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[EXPERIMENTAL] An R package to add something like list comprehensions

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Introduction
------------

We all know R is not the place to write for-loops, but some of us really, really like them. This package is inspired by Python's list comprehensions that make it possible to write for-loops in only a few keystrokes. Note that this package makes for-loops faster to write, not faster to run.

You can install the most recent version of `yuck` from GitHub:

``` r
devtools::install_github("tpq/yuck")
library(yuck)
```

Single loop comprehensions
--------------------------

A list comprehension allows a user to step through a for-loop and save each result in one line of code, similar to what `lapply` does. The `yuck` package introduces the `:=` operator to trigger a list comprehension by computing the expression on the right and saving it to the value on the left.

``` r
a := for(i in 1:5) i^2
a
#> [1]  1  4  9 16 25
```

Nested loop comprehensions
--------------------------

The `yuck` package allows for nested loop comprehensions.

``` r
a := for(i in 1:5) for(j in 1:7) (i - 1)^2 + (j - 1)^2
matrix(a, 7, 5)
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    0    1    4    9   16
#> [2,]    1    2    5   10   17
#> [3,]    4    5    8   13   20
#> [4,]    9   10   13   18   25
#> [5,]   16   17   20   25   32
#> [6,]   25   26   29   34   41
#> [7,]   36   37   40   45   52
```

Use cases
---------

The examples above are arbitrary and the problems presented have better (i.e., vectorized) solutions. Rather, the advantage of `yuck` comes from its use in combination with others tools. For example, we could use `yuck` to iterate across multiple machine learning parameters, models, and predictions. We can also combine `yuck` with `magrittr` to comprehend pipes.

``` r
library(e1071)
library(magrittr)
data(iris)
groups <- split(sample(1:nrow(iris)), 1:2)
train <- iris[groups[[1]],]
test <- iris[groups[[2]],]
confs := for(kernel in c("linear", "radial", "polynomial"))
  svm(Species ~ ., train, kernel = kernel) %>%
  predict(test) %>% table(test$Species)
confs
#> [[1]]
#>             
#> .            setosa versicolor virginica
#>   setosa         22          0         0
#>   versicolor      0         26         1
#>   virginica       0          0        26
#> 
#> [[2]]
#>             
#> .            setosa versicolor virginica
#>   setosa         22          0         0
#>   versicolor      0         26         2
#>   virginica       0          0        25
#> 
#> [[3]]
#>             
#> .            setosa versicolor virginica
#>   setosa         22          0         0
#>   versicolor      0         26         5
#>   virginica       0          0        22
```

I like using `yuck` to measure CPU time and RAM overhead.

``` r
library(peakRAM)
times := for(i in c(1e5, 1e6, 1e7)) data.frame(i,
  peakRAM(
    function() 1:i,
    1:i,
    1:i + 1:i,
    1:i * 2
  ))[,-3]
times
#> [[1]]
#>       i Function_Call Total_RAM_Used_MiB Peak_RAM_Used_MiB
#> 1 1e+05 function()1:i                0.4               0.4
#> 2 1e+05           1:i                0.4               0.4
#> 3 1e+05       1:i+1:i                0.4               0.8
#> 4 1e+05         1:i*2                0.8               1.2
#> 
#> [[2]]
#>       i Function_Call Total_RAM_Used_MiB Peak_RAM_Used_MiB
#> 1 1e+06 function()1:i                3.8               3.8
#> 2 1e+06           1:i                3.8               3.8
#> 3 1e+06       1:i+1:i                3.8               7.6
#> 4 1e+06         1:i*2                7.6              11.4
#> 
#> [[3]]
#>       i Function_Call Total_RAM_Used_MiB Peak_RAM_Used_MiB
#> 1 1e+07 function()1:i               38.1              38.1
#> 2 1e+07           1:i               38.1              38.1
#> 3 1e+07       1:i+1:i               38.1              76.2
#> 4 1e+07         1:i*2               76.2             114.4
```

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