Start here to learn R

Post by hadley » Mon, Jul 25, 2016, 12:40 PM UTC

vobios wrote: post #22

hadley wrote: post #18

Earlier quoted context omitted.

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 know ggplot2"... but you need to make a lot of plots to get the hang of ggplot2. The "+" syntax (not sure what the proper name for that is) alone is completely foreign and intimidating. 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?

Post by jhbadger » Mon, Jul 25, 2016, 2:19 PM UTC

RA_Fisher wrote: post #24

R_haterade wrote: post #11

Earlier quoted context omitted.

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…

S (R's ancestor) was developed in the mid-70s and 80s, at least a decade before Python was written. Then couldn't your argument be applied to those working on Python data science tools? Variety is the spice of life. :)

Post by projectramo » Mon, Jul 25, 2016, 3:08 PM UTC

R_haterade wrote: post #11

minimaxir wrote: post #2

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…

Post by vegabook » Mon, Jul 25, 2016, 3:24 PM UTC

hadley wrote: post #33

vegabook wrote: post #28

Earlier quoted context omitted.

I have to take this comment from whom it comes ie: the creator of the library obviously finds it intuitive. But there's definitely a big "brain paradigm shift" with ggplot2 which IMO would be a challenge to impose on the new user. I would argue that even you acknowedge this, since you start your Springer book with your own imperative qplot, and only get into the declarative grammar full-on in Part 2.

That's changed in the second edition of the book, based on the feedback I had from many people who were teaching ggplot2 to first time R users. If you've never used R before, neither base graphics nor ggplot2 is intuitive, so you're better off learning one paradigm and sticking to it.

Post by eanzenberg » Mon, Jul 25, 2016, 4:51 PM UTC

projectramo wrote: post #37

eanzenberg wrote: post #12

Earlier quoted context omitted.

Hadley's packages seem geared more towards data munging and transformations (I regularly use plyr and ggplot from the 'verse) and not machine learning. In my experience R has better implementations of machine learning algorithms than sklearn and rstudio is a better ide than any I found in python.

Not arguing that your statement is right or wrong, but I suggest checking out Spyder for python. I go back and forth between the two, and though I hate the syntactic gear shift, they are quite similar.