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I am recently starting R programming and I find it very interesting.
I found the package rattle for R which can handle statistical models without coding.
Also rapidminer can be integrated with R.
I am not advance coder. Is it possible to use tools like rattle and rapidminer with R to perform all tasks which SPSS can do?
Cheers.
I recently started using R and found the Rattle GUI interface a good gateway to coding in R. You can do much of what you want to do, load data, visualise, clean, run models etc.
The best part is Rattle saves all the underlying R code you are are utilising in the 'Log' tab of the GUI. That way you can learn basic commands you need to learn and save the log as a starting point for building your own R scripts.
To install:
install.packages("rattle", repos="http://rattle.togaware.com", type="source")
Edit: Once installed, to run the GUI:
library("rattle")
rattle()
The website for Rattle.
For other options have a look here.
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I want to make my R Shiny App multilingual, but I don't know, where to start from. Is there any libraries or Shiny widgets for switching the language in the Shiny App?
More generally, what are the best practices for internationalization in R? Is there any tutorials? I know, there is a gettext command, which comes from GNU gettext, but I'm not very handy with it. Should I read GNU gettext manual first (and what chapters will be enough for beginner)?
I was looking to do the same thing and found this. The creator explains how it works and there is a github repository if you want to use his code in your app.
It works great for me as it's similar to the Zend Framework implementation which I had already used in the past.
The projects I have found attempting to solve this issue:
https://github.com/Appsilon/shiny.i18n (28 stars, last activity 4 days ago, preparing for CRAN package publication)
https://github.com/chrislad/multilingualShinyApp (8 stars, last activity 4 years ago)
It seems shiny.i18n is the best bet at the moment. I added an issue requesting support for gettext https://github.com/Appsilon/shiny.i18n/issues/15 as per Translation of R script using gettext
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I wrote a simple R program and want to share it with my office. Most people in my office are not familiar with R and I imagine it could be kind of confusing for them to install R and all the required packages.
Is there an easy way I can share an R script that can take user inputs & share output that doesn't require R installation on the user's end? I'm thinking something like Shiny, but for desktop use.
Your users can run R in an R jupyter notebook that you have set up in advance on some server. Then it's not a "desktop app" per se but it's going to look like it is running in their local web browser like Firefox or Chrome etc. Sharing R code you wrote will work excellently this way.
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I am currently have a R query that can do the parallel processing within a loop using foreach. But it is done using a single server with 32 cores. Because of my data size, I am trying to find the r packages that can distribute the computing to different window servers and can work with foreach for paralleling.
Really appreciate for your help!
For several releases now, R has been shipping with a base library parallel. You could do much worse than starting to read its rather excellent (and still short) pdf vignette.
In a nutshell, you can just do something like
mclapply(1:nCores, someFunction())
and the function someFunction() will be run in parallel over nCores. A default value of half your physical cores may be a good start.
The Task View on High-Performance Computing has many more pointers.
SparkR is the answer. From "Announcing SparkR: R on Apache Spark":
SparkR, an R package initially developed at the AMPLab, provides an R frontend to Apache Spark and using Spark’s distributed computation engine allows us to run large scale data analysis from the R shell.
Also see SparkR (R on Spark).
To get started you need to set up a Spark cluster. This web page should help. The Spark documentation, without using Mesos or YARN as your cluster manager, is here. Once you have Spark set up, see Wendy Yu's tutorial on SparkR. She also shows how to integrate H20 with Spark which is referred to as 'Sparkling Water'.
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I am planning to run Monte Carlo simulations in an R environment (Windows 7). However I need to use old legacy statistical packages that are no longer executable in Windows 7, although I am aware that there are emulation solutions (Like VMWare) available. In addition I need to integrate these packages in a seamless workflow so that simulated data from R functions is pushed on the old package, processed, and pulled back for further analysis in R again.
I am aware that there are open source workflow tools (such as KNIME) that can integrate different software packages, but my internet searches tend to be swamped with references to WorkFlow Management software business applications that are irrelevant to me.
Is KNIME Analytics a suitable solution given my legacy software problem, and if not what workflow tool would you suggest?
Kind regards,
Giulio Flore
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First of all, I'm working with RStudio 0.98.1056.
I am writing a loop that produces several dataframes and I want to write each of them to a separate worksheet within an Excel workbook. (XLS and XLSX are both fine.) I know that there are several packages that do this, but they all seem to depend on other languages being present on the system -- the xlsx package depends on Java, as does the XLConnect package, and WriteXLS depends on Perl. I would like to avoid this because I want my code to be usable in contexts where installing an R package is trivial but installing support for another programming language is not.
So I have two questions:
1) Is there a package that duplicates this functionality using R only?
2) Are there serious obstacles to creating one?
The openxlsx package can write to separate sheets of an Excel file. Its only dependency is the Rcpp package. That's probably the most lightweight dependency of the contributed packages that can easily read and write Excel files.
The catch is that Rcpp requires a C++ compiler, but you can easily install that via Rtools for Windows or Xcode for Mac. Rcpp is a dependency for many contributed packages, so you might find that you already have it.