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Start here to learn R

r-exercises.com

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Re: Start here to learn R

#11

Post by R_haterade »

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 leading us to a local optimum.

Re: Start here to learn R

#12

Post by eanzenberg »

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…

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.

Re: Start here to learn R

#14

Post by vobios »

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:…

Without experiencing base R, you won't appreciate the tidyverse packages, which tend to have more of a learning curve.

For example, you can just run boxplot(x) in base R and it will make you a plot. Only after trying to make any modifications to it that you will see the benefit ggplot2.

As you mention yourself, "I almost quit R completely in frustration". I believe that is exactly why you appreciate the other packages you mention.

Re: Start here to learn R

#15

Post by vegabook »

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:…

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 dplyr et al are fantastic, it is really important to understand the "functional" aspects of R which are much better learned via the simple apply families.

Re: Start here to learn R

#16

Post by huac »

badminton1 wrote: post #10

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.

Friends and coworkers have also recommended Datacamp highly, though I've never used it.

Re: Start here to learn R

#17

Post by hatmatrix »

vobios wrote: post #14
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:…

Without experiencing base R, you won't appreciate the tidyverse packages, which tend to have more of a learning curve. For example, you can just run boxplot(x) in base R and it will make you a plot. Only after trying to make any modifications to it that you will see the benefit ggplot2. As you mention yourself, "I almost quit R completely in frustration". I believe that is exactly why you appreciate the other package…

Funny you mention this as an example. I find ggplot2 good for exploratory analysis on a data frame with many categorical variables that can be used for faceting/conditioning or grouping. When trying to make any modifications to its appearance, I usually return to base graphics.

Re: Start here to learn R

#18

Post by hadley »

vegabook wrote: post #15
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:…

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.

Re: Start here to learn R

#19

Post by haddr »

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 would say it is really important to know some basics, such as data.frame, *apply methods, etc. I also admit that as soon as possible I jump to data.table & dplyr when nobody's watching.

Re: Start here to learn R

#20

Post by haddr »

hatmatrix wrote: post #17
vobios wrote: post #14

Earlier quoted context omitted.

Without experiencing base R, you won't appreciate the tidyverse packages, which tend to have more of a learning curve. For example, you can just run boxplot(x) in base R and it will make you a plot. Only after trying to make any modifications to it that you will see the benefit ggplot2. As you mention yourself, "I almost quit R completely in frustration". I believe that is exactly why you appreciate the other package…

Funny you mention this as an example. I find ggplot2 good for exploratory analysis on a data frame with many categorical variables that can be used for faceting/conditioning or grouping. When trying to make any modifications to its appearance, I usually return to base graphics.

Same here. ggplot is nice, but whenever I need to do something more complex i suddenly find myself on stackoverflow. Base graph is super easy and usually get things done, even lattice is somehow more intuitive. ggplot when comes to details, seems awkward to me.
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