More Plots For A Numeric Variable

Grouped Bar Charts
Polar Area Diagrams
Recreating The Nightingale Visualization
Radar Charts
Bubble Charts

Grouped Bar Charts

Grouped bar charts can be used to show a quantitative variable within two classifications.

For the barley data from the lattice package the barchart function can show the average results for year within site:

gbsy <- group_by(barley, site, year)
absy <- summarise(gbsy, avg_yield = mean(yield))
## `summarise()` has grouped output by 'site'. You can override using the
## `.groups` argument.
barchart(avg_yield ~ site, group = year, data = absy, origin = 0,
         auto.key = TRUE)

Using ggplot2 this can be done by

assigning site to the x aesthetic;
assigning year to the fill aesrhetic;
specifying position = "dodge":

ggplot(absy) +
    geom_col(aes(x = site, y = avg_yield, fill = year),
             position = "dodge")

It is possible to use more than two or three classifications with a grouped bar chart but usually not a good idea.
The number of levels at the inner classification that are visually effective is limited.
Dot plots are often a better option.

The bars for the inner classification can also be placed in front of each other:

ggplot(absy) +
    geom_col(aes(x = site, y = avg_yield, fill = year),
             position = "identity")

This does not work well as the taller bars cover the shorter ones.
A re-ordering of the bars is needed to make this effective.
For the identity position this can be achieved by arranging the rows in decreasing order of avg_yield

ggplot(arrange(absy, desc(avg_yield))) +
    geom_col(aes(x = site, y = avg_yield, fill = year),
             position = position_identity())

Polar Area Diagrams

A classic, though now rarely used, visualization is a polar area chart, or coxcomb diagram, as introduced by Florence Nightingale:

The basic plot can be viewed as a bar chart drawn in polar coordinates.

The square root of the variable represented is used as the radius to make the areas of the wedges proportional to the magnitudes.
Specifying width = 1 ensures that there is no gap between the bars/wedges.

gbs <- group_by(barley, site)
abs <- summarise(gbs, avg_yield = mean(yield))
ggplot(abs) +
    geom_col(aes(y = sqrt(avg_yield), x = site, fill = site),
             width = 1, color = "black") +
    coord_polar()

The standard coxcomb diagram for a second classification positions the wedges in front of each other.

ggplot(arrange(absy, desc(avg_yield))) +
    geom_col(aes(y = sqrt(avg_yield), x = site, fill = year),
             width = 1, color = "black",
             position = "identity") +
    coord_polar()

As a visualization an ordinary bar chart is generally more effective.

The only advantage of a polar representation is to reflect a periodic feature, as in the original use.

Recreating The Nightingale Visualization

The data are available as the variable Nightingale in the HistData package.

library(HistData)
head(Nightingale)
##         Date Month Year  Army Disease Wounds Other Disease.rate Wounds.rate
## 1 1854-04-01   Apr 1854  8571       1      0     5          1.4         0.0
## 2 1854-05-01   May 1854 23333      12      0     9          6.2         0.0
## 3 1854-06-01   Jun 1854 28333      11      0     6          4.7         0.0
## 4 1854-07-01   Jul 1854 28722     359      0    23        150.0         0.0
## 5 1854-08-01   Aug 1854 30246     828      1    30        328.5         0.4
## 6 1854-09-01   Sep 1854 30290     788     81    70        312.2        32.1
##   Other.rate
## 1        7.0
## 2        4.6
## 3        2.5
## 4        9.6
## 5       11.9
## 6       27.7

The data set is in wide format, so needs some tidying.

First, select only variables that might be useful.

library(dplyr)
Night <- select(Nightingale, Date, Army, Disease, Wounds, Other)
head(Night)
##         Date  Army Disease Wounds Other
## 1 1854-04-01  8571       1      0     5
## 2 1854-05-01 23333      12      0     9
## 3 1854-06-01 28333      11      0     6
## 4 1854-07-01 28722     359      0    23
## 5 1854-08-01 30246     828      1    30
## 6 1854-09-01 30290     788     81    70

Next, convert to long format with variables cause and deaths:

library(tidyr)
Night <- gather(Night, cause, deaths, 3 : 5)
head(Night)
##         Date  Army   cause deaths
## 1 1854-04-01  8571 Disease      1
## 2 1854-05-01 23333 Disease     12
## 3 1854-06-01 28333 Disease     11
## 4 1854-07-01 28722 Disease    359
## 5 1854-08-01 30246 Disease    828
## 6 1854-09-01 30290 Disease    788

Add a variable with the month of the year:

library(lubridate)
Night <- mutate(Night, Month = month(Date, label = TRUE))
head(Night)
##         Date  Army   cause deaths Month
## 1 1854-04-01  8571 Disease      1   Apr
## 2 1854-05-01 23333 Disease     12   May
## 3 1854-06-01 28333 Disease     11   Jun
## 4 1854-07-01 28722 Disease    359   Jul
## 5 1854-08-01 30246 Disease    828   Aug
## 6 1854-09-01 30290 Disease    788   Sep

Finally, add a variable to distinguish periods before and after April 1, 1855:

Night <- mutate(Night,
                period = ifelse(Date < as.Date("1855-04-01"),
                                "before", "after"))
head(Night)
##         Date  Army   cause deaths Month period
## 1 1854-04-01  8571 Disease      1   Apr before
## 2 1854-05-01 23333 Disease     12   May before
## 3 1854-06-01 28333 Disease     11   Jun before
## 4 1854-07-01 28722 Disease    359   Jul before
## 5 1854-08-01 30246 Disease    828   Aug before
## 6 1854-09-01 30290 Disease    788   Sep before

The pair of plots can now be created as

p <- ggplot(arrange(Night, desc(deaths))) +
    geom_col(aes(y = deaths, x = Month, fill = cause),
             width = 1, color = "black", position = "identity") +
    scale_y_sqrt() +
    facet_grid(. ~ period) +
    coord_polar(start = pi) +
    scale_fill_manual(values = c(Wounds = "pink",
                                 Other = "darkgray",
                                 Disease = "lightblue"))
p

Some final theme adjustments:

p + theme(axis.title = element_blank(),
          axis.text.y = element_blank(),
          axis.ticks = element_blank(),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.border = element_blank())

Radar Charts

A polar coordinate transformation can also be used with a line chart. This leads to a radar chart, also called a spider web chart.

Using the global surface temperature data, the data can be treated as a single time series and draw as a single line showing temerature at each month as

lgast <- arrange(lgast, Year, Month)
library(lubridate)
past_year <- year(today()) - 1
lgast_last <- filter(lgast, Year == past_year)
p <- ggplot(lgast) +
    geom_path(aes(x = Month, y = Temp, group = 1, color = Year)) +
    geom_line(aes(x = Month, y = Temp, group = Year),
              data = lgast_last, color = "red")
p

The lines connecting December back to January are rendered more naturally with a radar chart.

A slightly modified version of coord_polar is needed to make this work properly. The definition is available in at least one package, but can also be included directly:

coord_radar <- function(theta = "x", start = 0, direction = 1) {
    theta <- match.arg(theta, c("x", "y"))
    r <- if (theta == "x") "y" else "x"
    ggproto("CordRadar", CoordPolar, theta = theta, r = r, start = start,
            direction = sign(direction),
            is_linear = function(coord) TRUE)
}

The radar chart is then

p + coord_radar()

Radar charts can be useful for periodic data.
Some fields use them heavily, so using them is expected.
They do have drawbacks, as described in this blog post.

Bubble Charts

A form of chart often seen in the popular press is the bubble chart.

The bubble chart uses ares of circles to represent magnitudes.
In on-line publications further information on each of the bubble is often provided through interactions, such as a mouse-over popup.
Other charts forms are almost always better for encoding the magnitude information.
It is also easy to get the encoding wrong:

(Corrected version)

ggplot bubble charts for the average yield values from the barley data and for the 2007 population sizes for the gapminder data: