R resources

• The R Project Homepage: http://www.r-project.org
• Quick R Homepage: http://www.statmethods.net
• Bioconductor: http://www.bioconductor.org
  
• An Introduction to R (long!): http://cran.r-project.org/doc/manuals/R-intro.html
• R for Data Science: https://r4ds.had.co.nz
• Google - tutorials, guides, demos, packages and more

Running R

• Need to make sure that you have R installed
  • locally or on a server
  • https://www.r-project.org
• Run R from the command line
  • just type R
  • can run it locally as well as on clusters
• Install a R Integrated Development Environment (IDE)
  • RStudio: http://www.rstudio.com
  • Makes working with R much easier, particularly for a new R user
  • Run on Windows, Mac or Linux OS
  • We’re running as a server on the AWS instances

RStudio

RMarkdown

• A great way to embed R code into descriptive files to keep your life organized
  • http://kbroman.org/knitr_knutshell/pages/Rmarkdown.html
  • https://bookdown.org/yihui/rmarkdown/
  • https://bookdown.org/yihui/bookdown/
• You can insert R chunks into Rmarkdown documents
• You will be doing more with markdown on Thursday

Assigning Variables

• A better way to do this is to assign variables
• Variables are assigned values using the <- operator.
• Variable names must begin with a letter, but other than that, just about anything goes.
• Do keep in mind that R is case sensitive.

Assigning Variables

x <- 2
x * 3

## [1] 6

y <- x * 3
y - 2

## [1] 4

These do not work

3y <- 3
3*y <- 3

Arithmetic operations on functions

y <- 67
print(y)

## [1] 67

x <- 124
z <- (x * y)^2
print(z)

## [1] 69022864

STRINGS

• Operations can be performed on character variables as well
• Note that “characters” need to be set off by quotation marks to differentiate them from numbers
• The c stands for concatenate
• Note that we are using the same variable names as we did previously, which means that we’re overwriting our previous assignment
• A good rule of thumb is to use new names for each variable, and make them short but still descriptive

STRINGS

x <- "I Love"
print(x)

## [1] "I Love"

y <- "Biostatistics"
print(y)

## [1] "Biostatistics"

z <- c(x, y)
print(z)

## [1] "I Love"        "Biostatistics"

VECTORS

• In general R thinks in terms of vectors
  • a list of characters, factors or numerical values (“I Love”)
  • it will benefit any R user to try to write scripts with that in mind
  • it will simplify most things
• Vectors can be assigned directly using the ‘c()’ function and then entering the exact values.

VECTORS

n <- c(2, 3, 4, 2, 1, 2, 4, 5, 10, 8, 9)
print(n)

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

z <- n + 3
print(z)

##  [1]  5  6  7  5  4  5  7  8 13 11 12

FACTORS

• The vector x is now what is called a list of character values (“I Love”).
• Sometimes we would like to treat the characters as if they were units for subsequent calculations.
• These are called factors, and we can redefine our character variables as factors.
• This might seem a bit strange, but it’s important for statistical analyses where we might want to see the mean or variance for two different treatments.

FACTORS

x_factor <- as.factor(x)
print(x_factor)

## [1] I Love
## Levels: I Love

• Note that factor levels are reported alphabetically

FACTORS

• We can also determine how R “sees” a variable using str() or class() functions.
• This is a useful check when importing datasets or verifying that you assigned a class correctly

str(x)

##  chr "I Love"

class(x)

## [1] "character"