Introduction to ggplot2

ggplot2 learning curve is steep but worth the effort

people <- data.frame(weight = c(80, 49, 62, 57),
                     height = c(1.82, 1.58, 1.71, 1.63),
                     gender = c("M", "F", "F", "F"))
people

  weight height gender
1     80   1.82      M
2     49   1.58      F
3     62   1.71      F
4     57   1.63      F

Want to make a scatterplot of height vs. weight:

• each observation is a point
• linear scaling of \( x \) and \( y \) axes
• cartesian coordinate system

\( \Rightarrow \) a plot can be found of as a mapping of data to geometric object (point, line, bar…) and their aesthetic attributes (shape, color, size…) \[ \mbox{height} \rightarrow x \qquad \mbox{weight} \rightarrow y \qquad \mbox{gender} \rightarrow \mbox{color} \]

Also scales and a coordinate system are needed to convert data unit to physical drawing units

Basic idea: specify different parts of the plot, and add them together using the + operator.

library(ggplot2)
ggplot(data = <DATA>, 
       aes(x = <X AXIS VARIABLE>,
           y = <Y AXIS VARIABLE>, ... ), ...) +

  geom_<TYPE>(aes(size = <SIZE VARIABLE>, ...),
                   data = <DATA>,
                   stat = <FUNCTION>,
                   position = <POSITION>,
                   color = <"COLOR">, ...) +

  scale_<AESTHETIC>_<TYPE>(name = <NAME>,
                   breaks = <WHERE>,
                   labels = <LABELS>, ... ) +

  theme(...) +
  facet_<TYPE>(<FORMULA>)

~54,000 round diamonds from http://www.diamondse.info with carat, colour, clarity, cut, total depth, table, depth, width, height, price

data(diamonds)
dim(diamonds)

[1] 53940    10

head(diamonds)

# A tibble: 6 x 10
  carat cut       color clarity depth table price     x     y     z
  <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 0.230 Ideal     E     SI2      61.5   55.   326  3.95  3.98  2.43
2 0.210 Premium   E     SI1      59.8   61.   326  3.89  3.84  2.31
3 0.230 Good      E     VS1      56.9   65.   327  4.05  4.07  2.31
4 0.290 Premium   I     VS2      62.4   58.   334  4.20  4.23  2.63
5 0.310 Good      J     SI2      63.3   58.   335  4.34  4.35  2.75
6 0.240 Very Good J     VVS2     62.8   57.   336  3.94  3.96  2.48

Use a subsample of 1,000 diamonds in the following graphids

Base graphics histogram example:

hist(diamonds$price, main = "", xlab = "Price", breaks = 50)

ggplot2 graphics histogram example:

ggplot(diamonds, aes(x = price)) + geom_histogram(bins = 50)

• title of the axes are guessed from their names

ggplot(diamonds, aes(x = price, fill = color)) + 
  geom_histogram(binwidth = 1000) + facet_wrap( ~ cut) + theme_bw()

• colours are automatically guessed from the factor color
• legend is automatically added
• data are split according to the factor cut (one panel for each type of cut)

## Create ggplot object
MyPlot <- ggplot(diamonds)
class(MyPlot)
summary(MyPlot); MyPlot

## Add aesthetics
MyPlot <- MyPlot + aes(x = carat, y = price, colour = cut)
summary(MyPlot)
MyPlot

## Add layer(s)
MyPlot <- MyPlot + geom_point(alpha=0.3)
summary(MyPlot) ; MyPlot  

MyPlot <- MyPlot + geom_smooth()
summary(MyPlot) ; MyPlot

## Scales for dimensions
MyPlot + scale_y_log10()

## Condition on variables
MyPlot + facet_grid(~ cut)

## More options
MyPlot + ggtitle("First example") + theme_bw()

• Create a plot object (class gg, ggplot2)

• Plot objects can be stored as variables; it's easy to share a plot.

• The plot object cannot be displayed without, at least, one layer

ggplot2 aesthetic = “something you can see”, set with the aes() function

Examples:

• position (i.e., on the x and y axes)
• color (“outside” color)
• fill (“inside” color)
• shape (of points)
• linetype
• size

All geom_XXX require some aesthetics (at least one)

?geom_point
...
Aesthetics
The following aesthetics can be used with geom_point. Aesthetics are mapped 
to variables in the data with the aes function: geom_point(aes(x = var))
x: x position (required)
y: y position (required)
shape: shape of point
colour: border colour
size: size
fill: internal colour
alpha: transparency

All of the following are correct (and equivalent):

ggplot(diamonds, aes(x = carat, y = price, color = cut)) + geom_point()

ggplot(diamonds) +  geom_point(aes(x = carat, y = price, color = cut))

ggplot(diamonds, aes(x = carat, y = price)) + geom_point(aes(color = cut))

Aesthetic mapping:

• Data value determines visual characteristic
• use aes()

ggplot(diamonds, aes(x = carat, y = price, color = clarity)) + geom_point()

Setting:

• Constant value determines visual characteristic
• Use parameter in geom_<TYPE>

ggplot(diamonds, aes(x = carat, y = price)) + geom_point(color = "red")

Tip: Size of bins is 0.2

ggplot2 geometric object = the actual marks put on the plot, a plot must have at least one geom

Examples:

• points: geom_point() for scatterplots, dot plots, etc.
• lines: geom_line() for time series, geom_smooth() for trend lines (spline by default), etc.
• boxplots and histograms: geom_boxplot() and geom_histogram()
• barplots: geom_bar()
• … and many more!

help.search("geom_", package = "ggplot2")

geom_abline       geom_jitter
geom_area             geom_line
geom_bar              geom_linerange 
geom_bin2d          geom_path 
geom_blank          geom_point 
geom_boxplot        geom_pointrange 
geom_contour        geom_polygon 
geom_crossbar       geom_quantile 
geom_density        geom_rect 
geom_density2d    geom_ribbon
geom_errorbar       geom_rug 
geom_errorbarh    geom_segment 
geom_freqpoly         geom_smooth 
geom_hex              geom_step 
geom_histogram    geom_text 
geom_hline          geom_tile
geom_vline        ...

• geom_rect requires xmin, xmax, ymin, ymax
• geom_tile requires x, y and optionnaly handles width and height

p + geom_tile(aes(x = as.numeric(cut), y = as.numeric(color), fill = depth))

/!\ Only the last value of depth is represented…

• geom_raster is a special case of geom_tile with all tiles having the same size

p + geom_raster(aes(x = as.numeric(cut), y = as.numeric(color), fill = depth))

ggplot(diamonds, aes(x = reorder(color, price), y = price, fill = color)) + 
  geom_boxplot(outlier.size = 0) +
  geom_point(aes(fill = color), alpha = 0.1, , shape = 21,
             position = position_jitter(w = .4))

• reorder is not a ggplot2 function. It deals with its first argument as a categorial variable (color) and reorder its level based on the value of a second variable (price). The third argument (FUN, default is mean) is the function to be applied to price for each level of color.

ggplot(diamonds, aes(x = carat, y = price, color = cut)) +
  geom_point(shape = 21) + geom_smooth() + geom_rug()

• Would you do the same (easily) with Base graphics?

check for the option position of geom_bar

Themes handle non-data plot elements like axis labels, plot background, legend appearance, …: theme_gray() (default), theme_bw(), theme_classic(), theme_linedraw(), …

ggplot(diamonds, aes(x = carat, y = price)) + geom_point() + 
  geom_smooth(aes(colour = cut)) + theme_bw()

library(RColorBrewer)
ggplot(diamonds, aes(x = cut, y = price, fill = clarity)) +
  geom_boxplot() + scale_fill_manual(name = "Clarity of\n diamond",
                                     labels = paste("C", 1:8),
                                     values = brewer.pal(8, "Set2"))

• Have a look at www.ColorBrewer.org

To plot several ggplot graphics together, use gridExtra:

library(gridExtra)
p1 <- ggplot(diamonds) + geom_point(aes(x = carat, y = price, color = cut))
p2 <- ggplot(diamonds) + geom_density(aes(x = price, fill = cut), alpha = 0.5)
grid.arrange(p1, p2, ncol = 2)

Use ggsave to save a ggplot (uses file name extension to determine file type: .ps, .eps, .tex, .pdf, .jpg, .tiff, .png, .bmp, .svg, .wmf)

p <- ggplot(...) + ...
ggsave("...", plot = p, width = 4, height = 4)