Simple functional programming in R · Posted: Mar 18, 2012

R can be a very unforgiving language. Obtuse error messages and a variety of differing yet similar data structures leads to a frustrating learning curve. Furthermore R is a functional programming language which, if you are used to imperative programming (e.g. Java/Perl/Python/Ruby), makes R seem like a crufty Perl for statistics.

The functional nature of R however is often understated. Myself when I first started to program in R, I programmed as if I was writing Java since this was the only other language I knew. This led to frustration at the poor object support and the seeming requirement to create large numbers of loops and temporary variables.

I’ve recently started to learn Clojure which has lead me appreciate the functional heart of R and how much it can simplify your code. R is almost a lisp dialect since it draws much inspiration from Scheme. This, for instance, is valid R:

    sqrt(
      '*'(
        '+'(1, 2),
        3))
    #=> 3

Having first learnt to program in Java everything I heard about “Functional programming” sounded alien and strange. I think this is in part due to the academic stereotype associated with functional programming. Languages such as Clojure however are proving that functional programming can be better than imperative approaches in terms of concise, clear code and for simplifying parallelisation. However for the point of this post I’m not going to discuss the pros or cons of functional programming but instead give two simple examples in R.

Converting a character vector to a binary variable

I have the following vector from a survey of bioinformaticians describing whether each respondent is married or not.

married <- c("Yes","Yes","No","No","Yes","Yes","","No","Yes")

What I’d like to do is convert this to a binary variable where Yes becomes 1 and No becomes 0. Here’s how I originally would have solved this problem using a loop:

binary.married <- c()
for(i in 1:length(married)) {
  if(married[i] == "Yes"){
    binary.married <- c(binary.married,1)
  } else {
    binary.married <- c(binary.married,0)
  }
}

This example will perform poorly when the married variable is very long since the new vector binary.married is recreated each time to add a new element onto the end. If you only take one point away from this post; avoid using c to repeatedly increment a vector and instead do this:

binary.married <- rep(0,length(married))
for(i in 1:length(married)) {
  if(married[i] == "Yes") binary.married[i] <- 1
}

This creates the same sized binary.married variable beforehand and the loop then replaces each variable with the binary version of either Yes or No. As I initialised the binary.married variable with 0s I only needed to replace the entries corresponding to Yes in the married vector.

I think however, in general, loops should be avoided in R. If you find yourself using a loop there is probably an easier, and possible faster way to do it. For instance I could perform a similar operation using vectorisation:

married[married == "Yes"] <- 1
married[married == "No"]  <- 0

This finds each index of Yes or No values and replaces them with the corresponding binary value. This approach is also a little safer too as only elements that match either Yes or No are replaced. As an example the original married variable contained an empty response, this code ignores this and produces:

married #=> c(1,1,0,0,1,1,"",0,1)

Therefore I’ll probably get an exception if I try to perform this numerical calculation on this data. The looping approach in contrast replaced the empty response with 0. The manual index-replace method however quickly becomes unwieldy with more factors than just Yes or No. Instead here is better way the married variable can be converted to binary:

married <- sapply(married,
                  function(x) switch(x, Yes = 1, No = 0, NA))

married #=> c(1,1,0,0,1,1,NA,0,1)

Here I’m using an apply-family function. These functions perform as you would expect and apply a function to each element in a vector/list/matrix depending on which apply is used. Using the apply family of functions is the essence of using R well and Neil Saunders has an excellent overview of the functions in the apply family. To expand on the code, this applies the defined function to each element in married and I used the syntactic sugar for creating an inline anonymous function (e.g. function(args) ...). This example can be written more verbosely for illustration:

character.to.binary <- function(x){
  switch(x,
    Yes = 1,
    No  = 0,
    NA)
}
married <- sapply(married,character.to.binary)

I think the previous anonymous function syntax function(x) ... is useful for writing inline anonymous when writing a complete function definition seems too much.

The switch statement is also worth clarifying too: I define the cases I want to match and the unmatched cases then return the default which is NA. Why use NA though? I could just leave this blank instead. For example when calculating the proportion of respondents married:

married <- sapply(married,
                  function(x) switch(x, Yes = 1, No = 0)) # No default case
married #=> c(1,1,0,0,1,1,"",0,1)

# Proportion of respondents married
mean(married[married != ""])

R functions however often provide an rm.na flag. So by following the convention of using NA for missing values this can be simplified to:

married <- sapply(married,
                  function(x) switch(x, Yes = 1, No = 0,NA))

# Proportion of respondents married
mean(married, na.rm = TRUE)

A slightly more complex example

Consider I have slightly more complex data. From the same survey, respondents were asked to input their gross income from a drop down menu. A sample of the responses is as follows:

income <- c("130,000 - 134,999", "55,000 - 59,999", "90,000 - 94,999",
            "70,000 - 74,999",   "10,000 - 14,999", "25,000 - 29,999",
            "20,000 - 24,999",   "25,000 - 29,999", "40,000 - 44,999",
            "20,000 - 24,999")

This is again a character vector which I want to convert to numeric data. Concretely I would like the numerical value for the midpoint of each entry. This might seem somewhat tricky but using sapply again I can just apply a series of functions to each element to get desired result:

to.numeric.midpoint <- function(x){
  sapply(strsplit(x,' - '),
         function(s) sum(as.numeric(gsub(',','',s))) / 2)
}

to.numeric.midpoint(income)
  #=> 132500  57500  92500  72500  12500  27500  22500  27500  42500  22500

There are two parts to this function. The first uses strsplit to break up each entry on " - " into a vector with two elements. The strsplit automatically vectorises this operation to perform this element-wise. The result of the strsplit operation is therefore as follows:

strsplit(income, ' - ')

[[1]]
[1] "130,000" "134,999"

[[2]]
[1] "55,000" "59,999"

[[3]]
[1] "90,000" "94,999"

# reminder omitted

A list of vectors is returned and sapply then calls the anonymous function on each entry in the list. The anonymous function simply removes the , characters from each element using gsub, converts each character element to a numeric value using as.numeric, then calculates the sum of both elements and divides the result by 2.

Further Reading

I hope these simple examples showed how using R functional nature can improve code performance and clarity. If you’re interested in leaning a little more about R I’d recommend two good books: The R Inferno PDF or hardcopy and The Art of R Programming. Both of these have a lot of information about the “right” way to program in R.

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