Omid - Challenge 321

data-challenges
advanced-exercises
🔰 Extract the correlations between the factors from Table 1 (X1 to X4) and Table 2 (Y1 to Y3), as shown in the result table.
Published

March 24, 2026

Illustration for Omid - Challenge 321

Challenge Description

🔰 Extract the correlations between the factors from Table 1 (X1 to X4) and Table 2 (Y1 to Y3), as shown in the result table.

Solutions

library(tidyverse)
library(readxl)
library(janitor)

path = "files/300-399/321/CH-321 Correlation.xlsx"
input1 = read_excel(path, range = "B2:F8")
input2 = read_excel(path, range = "B13:E19") %>%
  arrange(Year)
test  = read_excel(path, range = "H2:L5") %>%
  select(y = 1, everything()) %>%
  column_to_rownames(var = 'y')

correlation_matrix = cor(input2 %>% select(-Year), 
                         input1 %>% select(-Year), 
                         use = "pairwise.complete.obs")

all.equal(correlation_matrix, as.matrix(test))
# [1] TRUE

  • 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
import numpy as np

path = "300-399/321/CH-321 Correlation.xlsx"
input1 = pd.read_excel(path, usecols="B:F", skiprows=1, nrows=6)
input2 = pd.read_excel(path, usecols="B:E", skiprows=12, nrows=7).sort_values('Year')
test = pd.read_excel(path, usecols="H:L", skiprows=1, nrows=3).rename(columns=lambda c: c.replace('.1', ''))
test = test.rename(columns={test.columns[0]: "Y"}).set_index("Y")
test.index.name = None

mer = pd.merge(input1, input2, on='Year', how='inner')
corr = mer.corr().loc[['Y1', 'Y2', 'Y3'],['X1', 'X2', 'X3', 'X4']]

# Replace .equals with np.allclose for near equality
print(np.allclose(corr.values, test.values, atol=1e-8))

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • 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 business rule is readable, but the workbook still requires careful implementation to reach the expected layout.