# Data Types

R has a wide variety of data types including scalars, vectors (numerical, character, logical), matrices, data frames, and lists.

## Vectors

` a <- c(1,2,5.3,6,-2,4) # numeric vector`

b <- c("one","two","three") # character vector

c <- c(TRUE,TRUE,TRUE,FALSE,TRUE,FALSE) #logical vector

Refer to elements of a vector using subscripts.

` a[c(2,4)] # 2nd and 4th elements of vector`

## Matrices

All columns in a matrix must have the same mode(numeric, character, etc.) and the same length. The general format is

mymatrix <- **matrix(***vector*, **nrow=***r*, **ncol=***c*, **byrow=***FALSE*,

dimnames=list(*char_vector_rownames*, *char_vector_colnames***)) **

**byrow=TRUE** indicates that the matrix should be filled by rows. **byrow=FALSE** indicates that the matrix should be filled by columns (the default). **dimnames** provides optional labels for the columns and rows.

```
# generates 5 x 4 numeric matrix
```

y<-matrix(1:20, nrow=5,ncol=4)

# another example

cells <- c(1,26,24,68)

rnames <- c("R1", "R2")

cnames <- c("C1", "C2")

mymatrix <- matrix(cells, nrow=2, ncol=2, byrow=TRUE,

dimnames=list(rnames, cnames))

Identify rows, columns or elements using subscripts.

` x[,4] # 4th column of matrix`

x[3,] # 3rd row of matrix

x[2:4,1:3] # rows 2,3,4 of columns 1,2,3

## Arrays

Arrays are similar to matrices but can have more than two dimensions. See **help(array)** for details.

## Data Frames

A data frame is more general than a matrix, in that different columns can have different modes (numeric, character, factor, etc.). This is similar to SAS and SPSS datasets.

`d <- c(1,2,3,4)`

e <- c("red", "white", "red", NA)

f <- c(TRUE,TRUE,TRUE,FALSE)

mydata <- data.frame(d,e,f)

names(mydata) <- c("ID","Color","Passed") # variable names

There are a variety of ways to identify the elements of a data frame .

`myframe[3:5] # columns 3,4,5 of data frame`

myframe[c("ID","Age")] # columns ID and Age from data frame

myframe$X1 # variable x1 in the data frame

## Lists

An ordered collection of objects (components). A list allows you to gather a variety of (possibly unrelated) objects under one name.

`# example of a list with 4 components - `

#
a string, a numeric vector, a matrix, and a scaler

w <- list(name="Fred", mynumbers=a, mymatrix=y, age=5.3)

# example of a list containing two lists

v <- c(list1,list2)

Identify elements of a list using the [[]] convention.

` mylist[[2]] # 2nd component of the list`

mylist[["mynumbers"]] # component named mynumbers in list

## Factors

Tell R that a variable is **nominal ** by making it a factor. The factor stores the nominal values as a vector of integers in the range [ 1... k ] (where k is the number of unique values in the nominal variable), and an internal vector of character strings (the original values) mapped to these integers.

`# variable gender with 20 "male" entries and `

#
30 "female" entries

gender <- c(rep("male",20), rep("female", 30))

gender <- factor(gender)

# stores gender as 20 1s and 30 2s and associates

#
1=female, 2=male internally (alphabetically)

# R now treats gender as a nominal variable

summary(gender)

An ordered factor is used to represent an **ordinal variable**.

`# variable rating coded as "large", "medium", "small'`

rating <- ordered(rating)

# recodes rating to 1,2,3 and associates

#
1=large, 2=medium, 3=small internally

# R now treats rating as ordinal

R will treat factors as nominal variables and ordered factors as ordinal variables in statistical proceedures and graphical analyses. You can use options in the **factor( ) **and **ordered( )** functions to control the mapping of integers to strings (overiding the alphabetical ordering). You can also use factors to create value labels. For more on factors see the UCLA page.

## Useful Functions

`length(object) # number of elements or components`

str(object) # structure of an object

class(object) # class or type of an object

names(object) # names

c(object,object,...) # combine objects into a vector

cbind(object, object, ...) # combine objects as columns

rbind(object, object, ...) # combine objects as rows

object # prints the object

ls() # list current objects

rm(object) # delete an object

newobject <- edit(object) # edit copy and save as newobject

fix(object) # edit in place

## To Practice

To explore data types in R, try this free interactive introduction to R course