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Category: R

Comparing R Package Versions with Diffify

Published 2022-05-02 by Kevin Feasel

Clarissa Barratt and Parisa Gregg announce an interesting tool:

You know that sinking feeling that you get when you’re months into a big project and you log in one day and nothing works? Turns out something has updated and things have been removed that you needed and now you need to spend hours-days figuring out what’s changed and your masters deadline is getting closer and … ok, apparently this took me back to a very specific event.

But I’m sure most of that sounds familiar to you if you’ve ever programmed something over a longer period of time.

Over the last few months, Jumping Rivers have been working on a tool that will make it easier to see differences between R package versions: Diffify.

Read on to see it in action. It looks quite useful for troubleshooting issues in which a package suddenly changed API functionality, something which tends to happen frequently in the R and Python worlds.

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Combining flashlight and plotly in R

Published 2022-04-26 by Kevin Feasel

Michael Mayer analyzes candidate models:

Since almost all plots in flashlight are constructed with ggplot, it is super easy to turn them into interactive plotly objects: just add a simple ggplotly() to the end of the call.

However… it is not straightforward to show interactive plots in a blog! Thus, we show only screenshots of the resulting plots here and refer to the complete HTML report here: https://mayer79.github.io/flashlight_plotly/flashlight_plotly.html

We will use a sweet dataset with more than 20’000 houses to model house prices by a set of derived features such as the logarithmic living area. The location will be represented by the postal code.

Click through for the blog post or check out the report.

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String Concatenation in R

Published 2022-04-22 by Kevin Feasel

Benjamin Smith creates a function:

While it is possible to use the paste() or paste0() for string concatenation. I do understand how it can be messy to deal with, especially when working with loops and/or nested functions. In this short blog I share a remedy for this by writing a special function which can lend for cleaner code as opposed to using paste() or paste0().

It’s not quite as nice as a here string (e.g., @"{FirstName} just referenced the name here string at {UserTime}" user.FirstName DateTime.UtcNow) but this is a good reminder that operator creation in R is pretty easy. H/T R-Bloggers.

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R and Python Interop via Reticulate

Published 2022-04-18 by Kevin Feasel

Fabian Scheler combines R and Python:

I am way more experienced with R than with Python and prefer to code in this language when possible. This applies, especially when it is about visualizations. Plotly and ggplot2 are fantastic packages that provide a lot of flexibility. However, every language has its limitations, and the best results stem from their efficient combination.

This week, I created the candlestick below, and I think it’s an excellent case study to illustrate a few things:

Read on to learn more about using reticulate to execute Python code and interact with the results in R.

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Kubernetes for the R User

Published 2022-04-18 by Kevin Feasel

Roel M. Hogervorst provides an introduction to Kubernetes for R users:

Many R users come from an academic background, statistics and social sciences. That makes you an excellent problem solver with a deep knowledge of problems and a nuanced understanding of the world. You actually know what you are talking about!

But there is a different world, a world where the most important thing is showing an advertisement to as many people as possible. I’m joking, but the computer science world is where ideas like kubernetes were born. And like every other group specific words are used that can be hard to understand without context. That means that you have to use those words to find answers on your questions. This post will introduce some of those words and I have a list at the bottom. And now it is available to all of us, through a cloud provider in your area.

Read on for a light introduction to using Kubernetes.

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Retrieving Twitter Engagements in R

Published 2022-04-15 by Kevin Feasel

Bryan Shalloway continues looking at Twitter data:

This is a follow-up to a short post I wrote on R Access to Twitter’s v2 API. In this post I’ll walk through a few more examples of pulling data from twitter using a mix of Twitter’s v2 API as well as the {rtweet} package.

I’ll pull all Twitter users that I (brshallo) have recently been engaged by (e.g. they like my tweet) or engaged with (e.g. I like their tweet). I’ll lean towards using {rtweet} but will use {httr} in cases where it’s more convenient to use Twitter’s v2 API.

Click through for more information, including several R scripts.

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Accessing Twitter’s V2 API via R

Published 2022-04-08 by Kevin Feasel

Bryan Shalloway dives into the mess known as Twitter:

academictwitteR is probably the most established package that provides a quickstart entry point to the V2 API. However it requires creating an academic account in twitter, i.e. the user must be affiliated with a university. I also stumbled onto RTwitterV2 and voson.tcn which both also provide quickstarts on the V2 API, but did not explore these.

Instead I followed the tutorial Getting started with R and v2 of the Twitter API by Twitter Developer Advocate Jessica Garson that uses {httr} to interact more directly with the API. I highly recommend reading her tutorial. The code below is mostly just copied from there but changed to provide an example of getting the usernames of those that liked a tweet.

Read on for a how-to and some notes.

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