The fundamental R data structure is the vector, which stores an ordered set of values called elements. A vector can contain any number of elements. However, all the elements must be of the same type; for instance, a vector cannot contain both numbers and text.

There are several vector types commonly used in machine learning: integer (numbers without decimals), numeric (numbers with decimals), character (text data), or logical (TRUE or FALSE values). There are also two special values: NULL, which is used to indicate the absence of any value, and NA, which indicates a missing value.

It is tedious to enter large amounts of data manually, but simple vectors can be created by using the combine function c(). The vector can also be given a name using the arrow <- operator, which is R's assignment operator, used in a similar way to the = assignment operator in many other programming languages.

For example, let's construct a set of vectors containing data on three medical patients. We'll create a character vector named subject_name, which contains the three patient names, a numeric vector named temperature containing each patient's body temperature, and a logical vector flu_status containing each patient's diagnosis; TRUE if he or she has influenza, FALSE otherwise. As shown in the following listing, the three vectors are:

> subject_name <- c("John Doe", "Jane Doe", "Steve Graves")
> temperature <- c(98.1, 98.6, 101.4)
> flu_status <- c(FALSE, FALSE, TRUE)

Because R vectors are inherently ordered, the records can be accessed by counting the item's number in the set, beginning at 1, and surrounding this number with square brackets (for example, [ and ]) after the name of the vector. For instance, to obtain the body temperature for patient Jane Doe, or element 2 in the temperature vector simply type:

> temperature[2]
[1] 98.6

R offers a variety of convenient methods for extracting data from vectors. A range of values can be obtained using the colon operator. For instance, to obtain the body temperature of Jane Doe and Steve Graves, type:

> temperature[2:3]
[1] 98.6 101.4

Items can be excluded by specifying a negative item number. To exclude Jane Doe's temperature data, type:

> temperature[-2]
[1]  98.1 101.4

Finally, it is also sometimes useful to specify a logical vector indicating whether each item should be included. For example, to include the first two temperature readings but exclude the third, type:

> temperature[c(TRUE, TRUE, FALSE)]
[1] 98.1 98.6

As you will see shortly, the vector provides the foundation for many other R data structures. Therefore, knowing the various vector operations is crucial for working with data in R.