Software Carpentry Workshop

Lesson5: Data visualization with ggplot2

This lesson is based on this Software Carpentry lesson

Please make sure your directory structure is setup as described here

Introduction

R is one of the most powerful programming language for visualizing data. It isoften used to generate publication-quality graphics. Today, we will give you a flavor of some of the quality plotting you can produce using the R package ggplot2. ggplot2 is perhaps the best package for intuitive generation of high-quality figures. We will continue using the gapminder dataset and will visualize some of the trends in these data with ggplot2. Check out some of the interesting dynamic plots available from the Gapminder website. By the end of this lesson you will know the basic principles necessary to produce similar, static versions of many of the example plots you are looking at.

Objectives

To be able to use ggplot2 to generate publication-quality graphics.
To understand the basic grammar of graphics, including the aesthetics and geometry layers, adding statistics, transforming scales, and coloring or panelling by groups.

Keypoints

Use ggplot2 to create plots.
Think about graphics in layers: aesthetics, geometry, statistics, scale transformation, and grouping.

Background

Plotting our data is one of the best ways to quickly explore it and the various relationships between variables.

There are three main plotting systems in R, the base plotting system, the lattice package, and the ggplot2 package.

Today we'll be learning about the ggplot2 package, because it is the most effective for creating publication-quality graphics.

ggplot2 is built on the grammar of graphics, the idea that any plot can be generated from the same set of components: a data set, a coordinate system, and a set of geoms (the visual representation of data points).

The key to understanding ggplot2 is thinking about a figure in layers. This idea may be familiar to you if you have used image editing programs like Photoshop, Illustrator, or Inkscape. Main components of ggplot

Quickstart

We will be working in Lesson5_DataVisualizationR folder. Please copy gapminder.txt from Data folder to Lesson5_DataVisualizationR folder. Navigate to Lesson5_DataVisualizationR folder in RStudio and set this folder as your working directory.

We will need to install ggplot2 package. Let's use console window to do that:

install.packages("ggplot2")

Now let's work in scripting mode (text editor) so we can save all our commands to file. Open a new file, save as ggplot.R. Make sure your new file is visible in Lesson5_DataVisualizationRfolder under Files tab in the bottom-right pannel.

Remember that in order to work with ggplot2 package, we need to load it into R.

#load package into R
library(ggplot2)

Now let's load our gapminder dataset into R.

gapminder <- read.table("gapminder.txt", header=TRUE, sep="\t")

#view the dataset to know what variables are present (or what can be potentially plotted)
head(gapminder)
#or view as a table using environment tab in the top-right panel

We are now ready to plot. Let's start off with an example:

ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

So the first thing we do is call the ggplot function. This function lets R know that we're creating a new plot, and any of the arguments we give the ggplot function are the global options for the plot: they apply to all layers on the plot.

We've passed in two arguments to ggplot. First, we tell ggplot what data we want to show on our figure, in this example the gapminder data we read in earlier. For the second argument we passed in the aes function, which tells ggplot how variables in the data map to aesthetic properties of the figure, in this case the x and y locations. Here we told ggplot we want to plot the gdpPercap column of the gapminder data frame on the x-axis, and the lifeExp column on the y-axis. Notice that we didn't need to explicitly pass these columns to aes function (e.g. x = gapminder[, "gdpPercap"]), this is because ggplot is smart enough to know to look in the data for that column!

Let's break this long command into parts: By itself, the call to ggplot isn't enough to draw a figure:

ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp))

We need to tell ggplot how we want to visually represent the data, which we do by adding a new geom layer. In our example, we used geom_point, which tells ggplot we want to visually represent the relationship between x and y as a scatterplot of points:

ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

And here you have it - your first plot with ggplot2!
The three components that must be specified (minimum) to build a plot with ggplot2 are:

data
aesthetic mappings (aes)
geometric oblects (geom)

Challenge

Modify the example so that the figure shows how life expectancy has changed over time:
ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) + geom_point()
Hint: the gapminder dataset has a column called "year", which should appear on the x-axis.
Solution

Here is one possible solution:
ggplot(data = gapminder, aes(x = year, y = lifeExp)) + geom_point()
Challenge

In the previous examples and challenge we've used the aes function to tell the scatterplot geom about the x and y locations of each point. Another aesthetic property we can modify is the point color. Modify the code from the previous challenge to color the points by the "continent" column. What trends do you see in the data? Are they what you expected?
Solution

In the previous examples and challenge we've used the aes function to tell the scatterplot geom about the x and y locations of each point. Another aesthetic property we can modify is the point color. Modify the code from the previous challenge to color the points by the "continent" column. What trends do you see in the data? Are they what you expected?
ggplot(data = gapminder, aes(x = year, y = lifeExp, color=continent)) +
  geom_point()

Layers

Now let's talk about layers. In ggplot2, layers are added with + operator, giving an intuitive understanding about the content of each layer. In the above example, geom_point() layer contained points. Now, using a scatterplot probably isn't the best for visualizing change over time. Instead, let's tell ggplot to visualize the data as a line plot:

ggplot(data = gapminder, aes(x=year, y=lifeExp, by=country, color=continent)) +
  geom_line()

Instead of adding a geom_point layer, we've added a geom_line layer. We've added the by aesthetic, which tells ggplot to draw a line for each country.

But what if we want to visualize both lines and points on the plot? We can simply add another layer to the plot:

ggplot(data = gapminder, aes(x=year, y=lifeExp, by=country, color=continent)) +
  geom_line() + geom_point()

It's important to note that each layer is drawn on top of the previous layer. In this example, the points have been drawn on top of the lines. Here's a demonstration:

ggplot(data = gapminder, aes(x=year, y=lifeExp, by=country)) +
  geom_line(aes(color=continent)) + geom_point()

In this example, the aesthetic mapping of color has been moved from the global plot options in ggplot to the geom_line layer so it no longer applies to the points. Now we can clearly see that the points are drawn on top of the lines.

In general, each geom layer has 5 components that have different defaults depending on the geom type. Read more about layers here. For example, geom_point() is really a short way of writing:

layer(
  mapping = NULL, 
  data = NULL,
  geom = "point",
  stat = "identity",
  position = "identity"
)

When geom_point() is called, mapping(aes) and data take values specified in ggplot(), and stat and position arguments take specified defaults. But each of the components can be set for each layer separately, overriding global values. Different combinations of these components generate different geoms. You can have geom_line(), geom_bar(), geom_boxplot(), geom_text(), geom_errorbar()… over 30 geoms.

Here are the settings for geom_bar(). You will get to make a bar plot at the end of the lesson.

layer(
  mapping = NULL, 
  data = NULL,
  geom = "bar",
  stat = "count",
  position = "stack"
)

Here is a full documentation for ggplot2 package. And also here. What is important here is to know that you can specify these settings for every layer separately as we have seen in the previous example when we specified mapping for geom_line(): geom_line(aes(color=continent)).

Tip: Setting an aesthetic to a value instead of a mapping

So far, we've seen how to use an aesthetic (such as color) as a mapping to a variable in the data. For example, when we use geom_line(aes(color=continent)), ggplot will give a different color to each continent. But what if we want to change the colour of all lines to blue? You may think that geom_line(aes(color="blue")) should work, but it doesn't. Since we don't want to create a mapping to a specific variable, we simply move the color specification outside of the aes() function, like this: geom_line(color="blue").

ggplot(data = gapminder, aes(x=year, y=lifeExp, by=country)) +
     geom_line(color="blue") + geom_point()

Challenge

Switch the order of the point and line layers from the previous example. What happened?
Solution

Switch the order of the point and line layers from the previous example. What happened?
ggplot(data = gapminder, aes(x=year, y=lifeExp, by=country)) +
 geom_point() + geom_line(aes(color=continent))
The lines now get drawn over the points!

Transformations and statistics

ggplot also makes it easy to overlay statistical models over the data. To demonstrate we'll go back to our first example:

ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp, color=continent)) +
  geom_point()

Currently it's hard to see the relationship between the points due to some strong outliers in GDP per capita. We can change the scale of units on the x axis using the scale functions. These control the mapping between the data values and visual values of an aesthetic. We can also modify the transparency of the points, using the alpha function, which is especially helpful when you have a large amount of data which is very clustered.

ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.5) + scale_x_log10()

The log10 function applied a transformation to the values of the gdpPercap column before rendering them on the plot, so that each multiple of 10 now only corresponds to an increase in 1 on the transformed scale, e.g. a GDP per capita of 1,000 is now 3 on the x axis, a value of 10,000 corresponds to 4 on the x axis and so on. This makes it easier to visualize the spread of data on the x-axis.

Tip Reminder: Setting an aesthetic to a value instead of a mapping

Notice that we used geom_point(alpha = 0.5). As the previous tip mentioned, using a setting outside of the aes() function will cause this value to be used for all points, which is what we want in this case. But just like any other aesthetic setting, alpha can also be mapped to a variable in the data. For example, we can give a different transparency to each continent with geom_point(aes(alpha = continent)).

We can fit a simple relationship to the data by adding another layer, geom_smooth:

ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point() + scale_x_log10() + geom_smooth(method="lm")

We can make the line thicker by setting the size aesthetic in the geom_smooth layer:

ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point() + scale_x_log10() + geom_smooth(method="lm", size=1.5)

Challenge

Part 1.

Modify the color and size of the points on the point layer in the previous example.

Hint: do not use the aes function.
Solution to Part 1.

Modify the color and size of the points on the point layer in the previous example.

Hint: do not use the aes function.
ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
 geom_point(size=3, color="orange") + scale_x_log10() +
 geom_smooth(method="lm", size=1.5)

Challenge

Part 2.

Modify your solution to Part 1 so that the points are now a different shape and are colored by continent with new trendlines.
Hint: Use color argument inside aes(). See here about shapes
Solution to Part 2.

Modify your solution to Part 1 so that the points are now a different shape and are colored by continent with new trendlines.

Hint: Use color argument inside aes(). See here about shapes
ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp, color = continent)) +
geom_point(size=3, shape=17) + scale_x_log10() +
geom_smooth(method="lm", size=1.5)

Multi-panel figures

Earlier we visualized the change in life expectancy over time across all countries in one plot. Alternatively, we can split this out over multiple panels by adding a layer of facet panels. Focusing only on those countries with names that start with the letter "A" or "Z".

Tip

We start by subsetting the data. We use startsWith function to select names of countries that start with either letter 'A' or 'Z'. This function returns a logical vector. We can then use it to subset rows in gapminder dataset.

az_rows=startsWith(as.vector(gapminder$country), c('A','Z') )
az_countries <- gapminder[az_rows, ]
ggplot(data = az_countries, aes(x = year, y = lifeExp, color=continent)) +
  geom_line() + facet_wrap( ~ country)

The facet_wrap layer took a "formula" as its argument, denoted by the tilde (~). This tells R to draw a panel for each unique value in the country column of the gapminder dataset.

Modifying text

To clean this figure up for a publication we need to change some of the text elements. The x-axis is too cluttered, and the y axis should read "Life expectancy", rather than the column name in the data frame.

We can do this by adding a couple of different layers. The theme layer controls the axis text, and overall text size. Labels for the axes, plot title and any legend can be set using the labs function. Legend titles are set using the same names we used in the aes specification. Thus below the color legend title is set using color = "Continent", while the title of a fill legend would be set using fill = "MyTitle". Fill is used instead of color when geoms are filled with color, like bar plots, for example.

ggplot(data = az_countries, aes(x = year, y = lifeExp, color=continent)) +
  geom_line() + facet_wrap( ~ country) +
  labs(
    x = "Year",              # x axis title
    y = "Life expectancy",   # y axis title
    title = "Figure 1",      # main title of figure
    color = "Continent"      # title of legend
  ) +
  theme(axis.text.x=element_text(angle=45), axis.ticks.x=element_blank())

Exporting the plot

The ggsave() function allows you to export a plot created with ggplot. You can specify the dimension and resolution of your plot by adjusting the appropriate arguments (width, height and dpi) to create high quality graphics for publication. In order to save the plot from above, we first assign it to a variable lifeExp_plot, then tell ggsave to save that plot in png format.

lifeExp_plot <- ggplot(data = az_countries, aes(x = year, y = lifeExp, color=continent)) +
  geom_line() + facet_wrap( ~ country) +
  labs(
    x = "Year",              # x axis title
    y = "Life expectancy",   # y axis title
    title = "Figure 1",      # main title of figure
    color = "Continent"      # title of legend
  ) +
  theme(axis.text.x=element_text(angle=45), axis.ticks.x=element_blank())

ggsave(filename = "lifeExp.png", plot = lifeExp_plot, width = 12, height = 10, dpi = 300, units = "cm")

Note ggsave saves the last plot by default, so plot argument can be omitted. But if you created plots earlier in your script and want to save them later, you can specify which plot to save.

#saves the last plot by default
ggsave("myPlot.pdf")

#can specify what plot to save
ggsave("myPlot.pdf", plot=fp1)

This is a taste of what you can do with ggplot2. RStudio provides a really useful cheat sheet of the different layers available, and more extensive documentation is available on the ggplot2 website. Finally, if you have no idea how to change something, a quick Google search will usually send you to a relevant question and answer on Stack Overflow with reusable code to modify!

Now let's finish this lesson practicing making bar plots!

Challenge

Part 1.

Plot life expectancy over years for a country of your choice using geom_bar() Hint See help for geom_bar: ?geom_bar Understand the difference between stat="count" and stat="identity"

Part 2.

Prepare a figure for publication(add labels, titles, etc.,) to show population size over years for every country that starts with letter 'E'. Hint Follow life Expectancy over years example we discussed earlier. Use facet_wrap function.

Part 3.

Can you explain what information is plotted with this code?
ggplot(data=gapminder, aes(x=continent, y=lifeExp)) + 
       geom_bar(aes(fill = continent), stat="summary",fun.y="mean")
Solution to Part 1
# you must specify stat="identity" to get lifeExp on y axis:
ggplot(data =gapminder[gapminder$country=="Sweden", ], aes(x = year, y = lifeExp)) +
       geom_bar(fill='orange', stat="identity")

# the default stat for geom_bar() is stat="count". In this case, the number of observations
# for every year is plotted:
ggplot(data =gapminder[gapminder$country=="Sweden", ], aes(x = year)) +
       geom_bar(fill='orange')
Solution to Part 2
e_rows=startsWith(as.vector(gapminder$country), c('E') )
e_countries <- gapminder[e_rows, ]
ggplot(data = e_countries, aes(x = year, y = pop/1000000, fill=continent)) +
       geom_bar(stat="identity") + facet_wrap( ~ country) +
       labs(
       x = "Year",                     # x axis title
       y = "Population (Million)",     # y axis title
       title = "Figure 1",             # main title of figure
       fill = "Continent"              # title of legend
       ) +
       theme(axis.text.x=element_text(angle=45), axis.ticks.x=element_blank()) +
       theme_bw()
Solution to Part 3

A barplot of average life expectancy across years and countries for every continent.

Demo some more advanced features

If time permits, the following interactive activities can be run to showcase some of the cool stuff you can do with ggplot and R.

Let's do this in terms of one of the cooler visualizations directly from the Gapminder website. This visualization shows the relationship between income and life expectancy for all nations in the dataset. The points are scaled in size based on the population size of the nation and colored based on continent. So there is a lot of details here.

Challenge

Can we create something pretty similar using ggplot? What variables in our dataset needs to be mapped to what aesthetics to achive this?

Solution

x = income y = life expectancy size = population size color = continent

Challenge

Use mappings above to produce a similar plot for 2007. Bonus points if you can use dplyr to do it.
Solution
# note that you can save plot to variable and plot later
library(dplyr)
bubble <- gapminder %>%
   filter(year == 2007) %>%
   ggplot(aes(x = gdpPercap,
              y = lifeExp,
              color = continent,
              size = pop)) +
     geom_point() + 
     labs(
       x = "Income",            # x axis title
       y = "Life expectancy"    # y axis title
     )
bubble

This is great, but you'll notice that it this plot is static, while the online one is interactive. Turns out is is actually pretty easy to create an interactive version when you know a little ggplot, using a package called plotly. Let's demo this.

install.packages("plotly")
library(plotly)

# plotly has built in support for `ggplot` objects
bubble <- gapminder %>%
    filter(year == 2007) %>%
    ggplot(aes(x = gdpPercap,
               y = lifeExp,
               color = continent,
               size = pop,
               text = country)) +
    geom_point() + 
    labs(
        x = "Income",            # x axis title
        y = "Life expectancy"    # y axis title
    )
    
ggplotly(bubble)

Now you'll see an interactive plot, which is really awesome for exploring and for showing off your data.

The last thing we're missing from the Gapminder plot is the fact that it will cycle through years. We can't mimic this with with interactive plots at each time, but we can loop through plots for each year and make a movie-like animation. A pretty simple loop will do this.

for (yr in unique(gapminder$year)) {
   bubble <- gapminder %>%
      filter(year == yr) %>% 
      ggplot(aes(x = gdpPercap,
                 y = lifeExp,
                 color = continent,
                 size = pop)) +
         geom_point() + 
         labs(
           x = "Income", 
           y = "Life expectancy",
           title = yr
         ) 
    plot(bubble)    # need to do this in loops
    Sys.sleep(5)    # 5 sec pause between plots
}

Very cool. See how easy it is to produce fancy plots inR!

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Software Carpentry Workshop

Lesson5: Data visualization with ggplot2

Please make sure your directory structure is setup as described here

Introduction

Objectives

Keypoints

Background

Quickstart

Challenge

Solution

Challenge

Solution

Layers

Tip: Setting an aesthetic to a value instead of a mapping

Challenge

Solution

Transformations and statistics

Tip Reminder: Setting an aesthetic to a value instead of a mapping

Challenge

Solution to Part 1.

Challenge

Solution to Part 2.

Multi-panel figures

Tip

Modifying text

Exporting the plot

Challenge

Part 1.

Part 2.

Part 3.

Solution to Part 1

Solution to Part 2

Solution to Part 3

Demo some more advanced features

Challenge

Solution

Challenge

Solution