Insight Analysis and Predictions from Puzzles Utilizing R

#147–148

Puzzles

Author: ExcelBI

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

Puzzle #147

Picture above showing sudoku-like puzzles looks difficult, but puzzle itself is really hardcore. Probably one of the toughests since I join this puzzle solving. We get table filled with data like unsolved sudoku (that is theme of image). We need to populate it with proper data filling sometimes up, sometimes down. Easier to say than to do. Just look on this spreadsheet. There are even empty rows to fill.

So lets check how I did it. I’ll try to explain my chain of thoughts while solving.

Load libraries and data

library(tidyverse)
library(readxl)

input = read_excel("Power Query/PQ_Challenge_147.xlsx", range = "A1:D17")
test  = read_excel("Power Query/PQ_Challenge_147.xlsx", range = "F1:I17") %>%
  janitor::clean_names()

Transformation

reshape <- function(input) {
  input %>%
    janitor::clean_names() %>%
    mutate(nr = row_number()) %>%
    # for each column enumerate not empty cells
    mutate(across(c(cust_id, cust_name, amount, type),
                  ~ ifelse(is.na(.), NA, cumsum(!is.na(.))),
                  .names = "index_{.col}"),
    # for each row find max index which will be used to find cust_id per row
           max_index = pmax(index_cust_id, index_cust_name, index_amount, index_type, na.rm = TRUE)) %>%
    group_by(max_index) %>%
    mutate(across(c(cust_id, cust_name, amount, type),
                  ~ max(., na.rm = TRUE)),
    # for each max_index get first and last row in which it occurs
           min_row = min(nr, na.rm = TRUE),
           max_row = max(nr, na.rm = TRUE)) %>%
    ungroup() %>%
    # remove originally empty rows
    filter(!is.na(max_index)) %>%
    select(-starts_with("index_"), -max_index, -nr) %>%
    distinct() %>%
    # using first and last row per index make sequence and unnest it to rows
    mutate(row_seq = map2(min_row, max_row, seq)) %>%
    unnest(row_seq) %>%
    select(-min_row, -max_row, -row_seq) %>%
    group_by(cust_id) %>%
    # final touch. add original row number to type
    mutate(type = paste0(type, row_number())) %>%
    ungroup()
}


result = reshape(input)

Validation

identical(result, test)
# [1] TRUE

Puzzle #148

After one hardcore, comes one pretty nice and easy to solve. And it is about fruits. What we get is column with strings containing names of fruits. We need to split them separately, count them and put in some kind of crosstab. Little bit weird because both rows and columns has the same dimension, name of fruit. Let go into.

Load libraries and data

library(tidyverse)
library(readxl)

input = read_excel("Power Query/PQ_Challenge_148.xlsx", range = "A1:A12")
test  = read_excel("Power Query/PQ_Challenge_148.xlsx", range = "C1:N12") 

Transformation

result = input %>%
  separate_rows(Fruits, sep = ", ") %>%
  mutate(Fruits = str_remove_all(Fruits, " ")) %>%
  group_by(Fruits) %>%
  summarise(Count = n()) %>%
  ungroup() %>%
  mutate(Fruits2 = Fruits) %>%
  pivot_wider(names_from = Fruits2, values_from = Count) 

Validation

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

# Today for the first time I used all.equal() instead of identical().
# Main reason is because if there are NAs, NA is not identical to NA,
# it return NA instead of TRUE. But NA is equal to NA, so final df is not
# identical, but it is equal to given answer.
# In our eyes we can say they are the same. 

Feel free to comment, share and contact me with advices, questions and your ideas how to improve anything. Contact me on Linkedin if you wish as well.

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.

Continue reading: PowerQuery Puzzle solved with R

Insight Analysis and Predictions from Puzzles Utilizing R

In this article, we examine author ExcelBI’s puzzle solutions with the R programming language. Specifically, we evaluate puzzles #147 and #148. By doing this, we not only gain insight from the problem-solving methods used but also predict potential future developments in programming puzzles and provide actionable advice for R enthusiasts.

Puzzle #147

Puzzle #147 is described as an intensive, hardcore puzzle similar to unsolved Sudoku. Its complexity lies in the necessity to accurately populate a table with data consistently fluctuating upwards and downwards. Often, entire rows are left empty.

The puzzle was resolved using R libraries such as ‘tidyverse’ and ‘readxl,’ which are known for data manipulation and reading excel files respectively. An extensive transformation function was devised to methodically cleanse, adjust, enumerate and finally regenerate the data into the table.

Implications and Future Developments

Given the complexity of puzzles like #147, it showcases the efficiency of R in addressing intricate data manipulation tasks. In the future, similar problems could be made even more challenging by incorporating additional variables or conditions that further complicate the data handling process. A natural progression can be developing algorithms that automate these processes more effectively.

Actionable Advice

For those interesting in solving complex puzzles akin to #147, having a deep understanding of R libraries such as ‘tidyverse’ and ‘readxl’ is essential. Prospective problem solvers should also practice formulating complex transformation functions to better equip themselves for similar challenges.

Puzzle #148

Contrasting sharply with its predecessor, Puzzle #148 is a simpler problem focused on splitting, counting, and tabulating words within a string. Specifically focusing on names of fruits, values were separated, counted and then presented in a crosstab-like format.

The author uses the same R libraries, ‘tidyverse’ and ‘readxl,’ but employs different functions to resolve the puzzle effectively. The data is transformed through separating rows and string manipulation, followed by group summarization and ultimately dispersed into wider pivot tables.

Implications and Future Developments

Puzzle #148 showcases R’s ability to handle data wrangling tasks effectively. Future developments could revolve around parsing complex strings, creating opportunities for developing more advanced text mining techniques.

Actionable Advice

For R enthusiasts, puzzles like #148 give an opportunity to practice various data wrangling tasks. Familiarization with functions like ‘separate_rows’, ‘str_remove_all’, and ‘pivot_wider’ would be advantageous for these type of problems.

Concluding Thoughts

Both puzzles deliver different sets of insight but both affirm the dexterity of R programming. As problem-solving with R continues to evolve, understanding these tactics and learning from this analysis could serve an effective springboard for anyone interested in further honing their R puzzle-solving skills or even devising their own engaging puzzles.

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