Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Use the `geom_line()` aesthetic to draw line graphs and customize its styling using the `color` parameter. Specify which coordinates to use for each line with the `group` parameter.

• Create your first line graph using `geom_line()`
• Define how different lines are connected using the `group` parameter
• Change the line color of a line graph using the `color` parameter
```ggplot(___) +
geom_line(
mapping = aes(x = ___, y = ___,
group = ___,
color = ___)
)```

## Introduction to line graphs

Line graphs are used to visualize the trajectory of one numeric variable against another. Unlike scatter plots the x- and y-coordinates are not visualized through points but are instead connected through lines. Line graphs are most typically used if one variable changes continuously against another numeric variable which is the case for most time series charts (e.g. prices, customers, CO2 concentration, temperature over time), continuous functions (e.g. sine `sin(x)`) or other near-continuous relationships (real-world supply/demand curves).

## Quiz: Line Graphs

Which of the following statements about line graphs are correct?
• Line graphs are typically used to plot the relationship between categorical and numeric variables.
• Line graphs are typically used to plot variables of type `numeric`.
• For line graphs it is not necessary that the relationship between two variables shows continuity.
• Line graphs can be used to plot time series.
Start Quiz

## Creating a simple line graph

```ggplot(___) +
geom_line(
mapping = aes(x = ___, y = ___,
group = ___,
color = ___)
)```

Japan is among the countries with the highest life expectancy. Using the `gapminder_japan` dataset we determine how the life expectancy in Japan has developed over time. We need to:

1. Specify the dataset within `ggplot()`
2. Define the `geom_line()` plot layer
3. Map the `year` to the x-axis and the life expectancy `lifeExp` to the y-axis with the `aes()` function

Note that the ggplot2 library needs to be loaded first with `library(ggplot2)`.

```library(ggplot2)
ggplot(gapminder_japan) +
geom_line(
mapping = aes(x = year, y = lifeExp)
)```

## Exercise: Plot life expectancy of Brazil

Create your first line graph showing the life expectancy of people from Brazil over time.

1. Use the `ggplot()` function and specify the `gapminder_brazil` dataset as input
2. Add a `geom_line()` layer to the plot
3. Map the `year` to the x-axis and the life expectancy `lifeExp` to the y-axis with the `aes()` function
Start Exercise

```ggplot(___) +
geom_line(
mapping = aes(x = ___, y = ___,
group = ___,
color = ___)
)```

So far we only focused on single lines, but what if we have multiple countries in the dataset and want to somehow differentiate them?

Line graphs are often extended and used for the comparison of two or more lines. Multiple line graphs show the absolute differences between observations but also how the specific trajectories relate to each other. For example, let’s answer the question: How has life expectancy changed in the countries Austria and Hungary over time?

We first filter the dataset for both countries of interest. Then, we set the variable `country` as the `group` argument for the aesthetic mapping. The group argument tells ggplot which observations belong together and should be connected through lines. By specifying the `country` variable ggplot creates a separate line for each country. To make the lines easier to distinguish we also map `color` to the `country` so that each country line has a different color.

```gapminder_comparison <-
filter(gapminder, country %in% c("Austria", "Hungary"))

ggplot(data = gapminder_comparison) +
geom_line(mapping = aes(x = year, y = lifeExp,
group = country,
color = country)
)```

Note that ggplot also separates the lines correctly if only the `color` mapping is specified (the `group` parameter is implicitly set).

## Exercise: Compare life expectancy

Create a line graph to compare the life expectancy `lifeExp` in the countries Japan, Brazil and India.

1. Use the data set `gapminder_comparison` in your `ggplot()` function which contains only data for the countries `Japan`, `Brazil` and `India`.
2. Create a line graph with the `geom_line()` function
3. Map the `year` to the x-axis and the life expectancy `lifeExp` to the y-axis with the `aes()` function
4. Map the `group` and the `color` parameter to the `country` variable.
Start Exercise

## Exercise: Compare populations

Compare the population growth over the last decades in the countries Austria, Hungary and Serbia.

1. Use the data set `gapminder_comparison` in your `ggplot()` function which contains only data for the countries in question.
2. Create a line graph with `geom_line()`
3. Map the `year` to the x-axis and the population `pop` to the y-axis with `aes()`
4. Map the `group` and the `color` parameter to the `country` variable.
Start Exercise

## Quiz: Malformed Plot

```gapminder_comparison <- filter(gapminder, country %in% c("Brazil", "China", "India"))
ggplot(data = gapminder_comparison) +
geom_line(mapping = aes(x = year, y = pop))```
What has gone wrong in this plot?
• The population numbers are scaled differently in the plotted countries
• The `group` aesthetic should be used to map the population `pop` variable.
• The `color` aesthetic should be used to map the population `lifeExp` variable.
• The `group` aesthetic should be used to map the `year` variable.
• The `group` aesthetic should be used to map the `country` variable.
Start Quiz

Create a line graph with ggplot is an excerpt from the course Introduction to R, which is available for free at quantargo.com

VIEW FULL COURSE