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 are set or mapped using additional arguments.

• adding an argument of the form attribute = value sets attribute to value.
• adding an argument of the form attribute = ~ expression maps attribute to expression

where attribute is one of color, shape, etc., value is a constant (e.g. "red" or 0.5, as appropriate), and expression may be 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).

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 precisely how the mapping is done – which colors and sizes are used to represent which values of cyl and carb.)

• We also set the transparency 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) 

On-the-fly calculations

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.

library(dplyr)
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 quantitative 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 to indicate which groups of points are connected.

Here’s an example where gf_line() is useful. 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?
Converting to a line plot and coloring by day of week highlights the pattern and makes it easy to spot the unusual days.

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

The example above would look the same if we used gf_path() because the data set is already sorted by date. But in general, gf_path() and gf_line() produce different results. In the plots below, the first connects days chronologically (because the data are sorted by date) and the second in order of their low temperature.

Weather %>%
  filter(month == 5, year == 2017, city == "Chicago") %>%
  gf_path(high_temp ~ low_temp | city ~ year, color = ~ day) %>%
  gf_refine(scale_color_viridis_c(option = "C", begin = 0.2, end = 0.8))
Weather %>%
  filter(month == 5, year == 2017, city == "Chicago") %>%
  gf_line(high_temp ~ low_temp | city ~ year, color = ~ day) %>%
  gf_refine(scale_color_viridis_c(option = "C", begin = 0.2, end = 0.8))

Positions

position_dodge(), position_jitter(), and position_jitterdodge() can be used to adjust the positions at which glyphs are placed. Jittering adds some random noise and can be useful when many observations have the same value. Dodging moves groups of glyphs a fixed difference to make it easier to distinguish the groups.

gf_point(length ~ sex, color = ~ domhand, data = KidsFeet,
         position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.4))

Stats

A stat is a transformation that is applied to the data before a plot is generated. Several of the plots we have seen have made use of stats.

• gf_histogram() uses stat_bin() to bin the data and count the number of observations in each bin. It is equivalent to

gf_bar( ~ age, data = HELPrct, stat = "bin")

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

• gf_boxplot() uses stat_boxplot() to compute the five-number summary on which the boxplot is based.

• gf_violin(), gf_density(), and gf_density2d() use stats to compute a sequence of points along an estimated density. It is these points, and the not the raw data that are used to create the plot. gf_sina() uses the estimated density to jitter points.

gf_boxplot(age ~ substance, data = HELPrct)
gf_violin(age ~ substance, data = HELPrct) %>%
  gf_sina(alpha = 0.3)

There are also a number of stats that can be used to show a functional relationship between two variables.

• gf_smooth() plots a model fit using lm(), glm(), gam(), loess(), MASS::rlm().

• gf_lm() is gf_smooth() with the method set to lm() so it plots a least squares regression line.

• gf_spline() plots a spline fit to the data.

gf_point(length ~ width, data = KidsFeet, color = ~ sex) %>%
  gf_lm()
gf_point(births ~ date, color = ~wday,
         data = mosaicData::Births, alpha = 0.25) %>%
  gf_smooth()

## `geom_smooth()` using method = 'gam'

Confidence or prediction bands can be added to these as well.

gf_point(length ~ width | sex, data = KidsFeet, color = ~ sex) %>%
  gf_lm(interval = "prediction", fill = "red") %>%
  gf_lm(interval = "confidence", fill = "navy")

Mostly, the stats selected by default are just the ones you need. But sometimes it is useful to select a different stat. The stat_summary() and stat_summary_bin() stats are particularly useful in this respect. These stats use a function to aggregate over unique values of x or over bins of x values and save the user needing to do that data transformation manually.

The default function applied in each group is mean_se(), which computes the mean (and the mean plus and minus one standard error) This makes it simple to create an “interaction plot”.

gf_jitter(length ~ sex, color = ~ domhand, data = KidsFeet,
          width = 0.1, height = 0) %>%
  gf_line(length ~ sex, color = ~ domhand, data = KidsFeet,
          group = ~ domhand, stat="summary")

## No summary function supplied, defaulting to `mean_se()`

The other two values computed by mean_se() are available (starting with ggplot2 version 2.3) as stat(ymin) and stat(ymax).

gf_jitter(length ~ sex, color = ~ domhand, data = KidsFeet,
          width = 0.1, height = 0, alpha = 0.3) %>%
  gf_pointrange(length + stat(ymin) + stat(ymax) ~ sex, 
                color = ~ domhand, data = KidsFeet, 
                group = ~ domhand, stat="summary")

## No summary function supplied, defaulting to `mean_se()`

Custom functions can be used by defining fun.y, fun.ymin, and fun.ymax, or a single function fun.data that returns a data frame with variables named y, ymin, and ymax.

gf_point(length ~ sex, color = ~ domhand, data = KidsFeet,
          width = 0.1, height = 0, alpha = 0.5,
          position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.3)) %>%
  gf_pointrange(length + stat(ymin) + stat(ymax) ~ sex, 
                color = ~ domhand, data = KidsFeet, 
                group = ~ domhand, stat="summary",
                fun.y = median, fun.ymin = min, fun.ymax = max,
                position = position_dodge(width = 0.6))

## No summary function supplied, defaulting to `mean_se()`

Plotting functions

ggformula provides two way to plot functions: gf_function() and gf_fun(). They differ primarily in how one specifies the function to be plotted.

• gf_function() requires a (vectorized) function of one variable.
• gf_fun() requires a formula describing how the y-values are computed from the x-values.

If these are used as a first layer, the limits for the x-axis must be specified.

gf_function(fun = sqrt, xlim = c(0, 10)) %>%
  gf_fun(2 + 3 * cos(10 * x) ~ x, color = "red", n = 800)
f <- makeFun(lm(totalbill ~ poly(month, 2), data = mosaicData::Utilities))
gf_point(totalbill ~ month, data = mosaicData::Utilities, alpha = 0.6) %>%
  gf_fun(f(m) ~ m, color = "red")

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`.

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) 

Maps

Prior to ggplot2 version 2.3, basic maps could be created using gf_polygon().

if (require(maps) && require(dplyr)) {
  US <- map_data("state") %>%
    dplyr::mutate(name_length = nchar(region))
  States <- US %>%
    dplyr::group_by(region) %>%
    dplyr::summarise(lat = mean(range(lat)), long = mean(range(long))) %>%
    dplyr::mutate(name = abbreviate(region, 3))

  gf_polygon(lat ~ long, data = US, group = ~ group,
             fill = ~ name_length, color = "white") %>%
  gf_text(lat ~ long, label = ~ name, data = States,
    color = "gray70", inherit = FALSE) %>%
  gf_refine(mosaic::theme_map())
}

Starting with ggplot2 version 2.3, there is direct support for shape files. This allows each row of a data frame to contain all the geometry information for a region in a single column. There is also better support for overlaying information from multiple shape files.

if (require(maps) && require(dplyr) && require(sf) && require(purrr)) {
  USshape <- 
    sf::st_as_sf(maps::map('state', plot = FALSE, fill = TRUE)) %>%
    dplyr::mutate(
      name_length = nchar(as.character(ID)),
      centroid = purrr::map(geom, st_centroid),
      coords_x = purrr::map_dbl(centroid, 1),
      coords_y = purrr::map_dbl(centroid, 2)
    ) 
    
  gf_sf(fill = ~ factor(name_length), color = "white", data = USshape, alpha = 0.5) %>%
    gf_sf(data = sf::st_centroid(USshape), color = "white", alpha = 0.5, size = 3) %>%
    gf_text(coords_y ~ coords_x, label = ~ ID, color = "gray20", size = 2) %>%
    gf_labs(x = "", y = "") %>%
    gf_refine(mosaic::theme_map(), theme_bw()) %>%
    gf_labs(fill = "name length")
}

## Loading required package: sf

## Linking to GEOS 3.7.2, GDAL 2.4.2, PROJ 5.2.0

## Loading required package: purrr

## 
## Attaching package: 'purrr'

## The following object is masked from 'package:maps':
## 
##     map

## Warning in st_centroid.sf(USshape): st_centroid assumes attributes are constant
## over geometries of x

## Warning in st_centroid.sfc(st_geometry(x), of_largest_polygon =
## of_largest_polygon): st_centroid does not give correct centroids for longitude/
## latitude data

New types of plots

ggformula adds some additional plot options to ggplot2

gf_ash( ~ age, data = HELPrct, binwidth = 2) %>%
  gf_dhistogram( ~ age, data = HELPrct, binwidth = 2, alpha = 0.3) 
gf_ash( ~ age, data = HELPrct, binwidth = 10) %>% 
  gf_dhistogram( ~ age, data = HELPrct, binwidth = 2, alpha = 0.3) 

gf_dist("pois", lambda = 5)
gf_dist("pois", lambda = 5, kind = "cdf")
gf_dist("gamma", shape = 3, rate = 4, geom = "area")
gf_dist("gamma", shape = 3, rate = 4, geom = "area", fill = ~ (x <= 1))
gf_dist("gamma", shape = 3, rate = 4, kind = "cdf")

# size is used by the binomial distribution functions and when plotting
gf_dist("binom", size = 20, prob = 0.25, plot_size = 3)
gf_dist("binom", params = list(size = 20, prob = 0.25), size = 3)

x <- rgamma(1000, shape = 2, rate = 5)
gf_dhistogram( ~ x, alpha = 0.3) %>%
  gf_fitdistr(dist = "dnorm", color = ~ "Normal") %>% 
  gf_fitdistr(dist = "dgamma", color = ~ "Gamma") %>%
  gf_fitdistr(dist = "dweibull", color = ~ "Weibull")

## Warning in densfun(x, parm[1], parm[2], ...): NaNs produced

## Warning in densfun(x, parm[1], parm[2], ...): NaNs produced

## Warning in densfun(x, parm[1], parm[2], ...): NaNs produced

## Warning in densfun(x, parm[1], parm[2], ...): NaNs produced

## Warning in densfun(x, parm[1], parm[2], ...): NaNs produced

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_bar).

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_bar).

For convenience, a few modifications can also be made directly in the original function call:

gf_histogram( ~ age, data = Runners, alpha = 0.5, fill = "skyblue", color = "navy",
              binwidth = 5, 
              xlab = "age (years)", title = "Age of runners"
              )