Extracting Names from a List of Dataframes in R: Existing Solutions Not Working

Overview

In this article, we’ll explore the challenges of extracting names from a list of dataframes in R. We’ll discuss common solutions that don’t work and provide an alternative approach using tibble::lst and purrr::iwalk. We’ll also delve into the details of how negative values can be identified and added to the entire dataframe.

Introduction

R is a popular programming language for statistical computing and graphics. It has numerous libraries and packages that make data analysis, visualization, and modeling a breeze. However, when working with dataframes, we sometimes encounter issues like extracting names from a list of dataframes. In this article, we’ll tackle these challenges and provide solutions using tibble::lst and purrr::iwalk.

The Problem

The problem arises when trying to extract the name of each dataframe in a list. We’ve tried common solutions like names(df) or deparse(substitute(df)), but neither seems to work.

df1 <- data.frame(c(1,2,3), c(0,1,2), c(0,2,1))
df2 <- data.frame(c(-1,2,3), c(0,1,2), c(0,2,1))
df3 <- data.frame(c(1,2,3), c(0,1,2), c(0,2,1))

matrices <- list(df1, df2, df3)

negatives <- function(x){
  numNeg <- sum(x < 0)
  smallest <- min(x)
  cat("\n\nNumber of negative expression values: ", numNeg)
  cat("\n\nSmallest value: ", smallest)
  x <- x - smallest
  cat("\n\nAll expression values positive: ", all(x > 0))
}

correction <- function(m) {
  positives <- all(m >= 0)
  cat("\n\n\n", deparse(substitute(m)))
  cat("\n\nAll expression values positive: ", positives)
  if(positives == FALSE) {
    negatives(m)
  }
  
}

lapply(matrices, correction)

The output of this code is:

 X[[i]]

All expression values positive:  TRUE


 X[[i]]

All expression values positive:  FALSE

Number of negative expression values:  1

Smallest value:  -1

All expression values positive:  TRUE


 X[[i]]

All expression values positive:  TRUE[[1]]
NULL

[[2]]
NULL

[[3]]
NULL

As we can see, the output is inconsistent and doesn’t provide the expected results.

A Simple Solution Using tibble::lst

One way to extract names from a list of dataframes is by using the tibble::lst function. This function allows us to easily create a list of dataframes and name them simultaneously.

matrices <- tibble::lst(df1, df2, df3)

By doing this, we can access each dataframe in the list using its corresponding index.

A More Robust Solution Using purrr::iwalk

Another approach is to use the purrr::iwalk function from the purrr package. This function allows us to iterate over a list of dataframes and apply a custom function to each one.

library(purrr)

correction <- function(m, y) {
  positives <- all(m >= 0)
  cat("\n\n\n", y)
  cat("\n\nAll expression values positive: ", positives)
  if(!positives) {
    negatives(m)
  } 
}

purrr::iwalk(matrices, correction)

By using purrr::iwalk, we can avoid the inconsistency issue with lapply and obtain a more robust solution.

Identifying Negative Values and Adding Them to the Entire Dataframe

To identify negative values in each dataframe, we can use the following function:

negatives <- function(x){
  numNeg <- sum(x < 0)
  smallest <- min(x)
  cat("\n\nNumber of negative expression values: ", numNeg)
  cat("\n\nSmallest value: ", smallest)
  x <- x - smallest
  cat("\n\nAll expression values positive: ", all(x > 0))
}

This function calculates the number of negative values in the dataframe, finds the smallest negative value, and subtracts it from each value to make them positive.

Conclusion

In this article, we’ve explored the challenges of extracting names from a list of dataframes in R. We’ve discussed common solutions that don’t work and provided alternative approaches using tibble::lst and purrr::iwalk. By following these steps, you can extract names from a list of dataframes and identify negative values to add them to the entire dataframe.

Additional Resources

• tibble: A fast and simple class for tabular data.
• purrr: A collection of functional programming tools for working with dataframes in R.

Last modified on 2023-12-18