PowerQuery Puzzle solved with R

#231–232

Puzzles

Author: ExcelBI

All files (xlsx with puzzle and R with solution) for each and every puzzle are available on my Github. Enjoy.

Puzzle #231

Both puzzles this weekend are somehow related to logistics. In first one we need to complete data for invoice for each person placing order. We have classic transforming and joining problem. One tricky part is to add totals for rows and columns. Let’s do it.

Loading libraries and data

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

path = "Power Query/PQ_Challenge_231.xlsx"
input1 = read_excel(path, range = "A2:C5")
input2 = read_excel(path, range = "A8:B15")
test  = read_excel(path, range = "E2:J6")

Transformation

input = input1 %>%
  separate_rows(c(Items, Quantity), sep = ", ") %>%
  left_join(input2, by = "Items") %>%
  mutate(Amount = as.numeric(Quantity) * Price) %>%
  select(-c(Price, Quantity)) %>%
  pivot_wider(names_from = "Items", values_from = "Amount", values_fn = list(Amount = sum), values_fill = 0) %>%
  select(Name = Person,u, x, y, z) %>%
  arrange(Name) %>%
  adorn_totals(c("row", "col"))

Validation

all.equal(input, test, check.attributes = FALSE)
#> [1] TRUE

Puzzle #232

This time we have stock levels for each store. But not all days are recorded. So we need to make complete ranges of date and cumulative quantity for each and every store. It is not hard, and we can do it again using only one pipe of code. Find it out.

Loading libraries and data

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_232.xlsx"
input = read_excel(path, range = "A1:C7")
test  = read_excel(path, range = "E1:G13")

Transformation

result = input %>%
  group_by(Store) %>%
  complete(Date = seq(min(Date), max(Date), by = "day")) %>%
  ungroup() %>%
  mutate(has_val = cumsum(!is.na(Quantity))) %>%
  fill(Quantity) %>%
  mutate(Quantity = cumsum(Quantity), .by = c(Store, has_val)) %>%
  select(-has_val)

Validation

all.equal(result, test, check.attributes = FALSE)
#> [1] TRUE

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