https://danielgreenwood.ch/tags/r/ Recent content in R on The Salopian Scientific Collective Hugo 0.125.1 en-US Daniel Greenwood Sat, 25 May 2024 00:00:00 +0000 https://danielgreenwood.ch/2024/05/25/knitting-automated-reports-with-r-markdown/ Sat, 25 May 2024 00:00:00 +0000 https://danielgreenwood.ch/2024/05/25/knitting-automated-reports-with-r-markdown/ R Markdown is a simple markup language based based on Markdown, with added functionality for including R code and its output. This entire blog is written as R Markdown documents. I write the text and code in R Markdown, then: Run all the R code and knit the input and output together into a regular Markdown file. Knit that Markdown file into a static HTML page Run the Hugo program, which in a few fractions of a second turns that folder full of HTML pages (together with a few configuration files) into a fully functioning website with homepage, tags, menus etc. https://danielgreenwood.ch/2024/05/08/creating-an-interactive-data-gating-tool-with-plotly-and-shiny-in-r/ Wed, 08 May 2024 00:00:00 +0000 https://danielgreenwood.ch/2024/05/08/creating-an-interactive-data-gating-tool-with-plotly-and-shiny-in-r/ Interactive data gating allows researchers to visually select and analyze specific subsets of data points from complex data sets. This is particularly valuable in bioinformatics, where we often need to select clusters of points from large data sets - such as identifying a cell phenotype in a mixed population using molecular markers. The shiny package for R (and now also for Python) makes it easy to create interactive web applications to communicate our results. https://danielgreenwood.ch/2024/04/12/efficiently-handle-slightly-big-data-with-apache-arrow-in-r/ Fri, 12 Apr 2024 00:00:00 +0000 https://danielgreenwood.ch/2024/04/12/efficiently-handle-slightly-big-data-with-apache-arrow-in-r/ In systems biology, we often need to work with slightly big data. Not so big to justify setting up a database or using a high-performance cluster, but still a bit too big to comfortably work with in memory. We are talking about files in the 10 to 500 GB range, such as: Omics data like RNAseq or proteomics Single-cell phenotype data from high-content microscopy Large public data repositories, like the Human Cell Atlas The Arrow package for R lets us keep our data set on disk, dynamically loading only the rows and columns needed for our analysis.