Magic Functions to Obtain Results from for Loops in R

1. Overview

for() is one of the most popular functions in R. As you know, it is used to create loops.

For example, let’s calculate squared values for 1 to 3.

for (i in 1:3) {
  squared <- i ^ 2
  print(squared)
}
#> [1] 1
#> [1] 4
#> [1] 9

It is very easy.

However, it becomes too much hassle to change such codes to store the result. You must prepare some containers with correct length for storing the result and change print() to assignment statements.

result <- vector("numeric", 3) # prepare a container
for (i in 1:3) {
  squared <- i ^ 2
  result[i] <- squared         # change to assignment
}
result
#> [1] 1 4 9

Moreover, you may want to store the result as a data.frame with the iteration numbers.

result <- data.frame(matrix(nrow = 3, ncol = 2))
colnames(result) <- c("i", "squared")
for (i in 1:3) {
  squared <- i ^ 2
  result[i, 1] <- i
  result[i, 2] <- squared
}
result
#>   i squared
#> 1 1       1
#> 2 2       4
#> 3 3       9

What a bother!

In such or more troublesome situations like that you have to store many variables, the code will grow more complex.

The magicfor package makes to resolve the problem being kept readability.

You just add two lines before the for loop. First, load the library. Second, call magic_for(). Notice that the main for loop is kept intact.

library(magicfor)               # load library
magic_for(print, silent = TRUE) # call magic_for()

for (i in 1:3) {
  squared <- i ^ 2
  print(squared)
}

magic_result_as_dataframe()     # get the result
#>   i squared
#> 1 1       1
#> 2 2       4
#> 3 3       9

magic_for() takes a function name, and then reconstructs for() to remember values passed to the specified function in for loops. We call it “magicalization”. Once you call magic_for(), as you just run for() as usual, the result will be stored in memory automatically.

Here, we are using magic_result_as_dataframe() in order to get the stored values. It is one of the functions to obtain results from “magicalized for loops”, and means to take out the results as a data.frame.

Even if the number of observed variables increases, you can do it the same way.

magic_for(silent = TRUE)

for (i in 1:3) {
  squared <- i ^ 2
  cubed <- i ^ 3
  put(squared, cubed)
}

magic_result_as_dataframe()
#>   i squared cubed
#> 1 1       1     1
#> 2 2       4     8
#> 3 3       9    27

put() is the default function to store values in magicalized for loops. It allows to take any number of variables and can display them.

2. Installation

You can install the magicfor package from CRAN.

install.packages("magicfor")

The source code for magicfor package is available on GitHub at

• https://github.com/hoxo-m/magicfor.

3. Details

The magicfor package provides the functions as follows:

• magic_for(): Magicalize for.
• magic_free(): Free magicalization.
• Get results:
  • magic_result(): as a list.
  • magic_result_as_vetor(): as a vector.
  • magic_result_as_dataframe(): as a data.frame.
• put(): Display values.

In the following, we assume that the library is loaded to use the functions.

library(magicfor)

magic_for()

for (i in 1:3) {
  squared <- i ^ 2
  put(squared)
}
#> The loop is magicalized with put().
#> squared: 1
#> squared: 4
#> squared: 9

x <- 1
put(x)
#> x: 1

magic_result_as_vector()
#> [1] 1 4 9

magic_for(cat)

for (i in 1:3) {
  squared <- i ^ 2
  cat(squared, " ")
}
#> The loop is magicalized with cat().
#> 1  4  9

magic_for(progress = TRUE)

for (i in 1:3) {
  squared <- i ^ 2
  put(squared)
}

#> |=================================================================| 100%

magic_for(test = 2)

for (i in 1:100) {
  squared <- i ^ 2
  put(squared)
}
#> The loop is magicalized with put().
#> squared: 1
#> squared: 4

magic_for(temp = TRUE)
is_magicalized()
#> [1] TRUE

for (i in 1:3) {
  squared <- i ^ 2
  put(squared)
}
#> The loop is temporary magicalized with put().
#> squared: 1
#> squared: 4
#> squared: 9

is_magicalized()
#> [1] FALSE

magic_for()
is_magicalized()
#> [1] TRUE

magic_free()
is_magicalized()
#> [1] FALSE

magic_for(silent = TRUE)

for (i in 1:3) {
  squared <- i ^ 2
  put(squared)
}

magic_result_as_vector()
#> [1] 1 4 9

magic_free()
magic_result_as_vector()
#> NULL

magic_for(silent = TRUE)

for (i in 1:3) {
  squared <- i ^ 2
  put(squared)
}

magic_result()
#> $squared
#> $squared[[1]]
#> [1] 1
#> 
#> $squared[[2]]
#> [1] 4
#> 
#> $squared[[3]]
#> [1] 9

magic_result_as_vector()
#> [1] 1 4 9

magic_result_as_dataframe()
#>   i squared
#> 1 1       1
#> 2 2       4
#> 3 3       9

x <- 2
y <- 3
put(x)
#> x: 2
put(x, y)
#> x: 2, y: 3
put(x, x ^ 2, x ^ 3)
#> x: 2, x^2: 4, x^3: 8
put(x, squared = x ^ 2, cubed = x ^ 3)
#> x: 2, squared: 4, cubed: 8

magic_for()

for (i in 1:3) {
  put(x = i, squared = i ^ 2, cubed = i ^ 3)
}
#> The loop is magicalized with put().
#> x: 1, squared: 1, cubed: 1
#> x: 2, squared: 4, cubed: 8
#> x: 3, squared: 9, cubed: 27

magic_result_as_dataframe(F)
#>   x squared cubed
#> 1 1       1     1
#> 2 2       4     8
#> 3 3       9    27

4. Miscellaneous

Whenever you put just variables in magicalized for loops, their values will be stored regardless of target functions.

magic_for()

for (i in 1:3) {
  squared <- i ^ 2
  squared
}
#> The loop is magicalized with put().

magic_result_as_vector()
#> [1] 1 4 9

When you write trarget functions inside of if statements without else, NA will be inserted to represent missing.

magic_for()

for (i in 1:3) {
  squared <- i ^ 2
  if(i == 3) put(squared)
}
#> The loop is magicalized with put().
#> squared: 9

magic_result_as_vector()
#> [1] NA NA  9

Target functions work only top level lines or inside of if statements in magicalized for loops. For example, it does not work inside nested for loops.

magic_for()

for (i in 1:2) {
  for (j in 1:2) {
    put(i, j, i * j)
  }
}
#> The loop is magicalized with put().
#> i: 1, j: 1, i*j: 1
#> i: 1, j: 2, i*j: 2
#> i: 2, j: 1, i*j: 2
#> i: 2, j: 2, i*j: 4

magic_result_as_vector()
#> list()

5. Bug Reports

• https://github.com/hoxo-m/magicfor/issues