Publications by R on Nicola Rennie
Detecting heart murmurs from time series data in R
Time series data often contains information that’s not easily seen when simply visualising the data. We can uncover these hidden features using time series analysis, and use them to classify time series that exhibit similar features. Data In this blog post, we’ll extract features from and classify time series relating to sound recordings of pa...
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Scraping London Marathon data with {rvest}
There are lots of ways to get data into R: reading from local files or URLs, R packages containing data, using packages that wrap APIs, to name a few. But sometimes, none of those are an option. Let’s say you want get a table of data from a website (that doesn’t provide API access). You could copy and paste it into a spreadsheet, re-format it m...
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Making Pretty PDFs with Quarto
Quarto is an open-source scientific and technical publishing system that allows you to combine text with code to create fully reproducible documents in a variety of formats. One of those formats is PDF. The default outputs look reasonably good for academic articles, but if you’re making professional reports, a CV, or sending a letter – you prob...
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What’s new in {PrettyCols} 1.0.1?
Back in September 2022 I submitted {PrettyCols}, an R package containing aesthetically pleasing colour palettes, to CRAN. If you missed it, you can read the blog post introducing the package! After a few months, it was time for an update and this blog post will give you a brief overview of some of the new features and palettes! You can install {Pr...
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Seeing double? Building the same app in Shiny for R and Shiny for Python
Shiny is an R package that makes it easier to build interactive web apps straight from R. Back in July 2022 at rstudio::conf(2022), Posit (formerly RStudio) announced the release of Shiny for Python. As someone who knows Python but hasn’t written any Python code for quite a long time, I wanted to see how the two compared. So I did the only log...
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Using functional analysis to model air pollution data in R
Let’s say you need to understand how your data changes within a day, and between different days. For example, if you have hourly pollution data that follows a regular pattern throughout a day, but follows different patterns on a Wednesday and Saturday. Functional analysis is one approach of doing just that. During my PhD, I developed methods ba...
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30 Day Map Challenge 2022
The #30DayMapChallenge is a daily mapping, cartography, and data visualization challenge aimed at the spatial community. Every day in November participants create a map with a given theme (e.g. red, movement, two colours) and share their results on social media using the #30DayMapChallenge hashtag. Check out the challenge here. This year, I crea...
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How to make your own #RStats Wrapped!
Around the beginning of December social media accounts start to fill with images of people’s Spotify Wrapped showing their most listened to songs. If you’re an #RStats user, you might also be interested in which functions you use most often. This blog post will show you how to create your own #RStats Wrapped! Finding your most used functions...
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Another Year of #TidyTuesday
Last year, I wrote a blog post discussing of how I found participating in #TidyTuesday every week for a year. Well, this year I did the same again. And so I’m writing another blog post about it! If you’re unfamiliar with #TidyTuesday, it’s a weekly data challenge aimed at the R community. Every week a new data set is posted alongside a cha...
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Combining R and Python with {reticulate} and Quarto
The R versus Python debate has been going on for as long as both languages have existed. I’m not one to takes sides – I think you need to use the best tool for the job. Sometimes R will be better. Sometimes Python will be better. But what happens if you need both languages in the same workflow? Do you need to choose? No, is the simple answer....
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