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Familiarity with R

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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://rstudio.cloud/content/4701501

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] 0.24034637 0.36129627 0.31932912 0.36040619 0.10315605 0.66583121
##  [7] 0.15364120 0.02328397 0.62662163 0.46527359 0.09406921 0.43920132
## [13] 0.39845071 0.69523229 0.77945612 0.42430562 0.57525211 0.78171852
## [19] 0.29296575 0.64298070

rnorm(20)

##  [1] -0.2147803  0.3774138  0.6490794 -0.3913319 -0.3312433  0.7447508
##  [7]  0.5856689  0.9039691 -1.0058598  0.3497485  2.1102232 -0.8948942
## [13] -0.9326346 -1.7975214 -0.4048500 -0.9293515  0.4510814 -1.2083119
## [19]  0.3961602 -0.7003346

sample(month.abb, 3)

## [1] "Jan" "Feb" "Apr"

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] 0.39271638 0.73787278 0.89058362 0.34175404 0.21611711 0.86796133
##  [7] 0.84449887 0.54382415 0.79583754 0.02044313 0.67371211 0.89399457
## [13] 0.50483813 0.81232642 0.53471147 0.60363816 0.18086160 0.78866131
## [19] 0.11792667 0.84987164

Most descriptive stats functions are aggregate:

mean(x)

## [1] 0.5806076

median(x)

## [1] 0.6386751

sd(x)

## [1] 0.2826853

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")

Scripts

Top five advantages of using scripts over the console:

Exercise 2: Working with Scripts and Vectors

Exercise 2 Review

END!