Color scales and palettes in R Programming - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a color palette in R?

A color palette in R is a set of colors used together to make plots or graphics look nice and clear. It helps to keep colors consistent and visually appealing.

beginner

How do you create a simple color palette using base R?

You can create a simple color palette using the palette() function, for example: palette(c("red", "green", "blue")) sets the palette to red, green, and blue.

intermediate

What is the purpose of the colorRampPalette() function?

colorRampPalette() creates a function that generates a smooth gradient of colors between given colors. You can use it to make many colors blending from one to another.

beginner

Name two popular R packages for advanced color palettes.

Two popular R packages for color palettes are RColorBrewer and viridis. They provide many ready-to-use palettes for different purposes.

intermediate

What is the difference between sequential and diverging color scales?

Sequential color scales show a progression from light to dark or low to high values, good for ordered data. Diverging scales have two contrasting colors meeting in the middle, useful for showing deviation from a midpoint.

Which function in base R creates a smooth gradient of colors?

A palette()

B colorRampPalette()

C colors()

D colorGradient()

colorRampPalette() creates a function that generates gradients between colors.

Which package provides the brewer.pal() function for palettes?

A RColorBrewer

B scales

C ggplot2

D viridis

brewer.pal() is from the RColorBrewer package.

What type of data is best visualized with a diverging color scale?

A Data with a meaningful midpoint

B Random data

C Categorical data

D Binary data

Diverging scales highlight differences from a midpoint, like zero or average.

Which function sets the current color palette in base R?

A setPalette()

B colorRamp()

C colors()

D palette()

palette() sets or gets the current color palette.

The viridis package is known for palettes that are:

A Random colors

B Only black and white

C Colorblind-friendly and perceptually uniform

D Only pastel colors

viridis palettes are designed to be easy to read by everyone, including colorblind people.

Explain how to create and use a custom color palette in R using base functions.

Think about how you tell R which colors to use and then use them in a plot.

Describe the difference between sequential and diverging color scales and when to use each.

Consider how colors change and what the data represents.

Practice

1. What is the main difference between a color scale and a color palette in R visualization?

easy

A. Color scales and palettes are exactly the same in R.

B. Color scales assign fixed colors to categories, palettes show smooth color changes for continuous data.

C. Color scales are used only for bar charts, palettes only for scatter plots.

D. Color scales show smooth color changes for continuous data, palettes assign fixed colors to categories.

Solution

1. Step 1: Understand color scales

   Color scales map continuous data values to a gradient of colors smoothly changing from one to another.

2. Step 2: Understand color palettes

   Color palettes assign specific, fixed colors to discrete categories or groups.

3. Final Answer:

   Color scales show smooth color changes for continuous data, palettes assign fixed colors to categories. -> Option D

4. Quick Check:

   Color scales = continuous gradient, palettes = fixed category colors [OK]

Hint: Remember: scales = smooth gradient, palettes = fixed colors [OK]

Common Mistakes:

• Confusing continuous and categorical data use
• Thinking palettes create gradients
• Assuming scales only for discrete data

2. Which of the following is the correct syntax to apply a continuous color scale using the viridis palette in ggplot2?

easy

A. + scale_color_viridis_c()

B. + scale_color_manual(values = viridis)

C. + scale_fill_viridis_d()

D. + scale_color_gradient(low = 'blue', high = 'red')"

Solution

1. Step 1: Identify continuous color scale function

   In ggplot2, scale_color_viridis_c() applies a continuous viridis color scale.

2. Step 2: Check other options

   scale_color_manual() is for manual discrete colors, scale_fill_viridis_d() is for discrete fill colors, and scale_color_gradient() is a generic gradient, not viridis.

3. Final Answer:

   + scale_color_viridis_c() -> Option A

4. Quick Check:

   Continuous viridis scale = scale_color_viridis_c() [OK]

Hint: Use _c suffix for continuous scales in ggplot2 [OK]

Common Mistakes:

• Using _d suffix for continuous data
• Confusing fill and color scales
• Passing palette name to scale_color_manual incorrectly

3. What will be the output of this R code snippet?

library(ggplot2)
data <- data.frame(x = 1:5, y = 1:5, z = c(10, 20, 30, 40, 50))
ggplot(data, aes(x, y, color = z)) +
  geom_point(size = 5) +
  scale_color_gradient(low = 'yellow', high = 'red')

medium

A. Points colored with default ggplot2 palette ignoring scale

B. Points colored from yellow (low z) to red (high z) smoothly

C. Error because scale_color_gradient requires fill aesthetic

D. Points colored only yellow regardless of z values

Solution

1. Step 1: Understand the mapping

   The color aesthetic is mapped to numeric variable z, so a continuous color scale applies.

2. Step 2: Analyze scale_color_gradient

   This function creates a smooth gradient from low color (yellow) to high color (red) for the points.

3. Final Answer:

   Points colored from yellow (low z) to red (high z) smoothly -> Option B

4. Quick Check:

   scale_color_gradient maps numeric to gradient colors [OK]

Hint: Continuous color mapped with scale_color_gradient(low, high) [OK]

Common Mistakes:

• Thinking scale_color_gradient only works with fill
• Expecting discrete colors instead of gradient
• Ignoring the color aesthetic mapping

4. Identify the error in this R code that tries to use a palette for discrete colors:

library(ggplot2)
data <- data.frame(x = factor(c('A','B','C')), y = c(3,5,2))
ggplot(data, aes(x, y, fill = x)) +
  geom_bar(stat = 'identity') +
  scale_fill_gradientn(colors = c('red', 'green', 'blue'))

medium

A. geom_bar requires color aesthetic, not fill

B. Missing + sign before scale_fill_gradientn

C. Using scale_fill_gradientn for discrete fill instead of scale_fill_manual or scale_fill_brewer

D. Data x variable should be numeric, not factor

Solution

1. Step 1: Check data and aesthetics

   The fill aesthetic is mapped to a factor variable x, which is discrete.

2. Step 2: Analyze scale function

   scale_fill_gradientn() is for continuous gradients, not discrete palettes. For discrete fill, use scale_fill_manual() or scale_fill_brewer().

3. Final Answer:

   Using scale_fill_gradientn for discrete fill instead of scale_fill_manual or scale_fill_brewer -> Option C

4. Quick Check:

   Discrete fill needs manual or brewer palettes, not gradientn [OK]

Hint: Use manual or brewer scales for discrete colors, not gradientn [OK]

Common Mistakes:

• Using gradient scales for discrete variables
• Forgetting + sign before scale function
• Confusing fill and color aesthetics

5. You want to create a ggplot scatter plot with points colored by a numeric variable but want to highlight values above 50 in red and others in a blue-to-green gradient. Which approach correctly applies this custom color scale?

hard

A. Use scale_color_gradient with low='blue', high='green' and add geom_point with color='red' for points > 50 separately

B. Use a custom color scale with a conditional color vector inside scale_color_manual

C. Use scale_color_gradientn with colors = c('blue', 'green', 'red') and breaks at 50

D. Create a new factor variable for >50 and <=50, then use scale_color_manual with c('bluegreen', 'red')"

Solution

1. Step 1: Understand the requirement

   We want a gradient color for values <= 50 and red color for values > 50.

2. Step 2: Analyze options

   Use scale_color_gradient with low='blue', high='green' and add geom_point with color='red' for points > 50 separately suggests using a gradient scale for all points and layering a separate geom_point with red color for points > 50, which correctly highlights those points.

3. Step 3: Why others fail

   Use a custom color scale with a conditional color vector inside scale_color_manual tries conditional colors inside scale_color_manual which expects discrete values, not numeric. Use scale_color_gradientn with colors = c('blue', 'green', 'red') and breaks at 50 mixes gradient colors but cannot isolate >50 in red. Create a new factor variable for >50 and <=50, then use scale_color_manual with c('bluegreen', 'red')" changes variable to factor but loses gradient effect.

4. Final Answer:

   Use scale_color_gradient with low='blue', high='green' and add geom_point with color='red' for points > 50 separately -> Option A

5. Quick Check:

   Separate layers for highlight + gradient for others [OK]

Hint: Layer points with red color separately to highlight above threshold [OK]

Common Mistakes:

• Trying to mix discrete and continuous scales in one scale
• Using scale_color_manual for numeric gradient
• Not layering points for highlight