tidyverse](https://github.com/spcanelon/tour-of-the-tidyverse)

---
class: about-me, middle, center

## Silvia Canelón

### Postdoctoral Research Scientist

### University of Pennsylvania, Philadelphia, PA, USA

[<svg aria-label="Link" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><title>Link</title><path d="M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z"/></svg> silviacanelon.com](https://www.silviacanelon.com)<br/>
[<svg aria-label="Twitter" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><title>Twitter</title><path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"/></svg> @spcanelon](https://twitter.com/spcanelon)<br/>
[<svg aria-label="GitHub" role="img" viewBox="0 0 496 512" style="height:1em;width:0.97em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><title>GitHub</title><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg> @spcanelon](https://github.com/spcanelon)

.footnote[<span>Photo by <a href="https://unsplash.com/@lukehuff?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText">lucas huffman</a> on <a href="https://unsplash.com/s/photos/antarctica?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText">Unsplash</a></span>]

---
class: left, middle

# Acknowledgments

### [palmerpenguins](https://allisonhorst.github.io/palmerpenguins/articles/intro.html) 📦 developed by Drs. [Allison Horst](https://www.allisonhorst.com/), [Alison Hill](https://alison.rbind.io/), and [Kristen Gorman](https://www.uaf.edu/cfos/people/faculty/detail/kristen-gorman.php).

### Penguin artwork by Allison Horst ([@allison_horst](https://twitter.com/allison_horst))

### Slide inspiration from Alison Hill ([@apreshill](https://twitter.com/apreshill))'s recent education training materials "[Teaching in Production](https://rstudio-education.github.io/teaching-in-production/)"

### Slides made using Dr. Yihui Xie's [xaringan](https://github.com/yihui/xaringan) 📦 and Garrick Aden-Buie's [xaringanExtra](https://github.com/gadenbuie/xaringanExtra) 📦, and adapted from the [R-Ladies `xaringan` theme designed by Alison Hill](https://alison.rbind.io/post/2017-12-18-r-ladies-presentation-ninja/)

### Photographs from various photographers on Unsplash, and noted on the relevant slide

---
class: left, top
background-image: url(data:image/png;base64,#images/logo.png)
background-position: 1050px 50px
background-size: 80px

# Meet our penguin friends!

<div class="flex" style="margin: 0 0em;">
  <div class="column">
    <h3> Chinstrap </h3>
    <img src="https://github.com/spcanelon/slides/blob/main/tour-of-the-tidyverse/images/penguin_chinstrap.jpg?raw=true" style="width: 100%;">
  </div>
  
  <div class="column" style="margin: 0 1em;">
    <h3> Gentoo </h3>
    <img src="https://github.com/spcanelon/slides/blob/main/tour-of-the-tidyverse/images/penguin_gentoo.jpg?raw=true" style="">
  </div>
  
  <div class="column" style="margin: 0 0em;">
    <h3> Adélie </h3>
    <img src="https://github.com/spcanelon/slides/blob/main/tour-of-the-tidyverse/images/penguin_adelie.jpg?raw=true" style="">
  </div>
</div>

.footnote[🐧<span>Photos by <a href="https://unsplash.com/@longmaspirit?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText">Long Ma</a> on <a href="https://unsplash.com/collections/12240655/palmerpenguins/d5aed8c855e26061e5e651d3f180b76d?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText">Unsplash</a></span>
]

---
class: right, top
background-image: url(data:image/png;base64,#images/pptx/tidyverse.png)
background-size: 1150px

## Collection of R packages, including <br/> these 8 core packages (and more!)

---

.footnote[<span>Photo by <a href="https://unsplash.com/@eadesstudio?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText">James Eades</a> on <a href="https://unsplash.com/collections/12240655/palmerpenguins/d5aed8c855e26061e5e651d3f180b76d?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText">Unsplash</a></span>
]

---
background-image: url(data:image/png;base64,#images/hex/readr.png)
background-position: 1050px 50px
background-size: 80px
  
# readr: info

.pull-left[
### Importing data is the very first step! <br/> You can use `readr` to import rectangular data.
]
.pull-right[
### You can import...

- comma separated (CSV) files with `read_csv()`
- tab separated files with `read_tsv()`
- general delimited files with `read_delim()`
- fixed width files with `read_fwf()`
- tabular files where columns are separated by white-space with `read_table()`
- web log files with `read_log()`

]
]

### Package documentation: https://readr.tidyverse.org/
]
]

]

---
background-image: url(data:image/png;base64,#images/hex/readr.png)
background-position: 1050px 50px
background-size: 80px

# readr: exercise

.panelset[
.panel[.panel-name[Read data in]
.center[
### Both options below will get you the same dataset!]

Option 1

```r
# option 1: load using URL ----
raw_adelie_url <- read_csv("https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-pal.219.3&entityid=002f3893385f710df69eeebe893144ff")
```

Option 2

```r
# option 2: load using filepath ----
raw_adelie_filepath <- read_csv("tutorial/raw_adelie.csv")
```
]

Lucky for us, the `palmerpenguins` 📦 compiles data from all three species together for us!

```r
# saves package tibble into global environment
penguins <- palmerpenguins::penguins 
head(penguins)
## # A tibble: 6 × 8
##   species island    bill_length_mm
##   <fct>   <fct>              <dbl>
## 1 Adelie  Torgersen           39.1
## 2 Adelie  Torgersen           39.5
## 3 Adelie  Torgersen           40.3
## 4 Adelie  Torgersen           NA  
## 5 Adelie  Torgersen           36.7
## 6 Adelie  Torgersen           39.3
## # … with 5 more variables:
## #   bill_depth_mm <dbl>,
## #   flipper_length_mm <int>,
## #   body_mass_g <int>, sex <fct>,
## #   year <int>
```
]
.pull-right[
`penguins_raw` contains raw data

```r
penguins_raw <- palmerpenguins::penguins_raw
head(penguins_raw)
## # A tibble: 6 × 17
##   studyName `Sample Number` Species  Region
##   <chr>               <dbl> <chr>    <chr> 
## 1 PAL0708                 1 Adelie … Anvers
## 2 PAL0708                 2 Adelie … Anvers
## 3 PAL0708                 3 Adelie … Anvers
## 4 PAL0708                 4 Adelie … Anvers
## 5 PAL0708                 5 Adelie … Anvers
## 6 PAL0708                 6 Adelie … Anvers
## # … with 13 more variables: Island <chr>,
## #   Stage <chr>, Individual ID <chr>,
## #   Clutch Completion <chr>,
## #   Date Egg <date>,
## #   Culmen Length (mm) <dbl>,
## #   Culmen Depth (mm) <dbl>,
## #   Flipper Length (mm) <dbl>, …
```
]
]
]

---

.footnote[
<span>Photo by <a href="https://unsplash.com/@eadesstudio?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText">James Eades</a> on <a href="https://unsplash.com/collections/12240655/palmerpenguins/d5aed8c855e26061e5e651d3f180b76d?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText">Unsplash</a></span>
]

---
background-image: url(data:image/png;base64,#images/hex/tibble.png)
background-position: 1050px 50px
background-size: 80px
  
# tibble: info

.pull-left[
### A `tibble` is much like the `dataframe` in base R, but optimized for use in the Tidyverse.  
]
]

### Package documentation: https://tibble.tidyverse.org/
]
]

]

---
background-image: url(data:image/png;base64,#images/hex/tibble.png)
background-position: 1050px 50px
background-size: 80px

# tibble: exercise

```r
# try each of these commands in the console and see if you can spot the differences!

as_tibble(penguins)
as.data.frame(penguins)
```
]

```r
as_tibble(penguins)
## # A tibble: 344 × 8
##    species island    bill_length_mm
##    <fct>   <fct>              <dbl>
##  1 Adelie  Torgersen           39.1
##  2 Adelie  Torgersen           39.5
##  3 Adelie  Torgersen           40.3
##  4 Adelie  Torgersen           NA  
##  5 Adelie  Torgersen           36.7
##  6 Adelie  Torgersen           39.3
##  7 Adelie  Torgersen           38.9
##  8 Adelie  Torgersen           39.2
##  9 Adelie  Torgersen           34.1
## 10 Adelie  Torgersen           42  
## # … with 334 more rows, and 5 more
## #   variables: bill_depth_mm <dbl>,
## #   flipper_length_mm <int>,
## #   body_mass_g <int>, sex <fct>,
## #   year <int>
```
]
.pull-right[

```r
as.data.frame(penguins)
##     species    island bill_length_mm
## 1    Adelie Torgersen           39.1
## 2    Adelie Torgersen           39.5
## 3    Adelie Torgersen           40.3
## 4    Adelie Torgersen             NA
## 5    Adelie Torgersen           36.7
## 6    Adelie Torgersen           39.3
## 7    Adelie Torgersen           38.9
## 8    Adelie Torgersen           39.2
## 9    Adelie Torgersen           34.1
## 10   Adelie Torgersen           42.0
## 11   Adelie Torgersen           37.8
## 12   Adelie Torgersen           37.8
## 13   Adelie Torgersen           41.1
## 14   Adelie Torgersen           38.6
## 15   Adelie Torgersen           34.6
## 16   Adelie Torgersen           36.6
## 17   Adelie Torgersen           38.7
## 18   Adelie Torgersen           42.5
## 19   Adelie Torgersen           34.4
## 20   Adelie Torgersen           46.0
## 21   Adelie    Biscoe           37.8
## 22   Adelie    Biscoe           37.7
## 23   Adelie    Biscoe           35.9
## 24   Adelie    Biscoe           38.2
## 25   Adelie    Biscoe           38.8
## 26   Adelie    Biscoe           35.3
## 27   Adelie    Biscoe           40.6
## 28   Adelie    Biscoe           40.5
## 29   Adelie    Biscoe           37.9
## 30   Adelie    Biscoe           40.5
## 31   Adelie     Dream           39.5
## 32   Adelie     Dream           37.2
## 33   Adelie     Dream           39.5
## 34   Adelie     Dream           40.9
## 35   Adelie     Dream           36.4
## 36   Adelie     Dream           39.2
## 37   Adelie     Dream           38.8
## 38   Adelie     Dream           42.2
## 39   Adelie     Dream           37.6
## 40   Adelie     Dream           39.8
## 41   Adelie     Dream           36.5
## 42   Adelie     Dream           40.8
## 43   Adelie     Dream           36.0
## 44   Adelie     Dream           44.1
## 45   Adelie     Dream           37.0
## 46   Adelie     Dream           39.6
## 47   Adelie     Dream           41.1
## 48   Adelie     Dream           37.5
## 49   Adelie     Dream           36.0
## 50   Adelie     Dream           42.3
## 51   Adelie    Biscoe           39.6
## 52   Adelie    Biscoe           40.1
## 53   Adelie    Biscoe           35.0
## 54   Adelie    Biscoe           42.0
## 55   Adelie    Biscoe           34.5
## 56   Adelie    Biscoe           41.4
## 57   Adelie    Biscoe           39.0
## 58   Adelie    Biscoe           40.6
## 59   Adelie    Biscoe           36.5
## 60   Adelie    Biscoe           37.6
## 61   Adelie    Biscoe           35.7
## 62   Adelie    Biscoe           41.3
## 63   Adelie    Biscoe           37.6
## 64   Adelie    Biscoe           41.1
## 65   Adelie    Biscoe           36.4
## 66   Adelie    Biscoe           41.6
## 67   Adelie    Biscoe           35.5
## 68   Adelie    Biscoe           41.1
## 69   Adelie Torgersen           35.9
## 70   Adelie Torgersen           41.8
## 71   Adelie Torgersen           33.5
## 72   Adelie Torgersen           39.7
## 73   Adelie Torgersen           39.6
## 74   Adelie Torgersen           45.8
## 75   Adelie Torgersen           35.5
## 76   Adelie Torgersen           42.8
## 77   Adelie Torgersen           40.9
## 78   Adelie Torgersen           37.2
## 79   Adelie Torgersen           36.2
## 80   Adelie Torgersen           42.1
## 81   Adelie Torgersen           34.6
## 82   Adelie Torgersen           42.9
## 83   Adelie Torgersen           36.7
## 84   Adelie Torgersen           35.1
## 85   Adelie     Dream           37.3
## 86   Adelie     Dream           41.3
## 87   Adelie     Dream           36.3
## 88   Adelie     Dream           36.9
## 89   Adelie     Dream           38.3
## 90   Adelie     Dream           38.9
## 91   Adelie     Dream           35.7
## 92   Adelie     Dream           41.1
## 93   Adelie     Dream           34.0
## 94   Adelie     Dream           39.6
## 95   Adelie     Dream           36.2
## 96   Adelie     Dream           40.8
## 97   Adelie     Dream           38.1
## 98   Adelie     Dream           40.3
## 99   Adelie     Dream           33.1
## 100  Adelie     Dream           43.2
## 101  Adelie    Biscoe           35.0
## 102  Adelie    Biscoe           41.0
## 103  Adelie    Biscoe           37.7
## 104  Adelie    Biscoe           37.8
## 105  Adelie    Biscoe           37.9
## 106  Adelie    Biscoe           39.7
## 107  Adelie    Biscoe           38.6
## 108  Adelie    Biscoe           38.2
## 109  Adelie    Biscoe           38.1
## 110  Adelie    Biscoe           43.2
## 111  Adelie    Biscoe           38.1
## 112  Adelie    Biscoe           45.6
## 113  Adelie    Biscoe           39.7
## 114  Adelie    Biscoe           42.2
## 115  Adelie    Biscoe           39.6
## 116  Adelie    Biscoe           42.7
## 117  Adelie Torgersen           38.6
## 118  Adelie Torgersen           37.3
## 119  Adelie Torgersen           35.7
## 120  Adelie Torgersen           41.1
## 121  Adelie Torgersen           36.2
## 122  Adelie Torgersen           37.7
## 123  Adelie Torgersen           40.2
## 124  Adelie Torgersen           41.4
## 125  Adelie Torgersen           35.2
##     bill_depth_mm flipper_length_mm
## 1            18.7               181
## 2            17.4               186
## 3            18.0               195
## 4              NA                NA
## 5            19.3               193
## 6            20.6               190
## 7            17.8               181
## 8            19.6               195
## 9            18.1               193
## 10           20.2               190
## 11           17.1               186
## 12           17.3               180
## 13           17.6               182
## 14           21.2               191
## 15           21.1               198
## 16           17.8               185
## 17           19.0               195
## 18           20.7               197
## 19           18.4               184
## 20           21.5               194
## 21           18.3               174
## 22           18.7               180
## 23           19.2               189
## 24           18.1               185
## 25           17.2               180
## 26           18.9               187
## 27           18.6               183
## 28           17.9               187
## 29           18.6               172
## 30           18.9               180
## 31           16.7               178
## 32           18.1               178
## 33           17.8               188
## 34           18.9               184
## 35           17.0               195
## 36           21.1               196
## 37           20.0               190
## 38           18.5               180
## 39           19.3               181
## 40           19.1               184
## 41           18.0               182
## 42           18.4               195
## 43           18.5               186
## 44           19.7               196
## 45           16.9               185
## 46           18.8               190
## 47           19.0               182
## 48           18.9               179
## 49           17.9               190
## 50           21.2               191
## 51           17.7               186
## 52           18.9               188
## 53           17.9               190
## 54           19.5               200
## 55           18.1               187
## 56           18.6               191
## 57           17.5               186
## 58           18.8               193
## 59           16.6               181
## 60           19.1               194
## 61           16.9               185
## 62           21.1               195
## 63           17.0               185
## 64           18.2               192
## 65           17.1               184
## 66           18.0               192
## 67           16.2               195
## 68           19.1               188
## 69           16.6               190
## 70           19.4               198
## 71           19.0               190
## 72           18.4               190
## 73           17.2               196
## 74           18.9               197
## 75           17.5               190
## 76           18.5               195
## 77           16.8               191
## 78           19.4               184
## 79           16.1               187
## 80           19.1               195
## 81           17.2               189
## 82           17.6               196
## 83           18.8               187
## 84           19.4               193
## 85           17.8               191
## 86           20.3               194
## 87           19.5               190
## 88           18.6               189
## 89           19.2               189
## 90           18.8               190
## 91           18.0               202
## 92           18.1               205
## 93           17.1               185
## 94           18.1               186
## 95           17.3               187
## 96           18.9               208
## 97           18.6               190
## 98           18.5               196
## 99           16.1               178
## 100          18.5               192
## 101          17.9               192
## 102          20.0               203
## 103          16.0               183
## 104          20.0               190
## 105          18.6               193
## 106          18.9               184
## 107          17.2               199
## 108          20.0               190
## 109          17.0               181
## 110          19.0               197
## 111          16.5               198
## 112          20.3               191
## 113          17.7               193
## 114          19.5               197
## 115          20.7               191
## 116          18.3               196
## 117          17.0               188
## 118          20.5               199
## 119          17.0               189
## 120          18.6               189
## 121          17.2               187
## 122          19.8               198
## 123          17.0               176
## 124          18.5               202
## 125          15.9               186
##     body_mass_g    sex year
## 1          3750   male 2007
## 2          3800 female 2007
## 3          3250 female 2007
## 4            NA   <NA> 2007
## 5          3450 female 2007
## 6          3650   male 2007
## 7          3625 female 2007
## 8          4675   male 2007
## 9          3475   <NA> 2007
## 10         4250   <NA> 2007
## 11         3300   <NA> 2007
## 12         3700   <NA> 2007
## 13         3200 female 2007
## 14         3800   male 2007
## 15         4400   male 2007
## 16         3700 female 2007
## 17         3450 female 2007
## 18         4500   male 2007
## 19         3325 female 2007
## 20         4200   male 2007
## 21         3400 female 2007
## 22         3600   male 2007
## 23         3800 female 2007
## 24         3950   male 2007
## 25         3800   male 2007
## 26         3800 female 2007
## 27         3550   male 2007
## 28         3200 female 2007
## 29         3150 female 2007
## 30         3950   male 2007
## 31         3250 female 2007
## 32         3900   male 2007
## 33         3300 female 2007
## 34         3900   male 2007
## 35         3325 female 2007
## 36         4150   male 2007
## 37         3950   male 2007
## 38         3550 female 2007
## 39         3300 female 2007
## 40         4650   male 2007
## 41         3150 female 2007
## 42         3900   male 2007
## 43         3100 female 2007
## 44         4400   male 2007
## 45         3000 female 2007
## 46         4600   male 2007
## 47         3425   male 2007
## 48         2975   <NA> 2007
## 49         3450 female 2007
## 50         4150   male 2007
## 51         3500 female 2008
## 52         4300   male 2008
## 53         3450 female 2008
## 54         4050   male 2008
## 55         2900 female 2008
## 56         3700   male 2008
## 57         3550 female 2008
## 58         3800   male 2008
## 59         2850 female 2008
## 60         3750   male 2008
## 61         3150 female 2008
## 62         4400   male 2008
## 63         3600 female 2008
## 64         4050   male 2008
## 65         2850 female 2008
## 66         3950   male 2008
## 67         3350 female 2008
## 68         4100   male 2008
## 69         3050 female 2008
## 70         4450   male 2008
## 71         3600 female 2008
## 72         3900   male 2008
## 73         3550 female 2008
## 74         4150   male 2008
## 75         3700 female 2008
## 76         4250   male 2008
## 77         3700 female 2008
## 78         3900   male 2008
## 79         3550 female 2008
## 80         4000   male 2008
## 81         3200 female 2008
## 82         4700   male 2008
## 83         3800 female 2008
## 84         4200   male 2008
## 85         3350 female 2008
## 86         3550   male 2008
## 87         3800   male 2008
## 88         3500 female 2008
## 89         3950   male 2008
## 90         3600 female 2008
## 91         3550 female 2008
## 92         4300   male 2008
## 93         3400 female 2008
## 94         4450   male 2008
## 95         3300 female 2008
## 96         4300   male 2008
## 97         3700 female 2008
## 98         4350   male 2008
## 99         2900 female 2008
## 100        4100   male 2008
## 101        3725 female 2009
## 102        4725   male 2009
## 103        3075 female 2009
## 104        4250   male 2009
## 105        2925 female 2009
## 106        3550   male 2009
## 107        3750 female 2009
## 108        3900   male 2009
## 109        3175 female 2009
## 110        4775   male 2009
## 111        3825 female 2009
## 112        4600   male 2009
## 113        3200 female 2009
## 114        4275   male 2009
## 115        3900 female 2009
## 116        4075   male 2009
## 117        2900 female 2009
## 118        3775   male 2009
## 119        3350 female 2009
## 120        3325   male 2009
## 121        3150 female 2009
## 122        3500   male 2009
## 123        3450 female 2009
## 124        3875   male 2009
## 125        3050 female 2009
##  [ reached 'max' / getOption("max.print") -- omitted 219 rows ]
```
]
]

### What differences do you see?

You might see a `tibble` prints:
- variable classes
- only 10 rows
- only as many columns as can fit on the screen
- `NA`s are highlighted in console so they're easy to spot (font highlighting and styling in `tibble`)

Not so much a concern in an R Markdown file, but noticeable in the console. 
Print method makes it easier to work with large datasets.

]

.panel[.panel-name[More]
There are a couple of other main differences, namely in **subsetting** and **recycling**. Check them out in the [`vignette("tibble")`](https://tibble.tidyverse.org/articles/tibble.html)

Try it out here!

```r
vignette("tibble")
```
]
]

---

---
background-image: url(data:image/png;base64,#images/hex/ggplot2.png)
background-position: 1050px 50px
background-size: 80px
  
# ggplot2: info

### `ggplot2` uses the "Grammar of Graphics" and layers graphical components together to create a plot.
]

.right-column[
### R for Data Science: [Ch 3 Data visualization](https://r4ds.had.co.nz/data-visualisation.html)

### Package documentation: https://ggplot2.tidyverse.org/
]
]
]

---
background-image: url(data:image/png;base64,#images/hex/ggplot2.png)
background-position: 1050px 50px
background-size: 80px

# ggplot2: exercise

```r
View(penguins)
```
]

```r
glimpse(penguins)
## Rows: 344
## Columns: 8
## $ species           <fct> Adelie, Adelie,…
## $ island            <fct> Torgersen, Torg…
## $ bill_length_mm    <dbl> 39.1, 39.5, 40.…
## $ bill_depth_mm     <dbl> 18.7, 17.4, 18.…
## $ flipper_length_mm <int> 181, 186, 195, …
## $ body_mass_g       <int> 3750, 3800, 325…
## $ sex               <fct> male, female, f…
## $ year              <int> 2007, 2007, 200…
```
]
]

Let's see if body mass varies by penguin sex

```r
ggplot(data = penguins, 
*      aes(x = sex, y = body_mass_g)) +
  geom_point()
```
]

.pull-right[
<img src="data:image/png;base64,#index_files/figure-html/unnamed-chunk-18-1.png" width="504" />
]
]

```r
ggplot(data = penguins, 
       aes(x = sex, y = body_mass_g)) +
* geom_boxplot()
```
]

.pull-right[
<img src="data:image/png;base64,#index_files/figure-html/unnamed-chunk-20-1.png" width="504" />
]
]

```r
ggplot(data = penguins, 
       aes(x = sex, y = body_mass_g)) +
* geom_boxplot(aes(fill = species))
```

### <br/> What do you notice?
]

.pull-right[
<img src="data:image/png;base64,#index_files/figure-html/unnamed-chunk-22-1.png" width="504" />
]
]

### You might see...

.pull-left[
- Gentoo penguins have higher body mass than Adélie and Chinstrap penguins
- Higher body mass among male Gentoo penguins compared to female penguins
- Pattern not as discernible when comparing Adélie and Chinstrap penguins
- No *NA*s among Chinstrap penguin data points! **sex** was available for each observation
]

.pull-right[
<img src="data:image/png;base64,#index_files/figure-html/unnamed-chunk-23-1.png" width="504" />
]
]
]

---

---
background-image: url(data:image/png;base64,#images/hex/dplyr.png)
background-position: 1050px 50px
background-size: 80px
  
# dplyr: info

.pull-left[
### Data transformation helps you get the data in exactly the right form you need. <br/> With `dplyr` you can:

- create new variables
- create summaries
- rename variables
- reorder observations
- ...and more!
]
.pull-right[
- Pick observations by their values with `filter()`.
- Reorder the rows with `arrange()`.
- Pick variables by their names `select()`.
- Create new variables with functions of existing variables with `mutate()`.
- Collapse many values down to a single summary with `summarize()`.
- `group_by()` gets the above functions to operate group-by-group rather than on the entire dataset. 
- and `count()` + `add_count()` simplify `group_by()` + `summarize()` when you just want to count
]
]

.right-column[
### R for Data Science: [Ch 11 Data transformation](https://r4ds.had.co.nz/transform.html)

### Package documentation: https://dplyr.tidyverse.org/
]
]
]

---
background-image: url(data:image/png;base64,#images/hex/dplyr.png)
background-position: 1050px 50px
background-size: 80px

# dplyr: exercise

.panel[.panel-name[Select]
.center[ 
### Can you spot the difference in performing the same operation?
]
.pull-left[

```r
select(penguins, species, sex, body_mass_g)
## # A tibble: 344 × 3
##    species sex    body_mass_g
##    <fct>   <fct>        <int>
##  1 Adelie  male          3750
##  2 Adelie  female        3800
##  3 Adelie  female        3250
##  4 Adelie  <NA>            NA
##  5 Adelie  female        3450
##  6 Adelie  male          3650
##  7 Adelie  female        3625
##  8 Adelie  male          4675
##  9 Adelie  <NA>          3475
## 10 Adelie  <NA>          4250
## # … with 334 more rows
```
]

```r
penguins %>%
  select(species, sex, body_mass_g)
## # A tibble: 344 × 3
##    species sex    body_mass_g
##    <fct>   <fct>        <int>
##  1 Adelie  male          3750
##  2 Adelie  female        3800
##  3 Adelie  female        3250
##  4 Adelie  <NA>            NA
##  5 Adelie  female        3450
##  6 Adelie  male          3650
##  7 Adelie  female        3625
##  8 Adelie  male          4675
##  9 Adelie  <NA>          3475
## 10 Adelie  <NA>          4250
## # … with 334 more rows
```
]
]

We can use `arrange()` to arrange our data in descending order by **body_mass_g**

```r
penguins %>%
  select(species, sex, body_mass_g) %>%
* arrange(desc(body_mass_g))
## # A tibble: 344 × 3
##    species sex   body_mass_g
##    <fct>   <fct>       <int>
##  1 Gentoo  male         6300
##  2 Gentoo  male         6050
##  3 Gentoo  male         6000
##  4 Gentoo  male         6000
##  5 Gentoo  male         5950
##  6 Gentoo  male         5950
##  7 Gentoo  male         5850
##  8 Gentoo  male         5850
##  9 Gentoo  male         5850
## 10 Gentoo  male         5800
## # … with 334 more rows
```
]
]

.pull-left[
.middle[We can use `group_by()` to group our data by **species** and **sex**, and `summarize()` to calculate the average **body_mass_g** for each grouping.]
]

```r
penguins %>%
  select(species, sex, body_mass_g) %>%
* group_by(species, sex) %>%
* summarize(mean = mean(body_mass_g))
## # A tibble: 8 × 3
## # Groups:   species [3]
##   species   sex     mean
##   <fct>     <fct>  <dbl>
## 1 Adelie    female 3369.
## 2 Adelie    male   4043.
## 3 Adelie    <NA>     NA 
## 4 Chinstrap female 3527.
## 5 Chinstrap male   3939.
## 6 Gentoo    female 4680.
## 7 Gentoo    male   5485.
## 8 Gentoo    <NA>     NA
```
]
]

.panel[.panel-name[Counting 1]
If we're just interested in _counting_ the observations in each grouping, we can group and summarize with special functions `count()` and `add_count()`.

----

It involves...
1. using `mutate()` to create an intermediate variable **n_species** that adds up all observations per **species**, and
2. an `ungroup()`-ing step
]

```r
penguins %>% 
  group_by(species) %>%
* mutate(n_species = n()) %>%
* ungroup() %>%
  group_by(species, sex, n_species) %>%
  summarize(n = n())
## # A tibble: 8 × 4
## # Groups:   species, sex [8]
##   species   sex    n_species     n
##   <fct>     <fct>      <int> <int>
## 1 Adelie    female       152    73
## 2 Adelie    male         152    73
## 3 Adelie    <NA>         152     6
## 4 Chinstrap female        68    34
## 5 Chinstrap male          68    34
## 6 Gentoo    female       124    58
## 7 Gentoo    male         124    61
## 8 Gentoo    <NA>         124     5
```
]
]

.panel[.panel-name[Counting 2]
If we're just interested in _counting_ the observations in each grouping, we can group and summarize with special functions `count()` and `add_count()`.

----

.small-text[Example kindly [contributed by Alison Hill (@apreshill)](https://github.com/spcanelon/2020-rladies-chi-tidyverse/issues/2)]
]
.pull-right[

```r
penguins %>% 
  count(species, sex) %>%
* add_count(species, wt = n,
*           name = "n_species")
## # A tibble: 8 × 4
##   species   sex        n n_species
##   <fct>     <fct>  <int>     <int>
## 1 Adelie    female    73       152
## 2 Adelie    male      73       152
## 3 Adelie    <NA>       6       152
## 4 Chinstrap female    34        68
## 5 Chinstrap male      34        68
## 6 Gentoo    female    58       124
## 7 Gentoo    male      61       124
## 8 Gentoo    <NA>       5       124
```
]
]

.pull-left[
We can add to our counting example by using `mutate()` to create a new variable **prop**, which represents the proportion of penguins of each **sex**, grouped by **species**

.small-text[Example kindly [contributed by Alison Hill (@apreshill)](https://github.com/spcanelon/2020-rladies-chi-tidyverse/issues/2)]
]
.pull-right[

```r
penguins %>% 
  count(species, sex) %>%
  add_count(species, wt = n, 
            name = "n_species") %>%
* mutate(prop = n/n_species*100)
## # A tibble: 8 × 5
##   species   sex        n n_species  prop
##   <fct>     <fct>  <int>     <int> <dbl>
## 1 Adelie    female    73       152 48.0 
## 2 Adelie    male      73       152 48.0 
## 3 Adelie    <NA>       6       152  3.95
## 4 Chinstrap female    34        68 50   
## 5 Chinstrap male      34        68 50   
## 6 Gentoo    female    58       124 46.8 
## 7 Gentoo    male      61       124 49.2 
## 8 Gentoo    <NA>       5       124  4.03
```
]
]

.pull-left[
Finally, we can filter rows to only show us **Chinstrap** penguin summaries by adding `filter()` to our pipeline]

```r
penguins %>% 
  count(species, sex) %>%
  add_count(species, wt = n, 
            name = "n_species") %>%
  mutate(prop = n/n_species*100) %>%
* filter(species == "Chinstrap")
## # A tibble: 2 × 5
##   species   sex        n n_species  prop
##   <fct>     <fct>  <int>     <int> <dbl>
## 1 Chinstrap female    34        68    50
## 2 Chinstrap male      34        68    50
```
]
]

]

---

---
background-image: url(data:image/png;base64,#images/hex/forcats.png)
background-position: 1050px 50px
background-size: 80px
  
# forcats: info

### These are variables that have a fixed and known set of possible values, like **species**, **island**, and **sex** in our `penguins` dataset.
]

### Package documentation: https://forcats.tidyverse.org/
]
]
]

---
background-image: url(data:image/png;base64,#images/hex/forcats.png)
background-position: 1050px 50px
background-size: 80px

# forcats: exercise

.panelset[
.panel[.panel-name[Code]
.pull-left[
### Currently the **year** variable in `penguins` is continuous from 2007 to 2009.

### There may be situations where this isn't what we want and we might want to turn it into a categorical variable instead.
]

### The `factor()` function is perfect for this.

```r
penguins %>%
  mutate(year_factor = 
*          factor(year,
*                 levels = unique(year)))
```
]

]

.panel[.panel-name[Result]
### The result is a new factor **year_factor** with levels **2007**, **2008**, and **2009**
.pull-left[

```r
penguins_new <-
  penguins %>%
  mutate(year_factor = 
*          factor(year,
*                 levels = unique(year)))
penguins_new
## # A tibble: 344 × 9
##    species island    bill_length_mm
##    <fct>   <fct>              <dbl>
##  1 Adelie  Torgersen           39.1
##  2 Adelie  Torgersen           39.5
##  3 Adelie  Torgersen           40.3
##  4 Adelie  Torgersen           NA  
##  5 Adelie  Torgersen           36.7
##  6 Adelie  Torgersen           39.3
##  7 Adelie  Torgersen           38.9
##  8 Adelie  Torgersen           39.2
##  9 Adelie  Torgersen           34.1
## 10 Adelie  Torgersen           42  
## # … with 334 more rows, and 6 more
## #   variables: bill_depth_mm <dbl>,
## #   flipper_length_mm <int>,
## #   body_mass_g <int>, sex <fct>,
## #   year <int>, year_factor <fct>
```
]

```r
class(penguins_new$year_factor)
## [1] "factor"

levels(penguins_new$year_factor)
## [1] "2007" "2008" "2009"
```
]
]
]

---

---
background-image: url(data:image/png;base64,#images/hex/stringr.png)
background-position: 1050px 50px
background-size: 80px
  
# stringr: info

.pull-left[
### `stringr` helps us manipulate strings! The package includes many functions to help us with **regular expressions**, which are a concise language for describing patterns in strings.
]

-  detect matches
-  subset strings
-  manage string lengths
-  mutate strings
-  join and split strings
-  order strings
-  ...and more!
]

]

### Package documentation: https://stringr.tidyverse.org/
]
]
]

---
background-image: url(data:image/png;base64,#images/hex/stringr.png)
background-position: 1050px 50px
background-size: 80px

# stringr: exercise

### What does this chunk do?

```r
penguins %>%
  select(species, island) %>%
* mutate(ISLAND = str_to_upper(island))
## # A tibble: 344 × 3
##    species island    ISLAND   
##    <fct>   <fct>     <chr>    
##  1 Adelie  Torgersen TORGERSEN
##  2 Adelie  Torgersen TORGERSEN
##  3 Adelie  Torgersen TORGERSEN
##  4 Adelie  Torgersen TORGERSEN
##  5 Adelie  Torgersen TORGERSEN
##  6 Adelie  Torgersen TORGERSEN
##  7 Adelie  Torgersen TORGERSEN
##  8 Adelie  Torgersen TORGERSEN
##  9 Adelie  Torgersen TORGERSEN
## 10 Adelie  Torgersen TORGERSEN
## # … with 334 more rows
```
]

### How about this one?

```r
penguins %>%
  select(species, island) %>%
  mutate(ISLAND = str_to_upper(island)) %>%
* mutate(species_island = str_c(species, ISLAND, sep = "_"))
## # A tibble: 344 × 4
##    species island    ISLAND  species_island
##    <fct>   <fct>     <chr>   <chr>         
##  1 Adelie  Torgersen TORGER… Adelie_TORGER…
##  2 Adelie  Torgersen TORGER… Adelie_TORGER…
##  3 Adelie  Torgersen TORGER… Adelie_TORGER…
##  4 Adelie  Torgersen TORGER… Adelie_TORGER…
##  5 Adelie  Torgersen TORGER… Adelie_TORGER…
##  6 Adelie  Torgersen TORGER… Adelie_TORGER…
##  7 Adelie  Torgersen TORGER… Adelie_TORGER…
##  8 Adelie  Torgersen TORGER… Adelie_TORGER…
##  9 Adelie  Torgersen TORGER… Adelie_TORGER…
## 10 Adelie  Torgersen TORGER… Adelie_TORGER…
## # … with 334 more rows
```
]
]

---

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# tidyr: info

> There are three interrelated rules which make a dataset tidy:

> - Each variable must have its own column.
> - Each observation must have its own row.
> - Each value must have its own cell.
![](data:image/png;base64,#https://d33wubrfki0l68.cloudfront.net/6f1ddb544fc5c69a2478e444ab8112fb0eea23f8/91adc/images/tidy-1.png)

]

### Package documentation: https://tidyr.tidyverse.org/
]
]
]

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# tidyr: exercise

### Both penguin datasets are already tidy!

We can pretend that `penguins` wasn't tidy and that it looked instead like `untidy_penguins` below, where **body_mass_g** was recorded separately for *male*, *female*, and *NA* **sex** penguins.

```r
untidy_penguins <- 
  penguins %>% pivot_wider(names_from = sex, values_from = body_mass_g)
untidy_penguins
## # A tibble: 344 × 9
##    species island    bill_length_mm
##    <fct>   <fct>              <dbl>
##  1 Adelie  Torgersen           39.1
##  2 Adelie  Torgersen           39.5
##  3 Adelie  Torgersen           40.3
##  4 Adelie  Torgersen           NA  
##  5 Adelie  Torgersen           36.7
##  6 Adelie  Torgersen           39.3
##  7 Adelie  Torgersen           38.9
##  8 Adelie  Torgersen           39.2
##  9 Adelie  Torgersen           34.1
## 10 Adelie  Torgersen           42  
## # … with 334 more rows, and 6 more
## #   variables: bill_depth_mm <dbl>,
## #   flipper_length_mm <int>, year <int>,
## #   male <int>, female <int>, NA <int>
```

]

### Now let's make it tidy again!

We'll use the help of `pivot_longer()`

```r
untidy_penguins %>%
* pivot_longer(cols = male:`NA`,
*              names_to = "sex",
*              values_to = "body_mass_g")
## # A tibble: 1,032 × 8
##    species island    bill_length_mm
##    <fct>   <fct>              <dbl>
##  1 Adelie  Torgersen           39.1
##  2 Adelie  Torgersen           39.1
##  3 Adelie  Torgersen           39.1
##  4 Adelie  Torgersen           39.5
##  5 Adelie  Torgersen           39.5
##  6 Adelie  Torgersen           39.5
##  7 Adelie  Torgersen           40.3
##  8 Adelie  Torgersen           40.3
##  9 Adelie  Torgersen           40.3
## 10 Adelie  Torgersen           NA  
## # … with 1,022 more rows, and 5 more
## #   variables: bill_depth_mm <dbl>,
## #   flipper_length_mm <int>, year <int>,
## #   sex <chr>, body_mass_g <int>
```
]
]

---

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# purrr: info

### The `purrr` family of functions helps us replace for loops, making our code easier to read and more succint.
]

- Iterate over a single input with `map()`
- Iterate over two inputs in parallel with `map2()`
- Iterate with multiple arguments with `pmap()`
- Iterate with multiple arguments and functions with `invoke_map()`
- Call a function for its side-effects with `walk()`, `walk2()`, and `pwalk()`
]
]

### Package documentation: https://purrr.tidyverse.org/
]
]
]

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# purrr: exercise

.pull-left[
### Ok, we love our earlier boxplot showing us **body_mass_g** by **sex** and colored by **species**... but let's change up the colors to keep with our Antarctica theme!

### I'm a big fan of the color palettes in the `nord` 📦
]
.pull-right[
![](data:image/png;base64,#https://raw.githubusercontent.com/jkaupp/nord/master/man/figures/README-palettes-1.png)
]
]

.pull-left[
### Let's turn this plot
<img src="data:image/png;base64,#index_files/figure-html/unnamed-chunk-50-1.png" width="504" />
]

.pull-right[
### Into this one!
<img src="data:image/png;base64,#index_files/figure-html/unnamed-chunk-51-1.png" width="504" />

```r
library(nord)

# you can choose colors using 
# the color hex codes
nord::nord_palettes$frost
## [1] "#8FBCBB" "#88C0D0" "#81A1C1" "#5E81AC"
```

```r
# and assign them using the 
# `scale_fill_manual()` function
penguins %>%
  ggplot(aes(x = sex, y = body_mass_g)) +
  geom_boxplot(aes(fill = species)) +
* scale_fill_manual(values = c("#8FBCBB", "#88C0D0", "#81A1C1"))
```
]

.pull-right[
<img src="data:image/png;base64,#index_files/figure-html/unnamed-chunk-54-1.png" width="504" />
]
]

```r
penguins %>%
  ggplot(aes(x = sex, y = body_mass_g)) +
  geom_boxplot(aes(fill = species)) +
* scale_fill_manual(values = nord::nord_palettes$frost)
```

<img src="data:image/png;base64,#index_files/figure-html/unnamed-chunk-55-1.png" width="360" />
]

.pull-right[
And some color palette packages also come with their own functions like `scale_fill_nord()`

```r
penguins %>%
  ggplot(aes(x = sex, y = body_mass_g)) +
  geom_boxplot(aes(fill = species)) +
* nord::scale_fill_nord(palette = "frost")
```

<img src="data:image/png;base64,#index_files/figure-html/unnamed-chunk-56-1.png" width="360" />
]
]

.pull-left[
The `prismatic` 📦 helps us **see** the colors that correspond to each color hex code (mostly), with the `color()` function

```r
library(prismatic)
```

```r
prismatic::color(nord::nord_palettes$frost)
```
![](data:image/png;base64,#images/nord_frost.png)
]

.pull-right[
`purrr`'s `map()` function can help us iterate `color()` over all palettes in a palette package like `nord`!

```r
nord::nord_palettes %>% map(prismatic::color)
```

![](data:image/png;base64,#images/nord_multiple.png)
]
]
]
]

.pull-left[
### 🎨 [r-color-palettes repo](https://github.com/EmilHvitfeldt/r-color-palettes) from Emil Hvitfeldt

### Like this Wes Anderson themed one! And many, many others. 🤩
]

]

---
class: about-me, middle, center

# Thank you!

## Any questions?