Specifying attributes

In the gf_ functions, you specify the non-position graphical attributes using an extension of the basic formula. Attributes can be set to a constant value (e.g, set the color to “blue”; set the size to 2) or they can be mapped to a variable in the data or some expression involving the variables (e.g., map the color to sex, so sex determines the color groupings)

Attributes can be specified in 3 ways:

• adding term of the form attribute:value to the formula [auto chooses set or map]
• adding a term of the form attribute::value [map]
• adding an argument of the form attribute = value [set]

where attribute is one of color, shape, etc. and value is either a constant (e.g. "red" or 0.5, as appropriate), a variable (e.g. cyl), or some more general expression that can be computed using the variables in data (although often is is better to create a new variable in the data and to use that variable instead of an on-the-fly calculation within the plot). When using the : separator, mapping will be used if value is the name of a variable in data, otherwise setting will be used. The other two forms can be used to make things explicit when this auto-detection method doesn’t yield the desired results

The following plot, for instance,

• We use cyl to determine the color and carb to determine the size of each dot. Color and size are mapped to cyl and carb. A legend is provided to show us how the mapping is being done. (Later, we can use scales to control precisly how the mapping is done – which colors and sizes are used to represent which values of cyl and carb.)

• We also set the transpaency to 50%. The gives the same value of alpha to all glyphs in this layer.

gf_point(mpg ~ hp + color:cyl + size:carb + alpha:0.50, data = mtcars) 

Here is the same plot where mapping and setting are indicated explicitly.

# set alpha using a function argument instead of in the formula
gf_point(mpg ~ hp + color::cyl + size::carb, alpha = 0.50, data = mtcars) 

On-the-fly caclulations

ggformula allows for on-the-fly calculations of attributes, although the default labeling of the plot is often better if we create a new variable in our data frame. In the examples below, since there are only three values for carb, it is easier to read the graph if we tell R to treat cyl as a categorical variable by converting to a factor (or to a string). Except for the labeling of the legend, these two plots are the same.

gf_point(mpg ~ hp + color::factor(cyl) + size:carb + alpha:0.75, data = mtcars)
gf_point(mpg ~ hp + color:cylinders + size:carb + alpha:0.75, 
         data = mtcars %>% mutate(cylinders = factor(cyl)))

Using jitter and transparency to handle overlapping cases

Sometimes you have so many points in a scatter plot that they obscure one another. The ggplot2 system provides two easy ways to deal with this: translucency and jittering.

Use alpha:0.5 to make the points semi-translucent. If there are many points overlapping at one point, a much smaller value of alpha, say alpha:0.01. We’ve already seen this above.

Using gf_jitter() in place of gf_point() will move the plotted points to reduce overlap. Jitter and transparency can be used together as well.

gf_point(age ~ sex + alpha:0.05, data = Runners)
gf_jitter(age ~ sex + alpha:0.05, data = Runners)

Box and Whisker plots

Box and whisker plots show the distribution of a quantitative variable as a function of a categorical variable. The formula used in gf_boxplot() should have the quantitative variable to the left of the tilde. (To make horizontal boxplots using ggplot2 you have to make vertical boxplots and then flip the coordinates with coord_flip().)

gf_boxplot(net ~ sex + color:"red", data = Runners)
gf_boxplot(net ~ sex + color:start_position, data = Runners)

This plot may surprise you.

gf_boxplot(net ~ year, data = Runners)

## Warning: Continuous x aesthetic -- did you forget aes(group=...)?

This plot is placing a single box and whisker plot at the mean value of year. The warning message suggests that we need to tell R how to form the groups when using a quantitive variable for x. It suggests using the group aesthetic, and sometimes, this is just what we want.

gf_boxplot(net ~ year + group:year, data = Runners)

But often, is is better to convert a discrete quantitative variable used for grouping into a categorical variable (a factor or character vector). This can be done in several ways:

# add a new variable to the data
Runners$the_year <- as.character(Runners$year)               # in base R
Runners <- Runners %>% mutate(the_year = as.character(year)) # in dplyr
gf_boxplot(net ~ the_year + color:sex, data = Runners)
# or do it on the fly
gf_boxplot(net ~ factor(year) + color:sex, data = Runners)

2-dimensional density plots

Two-dimensional plots of density also have both a left and right component to the formula.

gf_density_2d(net ~ age, data = Runners)
gf_hex(net ~ age, data = Runners)

Paths and lines

The ggplot2 system offers two ways to connect points. gf_line() ignores the order of the points in the data, and draws the line going from left to right. gf_path() goes from point to point according to the order in the data. Both forms can use a color or group aesthetic as a flag to draw groupwise lines.

# create a categorical variable
mtcars <- mtcars %>% mutate(n_cylinders = as.character(cyl)) 
gf_line(mpg ~ hp, data = mtcars)
gf_path(mpg ~ hp, data = mtcars)
gf_line(mpg ~ hp + color:n_cylinders, data = mtcars)

The above are examples of bad plots. The viewer is unnecessarily distracted by the zigs and zags in the connecting lines. It would be better to use gf_point() here, but then you wouldn’t see how gf_line() and gf_path() work!

Here’s a more useful example. We begin with a scatter plot showing the number of live births in the US for each day of 1978.

library(mosaicData)
gf_point(births ~ date, data = Births78)

Can this interesting pattern be explained by a weekday/weekend effect? We could use color to show the days of the week.

library(mosaicData)
gf_point(births ~ date + color:wday, data = Births78)

Converting to a line plot highlights the pattern and makes it easier to spot the unusual days.

gf_line(births ~ date + color:wday, data = Births78)

Multiple layers

Often it is useful to overlay multiple layers onto a single plot. This can be done by chaining them with %>%, the “then” operator from magrittr. The data argument can be omitted if the new layers uses the same data as the first layer in the chain.

The following plot illustrates how histograms and frequency polygons are related.

gf_histogram( ~ age, data = Runners, alpha = 0.3, fill = "navy") %>%
  gf_freqpoly( ~ age)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

A 2-d density plot can be augmented with a scatterplot.

gf_density_2d(net ~ age, data = Runners) %>%
  gf_point(net ~ age + alpha:0.01) 

Faceting

The ggplot2 system allows you to make subplots — called “facets” — based on the values of one or two categorical variables. This is done by chaining with gf_facet_grid() or gf_facet_wrap(). These functions use formulas to specify which variable(s) are to be used for faceting.

gf_density_2d(net ~ age, data = Runners) %>% gf_facet_grid( ~ sex)
# the dot here is a bit strange, but required to make a valid formula
gf_density_2d(net ~ age, data = Runners) %>% gf_facet_grid( sex ~ .)
gf_density_2d(net ~ age, data = Runners) %>% gf_facet_wrap( ~ the_year)
gf_density_2d(net ~ age, data = Runners) %>% gf_facet_grid(start_position ~ sex)

One use of facets is to create side-by-side plots of different data sources.
First we merge the data into a single data frame with a variable to indicate the original data source. Then we use facets to separate the data into the various sources for the display. The redundant use of the y and color attributes for temperature makes it easier to compare across facets.

require(weatherData)
Temps <- NewYork2013 %>% mutate(city = "NYC") %>%
  bind_rows(Mumbai2013 %>% mutate(city = "Mumbai")) %>%
  bind_rows(London2013 %>% mutate(city = "London")) %>%
  mutate(date = lubridate::date(Time),
         month = lubridate::month(Time)) %>% 
  group_by(city, date) %>%
  summarise(
    hi = max(Temperature, na.rm = TRUE),
    lo = min(Temperature, na.rm = TRUE),
    mid = (hi + lo)/2
  )   
gf_ribbon(lo + hi ~ date, data = Temps, alpha = 0.3) %>%
  gf_facet_grid(city ~ .)

gf_linerange(lo + hi + color:mid ~ date, data = Temps) %>%
  gf_facet_grid(city ~ .) %>%
  gf_refine(scale_colour_gradientn(colors = rev(rainbow(5))))

Global plot adjustments

There are a number of things we may want to do to the entire plot – adjusting labels, colors, fonts, etc. ggformula provides wrappers to the ggplot2 functions for this so that the chaining syntax can be used.

gf_histogram( ~ age, data = Runners, alpha = 0.2, fill = "navy",
              binwidth = 5) %>%
  gf_freqpoly( ~ age, binwidth = 5) %>%
  gf_labs(x = "age (years)", title = "Age of runners") %>%
  gf_lims(x = c(20, 80)) %>%
  gf_theme(theme = theme_minimal)

## Warning: Removed 84 rows containing non-finite values (stat_bin).

## Warning: Removed 84 rows containing non-finite values (stat_bin).

## Warning: Removed 2 rows containing missing values (geom_path).

gf_histogram( ~ age, data = Runners, alpha = 0.5, fill = "white",
              binwidth = 5) %>%
  gf_freqpoly( ~ age, color = "skyblue", binwidth = 5, size = 1.5) %>%
  gf_labs(x = "age (years)", title = "Age of runners") %>%
  gf_lims(x = c(20, 80)) %>%
  gf_theme(theme = theme_dark)

## Warning: Removed 84 rows containing non-finite values (stat_bin).

## Warning: Removed 84 rows containing non-finite values (stat_bin).

## Warning: Removed 2 rows containing missing values (geom_path).