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ggauto

ggauto is an opinionated ggplot2 extension package to automatically choose the best chart type and styling, based on the types and values in the data.

[!WARNING] If you don’t like some (or all) of the opinionated choices in this package, make a fork and create your own version. Bug reports and/or fixes are extremely welcome for things that don’t work, but stylistic changes that are personal preferences will not be addressed.

In terms of styling, the defaults differ from ggplot2 in the following ways:

  • The use of a white background to improve contrast.
  • Larger text which is aligned horizontally to improve readability.
  • Improved styling for title and subtitle, including automatic text wrapping for long text.
  • Colours that are more likely to be accessible, using Paul Tol’s palettes.
  • Combined use of either shapes or direct labels alongside colour to improve accessibility.
  • Symmetric y axis, when 0 is included in the data (for some chart types), to enable comparison.
  • Errors when users try to make spaghetti line charts.

Installation

You can install the development version of ggauto from GitHub with:

# install.packages("pak")
pak::pak("nrennie/ggauto")

Load the package:

Mapping data types to chart types

Variable types

The available data types are based on the scale_x/y_ options in ggplot2:

  • Continuous
  • Discrete (categorical)
  • Date (including time and datetime)

Chart types

var1 var2 var3 Chart Type Implemented
Continuous - - Raincloud plot Yes
Continuous Continuous - Scatter plot Yes
Continuous Continuous Discrete Scatter plot with coloured shapes Yes
Discrete - - Bar chart (showing count of categories) Yes
Discrete Continuous - Bar chart (if one value per category) or raincloud plot (if multiple values per category) Yes
Discrete Discrete - Heatmap (showing count of category combinations) Yes
Discrete Discrete Continuous Heatmap (showing continuous variable) Not yet
Date Continuous - Line chart Yes
Date Continuous Discrete Line chart with coloured lines Yes

Examples

One continuous variable 

set.seed(123)
plot_data <- data.frame(
  v1 = rnorm(50, 1)
)

Pass columns of the data in as vectors:

ggauto(plot_data$v1)

Note the symmetric x-axis, which is added then 0 exists in the range of x values. Since the output of ggauto() is simply a ggplot2 chart, you can override this if you don’t want it:

ggauto(plot_data$v1) +
  scale_x_continuous()
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.

You’ll get a warning to say you are replacing the existing scale which you can ignore because it’s what you’re trying to do!

You can a title, subtitle, caption, and labels with the labs() function in ggplot2 as you normally would, or directly using the same arguments in ggauto(). The latter is recommended as the arguments are used a little abnormally to implement the styling. You can add markdown formatting into the title, subtitle, or caption:

ggauto(plot_data$v1,
  title = "Descriptive title goes here",
  subtitle = "More information about what's in the chart which can be a really, really long sentence that will wrap onto multiple lines automatically.",
  caption = "**Source**: where the data is from",
  xlab = "Nice variable name"
)

Two continuous variables

Let’s create a basic dataset with two continuous variables:

set.seed(123)
plot_data <- data.frame(
  v1 = runif(20),
  v2 = runif(20, -5, 5)
)
ggauto(plot_data$v1, plot_data$v2)

Compare to a basic, default scatter plot in ggplot2:

ggplot(
  data = plot_data,
  mapping = aes(x = v1, y = v2)
) +
  geom_point()

Two continuous variables, one discrete 

set.seed(123)
plot_data <- data.frame(
  v1 = runif(20),
  v2 = runif(20, -5, 5),
  v3 = sample(LETTERS[1:4], 20, replace = TRUE)
)
ggauto(plot_data$v1, plot_data$v2, plot_data$v3)

One date variable, one continuous variable 

set.seed(123)
plot_data <- data.frame(
  v1 = seq(
    as.Date("01-01-2024", tryFormats = "%d-%m-%Y"),
    as.Date("01-12-2024", tryFormats = "%d-%m-%Y"),
    by = "months"
  ),
  v2 = runif(12, -5, 5)
)
ggauto(plot_data$v1, plot_data$v2)

One date variable, one continuous variable, one discrete variable

Category labels are added at the end of each line automatically. At the moment, you will need to manually adjust the x axis to make sure they fit in (depending on the length of your category labels).

set.seed(123)
plot_data <- data.frame(
  v1 = rep(seq(
    as.Date("01-01-2024", tryFormats = "%d-%m-%Y"),
    as.Date("01-12-2024", tryFormats = "%d-%m-%Y"),
    by = "months"
  ), each = 4),
  v2 = runif(48),
  v3 = rep(LETTERS[1:4], times = 12)
)
ggauto(plot_data$v1, plot_data$v2, plot_data$v3) +
  scale_x_date(limits = as.Date(c("01-01-2024", "31-12-2024"),
    tryFormats = "%d-%m-%Y"
  ))

If you try to use more than 6 colours, an error will be returned. Future iterations of this package may automatically use small multiples (facets) in this situation.

set.seed(123)
plot_data <- data.frame(
  v1 = rep(seq(
    as.Date("01-01-2024", tryFormats = "%d-%m-%Y"),
    as.Date("01-12-2024", tryFormats = "%d-%m-%Y"),
    by = "months"
  ), each = 7),
  v2 = runif(84),
  v3 = rep(LETTERS[1:7], times = 12)
)
ggauto(plot_data$v1, plot_data$v2, plot_data$v3)
#> Error in `ggauto()`:
#> ! You cannot use more than 6 colours.

One discrete variable

If a character variable, will be sorted highest to lowest:

set.seed(123)
plot_data <- data.frame(
  v1 = sample(LETTERS[1:6], 20, replace = TRUE)
)
ggauto(plot_data$v1)

If a factor variable, the order is respected:

plot_data$v1 <-factor(plot_data$v1, levels = LETTERS[1:6])
ggauto(plot_data$v1)

One discrete variable, one continuous variable

If only one continuous value for each discrete variable, a bar chart is created e.g. if you’ve pre-computed the counts:

set.seed(123)
plot_data <- data.frame(
  v1 = LETTERS[1:6],
  v2 = rpois(6, 6)
)
ggauto(plot_data$v1, plot_data$v2)

If multiple continuous values for each discrete variable, a raincloud plot is created:

set.seed(123)
plot_data <- data.frame(
  v1 = rep(LETTERS[1:3], each = 25),
  v2 = rnorm(75)
)
ggauto(plot_data$v1, plot_data$v2)

Two discrete variables 

set.seed(123)
plot_data <- data.frame(
  v1 = sample(LETTERS[1:3], 200, replace = TRUE),
  v2 = sample(LETTERS[24:26], 200, replace = TRUE)
)
ggauto(plot_data$v1, plot_data$v2)

When there are many categories, labels are replaced with a legend:

set.seed(123)
plot_data <- data.frame(
  v1 = sample(LETTERS[1:13], 200, replace = TRUE),
  v2 = sample(LETTERS[14:26], 200, replace = TRUE)
)
ggauto(plot_data$v1, plot_data$v2)

Again, if one or both of the discrete variables is a factor, then the order is respected:

plot_data$v1 <- factor(plot_data$v1, levels = LETTERS[1:13])
ggauto(plot_data$v1, plot_data$v2)

You can also pass in column names and the data directly into data:

ggauto("v1", "v2", data = plot_data)

Or using the pipe:

plot_data |> 
  ggauto("v1", "v2", data = _)