Excel BI - PowerQuery Challenge 370

excel-challenges
power-query
Assume ‘Month 1’ begins on the date of the very first referral in the dataset (2024-01-01). Every 30 days counts as a new ‘forecasted month.’ Breach Calculation: Calculate the Forecasted Treatment Month (e.g., if a patient is in the 3rd slot for Cardiology, they are seen in Month 2, which is 30 days after the start).
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 370

Challenge Description

Assume “Month 1” begins on the date of the very first referral in the dataset (2024-01-01). Every 30 days counts as a new “forecasted month.” Breach Calculation: Calculate the Forecasted Treatment Month (e.g., if a patient is in the 3rd slot for Cardiology, they are seen in Month 2, which is 30 days after the start).

Solutions

library(tidyverse)
library(readxl)

path <- "Power Query/300-399/370/PQ_Challenge_370.xlsx"
input <- read_excel(path, range = "A1:D21")
test <- read_excel(path, range = "F1:K21")

start_date <- as.Date("2024-01-01")

result <- input %>%
  group_by(Department) %>%
  arrange(`Referral Date`, `Patient ID`) %>%
  mutate(
    position = row_number(),
    `Forecasted Month` = ceiling(position / 2),
    days_since_start = as.numeric(difftime(
      `Referral Date`,
      start_date,
      units = "days"
    )),
    referral_period = floor(days_since_start / 30) + 1,
    treatment_day = case_when(
      `Forecasted Month` == 1 ~ days_since_start,
      referral_period < `Forecasted Month` &
        referral_period == 1 ~ (`Forecasted Month` - 1) * 30 - 1,
      referral_period < `Forecasted Month` &
        referral_period >= 2 ~ (`Forecasted Month` - 1) * 30,
      referral_period == `Forecasted Month` ~ days_since_start,
      TRUE ~ (`Forecasted Month` - 1) * 30
    ),
    `Wait Days` = treatment_day - days_since_start,
    `SLA Status` = ifelse(
      `Wait Days` > `Target Days (SLA)`,
      "Breach",
      "Within SLA"
    )
  ) %>%
  ungroup() %>%
  select(
    `Patient ID`,
    Department,
    `Referral Date`,
    `Forecasted Month`,
    `Wait Days`,
    `SLA Status`
  ) %>%
  arrange(Department, `Referral Date`, `Patient ID`)

all.equal(result, test)

  • Logic:

    • Reads the workbook range needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • Builds helper columns that drive the final output

  • Strengths:

    • The R solution stays close to the workbook logic and keeps the transformation compact.

  • Areas for Improvement:

    • The code assumes the workbook layout and selected ranges remain stable.

  • Gem:

    • The best part of the solution is choosing the right intermediate shape before formatting the final output.
import pandas as pd
from datetime import datetime
import numpy as np

path = "Power Query/300-399/370/PQ_Challenge_370.xlsx"
input = pd.read_excel(path, usecols="A:D", skiprows=0, nrows=20)
test = pd.read_excel(path,  usecols="F:K", skiprows=0, nrows=20).rename(columns=lambda col: col.replace('.1', ''))

start_date = datetime(2024, 1, 1)

result = (
    input
    .sort_values(['Department', 'Referral Date', 'Patient ID'])
    .groupby('Department', group_keys=False)
    .apply(lambda g: g.assign(
        position=np.arange(1, len(g) + 1),
        forecast_month=(np.arange(1, len(g) + 1) + 1) // 2
    ))
    .assign(
        days_since_start=lambda x: (x['Referral Date'] - start_date).dt.days,
        reporting_period=lambda x: x['days_since_start'] // 30 + 1,
        target_date_days=lambda x: np.select(
            [
                x['forecast_month'] == 1,
                (x['reporting_period'] < x['forecast_month']) & (x['reporting_period'] == 1),
                (x['reporting_period'] < x['forecast_month']) & (x['reporting_period'] >= 2),
                x['reporting_period'] == x['forecast_month']
            ],
            [
                x['days_since_start'],
                (x['forecast_month'] - 1) * 30 - 1,
                (x['forecast_month'] - 1) * 30,
                x['days_since_start']
            ],
            (x['forecast_month'] - 1) * 30
        ),
        wait_days=lambda x: x['target_date_days'] - x['days_since_start'],
        sla_status=lambda x: np.where(x['wait_days'] > x['Target Days (SLA)'], 'Breach', 'Within SLA')
    )
    .sort_values(['Department', 'Referral Date', 'Patient ID'])
    [['Patient ID', 'Department', 'Referral Date', 'forecast_month', 'wait_days', 'sla_status']]
    .rename(columns={
        'forecast_month': 'Forecasted Month',
        'wait_days': 'Wait Days',
        'sla_status': 'SLA Status'
    })
    .reset_index(drop=True)
)
print(result.equals(test))

  • Logic:

    • Reads the workbook range needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • Builds helper columns that drive the final output

  • Strengths:

    • The Python version follows the same workbook rule in a direct pandas-oriented implementation.

  • Areas for Improvement:

    • As with the R version, any workbook layout change would require small adjustments.

  • Gem:

    • The implementation stays close to the source challenge instead of adding unnecessary abstraction.

Difficulty Level

This task is moderate:

  • It combines reshaping, grouping, or parsing steps that are common in Power Query style problems.

  • The main challenge is reproducing the workbook output structure exactly.