Category: R

John Mount notes that you should not assume that the View() function in R will work:

R tip: get out of the habit of calling View() directly.

View() only works correctly in interactive environments, not currently in RMarkdown contexts. It is better to call something else that safely dispatches to View(), or to something else depending if you are in an interactive or non-interactive session.

Click through for a script which is safe to run whether you’re in R Studio or using knitr to build a document.

John Mount explains why you might want to add drop = FALSE to your data.frame operations:

We were merely trying to re-order the rows and the result was converted to a vector. This happened because the rules for [ , ] change if there is only one result column. This happens even if the there had been only one input column. Another example is: d[,] is also vector in this case.

The issue is: if we are writing re-usable code we are often programming before we know complete contents of a variable or argument. For a data.frame named “g” supplied as an argument: g[vec, ] can be a data.frame or a vector (or even possibly a list). However we do know if g is a data.frame then g[vec, , drop = FALSE] is also a data.frame(assuming vec is a vector of valid row indices or a logical vector, note: NA induces some special cases).

We care as vectors and data.frames have different semantics, so are not fully substitutable in later code.

Definitely read the comments on this one as well, as John extends his explanation and others chime in with very useful notes.

Maelle Salmon performs some analysis on the Locke Data blog:

Often, the URL of a blog post can be guessed based on its title, e.g. this one can be read here. But even if the transition from the Markdown file information to an URL is logical, it was best to get URLs from the in situ blog posts, and then join them to the blog post information collected previously, since some special characters got special treatment that I could not fully understand by looking at blogdown source code.

I first extracted all posts URLs from the website map.

Check it out.

I have a quick post on removing HTML markup with stringr:

This is a quick post today on removing HTML tags using the stringr package in R.

My purpose here is in taking some raw data, which can include HTML markup, and preparing it for a vectorizer.  I don’t need the resulting output to look pretty; I just want to get rid of the HTML characters.

Click through for the script.  If you need to do something nice with the text afterward, my technique is probably too much sledgehammer for niceties, but it does the trick for pre-processing before vectorization.

Mike Spencer compares lapply (single core and its multi-core version) versus a for loop in R:

But how fast were they? Can we get faster? Thankfully R provides `system.time()` for timing code execution. In order to get faster, it makes sense to use all the processing power our machines have. The ‘parallel’ library has some great tools to help us run our jobs in parallel and take advantage of multicore processing. My favourite is `mclapply()`, because it is very very easy to take an `lapply` and make it multicore. Note that mclapply doesn’t work on Windows. The following script runs the `read_clean_write()` function in a for loop (boo, hiss), lapply and mclapply. I’ve run these as list elements to make life easier later on.

It’s interesting reading, particularly because I had expected lapply to do a little bit better.  Also interesting is the relative overhead cost of mclapply in this scenario:  going from 1 core to 4 cut the time to approximately 1/3, not 1/4.