Skip to main content

Getting Started with R Programming

The Beginner’s Guide to R Programming.

I'm very excited to start R Programming and I hope you are too. This is the second course in the Data Science Specialization and it focuses on the nuts and bolts of using R as a programming language.

The recommended background for this course is the course The Data Scientist's Toolbox. It is possible to take this class concurrently with that class but you may have to read ahead in the prerequisite class to get the relevant background for this class. For a complete set of course dependencies in the Data Science Specialization please see the course dependency chart, that has been posted on our blogpost.

The primary way to interact with me and the other students in this course is through the discussion forums which in our case are comments section under the lectures, social media and blogpost. Here, you can start new threads by asking questions or you can respond to other people's questions. If you have a question about any aspect of the course, I strongly suggest that you search through the discussion boards first to see if anyone has already asked that question. If you see something similar to what you want to ask, you should like that question comment to push the notification to get answered to that question quickly rather than asking your question separately. The more votes a question or comment gets, the more likely it is that I will see it and be able to respond quickly. Of course, if you don't see a question similar to the one you want to ask, then you should definitely start a new thread on the appropriate forum.

Finally, consider getting the course textbook, R Programming for Data Science, which is available for free. The content in the book tracks the material covered in the course and allows you to hang on to the material once the course is finished.



Course Description

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, discuss 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, and organizing and commenting R code. Topics in statistical data analysis and optimization will provide working examples.

Course Content:

  • Overview of R
  • R data types and objects
  • Reading and writing data
  • Control structures
  • Functions
  • Scoping rules for R programming
  • Dates and times
  • Loop functions
  • Debugging tools
  • Simulation in R
  • Code profiling

Programming Assignments

Programming assignments will be posted for you to practice on blogpost for each topic. Plus we will cover few guided assignments and projects, during and at the end of the course.

Swirl Programming Assignment (practice)

In this course, you have the option to use the swirl R package to practice some of the concepts we cover in lectures.

While these lessons will give you valuable practice and you are encouraged to complete them all for better understanding of the core concepts that will be covered in this course, so please complete them all.

What is swirl?

Swirl is the package that is developed by John Hopkins university for the R programming class.  It's called Statistics with Interactive R Learning or SWIRL for short.  And it was developed by Nick Carchedi, who was a student then at the Johns Hopkins department of bio-statistics. This is a system that allows you to interactively learn R at your own pace. And it will walk you through a bunch of lessons about different aspects of the R language and you can practice them as you go. So, rather than just watching a lecture and then doing an assignment and doing things piece by piece, you can actually work on R right in the R console in a  guided way. Rather than just figuring things out on your own. So, I think this, the SWIRL modules are really helpful and I encourage you to try to walk through them. And it will be great learning combining lecture videos and swirl practice assignments. I think it'll be a lot of fun.

Practical R Exercises in swirl

The swirl package turns the R console into an interactive learning environment. Using swirl will also give you the opportunity to be completely immersed in an authentic R programming environment. In this programming assignment, you'll have the opportunity to practice some key concepts from this course.

  • Install R

Swirl requires R 3.0.2 or later. If you have an older version of R, please update before going any further. If you're not sure what version of R you have, type R.version.string at the R prompt. You can download the latest version of R from https://www.r-project.org/.

Optional but highly recommended: Install R Studio. You can download the latest version of R Studio at https://www.rstudio.com/products/rstudio/.

  • Install swirl

Since swirl is an R package, you can easily install it by entering a single command from the R console:

install.packages("swirl")

If you've installed swirl in the past make sure you have version 2.2.21 or later. You can check this with:

packageVersion("swirl")

  • Load swirl

Every time you want to use swirl, you need to first load the package. From the R console:

library(swirl)

  • Install the R Programming course

swirl offers a variety of interactive courses, but for our purposes, you want the one called R Programming. Type the following from the R prompt to install this course:

install_from_swirl("R Programming")

  • Start swirl and complete the lessons

Type the following from the R console to start swirl:

swirl()

Then, follow the menus and select the R Programming course when given the option. 

I am very excited to start this course and I hope you enjoy this course and I anticipate a fun time in this course!

Labels:

Comments

Post a Comment

Type your comment here.
However, Comments for this blog are held for moderation before they are published to blog.
Thanks!

Popular posts from this blog

Installing R on Windows and MAC Operating System

The Beginner’s Guide to Installing R on Windows and MAC OS Hello and Welcome to next part of our first course in The Data Science Specialization. After getting familiar with what is Data science, Data, Data Science process and knowing what actually is Data Scientist, we move towards the next part of getting familiar with the tools that will be needed during our Data science specialization. First, let’s remind ourselves exactly what R is and why we might want to use it. R  is both a programming language and an environment, focused mainly on statistical analysis and graphics. It will be one of the main tools you use in this and following courses. R is downloaded from the  Comprehensive R Archive Network , or CRAN, and while this might be your first brush with it, we will be returning to CRAN time and time again, when we install packages - so keep an eye out! Why should you use R? Outside of this course, you may be asking yourself -  why should I use R? The rea...
1 comment
Read more

Mastering Subsetting Techniques and Vectorized Operations in R: A Comprehensive Guide

The Beginner’s Guide Subsetting and Vectorized Operations in R: Subsetting in R is a crucial part of data analysis and manipulation. It enables us to extract specific data elements from a larger dataset and perform operations on them. In this blog post, we will discuss several subsetting techniques in R, including partial matching , removing NA values , using the completecase function , vectorized operations on lists and matrices , and matrix multiplication and inverse . Partial Matching Partial matching in R is a useful technique for extracting subsets of data from larger datasets. It involves using a subset of a string to match against a larger string. For example, if you have a dataset with variable names such as "age", "height", and "weight", you can use partial matching to extract all variables that contain the substring "h". To do this, you can use the $ operator and the grep function as follows: data <- data.frame(age = c(20, 30, 40), h...
Post a Comment
Read more

Introduction to R Markdown

The Beginner’s Guide to R Markdown! We’ve spent a lot of time getting R and R Studio working, learning about Functionalities of R Studio and R Packages - you are practically an expert at this! There is one major functionality of R/R Studio that we would be remiss to not include in your introduction to R -  Markdown! Functionalities in R Studio Introduction to R Packages What is R Markdown? R Markdown is a way of creating fully reproducible documents, in which both text and code can be combined. In fact, these lessons are written using R Markdown! That’s how we make things: bullets bold italics links or run inline r code And by the end of this lesson, you should be able to do each of those things too, and more! Despite these documents all starting as plain text, you can render them into HTML pages, or PDFs, or Word documents, or slides! The symbols you use to signal, for example,  bold  or  italics  is compatible with all of those formats. Wh...
Post a Comment
Read more