Understanding the Problem and Context

In this article, we’ll explore how to conditional subset a list in R based on range in another column. The problem arises when dealing with unstructured data, where different columns may contain various types of information.

We’ll begin by understanding the context of the problem. We have a list of lists (my_list) containing data frames from multiple files. Each file has 10 sheets, and we’re trying to extract specific information from these data frames.

Creating a Toy Data Set

To demonstrate the solution, let’s create a toy data set in R:

# Load necessary libraries
library(purrr)
library(dplyr)

# Create my_list

list1 <- list(data.frame(cross = c("NA","NA","o","o","o","x","o","NA","NA"),
                         color = c("NA","NA","grey","black","white","yellow","blue","NA","NA"),
                         temperature = c("NA","NA","3","5","2","7","4","NA","NA")))

list2 <- list(data.frame(cross = c("NA","NA","o","x","o","o","o","NA","NA"),
                         color = c("NA","NA","grey","black","white","yellow","blue","NA","NA"),
                         temperature = c("NA","NA","8","6","1","6","9","NA","NA")))

my_list <- list(list1, list2)

Selecting a Single Value from the List

We can select a single value from my_list using the map_chr function from the purrr package:

# Subset a single value from the list
my_list %>% map_chr(c(1,3,7))
[1] "4" "9"

Understanding the Problem Statement

The problem statement asks how to select the color that has an “x” in the “cross” column, based on position 3 to 7. The output should be a vector of color names with no “NA” values.

To approach this problem, we need to understand that the data is messy, and there are many things in each column due to the nature of the original .xls files. This means we need to specify the range to look at in the “cross” column.

Solution Overview

We’ll use a combination of purrr, dplyr, and map functions to solve this problem. The solution involves:

1. Using map to apply a function to each element in the list.
2. Using filter from the dplyr package to select rows based on the “cross” column condition.
3. Using as.character to convert the color names to characters.

Step-by-Step Solution

Here’s a step-by-step solution:

Step 1: Define the Function

We’ll define a function that takes a data frame (tbl) and applies the following operations:

# Load necessary libraries
library(purrr)
library(dplyr)

# Define the function
map <- function(tbl){
  out_tbl <- tbl[3:7,] %>% 
    dplyr::filter(cross == "x")
  
  # If no rows match the condition, return NA
  if(nrow(out_tbl) == 0) return(NA)
  
  # Return the color names as characters
  out_tbl$color
}

# Apply the function to each element in my_list
map(my_list)

Step 2: Explanation of the Code

Here’s an explanation of the code:

• We define a function map that takes a data frame (tbl) and applies the following operations:

  • We select rows from the third row to the seventh row using tbl[3:7,].
  • We use dplyr::filter to select rows where “cross” equals “x”.
  • If no rows match the condition, we return NA.
  • Otherwise, we return the color names as characters using out_tbl$color.

• We apply this function to each element in my_list using map(my_list).

Step 3: Alternative Solution Using map2

Alternatively, you can use map2 from the purrr package to simplify the code:

# Load necessary libraries
library(purrr)
library(dplyr)

# Define the function
map2 <- function(tbl){
  out_tbl <- tbl %>% 
    filter(cross == "x", across(.cols = all_of("color")) != NA) %>% 
    select(color)
  
  # If no rows match the condition, return NA
  if(nrow(out_tbl) == 0) return(NA)
  
  # Return the color names as characters
  out_tbl$color
}

# Apply the function to each element in my_list
map2(my_list)

Step 4: Explanation of the Alternative Code

Here’s an explanation of the alternative code:

• We define a function map2 that takes a data frame (tbl) and applies the following operations:

  • We use filter to select rows where “cross” equals “x”.
  • We also filter out rows with missing values in any column using across(.cols = all_of("color")) != NA.
  • We select only the “color” column using select(color).
  • If no rows match the condition, we return NA.
  • Otherwise, we return the color names as characters using out_tbl$color.

• We apply this function to each element in my_list using map2(my_list).

Conclusion

We’ve solved the problem of conditional subsetting a list in R based on range in another column. The solution involves using a combination of purrr, dplyr, and map functions to filter rows, select columns, and return desired output.

By following these steps, you can write efficient and readable code to handle complex data processing tasks in R.

Last modified on 2023-06-18