Plotting Multiple Plots for All Variables of Listed DataFrames

In this tutorial, we’ll explore how to create plots for each variable in a list of dataframes. We’ll cover the basics of R programming and use popular libraries such as dplyr and ggplot2. By the end of this article, you should be able to plot multiple plots for all variables of listed dataframes.

What is a DataFrame?

A dataframe is a two-dimensional data structure in R that stores observations (rows) and variables (columns). It’s similar to an Excel spreadsheet or a SQL table. Dataframes are useful for data analysis, visualization, and machine learning tasks.

The Problem: Plotting Multiple Plots for All Variables of Listed Dataframes

Let’s consider the following example:

# Create three dataframes with different variables
df1 <- data.frame(ID = rep(c("P-1000", "P-10001", "P-10002"), 3),
                   Visit = c(rep("M1", 5), rep("M4", 5), rep("M17", 5)),
                   Value = runif(15))

df2 <- data.frame(ID = rep(c("P-1000", "P-10001", "P-10002"), 3),
                  Visit = c(rep("M1", 5), rep("M4", 5), rep("M17", 5)),
                  Value = runif(15))

df3 <- data.frame(ID = rep(c("P-1000", "P-10001", "P-10002"), 3),
                  Visit = c(rep("M1", 5), rep("M4", 5), rep("M17", 5)),
                  Value = runif(15))

# Create a list of dataframes
sampledata <- list(df1, df2, df3)

# Set the names of the dataframes
names(sampledata) <- c("A", "C", "Z")

# Plotting multiple plots for all variables of listed dataframes

The goal is to create plots for each variable in the list of dataframes. The current code tries to plot a separate boxplot for each variable in each dataframe, resulting in multiple plots for each ID.

The Solution: Using purrr::map2 and dplyr::group_map

To solve this problem, we’ll use two popular R packages: purrr and dplyr. Specifically, we’ll utilize the purrr::map2 function to apply a function across the list of dataframes and their names, and dplyr::group_map to apply functions by ID groups.

The Code

# Define a function to build a plot for each variable in a dataframe
build_plot <- function(sub_df) {
  # Filter out missing values
  sub_df <- sub_df %>% 
    filter(!is.na(Visit))
  
  # Convert Visit column to factor with desired levels
  sub_df <- sub_df %>% 
    mutate(Visit = factor(Visit, levels = c("M1", "M4", "M17")))
  
  # Create a boxplot for the Value variable
  plot <- ggplot(sub_df, aes(x = Visit, y = Value)) + 
    geom_boxplot() + 
    labs(title = paste("Value", sub_df$ID[1]), y = "Value", x = "Visit")
  
  return(plot)
}

# Define a function to build plots for each variable in a dataframe
run_groups <- function(main_df, df_name) {
  # Build plots by ID
  plot_list <- main_df %>% 
    group_by(ID) %>% 
    group_map(build_plot)
  
  # Save plots to single PDF
  ggplot2::ggsave(
    filename = paste0(df_name, "_plots.pdf"), 
    plot = gridExtra::marrangeGrob(plot_list, nrow = 1, ncol = 1),
    width = 15, height = 9
  )
  
  return(plot_list)
}

# Create PDFs by data frame
myplots <- purr::map2(sample_data, names(sample_data), run_groups)

# Run the code
run_groups(myplots, "MyPlots")

How it Works

1. We define two functions: build_plot and run_groups.
2. The build_plot function takes a dataframe as input, filters out missing values, converts the Visit column to a factor with desired levels, and creates a boxplot for the Value variable.
3. The run_groups function takes a dataframe and its name as input, builds plots by ID using group_map, and saves the plots to a single PDF file.
4. We use purrr::map2 to apply the build_plot function across the list of dataframes and their names, and dplyr::group_map to apply functions by ID groups.

The Result

The code generates three PDF files: one for each dataframe (A, C, Z). Each file contains a single plot with multiple boxplots for the Value variable. The plots are arranged in a single column per row.

In conclusion, we’ve demonstrated how to create plots for each variable in a list of dataframes using R programming and popular libraries such as dplyr and ggplot2. By utilizing purrr::map2 and dplyr::group_map, we can efficiently build plots by ID groups and save them to a single PDF file.

Last modified on 2025-02-20