library(tidyverse)
library(readxl)
path = "files/CH-169 Extract From Text Part 2.xlsx"
input = read_excel(path, range = "B2:B7")
test = read_excel(path, range = "D2:D7")
result = input %>%
mutate(result = str_match_all(Text, "(?<=\\{)[^{}]+(?=\\})|(?<=\\[)[^\\]]+(?=\\])|(?<=\\()[^\\)]+(?=\\))|(?<=\\*)[^\\*]+(?=\\*)")) %>%
mutate(result = map_chr(result, ~paste(., collapse = ", ")))
all.equal(result$result, test$Extracted, check.attributes = FALSE)
#> [1] TRUEOmid - Challenge 169
data-challenges
advanced-exercises
🔰 Extracted Challenge 169: Extract from the Text (Part 2)!

Challenge Description
🔰 Extracted Challenge 169: Extract from the Text (Part 2)!
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 = "CH-169 Extract From Text Part 2.xlsx"
input = pd.read_excel(path, usecols="B", skiprows=1, nrows=6)
test = pd.read_excel(path, usecols="D", skiprows=1, nrows=6)
def extract_text(text):
pattern = r"(?<=\{)[^{}]+(?=\})|(?<=\[)[^\]]+(?=\])|(?<=\()[^\)]+(?=\))|(?<=\*)[^\*]+(?=\*)"
matches = re.findall(pattern, text)
return ", ".join(matches)
input['result'] = input['Text'].apply(extract_text)
print(input["result"].equals(test["Extracted"])) # TrueLogic:
Reads the workbook ranges needed for the challenge
Parses the text patterns directly instead of relying on manual cleanup
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.