Goal

In this tutorial we look at some of the data on wealth and life expectancy of countries over time used by Hans Rosling in his famous TED talk. The goal is to introduce some simple 1D and 2D plots constructed using ggplot2.

How to work on this tutorial

  • In these notes, I supply some initial plots for a topic, and then pose some questions or extensions as Your turn.

  • You will learn best if you try to reproduce them yourself as you go along.

  • Open a new R script in R Studio, say, gapminder-tut.R.
    • Type the lines into the script, and run them as you go.
    • If you want to cheat a bit on typing, you can open the gapminder.R script that was created from this in a browser window and use some cut-and-paste.
  • There are a lot of steps here, and you may not complete them in this lab session. I urge you to make as much progress as you can today, and treat the rest as homework. Save your script somewhere you can access it later.

Load the packages we will use here:

library(ggplot2)
library(dplyr)    # data munging
library(scales)   # nicer axis scale labels

Data

The data has conviently been packaged in the R package gapminder, a subset of the original data set from (http://gapminder.org). For each of 142 countries, the package provides values for life expectancy, GDP per capita, and population, every five years, from 1952 to 2007.

It is unlikely that you have this package installed, so the first step is to install it and then load it.

install.packages("gapminder")
library(gapminder)

Tech note: Here is a way to make a script more portable by testing whether a package is available before installing it.

if(!require(gapminder)) {install.packages("gapminder"); library(gapminder)}

What variables are available, and what are their names? str() is your friend here.

str(gapminder)
## Classes 'tbl_df', 'tbl' and 'data.frame':    1704 obs. of  6 variables:
##  $ 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  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num  28.8 30.3 32 34 36.1 ...
##  $ pop      : int  8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num  779 821 853 836 740 ...

Tech note: The gapminder data set was constructed as a tibble, a generalization of a data.frame. The print() method gives an abbreviated printout. Normally, you would use head(gapminder) for this.

gapminder
## # A tibble: 1,704 x 6
##    country     continent  year lifeExp      pop gdpPercap
##    <fctr>      <fctr>    <int>   <dbl>    <int>     <dbl>
##  1 Afghanistan Asia       1952    28.8  8425333       779
##  2 Afghanistan Asia       1957    30.3  9240934       821
##  3 Afghanistan Asia       1962    32.0 10267083       853
##  4 Afghanistan Asia       1967    34.0 11537966       836
##  5 Afghanistan Asia       1972    36.1 13079460       740
##  6 Afghanistan Asia       1977    38.4 14880372       786
##  7 Afghanistan Asia       1982    39.9 12881816       978
##  8 Afghanistan Asia       1987    40.8 13867957       852
##  9 Afghanistan Asia       1992    41.7 16317921       649
## 10 Afghanistan Asia       1997    41.8 22227415       635
## # ... with 1,694 more rows

Overview

Normally, when starting with a new data set, it is useful to get some overview of the variables. The simplest one is summary().

summary(gapminder)
##         country        continent        year         lifeExp           pop              gdpPercap       
##  Afghanistan:  12   Africa  :624   Min.   :1952   Min.   :23.60   Min.   :6.001e+04   Min.   :   241.2  
##  Albania    :  12   Americas:300   1st Qu.:1966   1st Qu.:48.20   1st Qu.:2.794e+06   1st Qu.:  1202.1  
##  Algeria    :  12   Asia    :396   Median :1980   Median :60.71   Median :7.024e+06   Median :  3531.8  
##  Angola     :  12   Europe  :360   Mean   :1980   Mean   :59.47   Mean   :2.960e+07   Mean   :  7215.3  
##  Argentina  :  12   Oceania : 24   3rd Qu.:1993   3rd Qu.:70.85   3rd Qu.:1.959e+07   3rd Qu.:  9325.5  
##  Australia  :  12                  Max.   :2007   Max.   :82.60   Max.   :1.319e+09   Max.   :113523.1  
##  (Other)    :1632

We will want to look at trends over time by continent. How many countries are in this data set in each continent? There are 12 years for each country. Are the data complete? table() gives an answer.

table(gapminder$continent, gapminder$year)
##           
##            1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
##   Africa     52   52   52   52   52   52   52   52   52   52   52   52
##   Americas   25   25   25   25   25   25   25   25   25   25   25   25
##   Asia       33   33   33   33   33   33   33   33   33   33   33   33
##   Europe     30   30   30   30   30   30   30   30   30   30   30   30
##   Oceania     2    2    2    2    2    2    2    2    2    2    2    2

Tech note: table() doesn’t have a data= argument, so you have to qualify the names of variables using data$variable notation. Another way to do this is to use the with() function, that makes variables in a data set available directly. The same table can be obtained using:

with(gapminder, {table(continent, year)})

1D plots: Bar plots for discrete variables

Bar plots are often used to visualize the distribution of a discrete variable, like continent. With ggplot2, this is relatively easy:

ggplot(gapminder, aes(x=continent)) + geom_bar()

To make this (and other plots) more colorful, you can also map the fill attribute to continent.

ggplot(gapminder, aes(x=continent, fill=continent)) + geom_bar()

As you progress with ggplot2, you will also learn to:

The next step shows several of these features:

ggplot(gapminder, aes(x=continent, fill=continent)) + 
    geom_bar(aes(y=..count../12)) +
    labs(y="Number of countries") +
    guides(fill=FALSE)

Another ggplot2 feature is that every plot is a ggplot object. If you like a given plot, you can save it to some name by using mybar <- ggplot() + ..., or after you’ve done it, using mybar <- last_plot()

# record a plot for future use
mybar <- last_plot()

Then, we can add other layers or transform the coordinates with coord_ functions.

mybar + coord_trans(y="sqrt")

mybar + coord_flip()

mybar + coord_polar()

1D plots: density plots for continuous variables

There are several continuous variables in this data set: life expectancy (lifeExp), population (pop) and gross domestic product per capita (gdpPercap) for each year and country. For such variables, density plots provide a useful graphical summary.

We will start with lifeExp. The simplest plot uses this as the horizontal axis, aes(x=lifeExp) and then adds geom_density() to calculate and plot the smoothed frequency distribution.

ggplot(data=gapminder, aes(x=lifeExp)) + 
    geom_density()

To make such plots prettier, you can change the line thickness (size=), add a fill color (fill=""), and make the fill color partially transparent (alpha=). You can see the grid lines underneath the following plots

ggplot(data=gapminder, aes(x=lifeExp)) + 
    geom_density(size=1.5, fill="pink", alpha=0.3)

If you want, you can also add a histogram later. This is a little more complicated to get right, because historams are computed differently and need some additional arguments.

ggplot(data=gapminder, aes(x=lifeExp)) + 
    geom_density(size=1.5, fill="pink", alpha=0.5) +
    geom_histogram(aes(y=..density..), binwidth=4, color="black", fill="lightblue", alpha=0.5)

I don’t recommend such plots, because the density plot usually gives most of the information, and the histogram doesn’t add much.

Differences by continent

The plot of lifeExp is bimodal, and looks wierd. Maybe this is hiding a difference among countries in different continents. The easy way to see this in ggplot2 is to add another aesthetic attribute, fill=continent, which is inherited in geom_density(). Using transparent colors (alpha=) makes it easier to see the different distributions across continent.

ggplot(data=gapminder, aes(x=lifeExp, fill=continent)) +
    geom_density(alpha=0.3)

It is easy to see that African countries differ markedly from the rest.

boxplots and other visual summaries

Alternatively, you might want to view the distributions of life expectancy by another visual summary, grouped by continent. All you need to do is change the aesthetic to show continent on one axis, and life expectancy (lifeExp) on the other.

gap1 <- ggplot(data=gapminder, aes(x=continent, y=lifeExp, fill=continent))

Then, add a geom_boxplot() layer:

gap1 +
    geom_boxplot(outlier.size=2)

Your turn

  • Remove the legend from this plot
  • Also, make the plot horizontal
  • Instead of a boxplot, try geom_violin()

Effect ordering

The continents are a factor, and are ordered alphabetically by default. It might be more useful to order them by the mean or median life expectancy.

In this example, I use the dplyr “pipe” notation (%>%) to send the gapminder data to the dplyr:;mutate() function, and within that, reorder() the continents by their median life expectancy.

gapminder %>% 
    mutate(continent = reorder(continent, lifeExp, FUN=median))

(In other situations, you could use FUN=mean, FUN=sd, or FUN=max to sort the levels by their means, standard deviatons, maximums, or any other function.)

Continuing from here, you can pipe the result of this right into ggplot:

gapminder %>% 
    mutate(continent = reorder(continent, lifeExp, FUN=median)) %>%
    ggplot(aes(x=continent, y=lifeExp, fill=continent)) +
    geom_boxplot(outlier.size=2)

Looking at GDP

Let’s look at the distribution of gdpPercap in a similar way, starting with the unconditional distribution.

ggplot(data=gapminder, aes(x=gdpPercap)) + 
    geom_density()  

Hmm, that is extremely positively skewed.

Your turn

  • As we did for lifeExp plot the distributions separately for each continent
  • It is probably more useful to plot GDP on a log scale. Add another layer that transforms the x axis to log10(gdpPercap).
  • Make boxplots of gdpPercap by continent
  • Do the same, but plot GDP on a log scale.

1.5D: Time series plots

How has life expectancy changed over time? The simplest way to to plot a line for each country over year. To do this, we use the group aesthetic.

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