Introduction to the Tidyverse
DataCamp - David Robinson
12/4/2020
The gapminder dataset
Loading the gapminder and dplyr packages
Before you can work with the gapminder dataset, you’ll need to load two R packages that contain the tools for working with it, then display the gapminder dataset so that you can see what it contains.
This course introduces a lot of new concepts, so if you ever need a quick refresher, download the tidyverse for beginners Cheat Sheet and keep it handy!
# Load the gapminder package
library(gapminder)
# Load the dplyr package
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union# Look at the gapminder dataset
gapminder## # A tibble: 1,704 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <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 rowsThe filter verb
Filtering for one year
The filter verb extracts particular observations based on a condition. In this exercise you’ll filter for observations from a particular year.
library(gapminder)
library(dplyr)
# Filter the gapminder dataset for the year 1957
gapminder %>%
filter(year == 1957)## # A tibble: 142 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1957 30.3 9240934 821.
## 2 Albania Europe 1957 59.3 1476505 1942.
## 3 Algeria Africa 1957 45.7 10270856 3014.
## 4 Angola Africa 1957 32.0 4561361 3828.
## 5 Argentina Americas 1957 64.4 19610538 6857.
## 6 Australia Oceania 1957 70.3 9712569 10950.
## 7 Austria Europe 1957 67.5 6965860 8843.
## 8 Bahrain Asia 1957 53.8 138655 11636.
## 9 Bangladesh Asia 1957 39.3 51365468 662.
## 10 Belgium Europe 1957 69.2 8989111 9715.
## # ... with 132 more rowsFiltering for one country and one year
You can also use the filter() verb to set two conditions, which could retrieve a single observation.
Just like in the last exercise, you can do this in two lines of code, starting with gapminder %>% and having the filter() on the second line. Keeping one verb on each line helps keep the code readable. Note that each time, you’ll put the pipe %>% at the end of the first line (like gapminder %>%); putting the pipe at the beginning of the second line will throw an error.
library(gapminder)
library(dplyr)
# Filter for China in 2002
gapminder %>%
filter(country == "China", year == 2002)## # A tibble: 1 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 China Asia 2002 72.0 1280400000 3119.The arrange verb
Arranging observations by life expectancy
You use arrange() to sort observations in ascending or descending order of a particular variable. In this case, you’ll sort the dataset based on the lifeExp variable.
library(gapminder)
library(dplyr)
# Sort in ascending order of lifeExp
gapminder %>%
arrange(lifeExp)## # A tibble: 1,704 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Rwanda Africa 1992 23.6 7290203 737.
## 2 Afghanistan Asia 1952 28.8 8425333 779.
## 3 Gambia Africa 1952 30 284320 485.
## 4 Angola Africa 1952 30.0 4232095 3521.
## 5 Sierra Leone Africa 1952 30.3 2143249 880.
## 6 Afghanistan Asia 1957 30.3 9240934 821.
## 7 Cambodia Asia 1977 31.2 6978607 525.
## 8 Mozambique Africa 1952 31.3 6446316 469.
## 9 Sierra Leone Africa 1957 31.6 2295678 1004.
## 10 Burkina Faso Africa 1952 32.0 4469979 543.
## # ... with 1,694 more rows# Sort in descending order of lifeExp
gapminder %>%
arrange(desc(lifeExp))## # A tibble: 1,704 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Japan Asia 2007 82.6 127467972 31656.
## 2 Hong Kong, China Asia 2007 82.2 6980412 39725.
## 3 Japan Asia 2002 82 127065841 28605.
## 4 Iceland Europe 2007 81.8 301931 36181.
## 5 Switzerland Europe 2007 81.7 7554661 37506.
## 6 Hong Kong, China Asia 2002 81.5 6762476 30209.
## 7 Australia Oceania 2007 81.2 20434176 34435.
## 8 Spain Europe 2007 80.9 40448191 28821.
## 9 Sweden Europe 2007 80.9 9031088 33860.
## 10 Israel Asia 2007 80.7 6426679 25523.
## # ... with 1,694 more rowsThat’s right! Take a look at the countries with the highest and lowest life expectancy- is it similar to what you expected?
Filtering and arranging
You’ll often need to use the pipe operator (%>%) to combine multiple dplyr verbs in a row. In this case, you’ll combine a filter() with an arrange() to find the highest population countries in a particular year.
library(gapminder)
library(dplyr)
# Filter for the year 1957, then arrange in descending order of population
gapminder %>%
filter(year == 1957) %>%
arrange(desc(pop))## # A tibble: 142 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 China Asia 1957 50.5 637408000 576.
## 2 India Asia 1957 40.2 409000000 590.
## 3 United States Americas 1957 69.5 171984000 14847.
## 4 Japan Asia 1957 65.5 91563009 4318.
## 5 Indonesia Asia 1957 39.9 90124000 859.
## 6 Germany Europe 1957 69.1 71019069 10188.
## 7 Brazil Americas 1957 53.3 65551171 2487.
## 8 United Kingdom Europe 1957 70.4 51430000 11283.
## 9 Bangladesh Asia 1957 39.3 51365468 662.
## 10 Italy Europe 1957 67.8 49182000 6249.
## # ... with 132 more rowsGreat work! A lot of the exercises in this course will involve combining multiple steps with the %>% operator.
The mutate verb
Using mutate to change or create a column
Suppose we want life expectancy to be measured in months instead of years: you’d have to multiply the existing value by 12. You can use the mutate() verb to change this column, or to create a new column that’s calculated this way.
library(gapminder)
library(dplyr)
# Use mutate to change lifeExp to be in months
gapminder %>%
mutate(lifeExp = lifeExp * 12)## # A tibble: 1,704 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 346. 8425333 779.
## 2 Afghanistan Asia 1957 364. 9240934 821.
## 3 Afghanistan Asia 1962 384. 10267083 853.
## 4 Afghanistan Asia 1967 408. 11537966 836.
## 5 Afghanistan Asia 1972 433. 13079460 740.
## 6 Afghanistan Asia 1977 461. 14880372 786.
## 7 Afghanistan Asia 1982 478. 12881816 978.
## 8 Afghanistan Asia 1987 490. 13867957 852.
## 9 Afghanistan Asia 1992 500. 16317921 649.
## 10 Afghanistan Asia 1997 501. 22227415 635.
## # ... with 1,694 more rows# Use mutate to create a new column called lifeExpMonths
gapminder %>%
mutate(lifeExpMonths = lifeExp * 12)## # A tibble: 1,704 x 7
## country continent year lifeExp pop gdpPercap lifeExpMonths
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779. 346.
## 2 Afghanistan Asia 1957 30.3 9240934 821. 364.
## 3 Afghanistan Asia 1962 32.0 10267083 853. 384.
## 4 Afghanistan Asia 1967 34.0 11537966 836. 408.
## 5 Afghanistan Asia 1972 36.1 13079460 740. 433.
## 6 Afghanistan Asia 1977 38.4 14880372 786. 461.
## 7 Afghanistan Asia 1982 39.9 12881816 978. 478.
## 8 Afghanistan Asia 1987 40.8 13867957 852. 490.
## 9 Afghanistan Asia 1992 41.7 16317921 649. 500.
## 10 Afghanistan Asia 1997 41.8 22227415 635. 501.
## # ... with 1,694 more rowsCombining filter, mutate, and arrange
In this exercise, you’ll combine all three of the verbs you’ve learned in this chapter, to find the countries with the highest life expectancy, in months, in the year 2007.
library(gapminder)
library(dplyr)
# Filter, mutate, and arrange the gapminder dataset
gapminder %>%
filter(year == 2007) %>%
mutate(lifeExpMonths = 12 * lifeExp) %>%
arrange(desc(lifeExpMonths))## # A tibble: 142 x 7
## country continent year lifeExp pop gdpPercap lifeExpMonths
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 Japan Asia 2007 82.6 127467972 31656. 991.
## 2 Hong Kong, China Asia 2007 82.2 6980412 39725. 986.
## 3 Iceland Europe 2007 81.8 301931 36181. 981.
## 4 Switzerland Europe 2007 81.7 7554661 37506. 980.
## 5 Australia Oceania 2007 81.2 20434176 34435. 975.
## 6 Spain Europe 2007 80.9 40448191 28821. 971.
## 7 Sweden Europe 2007 80.9 9031088 33860. 971.
## 8 Israel Asia 2007 80.7 6426679 25523. 969.
## 9 France Europe 2007 80.7 61083916 30470. 968.
## 10 Canada Americas 2007 80.7 33390141 36319. 968.
## # ... with 132 more rowsGreat work! Notice how you can combine several dplyr operations to answer a more complicated question like this.
Visualizing with ggplot2
Variable assignment
Throughout the exercises in this chapter, you’ll be visualizing a subset of the gapminder data from the year 1952. First, you’ll have to load the ggplot2 package, and create a gapminder_1952 dataset to visualize.
By the way, if you haven’t downloaded it already, check out the tidyverse for beginners Cheat Sheet. It includes an overview of the most important concepts, functions and methods and might come in handy if you ever need a quick refresher!
# Load the ggplot2 package as well
library(gapminder)
library(dplyr)
library(ggplot2)
# Create gapminder_1952
gapminder_1952 <- gapminder %>%
filter(year == 1952)Comparing population and GDP per capita
In the video you learned to create a scatter plot with GDP per capita on the x-axis and life expectancy on the y-axis (the code for that graph is shown here). When you’re exploring data visually, you’ll often need to try different combinations of variables and aesthetics.
library(gapminder)
library(dplyr)
library(ggplot2)
gapminder_1952 <- gapminder %>%
filter(year == 1952)
# Change to put pop on the x-axis and gdpPercap on the y-axis
ggplot(gapminder_1952, aes(x = pop, y = gdpPercap)) +
geom_point()
Great work on your first graph! Each point represents a country: can you guess which country any of the points are?
Comparing population and life expectancy
In this exercise, you’ll use ggplot2 to create a scatter plot from scratch, to compare each country’s population with its life expectancy in the year 1952.
library(gapminder)
library(dplyr)
library(ggplot2)
gapminder_1952 <- gapminder %>%
filter(year == 1952)
# Create a scatter plot with pop on the x-axis and lifeExp on the y-axis
ggplot(gapminder_1952, aes(x = pop, y = lifeExp)) +
geom_point()
Great! You might notice the points are crowded towards the left side of the plot, making them hard to distinguish. This next video will help solve that problem.
Log scales
Putting the x-axis on a log scale
You previously created a scatter plot with population on the x-axis and life expectancy on the y-axis. Since population is spread over several orders of magnitude, with some countries having a much higher population than others, it’s a good idea to put the x-axis on a log scale.
library(gapminder)
library(dplyr)
library(ggplot2)
gapminder_1952 <- gapminder %>%
filter(year == 1952)
# Change this plot to put the x-axis on a log scale
ggplot(gapminder_1952, aes(x = pop, y = lifeExp)) +
geom_point() +
scale_x_log10()
Great! Notice the points are more spread out on the x-axis. This makes it easy to see that there isn’t a correlation between population and life expectancy.
Putting the x- and y- axes on a log scale
Suppose you want to create a scatter plot with population on the x-axis and GDP per capita on the y-axis. Both population and GDP per-capita are better represented with log scales, since they vary over many orders of magnitude.
library(gapminder)
library(dplyr)
library(ggplot2)
gapminder_1952 <- gapminder %>%
filter(year == 1952)
# Scatter plot comparing pop and gdpPercap, with both axes on a log scale
ggplot(gapminder_1952, aes(x = pop, y = gdpPercap)) +
geom_point() +
scale_x_log10() +
scale_y_log10()
Great! Notice that the y-axis goes from 1e3 (1000) to 1e4 (10,000) to 1e5 (100,000) in equal increments.
Additional aesthetics
Adding color to a scatter plot
In this lesson you learned how to use the color aesthetic, which can be used to show which continent each point in a scatter plot represents.
library(gapminder)
library(dplyr)
library(ggplot2)
gapminder_1952 <- gapminder %>%
filter(year == 1952)
# Scatter plot comparing pop and lifeExp, with color representing continent
ggplot(gapminder_1952, aes(x = pop, y = lifeExp, color = continent)) +
geom_point() +
scale_x_log10()
Good work! What differences can you see between continents, in terms of their population and life expectancy?
Adding size and color to a plot
In the last exercise, you created a scatter plot communicating information about each country’s population, life expectancy, and continent. Now you’ll use the size of the points to communicate even more.
library(gapminder)
library(dplyr)
library(ggplot2)
gapminder_1952 <- gapminder %>%
filter(year == 1952)
# Add the size aesthetic to represent a country's gdpPercap
ggplot(gapminder_1952, aes(x = pop, y = lifeExp, color = continent, size = gdpPercap)) +
geom_point() +
scale_x_log10()
Good work! Are you able to guess which point represents your own country?
Faceting
Creating a subgraph for each continent
You’ve learned to use faceting to divide a graph into subplots based on one of its variables, such as the continent.
library(gapminder)
library(dplyr)
library(ggplot2)
gapminder_1952 <- gapminder %>%
filter(year == 1952)
# Scatter plot comparing pop and lifeExp, faceted by continent
ggplot(gapminder_1952, aes(x = pop, y = lifeExp)) +
geom_point() +
scale_x_log10() +
facet_wrap(~ continent)
Great work! Faceting is a powerful way to understand subsets of your data separately.
Faceting by year
All of the graphs in this chapter have been visualizing statistics within one year. Now that you’re able to use faceting, however, you can create a graph showing all the country-level data from 1952 to 2007, to understand how global statistics have changed over time.
library(gapminder)
library(dplyr)
library(ggplot2)
# Scatter plot comparing gdpPercap and lifeExp, with color representing continent
# and size representing population, faceted by year
ggplot(gapminder, aes(x = gdpPercap, y = lifeExp, color = continent, size = pop)) +
geom_point() +
scale_x_log10() +
facet_wrap(~ year)
Awesome! That’s a lot of information you’re now able to share in one graph.
The summarize verb
Summarizing the median life expectancy
You’ve seen how to find the mean life expectancy and the total population across a set of observations, but mean() and sum() are only two of the functions R provides for summarizing a collection of numbers. Here, you’ll learn to use the median() function in combination with summarize().
By the way, dplyr displays some messages when it’s loaded that we’ve been hiding so far. They’ll show up in red and start with:
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':This will occur in future exercises each time you load dplyr: it’s mentioning some built-in functions that are overwritten by dplyr. You won’t need to worry about this message within this course.
library(gapminder)
library(dplyr)
# Summarize to find the median life expectancy
gapminder %>%
summarize(medianLifeExp = median(lifeExp))## # A tibble: 1 x 1
## medianLifeExp
## <dbl>
## 1 60.7That’s right! Note that this is the median across all countries and all years in the dataset.
Summarizing the median life expectancy in 1957
Rather than summarizing the entire dataset, you may want to find the median life expectancy for only one particular year. In this case, you’ll find the median in the year 1957.
library(gapminder)
library(dplyr)
# Filter for 1957 then summarize the median life expectancy
gapminder %>%
filter(year == 1957) %>%
summarize(medianLifeExp = median(lifeExp))## # A tibble: 1 x 1
## medianLifeExp
## <dbl>
## 1 48.4Great! Just like in Chapter 1, this chapter will often involve performing multiple dplyr steps in a row.
Summarizing multiple variables in 1957
The summarize() verb allows you to summarize multiple variables at once. In this case, you’ll use the median() function to find the median life expectancy and the max() function to find the maximum GDP per capita.
library(gapminder)
library(dplyr)
# Filter for 1957 then summarize the median life expectancy and the maximum GDP per capita
gapminder %>%
filter(year == 1957) %>%
summarize(medianLifeExp = median(lifeExp),
maxGdpPercap = max(gdpPercap))## # A tibble: 1 x 2
## medianLifeExp maxGdpPercap
## <dbl> <dbl>
## 1 48.4 113523.That’s right! Think about what other kinds of information about countries you might want to summarize within one year.
The group_by verb
Summarizing by year
In a previous exercise, you found the median life expectancy and the maximum GDP per capita in the year 1957. Now, you’ll perform those two summaries within each year in the dataset, using the group_by verb.
library(gapminder)
library(dplyr)
# Find median life expectancy and maximum GDP per capita in each year
gapminder %>%
group_by(year) %>%
summarize(medianLifeExp = median(lifeExp),
maxGdpPercap = max(gdpPercap))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 12 x 3
## year medianLifeExp maxGdpPercap
## <int> <dbl> <dbl>
## 1 1952 45.1 108382.
## 2 1957 48.4 113523.
## 3 1962 50.9 95458.
## 4 1967 53.8 80895.
## 5 1972 56.5 109348.
## 6 1977 59.7 59265.
## 7 1982 62.4 33693.
## 8 1987 65.8 31541.
## 9 1992 67.7 34933.
## 10 1997 69.4 41283.
## 11 2002 70.8 44684.
## 12 2007 71.9 49357.Great! Interesting: notice that median life expectancy across countries is generally going up over time, but maximum GDP per capita is not.
Summarizing by continent
You can group by any variable in your dataset to create a summary. Rather than comparing across time, you might be interested in comparing among continents. You’ll want to do that within one year of the dataset: let’s use 1957.
library(gapminder)
library(dplyr)
# Find median life expectancy and maximum GDP per capita in each continent in 1957
gapminder %>%
filter(year == 1957) %>%
group_by(continent) %>%
summarize(medianLifeExp = median(lifeExp),
maxGdpPercap = max(gdpPercap))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 5 x 3
## continent medianLifeExp maxGdpPercap
## <fct> <dbl> <dbl>
## 1 Africa 40.6 5487.
## 2 Americas 56.1 14847.
## 3 Asia 48.3 113523.
## 4 Europe 67.6 17909.
## 5 Oceania 70.3 12247.Great work! Which continent had the highest median life expectancy in 1957?
Summarizing by continent and year
Instead of grouping just by year, or just by continent, you’ll now group by both continent and year to summarize within each.
library(gapminder)
library(dplyr)
# Find median life expectancy and maximum GDP per capita in each continent/year combination
gapminder %>%
group_by(continent, year) %>%
summarize(medianLifeExp = median(lifeExp),
maxGdpPercap = max(gdpPercap))## `summarise()` regrouping output by 'continent' (override with `.groups` argument)## # A tibble: 60 x 4
## # Groups: continent [5]
## continent year medianLifeExp maxGdpPercap
## <fct> <int> <dbl> <dbl>
## 1 Africa 1952 38.8 4725.
## 2 Africa 1957 40.6 5487.
## 3 Africa 1962 42.6 6757.
## 4 Africa 1967 44.7 18773.
## 5 Africa 1972 47.0 21011.
## 6 Africa 1977 49.3 21951.
## 7 Africa 1982 50.8 17364.
## 8 Africa 1987 51.6 11864.
## 9 Africa 1992 52.4 13522.
## 10 Africa 1997 52.8 14723.
## # ... with 50 more rowsExcellent! In the next chapter, you’ll learn to turn this data into an informative graph.
Visualizing summarized data
Visualizing median life expectancy over time
In the last chapter, you summarized the gapminder data to calculate the median life expectancy within each year. This code is provided for you, and is saved (with <-) as the by_year dataset.
Now you can use the ggplot2 package to turn this into a visualization of changing life expectancy over time.
library(gapminder)
library(dplyr)
library(ggplot2)
by_year <- gapminder %>%
group_by(year) %>%
summarize(medianLifeExp = median(lifeExp),
maxGdpPercap = max(gdpPercap))## `summarise()` ungrouping output (override with `.groups` argument)# Create a scatter plot showing the change in medianLifeExp over time
ggplot(by_year, aes(x = year, y = medianLifeExp)) +
geom_point() +
expand_limits(y = 0)
Great! It looks like median life expectancy across countries is increasing over time.
Visualizing median GDP per capita per continent over time
In the last exercise you were able to see how the median life expectancy of countries changed over time. Now you’ll examine the median GDP per capita instead, and see how the trend differs among continents.
library(gapminder)
library(dplyr)
library(ggplot2)
# Summarize medianGdpPercap within each continent within each year: by_year_continent
by_year_continent <- gapminder %>%
group_by(continent, year) %>%
summarize(medianGdpPercap = median(gdpPercap))## `summarise()` regrouping output by 'continent' (override with `.groups` argument)# Plot the change in medianGdpPercap in each continent over time
ggplot(by_year_continent, aes(x = year, y = medianGdpPercap, color = continent)) +
geom_point() +
expand_limits(y = 0)
Great! You might be wondering how you can connect these points with lines. You’ll learn that in Chapter 4!
Comparing median life expectancy and median GDP per continent in 2007
In these exercises you’ve generally created plots that show change over time. But as another way of exploring your data visually, you can also use ggplot2 to plot summarized data to compare continents within a single year.
library(gapminder)
library(dplyr)
library(ggplot2)
# Summarize the median GDP and median life expectancy per continent in 2007
by_continent_2007 <- gapminder %>%
filter(year == 2007) %>%
group_by(continent) %>%
summarize(medianGdpPercap = median(gdpPercap),
medianLifeExp = median(lifeExp))## `summarise()` ungrouping output (override with `.groups` argument)# Use a scatter plot to compare the median GDP and median life expectancy
ggplot(by_continent_2007, aes(x = medianGdpPercap, y = medianLifeExp, color = continent)) +
geom_point()
Great work! Scatter plots are a very flexible tool for examining relationships.
Line plots
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.
library(gapminder)
library(dplyr)
library(ggplot2)
# Summarize the median gdpPercap by year, then save it as by_year
by_year <- gapminder %>%
group_by(year) %>%
summarize(medianGdpPercap = median(gdpPercap))## `summarise()` ungrouping output (override with `.groups` argument)# 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)
Great! Looks like median GDP per capita across countries has gone up over time.
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.
library(gapminder)
library(dplyr)
library(ggplot2)
# 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()` regrouping output by 'year' (override with `.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)
Excellent work! Take a look at the plot: did the growth in median GDP per capita differ between continents?
Bar plots
Visualizing median GDP per capita by continent
A bar plot is useful for visualizing summary statistics, such as the median GDP in each continent.
library(gapminder)
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))## `summarise()` ungrouping output (override with `.groups` argument)# Create a bar plot showing medianGdp by continent
ggplot(by_continent, aes(x = continent, y = medianGdpPercap)) +
geom_col()
Excellent! That’s three kinds of plots you’re now able to make with ggplot2.
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).
library(gapminder)
library(dplyr)
library(ggplot2)
# Filter for observations in the Oceania continent in 1952
oceania_1952 <- gapminder %>%
filter(continent == "Oceania", year == 1952)
# Create a bar plot of gdpPercap by country
ggplot(oceania_1952, aes(x = country, y = gdpPercap)) +
geom_col()
Good work! Looks like the GDP per capita of these two countries was similar in 1952.
Histograms
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.
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)
That’s right! Notice that most of the distribution is in the smallest (leftmost) bins. In the next exercise you’ll put the x-axis on a log scale.
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.
library(gapminder)
library(dplyr)
library(ggplot2)
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`.
Great! Notice that on a log scale, the distribution of country populations is approximately symmetrical.
Boxplots
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.
library(gapminder)
library(dplyr)
library(ggplot2)
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()
Looks good! What continents had countries with the highest GDP per capita?
Adding a title to your graph
There are many other options for customizing a ggplot2 graph, which you can learn about in other DataCamp courses. You can also learn about them from online resources, which is an important skill to develop.
As the final exercise in this course, you’ll practice looking up ggplot2 instructions by completing a task we haven’t shown you how to do.
library(gapminder)
library(dplyr)
library(ggplot2)
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")
Brilliant! Now you know how to look up additional methods for customizing graphs. That will be very useful in your career as an R user!
Don’t forget to download the Cheat Sheet for this course to keep track of what you’ve learned today!
Conclusion
