“R Solution for Excel Puzzles: Pythagorean Triplets, Padovan Sequence, Integer

Puzzles no. 484–488

Puzzles

Author: ExcelBI

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

Puzzle #484

I think we all know what Pythageorean Theorem is. Or at least I hope so… And today we need to use properties of right triangle to solve our challenge. We need to find groups of 3 numbers (called Pythagorean triplets), that are about two things. Sum of a squared and b squared are equal c squared, but also sum of a, b and c (means circumference) is equal to given number. There is possibility that given number has more then one triplet, and it is true for one case.

Loading libraries and data

library(tidyverse)
library(readxl)
library(gmp)

path = "Excel/484 Pythagorean Triplets for a Sum.xlsx"
input = read_xlsx(path, range = "A2:A10")
test  = read_xlsx(path, range = "B2:D10") %>%
  mutate(across(everything(), as.numeric))

Transformation

find_pythagorean_triplet <- function(P) {
  m_max <- floor(sqrt(P / 2))

  possible_values <- expand_grid(m = 2:m_max, n = 1:(m_max - 1)) %>%
    filter(m > n, m %% 2 != n %% 2, gcd(m, n) == 1)

  triplets <- possible_values %>%
    pmap(function(m, n) {
      k <- P / (2 * m * (m + n))
      if (k == floor(k)) {
        a <- k * (m^2 - n^2)
        b <- k * 2 * m * n
        c <- k * (m^2 + n^2)
        return(c(a, b, c))
      } else {
        return(NULL)
      }
    })

  triplet <- triplets %>%
    compact() %>%
    keep(~ sum(.x) == P)

  if (length(triplet) > 0) {
    result <- triplet[[1]]
  } else {
    result <- c(NA_real_, NA_real_, NA_real_)
  }

  tibble(a = result[1], b = result[2], c = result[3])
}

result = input %>%
  pmap_dfr(~ find_pythagorean_triplet(..1))

Validation

# in one case (for 132) I get another but correct result

Puzzle #485

Sequences, world is full of them, and some have even their name and well researched properties. It is the case with Padovan sequence, which is similar to Fibonacci’s but has little bit bigger step and another initial elements. Check it out.
PS. To run it efficiently and save some time I also use memoise.

Loading libraries and data

library(purrr)
library(memoise)
library(readxl)
library(tidyverse)

path = "Excel/485 Pandovan Sequence.xlsx"
input = read_excel(path, range = "A1:A10")
test  = read_excel(path, range = "B1:B10")

Transformation

padovan <- function(n) {
  if (n <= 2) {
    return(1)
  } else {
    return(padovan_memo(n - 2) + padovan_memo(n - 3))
  }
}
padovan_memo = memoise(padovan)

result = input %>%
  mutate(`Answer Expectecd` = map_dbl(n, padovan_memo))

Validation

identical(result$`Answer Expectecd`, test$`Answer Expectecd`)
# [1] TRUE

Puzzle #486

When we are measuring thing like ranges and distances, sometimes for better and more readable notes, we are shortening down sequences of consecutive numbers using hyphenated range notation. And that is our task today. Let’s get party started.

Loading libraries and data

library(tidyverse)
library(readxl)

path = "Excel/486 Create Integer Intervals.xlsx"
input = read_excel(path, range = "A1:A8")
test  = read_excel(path, range = "B1:B8")

Transformation

group_consecutive = function(number_string) {
  numbers <- str_split(number_string, ",") %>%
    unlist() %>%
    as.numeric()

  tibble(numbers = sort(numbers)) %>%
    mutate(group = cumsum(c(TRUE, diff(numbers) != 1))) %>%
    summarise(range = if_else(n() > 1,
                              paste0(min(numbers), "-", max(numbers)),
                              as.character(numbers[1])), .by = group) %>%
    pull(range) %>%
    paste(collapse = ", ")
}

result = input %>%
  mutate(`Answer Expected` = map_chr(Problem, group_consecutive))

Validation

identical(result$`Answer Expected`, test$`Answer Expected`)
#> [1] TRUE

Puzzle #487

In this puzzle we need to find most frequent characters in given strings. It was like warm up before warm up. With no further words, lets do it.

Loading libraries and data

library(tidyverse)
library(readxl)

path = "Excel/487 Maximum Frequency Characters.xlsx"
input = read_xlsx(path, range = "A2:A11")
test  = read_xlsx(path, range = "B2:C11")

Transformation

find_most_freq_char = function(input) {
  result = input %>%
    strsplit("") %>%
    unlist() %>%
    table()
  df = data.frame(
    Characters = names(result),
    Frequency = as.numeric(result)) %>%
    filter(Frequency == max(Frequency)) %>%
    summarise(Characters = paste(sort(Characters), collapse = ", "), .by = Frequency) %>%
    select(Characters, Frequency)
  return(df)
}

result = input$Strings %>%
  map(find_most_freq_char) %>%
  bind_rows()

Validation:

# Validation on eye, all correct. In three cases there is different sorting than
#  in provided example but with the same characters.

Puzzle #488

Combinations, possible inputs to given output. I am sure we all love it. Today we are given bunch of numbers and one output. We need to find all combinations of this numbers that will sum up to given target. Code is quite long, but I think also easy to understand. Let me show it.

Loading libraries and data

library(gtools)
library(tidyverse)
library(readxl)

path = "Excel/488 Numbers to Meet Target Sum.xlsx"
input = read_excel(path, range = "A1:A10")
target = read_excel(path, range = "B1:B2") %>% pull()
test = read_excel(path, range = "C1:C5")

Transformation

find_combinations <- function(numbers, target) {
  combs <- map(1:length(numbers), ~combinations(length(numbers), ., v = numbers))
  valid_combs <- combs %>%
     map(as_tibble) %>%
    bind_rows() %>%
    mutate(sum = rowSums(., na.rm = TRUE)) %>%
    filter(sum == target)

  return(valid_combs)
}

combinations <- find_combinations(input$Numbers, target) %>%
  unite("Combination", - sum, sep = ", ", remove = T, na.rm = T)

sort_numbers <- function(numbers) {
  paste(sort(as.numeric(strsplit(numbers, ",")[[1]])), collapse = ", ")
}

test <- test %>%
  mutate(Combination = map_chr(`Answer Expected`, sort_numbers))

Validation

identical(sort(combinations$Combination), sort(test$Combination))
# [1] TRUE

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.
On my Github repo there are also solutions for the same puzzles in Python. Check it out!

R Solution for Excel Puzzles was originally published in Numbers around us on Medium, where people are continuing the conversation by highlighting and responding to this story.

Continue reading: R Solution for Excel Puzzles

The Impact of Puzzles no. 484–488

The puzzles analyzed involve diverse concepts such as Pythagorean triplets, sequence calculations, range notation, character frequency, and number combinations for a specific sum. These exercises, designed by ExcelBI and implemented using the R programming language, have implications in data science, problem solving, program logic, and overall coding competence. The solutions are available on the author’s Github for reference, providing an opportunity for open-source contribution and exploration.

Long-Term Implications and Future Developments

The continuation of such puzzle-solving exercises amplifies coding and analytical skills, fostering learning by promoting engagement. More advanced problems involving larger data sets and more complex calculations could be forthcoming. Future iterations may involve machine learning models, statistics and probabilistic algorithms, thus pushing the frontier in computational problem-solving.

By solving these puzzles regularly, coders can better understand new or complex concepts. Over time, this cognition leads to data exploration proficiency, more efficient code writing, and improved algorithm understanding. These results in turn enhance the tasks of statistical analysis, modeling, forecasting, and data-driven decision-making tasks in data science.

Actionable Advice

Programming enthusiasts and data science learners should continually engage in these tasks to improve problem-solving ability, reinforce mathematical and conceptual understanding, and improve their coding skills. Collaboration with other coders and sharing insights on platforms such as Github or R-bloggers enable one to learn from a broader community, thereby broadening perspective and accelerating growth.

Continually practicing with different puzzles and challenges helps to foster a deep understanding of many versatile topics. Persistence in coding practice coupled with peer review is crucial for self-improvement.

Open-source contributors can leverage these puzzles and their solutions to identify potential areas of code optimization or alternative solution paths. A systematic comparison of solutions using different languages (since Python solutions are also provided by the author) can lead to a broader understanding about computational efficiency related to specific languages and libraries.

Lastly, as one works through these puzzles, documenting one’s thought process and recording any issues encountered can be helpful. This not only benefits self-revision, but it also assists others who may encounter similar problems or need clarification about a particular step or algorithm.

The continual push to solve new puzzles and challenges not only reinforces the fun aspect of coding but also nurtures a problem-solving mindset crucial for any aspirational data scientist or coder.

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