PowerQuery Puzzle solved with R

#221–222

Puzzles

Author: ExcelBI

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

Puzzle #221

We already had task to provide indices for some structured project, and here we go again. Once again I found out that perfect concept to use for such puzzles is factorization. Using it properly make solution of this task nice and pretty short.

Loading libraries and data

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_221.xlsx"
input = read_excel(path, range = "A1:C20")
test  = read_excel(path, range = "E1:J20")

Transformation

result = input %>%
  mutate(Project_Index = as.numeric(as.factor(Project))) %>%
  mutate(Task_Index = as.numeric(paste0(Project_Index,".",as.numeric(as.factor(Task)))) , .by = Project) %>%
  mutate(Activity_Index = paste0(Task_Index,".", as.numeric(as.factor(Activity))), .by = c(Project, Task))

Validation

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

Puzzle #222

Classic Power Query task — restructure table a little bit. And today we have list of exams scored by different students. But not all of them passed the exam, and we need to filter them out as well. There are several manouvers to follow and we get table with each student that passed the exam. It is pretty tricky one. So lets check it out.

Loading libraries and data:

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_222.xlsx"
input = read_excel(path, range = "A1:I7")
test  = read_excel(path, range = "A11:D17")

Transformation

result = input %>%
  pivot_longer(cols = -c(1), names_to = c(".value", "Type"), names_pattern = "(\\D+)(\\d+)") %>%
  mutate(rank = rank(desc(Marks), ties.method = "first"), .by = Test) %>%
  select(-Type) %>%
  unite("TM", Student, Marks, sep = " ") %>%
  pivot_wider(names_from = rank, values_from = TM, names_prefix = "Student")  %>%
  select(Subjects = Test, sort(names(.)[-1])) %>%
  filter(!is.na(Subjects)) %>%
  mutate(across(2:ncol(.), ~ifelse(as.numeric(str_extract(., "\\d+")) >= 40, str_remove(., "\\s\\d+"), NA_character_))) %>%
  select(where(~!all(is.na(.)))) %>%
  arrange(Subjects) 

Validation

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

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