Explore R for data science, from basics to machine learning. Learn data manipulation, visualization, version control, and workflow optimization for real-world challenges.

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Embark on an exciting journey with R as your trusted ally for data science. This comprehensive course will equip you with essential skills to leverage R's power for data manipulation and analysis. The course is suitable even if you have limited R experience, empowering you to perform data science tasks effectively.

The journey starts with R fundamentals to lay a strong foundation. You’ll master the tidyverse to create powerful and readable code. Then, you will explore the import of various data sources, visualization, and best practices before gaining hands-on machine learning experience. Also, you’ll learn version control with Git and GitHub and to optimize your R code for efficient data science workflows.

At course completion, you'll emerge as a confident R data scientist, ready to tackle real-world challenges. You'll be well-equipped to advance your career in data science with R's extensive capabilities in your toolbelt.

Data Science in R: From Basics to Machine Learning

R has six main components: base-R, the R console, CRAN, an IDE, scripts, and the working directory. This lesson will focus on base-R, the R console, scripts, and the IDE. We won't be using any extensions from CRAN yet, and we won't be setting up a working directory---these will be discussed in more detail in the upcoming lessons.




Any R script in this course will follow the same basic structure: 

1) Take in some data
2) Manipulate it and perform some statistical operations
3) Output the results in one form or another

Structuring our code with these three major sections is the best practice that delineates data science code in R. Over time, we'll add complications and sub-components, but this will always be the primary underlying structure.

That being said, we'll start with a typical “Hello, world!” example and build toward a data-driven model throughout this lesson.

#Take some input and assign it to a variable
VAR_InputText <- "Hello, world!"

#Print the variable’s contents
VAR_InputText

**Line 1** is an assignment operation. It puts the value “Hello, world!” into the variable `VAR_InputText`. Notice that there are no declared data types, as there would be in some other programming languages. In R, it's not necessary to specify what kind of data a variable will contain.

Also notice the operator `<-`, which is one of the assignment operators in R. There are three of these:

  * `<-`: This signifies assignment to the left (in the direction of the arrow).
  * `->`: This signifies assignment to the right (again in the direction of the arrow).
  * `=`: This signifies assignment to the left.

These operators assign a value to a variable. Therefore, the variable now contains that value. In this example, after the first line is executed, the variable `VAR_InputText` contains the value “Hello, world!”

Edit the code above to try out the other two assignment operators. They work the same way. However, when using the `->` operator, put the value on the left side and the variable name on the right side (information flows in the direction of the arrow).

However, as we advance, we'll almost exclusively use the `<-` operator for assignments. This directionality is standard and what we'll see in nearly all R code. While convenient, the `->` operator can be problematic because it makes code hard to read. Otherwise, the `=` operator is generally reserved for passing values to functions in R to maintain consistency in our code.





Learn the foundational skills for writing an R script from scratch, such as syntax, variables, and arithmetic operations. 

Why R?

R Fundamentals

R Fundamentals Exercises

Readable Coding with tidyverse

Tidyverse Exercises

Importing More Data Sources

Data Visualization with ggplot2

Best Practices for Data Scientists

Statistical Analysis and Machine Learning with tidymodels

Exploring tidymodels through Exercises

Useful Libraries for Data Science

Git Integration

Getting The Most Out of R

Appendix

Credit Card Fraud Detection using the R Language

Basic R Syntax

Code structure

Our first example

Value assignment