CAS-ggplot-workshop/ggplot-workshop-TEMPLATE.Rmd at main · blschum/CAS-ggplot-workshop · GitHub

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---
title: "2. Introduction to {ggplot2}"
author: "Britta Schumacher"
date: "Last updated: `r format(Sys.time(), '%d %B, %Y')`"
output:
  html_document:
    theme: yeti
    toc: yes
    toc_float: true
---

*FIRST*: Many thanks to Alison Horst for her gorgeous [aRt](https://github.com/allisonhorst/stats-illustrations).

# Data Visualization with `ggplot2`

### What is `ggplot2`?

<center>

![Artwork by the *incredible* Alison Horst](media/ggplot2_AHorst.jpg)

</center>

`ggplot2` is the bread and butter of data visualization; it takes clean, organized, manipulated data, and (with your direction) builds beautiful, & more importantly, *communicative*, plots. `ggplot2` is based on the *grammar of graphics*, which asserts that we can build every graph from the same few components: 1) a clean and tidy **data set**; 2) a set of **geoms** that map out data points; and 3) a **coordinate system** (in the broadest sense; i.e., how will the data be mapped onto a graphical surface).

To actually display data values, we map out our variables to aesthetic things like **size**, **color**, and **x** and **y** locations; we also tell `ggplot2` the type of visualization we are interested in building (e.g., bar graph, box plot, line graph, density plots, etc.). `ggplot2` opens up [data science](https://r4ds.had.co.nz/introduction.html) to broader audiences and helps all of us communicate our science. Download [this](https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf) cheat sheet and save it somewhere accessible--it's an incredible tool to refer back to!

`ggplot2` works with  `data.frames`, the data type we built in our previous `tidyverse` workshop. The data we feed into `ggplot2` consists of rows and columns that "live peacefully" together in a tidy `data.frame`. This means, often, we need to do some data cleaning up front to get our data into the **tidy** format we need for visualization.

# Now let's practice on some REAL*ish* data!

### Load the `tidyverse` and any additional required packages:

```{r}

```

### Explore:

We should first familiarize ourselves with the data.

```{r, eval = FALSE}

```


### Wrangle:

This dataset is **pretty big**--we'll want to wrangle it so that it only includes the information that we're interested in. We will:

a. filter for the African continent
b. select relevant columns of data
c. rename columns
d. create new columns.

Let's create an efficient workflow by combining **ALL** of these data wrangling steps into one; i.e., let's review harnessing the great POWERS of `tidyverse`!

```{r}

```

With this simplified and cleaned data set, we're ready to explore! Let's first isolate data we want to visualize by:

a. selecting Malawi, Tanzania, Mozambique, Zimbabwe, and Zambia;
b. grouping observations by country;
b. finding the average life expectancy, and per capita GDP for each country combination

```{r}

```

### Column graph

Now that we have our data summarised and in tidy format, we're ready to make a plot! We want to:

a. create a column graph showing the summarised by country
b. make it pretty

**Note:** Only the first 3 lines of the following code are necessary to make the plot. Everything else simply modifies the appearance and make it a bit more presentable. There are *tons* of ways to customize plots -- we explore only a few options below.

```{r, fig.align = 'center', fig.width = 15, fig.height = 10}


```

### Line graph

Let's visualize this data in a different way. Let's:

a. create a line graph that shows how GDP per capita and life expectancy relate by country
b. make it pretty

```{r fig.align = 'center', fig.width = 15, fig.height = 10}


```

### Histogram

Let's visualize this data in a different way; Let's:

a. create a histogram that shows the range of life expectancy by country
b. make it pretty

```{r}


```

### Plots, plots, plots, plots, plots, plots, plots!

There are MANY different ways to visualize the same data:

<center>

![Thank you stackoverflow, for this wild viz of all the ways we coulod visualize data about diamonds!](media/ggplots.png)

</center>

... In the end we just have to choose the method that makes the most sense for our audience, our research questions, and our data!

Additionally, aRtists everywhere are using R to make ridiculously cool pieces of [generative](https://en.wikipedia.org/wiki/Generative_art) and patterned art. Check out some talented RLadies [here](https://ijeamaka-anyene.netlify.app/) and [here](https://djnavarro.net/projects/)! Plus, [#TidyTuesday](https://github.com/rfordatascience/tidytuesday) submissions can be [absolutely](https://tanyadoesscience.com/project/tidytuesday/), [wildly](https://martindevaux.com/2021/01/tidytuesday-art-collections/), [brilliantly](https://codingwithavery.com/posts/2020-08-12-tidy-tuesday-avatar/), good.

# Bonus

What's *especially* neat about `ggplot2` is that it interfaces seamlessly with `sf`, a package for spatial data wrangling, analysis, and viz. `sf` treats its spatial objects as `data.frames` meaning.... we can use all of the magic & power of the `tidyverse` to play with the data! Plus, these data look just like any other data.frame, except attached to them is a column `geometry` that houses the coordinate information for the observation!

This is how we build the maps in our OSDS reports!

There are many, many ways to make maps beautiful maps in `ggplot` using `sf`. Anyone who says R isn't built for spatial viz just hasn't tried hard enough--check out [this](https://timogrossenbacher.ch/2019/04/bivariate-maps-with-ggplot2-and-sf/) if you don't believe me (just about the most gorgeous, creative, and well-built map I've ever seen).

-----------

# Resources
* Emily Burchfield's data viz materials, [here](https://www.emilyburchfield.org/courses/data_viz/intro_to_ggplot_tutorial).
* [Secrets of a happy graphing life](https://stat545.com/secrets.html).
* [The ggplot2 cheatsheet!](https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf)
* Chapters 5 - 12 in [this online textbook](https://clauswilke.com/dataviz/visualizing-amounts.html) provide a great overview of when to use different visualization strategies.