A roadmap for learning R

Oscar Baruffa 📊🇳🇱 — Wed, 06 May 2020 05:41:52 +0000

Some folks at work expressed an interest in how to get started learning R. There are lots of resources out there, but I thought I'd share with you what I shared with them as a pathway that I followed that's working out well for me.

You don’t need all these steps but I suggested starting from 1 and working your way through them that way.

Read R for Data Science (https://r4ds.had.co.nz/). I only made it to Chapter 16 and could already do a lot of the stuff I wanted to do. This book takes a really nice approach of FIRST getting you some results and then working through some programming-specific stuff, so you won’t just be working through a lot of programming concepts without knowing what you are working towards.
There are exercises (which I largely skipped), but if you do them and want to check your answers, you can read the companion book here: https://jrnold.github.io/r4ds-exercise-solutions/
If you get stuck, there is a super helpful Slack channel where you can post questions according to the chapter you are on and people will help you. Once you get the hang of manipulating data, I encourage you to practice with some datasets from the Tidy Tuesday challenge. This is a weekly challenge where people create visualisations of the same dataset. It is GREAT for learning how others do things because they often share their code and you can learn a lot. You don’t have to wait for a new dataset, go ahead and look for old datasets that interest you!
You can view past TidyTuesday submissions and their code here: https://nsgrantham.shinyapps.io/tidytuesdayrocks/ 1.If you want to participate in posting your own visualisations, you’ll need to join Twitter. Here’s a free guide on how to get started on Twitter for R programmers: https://www.t4rstats.com/
If you like podcasts, there is also a TidyTuesday podcast with short episodes that cover the previous week’s submissions and helpful tips.

I hope that helps you on your journey. Exciting times ahead :).

Image credit: Photo by oxana v on Unsplash

Book: Twitter for R Programmers!

Oscar Baruffa 📊🇳🇱 — Wed, 06 May 2020 04:58:29 +0000

People using R for analysis or data science might have heard that the R community is really active on Twitter, but feel a bit lost when trying to figure out how to use Twitter.

Like most social media apps, there's a bit of a learning curve to get going. In an effort to help newcomers get the most out of Twitter and join the great community, I have co-authored a short book to help you get started, and it's free :)!

You can find it at www.t4rstats.com

Choosing R over Python

Oscar Baruffa 📊🇳🇱 — Mon, 22 Jul 2019 14:25:24 +0000

In this post I’ll be setting out my view for why you may want to learn to code in R rather than in Python, or at least try R. At the end of this post I also share some suggestions for getting started with either.

Some background on my programming history (feel free to skip):

I’ve done bits of programming for quite a while. Whilst never being a star programmer or been able to do anything sophisticated for a long time, I have at one point or another written some code in QBASIC, Turbo Pascal, MATLAB, C , C++ and PHP.

I’ve dabbled in a bit of Javascript, HTML and CSS – even going so far as to write my own media-queries in CSS before I knew what responsive frameworks were!

Although not proficient in any of the above, I could do the necessary stuff for coursework and personal websites.

I’m a fair hand at Excel and some might say, pretty good at it. Yes it is kind-of a programming language, in the sense that it is more like programming than it is not.

After all of this, when it came to wanting to do very cool stuff, I discovered the world of Python and really got into it – Python 3 that is 🙂 . Reading many articles, doing online courses, listening to all the Talk Python To Me episodes and dabbling around enough to do some data cleaning and a bit of visualisation. I even bought a Raspberri Pi!

So you can see I am not stumbling into R completely blindly when I make my R-over-Python argument.

This brings me onto R. After somehow following a few people like Jesse Mostipak and Mara Averick on Twitter, they made R look so fun and welcoming (not that Python isn’t), that I decided to give it a try. Wow, mind blown! After having done all the other bits and pieces in other languages, R with its Tidyverse set of packages, really makes a lot of sense to me.

Before vs after Tidyverse

When I first started with Python, I had read many comments and articles stating that R had a steeper learning curve than Python, but I think this must be a reference R before days of the Tidyverse. The Tidyverse is the name for a collection of packages which all work together really well, with common syntax across them. I can’t tell exactly when the Tidyverse was released but I would guess that it has come into common use in the last two years or so, say from 2017 onwards. I noticed last year there was some of the “should we teach base R or Tidyverse first” type of discussions online, so it’s pretty recent.

My experience is that R has a less-steep learning curve than Python.

Data Analysis and visualisation

If your main aim in programming is to manipulate and visualise data, then you should give R a try. Especially if you are coming from an Excel-heavy background. Visualisation is much easier in R. My first participation in the TidyTuesday challenge really blew my mind. In a few lines of code I was able to do what would have been quite a painful experience in Python.

Again the reason is the Tidyverse. All the packages for manipulating and analysing data share a common underlying philosophy and syntax which makes it really a pleasure to work with. In contrast, if you want to make a decent visualisation in Python, you’ll need to string a number of packages together (Pandas, Seaborn & Matplotlib) which are just different enough to make it tough to customise.

Software engineering vs Data Analysis

I think the core difference comes down to the two different starting points of these languages. Python comes at data science from a software engineering perspective, whereas R comes from a data analysis perspective, so it isn’t surprising which seems to be more comfortable with handling data from an analysis and visualisation perspective.

Why choose?

Actually, you don’t have to . There isn’t a Python vs R holy-war, even though some people may be trying to make one happen. You can use one, or the other, or both. Of course the more time you spend on one the more proficient you’ll be at it. My suggestion is that if you are coming from Excel, and want to do analysis and visualisation, try R. You will probably love it.

Getting started with R

New to R or haven’t found a resource that “clicks” yet?

Have a look at the R4DS online book. It’s excellent and free. I didn’t get very far before I was hooked. I didn’t even complete half of it and I can already do some cool stuff.

After that, take part in the TidyTuesday challenge on Twitter. It very fun and very welcoming to newcomers – be sure to let us know if it’s your first post!

I have started a YouTube channel called “Other People’s Rstats” with screencasts covering R packages, tips and TidyTuesday posts. I highly recommend it :D.

Getting started with Python

The first step I suggest, especially for non-programmers, is to go through the amazing Automate the Boring Stuff with Python book online version. There’s a discount code in it to get a massive discount on the online Udemy course. This is a great intro to the power of Python to automate things and makes it really engaging to learn about the language.

The next step is another great online resource. Follow Kevin Markham’s youtube series introduction to the Pandas library. It makes data wrangling really fun and I wouldn’t dare do any in Python without this library. Kevin is a really great instructor.

To immerse yourself in Python, I also recommend listening to the Talk Python To Me podcast.

In conclusion

Don’t worry too much about which language you start with. Your decision is not set in stone.

Any learning you do will be valuable and there are many transferable skills if you do decide to switch.

I originally published this article on my blog

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