rturf

I’ve been doing some teaching, or demonstrating, how I use R software for data analysis in turf-related matters. See the full list of rturf posts. The initial project is about stimpmeter measurements and ClipVol.

Web applications for R that make calculations or show data.

Analyzing the tweets and the accounts of the turf industry on Twitter.

For the sixth year in a row, I’ve taken a look at “turfgrass Twitter” to find some of the most influential and active accounts from the previous year. I cast a broad net to find the industry accounts, and I minimize manual intervention of account addition and removal.

I’ll start with a tautology. Warm-season grass species grow, and are used as turfgrasses, in locations with warm temperatures. Where it gets interesting is the interaction of light—specifically the photosynthetically active radiation (PAR)—with temperature, and with water supply or drought stress.

For the fifth year in a row, I’ve taken a look at “turfgrass Twitter” to find some of the most influential and active accounts from the previous year. I cast a broad net to find the industry accounts, and I minimize manual intervention of account addition and removal.

I’ve made a number of Shiny apps, and have collected them all on this new page. These are designed either to show a set of data, or more likely, to make some calculations with the inputs allowed to vary.

I made a Global DLI app to generate summary charts of DLI for anywhere in the world. This app uses the nasapower R package to get satellite data from the NASA POWER Agroclimatology dataset.

I’ve repeated an analysis of tweets and accounts from another year in the turf industry. Last week I shared some overall results and category-specific rankings. I've repeated the #TurfTwitter analysis for 2020 and put the results in a searchable and sortable Shiny app.

You may have seen a few posts about #rturf on my personal site. I’m writing about that topic over there so I don’t clutter up the ATC site with too much data analysis and computer code.