Types of visualizations

The graphs you’ve made so far in this course have all been scatter plots, like this one.

These are useful for comparing two variables, such as GDP per capita and life expectancy, with each point representing one observation. But there are many other types of graphs you can make with ggplot2, each of which is useful for visualizing a particular type of data.

In this chapter, you’ll learn how to create line plots, bar plots, histograms, and boxplots. You’ll see how each plot requires different methods of data manipulation and preparation, and you’ll understand how each of these plot types plays a different role in data analysis.

Line plots

You’ll start by making a line plot, for visualizing a change over time.

In the last chapter, you created a plot like this to show the mean life expectancy in each continent in each year.

You can get a sense of the trends from this plot. But it’s a bit easier to understand as a line plot, where the observations within each continent are connected. This makes it clearer that what we care about is the upward or downward trend over time.

Line plots

Line plots

The way you make a line plot is to change one part of the code. geom_point() was the part of the code that specified you were making a scatter plot. Simply change that to geom_line() to make a line plot.

So this is how you make a line plot.

ggplot(by_year_continent, aes(x = year, y = medianLifeExp,color = continent))+
  geom_line()+
  expand_limits(y = 0)

Most of it looks exactly the same as a scatter plot: provide the data you’re plotting, the aesthetics of x, y, and color, and end with expand_limits(y = 0). The only difference is the type of plot: geom_line().

Exercise: Visualizing median GDP per capita over time

A line plot is useful for visualizing trends over time. In this exercise, you’ll examine how the median GDP per capita has changed over time.

Instructions

• Use group_by() and summarize() to find the median GDP per capita within each year, calling the output column medianGdpPercap. Use the assignment operator = to save it to a dataset called by_year.

# Summarize the median gdpPercap by year, then save it as by_year
by_year = gapminder%>%
            group_by(year)%>%
            summarize(medianGdpPercap = median(gdpPercap))
by_year

# A tibble: 12 × 2
    year medianGdpPercap
   <int>           <dbl>
 1  1952           1969.
 2  1957           2173.
 3  1962           2335.
 4  1967           2678.
 5  1972           3339.
 6  1977           3799.
 7  1982           4216.
 8  1987           4280.
 9  1992           4386.
10  1997           4782.
11  2002           5320.
12  2007           6124.

• Use the by_year dataset to create a line plot showing the change in median GDP per capita over time. Be sure to use expand_limits(y = 0) to include 0 on the y-axis.

Answer

# Summarize the median gdpPercap by year, then save it as by_year
by_year = gapminder%>%
            group_by(year)%>%
            summarize(medianGdpPercap = median(gdpPercap))

# Create a line plot showing the change in medianGdpPercap over time

ggplot(by_year, aes(x = year, y = medianGdpPercap))+
  geom_line()+
  expand_limits(y=0)

Exercise: Visualizing median GDP per capita by continent over time

In the last exercise you used a line plot to visualize the increase in median GDP per capita over time. Now you’ll examine the change within each continent.

Instructions

• Use group_by() and summarize() to find the median GDP per capita within each year and continent, calling the output column medianGdpPercap. Use the assignment operator = to save it to a dataset called by_year_continent.

# Summarize the median gdpPercap by year & continent, save as by_year_continent
suppressMessages({by_year_continent = gapminder%>%
                    group_by(year,continent)%>%
                    summarize(medianGdpPercap = median(gdpPercap))})
by_year_continent

# A tibble: 60 × 3
# Groups:   year [12]
    year continent medianGdpPercap
   <int> <fct>               <dbl>
 1  1952 Africa               987.
 2  1952 Americas            3048.
 3  1952 Asia                1207.
 4  1952 Europe              5142.
 5  1952 Oceania            10298.
 6  1957 Africa              1024.
 7  1957 Americas            3781.
 8  1957 Asia                1548.
 9  1957 Europe              6067.
10  1957 Oceania            11599.
# ℹ 50 more rows

• Use the by_year_continent dataset to create a line plot showing the change in median GDP per capita over time, with color representing continent. Be sure to use expand_limits(y = 0) to include 0 on the y-axis.

Answer

# Summarize the median gdpPercap by year & continent, save as by_year_continent
by_year_continent = gapminder%>%
                    group_by(year,continent)%>%
                    summarize(medianGdpPercap = median(gdpPercap))

`summarise()` has grouped output by 'year'. You can override using the
`.groups` argument.

# Create a line plot showing the change in medianGdpPercap by continent over time
ggplot(by_year_continent, aes(x = year, y = medianGdpPercap,color = continent))+
geom_line()+
expand_limits(y=0)

Bar plots

The next kind of plot you’ll learn to make is a bar plot. Bar plots are useful for comparing values across discrete categories, such as continents.

In the last chapter, you learned to calculate summarized values within groups. For example, this code finds the average life expectancy within each continent in the year 2007. That creates a table that looks like this, with one observation for each continent.

by_continent <- gapminder %>%
  filter(year == 2007) %>%
  group_by(continent) %>%
  summarize(meanLifeExp = mean(lifeExp))

by_continent

# A tibble: 5 × 2
  continent meanLifeExp
  <fct>           <dbl>
1 Africa           54.8
2 Americas         73.6
3 Asia             70.7
4 Europe           77.6
5 Oceania          80.7

Bar plots

Bar plots

Instead of just printing the table, you might want to represent the summary visually. For that, you would use a bar plot. This kind of plot represents the data using one bar for each continent, with the height of the bar representing the mean life expectancy.

ggplot(by_continent, aes(x = continent, y = meanLifeExp)) +
geom_col()

To create a bar plot, you use geom_col(), short for “column”. There are two aesthetics in a bar plot.

• X is the categorical variable: in this case, the continent.
• Y is the variable that determines the height of the bars: the mean life expectancy.

This makes it easy to see which continents have higher life expectancy than others. Notice that unlike scatter plots or line plots, bar plots always start at zero.

Exercise: Visualizing median GDP per capita by continent

A bar plot is useful for visualizing summary statistics, such as the median GDP in each continent.

Instructions

• Use group_by() and summarize() to find the median GDP per capita within each continent in the year 1952, calling the output column medianGdpPercap.
• Use the assignment operator = to save it to a dataset called by_continent.

# A tibble: 5 × 2
  continent medianGdpPercap
  <fct>               <dbl>
1 Africa               987.
2 Americas            3048.
3 Asia                1207.
4 Europe              5142.
5 Oceania            10298.

• Use the by_continent dataset to create a bar plot showing the median GDP per capita in each continent.

Answer

library(dplyr)
library(ggplot2)

# Summarize the median gdpPercap by continent in 1952

by_continent = gapminder%>%
                filter(year == 1952)%>%
                group_by(continent)%>%
                summarize(medianGdpPercap = median(gdpPercap))
# Create a bar plot showing medianGdp by continent
ggplot(by_continent,aes(x = continent,y = medianGdpPercap))+
  geom_col()

Exercise: Visualizing GDP per capita by country in Oceania

You’ve created a plot where each bar represents one continent, showing the median GDP per capita for each. But the x-axis of the bar plot doesn’t have to be the continent: you can instead create a bar plot where each bar represents a country.

In this exercise, you’ll create a bar plot comparing the GDP per capita between the two countries in the Oceania continent (Australia and New Zealand).

Instructions

• Filter for observations in the Oceania continent in the year 1952. Save this as oceania_1952.

# A tibble: 2 × 6
  country     continent  year lifeExp     pop gdpPercap
  <fct>       <fct>     <int>   <dbl>   <int>     <dbl>
1 Australia   Oceania    1952    69.1 8691212    10040.
2 New Zealand Oceania    1952    69.4 1994794    10557.

• Use the oceania_1952 dataset to create a bar plot, with country on the x-axis and gdpPercap on the y-axis.

Answer

library(gapminder)
library(dplyr)
library(ggplot2)

# Filter for observations in the Oceania continent in 1952
oceania_1952 = gapminder%>%
                filter(year == 1952, continent == 'Oceania')

# Create a bar plot of gdpPercap by country
ggplot(oceania_1952, aes(x = country, y = gdpPercap))+
geom_col()

Histograms

Each kind of graph offers a different way to investigate your data. So far we’ve been looking at relationships between two or more variables. But we can instead investigate one dimension of the data at a time, using a histogram.

Histograms

Histograms

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

A histogram shows a distribution. In this case, it’s the distribution of life expectancy across countries in the year 2007. Every bar represents a bin of life expectancies, and the height represents how many countries fall into that bin. This lets you get a sense of the distribution based on the histogram’s shape. We can see that most countries have a life expectancy between 70 to 80 years, but that another set of countries have life expectancies between 40 and 65.

ggplot(gapminder_2007, aes(x = lifeExp)) +
  geom_histogram()

A histogram is created with geom_histogram(). It has only one aesthetic: the x-axis, the variable whose distribution you are examining.

Adjusting bin width

Adjusting bin width

The width of each bin in the histogram is chosen automatically, and it has a large effect on how the histogram communicates the distribution. You may need to customize that width. You can do so with the binwidth option, which is set inside the parentheses of the geom_histogram() layer.

ggplot(gapminder_2007, aes(x = lifeExp)) +
  geom_histogram(binwidth = 5)

Setting binwidth equals 5 means that each of the bars in the histograms represents a width of five years. Setting a wide binwidth like this makes the histogram a bit blockier, which focuses on the general shape more than the small details. As you gain experience with histograms, you’ll learn how to customize this to give the clearest picture of your data.

Log x-axis

Log x-axis

In some cases, you may need to put the x-axis of a histogram on a log scale for it to be understandable, just like you did in several of the scatter plots in Chapter 2. Recall that you do this by adding a scale_x_log10 to the graph. You’ll practice doing so in the exercises.

Exercise: Visualizing population

A histogram is useful for examining the distribution of a numeric variable. In this exercise, you’ll create a histogram showing the distribution of country populations (by millions) in the year 1952.

Code for generating this dataset, gapminder_1952, is provided.

gapminder_1952 = gapminder %>%
  filter(year == 1952) %>%
  mutate(pop_by_mil = pop / 1000000)

Instructions

• Use the gapminder_1952 dataset to create a histogram of country population (pop_by_mil) in the year 1952.
• Inside the histogram geom, set the number of bins to 50.

Answer

library(gapminder)
library(dplyr)
library(ggplot2)

gapminder_1952 <- gapminder %>%
  filter(year == 1952) %>%
  mutate(pop_by_mil = pop / 1000000)

# Create a histogram of population (pop_by_mil)

ggplot(gapminder_1952,aes(x = pop_by_mil))+
geom_histogram(bins=50)

Exercise: Visualizing population with x-axis on a log scale

In the last exercise you created a histogram of populations across countries. You might have noticed that there were several countries with a much higher population than others, which causes the distribution to be very skewed, with most of the distribution crammed into a small part of the graph. (Consider that it’s hard to tell the median or the minimum population from that histogram).

To make the histogram more informative, you can try putting the x-axis on a log scale.

Instructions

gapminder_1952 <- gapminder %>%
  filter(year == 1952) 

• Use the gapminder_1952 dataset (code is provided) to create a histogram of country population (pop) in the year 1952, putting the x-axis on a log scale with scale_x_log10().

Answer

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Create a histogram of population (pop), with x on a log scale
ggplot(gapminder_1952,aes(x = pop))+
geom_histogram()+
scale_x_log10()

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Boxplots

In this final lesson, you’ll learn how to make one more type of graph: a box plot. Recall that we used a histogram when we wanted to examine the distribution of one variable, such as life expectancy, across all countries.

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Notice that a histogram combines all the life expectancies across all continents, without distinguishing them. But what if your goal is to compare the distribution of life expectancies among continents?

Box plots

Box plots

This is a box plot, which shows the distribution of life expectancies within each continent, so that you can compare them.

ggplot(gapminder_2007, aes(x = continent, y = lifeExp)) +
geom_boxplot()

It is created with geom_boxplot(), and it has two aesthetics: - x is the category (continent), - and y is the values that we’re comparing, which in this case is life expectancy.

A box plot takes a bit of practice to interpret, so here’s what each of the components means. The black line in the middle of each white box is the median of that continent’s distribution.

• The top and bottom of each box represent the 75th percentile and the 25th percentile of that group, meaning half of the distribution lies within that box.
• The lines going up and down from the box, called “whiskers”, cover additional countries.
• The two dots below the whiskers for Asia and the Americas represent outliers: countries with unusually low life expectancy relative to the rest of the distribution.

So there’s a lot that this plot tells us about differences in life expectancy across continents. We can see that the median life expectancy of Europe is one of the highest, and that the two countries in Oceana (Australia and New Zealand) both have very high values. We can also see that the distribution for Africa is unusually low, with about half of its countries having a life expectancy between 50 and 60 years.

Exercise: Comparing GDP per capita across continents

A boxplot is useful for comparing a distribution of values across several groups. In this exercise, you’ll examine the distribution of GDP per capita by continent. Since GDP per capita varies across several orders of magnitude, you’ll need to put the y-axis on a log scale.

Instructions

• Use the gapminder_1952 dataset (code is provided) to create a boxplot comparing GDP per capita (gdpPercap) among continents.
• Put the y-axis on a log scale with scale_y_log10().

Answer

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Create a boxplot comparing gdpPercap among continents
ggplot(gapminder_1952, aes(x = continent, y= gdpPercap))+
geom_boxplot()+
scale_y_log10()

Exercise: Adding a title to your graph

There are many other options for customizing a ggplot2 graph. You can also learn about them from online resources, which is an important skill to develop.

As the final exercise in this chapter, you’ll practice looking up ggplot2 instructions by completing a task we haven’t shown you how to do.

Instructions

• Add a title to the graph: Comparing GDP per capita across continents. Use a search engine, such as Google or Bing, to learn how to do so.

Answer

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Add a title to this graph: "Comparing GDP per capita across continents"
ggplot(gapminder_1952, aes(x = continent, y = gdpPercap)) +
  geom_boxplot() +
  scale_y_log10()+
  ggtitle("Comparing GDP per capita across continents")