R Data Structure

R Types are types for the smallest objects in R. Furthermore, these objects can form more complex data structures.

Vector

In R, every single value, like 5L, is considered a vector of length 1.
There is one rule about vectors: a vector can only contain objects of the same type.
If you combine objects of different types into a vector, the objects will be transformed into the same type.

Use function c (for "combine") to create a vector with more than 1 element.

length(5L)
x <- c(2L,5)
length(x) # 2
class(x) # numeric
class(x[1]) # numeric

For integers and numerics, a shortcut for generating a vector is the : operator.

c(1,2,3,4) == 1:4

Indexing

Use square brackets [] and index (starting from 1) to fetch the elements from a vector. Use a colon : to specify a range.

c(6,8)[1]
x <- c(6, 8, 7, 5, 3, 0, 9)
x[1:3]
x[c(1,4,5,8)]

Type Coercion

In vectors, implicit type coercions will be done. You can also do explicit type coercions.

as.character(c(6, 8))   # "6" "8"
as.logical(c(1,0,1,1))  # TRUE FALSE  TRUE  TRUE
# as.numeric("Bilbo")
# Warning message:
# NAs introduced by coercion    

Matrix

Matrix is a special two-dimensional data structure in R. The class of it is "matrix" "array".
Like a vector, the elements in a matrix should/will be of the same R Type.
To create a matrix, using the following function

M <- matrix([data =] data, [nrow =] 3, [ncol =] 2)

Indexing

Since a matrix is two-dimensional, the index has two arguments, like M[1,2]. When the argument is missing, it is interpreted as a colon : in Matlab Array - Indexing.

M <- matrix(1:4,2,2)
M[,]
M[1,]
M[,2] <- 5
M[]

Data Frame

Data frames are used to store tabular data (2d) in R. They are represented as a special type of list where every element of the list has to have the same length.

Data frames are usually created by reading in a dataset using the read.table() or read.csv(). However, data frames can also be created explicitly with the data.frame()function or they can be coerced from other types of objects like lists.

students <- data.frame(c("Cedric", "Fred", "George", "Cho", "Draco", "Ginny"),
                       c(       3,      2,        2,     1,       0,      -1),
                       c(     "H",    "G",      "G",   "R",     "S",     "G"))
names(students) <- c("name", "year", "house") # name the columns
class(students) # "data.frame"
students
# =>
#     name year house
# 1 Cedric    3     H
# 2   Fred    2     G
# 3 George    2     G
# 4    Cho    1     R
# 5  Draco    0     S
# 6  Ginny   -1     G
class(students$year)    # "numeric"
class(students[,3])     # "factor"
# find the dimensions
nrow(students)  # 6
ncol(students)  # 3
dim(students)   # 6 3

# There are many twisty ways to subset data frames, all subtly unalike
students$year       # 3  2  2  1  0 -1
students[, 2]       # 3  2  2  1  0 -1
students[, "year"]  # 3  2  2  1  0 -1

# To drop a column from a data.frame or data.table,
# assign it the NULL value
students$houseFounderName <- NULL

Array

An array in R is a multi-dimensional homogenous (of the same R Type) data structure, like Matlab Array.

array(c(c(c(2, 300, 4), c(8, 9, 0)), c(c(5, 60, 0), c(66, 7, 847))), dim = c(3, 2, 2))

List

A list is a multi-dimensional heterogeneous data structure.

x <- list(1, "a", TRUE, 1 + 4i)

Lists are like dictionaries: you can give each value a name:

x <- list(time = 1:40)
x$price = c(rnorm(40, .5 * x$time, 4))

# You can get items in the list like so
list1$time # by name
list1[["time"]] # by name
list1[[1]] # by index

by zcysxy