Rturf

A decade of flight data (2015–2024) totaling over one million miles is visualized using R, with Bangkok’s Don Mueang airport logging the most activity, while the post also details the technical process of updating legacy mapping code from the deprecated rgdal package to a modern sf-based workflow with the Winkel tripel projection.

I step away from the KPMG Women’s PGA Championship stimpmeter and clipping volume analysis to show why I suggest that clipping volume data be recorded using the layout from the sheet

In the last episode, I showed how R packages—also called libraries—can be loaded into the working environment. These packages contain useful functions. One that I use all the time is the summarise function in the dplyr package.

A screencast tutorial demonstrates how to use R packages to plot clipping volume against stimpmeter readings for putting greens over seven days, revealing that while no overall relationship is apparent, a day-by-day analysis suggests a negative relationship between clipping volume and green speed.

The post demonstrates how to use R’s merge function to combine two separate datasets—green speed measurements and clipping volume data from the 2019 Women’s PGA Championship—into a single data frame using shared date and hole columns, setting up analysis of the relationship between the two variables.

A walkthrough of two RStudio customizations the author uses: switching the editor theme to Solarized Dark (or Light for bright environments) and rearranging the pane layout to move the Console to the top right and minimize the rarely-used History tab, freeing up more space for the Source pane.

Using RStudio projects instead of setwd() simplifies file management when working with multi-file R projects involving data, code, and outputs by automatically handling working directories. The post includes a screencast demonstration and links to further resources from Jenny Bryan and Software Carpentry on project-oriented workflows.

A tutorial demonstrating how to write a custom R function (brede) to calculate turfgrass green speed from stimpmeter ball roll distances using the Brede equation, and how to use ifelse to correct speed measurements based on which stimpmeter notch was used.

A screencast tutorial demonstrating how to import a CSV file into R, calculate green speed using the Brede equation from uphill and downhill stimpmeter measurements, and visualize the resulting data with density plots and histograms using base graphics.

A screencast tutorial demonstrating how to read a CSV file into R using the read.csv function, illustrated with 2019 KPMG Women’s PGA Championship green speed data, and covering the distinction between opening a file and loading it into the R working environment for analysis.