Category: R

John Mount defends the honor of base R:

The graph summarizes the performance of four solutions to the “scoring logistic regression by hand” problem:

• Optimized Base R: a specialized “pre allocate and work with vectorized indices” method. This is fast as it is able to express our particular task in a small number of purely base R vectorized operations. We are hoping to build some teaching materials about this methodology.

• Idiomatic Base R (shown dashed): an idiomatic R method using stats::aggregate() to solve the problem. This method is re-plotted in both graphs as a dashed line and works as a good division between what is fast versus what is slow.

• data.table: a straightforward data.table solution (another possible demarcation between fast and slow).

• dplyr (no grouped filter): a dplyr solution (tuned to work around some known issues).

Read the whole thing, including the comments section, where there’s a good bit of helpful back-and-forth.

Theo Roe provides an introduction to tibbles in R:

Tibbles are a modern take on data frames, but crucially they are still data frames. Well, what’s the difference then? There’s a quote I found somewhere on the internet that decribes the difference quite well;

“keeping what time has proven to be effective, and throwing out what is not”.

Basically, some clever people took the classic data.frame(), shook it til the ineffective parts fell out, then added some new, more appropriate features.

I probably don’t do enough with tibbles, but the upside is that in most cases, there’s a smooth transition.

John Mount shows off some rquery benchmarks versus dplyr and data.table:

Let’s take a look at rquery’s new “ad hoc” mode (made convenient through wrapr‘s new “wrapr_applicable” feature). This is where rquery works on in-memory data.frame data by sending it to a database, processing on the database, and then pulling the data back. We concede this is a strange way to process data, and not rquery’s primary purpose (the primary purpose being generation of safe high performance SQL for big data engines such as Spark and PostgreSQL). However, our experiments show that it is in fact a competitive technique.

We’ve summarized the results of several experiments (experiment details here) in the following graph (graphing code here). The benchmark task was hand implementing logistic regression scoring. This is an example query we have been using for some time.

There are some nice early results, so it’ll be interesting to watch as this develops.

Julia Silge announces a new version of tidytext:

I am pleased to announce that tidytext 0.1.6 is now on CRAN!

Most of this release, as well as the 0.1.5 release which I did not blog about, was for maintenance, updates to align with API changes from tidytext’s dependencies, and bugs. I just spent a good chunk of effort getting tidytext to pass R CMD check on older versions of R despite the fact that some of the packages in tidytext’s Suggests require recent versions of R. FUN TIMES. I was glad to get it working, though, because I know that we have users, some teaching on university campuses, etc, who are constrained to older versions of R in various environments.

There are some more interesting updates. For example, did you know about the new-ish stopwords package? This package provides access to stopword lists from multiple sources in multiple languages. If you would like to access these in a list data structure, go to the original package. But if you like your text tidy, I GOT YOU.

Read on for examples and grab the latest version.

Dmitry Kisler shows how to geocode addresses in R using the OpenStreetMap API:

It is quite likely to get address info when scraping data from the web, but not geo-coordinates which may be required for further analysis like clustering. Thus geocoding is often needed to get a location’s coordinates by its address.

There are several options, including one of the most popular, google geocoding API. This option can be easily implemented into R with the function geocode from the library ggmap. It has the limitation of 2500 request a day (when it’s used free of charge), see details here.

To increase the number of free of charge geocoding requests, OpenStreetMap (OSM) Nominatim API can be used. OSM allows up to 1 request per second (see the usage policy), that gives about 35 times more API calls compared to the google geocoding API.

Click through for the script.

Rob Grant shows how to connect to Google Sheets with R:

That was a quick overview of the most basic functions of the google sheets package.

This is a really useful package. A lot of my work involves reading data in Google Sheets either before or after using R.

Googlesheets means I won’t have to bother with read.csv() or write.csv() as much in the future, saving me time.

Click through for a good tutorial.