PowerQuery Puzzle solved with R

#177–178

Puzzles

Author: ExcelBI

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

Puzzle #177

We are about to create reports for three students, each of them have to pass 3 exams for each subject. If even one of them has mark lower than 40, person fails the subject. Then we have to find their averages per person and rank acording to it. And make small adjustment in first columns, not to repeat names for every subject. Check the code.

Loading libraries and data

library(tidyverse)
library(readxl)

input = read_excel("Power Query/PQ_Challenge_177.xlsx", range = "A1:F10")
test  = read_excel("Power Query/PQ_Challenge_177.xlsx", range = "H1:M10")

Transformation

result = input %>%
  rowwise() %>%
  mutate(Result = if_else(any(c(Marks1, Marks2, Marks3) < 40), "Fail", "Pass"),
         total = Marks1 + Marks2 + Marks3) %>%
  ungroup() %>%
  mutate(average = mean(total), 
         rn = row_number(),
         .by = Name) 

aux_rank = result %>%
  select(Name, average) %>%
  distinct() %>%
  mutate(Rank = rank(-average))

result2 = result %>%
  left_join(aux_rank, by = "Name") %>%
  select(Name, Classs, Subject, `Total Marks` = total, Result, Rank, rn) %>%
  mutate(Name = ifelse(rn == 1, Name, NA_character_),
         Classs = ifelse(rn == 1, Classs, NA_real_),
         Rank = ifelse(rn == 1, Rank, NA_integer_)) %>%
  select(-rn)

Validation

identical(result2, test) 
#> [1] TRUE

Puzzle #178

HR should track how people they hire are advancing in positions, get promotions, but also if they are moving to another department or employer. We had input table containing of records of such events. But we need it to be more readable. Lets pivot the data.

Loading libraries and data

library(tidyverse)
library(readxl)

input = read_excel("Power Query/PQ_Challenge_178.xlsx", range = "A1:E5")
test  = read_excel("Power Query/PQ_Challenge_178.xlsx", range = "H1:K5")

Transformation

result = input %>%
  pivot_longer(-Emp, names_to = "Change", values_to = "Value") %>%
  separate(Change, into = c("Type", "Change"), sep = " ") %>%
  pivot_wider(names_from = Type, values_from = Value) %>%
  drop_na() 

# but one of my co-solver asked, why didn't I use only one pivot_longer
# check Anil Kumar Goyal's solution then.

input %>%
 pivot_longer(-Emp, names_to = c(".value", "Change"), names_sep = " ") %>%
 na.omit()

Validation

identical(result, test)
# [1] TRUE

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