A Fine Slice Of SQL Server
Thierry Moudiki checks an interval:
A previous post introduced the
crossvalidationpackage for R. This time, the focus is on probabilistic forecasting — evaluating not just how accurate point forecasts are, but how well-calibrated prediction intervals are, using empirical coverage rates and Winkler scores – andcrossvalidation.
Click through for the code and not much additional commentary. H/T R-Bloggers.
Leave a CommentYihui Xie explains how to switch test frameworks in R:
Back in 2013, I wrote about testing R packages when I first released testit. Thirteen years later, I still believe that unit testing should be nothing more than “tell me if something unexpected happened.” Recently I converted a large testthat test suite to testit, and I thought I’d share a practical guide for anyone considering the same move.
Click through for that guide.
Leave a CommentI tend to write a lot of functions that create specific graphics implemented with
ggplot2. Although I try to pick graphic parameters (e.g. colors, text size, etc.) that are reasonable, I will typically define all relevant aesthetics as parameters to my function. As a result, my functions tend to have a lot of parameters. When I need to debug the function I need to have all those parameters set in the global environment which usually requires me highlighting each assignment and running it. This function automates this process.
Click through to see how it works. H/T R-Bloggers.
Leave a CommentJonathan Carroll compares two approaches:
Thinking of a real-world project I could take for a spin, I decided to build some ingestion for my personal finances. I’ve used Quickbooks previously which connects up to my bank and helps categorise personal and business (as a freelance contractor) expenses. I decided I’ll build my own ‘slowbooks’ processing workflow based on some manual exports (I don’t think my bank has an API).
Both of the approaches I’ll compare here build on the idea of a
Makefilewhich connects up commands to run based on dependencies, and only runs what is needed; if all the input dependencies of a step have not changed, there’s no need to re-run that step. From what I understand, you could largely get away with just writing someMakefiles (or the newer implementationjust(just.systems)) but these two approaches help to better structure how that’s constructed.
Read on for Jonathan’s discovery process and ultimate findings. H/T R-Bloggers.
Leave a CommentGuillaume Pressiat shows off logrittr:
In SAS, every DATA step prints a log:
NOTE: There were 120000 observations read from WORK.SALES. NOTE: 7153 observations were deleted. NOTE: The data set WORK.RESULT has 112847 observations and 11 variables.R’s
dplyrpipelines are silent.logrittrfills that gap with%>=%, a drop-in pipe that logs row counts, column counts, added/dropped columns, and timing at every step, with no function masking.
Click through to see how logrittr helps. Back when I was using R heavily, I would have really enjoyed this package. H/T R-Bloggers.
Zhenguo Zhang makes a change to the legend:
In
ggplot2, the legend keys (the symbols next to the labels) usually inherit their size and appearance from the layers in the plot. For example, if you have a scatter plot with very small points, the legend keys will also be very small, making them hard to see.
Read on to see how you can change this and make the key size larger (or smaller).
Comments closedAnd how this set of functions really work?
Storing the daily progress in an RDS. Before running set of functions, you will prepare a location to store the RDS file (environment variables and game play)
Given how my plants tend to look, I probably shouldn’t try this.
Comments closedYihui Xie makes an announcement:
A few weeks ago I added preliminary support for Typst to knitr. The way it works is simple: if your file has the extension
.Rtyp, knitr will recognize it as a Typst document, knit it, and produce a.typoutput file. The chunk syntax follows the same Markdown-style fenced code block convention:```{r}to start a chunk and```to end it, with inline R expressions written as`r expr`. A minimal example (hello.Rtyp):
Click through for that example, as well as some notes on Typst and HTML.
Comments closedWhen feeding a program with command line arguments, single quotes and double quotes make a significant difference.
Click through to see how the behavior is different between a command line shell like bash, versus the R programming language itself, as well as R Console.
Comments closedWill Hipson lays out an update:
maestro has officially graduated to stable release with version 1.0.0 back in January 2026 and now its latest version 1.1.0. This marks a commitment to maintaining a stable API and increased reliance on using maestro in production. In our environment alone, maestro has orchestrated millions of pipeline executions over the course of a year, effectively making it the heartbeat of our entire data stack.
If you haven’t heard of maestro, it’s a pipeline orchestration package. You can learn more about it here.
Click through to see what’s changed between the 1.0.0 release and now. H/T R-Bloggers.
Comments closed