tour-of-the-tidyverse.Rmd

---
title: "An Antarctic Tour of the Tidyverse"
author: "Silvia P. Canelón, PhD (@spcanelon)"
date: "First created on Aug 31, 2020 (updated on Sept 22, 2020)"
institute: "University of Pennsylvania"
output:
  html_notebook:
    fig_width: 7.2
    highlight: pygments
    number_sections: no
    theme: lumen
    toc: yes
    toc_float: yes
  html_document:
    toc: yes
    df_print: paged
---

```{r}
knitr::opts_chunk$set(message = FALSE, warning = FALSE, collapse = TRUE)
```

# About {palmerpenguins}
- [{palmerpenguins} Documentation](https://allisonhorst.github.io/palmerpenguins/)
- [Allison Horst's GitHub repo for Palmer Penguins dataset](https://github.com/allisonhorst/palmerpenguins)

```{r, eval = FALSE}
# install.packages("remotes")
# remotes::install_github("allisonhorst/palmerpenguins")
```

# Loading packages
```{r}
# loading packages
library(tidyverse)
library(palmerpenguins)

# viewing data sets in package "palmerpenguins"
data(package = "palmerpenguins")
```

# readr 

Let's get data into R!

```{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: load using filepath ----
raw_adelie_filepath <- read_csv("raw_adelie.csv")
```

Lucky for us, Allison Horst compiled data from all three species together for us in the `{palmerpenguins}` package!

- `penguins` contains a clean dataset, and
- `penguins_raw` contains raw data

```{r}
# saves package tibble into global environment
penguins <- palmerpenguins::penguins 
head(penguins)

penguins_raw <- palmerpenguins::penguins_raw
head(penguins_raw)
```

# tibble 

A `tibble` is much like the `data frame` in base R, but optimized for use in the Tidyverse. Let's take a look at the differences.

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

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

# 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
- NAs 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.

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 tibble-vignette}
vignette("tibble")
```


## Taking a closer look at `penguins`

Get a full view of the dataset:
```{r penguins-view}
View(penguins)
```

Or catch a `glimpse`:
```{r penguins-glimpse}
glimpse(penguins)
```

# ggplot2 

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.

## Let's see if body mass varies by penguin sex

```{r ggplot2}
penguins %>%
  ggplot()

penguins %>%
  ggplot(aes(x = sex, y = body_mass_g))

penguins %>%
  ggplot(aes(x = sex, y = body_mass_g)) +
  geom_point()

# A scatter plot doesn't really tell us much.
# Let's try a different geometry

penguins %>%
  ggplot(aes(x = sex, y = body_mass_g)) +
  geom_boxplot()

# That's more informative!
# Let's see if there are differences by penguin species

penguins %>%
  ggplot(aes(x = sex, y = body_mass_g)) +
  geom_boxplot(aes(fill = species))

# What do you notice?
```
## What observations can you make from the plot?
You might see:

- Gentoo penguins have higher body mass than Adelie and Chinstrap penguins
- Higher body mass among male Gentoo penguins compared to female penguins
- Pattern not as discernable when comparing Adelie and Chinstrap penguins
- No `NA`s among Chinstrap penguin data points! `sex` was available for each observation

I wonder what percentage of observations are `NA` for each species? Let's get the tidyverse to help us with this!

Next stop, `dplyr`!

# dplyr 

```{r}
glimpse(penguins)
```

## select()
Selecting dataset columns with `select()`
```{r select}
penguins %>%
  select(species, sex, body_mass_g)
```

## arrange()
Reordering the data set with `arrange()`
```{r arrange}
penguins %>%
  select(species, sex, body_mass_g) %>%
  arrange(desc(body_mass_g))
```

## group_by() and summarize()
Summarizing the data using `group_by()` and `summarize()`

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 group-by-summarize}
penguins %>%
  select(species, sex, body_mass_g) %>%
  group_by(species, sex) %>%         
  summarize(mean = mean(body_mass_g))
```

## count() and add_count()
If we're just interested in _counting_ the observations in each grouping, we can group and summarize with special functions `count()` and `add_count()`.

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

```{r}
penguins %>% 
  group_by(species) %>%
  mutate(n_species = n()) %>%            
  ungroup() %>%                          
  group_by(species, sex, n_species) %>%
  summarize(n = n())
```

In contrast, `count()` and `add_count()` offer a simplified approach.

> Thank you to Alison Hill for [this suggestion](https://github.com/spcanelon/2020-rladies-chi-tidyverse/issues/2)!

```{r count}
penguins %>% 
  count(species, sex) %>%
  add_count(species, wt = n,    
            name = "n_species") 
```

## mutate()
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**

> Thank you to Alison Hill for [this suggestions](https://github.com/spcanelon/2020-rladies-chi-tidyverse/issues/2)!

```{r}
penguins %>% 
  count(species, sex) %>%
  add_count(species, wt = n, 
            name = "n_species") %>%
  mutate(prop = n/n_species*100) 
```


## filter()
Finally, we can filter rows to only show us **Chinstrap** penguin summaries by adding `filter()` to our pipeline
```{r filter}
penguins %>% 
  count(species, sex) %>%
  add_count(species, wt = n, 
            name = "n_species") %>%
  mutate(prop = n/n_species*100) %>%
  filter(species == "Chinstrap")
```

# forcats 

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)))
```

The result is a new factor `year_factor` with levels `2007`, `2008` and `2009`!

```{r}
penguins_new <-
  penguins %>%
  mutate(year_factor = factor(year, levels = unique(year)))
penguins_new
```

Double check the variable class and factor levels below:

```{r}
class(penguins_new$year_factor)
levels(penguins_new$year_factor)
```

# stringr 

Let's play around with strings a little bit!

From what we've learned so far, take a guess at what this code chunk will do before running it. 

```{r}
penguins %>%
  select(species, island) %>%
  mutate(ISLAND = str_to_upper(island))
```

How about this one? How is it different from the previous code chunk?

```{r}
penguins %>%
  select(species, island) %>%
  mutate(ISLAND = str_to_upper(island)) %>%
  mutate(species_island = str_c(species, ISLAND, sep = "_"))
```

# tidyr 

Both penguin datasets are already tidy!

We can pretend that it wasn't and that `body_mass_g` was recorded separately for `male`, `female`, and sex `NA` penguins. Like `untidy_penguins` below:
```{r}
untidy_penguins <-
  penguins %>%
    pivot_wider(names_from = sex,
                values_from = body_mass_g)
untidy_penguins
```

Now let's make it tidy again with the help of the `pivot_longer()` function!
`pivot_wider()`is another very popular tidying function. Have you seen it before? Hint: see the code chunk above!

```{r}
untidy_penguins %>%
  pivot_longer(cols = male:`NA`, 
               names_to = "sex",
               values_to = "body_mass_g")
```

# purrr 

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` `r emo::ji("package")`

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

Let's turn this plot:

```{r}
penguins %>%
  ggplot(aes(x = sex, y = body_mass_g)) +
  geom_boxplot(aes(fill = species))
```


Into this one!

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

Let's try out the `frost` palette.

```{r}
# we'll need to load the {nord} package
library(nord)

# you can choose colors using the color hex codes
nord::nord_palettes$frost
```



```{r}
# but you might prefer to use `scale_fill_manual()` 
# or more specialized functions like `scale_fill_nord()` 
# included in the {nord} package
penguins %>%
  ggplot(aes(x = sex, y = body_mass_g)) +
  geom_boxplot(aes(fill = species)) +
  scale_fill_manual(values = nord::nord_palettes$frost)
  #scale_fill_nord(palette = "frost")
```

Ok now for a handy package/function trio!

```{r}
# we'll have to load the {prismatic} package
library(prismatic)

prismatic::color(nord::nord_palettes$frost)
```

`purrr`'s `map()` function can help us iterate the `prismatic::color()` function over all palettes in a palette package like `nord`!

> Note: Not all colors will show well, like `polarnight` below. `prismatic::color()` relies on a package that kinda has limited functionality in this sense (`crayon`). It's doing its best :)

```{r, eval = FALSE}
nord::nord_palettes %>% map(prismatic::color)
```

# Extra material below!

## Recreating a {palmerpenguins} plot

Let's practice in real time!

![https://github.com/allisonhorst/palmerpenguins](https://raw.githubusercontent.com/allisonhorst/palmerpenguins/master/man/figures/README-flipper-bill-1.png)

```{r scatterplot-bill-length}
# scatterplot sequence ----
penguins %>%
  ggplot() + 
  geom_point(aes(x = flipper_length_mm, y = bill_length_mm)) # add aesthetics

penguins %>%
  ggplot() +
  geom_point(aes(x = flipper_length_mm, y = bill_length_mm, 
                 color = species)) # add color per species

penguins %>%
  ggplot() +
  geom_point(aes(x = flipper_length_mm, y = bill_length_mm, 
                 color = species, shape = species)) # add shape per species

penguins %>%
  ggplot() +
  geom_point(aes(x = flipper_length_mm, y = bill_length_mm, 
                 color = species, shape = species)) # add shape per species

penguins %>%
  ggplot() +
  geom_point(aes(x = flipper_length_mm, y = bill_length_mm, 
                 color = species, shape = species)) +
  geom_smooth(aes(x = flipper_length_mm, y = bill_length_mm, 
                  color = species))

penguins %>%
  ggplot(aes(x = flipper_length_mm, y = bill_length_mm)) + 
  geom_point(aes(color = species, shape = species)) +
  geom_smooth(aes(color = species), se = FALSE, method = "lm")
```

```{r scatterplot-body-mass}
penguins %>%
  ggplot() +
  geom_point(aes(x = flipper_length_mm, y = body_mass_g, 
                 color = species, shape = species))
```

```{r histogram}
penguins %>%
  ggplot() +
  geom_histogram(aes(x = flipper_length_mm))

penguins %>%
  ggplot() +
  geom_histogram(aes(x = flipper_length_mm, color = species))

penguins %>%
  ggplot() +
  geom_histogram(aes(x = flipper_length_mm, fill = species))

penguins %>%
  ggplot() +
  geom_histogram(aes(x = flipper_length_mm, fill = species, 
                     position = "identity", alpha = 0.5))
```


# TidyTuesday

## Getting started

- [Intro Blog and history](https://themockup.blog/posts/2018-12-11-tidytuesday-a-weekly-social-data-project-in-r/)
- [Intro Tweet by Tom Mock (RStudio)](https://twitter.com/thomas_mock/status/1287774575833616384?s=20)
- [TidyTuesdays GitHub](https://github.com/rfordatascience/tidytuesday/blob/master/README.md)
- [TidyTuesday Podcast](https://twitter.com/tidypod)
- [TidyTuesday Sight-Unseen on YouTube](https://www.youtube.com/watch?v=ImpXawPNCfM)
- [{tidytuesdayR} package to help you easily download the data set](https://github.com/thebioengineer/tidytuesdayR)

## tidytuesdayR
```{r eval=FALSE}
# install.packages("tidytuesdayR")
# remotes::install_github("thebioengineer/tidytuesdayR")

library(tidytuesdayR)

# load the data
tt_data <- tt_load("2020-07-27") # error message
tt_data <- tt_load("2020-07-28")
tt_data <- tt_load(2020, week=31)

# take a peek
readme(tt_data)
print(tt_data)
```

## Level up with modeling
- [Julia Silge (RStudio) teaches Tidymodels with {palmerpenguins} on YouTube](https://www.youtube.com/watch?v=z57i2GVcdww)

## Examples

- Specific to {palmerpenguins}:
  - Focus on fonts and formatting: https://twitter.com/ellamkaye/status/1289157139437621250?s=20
  - R-Ladies Ames: https://twitter.com/RLadiesAmes/status/1294425359388114944?s=20
  
- General to #tidytuesday:
  - R-Ladies Chicago TidyTuesday leader Ola!: https://twitter.com/AmazingSpeciali
  - R-Ladies Chicago TidyTuesday setup: https://twitter.com/RLadiesChicago/status/1192243170412761088?s=20