2.1 Aesthetic attributes

There is one scale for each aesthetic mapping in a plot. In the first plot, the value “blue” is scaled to a pinkish color, and a legend is added, because aesthetic mapping of color is set inside the aes(). If you want to set an aesthetic to a fixed value, without scaling it, do so in the individual layer outside of aes(), which is shown in the second plot.

library(patchwork)
f1 <- ggplot(mpg, aes(displ, hwy)) + geom_point(aes(color = "blue"))
f2 <- ggplot(mpg, aes(displ, hwy)) + geom_point(color = "blue")
f1 | f2

Two graphics are arranged horizontally by | (+ will do the same thing), an operator provided by the package patchwork .

2.2 Faceting

To facet a plot you simply add a faceting specification with facet_wrap(). For example, facet_wrap(~group,ncol = 1,scales="free") will show different groups in tables of graphics using different scales, arranged in one column.

2.3 Plot geoms

There are many geom functions other than geom_point(), here are some examples:

• geom_smooth() fits a smoother to the data and displays the smooth and its standard error (can be turned off with geom_smooth(se = FALSE)).
  • method = "loess", the default for small n, uses a smooth local regression.
  • method = "gam" fits a generalized additive model.
  • method = "lm" fits a linear model, giving the line of best fit.
  • method = "rlm" works like lm(), but uses a robust fitting algorithm so that outliers don’t affect the fit as much.
• geom_boxplot(), geom_jitter(), and geom_violin() produce plot to summarize the distribution of a set of points.
• geom_histogram() and geom_freqpoly() show the distribution of continuous variables.
• geom_bar() shows the distribution of categorical variables.
• geom_path() and geom_line() draw lines between the data points. However, geom_path connect points in the order of presentation.

More functions can be found in the cheatsheet.

2.4 Modifying the axes

The x- and y-axis labels can be modified by xlab() and ylab(). The limits of axes are controlled by xlim() and ylim(). Changing the axes limits sets values outside the range to NA. For continuous scales, use NA to set only one limit. Setting ylim inside coord_cartesian() will leave data unchanged.

# There are two ways of zooming the plot display: 
# with scales or with coordinate systems. 

p <- ggplot(mtcars, aes(disp, wt)) +
  geom_point() +
  geom_smooth()
p |

# Setting the limits on a scale converts 
# all values outside the range to NA.
p + scale_x_continuous(limits = c(325, 500)) |

# Setting the limits on the coordinate system
# The data is unchanged, and show a portion of it
# Note how smooth continues past the points visible.
p + coord_cartesian(xlim = c(325, 500))

2.5 Output

There are a few things you can do with a plot object:

• Render it on screen with print().
• Save it to disk with ggsave(). For example, use ggsave("plot.png", p, width = 5, height = 5) to save plot as png.
• Briefly describe its structure with summary().
• Save a cached copy of it to disk, with saveRDS(). This saves a complete copy of the plot object, so you can easily re-create it with readRDS().