In R, there are different data structures, including:

A matrix is a 2-dimensional object that can be build with the function matrix

( M <- matrix(1:6, ncol=3, byrow=FALSE) ) # Fill by columns (def.)

##      [,1] [,2] [,3]
## [1,]    1    3    5
## [2,]    2    4    6

( N <- matrix(1:6, ncol=3, byrow=TRUE) ) # Fill by rows

##      [,1] [,2] [,3]
## [1,]    1    2    3
## [2,]    4    5    6

M <- matrix(1:6, nrow=2, byrow=FALSE) 
dim(M)

## [1] 2 3

ncol(M)

## [1] 3

nrow(M)

## [1] 2

colnames(M) <- c("Sample_A", "Sample_B", "Sample_C") # Assign
rownames(M) <- c("Gene1", "Gene2") # Assign
M

##       Sample_A Sample_B Sample_C
## Gene1        1        3        5
## Gene2        2        4        6

colnames(M) # Query

## [1] "Sample_A" "Sample_B" "Sample_C"

rownames(M) # Query

## [1] "Gene1" "Gene2"

A data.frame is a 2-dimensional object (“matrix-like”) that can contain different types of data on its colums.

It can be created with the function data.frame

( DF <- data.frame(name=c("Mary", "John", "Lisa"),
  age=c(19, 30, 20),
  city=c("New York", "Seattle", "New York") ) )

##   name age     city
## 1 Mary  19 New York
## 2 John  30  Seattle
## 3 Lisa  20 New York

dim(DF)

## [1] 3 3

nrow(DF)

## [1] 3

ncol(DF)

## [1] 3

colnames(DF)

## [1] "name" "age"  "city"

M <- matrix(1:6, nrow=2, byrow=FALSE) 
colnames(M) <- c("Sample_A", "Sample_B", "Sample_C")
rownames(M) <- c("Gene1", "Gene2")
M

##       Sample_A Sample_B Sample_C
## Gene1        1        3        5
## Gene2        2        4        6

M[1,2] # Two indices: [row,column]

## [1] 3

Accessing the first row:

M[1,]

## Sample_A Sample_B Sample_C 
##        1        3        5

Removing the second column:

M[,-2]

##       Sample_A Sample_C
## Gene1        1        5
## Gene2        2        6

Elements of matrices and data.frames can be accessed also considering row and column names

M["Gene1",]

## Sample_A Sample_B Sample_C 
##        1        3        5

M[,"Sample_C"]

## Gene1 Gene2 
##     5     6

By default, R transforms one-dimensional objects into vectors, unless we specify drop = FALSE

v <- M[1,]

dim(v)

## NULL

m <- M[1,, drop = FALSE]

dim(m)

## [1] 1 3

Data.frames can be handled similarly to matrices

( DF <- data.frame(name=c("Mary", "John"), age=c(19, 30) ) ) 

##   name age
## 1 Mary  19
## 2 John  30

DF[,"age"]

## [1] 19 30

DF[1,2]

## [1] 19

A list is an object that can contain different objects (including lists!)

It can be created with the function list

v <- 1:3
M <- matrix(1:4, ncol=2)
( myList <- list(myVec=v, myMat=M) ) 

## $myVec
## [1] 1 2 3
## 
## $myMat
##      [,1] [,2]
## [1,]    1    3
## [2,]    2    4

length(myList)

## [1] 2

names(myList)

## [1] "myVec" "myMat"

myList <- list(v, M) 
names(myList)

## NULL

Elements in lists can be accessed using double square brackets

favorites <- list(colors=c("blue", "purple"),
  cities=c("Venice", "Innsbruck", "New York City"))

favorites[[1]]

## [1] "blue"   "purple"

favorites[["cities"]]

## [1] "Venice"        "Innsbruck"     "New York City"

(M1 <- matrix(1, ncol=2, nrow=2))

##      [,1] [,2]
## [1,]    1    1
## [2,]    1    1

(M2 <- matrix(2, ncol=2, nrow=2))

##      [,1] [,2]
## [1,]    2    2
## [2,]    2    2

cbind(M1,M2)

##      [,1] [,2] [,3] [,4]
## [1,]    1    1    2    2
## [2,]    1    1    2    2

rbind(M1,M2)

##      [,1] [,2]
## [1,]    1    1
## [2,]    1    1
## [3,]    2    2
## [4,]    2    2

( M <- rbind(c(1,1,1), c(2,2,2)) )

##      [,1] [,2] [,3]
## [1,]    1    1    1
## [2,]    2    2    2

apply(M,1,sum)

## [1] 3 6

M <- as.data.frame(M)
apply(M,2,sum)

## V1 V2 V3 
##  3  3  3

L <- list(group1 = c(1, 1, 2), 
          group2 = c(100, 50, 60),
          group3 = c(100, 100, 100, 1000, 2000)) 

lapply(L, mean)

## $group1
## [1] 1.333333
## 
## $group2
## [1] 70
## 
## $group3
## [1] 660

M3 <- rbind(M1, M2)
tail(M3, 2)

##      [,1] [,2]
## [3,]    2    2
## [4,]    2    2

head(L, 2)

## $group1
## [1] 1 1 2
## 
## $group2
## [1] 100  50  60