R is one of the most popular programming languages and framework for statistical

computing and data science application currently available.

The goal of this course is to bring you up to start this comprehensive software as quickly as possible, such that you can apply it effectively to your own data analysis.

This course is built for people of any age who trie to start programming in R at first time or even have never programmed at all. If you want to learn the basics of programming quickly so you can focus on interesting projects, and you like to test your

understanding of new concepts by solving meaningful problems, then this course is appropriate for you.

The course contents are divided into five main sections.

The first section -Get started with R environment

provides a brief overview of R programming language as well as the installation of R and RStudio, so will make you become familiar with the R environment.

Section 2 - R data structure and dataset

covers mainly the basics of R data structure, and how to import data files to create useful format for further analysis in R.

Section 3 provides a brief overview of some useful basic data management methods in R, which includes mainly data type conversions,recoding variables, handling dates and missing values, Selecting and dropping variables, Sorting, merging, and subsetting datasets.

Section 4, some of the more addvanced data management methods in R are explained. The main topics in this section include mathematical and statistical functions, functions for string and character, control flow in R, writing your own functions, and reshaping and aggregating datasets as well as summary statistics.

R graphics is covered in section 5. In this section, we will introduce the ggplot2 package, and how to creat some simple bivariate (two-variable) graphs, as well as using grouping and faceting to create multivariate graphs, how to save graphs in multiple formats, and how to plot some basic statistical plots.

Several virtual datasets suitable for R framework are followed during the course.

Each section has its own source code file written in R-format, which can be loaded into RStudio. you can download the source files from the course page.

By the end of the course you will be given an exercise which you can utilize all the knowledge learned to practice and evalutate what you have learned in this course.