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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>An Antarctic Tour of the Tidyverse</title>
<meta charset="utf-8" />
<meta name="author" content="Silvia Canelón, PhD" />
<meta name="date" content="2020-08-31" />
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</head>
<body>
<textarea id="source">
class: title-slide, center, bottom
<img src="images/lter_penguins.png" width="400" />
# An Antarctic Tour of the Tidyverse
## R-Ladies Chicago
### Silvia Canelón, PhD
### August 31, 2020
---
class: about-me, middle, center
## Silvia Canelón
### Postdoctoral Research Scientist
### University of Pennsylvania, Philadelphia, PA, USA
<img style="border-radius: 50%;" src="https://silvia.rbind.io/authors/silvia/avatar_hu5008cfaae4fe27558f3c3604a254cbf4_10721386_270x270_fill_lanczos_center_2.png" width="150px"/>
[<i class="fas fa-link "></i> silvia.rbind.io](https://silvia.rbind.io)<br/>
[<i class="fab fa-twitter "></i> @spcanelon](https://twitter.com/spcanelon)<br/>
[<i class="fab fa-github "></i> @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(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="images/penguin_chinstrap.jpg" style="width: 100%;">
</div>
<div class="column" style="margin: 0 1em;">
<h3> Gentoo </h3>
<img src="images/penguin_gentoo.jpg" style="">
</div>
<div class="column" style="margin: 0 0em;">
<h3> Adélie </h3>
<img src="images/penguin_adelie.jpg" 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(images/pptx/tidyverse.png)
background-size: 1150px
## Collection of R packages, including <br/> these 8 core packages (and more!)
---
class: penguin-tour
<img src="images/pptx/01-readr.png" width="1200" />
.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(images/hex/readr.png)
background-position: 1050px 50px
background-size: 80px
# readr: info
.panelset[
.panel[.panel-name[Overview]
.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()`
]
]
.panel[.panel-name[Cheatsheet]
<i class="fas fa-file-pdf "></i> PDF: https://github.com/rstudio/cheatsheets/raw/master/data-import.pdf
]
.panel[.panel-name[Reading]
.left-column[
<img src="images/r4ds-cover.png" width="222" />
]
.right-column[
### R for Data Science: [Ch 11 Data import](https://r4ds.had.co.nz/data-import.html)
### Package documentation: https://readr.tidyverse.org/
]
]
]
---
background-image: url(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")
```
]
.panel[.panel-name[Save data]
Lucky for us, the `palmerpenguins` 📦 compiles data from all three species together for us!
.pull-left[
`penguins` contains a clean dataset
```r
# saves package tibble into global environment
penguins <- palmerpenguins::penguins
head(penguins)
## # A tibble: 6 x 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year
## <fct> <fct> <dbl> <dbl> <int> <int> <fct> <int>
## 1 Adelie Torgersen 39.1 18.7 181 3750 male 2007
## 2 Adelie Torgersen 39.5 17.4 186 3800 female 2007
## 3 Adelie Torgersen 40.3 18 195 3250 female 2007
## 4 Adelie Torgersen NA NA NA NA <NA> 2007
## 5 Adelie Torgersen 36.7 19.3 193 3450 female 2007
## 6 Adelie Torgersen 39.3 20.6 190 3650 male 2007
```
]
.pull-right[
`penguins_raw` contains raw data
```r
penguins_raw <- palmerpenguins::penguins_raw
head(penguins_raw)
## # A tibble: 6 x 17
## studyName `Sample Number` Species Region Island Stage `Individual ID` `Clutch Complet… `Date Egg` `Culmen Length … `Culmen Depth (… `Flipper Length…
## <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <date> <dbl> <dbl> <dbl>
## 1 PAL0708 1 Adelie… Anvers Torge… Adul… N1A1 Yes 2007-11-11 39.1 18.7 181
## 2 PAL0708 2 Adelie… Anvers Torge… Adul… N1A2 Yes 2007-11-11 39.5 17.4 186
## 3 PAL0708 3 Adelie… Anvers Torge… Adul… N2A1 Yes 2007-11-16 40.3 18 195
## 4 PAL0708 4 Adelie… Anvers Torge… Adul… N2A2 Yes 2007-11-16 NA NA NA
## 5 PAL0708 5 Adelie… Anvers Torge… Adul… N3A1 Yes 2007-11-16 36.7 19.3 193
## 6 PAL0708 6 Adelie… Anvers Torge… Adul… N3A2 Yes 2007-11-16 39.3 20.6 190
## # … with 5 more variables: `Body Mass (g)` <dbl>, Sex <chr>, `Delta 15 N (o/oo)` <dbl>, `Delta 13 C (o/oo)` <dbl>, Comments <chr>
```
]
]
]
---
class: penguin-tour
<img src="images/pptx/02-tibble.png" width="1200" />
.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(images/hex/tibble.png)
background-position: 1050px 50px
background-size: 80px
# tibble: info
.panelset[
.panel[.panel-name[Overview]
.pull-left[
### A `tibble` is much like the `dataframe` in base R, but optimized for use in the Tidyverse.
]
]
.panel[.panel-name[Cheatsheet]
<i class="fas fa-file-pdf "></i> PDF (tidyr): https://github.com/rstudio/cheatsheets/raw/master/data-transformation.pdf
]
.panel[.panel-name[Reading]
.left-column[
<img src="images/r4ds-cover.png" width="222" />
]
.right-column[
### R for Data Science: [Ch 10 Tibbles](https://r4ds.had.co.nz/tibbles.html)
### Package documentation: https://tibble.tidyverse.org/
]
]
]
---
background-image: url(images/hex/tibble.png)
background-position: 1050px 50px
background-size: 80px
# tibble: exercise
.panelset[
.panel[.panel-name[Code]
Let's take a look at the differences!
```r
# try each of these commands in the console and see if you can spot the differences!
as_tibble(penguins)
as.data.frame(penguins)
```
]
.panel[.panel-name[Result]
.pull-left[
```r
as_tibble(penguins)
## # A tibble: 344 x 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year
## <fct> <fct> <dbl> <dbl> <int> <int> <fct> <int>
## 1 Adelie Torgersen 39.1 18.7 181 3750 male 2007
## 2 Adelie Torgersen 39.5 17.4 186 3800 female 2007
## 3 Adelie Torgersen 40.3 18 195 3250 female 2007
## 4 Adelie Torgersen NA NA NA NA <NA> 2007
## 5 Adelie Torgersen 36.7 19.3 193 3450 female 2007
## 6 Adelie Torgersen 39.3 20.6 190 3650 male 2007
## 7 Adelie Torgersen 38.9 17.8 181 3625 female 2007
## 8 Adelie Torgersen 39.2 19.6 195 4675 male 2007
## 9 Adelie Torgersen 34.1 18.1 193 3475 <NA> 2007
## 10 Adelie Torgersen 42 20.2 190 4250 <NA> 2007
## # … with 334 more rows
```
]
.pull-right[
```r
as.data.frame(penguins)
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year
## 1 Adelie Torgersen 39.1 18.7 181 3750 male 2007
## 2 Adelie Torgersen 39.5 17.4 186 3800 female 2007
## 3 Adelie Torgersen 40.3 18.0 195 3250 female 2007
## 4 Adelie Torgersen NA NA NA NA <NA> 2007
## 5 Adelie Torgersen 36.7 19.3 193 3450 female 2007
## 6 Adelie Torgersen 39.3 20.6 190 3650 male 2007
## 7 Adelie Torgersen 38.9 17.8 181 3625 female 2007
## 8 Adelie Torgersen 39.2 19.6 195 4675 male 2007
## 9 Adelie Torgersen 34.1 18.1 193 3475 <NA> 2007
## 10 Adelie Torgersen 42.0 20.2 190 4250 <NA> 2007
## 11 Adelie Torgersen 37.8 17.1 186 3300 <NA> 2007
## 12 Adelie Torgersen 37.8 17.3 180 3700 <NA> 2007
## 13 Adelie Torgersen 41.1 17.6 182 3200 female 2007
## 14 Adelie Torgersen 38.6 21.2 191 3800 male 2007
## 15 Adelie Torgersen 34.6 21.1 198 4400 male 2007
## 16 Adelie Torgersen 36.6 17.8 185 3700 female 2007
## 17 Adelie Torgersen 38.7 19.0 195 3450 female 2007
## 18 Adelie Torgersen 42.5 20.7 197 4500 male 2007
## 19 Adelie Torgersen 34.4 18.4 184 3325 female 2007
## 20 Adelie Torgersen 46.0 21.5 194 4200 male 2007
## 21 Adelie Biscoe 37.8 18.3 174 3400 female 2007
## 22 Adelie Biscoe 37.7 18.7 180 3600 male 2007
## 23 Adelie Biscoe 35.9 19.2 189 3800 female 2007
## 24 Adelie Biscoe 38.2 18.1 185 3950 male 2007
## 25 Adelie Biscoe 38.8 17.2 180 3800 male 2007
## 26 Adelie Biscoe 35.3 18.9 187 3800 female 2007
## 27 Adelie Biscoe 40.6 18.6 183 3550 male 2007
## 28 Adelie Biscoe 40.5 17.9 187 3200 female 2007
## 29 Adelie Biscoe 37.9 18.6 172 3150 female 2007
## 30 Adelie Biscoe 40.5 18.9 180 3950 male 2007
## 31 Adelie Dream 39.5 16.7 178 3250 female 2007
## 32 Adelie Dream 37.2 18.1 178 3900 male 2007
## 33 Adelie Dream 39.5 17.8 188 3300 female 2007
## 34 Adelie Dream 40.9 18.9 184 3900 male 2007
## 35 Adelie Dream 36.4 17.0 195 3325 female 2007
## 36 Adelie Dream 39.2 21.1 196 4150 male 2007
## 37 Adelie Dream 38.8 20.0 190 3950 male 2007
## 38 Adelie Dream 42.2 18.5 180 3550 female 2007
## 39 Adelie Dream 37.6 19.3 181 3300 female 2007
## 40 Adelie Dream 39.8 19.1 184 4650 male 2007
## 41 Adelie Dream 36.5 18.0 182 3150 female 2007
## 42 Adelie Dream 40.8 18.4 195 3900 male 2007
## 43 Adelie Dream 36.0 18.5 186 3100 female 2007
## 44 Adelie Dream 44.1 19.7 196 4400 male 2007
## 45 Adelie Dream 37.0 16.9 185 3000 female 2007
## 46 Adelie Dream 39.6 18.8 190 4600 male 2007
## 47 Adelie Dream 41.1 19.0 182 3425 male 2007
## 48 Adelie Dream 37.5 18.9 179 2975 <NA> 2007
## 49 Adelie Dream 36.0 17.9 190 3450 female 2007
## 50 Adelie Dream 42.3 21.2 191 4150 male 2007
## 51 Adelie Biscoe 39.6 17.7 186 3500 female 2008
## 52 Adelie Biscoe 40.1 18.9 188 4300 male 2008
## 53 Adelie Biscoe 35.0 17.9 190 3450 female 2008
## 54 Adelie Biscoe 42.0 19.5 200 4050 male 2008
## 55 Adelie Biscoe 34.5 18.1 187 2900 female 2008
## 56 Adelie Biscoe 41.4 18.6 191 3700 male 2008
## 57 Adelie Biscoe 39.0 17.5 186 3550 female 2008
## 58 Adelie Biscoe 40.6 18.8 193 3800 male 2008
## 59 Adelie Biscoe 36.5 16.6 181 2850 female 2008
## 60 Adelie Biscoe 37.6 19.1 194 3750 male 2008
## 61 Adelie Biscoe 35.7 16.9 185 3150 female 2008
## 62 Adelie Biscoe 41.3 21.1 195 4400 male 2008
## 63 Adelie Biscoe 37.6 17.0 185 3600 female 2008
## 64 Adelie Biscoe 41.1 18.2 192 4050 male 2008
## 65 Adelie Biscoe 36.4 17.1 184 2850 female 2008
## 66 Adelie Biscoe 41.6 18.0 192 3950 male 2008
## 67 Adelie Biscoe 35.5 16.2 195 3350 female 2008
## 68 Adelie Biscoe 41.1 19.1 188 4100 male 2008
## 69 Adelie Torgersen 35.9 16.6 190 3050 female 2008
## 70 Adelie Torgersen 41.8 19.4 198 4450 male 2008
## 71 Adelie Torgersen 33.5 19.0 190 3600 female 2008
## 72 Adelie Torgersen 39.7 18.4 190 3900 male 2008
## 73 Adelie Torgersen 39.6 17.2 196 3550 female 2008
## 74 Adelie Torgersen 45.8 18.9 197 4150 male 2008
## 75 Adelie Torgersen 35.5 17.5 190 3700 female 2008
## 76 Adelie Torgersen 42.8 18.5 195 4250 male 2008
## 77 Adelie Torgersen 40.9 16.8 191 3700 female 2008
## 78 Adelie Torgersen 37.2 19.4 184 3900 male 2008
## 79 Adelie Torgersen 36.2 16.1 187 3550 female 2008
## 80 Adelie Torgersen 42.1 19.1 195 4000 male 2008
## 81 Adelie Torgersen 34.6 17.2 189 3200 female 2008
## 82 Adelie Torgersen 42.9 17.6 196 4700 male 2008
## 83 Adelie Torgersen 36.7 18.8 187 3800 female 2008
## 84 Adelie Torgersen 35.1 19.4 193 4200 male 2008
## 85 Adelie Dream 37.3 17.8 191 3350 female 2008
## 86 Adelie Dream 41.3 20.3 194 3550 male 2008
## 87 Adelie Dream 36.3 19.5 190 3800 male 2008
## 88 Adelie Dream 36.9 18.6 189 3500 female 2008
## 89 Adelie Dream 38.3 19.2 189 3950 male 2008
## 90 Adelie Dream 38.9 18.8 190 3600 female 2008
## 91 Adelie Dream 35.7 18.0 202 3550 female 2008
## 92 Adelie Dream 41.1 18.1 205 4300 male 2008
## 93 Adelie Dream 34.0 17.1 185 3400 female 2008
## 94 Adelie Dream 39.6 18.1 186 4450 male 2008
## 95 Adelie Dream 36.2 17.3 187 3300 female 2008
## 96 Adelie Dream 40.8 18.9 208 4300 male 2008
## 97 Adelie Dream 38.1 18.6 190 3700 female 2008
## 98 Adelie Dream 40.3 18.5 196 4350 male 2008
## 99 Adelie Dream 33.1 16.1 178 2900 female 2008
## 100 Adelie Dream 43.2 18.5 192 4100 male 2008
## 101 Adelie Biscoe 35.0 17.9 192 3725 female 2009
## 102 Adelie Biscoe 41.0 20.0 203 4725 male 2009
## 103 Adelie Biscoe 37.7 16.0 183 3075 female 2009
## 104 Adelie Biscoe 37.8 20.0 190 4250 male 2009
## 105 Adelie Biscoe 37.9 18.6 193 2925 female 2009
## 106 Adelie Biscoe 39.7 18.9 184 3550 male 2009
## 107 Adelie Biscoe 38.6 17.2 199 3750 female 2009
## 108 Adelie Biscoe 38.2 20.0 190 3900 male 2009
## 109 Adelie Biscoe 38.1 17.0 181 3175 female 2009
## 110 Adelie Biscoe 43.2 19.0 197 4775 male 2009
## 111 Adelie Biscoe 38.1 16.5 198 3825 female 2009
## 112 Adelie Biscoe 45.6 20.3 191 4600 male 2009
## 113 Adelie Biscoe 39.7 17.7 193 3200 female 2009
## 114 Adelie Biscoe 42.2 19.5 197 4275 male 2009
## 115 Adelie Biscoe 39.6 20.7 191 3900 female 2009
## 116 Adelie Biscoe 42.7 18.3 196 4075 male 2009
## 117 Adelie Torgersen 38.6 17.0 188 2900 female 2009
## 118 Adelie Torgersen 37.3 20.5 199 3775 male 2009
## 119 Adelie Torgersen 35.7 17.0 189 3350 female 2009
## 120 Adelie Torgersen 41.1 18.6 189 3325 male 2009
## 121 Adelie Torgersen 36.2 17.2 187 3150 female 2009
## 122 Adelie Torgersen 37.7 19.8 198 3500 male 2009
## 123 Adelie Torgersen 40.2 17.0 176 3450 female 2009
## 124 Adelie Torgersen 41.4 18.5 202 3875 male 2009
## 125 Adelie Torgersen 35.2 15.9 186 3050 female 2009
## [ reached 'max' / getOption("max.print") -- omitted 219 rows ]
```
]
]
.panel[.panel-name[Chat]
### 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")
```
]
]
---
class: penguin-tour
<img src="images/pptx/03-ggplot2.png" width="1200" />
.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(images/hex/ggplot2.png)
background-position: 1050px 50px
background-size: 80px
# ggplot2: info
.panelset[
.panel[.panel-name[Overview]
### Let's start by making a simple plot of our data!
### `ggplot2` uses the "Grammar of Graphics" and layers graphical components together to create a plot.
]
.panel[.panel-name[Cheatsheet]
<i class="fas fa-file-pdf "></i> PDF: https://github.com/rstudio/cheatsheets/raw/master/data-visualization-2.1.pdf
]
.panel[.panel-name[Reading]
.left-column[
<img src="images/r4ds-cover.png" width="222" />
]
.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(images/hex/ggplot2.png)
background-position: 1050px 50px
background-size: 80px
# ggplot2: exercise
.panelset[
.panel[.panel-name[View the data]
.pull-left[
### Get a full view of the dataset:
```r
View(penguins)
```
]
.pull-right[
### Or catch a `glimpse`:
```r
glimpse(penguins)
## Rows: 344
## Columns: 8
## $ species <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie…
## $ island <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, To…
## $ 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, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9…
## $ 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, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2…
## $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186, 180, 182, 191, 198, 185, 195, 197, 184, 194, 174, 180, 189, 185, 180, 187, 183, …
## $ body_mass_g <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, 4250, 3300, 3700, 3200, 3800, 4400, 3700, 3450, 4500, 3325, 4200, 3400, 3600, 3800…
## $ sex <fct> male, female, female, NA, female, male, female, male, NA, NA, NA, NA, female, male, male, female, female, male, female, male, female, …
## $ year <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 20…
```
]
]
.panel[.panel-name[Scatterplot]
Let's see if body mass varies by penguin sex
.pull-left[
```r
ggplot(data = penguins,
* aes(x = sex, y = body_mass_g)) +
geom_point()
```
]
.pull-right[
<img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-18-1.png" width="504" />
]
]
.panel[.panel-name[Boxplot]
.pull-left[
```r
ggplot(data = penguins,
aes(x = sex, y = body_mass_g)) +
* geom_boxplot()
```
]
.pull-right[
<img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-20-1.png" width="504" />
]
]
.panel[.panel-name[By Species]
.pull-left[
```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="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-22-1.png" width="504" />
]
]
.panel[.panel-name[Chat]
### 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="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-23-1.png" width="504" />
]
]
]
---
class: penguin-tour
<img src="images/pptx/04-dplyr.png" width="1200" />
.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(images/hex/dplyr.png)
background-position: 1050px 50px
background-size: 80px
# dplyr: info
.panelset[
.panel[.panel-name[Overview]
.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
]
]
.panel[.panel-name[Cheatsheet]
<i class="fas fa-file-pdf "></i> PDF: https://github.com/rstudio/cheatsheets/raw/master/data-transformation.pdf
]
.panel[.panel-name[Reading]
.left-column[
<img src="images/r4ds-cover.png" width="222" />
]
.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(images/hex/dplyr.png)
background-position: 1050px 50px
background-size: 80px
# dplyr: exercise
.panelset[
.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 x 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
```
]
.pull-right[
```r
penguins %>%
select(species, sex, body_mass_g)
## # A tibble: 344 x 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
```
]
]
.panel[.panel-name[Arrange]
We can use `arrange()` to arrange our data in descending order by **body_mass_g**
.pull-left[
```r
glimpse(penguins)
## Rows: 344
## Columns: 8
## $ species <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie…
## $ island <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, To…
## $ 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, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9…
## $ 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, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2…
## $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186, 180, 182, 191, 198, 185, 195, 197, 184, 194, 174, 180, 189, 185, 180, 187, 183, …
## $ body_mass_g <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, 4250, 3300, 3700, 3200, 3800, 4400, 3700, 3450, 4500, 3325, 4200, 3400, 3600, 3800…
## $ sex <fct> male, female, female, NA, female, male, female, male, NA, NA, NA, NA, female, male, male, female, female, male, female, male, female, …
## $ year <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 20…
```
]
.pull-right[
```r
penguins %>%
select(species, sex, body_mass_g) %>%
* arrange(desc(body_mass_g))
## # A tibble: 344 x 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
```
]
]
.panel[.panel-name[Group By & Summarize]
.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.]
]
.pull-right[
```r
penguins %>%
select(species, sex, body_mass_g) %>%
* group_by(species, sex) %>%
* summarize(mean = mean(body_mass_g))
## # A tibble: 8 x 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()`.
----
.pull-left[
Counting can be done with `group_by()` and `summarize()`, but it's a little cumbersome.
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
]
.pull-right[
```r
penguins %>%
group_by(species) %>%
* mutate(n_species = n()) %>%
* ungroup() %>%
group_by(species, sex, n_species) %>%
summarize(n = n())
## # A tibble: 8 x 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()`.
----
.pull-left[
In contrast, `count()` and `add_count()` offer a simplified approach
.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 x 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
```
]
]
.panel[.panel-name[Mutate]
.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 x 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
```
]
]
.panel[.panel-name[Filter]
.pull-left[
Finally, we can filter rows to only show us **Chinstrap** penguin summaries by adding `filter()` to our pipeline]
.pull-right[
```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 x 5
## species sex n n_species prop
## <fct> <fct> <int> <int> <dbl>
## 1 Chinstrap female 34 68 50
## 2 Chinstrap male 34 68 50
```
]
]
]
---
class: penguin-tour
<img src="images/pptx/05-forcats.png" width="1200" />
.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(images/hex/forcats.png)
background-position: 1050px 50px
background-size: 80px
# forcats: info
.panelset[
.panel[.panel-name[Overview]
### Helps us work with **categorical variables** or factors.
### These are variables that have a fixed and known set of possible values, like **species**, **island**, and **sex** in our `penguins` dataset.
]
.panel[.panel-name[Cheatsheet]
<i class="fas fa-file-pdf "></i> PDF: https://github.com/rstudio/cheatsheets/raw/master/factors.pdf
]
.panel[.panel-name[Reading]
.left-column[
<img src="images/r4ds-cover.png" width="222" />
]
.right-column[
### R for Data Science: [Ch 15 Factors](https://r4ds.had.co.nz/factors.html)