How to Create a ggplot Line Plot in R

Intro

Line plots are used to show a continous varaible compared to an ordinal varaible. Most commonly line plots are used to show how some varaible changes over time. In this article, we will learn how to create ggplot line plots in R.

If you are in a rush

For those with little time, here is a quick snippet of Line plots. Read on for more details.

library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.1.0     v dplyr   1.0.5
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

data(economics)

ggplot(economics, aes(x = date, y = unemploy)) +
  geom_line()

Loading the Data

For our tutorial, we will use the economics data set that comes with the ggplot package.

library(tidyverse)

data(economics)

## Add year as a factor for later use
economics$year <- format(as.Date(economics$date, format="%d/%m/%Y"),"%Y")

glimpse(economics)

## Rows: 574
## Columns: 7
## $ date     <date> 1967-07-01, 1967-08-01, 1967-09-01, 1967-10-01, 1967-11-01, ~
## $ pce      <dbl> 506.7, 509.8, 515.6, 512.2, 517.4, 525.1, 530.9, 533.6, 544.3~
## $ pop      <dbl> 198712, 198911, 199113, 199311, 199498, 199657, 199808, 19992~
## $ psavert  <dbl> 12.6, 12.6, 11.9, 12.9, 12.8, 11.8, 11.7, 12.3, 11.7, 12.3, 1~
## $ uempmed  <dbl> 4.5, 4.7, 4.6, 4.9, 4.7, 4.8, 5.1, 4.5, 4.1, 4.6, 4.4, 4.4, 4~
## $ unemploy <dbl> 2944, 2945, 2958, 3143, 3066, 3018, 2878, 3001, 2877, 2709, 2~
## $ year     <chr> "1967", "1967", "1967", "1967", "1967", "1967", "1968", "1968~

Creating a Basic ggplot Line Plot

To create a Line plot in ggplot2, we can use the geom_line method after supplying a continuous variable to the y of our aes, aesthetic. In this example, we will plot unemployment over time.

ggplot(economics, aes(x = date, y = unemploy)) +
  geom_line()

We can also flip the plot to orient horizontally by using the coord_flip method.

ggplot(economics, aes(x = date, y = unemploy)) +
  geom_line() +
  coord_flip()

Customizing the ggplot Line Plot

We can customize our Line plots using some parameters on the geom_line method. For example, we can change the color using the color named parameter. Here is an example.

ggplot(economics, aes(x = date, y = unemploy)) +
  geom_line(color = 4,
               fill = 4,
               alpha = 0.25)

## Warning: Ignoring unknown parameters: fill

Adjusting the ggplot Line Plot Labels

We can adjust the title, x-label, and y-label of our Line plot using the labs method. We then pass the title, x and y parameters.

ggplot(economics, aes(x = date, y = unemploy)) +
  geom_line() +
  labs(
    title = "Comparison of Price by Cut",
    x = "Cut",
    y = "Price"
  )

Group by Color

We can color the separate groups of our violin plots by using the fill or colour aesthetic properties. Here is an example of using the colour to assign colors to each factor.

library(ggplot2)


ggplot(economics, aes(x = date, y = unemploy, colour = year)) +
  geom_line()

Facets Groups on a ggplot Line Plot

If we prefer to have separate plots, we can use the facet_ methods in ggplot. For example, here are plots separated by each year.

library(ggplot2)

ggplot(economics, aes(x = date, y = unemploy, colour = year)) +
  geom_line() +
  facet_grid(~year)

Limiting X and Y

If we would like to limit the y values of our plots, we can use the ylimit function

ggplot(economics, aes(x = date, y = unemploy)) +
  geom_line() +
  ylim(5000, 10000)

## Warning: Removed 41 row(s) containing missing values (geom_path).

Scaling X and Y

We can also scale the y axis using the scale_ function from ggplot. Here are some example of a log10 and sqrt scale of the y axis.

ggplot(economics, aes(x = date, y = unemploy)) +
  geom_line() +
  scale_y_log10()

ggplot(economics, aes(x = date, y = unemploy)) +
  geom_line() +
  scale_y_sqrt()

Color and Fill Scales

There are many color options in ggplot. We can use scale_ methods like scale_fill_brewer() to have ggplot automatically assign different themes based on our data set.

library(ggplot2)


ggplot(economics, aes(x = date, y = unemploy, colour = year)) +
  geom_line() +
  scale_fill_brewer()

Customizing the Legend of a ggplot Line Plot

When we have groups, ggplot will add a legend to the plot. We can customize the position of this legend using the theme method and the legend.position parameter. Here are example of moving the legend to the top, bottom, and hiding it.

ggplot(economics, aes(x = date, y = unemploy, colour = year)) +
  geom_line() +
  theme(legend.position="top")

ggplot(economics, aes(x = date, y = unemploy, colour = year)) +
  geom_line() +
  theme(legend.position="bottom")

ggplot(economics, aes(x = date, y = unemploy, colour = year)) +
  geom_line() +
  theme(legend.position="none")

Using Themes with a ggplot Line Plot

If we want to use built in styles for the full plot, ggplot provides themes to add to our plot. Here is an example of adding the theme_classic to our plot.

ggplot(economics, aes(x = date, y = unemploy, colour = year)) +
  geom_line() +
  theme_classic()

Adding Summary Information to a ggplot Line Plot

We can also add summary information to our Line plots to visualize in addition to our distributions. For example, we can use the stat_summary method to display the median like so.

ggplot(economics, aes(x = date, y = unemploy)) +
  geom_line() +
  stat_summary(
    fun.y = median,
    geom = "point",
    size = 2,
    color = "red"
   )

## Warning: `fun.y` is deprecated. Use `fun` instead.

Similarly, we can add the mean to each of our plots.

ggplot(economics, aes(x = date, y = unemploy)) +
  geom_line() +
  stat_summary(
    fun.y = mean,
    geom = "point",
    size = 2,
    color = "blue"
   )

## Warning: `fun.y` is deprecated. Use `fun` instead.