# Data Types in R

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.

## 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