#2 Data Classes (CloudStat)

November 5, 2011
By

(This article was first published on CloudStat, and kindly contributed to R-bloggers)

As stated in CloudStat Intro, we know that CloudStat is based on R Language, an object orientated language, everything in R is an object. Each object has a class. The simplest data objects are one-dimensional arrays called vectors, consisting of any number of elements. For example, the calculation:

Input:

1+1

Output

> 1+1

[1] 2

results in a vector, from the numeric class (as it contains a number), with just one element. Note that the command “1+1” is itself and object of the expression class

The simplest elements produce vectors of the following classes:

  • logical: The values T (or TRUE) and F (or FALSE).
  • integer: Integer values such as 2 or -5.
  • numeric: Floating-point real numbers (double-precision by default).
    Numerical values can be written as
    whole numbers (for example, 2, -5),
    decimal fractions (2.38, -23.125), or in
    scientific notation (2.38e57, 23e-98).
  • complex: Complex numbers of the form a + bi, where a and b are integers or numeric (for example, 5 + 4.67i).
  • character: character strings enclosed by matching double quotes (“) or apostrophes ( ’), for example, “CloudStat”, ’data analysis’.

Example: #2 Data Classes Example

> 1+1

[1] 2
>
> T; F #Logical
[1] TRUE
[1] FALSE
>
> 2; -5 #Integer
[1] 2
[1] -5
> 2; -5 #Whole number
[1] 2
[1] -5
> 2.38; -23.125 #Decimal fractions
[1] 2.38
[1] -23.125
> 2.38e57; 23e-98 #Scientific notation
[1] 2.38e+57
[1] 2.3e-97
> 5 + 4.67i #Complex number
[1] 5+4.67i
> "CloudStat"; "data analysis" #Character
[1] "CloudStat"
[1] "data analysis"
>

    Two other elements which are particularly useful are:

    • factors: These represent labelled observations. For example sex is a factor, generally incorporating two levels: male and female. These are generally used to represent qualitative effects in models.
    • ordered factors: A factor where the levels are ordered. For example there may be three responses to a question about quality, high, medium or low, but each level is not necessarily on a linear scale.

    #2 Data Classes Example

    Source: An Introduction to R: Examples for Actuaries by Nigel De Silva

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