Advanced Data Analysis in R: dplyr and Large Datasets

This level is for learners who are comfortable with data cleaning and charts. You will learn more powerful data steps, build prediction models, try machine learning, write cleaner code, and share results in reports and dashboards. At the end, you will complete a full data science project.

Advanced Data Transformation Using dplyr

Three dplyr tools save a lot of time. The case_when() function creates categories from several rules. The across() function applies one action to many columns. The slice_max() function returns the top rows in each group.

These tools replace long loops and keep your code short and readable. For example, you can label students as High, Medium, or Low, or find the best student in every section with one short command.

Analyzing Relationships Between Variables

A correlation matrix shows the link between many columns at once. In this data, study hours move closely with maths and science marks, at about 0.81 each. Attendance has no link with study hours, at 0.00.

This kind of check helps you choose which columns to use in a model and warns you when two columns carry the same information. Two columns with a very high correlation may carry similar information, so you may need only one.

Working with Larger Datasets Using Practical Examples

The same dplyr code works on hundreds of thousands of rows. For much larger files, packages such as data.table and arrow can be faster.

A few habits help at any size:

• Keep only the columns you need.
• Filter early.
• Avoid building a vector inside a loop.
• Measure your code with system.time() before trying to improve it.

Do not optimise too early. First make the code correct, then measure it, and only then make it faster.