How to Use scale_color_manual in R | A Complete Guide

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 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()

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. 

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"))

Change the colors of your plot by hex in scale_color_manual?

Change the colors of your plot by RGB in scale_color_manual?

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.