Outline

• 3.2 Atomic Vectors
• 3.3 Attributes
• 3.4 S3 Atomic Vectors
• 3.5 Lists
• 3.6 Dataframes and Tibbles
• 3.7 NULL

Vectors

• 2 types of vectors
  • Atomic
  • List

Atomic Vectors

• 4 common types

    a <- c(1,2,3,4) #Integer
    b <- c(TRUE, FALSE, T, F) #Logical
    c <- c(1.2, 2.3, 5.0) #Double
    d <- c("apple", "banana") #Character

• Rare types of Atomic Vectors: Raw and Complex

Missing Values

• R uses NA to represent missing values.

x <- c(NA, 5, NA, 10)
x == NA

## [1] NA NA NA NA

• Use is.na() to check for missing values

is.na(x)

## [1]  TRUE FALSE  TRUE FALSE

Testing

• Type of vectors can be tested with is.*() function.
• is.logical(), is.integer(), is.double(), and is.character()
• Avoid using is.vector(), is.atomic(), and is.numeric()

Coercion

• For atomic vectors, type is a property of the entire vector.
• When attempting to combine different types of elements, they will be coerced in a fixed order.
• character -> double -> integer -> logical

str(c("a", 1))

##  chr [1:2] "a" "1"

• Coercion happens automatically.

x <- c(FALSE, FALSE, TRUE)
as.numeric(x)

## [1] 0 0 1

sum(x) #Total number of TRUEs

## [1] 1

• Using as.*() allows us to deliberately coerce.
  • as.logical(),as.integer(), as.double() and as.character()

Attributes

• Attributes can be added to atomic vectors to build data structures like Arrays, Matrices, Factors or date-times.
• They can be individually set and retrieved using attr()

car = "CR-V"
attr(car,'manufacturer') <- 'Honda'
attr(car, 'manufacturer')

## [1] "Honda"

• Set multiple attributes using

car2 <- structure("Model S", manufacturer = "Tesla", year = 2020)

• You can retrieve multiple attributes by using

attributes(car2)

## $manufacturer
## [1] "Tesla"
## 
## $year
## [1] 2020

Names

x <- c(apple = 'a', banana = 'b') # 1
x

##  apple banana 
##    "a"    "b"

y <- c('a', 'b')
names(y) <- c('apple', 'banana') # 2
y

##  apple banana 
##    "a"    "b"

setNames(y, c('apple', 'banana')) # 3

##  apple banana 
##    "a"    "b"

Source: TonyElHabr Chapter 3 slide 8

Dimensions

• Allows vector to behave like a matrix or an array.

a <- matrix(1:6, nrow = 2, ncol = 3)
a

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

b <- matrix(1:6, c(1, 3, 2))
b

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

Unusual Behaviour

• Vectors without dim are thought of as 1-dimensionals but are NULL
• Matrices with 1 row or col and 1-dimensional arrays print the same but behave differently
• Use str() to reveal the differences.

S3 Atomic Vectors

• Objects that have a class attribute.
• 4 types of S3 vectors
  • factor (categorical)
  • Date (date)
  • POSIXct (date-time)
  • duration (difftime)

Factors

• Vectors that only contain pre-defined values
• Used for Categorical Data

sex_char <- c("m", "m", "m")
sex_factor <- factor(sex_char, levels = c("m", "f"))
table(sex_factor)

## sex_factor
## m f 
## 3 0

grade <- ordered(c("b", "b", "a", "c"), levels = c("c", "b", "a"))
grade

## [1] b b a c
## Levels: c < b < a

• Many base R functions automatically convert character vectors into factors.
• Use stringsAsFactors = FALSE to supress this behaviour.

Dates, POSIXct & Duration

• All built on top of double vectors.
• Dates

today <- Sys.Date()
typeof(today)

## [1] "double"

attributes(today)

## $class
## [1] "Date"

• Date-times
  • Two types of storing date-time: POSIXct & POSIXlt
  • Underlying value represents number of seconds since Jan 1, 1970.
  • tzone attribute

• Duration
  • Amount of time between date/date-time pairs.
  • Stored in difftimes
  • units attribute to determine how integer should be interpreted.

one_week_1 <- as.difftime(1, units = "weeks")
one_week_1

## Time difference of 1 weeks

typeof(one_week_1)

## [1] "double"

attributes(one_week_1)

## $class
## [1] "difftime"
## 
## $units
## [1] "weeks"

Lists

• Can be of any atomic type or contain other lists.

    e <- list(1, TRUE, 1.2, "apple", list(2, 4, 6))

• Elements of a list are references.
• c() combines several lists into one if there given a combination of atomic vectors and lists.

l4 <- list(list(1, 2), c(3, 4))
str(l4)

## List of 2
##  $ :List of 2
##   ..$ : num 1
##   ..$ : num 2
##  $ : num [1:2] 3 4

Data Frames

• S3 Vectors that are built on top of lists.

df1 <- data.frame(x = 1:3, y = letters[1:3])
typeof(df1)

## [1] "list"

attributes(df1)

## $names
## [1] "x" "y"
## 
## $class
## [1] "data.frame"
## 
## $row.names
## [1] 1 2 3

• Constraint
  • Length of each vector must be the same

Tibble

• Share the same structure as data frames
• class vectors are longer
• Default behaviour: stringsAsFactors = FALSE
• Discourages rownames
• "Nicer" Printing

NULL

• NULL has a unique type

typeof(NULL)

## [1] "NULL"

length(NULL)

## [1] 0

• Common uses
  • Represent an empty vector

c()

## NULL

• Represent an absent vector