A Collection of Useful Sites and People

Hướng Dẫn Tiếng Việt Về R 🇻🇳

Phân tích số liệu và biểu đồ bằng. Đây là nguồn tài nguyên tuyệt vời!

Getting started with R

I recently discovered this adorable giraffe themed R course 🦒, which I would recommend any new user to R start off with. It’s an aesthetically pleasing primer and best of all, it’s free!

People

Danielle Navarro is an #rstats guru who is also very humble, funny, and helpful. She blogs about her experiences (and often frustrations!) with R, which shows that learning R is a constant process. Plus, she has been updating her very popular book Learning Statistics with R, which has been invaluable for R users everywhere.

Maëlle Salmon has an extensive blog where she provides helpful tips and tricks and posts about interesting projects she is doing. Like Danielle, she is really active on Twitter.  

Mara Averick is another great person to follow on Twitter. She is renowned for posting tips and tricks to help with using R (and she uses A LOT of emojis 🥑💃💁‍♀️). ²  

And of course, Hadley Wickham is the man behind much of R’s syntax, including the cornerstone of data visualization, ggplot2. His book R for Data Science is a staple of R learning. ³  

“How do I…?”

Do anything? This is a pretty random assortment of useful commands. I would recommend trawling through it every so often and noting the commands you think might be useful for you in the future.

Read an R Help Page?. This is essential.

Read an R error message? And so is this.  

Asking for help

For general questions, start here. Maëlle Salmon’s guide to asking for help in R is a great starting point for figuring out how to ask for help.

Those of you who identify as female 👩 can always reach out to R-Ladies for help and support. R Ladies was founded to provide support and encourage positivity in the R community. They have been a huge success in encouraging R as a language of diversity and inclusivity, in a world where so many programming languages are dominated by men!

Stack Overflow is the go-to site for general questions asked by individuals of all genders 👨👩. But beware: it is notorious for being highly technical and for facilitating bullying. Nonetheless, it is a fantastic resource.  

Data Carpentry

This workshop by Aleeza Gerstein on data carpentry with dplyr is a great guide to beginning data carpentry. ⁴  

Data Visualization

Claus Wilke’s Fundamentals of Data Visualization was recently published, and should be essential reading for any R user embarking on data analysis (i.e., everyone!).

Kieran Healy is another big name in data visualization, and his comprehensive guide covers EVERYTHING. It’s a little “higher level” compared to Claus’s book, so start with Fundamentals of Data Visualization first.

If you’re reading this, you’ll hopefully be familiar with ggplot2. What you may not be as familiar with are the steps it takes to build a truly beautiful and informative ggplot. Luckily, Cédric Scherer has a guide.

The BBC have a great guide to creating BBC publication-worthy visualization with ggplot2.

A collection of themes that will make your plots look visually appealing can be found here.

Are your plots looking sad and boring? Here is a guide that will help you make them better.

Spatial Analysis

Katie Jolly is a spatial analyst R user, and she has a step by step [guide][(https://www.katiejolly.io/rladies-spatial/) to visualizing quantities in an area. Her website has other spatial data workings, all with code! ⁵

This is a really cool way to visualize cartographic lines, although I’m not sure how useful it is.

To make truly expert quality maps, there is no better resource than Timo Grossenbacher’s post. I still haven’t managed to make a map that looks this good in ggplot2, but I’m going to keep trying until I do!

Developing your skills

The R community is constantly growing and evolving 🌻. I used to find it daunting to keep up with everything, but R-weekly makes it easy to do just that! I recommend making it part of your Monday housekeeping to check it out.

Yihui Xie 🇨🇳 is a genius and one of the key thinkers behind much of “advanced R”, such as rmarkdown and xaringan. Keep an eye on what he’s up to, because his work is an indicator of the direction R is going in. And read his guides! He is one of the best R gurus for explaining advanced concepts thoroughly and clearly.

Emily Riederer wrote a highly influential blog post on using R markdown to create a better workflow and reproducible code. This will be a bit advanced for new users, but is also very much worth reading closely as you become more comfortable with R. I wish I had read something like this before I picked up a lot of bad habits!!

Package development

As you get more comfortable with R and R markdown, it is beneficial to learn, and/or become comfortable with, package development. This ensures reproducibility and can make your life a lot easier if you find yourself writing the same functions (or analyses!) over and over. The premier post that users galore have turned to is Hilary Parker’s “Writing an R package from scratch”. Also check out Maëlle Salmon’s post, which contains helpful tips around the development of R packages.

Reporting your results

Although I think it’s important to know how to report your statistical analyses yourself to ensure that you fully understand what you did, report is a dream come true for explaining your results in a clear and standardized way. Nonetheless, use this tool only when you are confident in your understanding of the analysis you are doing.

Speaking of xaringan…

Building slides for presentations in R is rewarding and perhaps most importantly, it’s EASY! Yihui Xie’s guide to xaringan can be found here.

xaringan doesn’t come with too many themes out of the box, but this R package by Garrick Aden-Buie provides some slick options.

I also just discovered this great presentation by Alison Hill where she gives tips on creating beautiful slides. I fully recommend working through her presentation.

Fun stuff

Make your graphs look kewl with the vapoRwave package.