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1. **[Introduction to R by Datacamp](https://www.datacamp.com/courses/free-introduction-to-r)**: Good intro course
2. **[Data Science: R Basics](https://www.edx.org/course/r-basics-2)**: This is by Harvard and is a part of the 9 courses in **[Data Science Certificate](https://www.edx.org/professional-certificate/harvardx-data-science)**. If you are interested in learning ML and Stats these are great courses.
3. **[Dataquest R Courses](https://www.dataquest.io/path/data-analyst-r/)**: Dataquest is similar to [Datacamp](www.datacamp.com), you can learn R coding in an interactive manner. Check out their free courses
- **[Introduction to Programming in R](https://www.dataquest.io/course/intro-to-r/)**
- **[Intermediate R Programming](https://www.dataquest.io/course/intermediate-r-programming/)**
- **[Data Visualization in R](https://www.dataquest.io/course/r-data-viz/)**
4. **[Swirl: Learn R interactively within R Studio](https://swirlstats.com/students.html)**: The swirl R package makes it fun and easy to learn R programming and data science. If you are new to R, have no fear.
5. **[R Bootcamp](https://www.datacamp.com/courses/rbootcamp)**: This is a short course covering the basics of [`ggplot`](https://ggplot2.tidyverse.org/), [`dplyr`](https://dplyr.tidyverse.org/), [`tidyr`](https://tidyr.tidyverse.org/) and `broom`.
Books
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1. **[R For Data Science](https://r4ds.had.co.nz/)**: This book is a great introduction to `R` and covers the components of the [`Data Science pipeline`](https://r4ds.had.co.nz/introduction.html) which we discussed in the session.
2. **[Hands-On Programming with R](https://rstudio-education.github.io/hopr/)**: This covers the programmatic aspects of the `R` language and would help you to be really clear with the basics.
3. **[Cookbook for R](http://www.cookbook-r.com)** The goal of the cookbook is to provide solutions to common tasks and problems in analyzing data. Most of the code in these pages can be copied and pasted into the R command window if you want to see them in action.
Tutorials
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1. **[R Programming](https://swcarpentry.github.io/r-novice-inflammation/)**
2. **[R for Reproducible Scientific Analysis](http://swcarpentry.github.io/r-novice-gapminder/)** : An introduction to R for non-programmers using gapminder data
3. **[Data Analysis and Visualization in R](https://datacarpentry.org/R-ecology-lesson/index.html)**
4. **[Data Wrangling with R](https://cengel.github.io/R-data-wrangling/)**
Videos
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1. **[What is R?](https://www.youtube.com/watch?v=XcBLEVknqvY)** : A very good introductory video on R.
2. **[Why Use R? - R Tidyverse Reporting and Analytics for Excel Users](https://www.youtube.com/watch?v=jn_3N_o2d6Q)**
3. **[Data Analysis Screencasts](https://www.youtube.com/watch?v=nx5yhXAQLxw&list=PLnH3UnphKJdvbQsOFoFcTrbn18I_NBvW3)** : [David Robinson](http://varianceexplained.org/) is a Data Scientest at Datacamp and does a weekly #TidyTuesday Screencast. You can do it along with him, very good exercise in Data Analysis using R.
Assignments
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1. **The Analytics Edge** course either on [OCW](https://ocw.mit.edu/courses/sloan-school-of-management/15-071-the-analytics-edge-spring-2017/index.htm) or [edx](https://edx.org/course/the-analytics-edge). Please check the assignments tab. They have provided the data for each assignment. You can readily check your answers on their website.
* Some of these assignments would expect you to know more than what was covered in class, so make extensive use of Google. This is a proper Masters level course taught at MIT in their Business Analytics program, so expect it to be challenging but you will definitely learn a lot if you are able to finish the assignments.
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