PowerQuery Puzzle solved with R

#225–226

Puzzles

Author: ExcelBI

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

Puzzle #225

Sometimes we have nice tables with so called tidy data, where each observation mean one row. But this can cause creating vast areas of data in spreadsheet, that are hard to find and interpret. That is why sometimes we need to fold data, squeeze them and so on to make maybe not tidy, but readable form like foldable map. In PQ Challenges we usually transform tables back and forth, and today we are squeezing and folding them.

Loading libraries and data

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_225.xlsx"
input = read_excel(path, range = "A1:D9")
test  = read_excel(path, range = "F1:G12")

Transformation

r1 = input %>%
  mutate(Id = consecutive_id(Group),
         `Emp ID` = as.character(`Emp ID`),
         Group = ifelse(Group == "Group A", "GroupA", Group))

r1_1 = r1 %>% select(Column1 = 1, Column2 = 2, ID = 5)
r1_2 = r1 %>% select(Column1 = 4, Column2 = 3, ID = 5)

r2 = rbind(r1_2, r1_1) %>%
  arrange(ID) %>%
  distinct() %>%
  select(-ID)

Validation

all.equal(r2, test, check.attributes = FALSE)
#> [1] TRUE

Puzzle #226

As I wrote few lines before, sometimes we have chart with data that is sometimes even redundant to itself. And we need to press them like fresh lemon to get valuable information. Check this one as well.

Loading libraries and data

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_226.xlsx"
input = read_excel(path, range = "A1:D13")
test  = read_excel(path, range = "F1:I19")

Transformation

result = input %>%
  fill(`Dept ID`) %>%
  select(-`Highest Paid Employee`) %>%
  pivot_longer(-`Dept ID`, values_to = "Value") %>%
  separate(Value, into = c("Emp Names", "Salary", "Promotion Date"), sep = "-") %>%
  select(-name) %>%
  filter(!is.na(`Emp Names`)) %>%
  arrange(`Dept ID`, `Emp Names`) %>%
  mutate(`Promotion Date` = as.POSIXct(`Promotion Date`, format = "%m/%d/%Y", tz = "UTC"),
         Salary = as.numeric(Salary)) %>%
  select(`Dept ID`, `Emp Names`, `Promotion Date`, Salary)

Validation

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

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