### Target audiences

- students

INTRODUCTION

__R Programming__

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.

R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows, and macOS.

In this course, you will learn **how to program in R and how to use R for effective data analysis**. You will learn **how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts** as they are implemented in a high-level statistical language. The course covers topics in statistical data analysis will provide working examples. **practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.**

**Skills you will gain**

- Data Analysis
- Debugging
- R Programming
- Rstudio

### Course Features

- Lectures 6
- Quizzes 2
- Duration 30 hours
- Language English
- Students 0
- Assessments Yes