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A paid alternative is also datacamp.com Has videos and interactive exercises you can do directly in the site. And btw, I am not affiliated with them, just a user.
Earlier quoted context omitted.
I could not disagree more. Base graphics are absolutely intuitive for users coming from an imperative style, and base graphics are extremely fast, which is seriously useful a lot of the time. This is not to say I dislike ggplot, I love it, only that it is not the starting point for learning R because its syntax really is not idiomatic R. You'd be imposing two orthogonal learning curves on the new user. Second, while…
I disagree. The inconsistencies across the apply family makes them hard to learn, and the absence of an apply function for data frames is particularly frustrating. I obviously also disagree on what is idiomatic R. If you know ggplot2, there are a relatively few advantages to learning base graphics, if you're mostly interested in graphics for data analysis.
If you want to make great graphs in R, you will need to learn ggplot2. If you just want to learn R, why not keep it simple at first?
A paid alternative is also datacamp.com Has videos and interactive exercises you can do directly in the site. And btw, I am not affiliated with them, just a user.
It's mostly Python (a little R), but just wondering what others think.
All the tutorials involve using base R packages. While OK for a tutorial, this is not reflective for real-world data analysis both in performance and usability, a lesson I have learned the hard way. (After using it for three semesters in college, I almost quit R completely in frustration) I recommend going straight to the Hadleyverse packages for the common use cases, and read the vignettes for the common use cases:…
While I appreciate the hard work Hadley has put into this ecosystem, and I detest the language wars, I can't help but feel that the world would be a better place if Hadley had put his effort into python DS modules instead. Hadley's work puts R almost at parity with python (at best!) for munging, and for academics, this long-term trend of domain languages like R or SAS becomes counterproductive. It's as if he's leadin…
All the tutorials involve using base R packages. While OK for a tutorial, this is not reflective for real-world data analysis both in performance and usability, a lesson I have learned the hard way. (After using it for three semesters in college, I almost quit R completely in frustration) I recommend going straight to the Hadleyverse packages for the common use cases, and read the vignettes for the common use cases:…
https://www.rstudio.com/online-learning/#R
For the Hadleyverse or Tiddyverse this is a starter book based on dplyr and other tools.
Hands-On Programming with R http://shop.oreilly.com/product/0636920028574.do#
Earlier quoted context omitted.
I could not disagree more. Base graphics are absolutely intuitive for users coming from an imperative style, and base graphics are extremely fast, which is seriously useful a lot of the time. This is not to say I dislike ggplot, I love it, only that it is not the starting point for learning R because its syntax really is not idiomatic R. You'd be imposing two orthogonal learning curves on the new user. Second, while…
I disagree. The inconsistencies across the apply family makes them hard to learn, and the absence of an apply function for data frames is particularly frustrating. I obviously also disagree on what is idiomatic R. If you know ggplot2, there are a relatively few advantages to learning base graphics, if you're mostly interested in graphics for data analysis.
Earlier quoted context omitted.
I could not disagree more. Base graphics are absolutely intuitive for users coming from an imperative style, and base graphics are extremely fast, which is seriously useful a lot of the time. This is not to say I dislike ggplot, I love it, only that it is not the starting point for learning R because its syntax really is not idiomatic R. You'd be imposing two orthogonal learning curves on the new user. Second, while…
I disagree. The inconsistencies across the apply family makes them hard to learn, and the absence of an apply function for data frames is particularly frustrating. I obviously also disagree on what is idiomatic R. If you know ggplot2, there are a relatively few advantages to learning base graphics, if you're mostly interested in graphics for data analysis.
All the tutorials involve using base R packages. While OK for a tutorial, this is not reflective for real-world data analysis both in performance and usability, a lesson I have learned the hard way. (After using it for three semesters in college, I almost quit R completely in frustration) I recommend going straight to the Hadleyverse packages for the common use cases, and read the vignettes for the common use cases:…
The Hadleyverse is the best thing that ever happened to R: sane, intuitive tools that do what you expect with common-sense usage and examples. And don't forget stringr!
https://www.udemy.com/data-science-and-machine-learning-boot...