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Data Structures

Data Types

So far we have only dealt with numeric values. However, we are not limited to just numbers we can store:

  • numeric - numeric values that can contain whole numbers and decimals
  • character - text value that is made a text value by adding quotes. So for example 1 2 3 is a numeric data, but "1" "2" "3" is character data
  • integer - limited to just whole numbers, but will take up less memory than numeric data. We can specify an integer by adding L to a number (e.g. 1L or 3L)
  • logical - These are boolean values so TRUE/T or FALSE/F.
  • complex - complex number such as 1+6i

Data Structures

So we have all this lovely data to play with and in R we typically organize in a few ways:

Vectors

Vectors are collections of data, like a:

collection of numbers - c(1,2,3) collection of characters - c("1","2","3") collection of logical values - c(TRUE,FALSE,TRUE)

Note

It should be noted that a vector needs to be a collection of the same type of data. You will also note that each list is separated by commas and surrounded by c(). This is necessary to create vectors so make sure to remember the c()!

Factors

Factors can be used to store categorical data and can be created like this:

size <- c("small", "medium", "small", "large", "medium")
size

[1] "small"  "medium" "small"  "large"  "medium"

size <- factor(size,levels = c("small","medium","large"))
size

[1] small  medium small  large  medium

Levels: small medium large

Now we have turned this character vector into a factor vector! These will come in handy when we start breaking down data by category.

Matrices

A matrix can be created by combining vectors of the same length and same data type. They are used frequently when performing operations on numeric data but can include other data types. In R we can create a matrix with the matrix() function:

matrix(data=1:9,nrow = 3,ncol=3)

     [,1] [,2] [,3]
[1,]    1    4    7
[2,]    2    5    8
[3,]    3    6    9

Here we take a vector and specify how many columns and how many rows we'd like.

Data Frames

Data frames are also collections of vectors of the same length. However, they do not need to be the same data type. Here we create a data.frame with the data.frame() function:

data.frame(
characters=c("past","present","future"),
numbers=c(1,2,3),
logical=c(TRUE,FALSE,TRUE),
integer=c(1L,2L,3L)
)

  characters numbers logical integer
1       past       1    TRUE       1
2    present       2   FALSE       2
3     future       3    TRUE       3

Lists

Lists are collections of data that do not need to be the same type or length. We can create lists with the list() function:

list(
data.frame=data.frame(numbers=1:3,characters=c("past","present","future")),
numbers=1:5,
characters=c("past","present","future")
)

$data.frame
  numbers characters
1       1       past
2       2    present
3       3     future

$numbers
[1] 1 2 3 4 5

$characters
[1] "past"    "present" "future"