library(tidyverse)
library(readxl)
path <- "300-399/339/CH-339 Column Splitting.xlsx"
input <- read_excel(path, range = "B3:B8")
test <- read_excel(path, range = "F3:H8")
result = input %>%
mutate(ID = str_replace_all(ID, "([A-Za-z]{2})([0-9]{2})", "\\1|\\2")) %>%
separate_wider_delim(
ID,
delim = "|",
names_sep = " ",
too_few = "align_start"
)
all.equal(result, test)
# [1] TRUEOmid - Challenge 339
data-challenges
advanced-exercises
🔰 Question Result ID XMS 21 ID 1 ID 2 ID 3

Challenge Description
🔰 Question Result ID XMS 21 ID 1 ID 2 ID 3
Solutions
Logic:
Reads the workbook ranges needed for the challenge
Builds the intermediate columns that drive the final result
Parses the text patterns directly instead of relying on manual cleanup
Strengths:
- The R solution stays close to the workbook rule and keeps the transformation compact.
Areas for Improvement:
- The code assumes the sheet structure and source ranges remain stable.
Gem:
- The strongest part of the solution is choosing the right intermediate representation before shaping the final output.
import pandas as pd
import re
path = "300-399/339/CH-339 Column Splitting.xlsx"
input = pd.read_excel(path, usecols="B", skiprows=2, nrows=6, dtype=str)
test = pd.read_excel(path, usecols="F:H", skiprows=2, nrows=6, dtype=str)
input["ID"] = input["ID"].str.replace(r"([A-Za-z]{2})([0-9]{2})", r"\1|\2", regex=True)
split_cols = input["ID"].str.split("|", expand=True)
split_cols.columns = [f"ID {i+1}" for i in range(split_cols.shape[1])]
print(split_cols.equals(test)) # TrueLogic:
Reads the workbook ranges needed for the challenge
Parses the text patterns directly instead of relying on manual cleanup
Applies the rule iteratively until the output stabilizes
Strengths:
- The Python version follows the same rule in a direct dataframe-oriented implementation.
Areas for Improvement:
- The code assumes the workbook layout remains stable, so any sheet redesign would require small adjustments.
Gem:
- The implementation stays close to the original workbook rule instead of adding unnecessary abstraction.
Difficulty Level
This task is moderate:
The core logic is clear, but the correct transformation pattern is not obvious from the raw input.
The challenge combines multiple reshaping, grouping, or parsing steps.