10 Examples to Master ggplot2: Line plots | by Soner Yıldırım | Oct, 2022

By Jessie Hobb On Oct 29, 2022

Plotting package for R

How you deliver information is just as important as the information itself. Data visualization is an imperative tool for delivering information, storytelling, or analysis in data science.

The two biggest players in the data science ecosystem are Python and R. Both have numerous packages to expedite and simplify the common tasks.

In this article, we will go over 10 examples to learn how to create and customize line plots with ggplot2, which is a data visualization package in tidyverse, a collection of R packages for data science.

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We will use 3 different datasets in the examples. You can download them from the datasets repository on my GitHub page.

The first one is a CSV file that contains the Apple stock prices in 2022. Let’s first create a data table by using the fread function of the data table package.

library(data.table)
library(ggplot2)apple <- fread("datasets/apple_stock_prices_2022.csv")head(apple)
# output         Date   High    Low   Open  Close    Volume Adj Close
1: 2022-01-03 182.88 177.71 177.83 182.01 104487900  181.2599
2: 2022-01-04 182.94 179.12 182.63 179.70  99310400  178.9595
3: 2022-01-05 180.17 174.64 179.61 174.92  94537600  174.1992
4: 2022-01-06 175.30 171.64 172.70 172.00  96904000  171.2912
5: 2022-01-07 174.14 171.03 172.89 172.17  86709100  171.4605
6: 2022-01-10 172.50 168.17 169.08 172.19 106765600  171.4804

Example 1

We will create a simple line plot that shows the data on the x-axis and closing price on the y-axis.

ggplot(apple, aes(x = Date, y = Close)) + 
geom_line()

The ggplot function specifies the data and the mappings to x and y. The aes represents aesthetic mappings that describe how variables in the data are mapped to visual properties of geoms (e.g. geom_line).

The geom_line is the function to draw a line plot. Here is the output of the above code snippet:

Example 2

We can do many customizations on the appearance. Let’s change the line size, and color, which can be done in the geom_line function.

ggplot(apple, aes(x = Date, y = Close)) + 
geom_line(size = 1.2, color = "blue")

We can also make it a dashed line using the linestyle parameter (linestyle = “dashed”).

Example 3

The range on the y axis is automatically defined based on the values in the dataset. However, it can be changed using the ylim function.

The defaults are usually fine but we sometimes need to adjust them to keep a standard between multiple plots or have an axis starting from zero. Let’s set the range to 100–200.

ggplot(apple, aes(x = Date, y = Close)) + 
geom_line(color = "darkgreen") + 
ylim(100, 200)

Example 4

We can add points to indicate the location of data points. This is helpful when we do not have a lot of data points (i.e. the density of observations is low).

In this example, we will use the measurements dataset.

measurements <- fread("datasets/measurements.csv")measurements
# output
day value
1:   1    80
2:   2    93
3:   3    94
4:   4    76
5:   5    63
6:   6    64
7:   8    85
8:   9    64
9:  10    95

Let’s create a line plot that shows the days on the x-axis and the values on the y-axis. We will also add points using the geom_point function.

ggplot(measurements, aes(x = day, y = value)) + 
geom_line() + 
geom_point()

The points are placed where we have an observation in the dataset. For instance, the dataset does not have day 7 so it is not shown.

Example 5

In the previous example, the x values of the observations are not very clear. In order to show each day value on the x-axis, we can convert it to a factor and use the group parameter of the ggplot function.

measurements[, day := factor(day)]ggplot(measurements, aes(x = day, y = value, group = 1)) + 
geom_line() + 
geom_point()

Example 6

We can have multiple lines on a line plot. We will use another dataset for this example, which contains the stock prices of Apple and Google in September, 2022.

stock <- fread("datasets/apple_google_stock_prices_092022.csv")head(stock)
# output         Date   High    Low   Open  Close   Volume Adj Close Stock
1: 2022-09-01 158.42 154.67 156.64 157.96 74229900    157.96  AAPL
2: 2022-09-02 160.36 154.97 159.75 155.81 76905200    155.81  AAPL
3: 2022-09-06 157.09 153.69 156.47 154.53 73714800    154.53  AAPL
4: 2022-09-07 156.67 153.61 154.82 155.96 87449600    155.96  AAPL
5: 2022-09-08 156.36 152.68 154.64 154.46 84923800    154.46  AAPL
6: 2022-09-09 157.82 154.75 155.47 157.37 68028800    157.37  AAPL

The stock column indicates the name of the stock.

For each day, we have two different values, one for Apple and one for Google. Thus, if we plot the date and closing price as we did earlier, we end up having a plot as shown below:

We need to show the Apple and Google stock values with different lines. There are a few different ways of doing this. For instance, we can use the colour parameter and specify the column that differentiates Apple and Google.

ggplot(stock, aes(x = Date, y = Close, colour = Stock)) + 
geom_line()

Example 7

Let’s recreate the previous plot but using different line styles for Apple and Google. We just need to use the linetype parameter instead of the colour.

ggplot(stock, aes(x = Date, y = Close, linetype = Stock)) + 
geom_line(size = 1.2)

Example 8

In examples 4 and 5, we added points to mark observations in the dataset. The size and shape of these points can also be customized.

Let’s add points to the plot in example 7 and also change the value range for y-axis.

ggplot(stock, aes(x = Date, y = Close, color = Stock)) + 
geom_line() +
geom_point(size = 3, shape = 22, fill = "white") +
ylim(90, 200)

Example 9

We may want to change the default axis labels or add a title to a plot. Let’s make our plot more informative and appealing by doing so.

The labs function can be used for adding a title and subtitle. The axis labels can be changed using the xlab and ylab functions.

ggplot(stock, aes(x = Date, y = Close, color = Stock)) + 
geom_line(size = 1) +
labs(title = "Apple vs Google Stock Prices",
subtitle = "September, 2022") +
xlab("") +
ylab("Closing Price")

Example 10

We can add a theme to the plots, which allows for making a lot of customizations including:

Changing the font size and style of title and subtitle
Changing the font size and style of axis labels
Changing the font size, style, and orientation of tick marks

Let’s use these to customize the plot in the previous example.

ggplot(stock, aes(x = Date, y = Close, color = Stock)) + 
geom_line(size = 1) +
labs(title = "Apple vs Google Stock Prices",
subtitle = "September, 2022") +
xlab("") +
ylab("Closing Price") +
theme(
plot.title = element_text(size = 18, face = "bold.italic"),
plot.subtitle = element_text(size = 16, face = "bold.italic"),
axis.title.y = element_text(size = 14, face = "bold"),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12)
)

Ggplot2 is a highly efficient library that offers a great amount of flexibility. I think it is similar to Matplotlib in terms of how we can customize pretty much anything on a plot.

The examples in this article cover most of what you need to create and customize line plots. There will be some edge cases where you need to do some further customizations but you can worry about them when it comes to that point.

Thank you for reading. Please let me know if you have any feedback.