Drawing stacked barcharts the tidyverse way
Don’t judge me, but I do spend quite a lot of time making stacked barcharts of survey data. I’m trying to go full tidyverse (see this blog, passim) and there is a very neat way of doing it in the tidyverse which is very easy to expand out into functions, or purrr, or whatever. Of course, me being me I can never remember what it is so I end up reading the same SO answers over and over again.
Produce tables in loop
Another one for me before I forget, I seem to have terrible problems writing headings and tables in a loop in RMarkdown. Everything gets crushed together because the line returns don’t seem to work properly and either the table or the headings get mangled. Well here it is, the thing I just did that worked: <pre class="brush: r; title: ; notranslate" title=""> for(i in 1:2){ cat("Heading ", i, "\n") cat("===========================") pandoc.
The shinyverse
I’m pretty sure I’ve made up the word shinyverse, to refer to all the packages that either use or complement Shiny. This is a non-exhaustive list of shinyverse packages, mainly for my benefit when I inevitably forget, but if it’s useful for you too then that’s good too. shiny.semantic adds support for the Semantic UI library to Shiny. Useful if you want your Shiny apps to look a bit different or there’s something in the library that you need.
Filling in non missing elements by row (Survey Monkey masterclass)
There must be other people out there doing this, so I’ll share this neat thing I found today. Quite often, you’ll have a dataset that has a lot of columns, only one of which will have something in it for each row. Survey Monkey, in particular, produces these kinds of sheets if you use branching logic. So if you work in one bit of the Trust, your team name will be found in one column, but if you branched elsewhere in the survey because you work somewhere else, it is in another column.
In praise of awkward questions
I went to a conference last week, more of a meet up really, and they presented the results of the work that we’ve all been doing, indicating that there were several statistically significant improvements in the expected direction. I’m sure the analysis is well intentioned and basically correct, so I didn’t really have any problem with it, but my arm shot up anyway, because I wanted to see more details of the analysis.
Jupyter, Python, interactive web frameworks, and more
A couple of days ago on Twitter I said the following: “Increasingly, RStudio’s products are so good that I feel a lot of my advice to my organisation is “buy a lot of RStudio products”. I love RStudio (I have a tattoo of their logo on my arm, even!) and they clearly give a lot of stuff away (we used their products for nothing for *years*). But I wish I could at least acknowledge some competition in this arena.
Producing several plots at once with RMarkdown and purrr
This is a very simple example of using purrr and RMarkdown to produce several plots all at once. invisible( # suppress console output from hist() map(c(5, 6, 7), function(x) { # values to feed to filter function iris %>% filter(Sepal.Length < x) %>% # just Sepal.Length < 5, 6, and 7 pull(Petal.Width) %>% # extract Petal.Width vector hist(breaks = 20, main = paste0("Histogram of < ", x)) # graph }) )
Shiny Pro in a Windows/ NHS environment, part 1: The curse of Kerberos
Right, here goes. Let’s tell the story. I’ve been working on this for months, not because it’s particularly hard, but because I really didn’t know what I was doing. We are a health trust in the UK (NHS), and we’ve been using Shiny on a privately hosted Linode server (Ubuntu) in the cloud for a long time. You can see it here. We’ve done this (hosted it in the wild) because none of the data is private.
Passing strings as variables from a Shiny app into dplyr
I’ve been sort of waiting until I understood this thoroughly, and I was going to write a very detailed blog post about it, and although I do understand it a lot better now than I did, I’m still not at the point where I would write an authoritative blog post about it confidently because there are too many things that I don’t understand. However, I’m aware that there are people out there right now who are trying to write dplyr code that takes strings as variable inputs, passed in from a Shiny interface, and I know how difficult it is to Google, because I Googled it myself.
Selectively hide text with JavaScript in RMarkdown
I guess this is one of those where I kind of did know it was possible, really. If I’d thought it through. But I’ve always not been sure how easy it would be and I’ve been in too much of a rush. So I’m trying to move my organisation onto HTML instead of Word. HTML is easier to output and parse, and it’s interactive with a bit of JavaScript. I used the DT package in R to put some tables into an RMarkdown document and of course that’s very nice because they’re pageable, orderable, and searchable out of the box.