library(tidyverse)
library(readxl)
path = "files/200-299/274/CH-274 Custom Grouping.xlsx"
input = read_excel(path, range = "B2:C18")
test = read_excel(path, range = "G2:I18")
result = input %>%
mutate(Group = cumsum(c(1, diff(Date) > 2)))
all.equal(result$Group, test$Group)
# [1] TRUEOmid - Challenge 274
data-challenges
advanced-exercises
🔰 Group Challenge 274: Custom Grouping!

Challenge Description
🔰 Group Challenge 274: Custom Grouping!
Solutions
Logic:
Reads the workbook ranges needed for the challenge
Builds the intermediate columns that drive the final result
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 = "200-299/274/CH-274 Custom Grouping.xlsx"
input = pd.read_excel(path, usecols="B:C", skiprows=1, nrows=16)
test = pd.read_excel(path, usecols="G:I", skiprows=1, nrows=16).rename(columns=lambda col: col.replace('.1', ''))
input['Group'] = input['Date'].diff().dt.days.gt(2).cumsum() + 1
print(input.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.