#adv-r

OOB stands for index out of bounds

[[]] and [] differ in their behavior when index is OOB

if a data frame (z) is entered: z<-data.frame(x=1:5,y=6:10)

if an atomic vector (a) is entered: a<-1:15

check the length of your object: (i.e. the # of indices)

> length(z) [1] 2

>length(z$x)

[1] 5

>length(z$y)

[1] 5

>length(c(z$x,z$y))

[1] 10

> length(a) [1] 15

now try to extract an object outside of these bounds using [[]] and []

> z[3] Error in `[.data.frame`(z, 3) : undefined columns selected

> z[[3]] Error in .subset2(x, i, exact = exact) : subscript out of bounds

> a[16] [1] NA

this table was taken from adv-r, Hadley WIckham

there is a difference between simplifying and preserving subsets

simplifying- returns the most simple data structure (can represents the output)

preserving- “keeps the structure of the output the same as the input “-- typically salient with programming (result will always be the same type)common error when subsetting: omitting drop=FALSE is a common mistake

So far it’s been established that: [] and $ selects columns/rows 

[[]] allows for more selective element operations--only allows you to pull out single values or columns in data frames--**note: when using lists, you can only use [[]]

we will create a variable (a) to understand this [[]] subset

a<-list(1:4,4:8,z=letters[1:4])

structurally: 

> str(a) List of 3 $  : int [1:4] 1 2 3 4 $  : int [1:5] 4 5 6 7 8 $ z: chr [1:4] "a" "b" "c" "d"

when using [[]]

it is most relevant for lists

you can specify a single element, or a string using c()

(1) using a single number or letter

> a[[1]] [1] 1 2 3 4

> a[['z']] [1] "a" "b" "c" "d"

> DATA FRAMES

(if you only have 1 var in the data frame, treated like a list--with 2 or more vectors, df behaves like matrix)

Taken directly from site:  How many of your friends complain regularly about their bodies? ~None: 147 ~A few: 411 ~Some: 188 ~Most: 141 ~All: 53

construct an object that reflects it:

> body_complain<-data.frame(x=c('None','A few','Some','Most','All'),y=c(147,411,188,141,53))

object $ title of column

___________________________________________________________

>DATA FRAMES>STRUCTURAL QUALITIES

the structure of data frame subsets are unique 

examine the differences using this variable (a)

> a  x y z 1 1 1 a 2 2 2 b 3 3 3 c 4 4 4 d

> str(a) 'data.frame':   4 obs. of  3 variables: $ x: int  1 2 3 4 $ y: int  1 2 3 4 $ z: Factor w/ 4 levels "a","b","c","d": 1 2 3 4

Now examine the structural differences which subsetting initiates in data frames

> str(a[c(1)]) 'data.frame':   4 obs. of  1 variable: $ x: int  1 2 3 4

> str(a[,c(1,3)]) 'data.frame':   4 obs. of  2 variables: $ x: int  1 2 3 4 $ z: Factor w/ 4 levels "a","b","c","d": 1 2 3 4

> str(a[c(3),]) 'data.frame':   1 obs. of  3 variables: $ x: int 3 $ y: int 3 $ z: Factor w/ 4 levels "a","b","c","d": 3

To access a subscript of a data frame use $ and []

>a$y[3]

[1] 3

__________________________________________________________

>DATA FRAME > REASSIGNMENT

S3 Objects

(1) Atomic Vectors

(2) Arrays

(3) Lists

S4 Objects

brackets [] are known as subsetting 

This is an exciting concept that will require an understanding of:

3 subsetting operators

the 6 types of subsetting

behavioral differences among different objects

subsetting with assignment