Mastering R Basics: Understanding Objects, Data Types (Vectors and Lists), and Coercion

The Beginner’s Guide R Objects and Data Types: "Vectors and Lists"

R is a programming language that is widely used for data analysis and statistical computing. It has a powerful set of data structures, including vectors, lists, and data frames, that allow users to work with data in a flexible and efficient way.

R Objects

Everything in R is an object, which means that it has a type, a value, and possibly some attributes. There are many different types of objects in R, including numbers, strings, and logical values, as well as more complex objects like functions and data frames.

Numbers

In R, there are two types of numbers: integers and doubles. Integers are whole numbers, while doubles are numbers with decimal places. When you create a number in R, it is automatically assigned a type based on its format. For example, if you type x <- 5, R will create an integer object, while if you type y <- 5.0, R will create a double object.

Attributes

Objects in R can have attributes, which are additional pieces of information that describe the object. For example, a vector might have an attribute that specifies its length, or a data frame might have an attribute that specifies the names of its columns. You can access an object's attributes using the attributes() function.

Data Types: Vectors and Lists

Vectors and lists are two of the most commonly used data types in R. Vectors are a basic data structure in R that allow you to store multiple values of the same type. For example, you might create a vector of integers like this:

x <- c(1, 2, 3, 4, 5)

Lists are a more complex data type in R that allow you to store multiple values of different types. For example, you might create a list like this:

my_list <- list(name = "John", age = 25, hobbies = c("reading", "swimming", "hiking"))

Data Coercion

Data coercion is the process of changing the type of an object in R. For example, you might need to coerce a character string to a numeric value in order to perform a calculation. You can use the as. functions to coerce data from one type to another. For example, to coerce a character string to a numeric value, you would use the as.numeric() function:

x <- "10" 

y <- as.numeric(x)

This would create a numeric object y with the value 10.

More on these topics have already been covered in the lecture.

Practice Material

Here are a few practice exercises to help beginners get started with R:

• Create a vector of even numbers from 2 to 20.

• Create a list with the following information about yourself: name, age, height, favorite color.

• Create a vector of five numeric values and then coerce it to a character string.

• Create a data frame with the following information about three people: name, age, height, weight.

• Create a vector of the numbers 1 to 10 and then extract the values that are greater than 5.

• For more practice you should start swirl's second, third and fourth lesson on  R Programming. Complete download process of swirl and R Programming is here, click on the link!

• You can look in to the practice and reading material that is provided in the text book, click here to download the textbook.

• Lecture slides can be downloaded from here. It would be great if you go through them too. 

I hope this blog post has been helpful in introducing R objects, numbers, attributes, data types, and data coercion. Good luck with your R programming journey!