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How to Use scale_color_manual in R | A Complete Guide

Learn to use scale_color_manual in R to create custom color scales for your plots. This guide will explain the concepts, benefits, and best practices.

Key Points

  • scale_color_manual is a function in R that allows you to specify the colors of your plots manually.
  • You can use color names, hex codes, or RGB values to choose the colors for scale_color_manual.
  • You can create different plots using different color palettes and scales, such as qualitative, sequential, diverging, discrete, continuous, or manual scales.
  • You can use scale_color_manual with different data types, such as categorical, numerical, or ordinal data, and various aesthetics, such as shape, size, or alpha.
  • You can use scale_color_manual to make your plots more appealing and informative and convey different meanings and messages with colors.
How to Use scale_color_manual in R | A Complete Guide

Functions and their Descriptions

Argument Option Description
aesthetics A character vector specifying which aesthetics to apply the scale to. By default, it is c("colour", "fill"), which means the scale will change both the color and the fill of the plot elements.
values A set of values that will be mapped to the aesthetic levels. It can be a vector of color names, hex codes, or RGB values, or a named vector that matches the aesthetic levels with the colors.
limits A character vector specifying the aesthetic levels shown on the plot. By default, it is NULL, which means all the levels will be shown.
breaks A character vector specifying the aesthetic levels shown on the legend. By default, it is NULL, which means the same as limits.
labels A character vector specifying the labels of the levels that will be shown on the legend. By default, it is NULL, which means the same as breaks.
na.value A value that will be used to replace missing values. By default, it is "grey50", which means a gray color.
guide A function or a logical value that will determine how the legend is displayed. By default, it is "legend", which means a standard legend.
Table of Contents

I am Zubair Goraya, a certified data analyst, a PhD scholar, and a freelancer with 5 years of experience. I have learned how to use scale_color_manual, a function of ggplot2 that lets you manually choose the colors of your plots. I am writing this article because I faced many problems while conducting my research, so you will get better results by following this guide. 

In this article, I will share with you what scale_color_manual is, how to use it with different types of plots, data, and aesthetics, and how to customize the legend of your graph. Scale_color_manual is a function that allows you to manually specify the colors of your plots instead of using the default colors that R provides. 

You can use it to change the colors of any graph you create with ggplot2, a popular package for data visualization. It will help you improve your data visualization skills and make your plots more attractive and informative.

Why should you care about scale_color_manual?

Well, because colors are not just for decoration. Colors can also convey meaning, emotion, and information. Colors can help you highlight the most important or interesting aspects of your data or show the relationships or differences between different groups or categories of data. 

Colors can also make your plots more appealing and readable or boring and confusing, depending on your choice.

What is scale_color_manual in R?

Scale_color_manual is a function in R that allows you to specify the colors of your plots manually. It is part of the ggplot2 package, which is a powerful and flexible package for data visualization in R. Ggplot2 uses a system of layers to create plots, where each layer represents a different aspect of the plot:
  • the data, 
  • the geometry, 
  • the aesthetics, 
  • the scales, 
  • the coordinates, 
  • the facets, 
  • the themes, 
  • the legends.

Scale_color_manual is one of the functions that belong to the scale layer, which controls how the data values are mapped to the aesthetic values, such as color, size, shape, etc. To use scale_color_manual, you need to have a plot that uses color as one of the aesthetics. 

For example, you can create a scatter plot with ggplot2, where the color of the points represents a categorical variable, such as the species of flowers. Here is an example of creating such a plot using the iris data set, which contains information about the sepal length, sepal width, petal length, and species of 150 flowers.

# Load the ggplot2 package
library(ggplot2)
# Load the iris data set
data(iris)
# Create a scatter plot with color as an aesthetic
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point()
A scatter plot of sepal length vs sepal width, with color representing species by using the scale_color_manual using ggplot2 in R

How to change the colors of your plot by using scale_color_manual?

As you can see, the plot uses the default colors that ggplot2 provides, which are blue, red, and green. These colors are correct, but they are neither very exciting nor informative. You may want to use more suitable colors for your data, appeal to your audience, or be more consistent with your theme. It is where scale_color_manual comes in handy. 

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You can use scale_color_manual to change the colors of your plot by specifying the values argument, a set of values that will be mapped to the levels of the color aesthetic. For example, you can use color names, such as “brown”, “purple”, and “orange”, to specify the colors. 

# Change the colors of the plot with scale_color_manual
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point() +
  scale_color_manual(values = c("brown", "purple", "orange"))
[A scatter plot of sepal length vs sepal width, with color representing species, and the colors changed to brown, purple, and orange

Change the colors of your plot by hex in scale_color_manual?

You can also use hex codes, such as “#FFC0CB”, “#800080”, and “#FFA500”, to specify the colors. Hex codes are six-digit codes representing a color's red, green, and blue components. Here is an example of how to use hex codes to specify the colors:

# Change the colors of the plot with scale_color_manual and hex codes
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point() +
  scale_color_manual(values = c("#FFC0CB", "#800080", "#FFA500"))
A scatter plot of sepal length vs sepal width, with color representing species, and the colors changed by using hex values

Change the colors of your plot by RGB in scale_color_manual?

You can also use RGB values, such as c(255, 192, 203), c(128, 0, 128), and c(255, 165, 0), to specify the colors. RGB values are three numbers that represent the red, green, and blue components of a color, ranging from 0 to 255. 

# Change the colors of the plot with scale_color_manual and RGB values
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point() +
  scale_color_manual(values = c(c(155, 192, 203), c(128, 0, 128), c(255, 165, 0)))
A scatter plot of sepal length vs sepal width, with color representing species, and the colors changed by using RGB color scheme

Depending on your preference and convenience, you can use any of these methods to choose the colors for scale_color_manual. However, it would help if you were careful not to use colors that are too similar, bright, or dark, as they may make your plot hard to read or interpret. It would be best to make sure that the colors match the number and order of the levels of the color aesthetic; otherwise, you may get unexpected results or errors. 

How do we use scale_color_manual with different types of data, and aesthetics?

You can use scale_color_manual with any plot that you create with ggplot2, as long as it uses color as one of the aesthetics. For example, you can use scale_color_manual with scatter plots, bar plots, box plots, line plots, and many more. 

You can also use scale_color_manual with different types of data, such as categorical, numerical, or ordinal data, and various kinds of aesthetics, such as shape, size, or alpha. In this section, I will show you some examples of how to use scale_color_manual with different types of plots, data, and aesthetics and explain the logic and the effect behind each one.

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Using scale_color_manual with different types of plots

One of the most common types of plots that you can use scale_color_manual with is a scatter plot. A scatter plot shows the relationship between two numerical variables by plotting them as points. You can use color as an aesthetic to show a third variable, either categorical or numerical. For example, you can use color to show the species of the flowers in the iris data set, as we did before. 

# Create a scatter plot with color as an aesthetic
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point() +
  scale_color_manual(values = c("brown", "purple", "orange"))
A scatter plot of sepal length vs sepal width, with color representing species, and the colors changed to brown, purple, and orange

You can also see that the plot has a legend that shows the mapping between the colors and the levels of the color aesthetic. The legend is automatically generated by ggplot2 based on the arguments and options you provide. 

You can customize the legend by changing the arguments and options of scale_color_manual, such as limits, breaks, labels, na.value, and guide. For example, you can change the titles of the legend to something more descriptive, such as “Setosa”, “Versicolor”, and “Virginica”, instead of the default “setosa”, “versicolor”, and “virginica”. 

# Change the labels of the legend with scale_color_manual
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point() +
  scale_color_manual(values = c("brown", "purple", "orange"),
                     labels = c("Setosa", "Versicolor", "Virginica"))
A scatter plot of sepal length vs sepal width, with color representing species, and the colors changed to brown, purple, and orange, and the labels of the legend changed to Setosa, Versicolor, and Virginica

You can see that the plot now has more descriptive labels on the legend, which makes it easier to understand and interpret. You can also change the order of the levels in the legend by changing the limits argument of scale_color_manual. 

For example, you can reverse the order of the levels so that the legend shows “Virginica”, “Versicolor”, and “Setosa” instead of the default “setosa”, “versicolor”, and “Virginia”. 

# Change the order of the levels on the legend with scale_color_manual
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point() +
  scale_color_manual(values = c("brown", "purple", "orange"),
                     labels = c("Setosa", "Versicolor", "Virginica"),
                     limits = c("virginica", "versicolor", "setosa"))
A scatter plot of sepal length vs sepal width, with color representing species, and the colors changed to brown, purple, and orange, and the order of the levels on the legend reversed to Virginica, Versicolor, and Setosa

You can see that the plot now has a different order of the levels on the legend, which may affect the perception and comparison of the data. You can also change the legend's appearance and position by changing the guide argument of scale_color_manual. 

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For example, you can change the legend to a horizontal layout instead of the default vertical layout by using the guide_legend function and setting the direction argument to "horizontal".

# Change the legend to a horizontal layout with scale_color_manual
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point() +
  scale_color_manual(values = c("brown", "purple", "orange"),
                     labels = c("Setosa", "Versicolor", "Virginica"),
                     guide = guide_legend(direction = "horizontal"))+
  theme(legend.position = "bottom")
A scatter plot of sepal length vs sepal width, with color representing species, and the colors changed to brown, purple, and orange, and the legend changed to a color bar and moved to the bottom

You can also remove the legend altogether, by setting the legend.position argument to “none”. 

# Remove the legend with scale_color_manual
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point() +
  scale_color_manual(values = c("brown", "purple", "orange"),
                     labels = c("Setosa", "Versicolor", "Virginica"),
                     guide = guide_legend(direction = "horizontal"))+
  theme(legend.position = "none")
A scatter plot of sepal length vs sepal width, with color representing species, and the colors changed to brown, purple, and orange, and the legend removed

Conclusion

In this article, you learned what scale_color_manual and scale_fill_manual are and how to use them with different plots, data, and aesthetics. You also learned how to choose the colors for your plots and customize the legend according to your needs and preferences. 

scale_color_manual and scale_fill_manual to create more attractive and informative plots and to communicate your data more effectively. You can also use them to explore and analyze your data and to discover new insights and patterns. You can apply scale_color_manual and scale_fill_manual to any plot you create with ggplot2, as long as it uses color or fill as one of the aesthetics. Combining them with other scales and functions creates more complex and customized plots.

Frequently Asked Questions (FAQs)

What is ggplot2, and how do I install it?

ggplot2 is a package for data visualization in R based on the grammar of graphics. It allows you to create plots with multiple layers, scales, coordinates, facets, and themes. You can install ggplot2 from CRAN by running install.packages("ggplot2") in your R console.

What is a geom, and how do I use it?

A geom is a function representing the shape or type of plot you want to create. For example, geom_point() creates a scatter plot, geom_bar() creates a bar chart, geom_line() creates a line graph, and so on. You can use a geom function as a layer in your ggplot and specify the data and aesthetic mappings you want for that layer.

What is an aesthetic, and how do I map it?

Aesthetics are the visual properties of a geom, such as color, size, shape, position, etc. You can map an aesthetic to a variable in your data so that the variable's values determine the geom's appearance. For example, you can map the color aesthetic to the species variable so that each species has a different color on the plot. You can map an aesthetic using the aes() function inside a geom function.

What is the difference between scale_color_manual and scale_fill_manual?

Both scale_color_manual and scale_fill_manual allow you to specify the colors for your plot manually. The difference is that scale_color_manual changes the color of the outline or stroke of the geom, while scale_fill_manual changes the color of the fill or background of the geom. 

For example, if you use geom_point(), scale_color_manual will change the point's color, while scale_fill_manual will have no effect. If you use geom_bar(), scale_color_manual will change the color of the bar's border, while scale_fill_manual will change the color of the inside of the bar.

How do I choose the colors for my plot?

You can choose the colors for your plot by providing a vector of color names or codes to the values argument of scale_color_manual or scale_fill_manual. For example, you can use scale_color_manual(values = c("red", "green", "blue")) to assign red, green, and blue colors to your plot. You can also use hexadecimal codes, such as scale_color_manual(values = c("#FF0000", "#00FF00", "#0000FF")). You can also use color palettes from other packages, such as RColorBrewer, viridis, or ggthemes.

How do I customize the legend of my plot?

You can customize the legend of your plot by changing the arguments and options of scale_color_manual or scale_fill_manual. For example, you can change the labels of the legend by providing a vector of character strings to the labels argument, such as scale_color_manual(labels = c("Setosa", "Versicolor", "Virginica")). 

You can also change the order of the levels on the legend by providing a vector of factor levels to the limits argument, such as scale_color_manual(limits = c("virginica", "versicolor", "setosa")). You can also change the appearance and the position of the legend by changing the guide argument, such as scale_color_manual(guide = guide_legend(direction = "horizontal")) or scale_color_manual(guide = guide_colorbar()).

How do I save my plot to a file?

You can save your plot to a file using the ggsave() function. You can specify the name, the format, the width, and the height of the file, such as ggsave("plot.png", width = 5, height = 5). The file format will be determined by the file extension, such as .png, .jpg, .pdf, etc.

How do I create a quick plot with ggplot2?

You can create a quick plot with ggplot2 by using the qplot() function. This function is similar to the base R plot() function but uses the ggplot2 syntax and defaults. You can provide the data, the geom, and the mappings as arguments, such as qplot(x = cty, y = hwy, data = mpg, geom = "point"). It will create a scatter plot of city and highway miles from the mpg data set.

How do I add multiple layers to my plot?

You can add multiple layers to your plot by using the + operator. You can add as many layers as you want if they are compatible with your plot's data and aesthetics. For example, you can add a smooth line to your scatter plot by using geom_point() and geom_smooth(), such as ggplot(mpg, aes(cty, hwy)) + geom_point() + geom_smooth().

How do I change the coordinate system of my plot?

You can change the coordinate system of your plot by using a coord_ function. For example, you can use coord_polar() to change your plot to a polar coordinate system, such as ggplot(mpg, aes(class, hwy)) + geom_bar() + coord_polar(). It will create a pie chart of highway mileage by category from the mpg data set. You can also use coord_flip() to flip the x and y axes, coord_fixed() to fix the aspect ratio, coord_trans() to transform the scales, etc.


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About the Author

Ph.D. Scholar | Certified Data Analyst | Blogger | Completed 5000+ data projects | Passionate about unravelling insights through data.

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