Analysis in R: If you want simple and clean graphs, here is a summary of ggplot2

The ggplot2 package is a very powerful plotting package. We have compiled a list of uses and commands as a reminder.

The data used in the introduction is converted to data frames from the diamonds included in the package.

tidyverse version is 1.3.1. R version 4.2.2 is confirmed.

Overview of ggplot2

One of the features of ggplot2 is that the commands for “reading” data, “plotting” figures from the data, and “decorating” figures are clearly separated.
It may take some getting used to, but it is a reasonable package because the commands have clear modification points, so the effort is low. Specifically, the “ggplot” command reads data, creates an object, plots, and then decorates the object.

Installing ggplot2

Run the following command. This tutorial uses the tidyverse package, which includes the ggplot2 package.

#install.packages("ggplot2")
install.packages("tidyverse")

Loading the ggplot2 library

Run the following command.

#Loading the library
#library("ggplot2")
library("tidyverse")

Data Read Command

There are two ways to read data with ggplot2: the “qplot” command, which specifies everything together from reading to plotting, and the “ggplot” command, which specializes in reading data.

Although “qplot” can be used as a precursor, we recommend using the “ggplot” command to take full advantage of ggplot2’s features: “read data” command, “plot figure from data” command, and “decorate figure” command.

Note that the x-axis, y-axis, groups, symbol colors, etc. must be specified with the aes command when loading data or within the plot command. The x- and y-axes must be specified according to the figure to be plotted.

#Data Read Command
Testdata <- diamonds

List of “aes options” that specify graph elements

List of aes options. Multiple options can be set.

Example of plotting read data

The elements are specified by aes from the geom_point command to plot the read data in a scatterplot. See the command comments for details.

#You can plot by specifying elements with aes
PlotData <- ggplot(Testdata)
PlotData +
  geom_point(aes(x = carat, y = price, color = color))

#To decorate a figure, you can plot it with the print command if you store an object in the argument
PointPlot <- PlotData + geom_point(aes(x = carat, y = price, color = color))
print(PointPlot)

Scatter plots to be plotted.

To save the file, use the ggsave command. The save in the working directory.

ggsave("Test.png", PointPlot)

Introduction to articles that may be helpful for graphing

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Split plot “facet command”

How to place a graph from the right. facet_grid(. ~”name or number of the row to split”)

PlotData +
  geom_point(aes(x = carat, y = price, color = color)) +
  facet_grid(.~color) +
  labs(x = "carat", y = "price", colour = "Colour")

How to place the graph from the top. facet_grid(“name of the column or column number you want to split”~.)

PlotData +
  geom_point(aes(x = carat, y = price, color = color)) +
  facet_grid(color~.) +
  labs(x = "carat", y = "price", colour = "Colour")

Adjust Legend and Axis Labels

First, the plot is normal. The legend and axis labels are not the row names of the data.

PlotData <- ggplot(Testdata, aes(x = Testdata[, 1],
                                 y = Testdata[, 7],
                                 color = Testdata[, 3],
                                 size = Testdata[, 1])) +
  geom_point()
print(PlotData)

The legend and axis names can be customized using the labs command, which also allows you to set titles.

PlotData2 <- PlotData +
  labs(x = "Calat", y = "Price", title = "TEST PLOT",
       color = "Color", size = "SIZE")
print(PlotData2)

Customize plot symbols

PlotData2 is used to change the transparency and color of the plot. The alpha option is added to the transparency with the aes command. If it is not set, the alpha legend will be displayed, so the guide command is used to prevent it from being displayed. The scale_colour_brewer command is used for color. For continuous data, it is better to use scale_colour_continuous, etc.

PlotData3 <- PlotData2 +
  aes(alpha = 0.01) +
  guides(alpha = "none") +
  scale_colour_brewer(palette = "Set1")
print(PlotData3)

Figure plotting commands commonly used with ggplot2

Commands for arranging the appearance of the entire figure

The character style is achieved using the theme command. The easiest way is to customize the provided theme_classic(), theme_minimal(), etc.

PlotData3 + theme_minimal()

List options for theme command

The usage is theme(element = corresponding element(option)).

Ex： theme(axis.title = element_text(face = “bold.italic”, colour = “red”))

Articles about ggplot2

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I hope this makes your analysis a little easier !!