You probably learned to make a line graph back in high school (or even middle school!). But the ggplot R package can make these graphs come to life. Let’s take a look at how to do this.

First, we need interesting data. For this post, we will use this earth surface temperature data. This dataset is pre-processed so we can explore climate trends right off the bat. Your data may need some coaxing to get it in a form that can be plotted. But once you have the data cleaned and formatted there is so much you can do to visualize it with ggplot.

Here let’s explore the temperature trends in Denmark. The dataset has four Danish cities: Aalborg, Aarhus, Copenhagen, and Odense. Temperatures begin in 1743 and continue through 2013.

The first call to ggplot() defines the dataframe and assigns Year to the x variable and AverageTemperature to the y variable. The next call to geom_point() creates a point for each datum. Next, the geom_line() call adds a line between points. Here we made the line ‘steelblue’ to stand out. We use scale_x_discrete() to display specific years on the x-axis which makes it easier to read the plot. Then facet_wrap() makes a separate panel for each city. The last two steps are labs() to add labels and theme() to adjust the appearance.

require(ggplot2)

line_graph <- ggplot(df_yearly, aes(x = Year, y = AverageTemperature, group = 1)) +
  geom_point() +
  geom_line(color="steelblue") +
  scale_x_discrete(breaks=seq(1743, 2013, 50)) +
  facet_wrap(~City) +
  labs(subtitle = "Mean Annual Temperatures",
       x = "Year",
       y = "Average Temperature") +
  theme_minimal() +
  theme(text = element_text(size = 20))

Thanks for reading and I hope this helps your R project! If there is a topic you would like me to explore, drop me a line through the contact me page.