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Introduction

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R Programming

Johns Hopkins University

4.6 (14,272 ratings) | 410K Students Enrolled

Course 2 of 10 in the Data Science Specialization

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 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. Topics in statistical data analysis will provide working examples.

Skills You'll Learn

Data Analysis, Debugging, R Programming, Rstudio

Reviews

4.6 (14,272 ratings)

5 stars
9,883 ratings
4 stars
3,187 ratings
3 stars
773 ratings
2 stars
241 ratings
1 star
188 ratings

AK

May 27, 2017

This was very engaging, however, the level of expectation and effort needed is much greater than course 1 - ToolBox.\n\nThis is perhaps the best course on R Programming designed for a small duration.

GB

Feb 03, 2016

Excelente opportunity to learn a lot. The course is very well prepared introduce you to R programing. Dont feel bad if you dont get it at te first moment. It will be a process of leaning worth trying

From the lesson

Week 1: Background, Getting Started, and Nuts & Bolts

This week covers the basics to get you started up with R. The Background Materials lesson contains information about course mechanics and some videos on installing R. The Week 1 videos cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data. I recommend that you watch the videos in the listed order, but watching the videos out of order isn't going to ruin the story.

Introduction1:20

Overview and History of R16:07

Getting Help13:53

R Console Input and Evaluation4:46

Data Types - R Objects and Attributes4:43

Data Types - Vectors and Lists6:27

Data Types - Matrices3:24

Data Types - Factors4:31

Data Types - Missing Values2:10

Data Types - Data Frames2:44

Data Types - Names Attribute1:49

Data Types - Summary0:43

Reading Tabular Data5:51

Reading Large Tables7:08

Textual Data Formats4:58

Connections: Interfaces to the Outside World4:35

Subsetting - Basics4:13

Subsetting - Lists4:45

Subsetting - Matrices2:50

Subsetting - Partial Matching1:42

Subsetting - Removing Missing Values3:59

Vectorized Operations3:46

Taught By

Roger D. Peng, PhD
Associate Professor, Biostatistics
Jeff Leek, PhD
Associate Professor, Biostatistics
Brian Caffo, PhD
Professor, Biostatistics

Hey everyone welcome to R Programming.

This is the second course in the Data Science Specialization and, as

the title suggests, we will be focusing on R as a programming language.

So in this course, we'll kind of start with the basic

building blocks of R kind of different data types in the

very, kind of low level details and then we'll kind of

move on to writing and formulating our programs or our scripts.

This involves things like control structures and writing r

functions and kind of doing some basic operations on data.

And then, and then we'll talk about kind of profiling your code, some of the

tools for debugging and kind of how to work,

how to work through longer pieces of code.

And then so after this course is done, I think you'll

have a pretty solid grasp of R as a programming language.

We're not going to cover every last feature of the R packages because of

their and some of the other courses in the specialization you'll learn more about,

for example, how to use the graphics system, how to make plots, how

to use some of the packages for

things like statistical inference and machine learning.

And so that will be covered in other classes.

So, the pur, purpose of this class is to really kind of

get you into R programming, in

particularly or if you're not very familiar

with the language and to make sure you kind of kind of get a

hand, get a sense of the on the of the nuts and bolts.

So I hope you enjoy it.

And I think after this course is done you'll be ready to move

on to a bunch of, to mo, to the other courses in the specialization.

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