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Testing your R package is crucial, and thankfully it only gets easier with time, thanks to experience… and awesome packages helping you setup and improve tests! In this post, we shall offer a roundup of packages for testing R packages, first in a section about general testing setup, and then in a section about testing “peculiar” stuff. General package testing infrastructure Create tests If you’re brand-new to unit testing your R package, I’d recommend reading this chapter from Hadley Wickham’s book about R packages.

The R community and the package ecosystem are awesome but it can be difficult to sustain your R packages when you only have so much free time. To make a stellar package you’ve got to keep on top of the issues, make great documentation, have that all important hex sticker, and generally have good quality code. This all takes time, time you don’t always have. We would like to help.

We at Locke Data maintain a few R packages that we’ve submitted to CRAN to help increase their userbase. After running devtools::release(), clicking in a confirmation email… what remains is waiting. Inspired by our experience, we’ve created a dashboard to help other package maintainers follow their package’s journey to CRANterbury. Read more about its making in this post! Why create the cransays dashboard? Sometimes, depending on the workload of CRAN volunteers, it can last a while before a package ends up on their way to CRAN.

Sometimes packages have functions that don’t do the things the way you want them to do them and you have to either re-build the function, or work with it as-is and add code around it to solve your issue. I’ve had to do this recently with the googleway package and it’s google_distance() function so I wanted to take you through step by step how I wrote code to go from a single value function to a function that handles many inputs and returns 4 rows per input.

As a heavy R and Tidyverse user, I’ve been playing with Microsofts m/Power Query language included in Excel and PowerBI from that perspective, looking for the functions to make my life easier, developing small code pipelines for my processing and trying to get a smooth, clear and maintainable data manipulation process in place. The Problem In PowerBI I have data generated from an API call to HubSpot, which deliveres a json which is flattened as the first step of the process into a table with hundreds of columns.

People use R for lots of reasons: “It’s great for the models I need”, “I like the functional approach”, “It’s the tool I’m most comfortable with”. People don’t use R for these reasons: “I have a favourite processor core, I don’t want to use the others”, “I love how my memory needs to fit all my data”. What if I told you that you didn’t need to worry about that any more?

How awesome is open source software? Quite awesome in our opinion! Locke Data maintains several open source repos on GitHub, in particular of R packages, and we’d like you to join in the fun! This month, we’re taking part in Hacktoberfest and will do our best to mentor you through your first open source contributions if you wish! Hacktoberfest is a month-long operation celebrating open source software. As an open source newbie, it’s the occasion to start participating in open source development!

We’re done with the basics of handling data in R. Now we want to know how to make sense of it. We know what kind of data it is, we know how to look at column names, dimensions and the like. If you’re trying to add value to this data however, that very often isn’t enough, so here’s a look at using the tools available to you to start figuring out how to do what you want.

Cosmos DB is a snazzy new(ish) Microsoft Azure product. I was able to go to Microsoft Office in London for three days of training on the database service, which was really well structured and well run, with a lot of knowledgeable Microsoft bods around to pass on their considerable knowledge. This post will extract out some key features and benefits of the service, and then discuss how this fit’s into a data scientists role.

To quickly recap, so far we’ve just worked with some single values to get to grips with how some of the various operations work. Of course, we rarely work with a single value! If we did, we could just use a calculator. This instalment you’ll get to grips with some different ways of storing data and how to manipulate your datasets in the “traditional” way. This will help you understand a lot of code written in the past, and will equip you to understand data manipulation of tabular data.