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Category: R

R and Python Interop via Reticulate

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

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

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

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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Printing ggplot2 Plots as Receipts

Bob Rudis has fun with a Point of Sale printer:

At the end of March, I caught a fleeting tweet that showcased an Epson thermal receipt printer generating a new “ticket” whenever a new GitHub issue was filed on a repository. @aschmelyun documents it well in this blog post. It’s a pretty cool hack, self-contained on a Pi Zero.

Andrew’s project birthed an idea: could I write an R package that will let me plot {ggplot2}/{grid} objects to it? The form factor of the receipt printer is tiny (~280 “pixels” wide), but the near infinite length of the paper means one can play with some data visualizations that cannot be done in other formats (and it would be cool to be able to play with other content to print to it in and outside of R).

Read on for a fun story which gets an entry in my most coveted category. H/T R-Bloggers

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Visualizing Air Pressure Spikes from the Hunga Tonga Eruption in R

Neil Saunders reviews some personal weather station data:

Wow. Now, pause for a moment and try to recall the last time you read any news about Tonga since the event.
The eruption sent an atmospheric pressure wave, clearly visible in this imagery, around the world. Friends online reported that this was detected by their personal weather stations (PWS) which made me wonder: was the wave apparent in online weather station data and can it be visualized using R?

The answers are yes and yes again.

Read on to see how.

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Coding Style in R

Maelle Salmon and Chrisophe Dervieux share some guidance on coding style:

Do you indent your code with one tab, two spaces, or eight spaces? Do you feel strongly about the location of the curly brace closing a function definition? Do you have naming preferences? You probably have picked up some habits along the way. In any case, having some sort of consistency in coding style will help those who read the code to understand, fix or enhance it. In this post, we shall share some resources about coding style, useful tools, and some remarks on etiquette.

It is pretty funny how picky we can be about coding style at the margins but ultimately, the primary goal of a coding style should be to give future maintainers as easy a time as possible in troubleshooting the code you write. This makes consistency the most important consideration. After that, there’s a lot of good advice in the post.

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Visualizing Networks of R Library Usage

Bryan Shalloway has fun with network plots:

In previous posts and threads I’ve alluded to the potential utility of visualizing the relationships between parsed functions/packages and files as a network plot.

I added the function network_plot() to funspotr. In this post I’ll simply output the network plots of the parsed-out packages from the code collections discussed in the prior two posts:

Click through for interactive plots of what different people in the R community use.

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