Importing CSV Data Based on Multiple AND and OR Conditions of File Names in R

When working with large datasets, particularly those stored in CSV files, efficiently importing data based on specific conditions can significantly streamline data analysis and processing tasks. In this article, we’ll explore how to import CSV data from a folder using multiple AND and OR conditions of the file names in R.

Introduction to Working with CSV Files in R

R provides an extensive set of functions for working with files, including those in the common Comma Separated Values (CSV) format. One of the most popular methods for importing CSV data into R is by utilizing the list.files() function along with regular expressions. This allows users to filter and select files based on various criteria.

Setting Up the Basics

Before diving into complex file name conditions, let’s review the basic steps involved in working with CSV files in R:

1. Importing Files: Utilize the list.files() function or other functions like dir() for directories.
2. Filtering Files: Apply conditions to select specific files using regular expressions (pattern argument).
3. Data Import and Processing: Use R’s built-in functions, such as read.csv(), to import selected CSV files into the workspace.

Understanding Regular Expressions in R

Regular expressions (regex) are a powerful tool for matching patterns in strings. In the context of file names, regex can be used to filter files that match specific criteria. The pattern argument within the list.files() function is where you specify your regex pattern.

For example:

## Importing all CSV files with "rcp85" in the name

all_paths <- list.files(path = "C:/#File.dir", pattern = "^rcp85.*\.csv$", full.names = TRUE)

In this case, ^ denotes the start of a line and .* matches any characters (including none). The \. at the end ensures we only select files with .csv as their extension.

Breaking Down Complex File Name Conditions

Given your specific requirements, let’s break down how to create conditions that filter by species, RCPs, and MSY. Since you have a list of 82 species, two RCPs (rcp45 and rcp85), and five MSY values, we can combine these using AND (&) and OR (|) operators within the regex pattern.

The file name conditions are as follows:

• Species: 60002, 60005, 600042, 600058, 600062, 600089, 600092, 600100 (non-sequential)
• MSY: either msy1 or msy3

Let’s write an R code snippet that creates a list of files matching these conditions:

## Creating the list of species and their corresponding file names

species.list <- c("60002", "60005", "600042", "600058", "600062",
                 "600089", "600092", "600100")

rcp_list <- c("rcp45", "rcp85")
msy_list <- c("msy1", "msy3")

## Combining conditions with AND and OR operators

combined_conditions <- function(species, rcp, msy) {
  file_name <- paste0(species, "-", rcp, "-", msy, ".csv")
  if (species %in% species.list & rcp %in% rcp_list && msy %in% msy_list) {
    return(file_name)
  }
}

filtered_files <- lapply(seq_along(species.list), function(i) {
  file_names <- lapply(1:length(rcp_list), function(j) {
    lapply(1:length(msy_list), function(k) {
      combined_conditions(species.list[i], rcp_list[j], msy_list[k])
    })
  }, simplify = FALSE)
  
  # Removing duplicates
  unique_file_names <- setdiff(unique(unlist(file_names)), na = TRUE)

  return(unique_file_names)
}, simplify = FALSE)

This code defines a function combined_conditions() that returns the file name if it matches both species and conditions (RCP, MSY). The filtered_files result includes all unique files that match either condition.

Conclusion

Importing CSV data based on multiple AND and OR conditions of the file names in R is feasible through regular expression filtering. By utilizing a combination of list.files() function with regex patterns and applying these to specific species, RCPs, and MSY values using AND (&) and OR (|), you can efficiently import only those files that meet your data selection criteria.

While this example covers the basic principles, remember that complex conditions or additional requirements may necessitate using external packages for enhanced functionality.

Last modified on 2024-03-05