R has four basic data structures: vectors, lists, matricies, and data frames.
Vectors contain data of a single type in 1 dimension. You can create vectors by using the c() function.
c(1, 2, 3)## [1] 1 2 3Since vectors have types, you can use them with operators to get predictable results.
1 + c(1, 2, 3)## [1] 2 3 4c(1, 2, 3) + c(4, 5, 6)## [1] 5 7 9c(1, 2, 3) * c(4, 5, 6)## [1] 4 10 18TRUE & c(TRUE, FALSE)## [1] TRUE FALSETRUE | c(TRUE, FALSE)## [1] TRUE TRUEc(TRUE, FALSE) & c(TRUE, FALSE)## [1] TRUE FALSEc(FALSE, TRUE) | c(TRUE, FALSE)## [1] TRUE TRUEAs you can see the & and | operators work with vectors the way && and || work with individual values.
You can extract individual values from vectors by “indexing” them using single brackets ([]).
a_vector <- c(6, 7, 8)
a_vector[1]## [1] 6a_vector[2]## [1] 7a_vector[3]## [1] 8To quickly create a sequence of integers, use the : operator:
1:7## [1] 1 2 3 4 5 6 7-4:2## [1] -4 -3 -2 -1 0 1 2To create a vector that repeats a value several times use the rep() function:
rep(5, times = 4)## [1] 5 5 5 5rep(3:4, times = 3)## [1] 3 4 3 4 3 4rep(3:4, times = 3, each = 2)## [1] 3 3 4 4 3 3 4 4 3 3 4 4rep(TRUE, times = 3)## [1] TRUE TRUE TRUErep("four times!", times = 4)## [1] "four times!" "four times!" "four times!" "four times!"To create a sequence of numbers use the seq() function:
seq(1, 5)## [1] 1 2 3 4 5seq(1, 2, by = 0.1)## [1] 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0seq(1, 2, length.out = 5)## [1] 1.00 1.25 1.50 1.75 2.00You can index vectors by using another vector to specify which values you want to extract. For example:
hundred <- 100:110
hundred[c(1, 3, 4)]## [1] 100 102 103hundred[5:7]## [1] 104 105 106You can also combine vectors by using c():
onetwothree <- c(1, 2, 3)
fourfivesix <- c(4, 5, 6)
c(onetwothree, fourfivesix)## [1] 1 2 3 4 5 6It’s also possible to change a value in a vector be re-assigning it:
onetwothree## [1] 1 2 3onetwothree[2] <- 100
onetwothree## [1] 1 100 3A matrix is like a two dimensional vector. Matricies contain data of a single type. You can create a matrix by using the matrix() function.
matrix(1:9, nrow = 3)## [,1] [,2] [,3]
## [1,] 1 4 7
## [2,] 2 5 8
## [3,] 3 6 9matrix(1:9, nrow = 3, byrow = TRUE)## [,1] [,2] [,3]
## [1,] 1 2 3
## [2,] 4 5 6
## [3,] 7 8 9Operators work in a predictable way:
mat <- matrix(2:5, nrow = 2)
1 + mat## [,1] [,2]
## [1,] 3 5
## [2,] 4 62 * mat## [,1] [,2]
## [1,] 4 8
## [2,] 6 10Notice that multiplying two matricies does not perform matrix multiplication according to matrix algebra:
mat * mat## [,1] [,2]
## [1,] 4 16
## [2,] 9 25To multiply matricies using linear algebra use the %*% operator:
mat %*% mat## [,1] [,2]
## [1,] 16 28
## [2,] 21 37You can index a matrix with the syntax a_matrix[row,column].
big_mat <- matrix(1:9, nrow = 3)
big_mat[1, 2]## [1] 4You can also get a row or a column of a matrix as a vector by leaving out the row or column:
big_mat[1,]## [1] 1 4 7big_mat[,2]## [1] 4 5 6Indexing with vectors also works:
big_mat[1:2, 1:2]## [,1] [,2]
## [1,] 1 4
## [2,] 2 5big_mat[3, 2:3]## [1] 6 9big_mat[c(1, 3), c(1, 3)] # The four "corners" of the matrix## [,1] [,2]
## [1,] 1 7
## [2,] 3 9You can also re-assign values in a matrix:
big_mat## [,1] [,2] [,3]
## [1,] 1 4 7
## [2,] 2 5 8
## [3,] 3 6 9big_mat[2, 2] <- 47
big_mat## [,1] [,2] [,3]
## [1,] 1 4 7
## [2,] 2 47 8
## [3,] 3 6 9You can even re-assign an entire row or column:
big_mat## [,1] [,2] [,3]
## [1,] 1 4 7
## [2,] 2 47 8
## [3,] 3 6 9big_mat[, 2] <- c(12, 13, 14)
big_mat[1,] <- c(39, 40, 41)
big_mat## [,1] [,2] [,3]
## [1,] 39 40 41
## [2,] 2 13 8
## [3,] 3 14 9Lists are the most adaptable data structures in R. You can create a list with the list() function. In its simplest form a list is a one dimensional data structure that can contain multiple types:
list1 <- list(1, TRUE, "three")
list1## [[1]]
## [1] 1
##
## [[2]]
## [1] TRUE
##
## [[3]]
## [1] "three"Lists can be indexed with double brackets:
list1[[2]]## [1] TRUElist1[[3]]## [1] "three"It’s also possible to name each element in a list:
list2 <- list(first = 85, second = "a word")
list2## $first
## [1] 85
##
## $second
## [1] "a word"You can access named list elements with the syntax a_list[["name"]] or a_list$name:
list2[["first"]]## [1] 85list2$second## [1] "a word"You can add named elements to existing lists with a similar synatx:
list2[["third"]] <- FALSE
list2$fourth <- "four"
list2## $first
## [1] 85
##
## $second
## [1] "a word"
##
## $third
## [1] FALSE
##
## $fourth
## [1] "four"The most useful thing about lists is that an element of a list can be anything, a single value, a vector, a matrix, or even another list:
list3 <- list() # This creates an empty list
list3$a_vector <- c("I", "am", "a", "vector")
list3$a_matrix <- matrix(1:8, nrow = 4)
list3$a_list <- list(one = "1", two = "2", three = "3")
list3## $a_vector
## [1] "I" "am" "a" "vector"
##
## $a_matrix
## [,1] [,2]
## [1,] 1 5
## [2,] 2 6
## [3,] 3 7
## [4,] 4 8
##
## $a_list
## $a_list$one
## [1] "1"
##
## $a_list$two
## [1] "2"
##
## $a_list$three
## [1] "3"A data frame is a two dimensional data structure that can contain multiple types of data. You can create a data frame using the function data.frame(). Unlike a list, a data frame can only contain single values, it cannot contain other data sctructures like a list can.
df <- data.frame(numbers = 9:12, truths = rep(TRUE, 4),
strings = c("Mary", "had a", "little", "lamb."),
stringsAsFactors = FALSE)
df## numbers truths strings
## 1 9 TRUE Mary
## 2 10 TRUE had a
## 3 11 TRUE little
## 4 12 TRUE lamb.Indexing data frames shares some similarities with matricies and lists:
df$numbers## [1] 9 10 11 12df$truths## [1] TRUE TRUE TRUE TRUEdf[2,2]## [1] TRUEdf[1,]## numbers truths strings
## 1 9 TRUE Marydf[2,]## numbers truths strings
## 2 10 TRUE had adf[,3]## [1] "Mary" "had a" "little" "lamb."