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Advanced R - Introduction and Prerequisites

Coding style(s)

Computers are cheap, and thinking hurts. -- Uwe Ligges

Simplicity, readability and consistency are a long way towards robust code.

Why?

Good coding style is like using correct punctuation. You can manage without it, but it sure makes things easier to read. -- Hadley Wickham

for consistency and readability.

Which one?

Examples

  • Place spaces around all infix operators (=, +, -, <-, etc., but not :) and after a comma (x[i, j]).
  • Spaces before ( and after ); not for function.
  • Use <- rather than =.
  • Limit your code to 80 characters per line
  • Indentation: do not use tabs, use 2 (HW)/4 (Bioc) spaces
  • Function names: use verbs
  • Variable names: camelCaps (Bioc)/ _ (HW) (but not a .)
  • Prefix non-exported functions with a ‘.’ (Bioc).
  • Class names: start with a capital
  • Comments: # or ## (from emacs)

formatR

library("formatR")
tidy_source(text = "a=1+1;a  # print the value
                    matrix ( rnorm(10),5)",
            arrow = TRUE)
## a <- 1 + 1
## a  # print the value
## matrix(rnorm(10), 5)

BiocCheck

$ R CMD BiocCheck package_1.0.0.tgz

* Checking function lengths................
  The longest function is 677 lines long
  The longest 5 functions are:
* Checking formatting of DESCRIPTION, NAMESPACE, man pages, R source,
  and vignette source...
    * CONSIDER: Shortening lines; 616 lines (11%) are > 80 characters
      long.
    * CONSIDER: Replacing tabs with 4 spaces; 3295 lines (60%) contain
      tabs.
    * CONSIDER: Indenting lines with a multiple of 4 spaces; 162 lines
      (2%) are not.

Style changes over time

Style changes over time

Ineractive use vs programming

Moving from using R to programming R is abstraction, automation, generalisation.

Interactive use vs programming: drop

head(cars)
head(cars[, 1])
head(cars[, 1, drop = FALSE])

Writing R functions

A function is made of

  • a name
  • some inputs (formal parameters)
  • a single output (return value)
  • a body
  • an environment, the map of the location of the functions variable
f <- function(x) {
    y <- x + 1
    return(x * y)
}

The apply functions

A functional approach to iteration:

apply(X, MARGIN, FUN, ...)
  • MARGIN = 1 for row, 2 for cols.
  • FUN = function to apply
  • ... = extra args to function.
  • simplify = should the result be simplified if possible.

To iterate over vectors and lists, there's sapply and lapply, where the margin is implicit.

lapply(X, FUN, ...) 
sapply(X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE)