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Number of registrants: 39

Familiarity with R

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Goal

Move in the direction of becoming functional with R!!

Learning Strategy

Today’s Focus

Learning Tips

Workshop Series

See also Getting Started with R resources.

R and RStudio

Exercise 1: RStudio Exploration and Basic Commands

RStudio Cloud project for this workshop:

https://posit.cloud/content/6638058

After it opens:

Break!

Exercise Review

Key vocabulary terms are in italic.

Naming Objects

The rules for naming objects are pretty flexible. You can use numbers, letters, and most special characters.

A few rules to take note of:

Naming Styles

There are a handful of popular naming styles. Pick one that you like, and be consistent!

Data Types

All variables have a class or data type, which you can view using class().

num_plots = 10
class(num_plots)

## [1] "numeric"

Other common data types:

Vectors

vectors are R objects that contain multiple values of the same class.

Example:

i = 4:12
i

## [1]  4  5  6  7  8  9 10 11 12

More examples:

Creating Vectors

In general, you need to use a function or operator to create a vector.

Sequence of numbers with the : operator:

1:10

##  [1]  1  2  3  4  5  6  7  8  9 10

Repeat function:

rep("Quercus lobata", 5)

## [1] "Quercus lobata" "Quercus lobata" "Quercus lobata" "Quercus lobata"
## [5] "Quercus lobata"

Combine elements of the same class with c():

yn <- c(TRUE, FALSE, TRUE)
yn

## [1]  TRUE FALSE  TRUE

Some built-in constants are also vectors:

LETTERS

##  [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S"
## [20] "T" "U" "V" "W" "X" "Y" "Z"

state.abb

##  [1] "AL" "AK" "AZ" "AR" "CA" "CO" "CT" "DE" "FL" "GA" "HI" "ID" "IL" "IN" "IA"
## [16] "KS" "KY" "LA" "ME" "MD" "MA" "MI" "MN" "MS" "MO" "MT" "NE" "NV" "NH" "NJ"
## [31] "NM" "NY" "NC" "ND" "OH" "OK" "OR" "PA" "RI" "SC" "SD" "TN" "TX" "UT" "VT"
## [46] "VA" "WA" "WV" "WI" "WY"

month.name

##  [1] "January"   "February"  "March"     "April"     "May"       "June"     
##  [7] "July"      "August"    "September" "October"   "November"  "December"

Random number functions:

runif(20)

##  [1] 0.17782436 0.64642744 0.89121214 0.70146090 0.79845666 0.26657743
##  [7] 0.63596431 0.19446843 0.40912918 0.69756948 0.24045534 0.87716139
## [13] 0.67787150 0.91604563 0.79698219 0.53838195 0.64656791 0.19914492
## [19] 0.09933631 0.99757726

rnorm(20)

##  [1]  0.314045995 -2.168952965 -1.035106500  2.050484172 -0.893925389
##  [6] -0.004147209  0.794782381  0.919572292  1.473193529  1.972762083
## [11]  0.142524334 -0.451737784  0.767048058 -1.034922964 -0.210531267
## [16] -0.528755024  0.016250052  1.439573960 -0.156178807 -0.359327657

sample(month.abb, 3)

## [1] "Aug" "Apr" "Feb"

How Vectors Behave

Vectorized operations

Many R functions and math operators are vectorized (i.e., operate on each individual element).

Examples

First we create two numeric vectors:

x = 0:4
x

## [1] 0 1 2 3 4

y = 11:15
y

## [1] 11 12 13 14 15

Are sin() & cos() vectorized?

sin(x)

## [1]  0.0000000  0.8414710  0.9092974  0.1411200 -0.7568025

cos(x)

## [1]  1.0000000  0.5403023 -0.4161468 -0.9899925 -0.6536436

Addition (and all math functions) is vectorized:

x + 1

## [1] 1 2 3 4 5

x + y

## [1] 11 13 15 17 19

Aggregate functions

Functions that accept a vector and spit out a single value are aggregate.

x = runif(20)
x

##  [1] 0.26211876 0.12979274 0.71034325 0.71292080 0.13439383 0.57130562
##  [7] 0.33741735 0.41426845 0.92729550 0.53577323 0.77293588 0.63427758
## [13] 0.02293783 0.38646294 0.31318852 0.99781761 0.36043713 0.75702638
## [19] 0.73384852 0.06540885

Most descriptive stats functions are aggregate:

mean(x)

## [1] 0.4889985

median(x)

## [1] 0.4750208

sd(x)

## [1] 0.288555

Other aggregate functions:

first(state.name)

## [1] "Alabama"

Subsetting Vectors

To extract a single element from a vector, use square bracket notation. Inside the square brackets, put the index of the element(s) you want.

Subset with indices

LETTERS[2]

## [1] "B"

To return multiple elements, pass a vector of indices.

LETTERS[2:4]

## [1] "B" "C" "D"

You can also use square brackets to extract elements in a different order.

LETTERS[4:2]

## [1] "D" "C" "B"

Subset with logicals

You can also insert a vector of Logical values (TRUE/FALSE) in the brackets. R will return the corresponding element for the TRUE values.

LETTERS[c(T,T,T,T,T,T,T,T,F,F,F,F,F,F,F,F,F,F,F,F,F,F,F,F,F,F)]

## [1] "A" "B" "C" "D" "E" "F" "G" "H"

Better still, use an expression that returns a vector of logical values:

state.abb[ substr(state.abb, 1, 1) == "N" ]

## [1] "NE" "NV" "NH" "NJ" "NM" "NY" "NC" "ND"

Plotting Vectors

Base R has simple plotting functions you can use to view the distribution of data.

Histograms

To make a histogram, use hist():

my_vals = rnorm(500)
hist(my_vals)

Box Plots

Prefer a box plot?

boxplot(my_vals)

Scatter Plots

The versatile plot() can be used to make a simple scatter plot:

plot(x = 1:20, y = runif(20))

Line Plots

plot(x = 1:20, y = runif(20), type = "b")

Exercise 2: Working with Scripts and Vectors

Exercise 2 Review

Scripts

Top five advantages of using scripts over the console:

END!