CSSS 508, Lecture 2

class: center, top, title-slide

.title[
# CSSS 508, Lecture 2
]
.subtitle[
## Visualizing Data
]
.author[
### Michael Pearce<br>(based on slides from Chuck Lanfear)
]
.date[
### April 5, 2023
]

---

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# Topics

Last time, we learned about,

1. R and RStudio
1. RMarkdown headers, syntax, and chunks
1. Basics of functions, objects, and vectors
1. Dataframes and basic plots

Today, we will cover,

1. Useful coding tips: packages, directories, and saving data
1. Basics of ggplot: layers and aesthetics
1. Advanced ggplot tools

---
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# 1. Useful Coding Tips

---

# Packages

Packages are **collections of functions and tools** that make your life easier! The best part of R is the huge number of user-created packages. The `Packages` tab in the bottom-right pane of RStudio lists your installed packages.

To install a new package in R, run the line of code:

```r
install.packages("gapminder") 
```
We always install packages in the **console**, because we only want to do it **once**

---

# Loading Packages

Installing a packages *does not* mean it's loaded in our R session. To do so, we call the package:

```r
library(gapminder)
```

**NOTE:** Use quotes when installing packages, but not when loading packages!

We need to run this code every time we open a new R session: **Where should we put this code?**

**Answer:** In R/Rmd files, and not the console!

---

# Working Directories

R saves files and looks for files to open in your current **working directory**. You
can ask R what this is:

```r
getwd()
```

```
## [1] "/Users/pearce790/CSSS508/Lectures/Lecture2"
```

Similarly, we can set a working directory like so:

```r
setwd("C:/Users/pearce790/CSSS508/HW2")
```

*Don't set a working directory in R Markdown documents!* They automatically set the directory they are in as the working directory.

---

# Managing Files

When managing R projects, it is normally best to give each project (such as a homework
assignment) its own folder. I use the following system:

* Every class or project has its own folder

* Each assignment or task has a folder inside that, which is the working directory for that item.

* `.Rmd` and `.R` files are named clearly and completely

For example, this presentation is located and named this:
`GitHub/CSSS508/Lectures/Lecture2/CSSS508_Lecture2_ggplot2.Rmd`

**Be consistent** so your projects are organized! You don't want to lose files!!

---
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# Aside: Gapminder Data

---

# Gapminder Data

In today's lecture and Homework 2, we'll use data from Hans Rosling's [Gapminder](http://www.gapminder.org) project. The data can be accessed through the `gapminder` R package.

If you didn't already, run in the console: `install.packages("gapminder")`, and then load the package:

```r
library(gapminder)
```

---

# Exploring Gapminder

The data is in a dataframe called `gapminder`, which is available after loading the package. Let's explore it using functions from last week:

.small[

```r
str(gapminder)
```

```
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num [1:1704] 28.8 30.3 32 34 36.1 ...
##  $ pop      : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
```
]

---

# What's Interesting Here?

* **Factor** variables `country` and `continent`

+ Factors are categorical data
   + We'll spend a lot of time on factors later!

* Many observations: `$n=1704$` rows

* For each observation, a few variables: `$p=6$` columns

* A nested/hierarchical structure: `year` in `country` in `continent`

+ These are panel data!

---
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# 2. Basics of `ggplot2`

---

## Basic Plots

.pull-left[
 .small[

```r
China <- subset(gapminder,
          gapminder$country == "China")
plot(lifeExp ~ year, 
     data = China, 
     xlab = "Year", 
     ylab = "Life expectancy",
     main = "Life expectancy in China", 
     col = "red", 
     pch = 16)
```

]
 The plot is made with *one function* and *many arguments*
]

.pull-right[
![](CSSS508_Lecture2_ggplot2_files/figure-html/unnamed-chunk-8-1.png)
]

Note: Don't worry about the code used to create the object `China`. We'll explore data manipulation next week!

---
## Fancier: `ggplot`

.pull-left[
 .small[

```r
ggplot(data = China, 
       aes(x = year, y = lifeExp)) +
  geom_point(color = "red", size = 3) +
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy in China") +
  theme_bw(base_size=18)
```
  ]
  This `ggplot` is made with *many functions* and *fewer arguments* in each.
]

.pull-right[
![](CSSS508_Lecture2_ggplot2_files/figure-html/unnamed-chunk-10-1.svg)
]

---
# `ggplot2`

The `ggplot2` package provides an alternative toolbox for plotting.

```r
# install.packages("ggplot2")
library(ggplot2)
```

The core idea underlying this package is the [**layered grammar of graphics**](https://doi.org/10.1198/jcgs.2009.07098): we can break up elements of a plot into pieces and combine them.

`ggplot`s are a bit harder to create, but are usually:

* prettier, 
* more professional, and
* more customizable!

---
# Structure of a ggplot

`ggplot` graphics objects consist of two primary components:

1. **Layers**, the components of a graph.

* We *add* layers to a `ggplot` object using `+`.
   * This includes adding lines, shapes, and text to a plot.

2. **Aesthetics**, which determine how the layers appear.

* We *set* aesthetics using *arguments* (e.g. `color="red"`) inside layer functions.
   * This includes modifying locations, colors, and sizes of the layers.

---

# Layers

**Layers** are the components of the graph, such as:

* `ggplot()`: initializes basic plotting object, specifies input data
* `geom_point()`: layer of scatterplot points
* `geom_line()`: layer of lines
* `geom_histogram()`: layer of a histogram
* `ggtitle()`, `xlab()`, `ylab()`: layers of labels
* `facet_wrap()`: layer creating multiple plot panels
* `theme_bw()`: layer replacing default gray background with black-and-white

Layers are separated by a `+` sign. For clarity, I usually put each layer on a new line.

---

# Aesthetics

**Aesthetics** control the appearance of the layers:

* `x`, `y`: `$x$` and `$y$` coordinate values to use
* `color`: set color of elements based on some data value
* `group`: describe which points are conceptually grouped together for the plot (often used with lines)
* `size`: set size of points/lines based on some data value (greater than 0)
* `alpha`: set transparency based on some data value (between 0 and 1)

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# Examples: Basic Jargon in Action!

We'll now build up two `ggplot`s together that demonstrate common layers and aesthetics.

---