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:

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:

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:

getwd()

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

Similarly, we can set a working directory like so:

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!!

Gapminder Data

In today's lecture and Homework 2, we'll use data from Hans Rosling's Gapminder 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:

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:

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!

Basic Plots

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)

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

Fancier: ggplot

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)

ggplot2

The ggplot2 package provides an alternative toolbox for plotting.

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

The core idea underlying this package is the layered grammar of graphics: we can break up elements of a plot into pieces and combine them.

ggplots 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)

Axis Labels, Points, No Background

1: Base Plot

ggplot(data = China,
       aes(x = year, y = lifeExp))

Initialize the plot with ggplot() and x and y aesthetics mapped to variables. These aesthetics will be accessible to any future layers since they're in the primary layer.

Axis Labels, Points, No Background

2: Scatterplot

ggplot(data = China, 
       aes(x = year, y = lifeExp)) +
  geom_point()

Add a scatterplot layer.

Axis Labels, Points, No Background

3: Point Color and Size

ggplot(data = China, 
       aes(x = year, y = lifeExp)) +
  geom_point(color = "red", size = 3)

Set aesthetics to make the points large and red.

Axis Labels, Points, No Background

4: X-Axis Label

ggplot(data = China, 
       aes(x = year, y = lifeExp)) +
  geom_point(color = "red", size = 3) +
  xlab("Year")

Add a layer to capitalize the x-axis label.

Axis Labels, Points, No Background

5: Y-Axis Label

ggplot(data = China, 
       aes(x = year, y = lifeExp)) +
  geom_point(color = "red", size = 3) +
  xlab("Year") + 
  ylab("Life expectancy")

Add a layer to clean up the y-axis label.

Axis Labels, Points, No Background

6: Title

ggplot(data = China, 
       aes(x = year, y = lifeExp)) +
  geom_point(color = "red", size = 3) +
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy in China")

Add a title layer.

Axis Labels, Points, No Background

7: Theme

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()

Pick a nicer theme with a new layer.

Axis Labels, Points, No Background

8: Text Size

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)

Increase the base text size.

Plotting All Countries

We have a plot we like for China...

... but what if we want all the countries?

Plotting All Countries

1: A Mess!

ggplot(data = gapminder,
       aes(x = year, y = lifeExp)) +
  geom_point(color = "red", size = 3) +
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy over time") +
  theme_bw(base_size=18)

We can't tell countries apart! Maybe we could follow lines?

Plotting All Countries

2: Lines

ggplot(data = gapminder, 
       aes(x = year, y = lifeExp)) +
  geom_line(color = "red", size = 3) +
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy over time") +
  theme_bw(base_size=18)

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.

ggplot2 doesn't know how to connect the lines!

Plotting All Countries

3: Grouping

ggplot(data = gapminder, 
       aes(x = year, y = lifeExp, 
           group = country)) +
  geom_line(color = "red", size = 3) +
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy over time") +
  theme_bw(base_size=18)

That looks more reasonable... but the lines are too thick!

Plotting All Countries

4: Size

ggplot(data = gapminder, 
       aes(x = year, y = lifeExp, 
           group = country)) +
  geom_line(color = "red") +
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy over time") +
  theme_bw(base_size=18)

Much better... but maybe we can do highlight regional differences?

Plotting All Countries

5: Color

ggplot(data = gapminder, 
       aes(x = year, y = lifeExp, 
           group = country, 
           color = continent)) +
  geom_line() +
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy over time") +
  theme_bw(base_size=18)

Patterns are obvious... but why not separate continents completely?

Plotting All Countries

6: Facets

ggplot(data = gapminder, 
       aes(x = year, y = lifeExp, 
           group = country, 
           color = continent)) +
  geom_line() +
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy over time") +
  theme_bw(base_size=18) + 
  facet_wrap(~ continent)

Now the text is too big!

Plotting All Countries

7: Text Size

ggplot(data = gapminder, 
       aes(x = year, y = lifeExp, 
           group = country, 
           color = continent)) +
  geom_line() +
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy over time") +
  theme_bw() +
  facet_wrap(~ continent)

Better. Do we even need the legend anymore?

Plotting All Countries

8: No Legend

ggplot(data = gapminder, 
       aes(x = year, y = lifeExp, 
           group = country, 
           color = continent)) +
  geom_line() +
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy over time") +
  theme_bw() +  
  facet_wrap(~ continent) +
  theme(legend.position = "none")

Looking good!

Storing Plots

We can assign a ggplot object to a name:

lifeExp_by_year <- 
  ggplot(data = gapminder, 
       aes(x = year, y = lifeExp, 
           group = country, 
           color = continent)) +
  geom_line() +
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy over time") +
  theme_bw() + 
  facet_wrap(~ continent) +
  theme(legend.position = "none")

Afterwards, you can display or modify ggplots...

Showing a Stored Graph

lifeExp_by_year

Adding More Layers

lifeExp_by_year +
    theme(legend.position = "bottom")

Saving ggplot Plots

If you want to save a ggplot, use ggsave():

ggsave("I_saved_a_file.pdf", plot = lifeExp_by_year,
       height = 3, width = 5, units = "in")

If you didn't manually set font sizes, these will usually come out at a reasonable size given the dimensions of your output file.

Changing the Axes

We can modify the axes in a variety of ways, such as:

• Change the $x$ or $y$ range using xlim() or ylim() layers

• Change to a logarithmic or square-root scale on either axis: scale_x_log10(), scale_y_sqrt()

• Change where the major/minor breaks are: scale_x_continuous(breaks =, minor_breaks = )

Axis Changes

ggplot(data = China, aes(x = year, y = gdpPercap)) +
    geom_line() +
    scale_y_log10(breaks = c(1000, 2000, 3000, 4000, 5000)) +
    xlim(1940, 2010) + ggtitle("Chinese GDP per capita")

Precise Legend Position

lifeExp_by_year +
  theme(legend.position = c(0.8, 0.2))

Instead of coordinates, you could also use "top", "bottom", "left", or "right".

Scales for Color, Shape, etc.

Scales are layers that control how the mapped aesthetics appear.

You can modify these with a scale_[aesthetic]_[option]() layer:

• [aesthetic] is color, shape, linetype, alpha, size, fill, etc.

• [option] is something like manual, continuous or discrete (depending on nature of the variable).

Examples:

• scale_linetype_manual(): manually specify the linetype for each different value
• scale_color_manual(): manually specify colors

Legend Name and Manual Colors

lifeExp_by_year +
  theme(legend.position = c(0.8, 0.2)) +
  scale_color_manual(
    name = "Which continent are\nwe looking at?", # \n adds a line break
    values = c("Africa" = "seagreen", "Americas" = "turquoise1", 
               "Asia" = "royalblue", "Europe" = "violetred1", "Oceania" = "yellow"))

End Result

We're going to slowly build up a really detailed plot now!

1. Base Plot

ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country))
  #
  #
  #  
  #
  #
  #
  #
  #
  #

2. Lines

ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_line()
  #
  #  
  #
  #
  #
  #
  #
  #

3. Continent Average

ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_line() +
  geom_line(stat = "smooth", method = "loess",
            aes(group = continent))
  #
  #
  #
  #
  #
  #

Note: A loess curve is something like a moving average.

4. Facets

ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_line() +
  geom_line(stat = "smooth", method = "loess", 
            aes(group = continent)) +
  facet_wrap(~ continent, nrow = 2)
  #
  #
  #
  #
  #

5. Color Scale

ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_line(aes(color = "Country")) +
  geom_line(stat = "smooth", method = "loess", 
            aes(group = continent, color = "Continent")) +
  facet_wrap(~ continent, nrow = 2) +
  scale_color_manual(name = "Life Exp. for:", values = c("Country" = "black", "Continent" = "blue"))
  #
  #
  #
  #

6. Size Scale

ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_line(aes(color = "Country", size = "Country")) +
  geom_line(stat = "smooth", method = "loess", 
            aes(group = continent, color = "Continent", size = "Continent")) +
  facet_wrap(~ continent, nrow = 2) +
  scale_color_manual(name = "Life Exp. for:", values = c("Country" = "black", "Continent" = "blue")) +
  scale_size_manual(name = "Life Exp. for:", values = c("Country" = 0.25, "Continent" = 3))
  #
  #
  #

7. Alpha (Transparency)

ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_line(alpha = 0.5, aes(color = "Country", size = "Country")) +
  geom_line(stat = "smooth", method = "loess", 
            aes(group = continent, color = "Continent", size = "Continent"), alpha = 0.5) +
  facet_wrap(~ continent, nrow = 2) +
  scale_color_manual(name = "Life Exp. for:", values = c("Country" = "black", "Continent" = "blue")) +
  scale_size_manual(name = "Life Exp. for:", values = c("Country" = 0.25, "Continent" = 3))
  #
  #
  #

8. Theme and Labels

ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_line(alpha = 0.5, aes(color = "Country", size = "Country")) +
  geom_line(stat = "smooth", method = "loess", 
            aes(group = continent, color = "Continent", size = "Continent"), alpha = 0.5) +
  facet_wrap(~ continent, nrow = 2) +
  scale_color_manual(name = "Life Exp. for:", values = c("Country" = "black", "Continent" = "blue")) +
  scale_size_manual(name = "Life Exp. for:", values = c("Country" = 0.25, "Continent" = 3)) +
  theme_minimal(base_size = 14) + ylab("Years") + xlab("")
  #
  #

9. Title and Subtitle

ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_line(alpha = 0.5, aes(color = "Country", size = "Country")) +
  geom_line(stat = "smooth", method = "loess", 
            aes(group = continent, color = "Continent", size = "Continent"), alpha = 0.5) +
  facet_wrap(~ continent, nrow = 2) +
  scale_color_manual(name = "Life Exp. for:", values = c("Country" = "black", "Continent" = "blue")) +
  scale_size_manual(name = "Life Exp. for:", values = c("Country" = 0.25, "Continent" = 3)) +
  theme_minimal(base_size = 14) + ylab("Years") + xlab("") + 
  ggtitle("Life Expectancy, 1952-2007", subtitle = "By continent and country")
  #

10. Angled Tick Values

ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_line(alpha = 0.5, aes(color = "Country", size = "Country")) +
  geom_line(stat = "smooth", method = "loess", 
            aes(group = continent, color = "Continent", size = "Continent"), alpha = 0.5) +
  facet_wrap(~ continent, nrow = 2) +
  scale_color_manual(name = "Life Exp. for:", values = c("Country" = "black", "Continent" = "blue")) +
  scale_size_manual(name = "Life Exp. for:", values = c("Country" = 0.25, "Continent" = 3)) +
  theme_minimal(base_size = 14) + ylab("Years") + xlab("") + 
  ggtitle("Life Expectancy, 1952-2007", subtitle = "By continent and country") +
  theme(axis.text.x = element_text(angle = 45))

Note: Fewer values might be better than angled labels!

11. Legend Position

ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_line(alpha = 0.5, aes(color = "Country", size = "Country")) +
  geom_line(stat = "smooth", method = "loess", 
            aes(group = continent, color = "Continent", size = "Continent"), alpha = 0.5) +
  facet_wrap(~ continent, nrow = 2) +
  scale_color_manual(name = "Life Exp. for:", values = c("Country" = "black", "Continent" = "blue")) +
  scale_size_manual(name = "Life Exp. for:", values = c("Country" = 0.25, "Continent" = 3)) +
  theme_minimal(base_size = 14) + ylab("Years") + xlab("") + 
  ggtitle("Life Expectancy, 1952-2007", subtitle = "By continent and country") +
  theme(legend.position=c(0.82, 0.15), axis.text.x = element_text(angle = 45))

Summary

ggplot2 can do a LOT! I won't expect you to remember all these tools.

My Solution

1: Set Base Layer

ggplot(gapminder,aes(x=lifeExp))

My Solution

2: Add Histogram Layer

ggplot(gapminder,aes(x=lifeExp))+
  geom_histogram(bins=30)

Setting the bins aesthetic removes a pesky message!

My Solution

3: Add Label Layers

ggplot(gapminder,aes(x=lifeExp))+
  geom_histogram(bins=30)+
  xlab("Life Expectancy")+
  ylab("Count")+
  ggtitle("Histogram of Life Expectancy 
          in Gapminder Data")