R stores both data and output from data analysis (as well as everything else) in objects.
Creating a new object is as easy as typing the object's name and assigning a value to it. There are multiple ways to assign values to objects in R.
As in many computer languages you can use the equal sign (=) as an assignment operator. Copy the following code into your script, and run it.
#assign the value of the square root of 100 to #the object, variable 'result' result=sqrt(100)
You are more likely, however, to see <- used to assign values to objects:
#assign the value of the square root of 100 to #the object, variable 'result' result<-sqrt(100)
Now we have created an object called result. It currently has the value of 10. To verify that, we can type the name of the object, and R will print the value stored for it:
result ## [1] 10
You can think of an object in R as a shoebox (object) with a name. Here, the name of the shoebox is "result." Imagine that it is holding a scrap of paper with the number 10 on it.
You can name your object anything you want, with a few exceptions. You can store any value in it, with a few exceptions.
For instance we could have named it ScoobyDoo if we wanted, but that's not a very informative name.
R provides many functions to examine features of objects. Two important ones:
class() - what kind of object is it (high-level)?length() - how long is it?For example, we can get some information about the result object that we just created:
#find class of object 'result' class(result) ## [1] "numeric" #find length of object 'result' length(result) ## [1] 1
There are several different data structures in R. If you take a longer introduction to R you'll learn about arrays, lists, matrices, etc.
We will skip to the two most common data types: vectors and data frames.
Guess what, you've been using them already!
Vectors are the building block of R. Simply, they are one-dimensional collections of values of the same type (integer, character, logical, etc.)
c() function…#A vector of numbers x<-c(5,9,11) x ## [1] 5 9 11
#A vector of logical values y<-c(TRUE,FALSE,TRUE,FALSE,FALSE) y ## [1] TRUE FALSE TRUE FALSE FALSE
#A vector of character strings
people<-c("Jack","Jill","Jim","June")
people
## [1] "Jack" "Jill" "Jim" "June"
#A vector with one value v<-c(15) v ## [1] 15
The elements of a vector must all have the same mode, or data type.
You can have a vector consisting of three character strings, or three integer elements, but not a vector with one integer element and two character string elements.
k<-c(1,2,3,"BOO!") k ## [1] "1" "2" "3" "BOO!"
Once your vector is defined, you will want to access values from it. For example, you may want to get the fourth value in the vector, or the second to last, or so forth.
The syntax for accessing values is vectorname[index] where vectorname is the name of the vector, and index is the index number of the element you are looking for.
Using the people vector we created earlier, here are some examples.
#print the people vector just for reference people ## [1] "Jack" "Jill" "Jim" "June"
#get the first value from the vector people[1] ## [1] "Jack"
#R will evaluate any math within the brackets... people[1+2] ## [1] "Jim" people[length(people)] ## [1] "June"
#print the 1st and 3rd values in different order people[c(1,3)] ## [1] "Jack" "Jim" people[c(3,1)] ## [1] "Jim" "Jack"
Datasets for statistical analysis are typically stored in data frames in R.
Data frames are rectangular, where the columns are variables and the rows are observations of those variables. This is very similar to a table in Excel or in a database.
Columns of data frames are… vectors!
And, like vectors, data frame columns can be of several different data types, but all entries in the same vector must be the same type.
Also, all columns in a data frame must be of the same equal length.
Data frames can be manually created with data.frame().
The elements of a data frame are almost always named.
mydata <- data.frame(diabetic = c(TRUE, FALSE, TRUE, FALSE),
height = c(65, 69, 71, 73))
mydata
## diabetic height
## 1 TRUE 65
## 2 FALSE 69
## 3 TRUE 71
## 4 FALSE 73
Data frames, unlike vectors, are two-dimensional structures. We subset them using the formula [rows, columns].
# row 3 column 2 mydata[3,2] ## [1] 71
# using column name mydata[1:2, "height"] ## [1] 65 69
# all rows of column "height" mydata[,"diabetic"] ## [1] TRUE FALSE TRUE FALSE
To subset an entire column of a dataframe, we use $ followed by that column name.
# subsetting creates a numeric vector mydata$height ## [1] 65 69 71 73
This function will be extremely important when we come to do operations on the data.
colnames(data_frame) returns the column names of the data frame.
colnames(data_frame) <- c("some", "names") will assign column names to the data frame.
# get column names colnames(mydata) ## [1] "diabetic" "height"
# assign column names
colnames(mydata) <- c("Diabetic", "Height")
colnames(mydata)
## [1] "Diabetic" "Height"
To change one column name, just use indexing.
colnames(mydata)[1] <- "Diabetes" colnames(mydata) ## [1] "Diabetes" "Height"