Table Separation and Formatting Using R

In this article, we’ll explore how to separate a column into single columns and format entries in R. We’ll use the tidyr, stringr, and purrr libraries to achieve this.

Introduction

Many data tables have complex entries with multiple values separated by commas or other characters. In these cases, it’s useful to separate each value into its own column. Additionally, formatting the entries according to specific rules can be challenging. We’ll use a combination of functions from the tidyr and stringr libraries to achieve this.

Problem Statement

Given a data table with two columns: id and info. The info column has values separated by commas. We want to separate these values into single columns, following specific formatting rules.

For example:

We want to separate the values in info into the following columns:

• type
• pBu (first value)
• pBo (last value)
• zTBu (first value)
• zTBo (last value)
• zV (fourth value)

Solution

We’ll use the following steps:

1. Use separate to separate values in info into new columns.
2. Clean up the pBu - pBo, zTBu - zTBo columns by removing unnecessary characters and splitting them into pBu and pBo.
3. Replace commas with periods (.) using map_df and gsub.
4. Remove left brackets from info values using map_df and gsub.

Step-by-Step Code

library(tidyr)
library(stringr)
library(purrr)

# Sample data
dt.test <- data.frame(
    id = c(8750, 3048, 3593, 8475, 9921),
    info = c("[J/B][10,00/10,00][1,500/1,500][1,00]", "[J*/POP][0.00/ 0.00 ][2.210/2.21]|1.50", "S", "KEINE", "[Q*/B[5,00/ 5,00][1,500/1,50]0.70"]
)

# Separate info into new columns
dt.test <- dt.test %>% 
    separate(info, into = c('type', 'pBu - pBo', 'zTBu - zTBo', 'zV'), sep = "\\]|\\/ ", remove = T) %>% 
    mutate(
        # Clean up pBu - pBo column
        'pBu' = word(`pBu - pBo`, 1, sep = "/"),
        'pBo' = word(`pBu - pBo`, -1, sep = "/"),
        'pBu - pBo' = NULL,
        
        # Clean up zTBu - zTBo column
        'zTBu' = word(`zTBu - zTBo`, 1, sep = "/"),
        'zTBo' = word(`zTBu - zTBo`, -1, sep = "/"),
        'zTBu - zTBo' = NULL
    ) %>% 
    # Replace commas with periods
    map_df(~ gsub(",", ".", .x)) %>% 
    # Remove left brackets from info values
    map_df(~ gsub("\\[", "", .x))

# Select required columns and keep data.table form
dt.test <- dt.test %>%
    select(id, type, pBu, pBo, zTBu, zTBo, zV) %>% 
    as.data.table()

Output

The code uses the tidyr library’s separate function to split the values in info into new columns. The mutate function is used to clean up and format the pBu - pBo and zTBu - zTBo columns. The map_df function replaces commas with periods, and the gsub function removes left brackets from info values. Finally, the code selects the required columns using select, keeps the data in a data.table format using as.data.table, and returns the output.

This solution demonstrates how to use R’s tidyr library to split, clean, and format data while maintaining its original structure and characteristics. It also showcases how to leverage other tidyverse libraries like stringr for string manipulation tasks.

Last modified on 2024-12-07