Computing Agroclimate Metrics:

Intro to R

Andy Lyons
March 24, 2025

https://ucanr-igis.github.io/agroclimR_ucm/

About Me…

Goals

Working with R

gain some experience doing data analysis with a command line language
understand some of the vocabulary used in R

package
vector
function
argument
piping
tidyverse
data frame
tibble
data wrangling
ggplot

Agroclimate Metrics

examples of agroclimate metrics
what can be predicted with agroclimate metrics
where to get weather data
hindcast almond bloom

R and RStudio

Why is R So Popular?

It’s free!
Huge user community (especially academics)
Thousands of add-ons (packages) that extend its capabilities
Particularly strong in plotting and reporting
Once you get over the initial hump, can work very efficiently
Makes it easy to get your code “out there”
Solid overall programming language

a programming language to write scripts
software (“environment”) that interprets scripts written in R
inspired by ‘S’ (1976), first beta version ~2000
lots of user-contributed packages:
- CRAN: >22,000
- Bioconductor: >2,000
- R-Forge: >2,000
- GitHub: >85,400

a very popular tool (IDE) to work with both R software and R scripts
Version 1.0 came out in late 2016
RStudio needs R to run, but you can use R without RStudio
Posit Cloud - run RStudio through a browser

online RStudio and Jupyter Notebooks
requires an account (free account gets you 25 hrs / month)
98% same as RStudio Desktop
have to upload your data to the cloud
everything gets saved between sessions
workspaces and projects can be shared with collaborators

Exercise 1: RStudio Exploration and Basic Commands

Exercise 1 Topics

Using R like a fancy calculator
Order of operations
Comparison operators
Saving the results of expressions to variable
Rules for naming variables

Posit Cloud project for this workshop:

https://bit.ly/agroclim-ucm

After it opens:

Exercise Review

Key vocabulary terms are in italic.

When you enter an expression at the console, R will evaluate the expression, and print the results at the console.
If you enter an incomplete expression, R will prompt you to finish the job by showing a ‘+’ symbol in the console
You can save the results of an expression to an object (variable) using an assignment operator
=
<-
R objects can be named almost anything (but no spaces or hyphens please)
R is case sensitive about everything
Once defined, R objects can be used in subsequent expressions
R objects can be updated (assigned a new value)
R objects are only saved in memory, and will disappear when you close RStudio
R has a few built-in constants, which are like objects but you don’t have to define them

R Packages

Packages are what R calls extensions or add-ons.

What’s in a package?

additional functions!
function help pages
tutorials (Vignettes)
datasets

Functions

The keys to R’s superpowers are functions! There are four things you need to know to use a function:

Which function should I use?

Finding the right R function, half the battle is.

- Jedi MasteR Yoda

Ask your friends

Ask Google

Cheatsheets!

Piping

Piping syntax is an alternative way of writing arguments into functions.

With piping, you use the pipe operator |> (or %>%) to ‘feed’ the result of one function into the next function.

Piping allows the results of one function to be passed as the first argument of the next function. Hence a series of commands to be written like a sentence.

Example

Consider the expression:

zoo(moo(boo(foo(99)),n=4))

Data Frames & Tibbles

R has two data classes that organize data in rows and columns:

data frames / tibble
matrices

Key concepts

columns of a data frame are vectors
columns have names
you can refer to columns by their name or position

Data Wrangling: What do we mean?

Whatever is needed to get your data frame ready
for the function(s) you want to use for analysis and visualization.

also called data munging, manipulation, transformation, etc.

Often includes one or more of:

dropping columns
renaming columns
changing the order of columns
creating new columns with an expression
filtering rows
sorting rows
going from ‘long’ to ‘wide’ formats
joining data frames based on a common field
merging data frames together
splitting tables
aggregating rows into groups

Data wrangling is a pain!

But R (specifically the tidyverse packages) makes it (a lot) easier.

Data Wrangling with `dplyr`

An alternative (usually better) way to wrangle data frames than base R.

Part of the tidyverse.

Best way to familiarize yourself - explore the cheat sheet:

Popular `dplyr` Functions

Row and Column Manipulations

subset rows	filter(), slice()
order rows	arrange()
pick column(s)	select(), pull()
add new columns	mutate()

Chaining dplyr functions

Most dplyr functions take a tibble as the first argument , and return a tibble.

This makes them very pipe friendly.

Forecasting Almond Bloom

Theoretical example:

Step 1: Predict when dormancy breaks

Nonpareil dormancy breaks when 23 chill portions accumulate starting September 1 (source)

Step 2: Predict when bloom occurs after dormancy

Bloom can be predicted using a degree day model after dormancy ends

Exercise 2: Import Weather Data & Compute Chill Portions

Exercise 2 Topics

Run code in a script
Import saved weather data (CIMIS Station 206)
Plot it
Compute chill portions