Crispo - Excel Challenge 09 2026

excel-challenges
weekly-exercises
Easy Sunday Excel Challenge
Published

March 1, 2026

Illustration for Crispo - Excel Challenge 09 2026

Challenge Description

Easy Sunday Excel Challenge

⭐ ⭐Count the Direct & Indirect and Total Reports

Solutions

library(tidyverse)
library(readxl)
library(igraph)

path <- "2026-03-01/Challenge 105.xlsx"
input <- read_excel(path, range = "B3:E19")
test <- read_excel(path, range = "G3:I7")

edges <- input %>%
  filter(!is.na(`Manager ID`)) %>%
  transmute(from = `Manager ID`, to = `Staff ID`)

g <- graph_from_data_frame(edges, directed = TRUE)

managers <- input %>%
  filter(Position == "Manager")

result <- managers %>%
  mutate(
    vertex = as.character(`Staff ID`),
    all_reports = map(
      vertex,
      ~ ego(g, order = Inf, nodes = .x, mode = "out")[[1]] %>% setdiff(.x)
    ),
    total_n = map_int(all_reports, length),
    direct_n = map_int(vertex, ~ degree(g, v = .x, mode = "out")),
    indirect_n = total_n - direct_n,
    `Direct & Indirect` = paste0(
      direct_n,
      " direct : ",
      indirect_n,
      " Indirect"
    )
  ) %>%
  select(Manager = Name, `Direct & Indirect`, `Total Reports` = total_n)

result
plot(g, vertex.label = V(g)$name, main = "Graph Visualization")

  • Logic:

    • Reads the workbook range needed for the challenge

    • Builds the intermediate helper columns that drive the final answer

  • Strengths:

    • The R solution stays compact and mirrors the workbook logic closely.

  • Areas for Improvement:

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

  • Gem:

    • The best part of the solution is choosing a tidy intermediate shape before producing the final answer.
import pandas as pd
import networkx as nx

path = "2026-03-01/Challenge 105.xlsx"
input = pd.read_excel(path, sheet_name="Sheet2", usecols="B:E", skiprows=2, nrows=16)
test = pd.read_excel(path, sheet_name="Sheet2", usecols="G:I", skiprows=2, nrows=4)

df = pd.DataFrame(input, columns=["Staff ID", "Manager ID", "Name", "Position"])
G = nx.DiGraph()
G.add_edges_from(
    df.dropna(subset=["Manager ID"])[["Manager ID", "Staff ID"]].itertuples(index=False)
)
managers = df[df["Position"] == "Manager"]
results = []
for _, row in managers.iterrows():
    manager_id = row["Staff ID"]
    name = row["Name"]
    direct = list(G.successors(manager_id))
    all_reports = nx.descendants(G, manager_id)
    direct_n = len(direct)
    total_n = len(all_reports)
    indirect_n = total_n - direct_n
    results.append({
        "Manager": name,
        "Direct & Indirect": f"{direct_n} direct : {indirect_n} Indirect",
        "Total Reports": total_n
    })
result_df = pd.DataFrame(results)

  • Logic:

    • Reads the workbook range needed for the challenge

    • Applies the rule iteratively until the output is complete

  • Strengths:

    • The Python version keeps the same rule in a direct pandas-oriented workflow.

  • Areas for Improvement:

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

  • Gem:

    • The implementation stays close to the stated challenge instead of adding unnecessary complexity.

Difficulty Level

This task is easy to moderate:

  • The business rule is readable, but the workbook still needs a few careful transformation steps.