Analysis in R: Easy use of ggplot2 “ggmatplot” package

The “ggplot2” package is a powerful tool for converting data into plots in R. However, there are times when you want to use it simply, and sometimes it is a hassle to convert the data to correspond to a number. This is an introduction to a package that solves such a problem and easily plots data as wide-type data.

An example, we also show a sample command to render each figure with the “ggplot2” package, except for the dot.

Package version is 0.1.1. Checked with R version 4.2.2.

Install Package

Run the following command.

#Install Package
install.packages("ggmatplot")

Example

See the command and package help for details.

#Loading the library
library("ggmatplot")

#Install the tidyverse package if it is not already there
if(!require("tidyverse", quietly = TRUE)){
  install.packages("tidyverse");require("tidyverse")
}

###Creating Data#####
set.seed(1234)
n <- 300
TestData <- tibble(Group = sample(paste0("Group", 1:2), n,
                                  replace = TRUE),
                   X_Data1 = sample(c(1:50), n, replace = TRUE),
                   X_Data2 = sample(c(1:100), n, replace = TRUE),
                   Y_Data1 = sample(c(1:50), n, replace = TRUE),
                   Y_Data2 = sample(c(1:100), n, replace = TRUE))
#Check
TestData
# A tibble: 300 x 5
   Group  X_Data1 X_Data2 Y_Data1 Y_Data2
   <chr>    <int>   <int>   <int>   <int>
1 Group2      19      48      17      25
2 Group1       3      65      24      75
3 Group2       6      33       9      77
4 Group1      22      64      39      77
5 Group2      43      18      11      63
6 Group2      32      14       3      44
7 Group2      34      92      34      35
8 Group1       2      63      27      10
9 Group1       4      55       8      95
10 Group1      34      19      24      97
# ... with 290 more rows
#######

#Plotting with wide type data: ggmatplot command
#Specify plot type:plot_type option;
#point,line,both(point + line),density,histogram,
#boxplot,dotplot,errorplot,violin,ecdf
#Other options are omitted as they can be understood without explanation,
#see Help.
ggmatplot(x = TestData %>% select(X_Data1, X_Data2),
          y = TestData %>% select(Y_Data1, Y_Data2),
          plot_type = "point", color = NULL,
          fill = NULL, shape = c(2, 4), linetype = NULL,
          log = NULL, main = "karada-good",
          xlab = "X Data", ylab = "Y Data",
          legend_label = NULL, legend_title = "karada-good",
          desc_stat = "mean_se")

###例:violin#####
ggmatplot(x = TestData %>% select(X_Data1, X_Data2),
          plot_type = "violin", color = NULL,
          fill = c("#a87963", "red"), alpha = 0.4, main = "karada-good",
          xlab = "X Data", ylab = "Value",
          legend_label = NULL, legend_title = "karada-good",
          desc_stat = "mean_se")

###In the case of ggplot2
TestData %>% select(X_Data1, X_Data2) %>%
  pivot_longer(cols = X_Data1:X_Data2,
               names_to = "Name", values_to = "Value") %>%
  ggplot(aes(x = Name, y = Value, fill = Name)) +
  geom_violin(alpha = 0.4) +
  scale_fill_manual(values = c("#a87963", "red")) +
  labs(title = "karada-good", x = "X Data",
       y = "Value", fill = "karada-good")

###Exsample:BoxPlot#####
ggmatplot(x = TestData %>% select(X_Data1, X_Data2),
          plot_type = "boxplot", color = c("#a87963", "red"),
          fill = c("#a87963", "red"), main = "karada-good",
          xlab = "X Data", ylab = "Value",
          legend_label = NULL, legend_title = "karada-good",
          desc_stat = "mean_se")

###In the case of ggplot2
TestData %>% select(X_Data1, X_Data2) %>%
  pivot_longer(cols = X_Data1:X_Data2,
               names_to = "Name", values_to = "Value") %>%
  ggplot(aes(x = Name, y = Value, fill = Name, col = Name)) +
  geom_boxplot(alpha = 0.5) +
  scale_fill_manual(values = c("#a87963", "red")) +
  scale_color_manual(values = c("#a87963", "red"),
                     guide = "none") +
  labs(title = "karada-good", x = "X Data",
       y = "Value", fill = "karada-good")

###Exsample:ecdf#####
ggmatplot(x = TestData %>% select(X_Data1, X_Data2),
          plot_type = "ecdf", color = NULL,
          fill = c("#a87963", "red"), main = "karada-good",
          xlab = "X Data", ylab = "Value",
          legend_label = NULL, legend_title = "karada-good",
          desc_stat = "mean_se")

###In the case of ggplot2
TestData %>% select(X_Data1, X_Data2) %>%
  pivot_longer(cols = X_Data1:X_Data2,
               names_to = "Name", values_to = "Value") %>%
  ggplot(aes(x = Value, col = Name)) +
  stat_ecdf(geom = "step") +
  scale_color_manual(values = c("#a87963", "red")) +
  labs(title = "karada-good", x = "X Data",
       y = "Value", col = "karada-good")

Output Example

・Scatter Plot

・Exsample:violin

・Exsample:BoxPlot

・Exsample:ecdf

I hope this makes your analysis a little easier !!