Looping ggplot over Subsets of Data Frame

Introduction

In data analysis and visualization, it’s often necessary to generate plots that cater to different subsets of the data. In this scenario, we’re dealing with a dataset df_cl containing various variables, including ‘FOV’. The goal is to create a flexible script that generates plots for each unique value in the ‘FOV’ column. This tutorial will guide you through the process of looping ggplot over subsets of the data frame.

Understanding Data Frames

Before we dive into the code, let’s first understand what a data frame is. In R, a data frame is a two-dimensional structure that stores data in rows and columns. It’s essentially a table with rows representing individual observations and columns representing different variables.

Data Frame Subsets

Subsetting a data frame means selecting specific rows or columns from the original dataset. This is done by specifying conditions that determine which rows or columns are included in the subset. For example, df_cl %>% filter(FOV == "a") would select only the rows where ‘FOV’ equals “a”.

ggplot2 and Data Frames

ggplot2 is a popular data visualization library in R that allows users to create high-quality plots using a grammar-based approach. When working with ggplot, it’s essential to understand how data frames are used as input.

In the provided code snippet, df_cl %>% filter(FOV == FOV) suggests that the original data frame is being filtered based on the ‘FOV’ column before being passed to ggplot. However, this will not produce different subsets for each unique value in the ‘FOV’ column.

The Issue and Solution

Let’s break down what’s happening in the code snippet:

FOV_list <- unique(df_cl$FOV): Extracts all unique values from the ‘FOV’ column of df_cl into a list called FOV_list.
ClEff_plots = list(): Creates an empty list to store plots for each FOV.
The for loop iterates over each value in FOV_list, assigning it to the variable FOV.

Inside this loop, we have:

ggplot(df_cl %>% filter(FOV == FOV), aes(x=ID)) + ...: Creates a new data frame by filtering df_cl based on the current value of FOV. However, notice that the column name in filter() is hardcoded to match the variable name ("FOV"), not the actual FOV value.
...: This is where we add plot elements like geom_points.

The problem lies in how we’re using the FOV values within the loop. Each iteration of the loop uses the same value, so the filtering condition remains the same (FOV == FOV). As a result, only one subset of data is being used for all plots.

To fix this issue, we need to create a new data frame for each unique FOV value, then pass it to ggplot. We’ll use dplyr and base R functions to achieve this.

Corrected Code

Here’s the corrected version of the code snippet:

#Getting all FOVs to loop  
FOV_list <- unique(df_cl$FOV)

#Create a list to store the ClEff plots  
ClEff_plots = list()

#looping over unique FOV names  
for (FOV in FOV_list) {  

  # Create new data frame for each FOV value  
  df_FOV <- df_cl %>% 
    filter(FOV == FOV)

  #Add plot elements to ggplot   
  ClEff_plots[[FOV]] = ggplot(df_FOV, aes(x=ID)) +   
    geom_point(aes(y=BB_ClEff, col="BB_ClEff"),size=0.1) + 
    geom_point(aes(y=GG_ClEff, col="GG_ClEff"),size=0.1) +   
    geom_point(aes(y=YY_ClEff, col="YY_ClEff"),size=0.1) +   
    geom_point(aes(y=RR_ClEff, col="RR_ClEff"),size=0.1) + 
    labs(title="ClEff",   
         y="Cleave %", x="Cycle") +    
    theme(axis.text.x = element_blank()) + 
    theme(axis.ticks.x = element_blank()) + 
    #theme(panel.border = element_rect()) + 
    facet_wrap(df_FOV$Cycle, nrow = 1, scales = "free_x", strip.position = "bottom") +  
    scale_color_manual(name="",   
                       values = c("BB_ClEff"="#0072B2", "GG_ClEff"="#009E73", "YY_ClEff"="#F0E442", "RR_ClEff"="#D55E00")) +   
  theme(panel.grid.minor = element_blank()) 

  print(ClEff_plots[[FOV]])

  ggsave(ClEff_plots[[FOV]], file=paste0('ClEff_plot_FOV_', FOV, '.png'),
         width = 44.45, height = 27.78, units = 'cm', dpi = 600, path = "~/desktop/Outputs/")
}

Changes Made:

Instead of using df_cl %>% filter(FOV == FOV), we create a new data frame df_FOV for each FOV value by using the %>% operator with filter(). This ensures that we’re working with different subsets of data for each plot.
We pass df_FOV to ggplot instead of filtering df_cl.

With these changes, you should now have a new plot for each unique FOV value.

Further Improvements

While the corrected code snippet works as expected, it can be further optimized. Here’s an alternative version using dplyr:

#Getting all FOVs to loop  
library(dplyr)
FOV_list <- unique(df_cl$FOV)

#Create a list to store the ClEff plots  
ClEff_plots = list()

#looping over unique FOV names  
for (FOV in FOV_list) {  

  #Add plot elements to ggplot using dplyr's filter()  
  df_FOV <- df_cl %>% 
    filter(FOV)

  #Add plot elements to ggplot   
  ClEff_plots[[FOV]] = ggplot(df_FOV, aes(x=ID)) +   
    geom_point(aes(y=BB_ClEff, col="BB_ClEff"),size=0.1) + 
    geom_point(aes(y=GG_ClEff, col="GG_ClEff"),size=0.1) +   
    geom_point(aes(y=YY_ClEff, col="YY_ClEff"),size=0.1) +   
    geom_point(aes(y=RR_ClEff, col="RR_ClEff"),size=0.1) + 
    labs(title="ClEff",   
         y="Cleave %", x="Cycle") +    
    theme(axis.text.x = element_blank()) + 
    theme(axis.ticks.x = element_blank()) + 
    #theme(panel.border = element_rect()) + 
    facet_wrap(df_FOV$Cycle, nrow = 1, scales = "free_x", strip.position = "bottom") +  
    scale_color_manual(name="",   
                       values = c("BB_ClEff"="#0072B2", "GG_ClEff"="#009E73", "YY_ClEff"="#F0E442", "RR_ClEff"="#D55E00")) +   
  theme(panel.grid.minor = element_blank()) 

  print(ClEff_plots[[FOV]])

  ggsave(ClEff_plots[[FOV]], file=paste0('ClEff_plot_FOV_', FOV, '.png'),
         width = 44.45, height = 27.78, units = 'cm', dpi = 600, path = "~/desktop/Outputs/")
}

This version is more concise and efficient since it leverages the filter() function from dplyr.

Last modified on 2025-01-21