Using `rbind` Problem Inside Nested For Loop

Introduction

In this article, we will explore the use of rbind function in R programming language and discuss its limitations when used inside nested for loops. We will also provide a solution to overcome these limitations.

Background

The rbind function is used to bind two or more data frames together along the rows. It creates a new data frame that combines all the input data frames into one, with each row from the individual data frames appearing in sequence. The first row of the resulting data frame comes from the first data frame, the second row from the second data frame, and so on.

However, when used inside nested for loops, rbind can lead to unexpected behavior. In this article, we will explore what happens when using rbind in a nested loop structure and provide a solution to overcome these limitations.

The Problem

Let’s consider an example where we have five folders with subfolders containing NetCDF files. We want to read the NetCDF files, extract certain variables, and store them in a data frame. The code is as follows:

setwd("E:/main_folder")
#1#  list all files in the main_folder
folders <- as.list(list.files("E:/main_folder"))

#2# make list of subfiles 
subfiles <- lapply(folders, function(x) as.list(list.files(paste("E:/main_folder",x, sep="/"))))

#3# list the netcdf files from each subfiles
files1 <- lapply(subfiles[[1]], function(x) list.files(paste(folders[1],x, sep = "/"),pattern='*.nc',full.names=TRUE))
files2 <- lapply(subfiles[[2]], function(x) list.files(paste(folders[2],x, sep = "/"),pattern='*.nc',full.names=TRUE))
files3 <- lapply(subfiles[[3]], function(x) list.files(paste(folders[3],x, sep = "/"),pattern='*.nc',full.names=TRUE))
files4 <- lapply(subfiles[[4]], function(x) list.files(paste(folders[4],x, sep = "/"),pattern='*.nc',full.names=TRUE))
files5 <- lapply(subfiles[[5]], function(x) list.files(paste(folders[5],x, sep = "/"),pattern='*.nc',full.names=TRUE))

#4# join all files in one list
filelist <- list(files1,files2,files3,files4,files5)



#5# Read the NetCDF and get the desired variables 
df <- data.frame()
MissionsData <- list()
for (i in seq_along(filelist)){
  n <- length(filelist[[i]])
  for (j in 1:n){
    for( m in 1:length( filelist[[i]][[j]])){
   nc <- nc_open(filelist[[i]][[j]][[m]])
lat <-  ncvar_get(nc, "glat.00")
lon <-  ncvar_get(nc, "glon.00")
ssh <-  ncvar_get(nc, "ssh.53")
jdn <- ncvar_get(nc, "jday.00")

df <- rbind(df,data.frame(lat,lon,ssh,jdn))
nc_close(nc)
    }
  }

  MissionsData[[i]] <- df

}

The problem with this code is that rbind is being used inside a nested for loop structure. As we will discuss later, this can lead to unexpected behavior and incorrect results.

A Solution

To overcome the limitations of using rbind in a nested for loop structure, we need to rethink our approach. Here’s an alternative solution that uses a different technique:

setwd("E:/main_folder")
#1#  list all files in the main_folder
folders <- as.list(list.files("E:/main_folder"))

#2# make list of subfiles 
subfiles <- lapply(folders, function(x) as.list(list.files(paste("E:/main_folder",x, sep="/"))))

#3# read netcdf file and get variables using lapply
read_files <- lapply(subfiles, function(subfile){
    files <- lapply(subfile, function(x) list.files(paste(folders[1],x, sep = "/"),pattern='*.nc',full.names=TRUE))
    dataframes <- lapply(files, function(file_i){
        nc <- nc_open(file_i)
        lat <-  ncvar_get(nc, "glat.00")
        lon <-  ncvar_get(nc, "glon.00")
        ssh <-  ncvar_get(nc, "ssh.53")
        jdn <- ncvar_get(nc, "jday.00") 
        nc_close(nc)  
        return(data.frame(lat,lon,ssh,jdn))
    })
    do.call(rbind, dataframes)
})

#4# bind the data frames together using rbind
df <- rbind(read_files[[1]],read_files[[2]],read_files[[3]],read_files[[4]],read_files[[5]])

MissionsData <- df

In this solution, we use lapply function to read the NetCDF files and extract the desired variables. We also use do.call(rbind, dataframes) to bind the individual data frames together along the rows.

Using lapply in a nested structure has several advantages over using rbind inside a nested for loop:

It avoids the need for manually indexing and looping through the individual files.
It provides more flexibility and scalability when working with large datasets.

Conclusion

In this article, we discussed the limitations of using rbind function in R programming language, especially when used inside nested for loops. We also provided a solution that uses a different technique to overcome these limitations.

When working with large datasets or complex data structures, it’s essential to consider alternative approaches and techniques that provide more flexibility and scalability.

Code Explanation

Here is the code explanation:

#1# list all files in the main_folder
folders <- as.list(list.files("E:/main_folder"))

#2# make list of subfiles 
subfiles <- lapply(folders, function(x) as.list(list.files(paste("E:/main_folder",x, sep="/"))))

#3# read netcdf file and get variables using lapply
read_files <- lapply(subfiles, function(subfile){
    files <- lapply(subfile, function(x) list.files(paste(folders[1],x, sep = "/"),pattern='*.nc',full.names=TRUE))
    dataframes <- lapply(files, function(file_i){
        nc <- nc_open(file_i)
        lat <-  ncvar_get(nc, "glat.00")
        lon <-  ncvar_get(nc, "glon.00")
        ssh <-  ncvar_get(nc, "ssh.53")
        jdn <- ncvar_get(nc, "jday.00") 
        nc_close(nc)  
        return(data.frame(lat,lon,ssh,jdn))
    })
    do.call(rbind, dataframes)
})

#4# bind the data frames together using rbind
df <- rbind(read_files[[1]],read_files[[2]],read_files[[3]],read_files[[4]],read_files[[5]])

MissionsData <- df

setwd("E:/main_folder"): sets the working directory to “E:/main_folder”.
folders <- as.list(list.files("E:/main_folder")): lists all files in the main folder using list.files() function and converts them into a list.
subfiles <- lapply(folders, function(x) as.list(list.files(paste("E:/main_folder",x, sep="/")))): makes a list of subfolders inside each folder using lapply(). Each element of subfiles is another list that contains the subfolder name.
- files <- lapply(subfile, function(x) list.files(paste(folders[1],x, sep = "/"),pattern='*.nc',full.names=TRUE)): lists all netcdf files in each subfolder using list.files() function and converts them into a list.
dataframes <- lapply(files, function(file_i){ ... }): reads the netcdf file specified by file_i and extracts variables. For each file, it opens the netcdf file using nc_open(), gets the variables using ncvar_get(), and returns data.frame with extracted values.
- do.call(rbind, dataframes): binds all individual data frames together along rows.
df <- rbind(read_files[[1]], read_files[[2]], read_files[[3]], read_files[[4]], read_files[[5]]): binds the five data frames obtained by lapply() function for each subfolder together using rbind().
MissionsData <- df: assigns the combined data frame to list called MissionsData.

Last modified on 2025-03-28

Using rbind Problem Inside Nested For Loop