R Graphics Cookbook

Chapter 4. Line Graphs

Line graphs are typically used for visualizing how one continuous variable, on the y-axis, changes in relation to another continuous variable, on the x-axis. Often the x variable represents time, but it may also represent some other continuous quantity, like the amount of a drug administered to experimental subjects.

As with bar graphs, there are exceptions. Line graphs can also be used with a discrete variable on the x-axis. This is appropriate when the variable is ordered (e.g., “small”, “medium”, “large”), but not when the variable is unordered (e.g., “cow”, “goose”, “pig”). Most of the examples in this chapter use a continuous x variable, but we’ll see one example where the variable is converted to a factor and thus treated as a discrete variable.

Making a Basic Line Graph

Problem

You want to make a basic line graph.

Solution

Use ggplot() with geom_line(), and specify what variables you mapped to x and y (Figure 4-1):

ggplot(BOD, aes(x=Time, y=demand)) + geom_line()

Figure 4-1. Basic line graph

Discussion

In this sample data set, the x variable, Time, is in one column and the y variable, demand, is in another:

BOD

 Time demand
    1    8.3
    2   10.3
    3   19.0
    4   16.0
    5   15.6
    7   19.8

Line graphs can be made with discrete (categorical) or continuous (numeric) variables on the x-axis. In the example here, the variable demand is numeric, but it could be treated as a categorical variable by converting it to a factor with factor() (Figure 4-2). When the x variable is a factor, you must also use aes(group=1) to ensure that ggplot() knows that the data points belong together and should be connected with a line (see Making a Line Graph with Multiple Lines for an explanation of why group is needed with factors):

BOD1 <- BOD # Make a copy of the data
BOD1$Time <- factor(BOD1$Time)
ggplot(BOD1, aes(x=Time, y=demand, group=1)) + geom_line()

Figure 4-2. Basic line graph with a factor on the x-axis (notice that no space is allocated on the x-axis for 6)

In the BOD data set there is no entry for Time=6, so there is no level 6 when Time is converted to a factor. Factors hold categorical values, and in that context, 6 is just another value. It happens to not be in the data set, so there’s no space for it on the x-axis.

With ggplot2, the default y range of a line graph is just enough to include the y values in the data. For some kinds of data, it’s better to have the y range start from zero. You can use ylim() to set the range, or you can use expand_limits() to expand the range to include a value. This will set the range from zero to the maximum value of the demand column in BOD (Figure 4-3):

# These have the same result
ggplot(BOD, aes(x=Time, y=demand)) + geom_line() + ylim(0, max(BOD$demand))
ggplot(BOD, aes(x=Time, y=demand)) + geom_line() + expand_limits(y=0)

Figure 4-3. Line graph with manually set y range

See Also

See Setting the Range of a Continuous Axis for more on controlling the range of the axes.

Adding Points to a Line Graph

Problem

You want to add points to a line graph.

Solution

Add geom_point() (Figure 4-4):

ggplot(BOD, aes(x=Time, y=demand)) + geom_line() + geom_point()

Figure 4-4. Line graph with points

Discussion

Sometimes it is useful to indicate each data point on a line graph. This is helpful when the density of observations is low, or when the observations do not happen at regular intervals. For example, in the BOD data set there is no entry for Time=6, but this is not apparent from just a bare line graph (compare Figure 4-3 with Figure 4-4).

In the worldpop data set, the intervals between each data point are not consistent. In the far past, the estimates were not as frequent as they are in the more recent past. Displaying points on the graph illustrates when each estimate was made (Figure 4-5):

library(gcookbook) # For the data set

ggplot(worldpop, aes(x=Year, y=Population)) + geom_line() + geom_point()

# Same with a log y-axis
ggplot(worldpop, aes(x=Year, y=Population)) + geom_line() + geom_point() +
    scale_y_log10()

Figure 4-5. Top: points indicate where each data point is; bottom: the same data with a log y-axis

With the log y-axis, you can see that the rate of proportional change has increased in the last thousand years. The estimates for the years before 0 have a roughly constant rate of change of 10 times per 5,000 years. In the most recent 1,000 years, the population has increased at a much faster rate. We can also see that the population estimates are much more frequent in recent times—and probably more accurate!

See Also

To change the appearance of the points, see Changing the Appearance of Points.

Making a Line Graph with Multiple Lines

Problem

You want to make a line graph with more than one line.

Solution

In addition to the variables mapped to the x- and y-axes, map another (discrete) variable to colour or linetype, as shown in Figure 4-6:

# Load plyr so we can use ddply() to create the example data set
library(plyr)
# Summarize the ToothGrowth data
tg <- ddply(ToothGrowth, c("supp", "dose"), summarise, length=mean(len))

# Map supp to colour
ggplot(tg, aes(x=dose, y=length, colour=supp)) + geom_line()

# Map supp to linetype
ggplot(tg, aes(x=dose, y=length, linetype=supp)) + geom_line()

Figure 4-6. Left: a variable mapped to colour; right: a variable mapped to linetype

Discussion

The tg data has three columns, including the factor supp, which we mapped to colour and linetype:

tg

 supp dose length
   OJ  0.5  13.23
   OJ  1.0  22.70
   OJ  2.0  26.06
   VC  0.5   7.98
   VC  1.0  16.77
   VC  2.0  26.14

str(tg)

'data.frame': 6 obs. of  3 variables:
 $ supp  : Factor w/ 2 levels "OJ","VC": 1 1 1 2 2 2
 $ dose  : num  0.5 1 2 0.5 1 2
 $ length: num  13.23 22.7 26.06 7.98 16.77 ...

Note

If the x variable is a factor, you must also tell ggplot() to group by that same variable, as described momentarily.

Line graphs can be used with a continuous or categorical variable on the x-axis. Sometimes the variable mapped to the x-axis is conceived of as being categorical, even when it’s stored as a number. In the example here, there are three values of dose: 0.5, 1.0, and 2.0. You may want to treat these as categories rather than values on a continuous scale. To do this, convert dose to a factor (Figure 4-7):

ggplot(tg, aes(x=factor(dose), y=length, colour=supp, group=supp)) + geom_line()

Figure 4-7. Line graph with continuous x variable converted to a factor

Notice the use of group=supp. Without this statement, ggplot() won’t know how to group the data together to draw the lines, and it will give an error:

ggplot(tg, aes(x=factor(dose), y=length, colour=supp)) + geom_line()

  geom_path: Each group consists of only one observation. Do you need to adjust the 
group aesthetic?

Another common problem when the incorrect grouping is used is that you will see a jagged sawtooth pattern, as in Figure 4-8:

ggplot(tg, aes(x=dose, y=length)) + geom_line()

Figure 4-8. A sawtooth pattern indicates improper grouping

This happens because there are multiple data points at each y location, and ggplot() thinks they’re all in one group. The data points for each group are connected with a single line, leading to the sawtooth pattern. If any discrete variables are mapped to aesthetics like colour or linetype, they are automatically used as grouping variables. But if you want to use other variables for grouping (that aren’t mapped to an aesthetic), they should be used with group.

Note

When in doubt, if your line graph looks wrong, try explicitly specifying the grouping variable with group. It’s common for problems to occur with line graphs because ggplot() is unsure of how the variables should be grouped.

If your plot has points along with the lines, you can also map variables to properties of the points, such as shape and fill (Figure 4-9):

ggplot(tg, aes(x=dose, y=length, shape=supp)) + geom_line() +
    geom_point(size=4)           # Make the points a little larger

ggplot(tg, aes(x=dose, y=length, fill=supp)) + geom_line() +
    geom_point(size=4, shape=21) # Also use a point with a color fill

Figure 4-9. Left: line graph with different shapes; right: with different colors

Sometimes points will overlap. In these cases, you may want to dodge them, which means their positions will be adjusted left and right (Figure 4-10). When doing so, you must also dodge the lines, or else only the points will move and they will be misaligned. You must also specify how far they should move when dodged:

ggplot(tg, aes(x=dose, y=length, shape=supp)) +
    geom_line(position=position_dodge(0.2)) +         # Dodge lines by 0.2
    geom_point(position=position_dodge(0.2), size=4)  # Dodge points by 0.2

Figure 4-10. Dodging to avoid overlapping points

Changing the Appearance of Lines

Problem

You want to change the appearance of the lines in a line graph.

Solution

The type of line (solid, dashed, dotted, etc.) is set with linetype, the thickness (in mm) with size, and the color of the line with colour.

These properties can be set (as shown in Figure 4-11) by passing them values in the call to geom_line():

ggplot(BOD, aes(x=Time, y=demand)) +
    geom_line(linetype="dashed", size=1, colour="blue")

Figure 4-11. Line graph with custom linetype, size, and colour

If there is more than one line, setting the aesthetic properties will affect all of the lines. On the other hand, mapping variables to the properties, as we saw in Making a Line Graph with Multiple Lines, will result in each line looking different. The default colors aren’t the most appealing, so you may want to use a different palette, as shown in Figure 4-12, by using scale_colour_brewer() or scale_colour_manual():

# Load plyr so we can use ddply() to create the example data set
library(plyr)
# Summarize the ToothGrowth data
tg <- ddply(ToothGrowth, c("supp", "dose"), summarise, length=mean(len))

ggplot(tg, aes(x=dose, y=length, colour=supp)) +
    geom_line() +
    scale_colour_brewer(palette="Set1")

Figure 4-12. Using a palette from RColorBrewer

Discussion

To set a single constant color for all the lines, specify colour outside of aes(). The same works for size, linetype, and point shape (Figure 4-13). You may also have to specify the grouping variable:

# If both lines have the same properties, you need to specify a variable to
# use for grouping
ggplot(tg, aes(x=dose, y=length, group=supp)) +
    geom_line(colour="darkgreen", size=1.5)

# Since supp is mapped to colour, it will automatically be used for grouping
ggplot(tg, aes(x=dose, y=length, colour=supp)) +
    geom_line(linetype="dashed") +
    geom_point(shape=22, size=3, fill="white")

Figure 4-13. Left: line graph with constant size and color; right: with supp mapped to colour, and with points added

See Also

For more information about using colors, see Chapter 12.

Changing the Appearance of Points

Problem

You want to change the appearance of the points in a line graph.

Solution

In geom_point(), set the size, shape, colour, and/or fill outside of aes() (the result is shown in Figure 4-14):

ggplot(BOD, aes(x=Time, y=demand)) +
    geom_line() +
    geom_point(size=4, shape=22, colour="darkred", fill="pink")

Figure 4-14. Points with custom size, shape, color, and fill

Discussion

The default shape for points is a solid circle, the default size is 2, and the default colour is "black". The fill color is relevant only for some point shapes (numbered 21–25), which have separate outline and fill colors (see Using Different Point Shapes for a chart of shapes). The fill color is typically NA, or empty; you can fill it with white to get hollow-looking circles, as shown in Figure 4-15:

ggplot(BOD, aes(x=Time, y=demand)) +
    geom_line() +
    geom_point(size=4, shape=21, fill="white")

Figure 4-15. Points with a white fill

If the points and lines have different colors, you should specify the points after the lines, so that they are drawn on top. Otherwise, the lines will be drawn on top of the points.

For multiple lines, we saw in Making a Line Graph with Multiple Lines how to draw differently colored points for each group by mapping variables to aesthetic properties of points, inside of aes(). The default colors are not very appealing, so you may want to use a different palette, using scale_colour_brewer() or scale_colour_manual(). To set a single constant shape or size for all the points, as in Figure 4-16, specify shape or size outside of aes():

# Load plyr so we can use ddply() to create the example data set
library(plyr)
# Summarize the ToothGrowth data
tg <- ddply(ToothGrowth, c("supp", "dose"), summarise, length=mean(len))

# Save the position_dodge specification because we'll use it multiple times
pd <- position_dodge(0.2)

ggplot(tg, aes(x=dose, y=length, fill=supp)) +
    geom_line(position=pd) +
    geom_point(shape=21, size=3, position=pd) +
    scale_fill_manual(values=c("black","white"))

Figure 4-16. Line graph with manually specified fills of black and white, and a slight dodge

See Also

See Using Different Point Shapes for more on using different shapes, and Chapter 12 for more about colors.

Making a Graph with a Shaded Area

Problem

You want to make a graph with a shaded area.

Solution

Use geom_area() to get a shaded area, as in Figure 4-17:

# Convert the sunspot.year data set into a data frame for this example
sunspotyear <- data.frame(
    Year     = as.numeric(time(sunspot.year)),
    Sunspots = as.numeric(sunspot.year)
)

ggplot(sunspotyear, aes(x=Year, y=Sunspots)) + geom_area()

Figure 4-17. Graph with a shaded area

Discussion

By default, the area will be filled with a very dark grey and will have no outline. The color can be changed by setting fill. In the following example, we’ll set it to "blue", and we’ll also make it 80% transparent by setting alpha to 0.2. This makes it possible to see the grid lines through the area, as shown in Figure 4-18. We’ll also add an outline, by setting colour:

ggplot(sunspotyear, aes(x=Year, y=Sunspots)) +
    geom_area(colour="black", fill="blue", alpha=.2)

Figure 4-18. Graph with a semitransparent shaded area and an outline

Having an outline around the entire area might not be desirable, because it puts a vertical line at the beginning and end of the shaded area, as well as one along the bottom. To avoid this issue, we can draw the area without an outline (by not specifying colour), and then layer a geom_line() on top, as shown in Figure 4-19:

ggplot(sunspotyear, aes(x=Year, y=Sunspots)) +
    geom_area(fill="blue", alpha=.2) +
    geom_line()

Figure 4-19. Line graph with a line just on top, using geom_line()

See Also

See Chapter 12 for more on choosing colors.

Making a Stacked Area Graph

Problem

You want to make a stacked area graph.

Solution

Use geom_area() and map a factor to fill (Figure 4-20):

library(gcookbook) # For the data set

ggplot(uspopage, aes(x=Year, y=Thousands, fill=AgeGroup)) + geom_area()

Figure 4-20. Stacked area graph

Discussion

The sort of data that is plotted with a stacked area chart is often provided in a wide format, but ggplot2() requires data to be in long format. To convert it, see Converting Data from Wide to Long.

In the example here, we used the uspopage data set:

uspopage

 Year AgeGroup Thousands
 1900       <5      9181
 1900     5-14     16966
 1900    15-24     14951
 1900    25-34     12161
 1900    35-44      9273
 1900    45-54      6437
 1900    55-64      4026
 1900      >64      3099
 1901       <5      9336
 1901     5-14     17158
...

The default order of legend items is the opposite of the stacking order. The legend can be reversed by setting the breaks in the scale. This version of the chart (Figure 4-21) reverses the legend order, changes the palette to a range of blues, and adds thin (size=.2) lines between each area. It also makes the filled areas semitransparent (alpha=.4), so that it is possible to see the grid lines through them:

ggplot(uspopage, aes(x=Year, y=Thousands, fill=AgeGroup)) +
    geom_area(colour="black", size=.2, alpha=.4) +
    scale_fill_brewer(palette="Blues", breaks=rev(levels(uspopage$AgeGroup)))

Figure 4-21. Reversed legend order, lines, and a different palette

To reverse the stacking order, we’ll put order=desc(AgeGroup) inside of aes() (Figure 4-22):

library(plyr) # For the desc() function
ggplot(uspopage, aes(x=Year, y=Thousands, fill=AgeGroup, order=desc(AgeGroup))) +
    geom_area(colour="black", size=.2, alpha=.4) +
    scale_fill_brewer(palette="Blues")

Figure 4-22. Reversed stacking order

Since each filled area is drawn with a polygon, the outline includes the left and right sides. This might be distracting or misleading. To get rid of it (Figure 4-23), first draw the stacked areas without an outline (by leaving colour as the default NA value), and then add a geom_line() on top:

ggplot(uspopage, aes(x=Year, y=Thousands, fill=AgeGroup, order=desc(AgeGroup))) +
    geom_area(colour=NA, alpha=.4) +
    scale_fill_brewer(palette="Blues") +
    geom_line(position="stack", size=.2)

Figure 4-23. No lines on the left and right of the graph

See Also

See Converting Data from Wide to Long for more on converting data from wide to long format.

For more on reordering factor levels, see Changing the Order of Factor Levels.

See Chapter 12 for more on choosing colors.

Making a Proportional Stacked Area Graph

Problem

You want to make a stacked area graph with the overall height scaled to a constant value.

Solution

First, calculate the proportions. In this example, we’ll use ddply() to break uspopage into groups by Year, then calculate a new column, Percent. This value is the Thousands for each row, divided by the sum of Thousands for each Year group, multiplied by 100 to get a percent value:

library(gcookbook) # For the data set
library(plyr)      # For the ddply() function

# Convert Thousands to Percent
uspopage_prop <- ddply(uspopage, "Year", transform,
                       Percent = Thousands / sum(Thousands) * 100)

Once we’ve calculated the proportions, plotting is the same as with a regular stacked area graph (Figure 4-24):

ggplot(uspopage_prop, aes(x=Year, y=Percent, fill=AgeGroup)) +
    geom_area(colour="black", size=.2, alpha=.4) +
    scale_fill_brewer(palette="Blues", breaks=rev(levels(uspopage$AgeGroup)))

Figure 4-24. A proportional stacked area graph

Discussion

Let’s take a closer look at the data and how it was summarized:

uspopage

 Year AgeGroup Thousands
 1900       <5      9181
 1900     5-14     16966
 1900    15-24     14951
 1900    25-34     12161
 1900    35-44      9273
 1900    45-54      6437
 1900    55-64      4026
 1900      >64      3099
 1901       <5      9336
 1901     5-14     17158
...

We’ll use ddply() to split it into separate data frames for each value of Year, then apply the transform() function to each piece and calculate the Percent for each piece. Then ddply() puts all the data frames back together:

uspopage_prop <- ddply(uspopage, "Year", transform,
                       Percent = Thousands / sum(Thousands) * 100)

 Year AgeGroup Thousands   Percent
 1900       <5      9181 12.065340
 1900     5-14     16966 22.296107
 1900    15-24     14951 19.648067
 1900    25-34     12161 15.981549
 1900    35-44      9273 12.186243
 1900    45-54      6437  8.459274
 1900    55-64      4026  5.290825
 1900      >64      3099  4.072594
 1901       <5      9336 12.033409
 1901     5-14     17158 22.115385
...

See Also

For more on summarizing data by groups, see Summarizing Data by Groups.

Adding a Confidence Region

Problem

You want to add a confidence region to a graph.

Solution

Use geom_ribbon() and map values to ymin and ymax.

In the climate data set, Anomaly10y is a 10-year running average of the deviation (in Celsius) from the average 1950–1980 temperature, and Unc10y is the 95% confidence interval. We’ll set ymax and ymin to Anomaly10y plus or minus Unc10y (Figure 4-25):

library(gcookbook) # For the data set

# Grab a subset of the climate data
clim <- subset(climate, Source == "Berkeley",
               select=c("Year", "Anomaly10y", "Unc10y"))

clim

Year Anomaly10y Unc10y
 1800     -0.435  0.505
 1801     -0.453  0.493
 1802     -0.460  0.486
...
 2003      0.869  0.028
 2004      0.884  0.029

# Shaded region
ggplot(clim, aes(x=Year, y=Anomaly10y)) +
    geom_ribbon(aes(ymin=Anomaly10y-Unc10y, ymax=Anomaly10y+Unc10y),
                alpha=0.2) +
    geom_line()

Figure 4-25. A line graph with a shaded confidence region

The shaded region is actually a very dark grey, but it is mostly transparent. The transparency is set with alpha=0.2, which makes it 80% transparent.

Discussion

Notice that the geom_ribbon() is before geom_line(), so that the line is drawn on top of the shaded region. If the reverse order were used, the shaded region could obscure the line. In this particular case that wouldn’t be a problem since the shaded region is mostly transparent, but it would be a problem if the shaded region were opaque.

Instead of a shaded region, you can also use dotted lines to represent the upper and lower bounds (Figure 4-26):

# With a dotted line for upper and lower bounds
ggplot(clim, aes(x=Year, y=Anomaly10y)) +
    geom_line(aes(y=Anomaly10y-Unc10y), colour="grey50", linetype="dotted") +
    geom_line(aes(y=Anomaly10y+Unc10y), colour="grey50", linetype="dotted") +
    geom_line()

Figure 4-26. A line graph with dotted lines representing a confidence region

Shaded regions can represent things other than confidence regions, such as the difference between two values, for example.

In the area graphs in Making a Stacked Area Graph, the y range of the shaded area goes from 0 to y. Here, it goes from ymin to ymax.