Basics of R

• Public domain software
• Superior (if not comparable) to commercial alternatives
• Available on all platforms
• Not just for statistics, but also general purpose programming
• Is object-oriented and functional

• Reproducible
• Save time
• Large user base (get help!)
• Valued skill (get a job!)

Four sections in RStudio:

• Console: Where commands are run

• Scripts: Where commands are written & saved

• Environments: Where objects are stored

• Plots & Help: View data, help

Type directly into console or type into script then run (CTRL + ENTER)

A script is a plain text file with R commands in it.

Ends in the file extension .R or .txt

R has many mathematical operators

• + Addition
• - Subtraction
• * Multiplication
• / Division
• ^ Exponentiation
• %% Modulus (finds remainder)
• %/% Integer division (leaves off remainder)

R has many logical operators

• < Less than
• <= Less than or equal to
• > Greater than
• >= Greater than or equal to
  
• == Exactly equal to
• != not equal to
• ! NOT
• | OR
• & AND

1. What is 17 multiplied by 365?

2. What is 7 cubed?

3. Is 9 to the fourth equal to the sum of 2000 and 187 multiplied by 3?

An object is the fundamental unit in R.

All expressions can be saved as an object.

To create an object from an expression we use the assignment operater <-

<- assigns values on the right to objects on the left.

a <- 12+180
a

[1] 192

The object a is now the output of the expression 12+180.

Check your Environment (upper right panel)

Do not used a = 12+180 for assignment in R.

Google's R Style Guide

Advanced R: Style Guide

Use # signs to comment on your script.

Anything to the right of a # is ignored.

Good scripts (and homework) have comments before every major block of code.

5 + 5 # this adds five and five 

[1] 10

# 10 + 10 this does not add ten and ten 

The beauty of R is that objects can be combined into other, larger, and more complex objects.

a <- 8*10
b <- 2*10
d <- a*b
d

[1] 1600

# This is equivalent to: 
d <- 8*10*2*10
d

[1] 1600

1. Create an object that is your age. Create another object that is the age of the person to your right. Find the difference between these objects.

R has ~5 common data structures.

We will start with the simplest: vectors.

Vectors are one dimensional strings of numbers, character, or objects.

A vector is made using the function c().

a <- c(3,4,5)
a

[1] 3 4 5

b <- c("q","r","s")
b

[1] "q" "r" "s"

# make some objects 
a <- 4*7
b <- 6*5
g <- 9*2

# combine 'em
d <- c(a,b,g)
d

[1] 28 30 18

You can access any position of the vector using the square brackets [ ]

# make a vector 
d <- c(72,6)
d

[1] 72  6

d[1] # get the 1st object in the vector 

[1] 72

d[2] # get the 2nd 

[1] 6

1. What is the 9th and 12th position of seq(1,27,0.5)?

2. Bonus! Can you find those positions simultaneously?

3. Bonus! Make a vector “Green”, “Eggs”. How would you add Ham to that vector?

A function is a saved object that performs a task given some inputs.

All functions are made up of smaller objects.

Functions are used in the format:

name_of_function(inputs)

The output of a function can be saved to an object:

output <- name_of_function(inputs)

You have already used the funcion c().

It combines the arguments you give it into a vector.

The arguments are separated by commas.

c(3,4,5)

[1] 3 4 5

You have already also seen the funcion seq().

It generates regular sequences given three arguments.

seq(from=1, to=10, by=1)

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

Not necessary to explicitly name arguments (but it is often helpful).

seq(1,10,1)

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

Use sum() to take the sum a vector:

sum(c(3,4,5))

[1] 12

Use mean() to take the mean of a vector:

mean(c(3,4,5))

[1] 4

Functions can act on an object

# assign the numbers to a vector 
a <- c(3,4,5)

# use the vector as the input to the function
mean(a)

[1] 4

All functions come with a help screen.

It is critical that you learn to read the help screens, since they provide important information on what the function does, how it works, and ususally provide examples at the very bottom.

help(mean)

Even quicker…

Use ? before a function name to view the help screen

?mean # Same as help(mean) 

?sort # Same as help(sort)

Some R functions are easy to guess their name

Most functions are abbreviated to save time and space.

Use ?? to search for functions

e.g., Search for any function whose help screens contain the word “robust”

??robust

Note: This will only work for installed packages

1. What is the median of 34, 16, 105, 27?
   Remember: functions are often named intuitively.

2. What does the function range() do, what is the sample example in the help file?

3. Bonus! Is mean(4,5) different than mean(c(4,5))?

We will be exploring functions in much greater detail throughout this course.

Functions are the soul of R, that is why we use it.

Functions are kept inside packages, some of which come pre-installed with R.

Others must be downloaded.

There are tons of R packages!

List of R Packages and search with your favorite keyword!

Ecology, paleo, dispersal, population, time series, phylogenetic, community, Bayes

Often you will need to install a package to access a certain library of functions.

# Install a new package
install.packages("picante") 

# If RStudo asks, pick a closeby mirror and say "okay"" if it asks to create a new folder

Remember to surround the package name in quotation marks.

Installing a package just downloads its to your computer.

To actually use a function from an outside package you have to “load it.”

This let's R know what packages to load in, and not waste time with all potential functions.

# Two ways to load packages:
library(ggplot2)
require(ggplot2)

Note: no quotation marks needed

Good scripts (and homeworks) have a series of require() or library() statements at the top of the script.

1. Search & find a fascinating package. What is it? What is one interesting function?

2. Install the package to your computer.

List of R Packages

ls() will list all of the obects in your environment.

rm() will remove individual objects

x <- 10
rm(x) 

rm(list=ls()) will remove everthing in your workspace

a <- 1; b <- 2; d <- 3; e <- 4
rm(list=ls()) 

Always remember to save scripts.

For now we will not save workspaces.

Type in quit() or q() and answer N to quit.