An Antarctic Tour of the Tidyverse

<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

[<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>]

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# 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

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# 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>
]

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## 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>
]

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# 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()`

]
]

<i class="fas  fa-file-pdf "></i> PDF: https://github.com/rstudio/cheatsheets/raw/master/data-import.pdf
![](https://raw.githubusercontent.com/rstudio/cheatsheets/master/pngs/thumbnails/data-import-cheatsheet-thumbs.png)
]

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

]

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# 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 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>
```
]
]
]

---

.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>
]

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# tibble: info

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

<i class="fas  fa-file-pdf "></i> PDF (tidyr): https://github.com/rstudio/cheatsheets/raw/master/data-transformation.pdf
![](https://raw.githubusercontent.com/rstudio/cheatsheets/master/pngs/thumbnails/data-import-cheatsheet-thumbs.png)
]

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

]

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# 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 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 ]
```
]
]

### 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")
```
]
]

---

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background-position: 1050px 50px
background-size: 80px
  
# ggplot2: info

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

<i class="fas  fa-file-pdf "></i> PDF: https://github.com/rstudio/cheatsheets/raw/master/data-visualization-2.1.pdf
![](https://raw.githubusercontent.com/rstudio/cheatsheets/master/pngs/thumbnails/data-visualization-cheatsheet-thumbs.png)
]

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

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

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# ggplot2: exercise

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

```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…
```
]
]

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="2020-rladies-chi-tidyverse_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="2020-rladies-chi-tidyverse_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="2020-rladies-chi-tidyverse_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="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-23-1.png" width="504" />
]
]
]

---

---
background-image: url(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
]
]

<i class="fas  fa-file-pdf "></i> PDF: https://github.com/rstudio/cheatsheets/raw/master/data-transformation.pdf
![](https://raw.githubusercontent.com/rstudio/cheatsheets/master/pngs/thumbnails/data-transformation-cheatsheet-thumbs.png)
]

.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

.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
```
]

```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
```
]
]

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 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
```
]
]

.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 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()`.

----

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 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()`.

----

.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
```
]
]

.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
```
]
]

.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 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
```
]
]

]

---

---
background-image: url(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.
]

<i class="fas  fa-file-pdf "></i> PDF: https://github.com/rstudio/cheatsheets/raw/master/factors.pdf
![](https://raw.githubusercontent.com/rstudio/cheatsheets/master/pngs/thumbnails/forcats-cheatsheet-thumbs.png)
]

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

---
background-image: url(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 x 9
##    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex     year year_factor
##    <fct>   <fct>              <dbl>         <dbl>             <int>       <int> <fct>  <int> <fct>      
##  1 Adelie  Torgersen           39.1          18.7               181        3750 male    2007 2007       
##  2 Adelie  Torgersen           39.5          17.4               186        3800 female  2007 2007       
##  3 Adelie  Torgersen           40.3          18                 195        3250 female  2007 2007       
##  4 Adelie  Torgersen           NA            NA                  NA          NA <NA>    2007 2007       
##  5 Adelie  Torgersen           36.7          19.3               193        3450 female  2007 2007       
##  6 Adelie  Torgersen           39.3          20.6               190        3650 male    2007 2007       
##  7 Adelie  Torgersen           38.9          17.8               181        3625 female  2007 2007       
##  8 Adelie  Torgersen           39.2          19.6               195        4675 male    2007 2007       
##  9 Adelie  Torgersen           34.1          18.1               193        3475 <NA>    2007 2007       
## 10 Adelie  Torgersen           42            20.2               190        4250 <NA>    2007 2007       
## # … with 334 more rows
```
]

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

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

---

---
background-image: url(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!
]

]

<i class="fas  fa-file-pdf "></i> PDF: https://github.com/rstudio/cheatsheets/raw/master/strings.pdf
![](https://raw.githubusercontent.com/rstudio/cheatsheets/master/pngs/thumbnails/strings-cheatsheet-thumbs.png)
]

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

---
background-image: url(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 x 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 x 4
##    species island    ISLAND    species_island  
##    <fct>   <fct>     <chr>     <chr>           
##  1 Adelie  Torgersen TORGERSEN Adelie_TORGERSEN
##  2 Adelie  Torgersen TORGERSEN Adelie_TORGERSEN
##  3 Adelie  Torgersen TORGERSEN Adelie_TORGERSEN
##  4 Adelie  Torgersen TORGERSEN Adelie_TORGERSEN
##  5 Adelie  Torgersen TORGERSEN Adelie_TORGERSEN
##  6 Adelie  Torgersen TORGERSEN Adelie_TORGERSEN
##  7 Adelie  Torgersen TORGERSEN Adelie_TORGERSEN
##  8 Adelie  Torgersen TORGERSEN Adelie_TORGERSEN
##  9 Adelie  Torgersen TORGERSEN Adelie_TORGERSEN
## 10 Adelie  Torgersen TORGERSEN Adelie_TORGERSEN
## # … with 334 more rows
```
]
]

---

---
background-image: url(images/hex/tidyr.png)
background-position: 1050px 50px
background-size: 80px
  
# 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.
![](https://d33wubrfki0l68.cloudfront.net/6f1ddb544fc5c69a2478e444ab8112fb0eea23f8/91adc/images/tidy-1.png)

]

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

---
background-image: url(images/hex/tidyr.png)
background-position: 1050px 50px
background-size: 80px

# 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 x 9
##    species island    bill_length_mm bill_depth_mm flipper_length_mm  year  male female  `NA`
##    <fct>   <fct>              <dbl>         <dbl>             <int> <int> <int>  <int> <int>
##  1 Adelie  Torgersen           39.1          18.7               181  2007  3750     NA    NA
##  2 Adelie  Torgersen           39.5          17.4               186  2007    NA   3800    NA
##  3 Adelie  Torgersen           40.3          18                 195  2007    NA   3250    NA
##  4 Adelie  Torgersen           NA            NA                  NA  2007    NA     NA    NA
##  5 Adelie  Torgersen           36.7          19.3               193  2007    NA   3450    NA
##  6 Adelie  Torgersen           39.3          20.6               190  2007  3650     NA    NA
##  7 Adelie  Torgersen           38.9          17.8               181  2007    NA   3625    NA
##  8 Adelie  Torgersen           39.2          19.6               195  2007  4675     NA    NA
##  9 Adelie  Torgersen           34.1          18.1               193  2007    NA     NA  3475
## 10 Adelie  Torgersen           42            20.2               190  2007    NA     NA  4250
## # … with 334 more rows
```

]

### 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 x 8
##    species island    bill_length_mm bill_depth_mm flipper_length_mm  year sex    body_mass_g
##    <fct>   <fct>              <dbl>         <dbl>             <int> <int> <chr>        <int>
##  1 Adelie  Torgersen           39.1          18.7               181  2007 male          3750
##  2 Adelie  Torgersen           39.1          18.7               181  2007 female          NA
##  3 Adelie  Torgersen           39.1          18.7               181  2007 NA              NA
##  4 Adelie  Torgersen           39.5          17.4               186  2007 male            NA
##  5 Adelie  Torgersen           39.5          17.4               186  2007 female        3800
##  6 Adelie  Torgersen           39.5          17.4               186  2007 NA              NA
##  7 Adelie  Torgersen           40.3          18                 195  2007 male            NA
##  8 Adelie  Torgersen           40.3          18                 195  2007 female        3250
##  9 Adelie  Torgersen           40.3          18                 195  2007 NA              NA
## 10 Adelie  Torgersen           NA            NA                  NA  2007 male            NA
## # … with 1,022 more rows
```
]
]

---

---
background-image: url(images/hex/purrr.png)
background-position: 1050px 50px
background-size: 80px
  
# 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()`
]
]

<i class="fas  fa-file-pdf "></i> PDF: https://github.com/rstudio/cheatsheets/raw/master/purrr.pdf
![](https://raw.githubusercontent.com/rstudio/cheatsheets/master/pngs/thumbnails/purrr-cheatsheet-thumbs.png)
]

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

---
background-image: url(images/hex/purrr.png)
background-position: 1050px 50px
background-size: 80px

# 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[
![](https://raw.githubusercontent.com/jkaupp/nord/master/man/figures/README-palettes-1.png)
]
]

.pull-left[
### Let's turn this plot
<img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-50-1.png" width="504" />
]

.pull-right[
### Into this one!
<img src="2020-rladies-chi-tidyverse_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="2020-rladies-chi-tidyverse_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="2020-rladies-chi-tidyverse_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="2020-rladies-chi-tidyverse_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)
```
![](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)
```

![](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?