1 One Line in a Plot
2 Multiple Lines in a Plot
3 Axis Labels
4 Colours and Legend
- 4.1 Edit the Default Colours
5 Line Sizes
6 Line Types
7 Editing Axes and Gridlines
8 Multiple Data Sets
9 Error Bars
- 9.1 Automatic Methods
10 Save Plot

1 One Line in a Plot

To plot a line graph in ggplot2, you need:

A data frame
A ggplot() object
A geom_line() object with a defined aesthetic mapping (aes())

Here’s an example:

library(ggplot2)

df <- data.frame(
    x = c(0, 1, 2, 3, 4, 5, 6),
    y = c(0, 1, 4, 9, 16, 25, 36)
)

p <- ggplot()
p <- p + geom_line(data = df, aes(x = x, y = y))
print(p)

In the above example, the y-values were simply the x-values squared, so we could have achieved the same thing with an equation as opposed to explicit values:

df <- data.frame(
    x = c(0, 1, 2, 3, 4, 5, 6)
)

p <- ggplot()
p <- p + geom_line(data = df, aes(x = x, y = x**2))
print(p)

2 Multiple Lines in a Plot

If you want to plot additional lines on the same set of axes, add more geom_line() objects:

df <- data.frame(
    x = c(0, 1, 2, 3, 4, 5, 6)
)

p <- ggplot()
# Add a straight line
p <- p + geom_line(data = df, aes(x = x, y = x))
# Add a parabola
p <- p + geom_line(data = df, aes(x = x, y = x**2))
print(p)

3 Axis Labels

To change the title and labels, create ggtitle(), ylab() and xlab() objects:

df <- data.frame(
    x = c(0, 1, 2, 3, 4, 5, 6)
)

p <- ggplot()
p <- p + geom_line(data = df, aes(x = x, y = x))
p <- p + geom_line(data = df, aes(x = x, y = x**2))
p <- p + ggtitle("y = x and y = x^2")
p <- p + ylab("y-values")
p <- p + xlab("x-values")
print(p)

Of course, having “x^2” instead of “x²” in the title doesn’t look great. To improve this text formatting we have three options, each with their own pros and cons:

Copy-and-paste glyphs in directly
Use Unicode
Use Latex

Here are examples of each option:

3.1 Glyphs in Labels

This is the simplest option: find the symbols you want (eg by Googling them) and copy-paste them into your code:

df <- data.frame(x = seq(0, 2, 0.01))

p <- ggplot()
p <- p + geom_line(data = df, aes(x = x, y = x))
p <- p + geom_line(data = df, aes(x = x, y = x**2))
p <- p + ggtitle("β = α and β = α² for 0 ≥ α ≥ 2")
p <- p + ylab("Output, β (mΩ)")
p <- p + xlab("Input, α (μs)")
print(p)

3.2 Unicode in Labels

A slightly more complicated option is to look up the Unicode codes for the symbols and use those prepended by a " to let R know you want to interpret it as Unicode:

df <- data.frame(x = seq(0, 2, 0.01))

p <- ggplot()
p <- p + geom_line(data = df, aes(x = x, y = x))
p <- p + geom_line(data = df, aes(x = x, y = x**2))
p <- p + ggtitle("\U03B2 = \U03B1 and \U03B2 = \U03B1\U00B2 for 0 \U2265 \U03B1 \U2265 2")
p <- p + ylab("Output, \U03B2 (m\U03A9)")
p <- p + xlab("Input, \U03B1 (\U03BCs)")
print(p)

3.3 Latex in Labels

By using the latex2exp package, you can use the full power of Latex to generate proper-looking labels. Note, however, that backslashes have a special meaning in R and you need to escape this by using a second backslash (an escaping backslash) in order to allow the first backslash be interpreted as such by Latex:

library(latex2exp)

df <- data.frame(x = seq(0, 2, 0.01))

p <- ggplot()
p <- p + geom_line(data = df, aes(x = x, y = x))
p <- p + geom_line(data = df, aes(x = x, y = x**2))
p <- p + ggtitle(TeX("\\beta = \\alpha and \\beta = \\alpha$^2$ for 0\\geq\\alpha\\geq{}2"))
p <- p + ylab(TeX("Output, \\beta (m\\Omega)"))
p <- p + xlab(TeX("Input, \\alpha (\\mu{}s)"))
print(p)

4 Colours and Legend

Colours and legends are added automatically by ggplot2:

df <- data.frame(
    human_age = c(1:16, 1:16, 1:16),
    dog_age = c(
        15, 24, 28, 32, 36, 45, 50, 55, 61, 66, 72, 77, 82, 88, 93, 120,
        15, 24, 28, 32, 36, 42, 47, 51, 56, 60, 65, 69, 74, 78, 83, 87,
        15, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80
    ),
    dog_size = c(rep("Large", 16), rep("Medium", 16), rep("Small", 16))
)

p <- ggplot(
    df, aes(x = human_age, y = dog_age, group = dog_size, col = dog_size)
)
p <- p + geom_line()
p <- p + ggtitle("How Old is a Dog in Dog Years?")
p <- p + ylab("Age in Dog Years")
p <- p + xlab("Age in Human Years")
p <- p + labs(colour = "Dog Size")
print(p)

FYI, the above plot comes from this page.

4.1 Edit the Default Colours

If you don’t like the colours ggplot2 chooses for you, you can change them with the scale_color_manual() function:

p <- ggplot(
    df, aes(x = human_age, y = dog_age, group = dog_size, col = dog_size)
)
p <- p + geom_line()
p <- p + ggtitle("How Old is a Dog in Dog Years?")
p <- p + ylab("Age in Dog Years")
p <- p + xlab("Age in Human Years")
p <- p + labs(colour = "Dog Size")
p <- p + scale_color_manual(values = c("blue", "red", "orange"))
print(p)

5 Line Sizes

The thicknesses of the lines can be changed with the scale_size_manual() function:

p <- ggplot(
    df, aes(
        x = human_age, y = dog_age,
        group = dog_size, colour = dog_size, size = dog_size
    )
)
p <- p + geom_line()
p <- p + ggtitle("How Old is a Dog in Dog Years?")
p <- p + ylab("Age in Dog Years")
p <- p + xlab("Age in Human Years")
p <- p + labs(colour = "Dog Size", size = "Dog Size")
p <- p + scale_color_manual(values = c("blue", "red", "orange"))
p <- p + scale_size_manual(values = c(3, 2, 1))
print(p)

6 Line Types

The style of the lines can be changed with the scale_linetype_manual() function:

p <- ggplot(
    df, aes(
        x = human_age, y = dog_age,
        group = dog_size, colour = dog_size,
        size = dog_size, linetype = dog_size
    )
)
p <- p + geom_line()
p <- p + ggtitle("How Old is a Dog in Dog Years?")
p <- p + ylab("Age in Dog Years")
p <- p + xlab("Age in Human Years")
p <- p + labs(colour = "Dog Size", size = "Dog Size", linetype = "Dog Size")
p <- p + scale_color_manual(values = c("blue", "red", "orange"))
p <- p + scale_size_manual(values = c(1, 1, 1))
p <- p + scale_linetype_manual(values = c("twodash", "longdash", "dotted"))
print(p)

7 Editing Axes and Gridlines

p <- ggplot(
    df, aes(
        x = human_age, y = dog_age,
        group = dog_size, colour = dog_size,
        size = dog_size, linetype = dog_size
    )
)
p <- p + geom_line()
p <- p + ggtitle("How Old is a Dog in Dog Years?")
p <- p + ylab("Age in Dog Years")
p <- p + xlab("Age in Human Years")
p <- p + labs(colour = "Dog Size", size = "Dog Size", linetype = "Dog Size")
p <- p + scale_color_manual(values = c("blue", "red", "orange"))
p <- p + scale_size_manual(values = c(1, 1, 1))
p <- p + scale_linetype_manual(values = c("twodash", "longdash", "dotted"))
p <- p + theme(panel.grid.major = element_blank())
p <- p + theme(panel.grid.minor = element_blank())
p <- p + scale_y_continuous(limits = c(0, 120))
p <- p + scale_x_continuous(limits = c(0, 16))
print(p)

8 Multiple Data Sets

For this example, the raw data will be imported from a CSV file:

# Import raw data
df <- read.csv(
    "https://github.com/rowannicholls/rowannicholls.github.io/blob/master/R/graphs/ggplot2/Points.csv?raw=true",
    check.names = FALSE
)

This CSV contains the number of log points for each team in the English Premier League for each match week of the 2018-19 season:

p <- ggplot(df, aes(x = matchweek, y = points, group = team, col = team))
p <- p + geom_line()
p <- p + ggtitle("Premier League 2018-19 Log Points")
p <- p + ylab("Points")
p <- p + xlab("Match Week")
p <- p + labs(colour = "")
p <- p + scale_color_manual(
    values = c(
        "#ff0000", "#8b0304", "#005daa", "#80bfff", "#224781", "#0000dd",
        "#0a4af5", "#274488", "#000000", "#176fc0", "#0101e8", "#dd0000",
        "#6caddf", "#e80909", "#000000", "#ed1a3b", "#132257", "#fbee23",
        "#7f0000", "#fdbc02"
    )
)
print(p)

9 Error Bars

For this example, use the pre-installed ‘ToothGrowth’ data set. This has data from a study on the effect of vitamin C on tooth growth in guinea pigs. Here’s what the head of the data frame looks like:

head(ToothGrowth)

##    len supp dose
## 1  4.2   VC  0.5
## 2 11.5   VC  0.5
## 3  7.3   VC  0.5
## 4  5.8   VC  0.5
## 5  6.4   VC  0.5
## 6 10.0   VC  0.5

…and here is the data plotted with its associated error bars:

df <- ToothGrowth

# Initialise vector to store maximums for each group
upper <- vector()
# Initialise vector to store minimums for each group
lower <- vector()
# Initialise vector to store averages for each group
average <- vector()
# Initialise vector to store sample sizes for each group
n <- vector()
# Analyse each dosage group
doses <- unique(df$dose)
for (dosage in doses) {
    sub <- subset(df, dose == dosage)
    upper <- c(upper, max(sub$len))
    lower <- c(lower, min(sub$len))
    average <- c(average, mean(sub$len))
    n <- c(n, length(sub$len))
}
# Get values of error bars
ytop <- max(upper)
ybottom <- min(lower)
yrange <- ytop - ybottom
df <- data.frame(doses, average, upper, lower)

# Plot
p <- ggplot(df, aes(x = doses, y = average))
p <- p + geom_point(size = 4)
p <- p + geom_line()
p <- p + geom_errorbar(aes(ymin = lower, ymax = upper), width=0.2)
p <- p + annotate(
    'text', x = doses, y = average - 0.1 * yrange,
    label = paste('mean =', average)
)
p <- p + annotate(
    'text', x = doses, y = ybottom - 0.1 * yrange, label = paste('n =', n)
)
# Add more space at the bottom in order to fit the annotations
p <- p + ylim(ybottom - 0.1 * yrange, ytop)

print(p)

9.1 Automatic Methods

A similar graph can be plotted with help from the ggpubr package. This approach can be useful for certain plots but can make it more difficult to control its look.

library(ggpubr)

ggline(
    ToothGrowth, x = "dose", y = "len", add = c("mean_ci"), color = "supp",
    palette = "jco"
)

10 Save Plot

Finally, save your plot to your computer as an image using one of the following (depending on what format you want the image to be in):

png("File Name.png")
pdf("File Name.pdf")
ggsave("File Name.png")

If you use one of the first two, it must come before you start plotting the graph (ie before you call ggplot()). If you use the last one (ggsave()) it must come after you’ve plotted the graph.

⇦ Back

Graphs in R using ggplot2:Line Plots