Course Description

This is an introduction to the programming language R, focused on a powerful set of tools known as the Tidyverse. You’ll learn the intertwined processes of data manipulation and visualization using the tools dplyr and ggplot2. You’ll learn to manipulate data by filtering, sorting, and summarizing a real dataset of historical country data in order to answer exploratory questions. You’ll then learn to turn this processed data into informative line plots, bar plots, histograms, and more with the ggplot2 package. You’ll get a taste of the value of exploratory data analysis and the power of Tidyverse tools. This is a suitable introduction for those who have no previous experience in R and are interested in performing data analysis.

Data wrangling

In this chapter, you’ll learn to do three things with a table: filter for particular observations, arrange the observations in a desired order, and mutate to add or change a column. You’ll see how each of these steps allows you to answer questions about your data.

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.

To your right, you’ll see two windows inside which you can enter code: The script.R window, and the R Console. All of your code to solve each exercise must go inside script.R.

If you hit Submit Answer, your R script is executed and the output is shown in the R Console. DataCamp checks whether your submission is correct and gives you feedback. You can hit Submit Answer as often as you want. If you’re stuck, you can ask for a hint or a solution.

You can use the R Console interactively by simply typing R code and hitting Enter. When you work in the console directly, your code will not be checked for correctness so it is a great way to experiment and explore.

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!

Use the library() function to load the dplyr package, just like we’ve loaded the gapminder package for you.
Type gapminder, on its own line, to look at the gapminder dataset.

# Load the gapminder package
library(gapminder)

# Load the dplyr package
library(dplyr)

# Look at the gapminder dataset
gapminder

Understanding a data frame

Now that you’ve loaded the gapminder dataset, you can start examining and understanding it.

We’ve already loaded the gapminder and dplyr packages. Type gapminder in the console, to display the object.

How many observations (rows) are in the dataset?

1704
6
1694
1952

The 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.

Add a filter() line after the pipe (%>%) to extract only the observations from the year 1957. Remember that you use == to compare two values.

library(gapminder)
library(dplyr)

# Filter the gapminder dataset for the year 1957
gapminder %>%
  filter(year == 1957)

Filtering 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.

Filter the gapminder data to retrieve only the observation from China in the year 2002.

library(gapminder)
library(dplyr)

# Filter for China in 2002
gapminder %>%
  filter(country == "China", year == 2002)

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.

Sort the gapminder dataset in ascending order of life expectancy (lifeExp).
Sort the gapminder dataset in descending order of life expectancy.

library(gapminder)
library(dplyr)

# Sort in ascending order of lifeExp
gapminder %>%
  arrange(lifeExp)
  
# Sort in descending order of lifeExp
gapminder %>%
  arrange(desc(lifeExp))

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.

Use filter() to extract observations from just the year 1957, then use arrange() to sort in descending order of population (pop).

library(gapminder)
library(dplyr)

# Filter for the year 1957, then arrange in descending order of population
gapminder %>%
  filter(year == 1957) %>%
  arrange(desc(pop))

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.

Use mutate() to change the existing lifeExp column, by multiplying it by 12: 12 * lifeExp.
Use mutate() to add a new column, called lifeExpMonths, calculated as 12 * lifeExp.

library(gapminder)
library(dplyr)

# Use mutate to change lifeExp to be in months
gapminder %>%
  mutate(lifeExp = lifeExp * 12)

# Use mutate to create a new column called lifeExpMonths
gapminder %>%
  mutate(lifeExpMonths = lifeExp * 12)

Combining 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.

In one sequence of pipes on the gapminder dataset:
filter() for observations from the year 2007,
mutate() to create a column lifeExpMonths, calculated as 12 * lifeExp, and
arrange() in descending order of that new column

library(gapminder)
library(dplyr)

# Filter, mutate, and arrange the gapminder dataset
gapminder %>%
  filter(year == 2007) %>%
  mutate(lifeExpMonths = 12 * lifeExp) %>%
  arrange(desc(lifeExpMonths))

Data visualization

Often a better way to understand and present data as a graph. In this chapter, you’ll learn the essential skills of data visualization using the ggplot2 package, and you’ll see how the dplyr and ggplot2 packages work closely together to create informative graphs.

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 after the gapminder and dplyr packages.
Filter gapminder for observations from the year 1952, and assign it to a new dataset gapminder_1952 using the assignment operator (<-).

# 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 has been provided in the exercise code). When you’re exploring data visually, you’ll often need to try different combinations of variables and aesthetics.

Change the scatter plot of gapminder_1952 so that (pop) is on the x-axis and GDP per capita (gdpPercap) is on the y-axis.

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()

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.

Create a scatter plot of gapminder_1952 with population (pop) is on the x-axis and life expectancy (lifeExp) on the y-axis.

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()

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.

Change the existing scatter plot (code provided) to put the x-axis (representing population) 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()

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.

Create a scatter plot with population (pop) on the x-axis and GDP per capita (gdpPercap) on the y-axis. Put both the x- and y- axes on a log scale.

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()

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.

Create a scatter plot with population (pop) on the x-axis, life expectancy (lifeExp) on the y-axis, and with continent (continent) represented by the color of the points. Put the x-axis on a log scale.

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()

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.

Modify the scatter plot so that the size of the points represents each country’s GDP per capita (gdpPercap).

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()

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.

Create a scatter plot of gapminder_1952 with the x-axis representing population (pop), the y-axis representing life expectancy (lifeExp), and faceted to have one subplot per continent (continent). Put the x-axis on a log scale.

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)

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.

Create a scatter plot of the gapminder data:
Put GDP per capita (gdpPercap) on the x-axis and life expectancy (lifeExp) on the y-axis, with continent (continent) represented by color and population (pop) represented by size.
Put the x-axis on a log scale
Facet by the year variable

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)

Grouping and summarizing

So far you’ve been answering questions about individual country-year pairs, but you may be interested in aggregations of the data, such as the average life expectancy of all countries within each year. Here you’ll learn to use the group by and summarize verbs, which collapse large datasets into manageable summaries.

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.

Use the median() function within a summarize() to find the median life expectancy. Save it into a column called medianLifeExp.

library(gapminder)
library(dplyr)

# Summarize to find the median life expectancy
gapminder %>%
  summarize(medianLifeExp = median(lifeExp))

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.

Filter for the year 1957, then use the median() function within a summarize() to calculate the median life expectancy into a column called medianLifeExp.

library(gapminder)
library(dplyr)

# Filter for 1957 then summarize the median life expectancy
gapminder %>%
  filter(year == 1957) %>%
  summarize(medianLifeExp = median(lifeExp))

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.

Find both the median life expectancy (lifeExp) and the maximum GDP per capita (gdpPercap) in the year 1957, calling them medianLifeExp and maxGdpPercap respectively. You can use the max() function to find the maximum.

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))

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.

Find the median life expectancy (lifeExp) and maximum GDP per capita (gdpPercap) within each year, saving them into medianLifeExp and maxGdpPercap, respectively.

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))

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.

Filter the gapminder data for the year 1957. Then find the median life expectancy (lifeExp) and maximum GDP per capita (gdpPercap) within each continent, saving them into medianLifeExp and maxGdpPercap, respectively.

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))

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.

Find the median life expectancy (lifeExp) and maximum GDP per capita (gdpPercap) within each combination of continent and year, saving them into medianLifeExp and maxGdpPercap, respectively.

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))

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.

Use the by_year dataset to create a scatter plot showing the change of median life expectancy over time, with year on the x-axis and medianLifeExp on the y-axis. Be sure to add expand_limits(y = 0) to make sure the plot’s y-axis includes zero.

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

by_year <- gapminder %>%
  group_by(year) %>%
  summarize(medianLifeExp = median(lifeExp),
            maxGdpPercap = max(gdpPercap))

# 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)

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.

Summarize the gapminder dataset by continent and year, finding the median GDP per capita (gdpPercap) within each and putting it into a column called medianGdpPercap. Use the assignment operator <- to save this summarized data as by_year_continent.
Create a scatter plot showing the change in medianGdpPercap by continent over time. Use color to distinguish between continents, and be sure to add expand_limits(y = 0) so that the y-axis starts at zero.

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))

# 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)

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.

Filter the gapminder dataset for the year 2007, then summarize the median GDP per capita and the median life expectancy within each continent, into columns called medianLifeExp and medianGdpPercap. Save this as by_continent_2007.
Use the by_continent_2007 data to create a scatterplot comparing these summary statistics for continents in 2007, putting the median GDP per capita on the x-axis to the median life expectancy on the y-axis. Color the scatter plot by continent. You don’t need to add expand_limits(y = 0) for this plot.

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))

# 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()

Types of visualizations

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

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.

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.
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.

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))

# 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)

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.

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.
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.

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))

# 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

Visualizing median GDP per capita by continent

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

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.
Use the by_continent dataset to create a bar plot showing the median GDP per capita 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))

# Create a bar plot showing medianGdp by continent
ggplot(by_continent, aes(x = continent, y = medianGdpPercap)) +
  geom_col()

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).

Filter for observations in the Oceania continent in the year 1952. Save this as oceania_1952.
Use the oceania_1952 dataset to create a bar plot, with country on the x-axis and gdpPercap on the y-axis.

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()

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.

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.

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)

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.

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().

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()

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.

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().

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()

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.

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.
After this exercise you are almost done with your course. If you enjoyed the material, feel free to send Dave a thank you via twitter. He’ll appreciate it. Tweet to Dave

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")

Conclusion

Congratulations on completing this Introduction to R via the Tidyverse. You’ve been introduced to the principles

Transforming and visualizing data with R

of transforming and visualizing data with R, and in the process learned some real insights from the Gapminder dataset. This course forms a great foundation for other DataCamp courses where you can continue learning how to use these powerful tools to explore data. You can take

Next steps: Data visualization

courses about ggplot2 to learn to create more informative and customized data visualizations. You can learn much more about using dplyr to transform your data, such as how to join multiple tables together. To analyze other data that you’re interested in, you can take the course on importing and cleaning datasets. And you can practice your data wrangling and visualization skills in my own course “Exploratory Data Analysis with R”, which offers a case study of analyzing United Nations voting over time. These are just a few of the many resources you have to continue learning about data science and R.

Enjoy your data science journey!

I hope you had fun in this course, and continue to enjoy your data science journey!

Introduction to the Tidyverse

David Robinson

30 March 2023

Data wrangling

The gapminder dataset

Loading the gapminder and dplyr packages

Understanding a data frame

The filter verb

Filtering for one year

Filtering for one country and one year

The arrange verb

Arranging observations by life expectancy

Filtering and arranging

The mutate verb

Using mutate to change or create a column

Combining filter, mutate, and arrange

Data visualization

Visualizing with ggplot2

Variable assignment

Comparing population and GDP per capita

Comparing population and life expectancy

Log scales

Putting the x-axis on a log scale

Putting the x- and y- axes on a log scale

Additional aesthetics

Adding color to a scatter plot

Adding size and color to a plot

Faceting

Creating a subgraph for each continent

Faceting by year

Grouping and summarizing

The summarize verb

Summarizing the median life expectancy

Summarizing the median life expectancy in 1957

Summarizing multiple variables in 1957

The group_by verb

Summarizing by year

Summarizing by continent

Summarizing by continent and year

Visualizing summarized data

Visualizing median life expectancy over time

Visualizing median GDP per capita per continent over time

Comparing median life expectancy and median GDP per continent in 2007

Types of visualizations

Line plots

Visualizing median GDP per capita over time

Visualizing median GDP per capita by continent over time

Bar plots

Visualizing median GDP per capita by continent

Visualizing GDP per capita by country in Oceania

Histograms

Visualizing population

Visualizing population with x-axis on a log scale

Boxplots

Comparing GDP per capita across continents

Adding a title to your graph

Conclusion

Conclusion

Transforming and visualizing data with R

Next steps: Data visualization

Enjoy your data science journey!