Super minor nitpick:

R Studio is the development environment.

R is the language.

Presumably you want to become well versed in the latter rather than the former. It's an easy mistake to make though, since the two are so intertwined for most people as to become almost indistinguishable.

More to your point though:

Before learning anything, it's a good idea to ask yourself why you want to learn it, and what you hope to be able to do with it. Now, you mentioned two things,

• Hypothesis testing.
  
  
• Graphing 4 variables.
  
  Both of these are relatively simple, and if you have even the most rudimentary understanding of R, you could learn to do in a couple of minutes.
  
  So, my question to you would be, in using R is your goal to get quick, simple answers to straightforward questions OR are you ultimately looking to be able to do much more complicated tasks? This isn't a judgemental question, not everyone needs to aspire to become an R god, just needing something quick and dirty is perfectly okay.
  
  If the things you mentioned are more or less the extent of your needs, I'd suggest just googling what you need to do at the time and pick up what you need, more or less, through osmosis.
  
  However, if you have designs on being able to do amazingly complicated things, if you want to push R to its fullest, you'll need a more structured approach.
  
  One thing you absolutely must understand is R is a package based language. What this means for you is that beyond the numerous ways you can do any task in any language, people have written countless* packages which contain all sorts of handy functions to do just about anything you could conceivably want to do.
  
  >* Okay, it's not really countless, there are (as of this writing 12,620 packages on CRAN and 1,560 additional packages on bioconductor. There are bunches more of unofficial ones scattered about GitHub and others privately maintained, but you get the point, there's lots of them.
  
  So, for anything you want to do, you can approach it in one of two, very broad, ways:
  
  
• Base R.
  
  
• Using packages.
  
  When you are starting out, I think it's very important to get a good handle on Base R.
  
  I would start out with basically any introductory R book. Search on Amazon and just find one you like.
  
  Personally, I can recommend Using R for Introductory Statistics by John Verzani. It isn't for everyone, but if you're truly a beginner to both R and statistics more generally, it's a good reference text.
  
  After that it's, up to you. Where you want to take it. For me, the pantheon of R gods* I would pay tribute to are these four:
  
  
• The god of tidiness - Hadley Wickham GitHub/u/hadley
  
  
• The god of speed - Dirk Eddelbuettel GitHub
  
  
• The god of art - Winston Chang GitHub
  
  
• The god of sharing - Yihui Xie GitHub
  
  >*I'm sure every single person on that list would balk at being called a "god," but they'd be lying.
  
  It's no mistake that 3/4 of them work for R Studio.
  
  

  The god of tidiness.
  

  
  Hadley must be a complete neat-freak because he's the driving force behind the tidyverse,
  
  >The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.
  
  Once you branch out of base R, the tidyverse should be your first destination. It's not quite a new language unto itself, more like a very sophisticated dialect of the language you already know. Once you can speak "tidy," you can still communicate with the "base" speaking plebs, you just won't be able to imagine every wanting to.*
  >* this is not exactly true, and might come across as gross and elitist, but the tidy paradigm really is substantially better. If you were designing a completely new language to do statistical competing, from scratch, today, the language would probably feel a lot like the tidyverse.
  
  Anyway, any book by Hadley Wickham is gold, and they're all available online for free. But R for Data Science is a good first step into a larger world.
  
  

  The god of speed.
  

  
  I imagine Dirk is not a patient man. He's very active on forums, basically every meaningful response on stackexchange for an Rcpp related question is his (or his collaborator, lesser-god Romain Francois), but sometimes his responses can seem a little... terse?
  
  Now, R is notoriously slow. It's much maligned for this, usually fairly, sometimes not.
  
  Much of the perceived slowness can be mitigated in base R by learning the suite of apply functions which are vectorized. That is they take a multivalued variable (a vector, matrix, or list) and they apply the same function to each element. Its typically much, much faster than using a for-loop. However, you can't always get away from needing a for-loop, and sometimes your loop will need to run thousands (or millions) of times. That's where the Rcpp package which Dirk maintains comes into play.
  
  It is an interface between R and C++, there's not much to say about the package itself. You'll need to learn at least some rudimentary C++ to make use of it, but simply breaking out a computationally intensive for-loop into an Rcpp function can yield a huge improvement in run times. 10x-100x (or more) depending on how well (or poorly) optimized your R and C++ code is. There's some weirdness involved (like you can't call an Rcpp function in a parallel apply function (separate package) unless your Rcpp function is loaded as part of a package, so for maximum benefit you'll need to learn how to write your own packages - praise be to Hadley).
  
  Rcpp includes some semantic "sugar" which allows you to write some things in C++ more like you would in R, but that's yet a third thing to learn.
  
  Also Rcpp, much like the tidyverse is more an ecosystem of interconnected packages than a single package.
  
  

  The god of art.
  

  
  Base R plots are ugly as sin. They just are, no one should use them ever, for any reason.*
  
  >*Exaggeration.
  
  That said, Winston's* ggplot2 is a revelation and a revolution in how graphics are created and presented.
  
  >* Yes, technically ggplot2 is also Hadley's and is part of the tidyverse, but Winston literally wrote the book on it. Okay, okay, Hadley technically created the package and has written books about it, I just find Chang's book more fitting to my needs.
  
  The "gg" in ggplot2 stands for "grammer of graphics", a common structure for describing the components of a visualization in a concise way.
  
  Learning ggplot2 will take you a long way toward being able to make beautiful graphical visualizations.
  
  

  The god of sharing.
  

  
  After you've learned all of the above. You can wrangle your messy data into something tidy and manageable, you can work on it cleanly and power through massive computations, and you can create stunning images from your data, it all means nothing if you're the only one who sees it.
  
  This is where Yihui shines. He is the maintainer for the knitr package, and the author of Dynamic Documents with R and knitr. This will allow you to turn all of your work into PDFs or web pages to share with the world.
  
  It's super easy to get started with, much more complicated to master, but definitely worth it.
  
  To use it effectively, you'll need to learn rmarkdown also by Yihui. You'll also want to start dabbling with LaTeX (if your not proficient already) and to truly bend documents to your whim you'll need to learn to tinker with YAML.
  
  

  Closing remarks.
  

  
  It's a lot to master. Few ever will. Not everyone will agree on everything I've said, but I think the park to true mastery looks something like that.
  
  Best of luck!

Someone over on r/rlanguage posted this link to a list of R help resources. As we don't know quite what level you're at, you may want to look through there to see what's applicable to you.
If you are a total novice, one site I've had recommended (and is also linked on the above blog) is datacamp. Personally I found this useful as a start to learning some of R's commands, but the first chunk of the course left me unable to actually make or run a program as it didn't fill in the basics (eg what a working directory is, how to actually download R). So I used that website in conjunction with the book getting started with R - whilst it is targeted at biologists, the first half is certainly applicable to anyone getting to grips with R.
You'll have to decide yourself whether it's worth spending money on books if you'll only be using them for this one class or whether it would be better trying out some of the free online resources (or seeing if you find free ebook versions!).
As u/fang_xianfu said, a specific question will probably give you more targeted help and advice, so ask away!

Awesome R Shiny Awesome

A curated list of resources for R Shiny. This awesome list was inspired by https://github.com/dpastoor/awesome-shiny.

• Resources
  
  • General
    
  • Community
    
  • Services
    
  • Tutorials
    
• Tools
  
  • Boilerplates
    
  • Libraries
    
  • Integrations
    
• People
  
• Books
  
• Galleries
  
• Examples
  
• Contribution
  
  
  ----------
  
  

  Resources
  

  
  

  General
  

• Official Website
  
  • Blog
    
  • Articles
    
  • Webinars
    
  • 2017 Conference
    
  • Training
    
  • Function References
    
• GitHub
  
  

  Community
  

• Shiny Server Forum
  
• StackOverflow Questions
  
• R-bloggers
  
• Google Group
  
• rOpenSci
  
  

  Services
  

• R-Studio Server
  
• Shiny Server
  
• ShinyApps.io
  
• Digital Ocean
  
• RStudio Connect
  
  

  Tutorials
  

• R-Studio (video)
  
  • Effective Reactive Programming – Part 1 & Part 2
    
  • Coordinated multiple views (linked brushing)
    
  • Building interactive tools for exploratory data analysis (gadgets)
    
  • Improvements in deploying apps
    
  • Modularizing Shiny app code
    
  • Shiny UI
    
  • Debugging Techniques
    
  • Profiling and Performance
    
  • Interfacing DataTables
    
  • Complex application layouts with Grid Style Sheets
    
  • Building Dashboards
    
• R-Studio (YouTube)
  
• R-Studio (Vimeo)
  
• Dean Attali
  
  • Blog Post: Building Shiny apps - an interactive tutorial
    
  • Blog Post: Shiny tips & tricks for improving your apps and solving common problems
    
  • Blog Post: How to get your very own RStudio Server and Shiny Server with DigitalOcean
    
  • Blog Post: Mimicking a Google Form with a shiny app
    
  • Blog post: Persistent data storage (and retrieval) in Shiny apps
    
  • Course: Stat545
    
• Abhinav Agrawal (YouTube)
  
• AHmed HAsan (YouTube)
  
• Zev-Ross
  
• AHmed HAsan (YouTube)
  
• Cheat Sheets
  
  
  
  

  Tools
  

  
  

  Packages
  

• [R-Studio]()
  
  • shinydashboard
    
  • shinythemes
    
  • shinygadgets
    
• shinyAce - integrate ace editor with shiny
  
• shinyBS - twitter bootstrap components for shiny
  
• shinyjs - common javascript operations in shiny via R code
  
• colourpicker
  
• shinyEvents
  
• shinyBS
  
• ShinySky
  
• radiant
  
• shinyTree
  
• shinystan - ShinyStan provides visual and numerical summaries of model parameters and convergence diagnostics for MCMC simulations.
  
• shinyjqui - jQuery UI interactions and effects for shiny
  
• bsplus - Shiny and R Markdown addons to Bootstrap 3
  
  

  Integrations
  

• Tableau
  
• HTML Widgets/Javascript
  
  • DT
    
  • leaflet
    
  • d3heatmap
    
  • dygraphs
    
  • DiagrammeR
    
  • MetricsGraphics
    
  • networkD3
    
  • rthreejs
    
  • rbokeh
    
  • ggvis
    
  • plotly
    
  • formattable
    
    

    People
    

• Dean Attali
  
• John Cheng
  
• Winston Chang
  
• Barbara Borges Ribeiro
  
• Ramnath Vaidyanathan
  
• Jeff Allen
  
• Vincent Nijs
  
• Sebastian Kranz
  
  

  Books
  

• Web Application and Development Using Shiny (2nd edition) - by Chris Beely
  
• Learing Shiny - by Hernán G. Resnizky
  
  

  Galleries
  

• Shiny User Showcase
  
• showmeshiny - a huge gallery of Shiny apps
  
  

  Examples
  

• Waze - Community based real-time traffic and navigation info.
  
• Astra Zenca - Data science tools used to create medicines more efficiently.
  
• shiny-salesman - traveling salesman app
  
• shinyEd - statistics education apps
  
• shinyData - interactive data analysis and visualization
  
• STARTapp -
  
• shiny-phyloseq -
  
• Google Analytics Dashboard - A demo on how to build your own Google Analytics dashboard with R, Shiny and MySQL
  
• BallR - BallR uses the NBA Stats API to visualize every shot taken by a player during an NBA season dating back to 1996.
  
• DDCV - A shiny app to evaluate drug-drug interactions.
  
• Github: Hot or Not - A Shiny App that analyzes what repos are hot on github.
  
• GenMap-Comparator - An application to compare genetic maps with D3 & Shiny.
  
• MAVIS - MAVIS: Meta Analysis via Shiny
  ----------
  
  

  Contributors
  

• Rob Gilmore
  
• S. Hutchins
  
• Devin Pastoor
  
• Dean Attali
  

From my coursework, I found these resources helpful:

http://www-bcf.usc.edu/~gareth/ISL/

https://www.amazon.com/Art-Programming-Statistical-Software-Design/dp/1593273843/ref=sr_1_3?ie=UTF8&qid=1527726606&sr=8-3&keywords=programming+with+R

https://www.amazon.com/Cookbook-Analysis-Statistics-Graphics-Cookbooks/dp/0596809158/ref=sr_1_9?ie=UTF8&qid=1527726606&sr=8-9&keywords=programming+with+R

https://www.amazon.com/Mastering-RStudio-Develop-Communicate-Collaborate-ebook/dp/B0123RVFZG/ref=sr_1_1_sspa?s=books&ie=UTF8&qid=1527726659&sr=1-1-spons&keywords=Rstudio&psc=1