Opinion/Tutorial: The best way to learn R
If you’re a college student your university may pay for your access to Lynda.com (mine did, many students from other schools that I have talked to say their’s did). You can also get access to all these courses on LinkedIn Learning if you have LinkedIn Premium. Wether you have LinkedIn Learning or Lynda.com, you can publish the certificates of these courses onto your LinkedIn. Once you have access, I would watch the following courses in the following order. As I have mentioned in another blog post, you can publish
R Statistics Essential Training with Barton Poulson (5hr 59min)
- This is a great foundation to R, and you can quickly see how easy it is.
Data Wrangling in R with Mike Chapple (4hr 12min)
- Data Wrangling is a big part of being a data scientist, spending a few hours to learn this will save you many more in the future.
Data Visualization in R with ggplot2 with Mike Chapple (2hr 26min)
- If you want to look like a pro with R, learning ggplot is essential.
R for Data Science: Lunchbreak lessons with Mark Niemann-Ross (4hr)
- This really emphasizes the fact that if you are using R, you don’t have to be writing a lot of code. Chances are there is already a function out there that does what you want. This course explores a lot of the common functions.
Cleaning Bad Data in R with Mike Chapple (1hr 54min)
- This has a bit of overlap with the data wrangling course, but it is still hepful.
There are other courses on there in R, I’d recommend avoiding the ones taught my Martin John Hadley. After completeing these you should move onto the videos on RStudio’s website and move through them as you see fit.