Topics

In this sesssion we’ll be covering:

Other sessions we’ll cover today and tomorrow:

Introductions

Before we get started


What is R

R is many things…


Really cool graph made in R


R vs. Excel


Advantages of Excel

  • Easy to use
  • Familiar
  • Easy to sort and scroll through data


Disadvantages of Excel

  • Easy to make a mistake
  • Limits on data size, not a huge deal anymore. But with large datasets, can really bog down your computer
  • Difficult to perform a series of steps
  • Without very very good documentation, difficult to describe steps of an analysis


Disadvantages of R

  • Difficult to use at first
  • Figuring out errors can be difficult
  • Relies on the user finding answers via google searches, no structured support
  • Often times your colleague may not be familar with it, so sharing data analyses with them can be difficult


Advantages of R

  • Because you can see all of your steps, much harder to make an error
  • If you know the right tricks, almost unlimited size of data
  • Easily reproducible and repeatable
  • Active user community means lots of internet sources and rapid releases of new technology


When to Use Excel

  • Doing a one-time analysis, small dataset, basic graphics


When to Use R

  • Doing repeated analyses, lots of variables, advanced graphics
  • Most likely will begin as a hybrid user and migrate over time


R and RStudio


R

  • To download R for Windows, see this page
  • If you open R itself, it will look very plain

plain R console

RStudio

  • RStudio makes R a little more user friendly
  • It’s free and can be downloaded at rstudio.com

  • It’s not necessary to open RStudio to use R, but in these sessions we will assume that RStudio is your interface to R

  • When you first open RStudio, this is what you see:

first opening RStudio


  • The left panel is the console for R
  • Type 1 + 1 then hit “Enter” and R will return the answer


RStudio 1 + 1


  • It’s a good idea to use a script so you can save your code
  • Open a new script by selecting “File” -> “New File” -> “R Script” and it will appear in the top left panel of RStudio


RStudio open script


  • This is basically a text document that can be saved (go to “File” -> “Save As”)
  • You can type and run more than one line at a time by highlighting and clicking the “Run” button on the script tool bar


RStudio many lines

  • The bottom right panel can be used to find and open files, view plots, load packages, and look at help pages
  • The top right panel gives you information about what variables you’re working with during your R session
  • We’ll explain more about what to look for in those panels later

R basics

Doing math

  • Open up a script if you haven’t already (“File” -> “New File” -> “R Script”)
  • Try some math by either typing the lines below or copying and pasting the lines into your script
10 + 5
10 - 5
10 * 5
10 / 5
10 ^ 5
  • Remember, to run the lines, highlight your code and click the “Run” button on the toolbar of the script panel

Reference table of arithmetic operators

Operator Meaning Example
+ addition 2 + 2
- subtraction 2 - 2
* multiplication 2 * 2
/ division 2 / 2
^ or ** exponentiation 2 ^ 2

Order of operations

R follows the usual order of arithmetical operations and uses parentheses for grouping operations.

10 - 3 / 5    # R will first do division operation then subtraction
## [1] 9.4
(10 - 3) / 5  # Use parentheses to group and prioritize operations.
## [1] 1.4

Note on commenting

  • To write a comment in your script that will not be evaluated, type # in front of your comment
  • The text after# will not be evaluated
  • Run all of the code below and see what gets returned in the R console (bottom left panel in RStudio)
# Full line comment
x # partial line comment


Creating objects

  • An object is used to store information in R
  • To create an object or variable in R we use an arrow symbol pointing left <-. You can use = or -> as well, but this is much less common and you will typically see the left arrow used.
  • On the right we’ve created the variables x and y by assigning some numbers to them
x <- 10
y <- 5
x + y
## [1] 15
Above, the top panel is what you run in your script, the bottom panel is the output


  • In RStudio, you will see the variables we created in the top right panel


variables

  • If you’ve already created a variable, you can replace the value with another value 
x
## [1] 10
x <- 20
x
## [1] 20
  • In the top right panel you can see that the number stored in the variable x has changed


variables2


  • You can basically use anything for a variable name, x, X, x2, my.data, my.text, etc.
  • However, there are 3 important naming conventions to remember:
    1. You can’t start your variable with a number.
    2. You can’t use any spaces or special characters ($,%,#,-). Periods (.) are ok.
    3. Capitalization DOES matter in R. That is, R will consider ‘my.data’ and ‘My.data’ or ‘x’ and ‘X’ to be different objects.
  • Try running the following code and you will see that in your global environment there are two different objects listed.
x <- 5
X <- 5

R Object types

R has three main object types:

Type Description Examples
character letters and words "z", "red", "H2O"
numeric numbers 1, 3.14, log(10)
logical binary TRUE, FALSE


Grouping Data

There are several ways to group data to make them easier to work with:

  • Vectors: contain multiple values of the same type (e.g., all numbers or all words)
  • Lists: contain multiple values of different types (e.g., some numbers and some words)
  • Matrices: a table, like a spreadsheet, with only one data type
  • Data Frames: Like a matrix, but you can mix data types

Vectors

  • Vectors are variables with an ordered set of values
  • They contain only one type of data (numeric, character, or logical)
  • We use c( ) as a container for vector elements. Think of the c as combining elements.
x <- c(1, 2, 3, 4, 5)
x
## [1] 1 2 3 4 5
fruit <- c("apples","bananas","oranges")
fruit
## [1] "apples"  "bananas" "oranges"

If you try to type in text without using quotations marks (either single or double), R will throw an error. Try the text below.

fruit <- c(apples, bananas, oranges)
fruit


This is because R will interpret text without quotes as the names of variables. Since we don’t have any variables named apples, bananas, or oranges, R can’t find them.


Lists

  • Lists are like vectors but can contain any mix of data types
  • We use list() as a container for list items
x <- list("Benzene", 1.3, TRUE)
x
## [[1]]
## [1] "Benzene"
## 
## [[2]]
## [1] 1.3
## 
## [[3]]
## [1] TRUE


Data frames

  • Data frames are ‘spreadsheet-like’ tables in R
  • We use data.frame() as a container for many vectors of the same length
  • Data frames are the most common objects that we work with while analyzing environmental data, but vectors and lists can be important when you start using R as a programming language. We’ll talk about that later…


pollutant <- c("Benzene", "Toluene", "Xylenes")
concentration <- c(1.3, 5.5, 6.0)
carcinogen <- c(TRUE, FALSE, FALSE)
my.data <- data.frame(pollutant, concentration, carcinogen)
my.data
##   pollutant concentration carcinogen
## 1   Benzene           1.3       TRUE
## 2   Toluene           5.5      FALSE
## 3   Xylenes           6.0      FALSE


If you try to input a data frame where the elements are not all the same length, this will cause an error

pollutant <- c("Benzene", "Toluene")
concentration <- c(1.3, 5.5, 6.0)
carcinogen <- c(TRUE, FALSE, FALSE)
my.data <- data.frame(pollutant, concentration, carcinogen)
## Error in data.frame(pollutant, concentration, carcinogen): arguments imply differing number of rows: 2, 3

Now let’s try some exercises to test our understanding of R basics and data types.

Exercise 1

http://rpubs.com/kfrost14/Ex1