PowerQuery Puzzle solved with R

#199–200

Puzzles

Author: ExcelBI

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

Puzzle #199

Data mining, maybe it is too big word for challenge we face today, but we need to dig up some information from given texts. Each of them have part number and order or delivery dates for each. Sometimes more than one. We need to extract them, clean if needed and sort by part and then by date. Little cleaning were needed so code is not as short as it could be. Check it.

Loading libraries and data

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_199.xlsx"
input = read_excel(path, range = "A1:A5")
test  = read_excel(path, range = "C1:D8")

Transformation

pattern_no = "\\d{3}"
pattern_date = "\\d{1,2}/+\\d{1,2}/+\\d{2}"

result = input %>%
  mutate(`Part No.` = str_extract_all(String, pattern_no),
         Date = str_extract_all(String, pattern_date)) %>%
  unnest(Date, `Part No.`) %>%
  mutate(Date = str_replace_all(Date, "//", "/")) %>%
  select(-String) %>%
  mutate(`Part No.` = as.numeric(`Part No.`),
         Date = as.POSIXct(Date, format = "%m/%d/%y", tz = "UTC")) %>%
  arrange(`Part No.`, Date)

Validation

identical(result, test)
# [1] TRUE

Puzzle #200

What we have here today. Exam reports of 6 students on 4 exams in 2 parts. Not a perfect situation, because we all like data in one place in nice structure, we like them tidy. So we have to tidy them up. Fortunatelly it is not so hard as it can look. Find out yourself.

Loading libraries and data

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_200.xlsx"
input1 = read_excel(path, range = "A1:D6")
input2 = read_excel(path, range = "F1:I6")
test = read_excel(path, range = "A11:E17")

Transformation

in1 = input1 %>%
  pivot_longer(cols = -c(1), names_to = "subject", values_to = "score")
in2 = input2 %>%
  pivot_longer(cols = -c(1), names_to = "subject", values_to = "score")

result = bind_rows(in1, in2) %>%
  summarise(max = max(score), .by = c("subject", "Student")) %>%
  pivot_wider(names_from = "subject", values_from = "max") %>%
  arrange(Student) 

result = result %>%
  select(Student, sort(names(result)[2:5]))

Validation

identical(result, test)
# [1] TRUE

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