# Good practices in R programming

R is a free software environment for statistical computing and graphics, available from The R Project for Statistical Computing. At Indiana University, R is available on research computing systems. R is also available via IUanyWare.

Following are guidelines and code examples that illustrate good practices in R programming. For additional help with developing R programs, contact the UITS Research Applications and Deep Learning team.

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## Avoid unnecessary operators

R is an interpreted language; every operator in your R scripts requires a name lookup every time you use it.

The following two code examples are functionally equivalent. However, the first code example takes about twice as much processing time due to the multiple parentheses.

Example1 | Example2 |
---|---|

system.time({ I = 0 while (I<100000) { ((((((((((10)))))))))) I = I + 1 } }) user system elapse 0.125 0.000 0.125 |
system.time({ I = 0 while (I<100000) { 10 I = I + 1 } }) user system elapse 0.055 0.000 0.055 |

## Avoid growing objects inside loops

Always pre-allocate objects to be used inside loops. Executing loops in R is slow, and growing objects inside loops will make your R program particularly slow. You should always try to pre-allocate vectors, lists, and data frames accessed inside any loops.

Consider the following two code examples. The first accesses and grows a vector inside the for loop while the second pre-allocates the vector and accesses the vector inside the for loop without growing its size.

Example1 | Example2 |
---|---|

square_loop_noinit <- function (n) { x <- c() for (i in 1:n) { x <- c(x, i^2) } system.time({ square_loop_noinit(200) }) user system elapse 0.257 0.000 0.257 |
square_loop_noinit <- function (n) { x <- integer(n) for (i in 1:n) { x[i] <- i^2 } system.time({ square_loop_noinit(200) }) user system elapse 0.099 0.000 0.099 |

## Use vectorization if possible

In R, everything is a vector. In your R script, you should always write vectorized code or use pre-existing compiled kernels (which are already vectorized and optimized) to avoid interpreter overhead.

Consider the following two code examples. The second example achieves a 38-fold speedup by using vectorized code provided by compiled kernels.

Example1 | Example2 |
---|---|

Ply <- function(x) lapply (rep(1, 1000), rnorm) system.time({ Ply() }) user system elapse 0.348 0.000 0.348 |
vec <- function(x) rnorm(1000) system.time({ vec() }) user system elapse 0.009 0.000 0.009 |

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*Last modified on* 2019-03-27 14:49:48*.*