Monday december 7: R Basics

• What is R
• The R GUI, Workspace
• Objects, operators, functions
• Basic Analyses in R

Monday december 14: Getting Started with Data Analysis in R

• Loading and exporting datafiles
• Installing and using R packages
• Advanced analyses in R

• R is a language and environment for statistical computing and for graphics

• GNU project

• Managed by the R Foundation for Statistical Computing, Vienna, Austria.

• Based on the object-oriented language S (1975)

• Free

• Open Source

• Community Driven

• Many resources available

• Everything on the Comprehensive R Archive Network (CRAN) and is aimed at R users, must be accompanied by a comprehensive help file. - Type ?anova or help(anova) in the console.
• Type ?? followed by your search criterion. For example ??anova returns a list of all help pages that contain the word 'anova'
• Alternatively, the rest of internet will help. For example: http://www.stackoverflow.com, http://www.stackexchange.com
• If you google R related issues; use 'R:' as a prefix in your search term

• Operators: built in functions to do the most basic operations

• Operators: built in functions to do the most basic operations

  • Arithmetic operators: +, -, /, *, ^, :
  • Logical operators: <, <=, >, >=, ==, !=

(2+3)/5

## [1] 1

2>3

## [1] FALSE

2/3==4*2/12

## [1] TRUE

• Store data in "objects"
• Store "functions" in objects
• Perform calculations on objects with data, using "operators" or functions
• These operators and functions return results
• which can in turn be stored in objects.

• You program them yourself
• They are stored as part of basic R functionality
• They are stored in packages made for R that you have installed

• R works with objects that consist out of elements (data).
• The elements are 'assigned' to objects.
• Once you have assigned an element to an object, it is stored in the global environment.

• Assigning things in R is very straightforward:

  • you just use <- (or =)

a <- 100

• Calling things in R is also very straightforward:

  • you just use type the name you have given to the object

• For example, we assigned the value 100 to the object called a. To call object a, we would type

a

## [1] 100

• Numeric (numbers)
• Character or String (text)
• Logical (True or False)
• Many other (sub)categories which are beyond the scope of this course.

a <- 100
mode(a)

## [1] "numeric"

b <- "apple"
mode(b)

## [1] "character"

c <- "100"
mode(c)

## [1] "character"

d <- as.numeric(c)
d

## [1] 100

e <- 3>2
mode(e)

## [1] "logical"

• Missing data is coded as NA.
• Data that are the result of ´impossible' operations are coded as NaN

f <- NA
is.na(f)

## [1] TRUE

sqrt(-1)

## Warning in sqrt(-1): NaNs produced

## [1] NaN

• vectors
• matrices
• arrays (beyond the scope of this course)
• lists

• dataframes

  • Each type of object can only store either numeric data, or string data (but NA and NaN may be mixed in)
  • with the exception of dataframes and lists.

• A vector is a 1-dimensional concatenation of one or more elements (either all strings, or numbers)

a <- c(1, 2, 3, 4, 5)
a

## [1] 1 2 3 4 5

b <- 6:10
b

## [1]  6  7  8  9 10

If we would want just the third element, we would type

b[3]

## [1] 8

• A matrix contains one or multiple vectors
• a matrix is 2-dimensional: nrow specifies the number of rows, ncol the number of columns

c <- matrix(a, nrow = 5, ncol = 2)
c

##      [,1] [,2]
## [1,]    1    1
## [2,]    2    2
## [3,]    3    3
## [4,]    4    4
## [5,]    5    5

• The first row is called by

c[1, ]

## [1] 1 1

• The second column is called by

c[, 2]

## [1] 1 2 3 4 5

• The intersection of the first row and second column is called by

c[1, 2]

## [1] 1

In short; square brackets [] are used to call elements, rows, and columns

• The object type that R uses for fitting statistical models to
• Look and work similar to matrices
• Contain multiple vectors, organized in rows and columns
• May contain both vectors with characters, and vectors with numbers!

head(ToothGrowth)

##    len supp dose
## 1  4.2   VC  0.5
## 2 11.5   VC  0.5
## 3  7.3   VC  0.5
## 4  5.8   VC  0.5
## 5  6.4   VC  0.5
## 6 10.0   VC  0.5

• A list is an object that contains a collection of other objects.

l = c(2, 3, 5) 
m = c("aa", "bb", "cc", "dd", "ee") 
x = list(l,m)   
x

## [[1]]
## [1] 2 3 5
## 
## [[2]]
## [1] "aa" "bb" "cc" "dd" "ee"

• Most R model fitting functions return the analysis results in lists.

• functions are objects that are used to do calculations on other objects
• all functions have a name, and they can be called using that name.

• functions are objects that are used to do calculations on other objects
• all functions have a name, and they can be called using that name.
• if you want to know how a function works and what it does, call ?functionname

?mean()

• all functions work by specifying one or more 'arguments' for that function (like setting options)

x <- c(2,4,3,NA,5)
mux <- mean(x, na.rm=TRUE)
mux

## [1] 3.5

• Many basic functions are available in R from the start. You have already seen these: mean(), c(), matrix(), list(),head(),mode(), as.numeric(), is.na(), sqrt()
• Many more are available in packages
• And you can make new ones yourself

• use the help resources available online
• Don't be afraid to just try things
• Save your code regularly