PowerQuery Puzzle solved with R

#213–214

Puzzles

Author: ExcelBI

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

Puzzle #213

Two warehouses gave us some summary, but products there are sometimes in packages, sometimes in batches. So we need to find out how many products and groups are in both warehouses. So we need to merge, split and pivot data, but in certain order. Find out how.

Loading libraries and data

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

path = "Power Query/PQ_Challenge_213.xlsx"

T1 = read_excel(path, range = "A2:C8")
T2 = read_excel(path, range = "A12:C16")

test = read_excel(path, range = "F2:K9")

Transformation

T_full = bind_rows(T1, T2) %>%
  separate_rows(Item, sep = ", ") %>%
  separate_rows(Group, sep = ", ") %>%
  pivot_wider(names_from = Item, values_from = Stock, values_fn = sum) %>%
  adorn_totals(c("row", "col")) 

Validation

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

Puzzle #214

Sometimes we have to do transformation in exactly oposite direction. And today we have to split one table into three that are specific to one of three zoos. Fortunatelly pivoting is powerful and we can do some tricks in it.

Loading libraries and data

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_214.xlsx"
input = read_excel(path, range = "A1:C13")
test  = read_excel(path, range = "E1:J7")

Transformation

result = input %>%
  arrange(Animals, .by = Zoo) %>%
  mutate(nr = row_number(), .by = Zoo) %>%
  pivot_wider(
    names_from = Zoo,
    values_from = c(Animals, Count),
    names_glue = "{.value}_{Zoo}"
  ) %>%
  select(contains("Zoo1"), contains("Zoo2"), contains("Zoo3"))

colnames(result) = colnames(test)

Validation

all.equal(result, test)
#> [1] TRUE

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