R | Vectors vs List vs Array vs Matrix vs dataframe

For a non-R programmers some of the R data-types might be a bit confusing.

R	Non-R
InR, vector and list and arrays are different and has different properties. List can be named or un-named	Un-named list are Arrays Named lists are dictionary However Array and Dictionary can contain another Array or a dictionary, but its still called Arrays and dictionaries.

Vector

• Simplest form of R object
• All values in a vector should be of same obj/class.
• However they cant contain another vector or array or list.
• (non-R programmers terms) its like array but with all values in the array having same data-types

List

• It is like a vector but can contain any obj/class.
• class also have named attributes.
• can contain list within a list etc.
• (non-R programmers terms) its like array (if not named) and dictionaries( if named)

Array

• Its a list with one, two or more dimensions
• dimensions can be named
• (non-R programmers terms) its like array within another array

Matrix

• Its more like an array but with only 2 dimensions
• (non-R programmers terms) its still a matrix :)

DataFrame

• data in table format, with rows and each column is an attribute.
• when you read a csv file, you normally read that as dataFrames
• (non-R programmers terms) Imagine table with rows and columns( with columnames)

Examples 

Vector

> x<- c(1:2, 1:3)

> class(x)

[1] "integer"

> x

[1] 1 2 1 2 3

> y<- c(1:2, 1:3)

> x<- c(1:2, 1:3,y)

> x

 [1] 1 2 1 2 3 1 2 1 2 3

List

> y<- c(1:2, 1:3)

> x<- list(1:2, 1:3,y)

> x

[[1]]

[1] 1 2

[[2]]

[1] 1 2 3

[[3]]

[1] 1 2 1 2 3

> x<- list(a=1:2, b=1:3, c=y)

> x

$a

[1] 1 2

$b

[1] 1 2 3

$c

[1] 1 2 1 2 3

> x$b

[1] 1 2 3

> 

Array

> x <- array(1:20, dim=c(5,2,2), dimnames=list( NULL, c("ht", "wt"), c("male", "female")))

> x

, , male

     ht wt

[1,]  1  6

[2,]  2  7

[3,]  3  8

[4,]  4  9

[5,]  5 10

, , female

     ht wt

[1,] 11 16

[2,] 12 17

[3,] 13 18

[4,] 14 19

[5,] 15 20

Matrix

> x<- matrix(1:20, nrow=10, ncol=2)

> x

      [,1] [,2]

 [1,]    1   11

 [2,]    2   12

 [3,]    3   13

 [4,]    4   14

 [5,]    5   15

 [6,]    6   16

 [7,]    7   17

 [8,]    8   18

 [9,]    9   19

[10,]   10   20

DataFrames

df_data <- read.csv(file="/path/to/csvfilename.csv")