Course Orientation
JuL 13, 2023
DataSciencePursuit
Welcome to our Intro to R Course for Beginners
Welcome to our introductory R programming course. This course is mostly designed for absolute beginners to programming. No prior programming knowledge is required. We will start from the very beginning and progress towards more challenging concepts. All you need is basic math to begin.
If you havent already, please read our page on what programming is. It provides some context that may be helpful when learning to program.
About R
R is the most popular programming language for data science. It’s is also free and open source and it was specifically designed for statistical analysis, but it has evolved to be a great language for data science.
About this Course
This course will cover the basics of R programming with a focus on what is needed to get started with data science. To be clear, this course will not cover data science specific topics yet. We will cover only the necessary fundamental topics to help you better understand more advanced applications of R, for data science. These applications will be covered in future courses.
For aspiring data scientists, we recommend that you don't spend too much time on the basics. The sooner you can start doing data science projects the better, especially for those seeking jobs.
What is Covered?
The fundamentals in any case are very important as they are the building blocks for whatever you need to do. However, learning the basics can be a little boring (or sometimes it feels pointless), so let’s link what we will cover in this course to what we will need as data scientists in the future. In data science, we work with data in different formats, the example mentioned previously was table format. See the example below:
Here is an example of the data you will be working with:
Country | Age | Employed Indicator |
---|---|---|
Canada | 40 | Yes |
USA | 60 | No |
South Africa | 50 |
Tables are made up of different components. They are a collection of data entries that are arranged in rows and columns. We are going to need to work with the entire table and its also individual components as we go through the data science process. In this course, instead of looking at the entire table from the get-go, we will start from the ground up. We will look at the individual components and then build up to working with the entire table or other data formats. Specifically:
- We will start by getting R either working on it from a free online resource or downloading and installing it on our computer.
- Then we will work with R as a calculator to get us to understand how to write and run/execute code in R.
- We will also explore some R functions. Doing this on basic math can help you focus on just learning the new concepts.
- Then we will learn about data types and how to work with them in R. From our table, some data entries are numbers like age, some are words or text like country and some data entries are empty (missing values). Different data types are treated differently in R, and they have different rules. This will help us a lot later when we need to clean data.
- We will then look at data structures which are collections of data. First, we have vectors which are like rows or columns in a table. We will learn some functions and features to help manipulate/work with these vectors. We will then bring all we have learnt together to see how a table like structure called data frames can be made in R.
- Lastly, we will learn what packages are and the different packages that are used by data scientists. How to work with these packages will be covered in detail in future courses as we go through the data science process.
As you learn the basics, always keep the bigger picture in mind. Everything you learn can come together to help you make and do some awesome things.
Project and exercises
Throughout the course, we will work on creating a simple rock paper scissors game. I chose this game because it was the reason I fell in love with programming in university. It was on an assignment we were given in first year and it was the first time the theory we were learning became practical to me. I even called my mom to show her. Making the game will incorporate almost all the topics we will cover.
We will do other coding exercises as well, and provide examples along the way, a couple of which are a node to what will we will do later for data science. We encourage you to code along. That may help you understand things better and develop problem-solving skills that are key to programming. You can also think of something else you may want to build, and build it as you go through the course. This can be right away or after you learn a certain topic you can think of an application for it. The end goal is to be able to create something simple with the basics ( connect everything to make something else).
We hope along the way that you will also learn to be comfortable with googling and asking for help, even from AI (although you should always be cautious when it comes to code). The best code is the code written by someone else.
After completing this course, you should be ready to go through programming for the data science process.
Next step
Let’s get started, first order of business, get R and RStudio!