README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```

## 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, eval = FALSE}
devtools::install_github("tpq/yuck")
library(yuck)
```

```{r, echo = FALSE}
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
```

## 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)
```

## 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
```

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
```