Data Visualizations with ggplot2 in R/

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Line Chart

Line Chart

Learn how to create line charts with ggplot2.

We'll cover the following...

Basic line chart in ggplot2

In this lesson, we’ll build line charts, which are great for showing trends and are frequently used in time series.

Let’s begin with importing the gapminder package, which contains a subset of the gapminder dataset on life expectancy, GDP per capita, and population by country. We’ll also print the first few rows of the gapminder dataset.

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library(gapminder)

head(gapminder)
  • Line 1: We import the gapminder package with the library() command in R. This also loads the built-in gapminder dataset.
  • Line 2: We print the first few rows of the dataset with the head() function.

Note: The library and the dataset have the same name.

Suppose we want to plot a basic line graph for GDP vs. year for a particular country, the United States. However, because there is no column with the GDP values in the dataset, we’ll need to calculate the corresponding values from the entire dataset.

To get the values we need for building a line plot, we have to use some of the dplyr functions for processing the required data, as shown below:

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us_data <- gapminder %>%
  filter(continent == "Americas", country == "United States")
  • Line 1: We create a new data frame, us_data, to store the output. We use the pipe operator, %>%, to pass the gapminder dataset to the filter() function.
  • Line 2: The filter() function filters the data for the continent Americas and the country United States.

Note: In the above code, we use the %>% pipe operator in R, which is written as (percentage—larger than—percentage) to pass what’s on the left as a first argument to the function on the right.

Another useful function we need to use here is the mutate() function, which modifies or creates new columns in a data frame.

Next, we’ll create a new column titled gdp (Gross Domestic Product) and filter and prepare the us_data data frame for generating a line chart for a single country like the United States.

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us_data <- gapminder %>%
  mutate(gdp=gdpPercap*pop)%>%
  filter(continent == "Americas", country == "United States")
head(us_data)
  • Line 1: We create a new data frame, us_data, to store the output and use the %>% operator to pass the gapminder dataset to the mutate() function.
  • Line 2: The mutate() function creates a new column with the name gdp by multiplying the gdpPercap and pop columns.
  • Line 3: We use the %>% operator again to pass the output of the mutate() function to the filter() function, where the filter() function filters the data for the Americas continent and the United States country.
  • Line 4: We print the first few rows of the newly created us_data data frame.

Now, we’ll build our first line graph using the geom_line() function for the filtered data frame.

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ggplot(us_data) +
    geom_line(aes(x = year, y = gdp))
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