rturf 10: summarising by group, and chart facets

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.

rturf 9: libraries, plotting data, and decreasing green speed

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.

rturf 8: the merge function to combine green speed and clipping volume

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.

rturf 7: how I setup panes and theme in RStudio

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.

rturf 6: RStudio projects or setwd

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.

rturf 5: writing a function, and using ifelse

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.

rturf 4: reading a file, making a calculation, and common summary plots

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

rturf 3: reading a file into R

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.

What I did to learn R

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.

Getting started with #rturf

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.