**Left Censored » R**, and kindly contributed to R-bloggers)

Last week markbulling over at Drunks & Lampposts posted a method of using `sapply`

to filter a list by a predicate. Today the @RLangTip tip of the day was to use `sapply`

similarly. This made makes me wonder if **R**‘s very useful higher-order functions aren’t as well known as they should be. In this case, the `Filter`

higher-order function would be the tool to use. `Filter`

works more or less like the `*apply`

family of functions, but it performs the subsetting (the filtering) of a list based on a predicate in a single step.

As an example, let’s say we have a list of 1000 vectors, each of length 2 with \(x_1,\,x_2 \in [0,\,1]\), and we want to select only those vectors where the elements of the list sum to a value greater than 1. With `Filter`

, this is all we have to do:

mylist <- lapply(1:1000, function(i) c(runif(1), runif(1))) method.1 <- Filter(function(x) sum(x) > 1, mylist)

Which is at least a bit more transparent than the `sapply`

alternative:

method.2 <- mylist[sapply(mylist, function(x) sum(x) > 1)]

In some very quick tests, I found no performance difference between the two approaches.

There are other useful higher-order functions. If you are interested, check out `?Filter`

.

**leave a comment**for the author, please follow the link and comment on his blog:

**Left Censored » R**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...