The aim of this course is to introduce you to using R, a powerful and flexible interactive environment for statistical computing and research. R in itself is not difficult to learn, but as with learning any new language (spoken or computer) the initial learning curve can be a little steep and somewhat daunting. I have tried to simplify the content of this course as much as possible and have based it on my own personal experience of using (and learning) R over the last 5 years.

Who is the Target Audience?

Introduction to R programming for data analysis course is created for those who are brand new to R. You are going to find this course useful, if you are:

• New to R programming
• New to programming in general and so far have not written a single line of code
• Business Managers, who work with data, but would like to understand more about open source programming languages used for data science
• A student of statistics
• A data enthusiast
• Anyone who works with data and interested in learning open-source data science programming tools such as R

What do I learn in this course?

It is impossible to cover everything there is to know about R. However, this course casts a wider net on the R programming language in general, however the main theme of discussion shall be to show you features of R language used to carry out the data analysis. 

We will cover following topics.

- Installation of R from CRAN server

- Installation of RStudio

- IDE used to carry out data analysis in R

- Rstudio orientation

- R package system

- R help documentation

- R data types and data structures

- Loops and conditional statements

- Writing custom Functions in R

- Vector functions - apply, sapply, tapply

- Data visualisation using base R

- Handling time series data using base R

What I will NOT learn in this course?

This course will not cover any software development fundamentals. This is introductory course hence advance concepts in R programming such as classes, parallel computation etc. won't be discussed.

The following topics are excluded:

- R as software development language

- R as object oriented programming language

- Internal architecture of R language

- Error handling and performance improvement techniques in R