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One Year with R

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Re: One Year with R

#171

Post by Mikeb85 »

rvanlaar wrote: post #142
clove wrote: post #52

Earlier quoted context omitted.

I've used R for 19 years and do not have any other programming language ability. I am curious: What is frustrating to you about R relative to other languages?

I'm not OP. To me, it's the tooling around it. Everything is done in R-studio and it's focus is to generate statistical documents. The result is a sub optimum solution. It lacks good tooling around installing and running R programs. R programs don't import, they include. It doesn't make it more readable. R-studio is very Emacs like in the sense that it just lacks a decent editor. Due to R-studio being the default, th…

> Due to R-studio being the default, there's not much support for other editors.

But RStudio is amazing... Easily best environment I've used for any programming language (well, except Pharo).

Re: One Year with R

#172

Post by mbreese »

hadley wrote: post #108
epistasis wrote: post #73

Earlier quoted context omitted.

Thanks for taking my aggressive comment with such spirit, it really speaks to a good community. (Sleep training an infant has me a bit frazzled) I should have been more specific, the ... frustration for me comes up mostly in ggplot, Which usually directs you to layer(). Which gets parameter string documentation like: * geom - The geometric object to use display the data * stat - The statistical transformation to use…

Ah yeah, connecting the dots in ggplot2 docs is hard. It's hard for us to document because, under the hood, the pieces quite decoupled and different pieces are responsible for different arguments. But since we last took a deep dive on the ggplot2 docs, we've gotten much better at generating docs with code, so maybe it's time to have another look. I've filed an issue ( https://github.com/tidyverse/ggplot2/issues/4770…

Is there a tutorial someplace that explains how ggplot actually manages plotting? Or the architecture and layers between the high level code and how a plot is drawn? Meaning, I love being able to express what I want and ggplot figures out a good plot for me. But I know there are many layers that can be manipulated, but I just don’t understand the layers.

One of the best compliments I can think of is that with ggplot, easy things are easy and hard things are possible. But I haven’t been able to figure out how to fully work the system.

(Thanks for all of the work!)

Re: One Year with R

#173

Post by JZL003 »

I've grown to really rely on R pipes, it's just how I like thinking about problems with small short lambdas. I wish python had better support (and better lambdas, without typing out 'lambda' and with tuple arguments) but for when I need to use it, toolz's pipe is pretty ok as long as you use lazy map/filter/etc

It's just so close to being as good, but not quite

Re: One Year with R

#174

Post by jghn »

canjobear wrote: post #138
delusional wrote: post #117

Earlier quoted context omitted.

> R is a highly optimized, well-oiled machine if you're using it for its highly-optimized, well-oiled purposes. This hits home for me. We are just starting to use R for risk modeling where I work. R, more than any language I've ever used, makes me appreciate "worse is better". From a theoretical "aesthetic" perspective R is a mess. Yet for data processing all those theoretical concerns don't matter. It just works. It…

R "just works" now because a huge amount of effort has gone into improving the language over the last 10 or so years, in part spurred by the tidyverse movement, although not restricted in scope to tidyverse. When I was starting grad school around 2010, if someone sent you some R code, the chances that you would be able to "just run" it were basically zero: there would be weird version mismatches in how functions work…

I suspect that has as much to do with the maturation of the data science community as it does the language environment. There have been pockets of R users who put much effort into reproducibility before the era you cite, such as Bioconductor.

When I think back to the era you're describing what I recall was people winging around hacky scripts being the norm regardless of their environment. While still not something I'd think of as software engineering best practices, what I see now is less Wild West.

Re: One Year with R

#175

Post by lottin »

rvanlaar wrote: post #142
clove wrote: post #52

Earlier quoted context omitted.

I've used R for 19 years and do not have any other programming language ability. I am curious: What is frustrating to you about R relative to other languages?

I'm not OP. To me, it's the tooling around it. Everything is done in R-studio and it's focus is to generate statistical documents. The result is a sub optimum solution. It lacks good tooling around installing and running R programs. R programs don't import, they include. It doesn't make it more readable. R-studio is very Emacs like in the sense that it just lacks a decent editor. Due to R-studio being the default, th…

Emacs has had excellent R support since much before R-studio came around. In fact to me R-studio has always felt like a stand-alone implementation of ESS (minus Emacs).

Re: One Year with R

#176

Post by bscphil »

magnio wrote: post #60
epistasis wrote: post #53

Earlier quoted context omitted.

The tidyverse docs are the only ones with the super frustrating ... of impenetrable gnostic "documentation" that I know of. In general the tidyverse documentation is horrible, almost as bad as typical Python docs, IMHO. Other parts of base R are wonderfully documented in my opinion.

> almost as bad as typical Python docs I found numpy, scipy, pandas, and plotly docs to be quite clear and extensive. The only docs I have found to be confusing are matplotlib's and the Python standard library's. Not sure what packages you are referring to?

On the other hand, I'm curious what you've found to be lacking about the standard library documentation. I've found it to be generally very thorough, in some cases fantastic, though there's an occasional weak point.

Re: One Year with R

#177

Post by claytonjy »

sntscy wrote: post #144
CapmCrackaWaka wrote: post #78

Earlier quoted context omitted.

I also use R for any heavy data manipulation, but I primarily use the data.table package. The efficiency that both of these packages unlock is absolutely unparalleled in any other tabular data manipulation library, in any other language that I have used. And R has the top 2!! My skin writhes every time I need to type: table.loc[(table.column > 2) | (table.column2 when I want to subset a table.

Can't get around resetting the index, as far as I know, but for the filtering you can also do, `table.query("column > 2 and column2 < 3")`

do any IDEs help you autocomplete the string argument? that's a bit part of the non-standard evaluation magic in the tidyverse these days; you can use bare, _unquoted_ names, _and_ get excellent autocomplete, at least in RStudio.

Re: One Year with R

#178

Post by svnt »

clove wrote: post #52
crispyambulance wrote: post #28

I was expecting a rant, but the OP's article is actually very thoughtful. He definitely knows what he's talking about. The thing about R, for me and many others, is that it's very much an everyday grind language. Especially with Rstudio, its natural domain is as one of "notebook" languages like python, julia, matlab, and mathematica but with a more clear focus towards the tasks of data-analysis. I just tell the BI-to…

I've used R for 19 years and do not have any other programming language ability. I am curious: What is frustrating to you about R relative to other languages?

In decades I have not encountered any development environment where the obscurity of error message presentation can touch R. If one uses other languages or build environments the error messages can frequently be used to diagnose the issue.

In R they default to just vomiting some internal exception often with no context, and I can count the times I have encountered helpful or even seemingly deliberately constructed error messaging in single-place base five. Even kernel development is in some sense better because at least there you are in context and the layers are traversable.

Seemingly one of the primary skills of an R programmer is serving as an informal database of “what the fuck does this mean?” when the issue is something trivially detectable like passing a list vs a vector.

Re: One Year with R

#179

Post by crispyambulance »

clove wrote: post #52
crispyambulance wrote: post #28

I was expecting a rant, but the OP's article is actually very thoughtful. He definitely knows what he's talking about. The thing about R, for me and many others, is that it's very much an everyday grind language. Especially with Rstudio, its natural domain is as one of "notebook" languages like python, julia, matlab, and mathematica but with a more clear focus towards the tasks of data-analysis. I just tell the BI-to…

I've used R for 19 years and do not have any other programming language ability. I am curious: What is frustrating to you about R relative to other languages?

FWIW, I do prefer R to all other "notebook" languages. It has everything I need for answering questions about data, working with files, text, and visualization. Libraries that do everything I could imagine are easily available.

For me it's a pragmatic workhorse tool that I use and aside from frequently getting frustrated with the task at hand, it has never failed me in the end.

I think R is much like a handheld power tool. I have no interest in diving deep into the workings of the tool because when I need to use a drill, for instance, I just need to drill holes and anything else is an annoying distraction (I realize that sounds bad!).

I've also worked with Mathematica and JMP in the past. They're very capable, but not as good at general-purpose data-wrangling as R is today (given Rstudio, knitr, shiny, all the specialized libraries, and most especially the tidyverse).

Re: One Year with R

#180

Post by claytonjy »

discordance wrote: post #132
alexilliamson wrote: post #95

Earlier quoted context omitted.

Not to mention the auto complete that comes with RStudio. Is there any way to get equivalent functionality in Jupyter?

If you set up use the Jupyter extension[0] and open your notebooks in VS Code you get Intellisense (code completion, method info and hints etc). 0: https://marketplace.visualstudio.com/items?itemName=ms-tools...

IME this is strictly worse than the RStudio experience; most of the time I hit tab in a VSCode notebook I get way too many options that IMO are clearly not what I want, though at some level it's more pandas' fault (too many methods & attributes even before attaching every column name as an attribute) than VSCode or Intellisense.
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