R Programming Tutorial

Data visualization is an essential skill for data scientists. It combines statistics and design in meaningful and appropriate ways.

Learn how to sort vectors, data frames, and more in R using order(), sort(), and dplyr's arrange() with practical examples.

Learn about generalized linear models (GLM) and how they differ from linear models.

Learn how to subset data frames in R using brackets [], subset(), and dplyr. Includes code examples and a comparison of methods.

Learn about Object-Oriented Programming (OOP) along with R's objects, different classes like S3 and S4, along with its construction, creating its generic function with examples and many more.

Learn about the factor function in R, along with an example, and it's structure, order levels, renaming of the levels, and finally, with the ordering of categorical values.

Learn in detail about the ifelse() function, including syntax, along with finding whether a number is odd or even, and finally, with an example to see whether a student passed or failed their exam.

Learn all about R's matrix, naming rows and columns, accessing elements also with computation like addition, subtraction, multiplication, and division.

Learn about data reshaping in R, different functions like rbind(), cbind(), along with Melt(), Dcast(), and finally about the transpose function.

Kevin Systrom published a really interesting article on estimating $R_t$, the measure known as effective reproduction number. We will use the same approach outlined by Kevin in his article to estimate COVID-19's Rt in real-time.

Learn about Arrays in R, including indexing with examples, along with the creation and addition of matrices and the apply() function.

Learn about R's Strings: its rules, concatenation, along with essential properties of the string, extracting and replacing a character string, and formatting a string.