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.
After all those previous posts in this series, we are finally ready to take a look at the clipping volume and green speed data together. In this screencast, I loaded some R packages, or libraries.
Previous posts in this series showed how I read the green speed data from the 2019 Women’s PGA Championship into the R working environment. The reason I want to have these speeds in the working environment is so I can have a look at how the green speeds were related to clipping volume that week.
Before continuing with reading in data and making calculations about green speed and clipping volume, I wanted to show how I set up RStudio on my computer, because I make a few customizations to how it looks after a fresh install.
Sometimes I want to use R to do something real quick. But I’m struggling to think of a good example right now. More often what I’m doing can be considered a project—it will have more than one file.
It is convenient to write functions that will make calculations or produce specific output. In this rturf screencast, I demonstrate the creation of a brede function that when given two inputs, which should be the uphill and downhill ball roll distances in inches, returns the green speed in feet.
Continuing with the rturf series, this screencast shows how I would typically:
read a file of data into the working environment by first placing the file into a /data/ folder within the project directory
I made this screencast to show how I would typically get data into the R working environment.
This is a series about R for turfgrass, so for the example data file I chose one that includes morning green speed (stimpmeter) measurements from the 2019 KPMG Women’s PGA Championship at Hazeltine National Golf Club.
Here’s how I got started learning R back in 2011. After this post, I’ll move on to something where we actually work with some data.
Stage 1 I installed R software on my computer.
I started using R in 2011. I’ve read some books, done a lot of Google searches, read a lot of questions and answers that turned up on stackoverflow after executing those searches, and read a lot of blog posts and package vignettes and manuals and documentation.