Essential Fundamentals of R

A data frame is used for storing data tables. It is a list of vectors of equal length. For example, the following variable df is a data frame containing three vectors n, s, b.

> n = c(2, 3, 5)
> s = c("aa", "bb", "cc")
> b = c(TRUE, FALSE, TRUE)
> df = data.frame(n, s, b) # df is a data frame

We use built-in data frames in R for our tutorials. For example, here is a built-in data frame in R, called mtcars.

> mtcars
mpg cyl disp hp drat wt ...
Mazda RX4 21.0 6 160 110 3.90 2.62 ...
Mazda RX4 Wag 21.0 6 160 110 3.90 2.88 ...
Datsun 710 22.8 4 108 93 3.85 2.32 ...
............

The top line of the table, called the header, contains the column names. Each horizontal line afterward denotes a data row, which begins with the name of the row, and then followed by the actual data. Each data member of a row is called a cell.

To retrieve data in a cell, we would enter its row and column coordinates in the single square bracket "[]" operator. The two coordinates are separated by a comma. In other words, the coordinates begins with row position, then followed by a comma, and ends with the column position. The order is important.

Here is the cell value from the first row, second column of mtcars.

> mtcars[1, 2]
[1] 6

Moreover, we can use the row and column names instead of the numeric coordinates.

> mtcars["Mazda RX4", "cyl"]
[1] 6

Lastly, the number of data rows in the data frame is given by the nrow function.

> nrow(mtcars) # number of data rows
[1] 32

And the number of columns of a data frame is given by the ncol function.

> ncol(mtcars) # number of columns
[1] 11

Further details of the mtcars data set is available in the R documentation.

> help(mtcars)

One of the most important aspects of computing with data is the ability to manipulate it, to enable subsequent analysis and visualization. R offers a wide range of tools for this purpose.

One of the great strengths of R is the user's ability to add functions. In fact, many of the functions in R are actually functions of functions. The structure of a function is given below.

myfunction <- function(<em>arg1, arg2, ... </em>){<br> <em>statements</em><br> return(<em>object</em>)<br> }

Objects in the function are local to the function. The object returned can be any data type.

The classic, Fortran-like loop is available in R. The syntax is a little different, but the idea is identical; you request that an index, i, takes on a sequence of values, and that one or more lines of commands are executed as many times as there are different values of i. Here is a loop executed five times with the values of i from 1 to 5: we print the square of each value:

 for (i in 1:5) print(i∧2) [1] 1 [1] 4 [1] 9 [1] 16 [1] 25 

The purpose of the R function function() is to create functions. For instance, consider this code:

 inc <- function(x) return(x+1) 

It instructs R to create a function that adds 1 to its argument and then assigns that function to inc. However, that last step—the assignment—is not always taken. We can simply use the function object created by our call to function() without naming that object. The functions in that context are called anonymous, since they have no name. (That is somewhat misleading, since even nonanonymous functions only have a name in the sense that a variable is pointing to them.)

"Apply" functions keep you from having to write loops to perform some operation on every row or every column of a matrix or data frame, or on every element in a list. For example, the built-in data set state.x77 contains eight columns of data describing the 50 U.S. states in 1977. If you wanted the average of each of the eight columns, you could do this:

 > avgs <- numeric (8) > for (i in 1:8) + avgs[i] <- mean (state.x77[,i]) # The "+" is R's continuation character; don't type it > avgs [1] 4246.4200 4435.8000 1.1700 70.8786 7.3780 53.1080 104.4600 70735.8800 

 > apply (state.x77, 2, median) Population Income Illiteracy Life Exp Murder HS Grad Frost Area 2838.5 4519 0.95 70.675 6.85 53.25 114.5 54277 

The reshape package in R l

Recoding data allows you to change a data type, for example, or to calculate new data columns from existing ones.

Recoding data allows you to change a data type, for example, or to calculate new data columns from existing ones.