similar to: Data cleaning & Data preparation, what do R users want?

Hi again, Typo in the last email. Should read "about 40 standard deviations". Jim On Thu, Nov 30, 2017 at 10:54 AM, Jim Lemon <drjimlemon at gmail.com> wrote: > Hi Robert, > People want different levels of automation in the software they use. > What concerns many of us is the desire for the function >

I don't think my view is of interest to many, so offlist. I reject this: " I would consider data analysis work to be three stages: data preparation, statistical analysis, and producing the report." For example, there is no such thing as "outliers" -- data to be removed as part of cleaning/preparation -- without a statistical model to be an "outlier" **from**,

Hi Robert, People want different levels of automation in the software they use. What concerns many of us is the desire for the function "figure-out-what-this-data-is-import-it-and-get-rid-of-bad-values". Such users typically want something that justifies its use by being written by someone who seems to know what they're doing and lots of other people use it. One advantage of many R

Great question. What do I want? I want my co-workers to stop using Excel spreadsheets for data entry, storage, and sharing! I want them to understand the value of data discipline. But alas . . . . I work in a county health department in the US. Between dplyr, stringr, grep, grepl, and the base R read() functions, I'm doing OK. I need to learn more about APIs, so I can see if I can make R

I would agree that getting data into R from various sources is the biggest pain point. Even if there is an api, the results are not always consistent and you have to do lots of dimension checking to get it right. Or there isn't an open api at all and you have to hack it by web scraping or otherwise- http://enpiar.com/2017/08/11/one-hour-package/ On Thu, Nov 30, 2017 at 1:00 AM, Jim Lemon

Dominik (and others) If it is indeed still the biggest paint point, even in 2017, then maybe we can do something about that, with more efforts at different user interface design and try-outs with them on specialized datasets. [ The fact that in some specialties, such as clinical trials, for example, getting access to public domain datasets (and not having to use a tiny "toy" dataset,

How difficult is it to get a good feel for the internals of R, if you want to learn the general code base, but also the CPU intensive stuff ( much of it in C or Fortran?) and the ways in which the general code and the CPU intensive stuff is connected together? R has a very large audience, but my understanding is that only a small group have a good understanding of the internals (and some of those

Will R have more glitches on one operating system as opposed to another, or is it pretty much the same? robert

As noted on the R-project web site itself ( www.r-project.org -> Manuals -> R Data Import/Export ), it can be cumbersome to prepare messy and dirty data for analysis with the R tool itself. I've also seen at least one S programming book (one of the yellow Springer ones) that says, more briefly, the same thing. The R Data Import/Export page recommends examples using SAS, Perl, Python, and

1) What is easy for one person may be very hard for another, so your question is really unanswerable. You do need to know C and Fortran to get through the source code. Get started soon reading the R Internals document if it sounds interesting to you... you are bound to learn something even if you don't stick with it. If you have questions about the internals though, you should read the Posting

what does R have to compare with , say , proc surveymeans, estimate survey means/proportions with standard errors, using Taylor methods? [[alternative HTML version deleted]]

Dear community, In preparation for the BoF on Parallel IR at the US developers meeting we would like to collect feedback from the whole community. The concerns, ideas, etc. will be summarized in the BoF and should provide a good starting point for a discussion. We know that over the years the topic of a parallel extension for LLVM was discussed on the mailing list [0, 1, 2], workshops [3, 4] or

Hello all, Before getting to my question, I would like to apologize for asking this question here. My question is not directly an R question, however, I still find the topic relevant to R community of users - especially due to only * partial* (current) support for interactive data visualization (see here: http://cran.r-project.org/web/views/Graphics.html were with iplots we are waiting for

Hi Everyone, I'm writing to announce my new R beginner's guide book and answer questions related to it. The primary focus of Statistical Analysis with R is helping new users become accustomed to R and empowering them to apply R to suit their own needs. It is a beginner's guide written for a broad audience and should be well received by businesspeople, IT professionals, researchers,

dear all, could somebody tell me how I can plot mild outliers as a circle(?) and extreme outliers as an asterisk(*) in a box-whisker plot? Thanks very much in advance -- View this message in context: http://www.nabble.com/mild-and-extreme-outliers-in-boxplot-tp25040545p25040545.html Sent from the R help mailing list archive at Nabble.com.

Greetings LLVM Devs! I am a PhD student in the Secure Systems and Software Lab at UC Irvine. We have been working on adding randomness into code generation to create a diverse population of binaries. This diversity prevents code-reuse attacks such as return-oriented-programming (ROP) by denying the attacker information about the exact code layout. ROP has been used is several high-profile recent

Dear Friends, I'm contributing to a paper on a new R package for a clinical (medicine, ophthalmology) audience, and part of the mission is to encourage people who might be occasional users of Excel or SPSS, to become more familiar with R. I'd really appreciate any pointers to more recent papers that describe R, it's growth (statistics on user base, number of packages, volume of help

In clinical trial data preparation and many other data situations, the statistical programmer needs to merge and re-merge multiple input files countless times. A syntax for merging files that is clear and concise is very important for the statistical programmer's productivity. Here is how Vilno does it: inlist dataset1 dataset2 dataset3 ; joinby variable1 variable2 where ( var3<=var4 ) ;

Ashlee Vance's article on R in the New York Times. This is typical of the New York Times. Because they get to coast on the prestige and reputation of their brand , they have a history of just this sort of journalistic sloppiness. Whether it's the author or the editor at fault doesn't really matter, they do this screw-up all the time. Look, if you write an article on the first page of

That may sound like a stupid question, but if it confuses me, I'm sure it confuses others as well. I've tried to find that information on the R mail-group info pages, can't seem to find it. Is it something obvious? To begin a brand new discussion, you do your post as an e-mail sent to r-help at stat.math.ethz.ch . As I am doing right now. How do I do an additional post that gets