An user guide to walk through data visualization by box plot in R.
Please see the tutorials for more details.

• single-batch_boxplot_v01.pdf

• sample1.xlsx

single-batch_boxplot.R

# Aim: This R script is about making single batch boxplot.
 
# Data Description: One batch with replicates
# - sample1.xlsx
#   - Sheet1: Contains raw data across two columns: `Strain` and `Value`. Each strain includes 20 replicates.
#   - Sheet2: Contains the statistical test result (Tukey's HSD).
#
# Output (both pdf and png format):
# - Version 1: Displays all data points using a single color.
# - Version 2: Colors the data points by strain and adds statistical significance group labels at the top.
#---------------------------------------------------------------------------------------------------------------
 
### Step 0, Check packages
 
# Required packages
packages <- c("ggplot2", "readxl")
 
# Check and install required packages
for (pkg in packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg, repos = "https://cloud.r-project.org")
  }
}
 
#---------------------------------------------------------------------------------------------------------------
 
### Step 1. Configuration
 
## Set up a dedicated working directory to store your raw files and output plots.
 
# working directory
working_path = "/scratch/hychang/R_wd/Rana001/Rana001.01/single-batch/"
 
# input data located directory
input_path = "/scratch/hychang/R_wd/Rana001/Rana001.01/single-batch/"
 
# output folder for storing plots
output_path = "/scratch/hychang/R_wd/Rana001/Rana001.01/single-batch/plots/"
 
## Set Input and output
 
# File name of the raw data table, including the file extension
input_file_raw = "sample1.xlsx"
 
# Worksheet name of the raw data table
input_sheet_raw =  "Sheet1"
 
# (Optional) File name of the statistical test result table, including the file extension
input_file_stat = "sample1.xlsx"
 
# (Optional) Worksheet name of the statistical test result table
input_sheet_stat = "Sheet2"
 
# Column names in the input data
x_column = "Strain"
y_column = "Value"
 
# Column name of statistical significance group letters, skip if the statistical test not available
stat_column = "groups"
 
# Output file names of the figures, including the file extension
output_file_1 = "single-batch_v1.pdf"
output_file_2 = "single-batch_v2.pdf"
output_file_3 = "single-batch_v1.png"
output_file_4 = "single-batch_v2.png"
 
# Set up a dedicated working directory to store your raw files and output plots.
dir.create(working_path, recursive = TRUE, showWarnings = FALSE)
dir.create(input_path, recursive = TRUE, showWarnings = FALSE)
dir.create(output_path, recursive = TRUE, showWarnings = FALSE)
 
## Setting figure parameters
 
# the names of x and y axis
x_lab = "Strain"
y_lab = "Value"
 
# y axis range and breaks
y_range = c(0, 7)
y_breaks = c(0, 2, 4, 6)
 
# font size of x and y axis title (axis label)
axis_title_size = 16
 
# font size of x and y axis text (tick label)
axis_text_size = 14
 
# sample ordering
x_order = c("A", "B", "C")
 
# size of data points
point_size= 2
 
# box size
box_size= 0.4
 
# use a single color for all data points
color_single = c("#003175")
 
# use multiple colors for the data points; the number of colors should match the number of samples
color_multiple = c(c("#1B9E77", "#D95F02", "#7570B3"))
 
## Export setting
pdf_width = 6
pdf_height = 4
png_width = 600
png_height = 400
 
#---------------------------------------------------------------------------------------------------------------
 
### Step 2. Setup and Import data
 
# Set working directory
setwd(working_path)
 
# load packages
library(readxl)
library(ggplot2)
 
# Import raw data
df1= read_xlsx(file.path(input_path, input_file_raw), sheet = input_sheet_raw)
 
# (Optional) Import statistical test result. Skip this step if you don't have the results.
df2= read_xlsx(file.path(input_path, input_file_stat), sheet = input_sheet_stat)
 
# Sample ordering
df1[[x_column]] = factor(df1[[x_column]], levels = x_order)
df2[[x_column]] = factor(df2[[x_column]], levels = x_order) # skip this if df2 not available
 
# Display the data frame and view its dimensions (number of rows and columns).
df1; dim(df1)
df2; dim(df2) # skip this if df2 not available
 
# show sample counts and if there is NA value.
table(df1[[x_column]], useNA = "always")
 
# range of value
range(df1[[y_column]])
#---------------------------------------------------------------------------------------------------------------
 
#### Step 3. Generate Plots
 
# Version 1
# all data points using a single, uniform color.
p1 = ggplot(data = df1, aes(x = .data[[x_column]], y = .data[[y_column]])) +
  geom_boxplot(width = box_size,
               outlier.shape = NA) +
  geom_point(color = color_single,
             size = point_size,
             position = position_jitter(width = 0.2, height = 0)) +
  scale_y_continuous(limits = y_range,
                     breaks = y_breaks) +
  labs(x = x_lab,
       y = y_lab) +
  theme_classic() +
  theme(axis.title = element_text(size = axis_title_size),
        axis.text = element_text(size = axis_text_size))
 
# show plot
p1
 
 
# Version 2
# colors data points by Strain and adds statistical significance group labels at the top.
p2 = ggplot(data = df1, aes(x = .data[[x_column]], y = .data[[y_column]])) +
  geom_boxplot(width = box_size, outlier.shape = NA) +
  geom_point(aes(color = .data[[x_column]]),
             size = point_size,
             position = position_jitter(width = 0.2, height = 0)) +
  geom_text(data = df2,
            aes(x = .data[[x_column]], y = Inf, label = .data[[stat_column]]),
            vjust = "inward") +
  scale_color_manual(values = c(color_multiple)) +
  scale_y_continuous(limits = y_range,
                     breaks = y_breaks) +
  labs(x = x_lab,
       y = y_lab) +
  theme_classic() +
  theme(axis.title = element_text(size = axis_title_size),
        axis.text = element_text(size = axis_text_size))
 
# show plot
p2
 
 
#---------------------------------------------------------------------------------------------------------------
#### Step 4. Export Plots
 
## Export PDF
 
# version 1 PDF export
pdf(file.path(output_path, output_file_1), width = pdf_width, height = pdf_height)
p1
dev.off()
 
# version 2 PDF export
pdf(file.path(output_path, output_file_2), width = pdf_width, height = pdf_height)
p2
dev.off()
 
 
 
## Export PNG
 
# Version 1 PNG export
png(file.path(output_path, output_file_3), width = png_width, height = png_height)
p1
dev.off()
 
# Version 2 PNG export
png(file.path(output_path, output_file_4), width = png_width, height = png_height)
p2
dev.off()
 
 
### END this script

Plots can be exported as PDF or PNG files. Here shows the PNG files.

• Version 1: Displays all data points using a single, uniform color.
  • PNG: setting width 600 and height 400.
  • 
  • PDF: width 6 inch and height 4 inch. single-batch_v1.pdf
• Version 2: Colors data points by Strain and adds statistical significance group labels at the top.
  • PNG: setting width 600 and height 400.
  • 
  • PDF: width 6 inch and height 4 inch. single-batch_v2.pdf

• multiple-batches_boxplot_v01.pdf

• sample2.xlsx

multiple-batches_boxplot.R

# Aim: This R script is about generating multiple batches boxplot.
 
# Data Description: Multiple batches with replicates
# - sample1.xlsx
#   - Sheet1: Contains raw data across three columns: `Strain`, `Batch`, `Value`. Each strain has three batches.
#   - Sheet2: Contains the statistical test result (Tukey's HSD).
#
# Output (both pdf and png format):
# - Version 1: Displays data with batches shown in distinct colors.
# - Version 2: Displays batches in distinct colors and adds statistical significance group labels at the top.
#---------------------------------------------------------------------------------------------------------------
 
### Step 0, Check packages
 
# Required packages
packages <- c("ggplot2", "readxl")
 
# Check and install required packages
for (pkg in packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg, repos = "https://cloud.r-project.org")
  }
}
 
#---------------------------------------------------------------------------------------------------------------
 
### Step 1. Configuration
 
## Set up a dedicated working directory to store your raw files and output plots.
 
# working directory
working_path = "/scratch/hychang/R_wd/Rana001/Rana001.01/multiple-batches/"
 
# input data located directory
input_path = "/scratch/hychang/R_wd/Rana001/Rana001.01/multiple-batches/"
 
# output folder for storing plots
output_path = "/scratch/hychang/R_wd/Rana001/Rana001.01/multiple-batches/plots/"
 
## Set Input and output
 
# File name of the raw data table, including the file extension
input_file_raw = "sample2.xlsx"
 
# Worksheet name of the raw data table
input_sheet_raw = "Sheet1"
 
# File name of the statistical test result table, including the file extension
input_file_stat = "sample2.xlsx"
 
# Worksheet name of the statistical test result table
input_sheet_stat = "Sheet2"
 
# Column names in the input data
x_column = "Strain"
y_column = "Value"
batch_column = "Batch"
 
# Column name of statistical significance group letters, 
# skip if the statistical test not available
stat_column = "groups"  
 
# Output file names of the figures, including the file extension
output_file_1 = "mult-batches_v01.pdf"
output_file_2 = "mult-batches_v02.pdf"
output_file_3 = "mult-batches_v01.png"
output_file_4 = "mult-batches_v02.png"
 
# Set up a dedicated working directory to store your raw files and output plots.
dir.create(working_path, recursive = TRUE, showWarnings = FALSE)
dir.create(input_path, recursive = TRUE, showWarnings = FALSE)
dir.create(output_path, recursive = TRUE, showWarnings = FALSE)
 
## Setting figure parameters
 
# the names of x and y axis
x_lab = "Strain"
y_lab = "Value"
 
# y axis range and breaks
y_range = c(0, 6)
y_breaks = c(0, 2, 4, 6)
 
# font size of x and y axis title (axis label)
axis_title_size = 16
 
# font size of x and y axis text (tick label)
axis_text_size = 14
 
# sample ordering
x_order = c("A", "B", "C")
 
# size of data points
point_size= 2
 
# box size
box_size= 0.4
 
# space between batches (points)
space_batch = 0.3
 
# use multiple colors for the data points; 
# the number of colors should match the number of batches
color_multiple = c(c("#1B9E77", "#D95F02", "#7570B3"))
 
## Export setting
pdf_width = 6
pdf_height = 4
png_width = 600
png_height = 400
 
#---------------------------------------------------------------------------------------------------------------
 
### Step 2. Setup and Import data
 
# Set working directory
setwd(working_path)
 
# load packages
library(readxl)
library(ggplot2)
 
# Import raw data
df1= read_xlsx(file.path(input_path, input_file_raw), sheet = input_sheet_raw)
 
# (Optional) Import statistical test result. Skip this step if you don't have the results.
df2= read_xlsx(file.path(input_path, input_file_stat), sheet = input_sheet_stat)
 
# Sample ordering
df1[[x_column]] = factor(df1[[x_column]], levels = x_order)
df2[[x_column]] = factor(df2[[x_column]], levels = x_order) # skip this if df2 not available
 
# Display the data frame and view its dimensions (number of rows and columns).
df1; dim(df1)
df2; dim(df2) # skip this if df2 not available
 
# Show sample counts, including NA values
table(df1[[x_column]], useNA = "always")
 
# Show the range of values
range(df1[[y_column]])
 
# check batch counts, including NA values
table(df1[[batch_column]], useNA = "always")
#---------------------------------------------------------------------------------------------------------------
 
#### Step 3. Generate Plots
 
# Version 1
# all data points using a single, uniform color.
p1= ggplot(data= df1, aes(x= .data[[x_column]], y= .data[[y_column]]))+
  geom_boxplot(width = box_size, 
               outlier.shape = NA)+
  geom_point(data= df1, 
             aes(fill= .data[[batch_column]]),
             shape = 21,
             size = point_size,
             position= position_dodge(width = space_batch))+
  scale_fill_manual(values = color_multiple)+
  scale_y_continuous(limits = y_range,
                     breaks = y_breaks)+
  labs(x= x_lab, y= y_lab)+ 
  theme_classic()+
  theme(axis.title = element_text(size = axis_title_size),
        axis.text = element_text(size = axis_text_size))
 
# show plot
p1
 
 
# Version 2
# colors data points by Strain and adds statistical significance group labels at the top.
p2= ggplot(data = df1, aes(x= .data[[x_column]], y= .data[[y_column]]))+
  geom_boxplot(width = box_size, outlier.shape = NA)+
  geom_point(aes(fill= .data[[batch_column]]),
             shape = 21, 
             size = point_size,
             position= position_dodge(width = space_batch))+
  geom_text(data = df2, 
            aes(x= .data[[x_column]], y= Inf, label = .data[[stat_column]]), 
            vjust= "inward")+
  scale_fill_manual(values = color_multiple)+
  scale_y_continuous(limits = y_range,
                     breaks = y_breaks)+
  labs(x= x_lab, y= y_lab)+ 
  theme_classic()+
  theme(axis.title = element_text(size = axis_title_size),
        axis.text = element_text(size = axis_text_size))
 
# show plot
p2
 
 
#---------------------------------------------------------------------------------------------------------------
#### Step 4. Export Plots
 
## Export PDF
 
# version 1 PDF export
pdf(file.path(output_path, output_file_1), width = pdf_width, height = pdf_height)
p1
dev.off()
 
# version 2 PDF export
pdf(file.path(output_path, output_file_2), width = pdf_width, height = pdf_height)
p2
dev.off()
 
 
 
## Export PNG
 
# Version 1 PNG export
png(file.path(output_path, output_file_3), width = png_width, height = png_height)
p1
dev.off()
 
# Version 2 PNG export
png(file.path(output_path, output_file_4), width = png_width, height = png_height)
p2
dev.off()
 
 
### END this script

Plots can be exported as PDF or PNG files. Here shows the PNG files.

• Version 1: Displays data with batches shown in distinct colors.
  • PNG: setting width 600 and height 400.
  • 
  • PDF: width 6 inch and height 4 inch. mult-batches_v01.pdf
• Version 2: Displays batches in distinct colors and adds statistical significance group labels at the top.
  • PNG: setting width 600 and height 400.
  • 
  • PDF: width 6 inch and height 4 inch. mult-batches_v02.pdf