#189–190

Puzzles

Author: ExcelBI

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

Puzzle #189

We have sequential report of some weird acceptance process. Unfortunatelly we don’t know the rules, and we are suppose to only clean report to more digestible form. We need to check if after any number there is message “Yes”, if so, this number passes, if not it fails. We need to clean sequence from message rows. Check it out.

Loading libraries and data

library(tidyverse)
library(readxl)

input = read_excel("Power Query/PQ_Challenge_189.xlsx", range = "A1:B11")
test  = read_excel("Power Query/PQ_Challenge_189.xlsx", range = "D1:F8")

Transformation

result = input %>%
  mutate(Result = if_else(lead(Code) == "Yes", "Pass", NA)) %>%
  mutate(Result = if_else(is.na(Result) & str_detect(Code, "\\d"), "Fail", Result)) %>%
  filter(!is.na(Result)) %>%
  mutate(Code = as.numeric(Code))

Validation

identical(result, test)
# [1] TRUE

Puzzle #190

Wow, that’s the puzzle I like, mine in dirty (aka untidy) data to dig info we really need. It seems that somebody took data from system, and forgot any separators. Somehow colons survived. And knowing what data we need, we have to prepare mechanism that will get every needed chunk of text. Let’s go, let’s use some Regex.

Later I found out another, shorter solution, which will be also below.

Loading libraries and data

library(tidyverse)
library(readxl)
library(rebus)

input = read_excel("Power Query/PQ_Challenge_190.xlsx", range = "A1:A3")
test  = read_excel("Power Query/PQ_Challenge_190.xlsx", range = "A6:E8")

Transformation

name_pattern = "Name:" %R% capture(one_or_more(WRD)) %R% "Org:"
org_pattern = "Org:" %R% capture(one_or_more(WRD)) %R% "City:"
city_pattern = "City:" %R% capture(one_or_more(WRD)) %R% "FromDate:"
from_date_pattern = "FromDate:" %R% capture(one_or_more(WRD)) %R% "ToDate:"
to_date_pattern = "ToDate:" %R% capture(one_or_more(WRD))

extract_and_space <- function(a, name_pattern) {
  extracted <- str_match(a, name_pattern)
  result <- extracted %>% 
    pluck(2) %>%
    {if (is.na(.)) extracted %>% pluck(1) else .} %>%
    str_replace_all("([a-z])([A-Z])", "\\1 \\2") %>%
    str_replace_all("([A-Z])([A-Z][a-z])", "\\1 \\2")
  
  return(result)
}

result = input %>%
  mutate(Name = map_chr(Data, ~extract_and_space(.x, name_pattern)),
         Org = map_chr(Data, ~ str_match(.x, org_pattern) %>% pluck(2)),
         City = map_chr(Data, ~ extract_and_space(.x, city_pattern)),
         `From Date` = map_chr(Data, ~ str_match(.x, from_date_pattern) %>% pluck(2)),
         `To Date` = map_chr(Data, ~ str_match(.x, to_date_pattern) %>% pluck(2))) %>%
  mutate(`From Date` = ymd(`From Date`) %>% as.POSIXct(),
         `To Date` = ymd(`To Date`) %>% as.POSIXct()) %>%
  select(-Data)

Transformation v2

pattern = 'Name:(\\w+)Org:(\\w+)City:(\\w+)FromDate:(\\d+)ToDate:(\\d+)'

result2 <- input %>%
 extract(Data, into = c("Name", "Org", "City", "From Date", "To Date"), regex = pattern, remove = FALSE) %>%
 mutate(across(c(`From Date`, `To Date`), ~ ymd(.x) %>% as.POSIXct())) %>%
 mutate(across(c(Name, City), ~ str_replace_all(.x, "([A-Z])", " \\1") %>% trimws(which = "left"))) %>%
 select(-Data)

Validation

identical(result, test)
# [1] TRUE

identical(result2, test)
# [1] TRUE

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PowerQuery Puzzle solved with R was originally published in Numbers around us on Medium, where people are continuing the conversation by highlighting and responding to this story.