The Basics of R and RStudio

Introduction

R and RStudio utilize multiple data types to store different kinds of data.

The most common data types in R are listed below.

Data Type	Description
Numeric	The most common data type. The values can be numbers or decimals (all real numbers).
Integer	Special case of numeric data without decimals.
Logical	Boolean data type with only 2 values (`TRUE` or `FALSE`).
Complex	Specifies imaginary values in R.
Character	Assigns a character or string to a variable. The character variables are enclosed in single quotes (‘character’) while the string variables are enclosed in double quotes (“string”).
Factor	Special type of character variable that represents a categorical such as gender.
Raw	Specifies values as raw bytes. It uses built-in functions to convert between raw and character (charToRaw() or rawToChar()).
Dates	Specifies the date variable. Date stores a date and POSIXct stores a date and time. The output is indicated as the number of days (Date) or number of seconds (POSIXct) since 01/01/1970.

Data types

1. Numeric

89.98

[1] 89.98

[1] 55

2. Integer

5L

[1] 5

5768L

[1] 5768

3. Logical

TRUE

[1] TRUE

FALSE

[1] FALSE

4. Complex

10 + 30i

[1] 10+30i

287 + 34i

[1] 287+34i

5. Character or String

'abc'

[1] "abc"

"def"

[1] "def"

"I like learning R"

[1] "I like learning R"

6. Dates

"2022-06-23 14:39:21 EAT"

[1] "2022-06-23 14:39:21 EAT"

"2022-06-23"

[1] "2022-06-23"

Examining various data types

Several functions exist to examine the features of the various data types. These include:

typeof() – what is the data type of the object (low-level)?
class() – what is the data type of the object (high-level)?
length() – how long is the object?
attributes() – any metadata available?

Let’s look at how these functions work with a few examples

a <- 45.84
b <- 858L
c <- TRUE
d <- 89 + 34i
e <- 'abc'

1. Examine the data type at a low-level with `typeof()`

typeof(a)

[1] "double"

typeof(b)

[1] "integer"

typeof(c)

[1] "logical"

typeof(d)

[1] "complex"

typeof(e)

[1] "character"

2. Examine the data type at a high-level with `class()`

class(a)

[1] "numeric"

class(b)

[1] "integer"

class(c)

[1] "logical"

class(d)

[1] "complex"

class(e)

[1] "character"

3. Use the `is.____()` functions to determine the data type

To test whether the variable is of a specific type, we can use the is.____() functions.

First, we test the variable a which is numeric.

is.numeric(a)

[1] TRUE

is.integer(a)

[1] FALSE

is.logical(a)

[1] FALSE

is.character(a)

[1] FALSE

Second, we test the variable c which is logical.

is.numeric(c)

[1] FALSE

is.integer(c)

[1] FALSE

is.logical(c)

[1] TRUE

is.character(c)

[1] FALSE

Converting between various data types

To convert between data types we can use the as.____() functions. These include: as.Date(), as.numeric(), and as.factor(). Additionally, other helpful functions include factor() which adds levels to the data and nchar() which provides the length of the data.

Examples

as.integer(a)

[1] 45

as.logical(0)

[1] FALSE

as.logical(1)

[1] TRUE

nchar(e)

[1] 3