If you’re a regular reader of this blog you may have noticed the distinct lack of charts.
I’ve written about my journey with R before, but not too much about what I do actually use R for. In short, I use it mainly for software engineering type work in order to automate other things in R, or to support the work of data scientists. There’s perhaps a bit more to it than that, but that’s the main thrust of it.
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I use R both inside and a outside of work quite a bit. As someone who deals primarily with R infrastructure, this usually revolves around Linux and RStudio Server and the rest of R’s ecosystem.
Outside of work I generally use a Chromebook for most stuff, along with a Linux server (with RStudio Server on it) to mess around, learn new things and work on my various personal projects. My Chromebook isn’t the newest or the most powerful, but it allows me to get stuff done and is fun to use, so I’m pretty happy with it.
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Welcome to the last in this little series of R functions built for fun. If you’ve not been following along from the start feel free to head back to the first post in the series and check that out too. That post explains the thinking behind the series and has an index to the other posts.
Sleigh or ISS Over the Christmas period, when you look upwards and see a bright object streaking across the sky you might wonder to yourself: “Is that a sleigh, or the International Space Station?
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Nearly at the end now! If you want to go back and see where we started, check out this post.
Not quite rot13 I confess, I cheated a little with this one. This function has two other functions declared inside it, technically making this ’the 14 R functions of xmas’. It’s also fine example of how not to design a function.
This function actually implements a variation on the real ROT13, which is a simple substitution cipher that replaces each letter with the letter 13 steps after it in the alphabet, looping back to “a” when you reach “z”.
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Wow, we’re already up to part 4 of this little series! If you want to go back and see where we started, check out this post.
Today’s two (mostly pointless) R functions are evolutions of things we seen before. In the first we have more URL manipulation and in the second, more fun with sample().
Simple tweet In the 2nd installment we wrote a small function to launch a gif search on Giphy.
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Now that we’re at day three, you hopefully know the drill (if not, check out the first of these posts). I wrote 12 small and simple R functions, basically just for a bit of fun and I’m sharing them here to spread the fun around a little.
Today’s functions are a number guessing game that reads user input and a function that tries to make us feel like we’ve been sucked into a retro sci-fi movie!
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Yesterday I posted the first two of the 12 small and hopefully fun R functions that I challenged myself to write last weekend. If you read that post, you’ll already get the idea. They’re small and fun functions that also do something small and fun!
I wanted to stick to no more than a single function (though I may have cheated a bit in at least one of them) in order to ensure that the scope stayed small and I didn’t distract myself with too many unnecessary features.
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For the other posts in this series check out the following links:
Part 1 - barcode_game and days_until_xmas (This post) Part 2 - giphy_search and roll_dice Part 3 - what_number and sci-fi_terminal Part 4 - simple_tweet and magic_R_ball Part 5 - rot13 and awesome_blogdown Part 6 - sleigh_or_iss, twelve_days and unwrap_package A couple of weeks ago I tweeted about coding as a form of procrastination. It’s something I do quite a bit, but I also do something far more annoying that keeps me from coding altogether.
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Update (2018-12-8): I completely forgot to point out in the original post that RSPM can be used to publish rpackages directly from a git repo. This is a brilliant solution for continuous deployment type scenarios. Check the docs for more information!
If you’ve read any of my previous posts you’ll know I’m a big fan of RStudio Package Manager (RSPM). It’s a great tool for enterprise users of R to take control of the way they distribute R packages within their organisation.
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If you’re working with R in a Red Hat Linux based environment with no access to the internet and need to install the stringi package you’ll no doubt run into issues with ICU library that it relies on.
At the time of writing, stringi (1.2.4) relies on libicu (called ’libicu-devel’ on Red Hat based systems). Red Hat 7.5 only has libicu-devel version 50 and stringi needs 52 or higher. During installation, if it can’t find the version it needs, it tries to download a version it can compile against from the internet.
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