Default colours along a variable in INBOtheme

Introduction

Some themes impact the scales when using a colour or fill along a variable. This vignette focusses on the different styles of colour scales. The fill scales uses the same gradients. To keep the vignette small, we only give examples with different colours. We illustrate the other effects of the themes in vignette("colour", package = "INBOtheme"), again to keep the size of the vignettes to a minimum. Note that you need to use the appropriate scale provided by INBOtheme in case of a gradient along a continuous variable.

library(ggplot2)
library(INBOtheme)

Default: `theme_inbo()`

Continuous variable

ggplot(diamonds, aes(x = carat, y = depth, colour = price)) +
  geom_point() +
  scale_colour_gradient()

Coloured along a continuous variable.

ggplot(diamonds, aes(x = carat, y = depth, colour = price)) +
  geom_point() +
  scale_colour_gradient2(midpoint = 1e4)

Coloured along a continuous variable using a gradient with midpoint.

Ordinal variable

ggplot(diamonds, aes(x = carat, y = depth, colour = cut)) +
  geom_point()

Coloured along an ordinal variable.

Categorical variable

Here we illustrate figures with a different number of categories. We choose for scatter plots with random x, y and category. This is the most difficult situation to distinguish the colours. Be careful when you use more than 5 colours.

data.frame(
  x = runif(1e3), y = runif(1e3),
  category = sample(LETTERS[1:2], 1e3, replace = TRUE)
) |>
  ggplot(aes(x = x, y = y, colour = category)) +
  geom_jitter()

Coloured by a two level categorical variable.

data.frame(
  x = runif(1e3), y = runif(1e3),
  category = sample(LETTERS[1:3], 1e3, replace = TRUE)
) |>
  ggplot(aes(x = x, y = y, colour = category)) +
  geom_jitter()

Coloured by a three level categorical variable.

data.frame(
  x = runif(1e3), y = runif(1e3),
  category = sample(LETTERS[1:4], 1e3, replace = TRUE)
) |>
  ggplot(aes(x = x, y = y, colour = category)) +
  geom_jitter()

Coloured by a four level categorical variable.

data.frame(
  x = runif(1e3), y = runif(1e3),
  category = sample(LETTERS[1:5], 1e3, replace = TRUE)
) |>
  ggplot(aes(x = x, y = y, colour = category)) +
  geom_jitter()

Coloured by a five level categorical variable.

data.frame(
  x = runif(1e3), y = runif(1e3),
  category = sample(LETTERS[1:6], 1e3, replace = TRUE)
) |>
  ggplot(aes(x = x, y = y, colour = category)) +
  geom_jitter()

Coloured by a six level categorical variable.

data.frame(
  x = runif(1e3), y = runif(1e3),
  category = sample(LETTERS[1:7], 1e3, replace = TRUE)
) |>
  ggplot(aes(x = x, y = y, colour = category)) +
  geom_jitter()

Coloured by a seven level categorical variable.

data.frame(
  x = runif(1e3), y = runif(1e3),
  category = sample(LETTERS[1:8], 1e3, replace = TRUE)
) |>
  ggplot(aes(x = x, y = y, colour = category)) +
  geom_jitter()

Coloured by an eight level categorical variable.

`theme_vlaanderen2015()`

Continuous variable

ggplot(diamonds, aes(x = carat, y = depth, colour = price)) +
  geom_point() +
  scale_colour_gradient()

Coloured along a continuous variable.

ggplot(diamonds, aes(x = carat, y = depth, colour = price)) +
  geom_point() +
  scale_colour_gradient2(midpoint = 1e4)

Coloured along a continuous variable using a gradient with midpoint.

Ordinal variable

ggplot(diamonds, aes(x = carat, y = depth, colour = cut)) +
  geom_point()

Coloured along an ordinal variable.

Categorical variable

data.frame(
  x = runif(1e3), y = runif(1e3),
  category = sample(LETTERS[1:2], 1e3, replace = TRUE)
) |>
  ggplot(aes(x = x, y = y, colour = category)) +
  geom_jitter()

Coloured by a two level categorical variable.

data.frame(
  x = runif(1e3), y = runif(1e3),
  category = sample(LETTERS[1:3], 1e3, replace = TRUE)
) |>
  ggplot(aes(x = x, y = y, colour = category)) +
  geom_jitter()

Coloured by a three level categorical variable.

data.frame(
  x = runif(1e3), y = runif(1e3),
  category = sample(LETTERS[1:4], 1e3, replace = TRUE)
) |>
  ggplot(aes(x = x, y = y, colour = category)) +
  geom_jitter()

Coloured by a four level categorical variable.

data.frame(
  x = runif(1e3), y = runif(1e3),
  category = sample(LETTERS[1:5], 1e3, replace = TRUE)
) |>
  ggplot(aes(x = x, y = y, colour = category)) +
  geom_jitter()

Coloured by a five level categorical variable.

data.frame(
  x = runif(1e3), y = runif(1e3),
  category = sample(LETTERS[1:6], 1e3, replace = TRUE)
) |>
  ggplot(aes(x = x, y = y, colour = category)) +
  geom_jitter()

Coloured by a six level categorical variable.

data.frame(
  x = runif(1e3), y = runif(1e3),
  category = sample(LETTERS[1:7], 1e3, replace = TRUE)
) |>
  ggplot(aes(x = x, y = y, colour = category)) +
  geom_jitter()

Coloured by a seven level categorical variable.

data.frame(
  x = runif(1e3), y = runif(1e3),
  category = sample(LETTERS[1:8], 1e3, replace = TRUE)
) |>
  ggplot(aes(x = x, y = y, colour = category)) +
  geom_jitter()

Coloured by an eight level categorical variable.

`theme_elsevier()`

Continuous variable

ggplot(diamonds, aes(x = carat, y = depth, colour = price)) +
  geom_point() +
  scale_colour_gradient()

Coloured along a continuous variable.

ggplot(diamonds, aes(x = carat, y = depth, colour = price)) +
  geom_point() +
  scale_colour_gradient2(midpoint = 1e4)

Coloured along a continuous variable using a gradient with midpoint.

Ordinal variable

ggplot(diamonds, aes(x = carat, y = depth, colour = cut)) +
  geom_point()

Coloured along an ordinal variable.

Categorical variable

data.frame(
  x = runif(1e3), y = runif(1e3),
  category = sample(LETTERS[1:2], 1e3, replace = TRUE)
) |>
  ggplot(aes(x = x, y = y, colour = category)) +
  geom_jitter()

Coloured by a two level categorical variable.

data.frame(
  x = runif(1e3), y = runif(1e3),
  category = sample(LETTERS[1:3], 1e3, replace = TRUE)
) |>
  ggplot(aes(x = x, y = y, colour = category)) +
  geom_jitter()

Coloured by a three level categorical variable.

data.frame(
  x = runif(1e3), y = runif(1e3),
  category = sample(LETTERS[1:4], 1e3, replace = TRUE)
) |>
  ggplot(aes(x = x, y = y, colour = category)) +
  geom_jitter()

Coloured by a four level categorical variable.

data.frame(
  x = runif(1e3), y = runif(1e3),
  category = sample(LETTERS[1:5], 1e3, replace = TRUE)
) |>
  ggplot(aes(x = x, y = y, colour = category)) +
  geom_jitter()

Coloured by a five level categorical variable.

data.frame(
  x = runif(1e3), y = runif(1e3),
  category = sample(LETTERS[1:6], 1e3, replace = TRUE)
) |>
  ggplot(aes(x = x, y = y, colour = category)) +
  geom_jitter()

Coloured by a six level categorical variable.

data.frame(
  x = runif(1e3), y = runif(1e3),
  category = sample(LETTERS[1:7], 1e3, replace = TRUE)
) |>
  ggplot(aes(x = x, y = y, colour = category)) +
  geom_jitter()

Coloured by a seven level categorical variable.

data.frame(
  x = runif(1e3), y = runif(1e3),
  category = sample(LETTERS[1:8], 1e3, replace = TRUE)
) |>
  ggplot(aes(x = x, y = y, colour = category)) +
  geom_jitter()

Coloured by an eight level categorical variable.

Thierry Onkelinx

Introduction

Default: theme_inbo()

Continuous variable

Ordinal variable

Categorical variable

theme_vlaanderen2015()

Continuous variable

Ordinal variable

Categorical variable

theme_elsevier()

Continuous variable

Ordinal variable

Categorical variable

Default: `theme_inbo()`

`theme_vlaanderen2015()`

`theme_elsevier()`