Data Types

Md Zulquar Nain

Objects

• Most statistical software (e.g., SPSS, Stata) operates on datasets, which consist of rows of observations and columns of variables
• R is an object-oriented programming language (like Python,JavaScript).
• So, what is an object?
• In computer language - complex definition
• Anything to which you assigned a value

Important

Objects are like boxes in which we can put things: data, functions, and even other objects. Ben Skinner

Data Objects

• Data usually obtained from external sources
• Collected by researchers using survey
• In R import those data files into R Objects

Data Types

• Use function typeof() to check the type of data
• the function typeof() returns the Data type or data structure
• Data Types are
  • Double
  • Integer
  • Complex
  • Logical
  • Character
  • Factor
  • Date and time

Data Types

• Double
  • Doubles are numbers like 2.0, 2.2, 2.999
  • May or may not include decimals
  • Mostly used to represent continuous variables like wage, height, age
  • In R by default all numbers are Double

x <- 10.5
is.double(x)

[1] TRUE

Data Types

• Integer
  • Integers are natural numbers
  • Mostly used for counting variables
  • However, in R by default treated as Double

x <- 6
typeof(x)

[1] "double"

x <- as.integer(9)
typeof(x)

[1] "integer"

Data Types

• Complex
  • Complex like \(x+yi\)

x <- -1
typeof(x)

[1] "double"

sqrt(x)

[1] NaN

# Complex
y <--1+(0+0i)
sqrt(y)

[1] 0+1i

Data Types

• Logical
  • Logical: a variable of logical type data has values like TRUE or FALSE
  • Usually used to represent conditional statements

x <- 20
y <- 15
a <- x > y
a

[1] TRUE

typeof(a)

[1] "logical"

Data Types

• Character
  • Characters represents a string values in R
  • Usually a string or collection of characters are kept indouble quotes
  • Anything in a double quote is considered a string in R

x <- "You are smart"
print(x)

[1] "You are smart"

y <- "5"
typeof(y)

[1] "character"

Data Types

• Factor
  • Factor represents categorical data
  • For example gender(Male, Female), employed and unemployed
  • Also handy in case of panel and longitudinal data
  • Factor objects can be created from character or numeric objects

# Created from Characters
cat <- c("A","B","C")
# Use functions to convert into factor type
cat=as.factor(cat)
cat 

[1] A B C
Levels: A B C

levels(cat)

[1] "A" "B" "C"

Data Types

• Date and Time
  • We need quite often especially when dealing with time series models
  • Function as.Date is used to create Date and Time objects
    
  • See help(as.Date)

today <-"25-03-2023" 
today <- as.Date(today,"%d-%m-%Y")
today

[1] "2023-03-25"

today1 <- Sys.Date()
today1

[1] "2024-02-15"

today2 <- format(today1, "%d %B %Y")
today2

[1] "15 February 2024"

Missing Values in R

• In R missing data is represented by NA meaning Not Available
• Another symbol appears in R is NaN meaning Not a Number
• NULL in R represent an object with ZERO length
• -Inf and Inf represents negative and positive infinity

# Create a vector using function `c()`
ownd <- c("10","30","missing")
# converting to double
ownd <- as.double(ownd)
  # check for the missing value
is.na(ownd)

[1] FALSE FALSE  TRUE

THANKS