In this post I explore ways of using ggplot2 and ggflags to plot album sales and chart data from the Taylor Swift and Beyoncé Tidy Tuesday dataset (as shown here). I then create charts comparing sales in the UK and US specifically.
This post focuses on ways to customize summary rows in gt tables, to create the summary table shown here (based on the Tidy Tuesday Taylor Swift and Beyoncé data).
In this post, I walk through the various steps involved in creating the summary table shown here (based on the Tidy Tuesday Taylor Swift data), showcasing various capabilities of the gt package.
Following on from my previous post, I use SELECT and JOIN statements to pivot the Taylor Swift and Beyoncé Tidy Tuesday data using RSQLite (after normalizing the underlying database), output the results to R, and create a relatively simple summary table using gt.
In this post I talk through, step-by-step, a process I’ve been using to document the SQL learning I’ve been doing using R Markdown. I draw on a tutorial by Andrew Couch to create a database I can manipulate using RSQLite, using a Tidy Tuesday dataset featuring Taylor Swift and Beyoncé album data.
Hello world! In this, my first post, let’s start with a variant on two of those old existential chestnuts, ‘Who am I?’ and ‘Why am I here?’ Or more precisely, what brings me to rstats and why am I wrestling with GitHub, blogdown and Hugo to create this blog at this particular time in my life?
Show and tell contribution for R Ladies Sydney.
I am currently working on several projects in R using the Longitudinal Surveys of Australian Youth (LSAY) and Programme for International Student Assessment (PISA) data, plus various data from the Australian Bureau of Statistics, to build my to build my skills and experience using R to visualize, analyse and report on educational data
This report replicates and extends analyses conducted in Lin, Chai, and Jong (2019), to introduce a number of packages that can be used with R for data analysis, reporting and visualization.