Show HN: New IDE and GUI for R language
fasterstat.com
fasterstat.com
Excel add-on, on the other hand, seems very interesting. I lost lots of time in Excel before I decided to get rid of it instead in favour of R+SQLite. I wonder if it would be possible to make the add-on work under wine though.
Edit: just tried to get FasteR add-on under wine. Unfortunately, it requires .NET 4.5 with which I didn't have much success previously...
Edit: actually I managed to click through to find this screenshot: http://fasterstat.com/Images/Desktop/12_R_gui.png which looks novel (can't currently watch the video unfortunately)
FasteR - desctop application
This tools hepls
or as [an] application
http://fasterstat.com/Product produsing -> producing poverfull -> powerful
http://fasterstat.com/ anoher -> another
There is also inconsistent use of 'Addin' and 'Add-In'.
It was hard/impossible for me to read the R code in the video while playing it at 1080p.
Recommendation for the developer (if you're not doing this already): market hard to finance businesses. You'll make a killing.
Personally I would NOT want to have anyone use R with a spreadsheet program.
1) I want manipulated data that is clear to replicate
2) I want my output to be plots and reports not another spreadsheet
Knitr for the win! Seriously great package (and integrated nicely into R Studio), I use it for all my reports, presentations, etc...
- Works with just about any language you can imagine, not just R or the few other hypothetically supported languages
- Allows me to write in a lightweight Markdown-comparable, LaTeX-augmentable syntax, except org-mode supports internal references.
- Lets you do truly obscene things like pass values from bash, to python, to julia, to R
As Emacs begins to move more and more to Guile, I hope that the org-* functionality can be instrumented as embeddable libraries. I understand that Emacs isn't for everyone, and thus I wish org-mode (the format) and org-babel were available to the rest of the world. I was surprised to find how few edge-cases aren't better handled by org-* than knitr / RMarkdown.
R and Excel integration already exists: http://www.r-bloggers.com/a-million-ways-to-connect-r-and-ex...
Not that I think it's a good idea. Also, R is already a thing in Finance, quite a few job postings list it as a useful skill, and Linkedin has R/Finance groups.
Anyhow, it's much easier to manipulate data with scripts than spreadsheets, especially with R packages like quantmod. Looking at giant spreadsheets makes my eyes bleed and my head hurt, plus I think R Studio makes more sense for newbies to R anyway (and R Studio Server is fantastic for businesses).
Also, why would I pay €399 for FasteR while its main competitor is more mature and completely free? Aside from Excel add-in, the two other reasons I've found are in the comments here: "graphical user interface for selected packages/functions, support for multiple monitors". Am I missing something?
399 is for both editions of software for next two years. There are multiple reasons not only two... Mainly the GUI, but this is not in current 0.4.1 version available in the form as I imagine. I only follow the advice: Release something before you are satisfied.
- I think that developing software for “Windows only” is not a mistake. If you make the first version with a small team or in one person, it removes a lot of compatibility issues and problems. If the software will be successful on Windows, it can be extended to other platforms. - "It's hard to compete with R Studio" - yes it is true. Therefore, it is necessary to go a little different path. This will be visible later, after several iterations. It is now difficult to compare the first public version of FasteR with mature R Studio. But competition is always a good think. - "Excel is bad" - According to statistics here is a 750M of Excel users. Every statistician knows about problems of Excel (or all spreadsheet tools). On the other hand, it is standard in a number of areas. If you look around (not only in the community of developers and designers) you will find that with Excel spreadsheets works tremendous amount of companies and institutions, particularly because of its simplicity. For some issues is not sufficient and here is it possible to use for example the integration with FasteR.
It could be almost like a philosophical point: "GNU software is utilitarian. We don't waste time on fancy graphics". Or it could be that GNU developers tend to be... developers. No designers in sight, and no appreciation for visual aesthetics.
True, but since it's Free Software you can release a version of the same code by yourself with a better logo. Nobody prevents you from changing anything, that's what's beautiful with GNU tools.
I urge you to make a Chocolatey package to make updating easy.
There's also a lot of low-hanging fruit in terms of usability. e.g. the objects browser should automatically refresh when you "transfer data". Also I'd love to be able to make "transfer data" use a data.table instead of data.frame.
Killing the R process then hitting refresh on the object browser results in a crash.
I personally use R Studio and see no reason why I'd prefer a spreadsheet, R Studio does let you see a table representation of data anyway, and can very easily import spreadsheets and manipulate them.
Quick typo check: 'univerties' under Licensing
"This tools hepls"
(Yes, I understand imperative languages are faster, but why not just keep the imperative parts for the "inner loops", nicely isolated, and a functional core for everything else?)
The drawcard of R is the breadth of specialised statistical packages. (Often with inner loops written in C for performance.)
BUT R is not slow when it comes to parallel processing or using Revolution Anayltics also has speed ups if you need it. R also has dplyr is speedy and the data.tables is even faster. I think the original Julia speed claims were a little biased to Julia and well there is plenty of awesome things about R, but "slow" isn't a far statement. There is a reason why R has grown so much.
Interesting argument for R usage: https://matloff.wordpress.com/2014/05/21/r-beats-python-r-be...
(This comes up often, and I'm not sure why I'm compelled to reply today, but there it is.)
As for why this can matter: a big part of my job is designing and prototyping new statistical estimators. "Prototyping" means running them through lots of simulations to explore their properties in small samples. My two options in R are:
1. Program up the estimator in pure R, in which case the simulations can take days to weeks to complete.
2. Program up the estimator in C and write an R interface, in which case the simulations will run 10 to 100 times faster. (Depending on the exact operations used.) The code will take much longer to adjust if I need to change the estimator, which happens frequently, and debugging will take longer.
3. Program up the estimator and simulations in pure C, making it still faster but much more brittle.
This is a fairly iterative process -- I'll typically discover errors in the math as I run the simulations or after I've run them, or the simulations will reveal unappealing properties that need to be fixed. But once I have "good" estimator and "good" simulations, I write it up in a paper and am done with it. (Gross simplification of my actual job, but accurate enough.)
So the distinction between "R's speed" and "C's speed" very much matters, and if R were actually as fast as C it would make my life much easier.
Of course, for people who are "coding in R" by running data analysis in a script the distinction doesn't matter. But I already said that in the comment you're replying to.
Julia as a language is faster, but I'm not sure it's actually faster than R to process data if you're using R as most people do...
I don't think this all of the difficulty is intrinsic to FP, but FP language designers don't usually care about newbie-friendliness. The closest thing to a user-friendly FP language I know about is Scala, and it, too, has lots and lots of arbitrary symbols and hideously complicated function signatures.
Remember, the typical scientist has no patience for monads, currying, inscrutable value names or functions-as-arguments and compiler complaints about function signatures are more likely to instill deep hatred than anything else.
I know it's an extreme example, but a statistician will never try to understand what something like "def map[B, That](f: A => B)(implicit bf: CanBuildFrom[Repr, B, That]): That" means, he'll just move on to a language that's easier.
R isn't exactly what I'd call your typical imperative language. It's more like the love child of APL and Scheme, with random bits thrown in by a bunch of statisticians.
Loops are almost never used in R, since pretty much everything is a vector (or some sort of data structure made of vectors), you simply apply functions to vectors and it maps over the whole thing.
Of course, the C, C++ and Fortran bits are 'imperative', which is kind of the structure you describe anyway (functional, vectorized R language which calls down to a bunch of C/Fortran functions).
As do I. It serves as a fine basic computational substrate for the kind of stuff I do, mostly hassle free.
Excel is a poor choice for statistical analysis beyond textbook examples, the simplest descriptive statistics, or for more than a very few columns. The problems I encountered that led to this conclusion are in four general areas:
[1] Missing values are handled inconsistently, and sometimes incorrectly.
[2] Data organization differs according to analysis, forcing you to reorganize your data in many ways if you want to do many different analyses.
[3] Many analyses can only be done on one column at a time, making it inconvenient to do the same analysis on many columns.
[4] Output is poorly organized, sometimes inadequately labeled, and there is no record of how an analysis was accomplished.
'Here’s an example of how the numerical inaccuracies in Excel can get you into trouble.'
http://pages.stern.nyu.edu/~jsimonof/classes/1305/pdf/excelr...
As someone previously mentioned here 'RStudio' is great. I would also add R-Project for Linux distribution.
Excel is a wonderful tool for many things. Statistics is not among them.
P.S. Happened a couple of times that articles posted on 'Journal of Econometrics' was denied (even by college students) for errors due to Excel.
1) Excel your end result can not be replicated
2) Excel use in most shops use macros extensively and it is easy to not have your Excel environment available
3) The graphing capabilities of Excel is the reason why people still use pie charts
4) When doing analysis you want everything scripted? Excel as part of the tool chain is not something scriptable.
5) People usually have bad workflows with Excel and it takes learning R or Padas or Matlab or another statistics language to leave their current mindset
6) I can't think of one added benefit for having Excel as part of a good practice workflow unless you are mandated to produce some Excel spreadsheets which R can also do without touching Excel
I could keep going on....