Recoding to NA with dplyr
Just very quickly again, still horribly busy, but this has been annoying me for ages and I finally figured it out. When you’re using recode from dplyr, you can’t recode to NA if you’re recoding to other things that aren’t NA, because it complains that the types aren’t compatible. So don’t do this: <pre class="brush: r; title: ; notranslate" title=""> mutate(Relationship = recode(Relationship, "Co-Habiting" = "Co", "Divorced" = "Di", "Married" = "Ma", "Civil partnership" = "CI" "Single" = "Si", "Widowed" = "Wi", "Separated" = "Se", "Prefer not to say" = NA)) Use na_if() instead:
Checking memory usage in R
Wow, I cannot believe I didn’t blog this. I just tried to find it on my own blog and I must have forgotten. Anyway, it’s an answer I gave on Stack Overflow a little while ago, to do with measuring how much memory your R objects are using. <pre class="brush: r; title: ; notranslate" title=""> install.packages("pryr") library(pryr) object_size(1:10) ## 88 B object_size(mean) ## 832 B object_size(mtcars) ## 6.74 kB This is from Hadley Wickham’s advanced R.
Convert icons to different colours
I’m horribly, horribly busy (stay tuned until mid May-ish to hear about what I’ve been up to) but I’ve just done something so quickly and easily that I couldn’t resist sharing it. So I’ve downloaded a little man icon, I’m going to use it in a pictogram to show the percentage of people who agree with something. Just a bit of fun for a presentation. The man is black, but I thought it would be nice to have green men who are saying happy things (“I felt supported”, that kind of thing) and red men who are saying unhappy things (“I felt isolated”, e.
Securing Shiny Server over SSL with Apache on Ubuntu
Well, I couldn’t find a guide for precisely this on the Internet, securing your Shiny Server over SSL (with Apache), but I did find the following links which were very useful and which I only adapted slightly. Do have a read of the following if you’re so inclined, they’re both very useful, but neither told me exactly what to do. https://ipub.com/shiny-https/ https://support.rstudio.com/hc/en-us/articles/213733868-Running-Shiny-Server-with-a-Proxy This guide is for Ubuntu but I imagine the configuration will work on any Apache server.
Scraping the RStudio webinar list
I only just found this list of RStudio webinars, there’s loads of stuff on there, I really need to plow through a lot of it. What I really wanted was a list of them with links that I could archive and edit and rearrange so I could show which ones I am interested in, which I’ve already watched, and so on. Well, if you’ve got a problem, and no-one else can help, then maybe you need… The R Team.
One weird trick to getting column types right with read_csv
Using read_csv from the tidyverse is so easy that I didn’t bother to look at the readr documentation for a long time. However, I’m glad I did, because there is, as they say in the click bait world, one weird trick to get your column types right with read_csv. read_csv (or the other delimited file reading functions like read_tsv) does a brilliant job guessing what column types things are but by default it only looks at 1000 rows.
Analysing runs from the Polar web flow service
Well, we’re still in New Year’s resolutions territory, so what better time to have a look at using R to analyse data collected from a run? For this analysis I have used the Polar Flow web service to download two attempts at the same Parkrun, recorded on a Polar M600 (which I love, by the way, if you’re looking for a running/ smartwatch recommendation). The background to the analysis is in the second of the two runs I thought I was doing really well and was going to crush my PB and it ended up being exactly the same as the previous run, in terms of total time taken, but with my heart rate a lot lower.
New Year blog post
So my favourite productivity guru, David Allen, he of Getting Things Done fame, has a suggestion for a New Year review that you should carry out each year. His suggested structure can be found here. I think this is an excellent idea so I have set myself a recurring reminder to do it each year, starting now. And I may as well blog it so I can find it again and anyone else who’s interested can have a read and maybe be inspired to do their own.
Gathering data (wide to long) in the tidyverse
I seem to have a pretty severe mental block about the gather() function from dplyr so this is yet another post that to be honest is basically for me to refer to in 6 months when I forget all this stuff. So I’m going to address the mental block I have very specifically and show some code; hopefully it will help someone else out there. So whenever I use gather I put the whole dataframe in.
Failure to produce pdf with RMarkdown tidyverse
I’m using tidyverse for everything now, as I’ve mentioned in previous posts, when I want a cup of tea I just run: <pre class="brush: r; title: ; notranslate" title=""> house %>% filter(kitchen == 1) %>% select(tea, kettle) %>% infuse() I just ran code(in a gist because the markup makes Hugo go strange) in a vanilla RStudio setup with pdflatex installed This is the code that you get if you set up an RMarkdown project in RStudio and select “compile to LaTeX”, and you want to do some data stuff with the tidyverse package.