library(tidyverse)
library(readxl)
path = "files/300-399/320/CH-320 Text Cleaning.xlsx"
input = read_excel(path, range = "B1:B7")
test = read_excel(path, range = "C1:C7")
result = input %>%
transmute(`Result ID` = parse_number(`Question ID`))
all.equal(result, test)
# [1] TRUEOmid - Challenge 320
data-challenges
advanced-exercises
🔰 Remove the initial caracters and zeoros to convert the IDs into numbers

Challenge Description
🔰 Remove the initial caracters and zeoros to convert the IDs into numbers
Solutions
Logic:
- Reads the workbook ranges needed for the challenge
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
path = "300-399/320/CH-320 Text Cleaning.xlsx"
input = pd.read_excel(path, usecols="B", nrows=7)
test = pd.read_excel(path, usecols="C", nrows=7)
result = pd.DataFrame({
'Result ID': input.iloc[:, 0].str.extract(r'(\d+)').astype(int).iloc[:, 0]
})
print(result.equals(test)) # TrueLogic:
- Reads the workbook ranges needed for the challenge
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 business rule is readable, but the workbook still requires careful implementation to reach the expected layout.