Sharon’s list contains many neat tricks, some of which less well-known base functions, others features of more niche packages. Here’s the ones I am definitely adding to my R tricks overview and want to highlight here as well:
Categorize values into interval cut()
Convert numbers that came in as strings with commas to R numbers with readr::parse_number(mydf$mycol)
Create a searchable, sortable HTML table in 1 line of code with DT::datatable(mydf, filter = 'top')
Display a fraction between 0 and 1 as a percentage with scales::percent(myfraction)
Generate a vector of 1:length(myvec) with seq_along(myvec)
rstudio::conf is theyearly conference when it comes to R programming and RStudio. In 2017, nearly 500 people attended and, last week, 1100 people went to the 2018 edition. Regretfully, I was on holiday in Cardiff and missed out on meeting all my #rstats hero’s. Just browsing through the #rstudioconf Twitter-feed, I already learned so many new things that I decided to dedicate a page to it!
Fortunately, you can watch the live streams taped during the conference:
Restart R often. This ensures your code is still working as intended. Use Shift-CMD-F10 to do so quickly in RStudio.
Use stable instead of absolute paths. This allows you to (1) better manage your imports/exports and folders, and (2) allows you to move/share your folders without the code breaking. For instance, here::here("data","raw-data.csv") loads the raw-data.csv-file from the data folder in your project directory. If you are not using the here package yet, you are honestly missing out! Alternatively you can use fs::path_home(). normalizePath() will make paths work on both windows and mac. You can usebasename instead of strsplit to get name of file from a path.
To upload an existing git directory to GitHub easily, you can usethis::use_github().
If you include the below YAML header in your .R file, you can easily generate .md files for you github repo.
#' output: github_document
Moreover, Jenny proposed these useful default settings for knitr:
Another of Jenny Bryan‘s talks was named Data Rectangling and although you might not get much out of her slides without her presenting them, you should definitely try the associated repurrrsivetutorial if you haven’t done so yet. It’s a poweR up for any useR!
I can’t remember who shared it, but a very cool trick is to name the viewing tab of any dataframe you pipe into View() using df %>% View("enter_view_tab_name").
These probably only present a minimal portion of the thousands of tips and tricks you could have learned by simply attending rstudio::conf. I will definitely try to attend next year’s edition. Nevertheless, I hope the above has been useful. If I missed out on any tips, presentations, tweets, or other materials, please reply below, tweet me or pop me a message!
Many people have criticized the pie chart. The most important critique is that we, humans, are good in comparing lengths and heights, but angles and areas not so much. The following three charts by Kristin Henry demonstrate the phenomenon. Can you spot how the two pie charts below are different?
And how about now?
OK, I admit that the order of the categories matters quite a lot in the chart above. But alternatively, you can transform the pie charts into grouped bar charts, that will immediately show the difference:
In general, pie charts should be avoided when a large number of items is considered. Simple pie charts displaying 2-3 categories or one category as opposed to the others may work just fine, but when displaying more data, it is better to choose a different chart type. Oracle hosted a different example some years back: