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My favorite GUI (or one of them) R Commander has a relatively new plugin called KMGGplot2. Until now Deducer was the only GUI with ggplot features , but the much lighter and more popular R Commander has been a long champion in people wanting to pick up R quickly.
RcmdrPlugin.KMggplot2: Rcmdr Plug-In for Kaplan-Meier Plot and Other Plots by Using the ggplot2 Package
As you can see by the screenshot- it makes ggplot even easier for people (like R newbies and experienced folks alike)
This package is an R Commander plug-in for Kaplan-Meier plot and other plots by using the ggplot2 package.
|Depends:||R (≥ 2.15.0), stats, methods, grid, Rcmdr (≥ 1.8-4), ggplot2 (≥ 0.9.1)|
|Imports:||tcltk2 (≥ 1.2-3), RColorBrewer (≥ 1.0-5), scales (≥ 0.2.1), survival (≥ 2.36-14)|
|Author:||Triad sou. and Kengo NAGASHIMA|
|Maintainer:||Triad sou. <triadsou at gmail.com>|
|CRAN checks:||RcmdrPlugin.KMggplot2 results|
---------------------------------------------------------------- NEWS file for the RcmdrPlugin.KMggplot2 package ---------------------------------------------------------------- ---------------------------------------------------------------- Changes in version 0.1-0 (2012-05-18) o Restructuring implementation approach for efficient maintenance. o Added options() for storing package specific options (e.g., font size, font family, ...). o Added a theme: theme_simple(). o Added a theme element: theme_rect2(). o Added a list box for facet_xx() functions in some menus (Thanks to Professor Murtaza Haider). o Kaplan-Meier plot: added confidence intervals. o Box plot: added violin plots. o Bar chart for discrete variables: deleted dynamite plots. o Bar chart for discrete variables: added stacked bar charts. o Scatter plot matrix: added univariate plots at diagonal positions (ggplot2::plotmatrix). o Deleted the dummy data for histograms, which is large in size. ---------------------------------------------------------------- Changes in version 0.0-4 (2011-07-28) o Fixed "scale_y_continuous(formatter = "percent")" to "scale_y_continuous(labels = percent)" for ggplot2 (>= 0.9.0). o Fixed "legend = FALSE" to "show_guide = FALSE" for ggplot2 (>= 0.9.0). o Fixed the DESCRIPTION file for ggplot2 (>= 0.9.0) dependency. ---------------------------------------------------------------- Changes in version 0.0-3 (2011-07-28; FIRST RELEASE VERSION) o Kaplan-Meier plot: Show no. at risk table on outside. o Histogram: Color coding. o Histogram: Density estimation. o Q-Q plot: Create plots based on a maximum likelihood estimate for the parameters of the selected theoretical distribution. o Q-Q plot: Create plots based on a user-specified theoretical distribution. o Box plot / Errorbar plot: Box plot. o Box plot / Errorbar plot: Mean plus/minus S.D. o Box plot / Errorbar plot: Mean plus/minus S.D. (Bar plot). o Box plot / Errorbar plot: 95 percent Confidence interval (t distribution). o Box plot / Errorbar plot: 95 percent Confidence interval (bootstrap). o Scatter plot: Fitting a linear regression. o Scatter plot: Smoothing with LOESS for small datasets or GAM with a cubic regression basis for large data. o Scatter plot matrix: Fitting a linear regression. o Scatter plot matrix: Smoothing with LOESS for small datasets or GAM with a cubic regression basis for large data. o Line chart: Normal line chart. o Line chart: Line char with a step function. o Line chart: Area plot. o Pie chart: Pie chart. o Bar chart for discrete variables: Bar chart for discrete variables. o Contour plot: Color coding. o Contour plot: Heat map. o Distribution plot: Normal distribution. o Distribution plot: t distribution. o Distribution plot: Chi-square distribution. o Distribution plot: F distribution. o Distribution plot: Exponential distribution. o Distribution plot: Uniform distribution. o Distribution plot: Beta distribution. o Distribution plot: Cauchy distribution. o Distribution plot: Logistic distribution. o Distribution plot: Log-normal distribution. o Distribution plot: Gamma distribution. o Distribution plot: Weibull distribution. o Distribution plot: Binomial distribution. o Distribution plot: Poisson distribution. o Distribution plot: Geometric distribution. o Distribution plot: Hypergeometric distribution. o Distribution plot: Negative binomial distribution.
R Commander ( see paper by Prof J Fox at http://www.jstatsoft.org/v14/i09/paper ) is a well known and established graphical user interface to the R analytical environment.
While the original GUI was created for a basic statistics course, the enabling of extensions (or plug-ins http://www.r-project.org/doc/Rnews/Rnews_2007-3.pdf ) has greatly enhanced the possible use and scope of this software. Here we give a list of all known R Commander Plugins and their uses along with brief comments.
- DoE – http://cran.r-project.org/web/packages/RcmdrPlugin.DoE/RcmdrPlugin.DoE.pdf
- epack- http://cran.r-project.org/web/packages/RcmdrPlugin.epack/RcmdrPlugin.epack.pdf
- Export- http://cran.r-project.org/web/packages/RcmdrPlugin.Export/RcmdrPlugin.Export.pdf
- MAc- http://cran.r-project.org/web/packages/RcmdrPlugin.MAc/RcmdrPlugin.MAc.pdf
- qcc- http://cran.r-project.org/web/packages/RcmdrPlugin.qcc/RcmdrPlugin.qcc.pdf and http://cran.r-project.org/web/packages/qcc/qcc.pdf
- Teaching Demos
Note the naming convention for above e plugins is always with a Prefix of “RCmdrPlugin.” followed by the names above
Also on loading a Plugin, it must be already installed locally to be visible in R Commander’s list of load-plugin, and R Commander loads the e-plugin after restarting.Hence it is advisable to load all R Commander plugins in the beginning of the analysis session.
However the notable E Plugins are
1) DoE for Design of Experiments-
Full factorial designs, orthogonal main effects designs, regular and non-regular 2-level fractional
factorial designs, central composite and Box-Behnken designs, latin hypercube samples, and simple D-optimal designs can currently be generated from the GUI. Extensions to cover further latin hypercube designs as well as more advanced D-optimal designs (with blocking) are planned for the future.
2) Survival- This package provides an R Commander plug-in for the survival package, with dialogs for Cox models, parametric survival regression models, estimation of survival curves, and testing for differences in survival curves, along with data-management facilities and a variety of tests, diagnostics and graphs.
3) qcc -GUI for Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts
4) epack- an Rcmdr “plug-in” based on the time series functions. Depends also on packages like , tseries, abind,MASS,xts,forecast. It covers Log-Exceptions garch
and following Models -Arima, garch, HoltWinters
5)Export- The package helps users to graphically export Rcmdr output to LaTeX or HTML code,
via xtable() or Hmisc::latex(). The plug-in was originally intended to facilitate exporting Rcmdr
output to formats other than ASCII text and to provide R novices with an easy-to-use,
easy-to-access reference on exporting R objects to formats suited for printed output. The
package documentation contains several pointers on creating reports, either by using
conventional word processors or LaTeX/LyX.
6) MAc- This is an R-Commander plug-in for the MAc package (Meta-Analysis with
Correlations). This package enables the user to conduct a meta-analysis in a menu-driven,
graphical user interface environment (e.g., SPSS), while having the full statistical capabilities of
R and the MAc package. The MAc package itself contains a variety of useful functions for
conducting a research synthesis with correlational data. One of the unique features of the MAc
package is in its integration of user-friendly functions to complete the majority of statistical steps
involved in a meta-analysis with correlations. It uses recommended procedures as described in
The Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009).
A query to help for ??Rcmdrplugins reveals the following information which can be quite overwhelming given that almost 20 plugins are now available-
Glossary for DoE terminology as used in
RcmdrPlugin.DoE Linear Model Dialog for
RcmdrPlugin.DoE response surface model Dialog
for experimental data
R-Commander plugin package that implements
design of experiments facilities from packages
DoE.base, FrF2 and DoE.wrapper into the
Functions used in menus
Internal RcmdrPlugin.doex objects
Install the DOEX Rcmdr Plug-In
Internal functions for menu system of
Help with EHES sampling
Graphically export objects to LaTeX or HTML
Internal RcmdrPlugin.FactoMineR objects
Graphical User Interface for FactoMineR
An IPSUR Plugin for the R Commander
Meta-Analysis with Correlations (MAc) Rcmdr
Meta-Analysis with Mean Differences (MAd) Rcmdr
RcmdrPlugin.orloca: A GUI for orloca-package
RcmdrPlugin.orloca: A GUI for orloca-package
RcmdrPlugin.orloca.es: Una interfaz grafica
para el paquete orloca
Install the Demos Rcmdr Plug-In
Internal RcmdrPlugin.qual objects
Install the quality Rcmdr Plug-In
Internal RcmdrPlugin.SensoMineR objects
Graphical User Interface for SensoMineR
RcmdrPlugin.SLC: A GUI for slc-package
RcmdrPlugin.SLC: A GUI for SLC R package
Efficiently search R Help pages
RcmdrPlugin.steepness: A GUI for
steepness-package (internal functions)
RcmdrPlugin.steepness: A GUI for steepness R
Internal RcmdrPlugin.survival Objects
Rcmdr Plug-In Package for the survival Package
Install the Demos Rcmdr Plug-In
- New edition of “R Companion to Applied Regression” – by John Fox and Sandy Weisberg (r-bloggers.com)
- Reasons for Transitioning to Vim: Bringing LaTeX, R, Sweave and More under One Roof (r-bloggers.com)
R is bad for you because -
1) It is slower with bigger datasets than SPSS language and SAS language .If you use bigger datasets, then you should either consider more hardware , or try and wait for some of the ODBC connect packages.
2) It needs more time to learn than SAS language .Much more time to learn how to do much more.
3) R programmers are lesser paid than SAS programmers.They prefer it that way.It equates the satisfaction of creating a package in development with a world wide community with the satisfaction of using a package and earning much more money per hour.
4) It forces you to learn the exact details of what you are doing due to its object oriented structure. Thus you either get no answer or get an exact answer. Your customer pays you by the hour not by the correct answers.
5) You can not push a couple of buttons or refer to a list of top ten most commonly used commands to finish the project.
6) It is free. And open for all. It is socialism expressed in code. Some of the packages are built by university professors. It is free.Free is bad. Who pays for the mortgage of the software programmers if all softwares were free ? Who pays for the Friday picnics. Who pays for the Good Night cruises?
7) It is free. Your organization will not commend you for saving them money- they will question why you did not recommend this before. And why did you approve all those packages that expire in 2011.R is fReeeeee. Customers feel good while spending money.The more software budgets you approve the more your salary is. R thReatens all that.
8) It is impossible to install a package you do not need or want. There is no one calling you on the phone to consider one more package or solution. R can make you lonely.
10) R forces you to learn new stuff by the month. You prefer to only earn by the month. Till the day your job got offshored…
Written by a R user in English language
( which fortunately was not copyrighted otherwise we would be paying Britain for each word)
- Install and load R package “Rcmdr” to quickly install lots of other packages (r-bloggers.com)
- A Beginner’s Guide to Integrated Development Environments (mashable.com)
- IPSUR – A Free R Textbook (r-bloggers.com)
- Trrrouble in land of R…and Open Source Suggestions (r-bloggers.com)
- R is Hot: Part 1 (r-bloggers.com)
- The Big Data Explosion and the Demand for the Statistical Tools to Analyze It (readwriteweb.com)
- Teach Yourself How to Use the Ubuntu Command Line (helpdeskgeek.com)
Ajay- The above post was reprinted by personal request. It was written on Jan 2009- and may not be truly valid now. It is meant to be taken in good humor-not so seriously.
It was really nice to see the latest version of R Excel at http://rcom.univie.ac.at/ and bundled together in an aptly named package called R and Friends.
The look and feel of the package as well as ease of installing are really professional. I also liked the commercial equivalent at http://www.statconn.com/
However much older-guardians and die- hards of command line, feel that GUI is like putting lipstick on a pig, but we respectfully demur.
What does R Excel do? Well for one it can put the R Commander Interface INSIDE your Excel Spreadsheet. That makes it easy to use and a familiar interface even if you are newbie to R- (assuming you have done some Excel)
Download the latest version here
This package will automatically install and configure
- R 2.11.1
- rscproxy 1.3-1
- rcom 2.2-1
It will also download and install a suitable version of the statconnDCOM server and of RExcel during installation. Therefore you will need a working Internet connection during the installation process.
This version of RAndFriends was created 20100516.
We also give you information how to download all sources for R and the R packages included in RAndFriends.
Also read a paper on R and SAS interoperability (using HMisc package from Dr Harrell) at Holland Numerics
Some nice updates for R followers-
1) Rather than have an Icon for R – There is a seperate icon for RCmdr in Ubuntu Karmic Koala – Thus the default screen on opening is R Cmdr.
2) REvolution Computing has managed a coup with their bundling of their libraries with the R Distribution in Ubuntu Karmic Koala( see screenshot). We however still are waiting for who gets the credit for that ( Daneese Cooper or the long suffering Mr Smith)
3)Karmic Koala offers 2 GB free space for storing data in the cloud for every user and 50 GB at 10$ a month. This helps with your storage costs. Data is protected thanks to an oauth login id and machine specific tie-in.
4) RCmdr has a great new plugin for DOE (Design off Experiments) students. DOE is a powerful and under utilized technique especially in Web Analytics. This is promising given that Dr John Fox ( whom we interviewed on this website) has going on ahead and seems clearly to have established RCmdr as the introductory GUI for beginners to R.
(see screenshot 2 below)
5) The Karmic Koala is very easy to install and very intuitive to use- Don’t want to give up your Windows ( well just install a dual boot which takes less than 1 hour on a fast internet connection or 15 minutes if you have a DVD)
6) What are other Statistics softwares doing? If they are not too keen on helping Microsoft get more sales ( especially student OS licenses) why don’t they offer the Ubuntu version free for students ( and besides once and for all put to rest the open source credential controversy)
Ajay- Describe your career in science from your high school days to the science books you have written. What do you think can be done to increase interest in science in young people.
John Fox- I’m a sociologist and social statistician, so I don’t have a career in science, as that term is generally understood. I was interested in science as a child, however: I attended a science high school in New York City (Brooklyn Tech), and when I began university in 1964 at New York’s City College, I started in engineering. I moved subsequently through majors in philosophy and psychology, before finishing in sociology — had I not graduated in 1968 I probably would have moved on to something else. I took a statistics course during my last year as an undergraduate and found it fascinating. I enrolled in the sociology graduate program at the University of Michigan, where I specialized in social psychology and demography, and finished with a PhD in 1972 when I was 24 years old. I became interested in computers during my first year in graduate school, where I initially learned to program in Fortran. I also took quite a few courses in statistics and math.
I haven’t written any science books, but I have written and edited a number of books on social statistics, including, most recently, Applied Regression Analysis and Generalized Linear Models, Second Edition (Sage, 2008).
I’m afraid that I don’t know how to interest young people in science. Science seemed intrinsically interesting to me when I was young, and still does.
Ajay- What prompted you to R Commander. How would you describe R Commander as a tool, say for a user of other languages and who want to learn R, but get afraid of the syntax.
John- I originally programmed the R Commander so that I could use R to teach introductory statistics courses to sociology undergraduates. I previously taught this course with Minitab or SPSS, which were programs that I never used for my own work. I waited for someone to come up with a simple, portable, easily installed point-and-click interface to R, but nothing appeared on the horizon, and so I decided to give it a try myself.
I suppose that the R Commander can ease users into writing commands, inasmuch as the commands are displayed, but I suspect that most users don’t look at them. I think that serious prospective users of R should be encouraged to use the command-line interface along with a script editor of some sort. I wouldn’t exaggerate the difficulty of learning R: I came to R — actually S then — after having programmed in perhaps a dozen other languages, most recently at that point Lisp, and found the S language particularly easy to pick up.
Ajay- I particularly like the R Cmdr plugins. Is it possible for anyone to increase R Commander with a customized package- plugin.
John- That’s the basic idea, though the plug-in author has to be able to program in R and must learn a little Tcl/Tk.
Ajay- Have you thought of using the R Commander GUI on an Amazon EC2 and thus making R high performance computing say available on demand ( similar to Zementis model deployment using Amazon Ec2). What are you views on the future of statistical computing
John- I’m not sure whether or how an interface like the Rcmdr, which is Tcl/Tk-based, can be adapted to cloud computing. I also don’t feel qualified to predict the future of statistical computing.
I think that R is where the action is for the near future.
Ajay-What are the best ways for using R Commander as a teaching tool ( I noticed the help is a bit outdated).
John- Is the help a bit outdated? My intention is that the R Commander should be largely self-explanatory. Most people know how to use point-and-click interfaces. In the basic courses for which it is principally designed, my goals are to teach the essential ideas of statistical reasoning and some skills in data analysis. In this kind of course, statistical software should facilitate the basic goals of the course.
As I said, for serious data analysis, I believe that it’s a good idea to encourage use of the command-line interface.
Ajay- What are your views on R being recognized by SAS Institute for it’s IML product. Do you think there can be a middle way for open source and proprietary software to exist.
John- I imagine that R is a challenge for producers of proprietary software like SAS, partly because R development moves more quickly, but also because R is giving away something that SAS and other vendors of proprietary statistical software are selling. For example, I once used SAS quite a bit but don’t anymore. I also have the sense that for some time SAS has directed its energies more toward business uses of its software than toward purely statistical applications.
Ajay- Do people in R Core team recognize the importance of GUI? What does the rest of R community feel? What has the feedback of users ben to you. Any plans to corporate sponsors for R Commander ( Rattle , an R language data mining GUI has a version called Rstat at http://www.informationbuilders.com/products/webfocus/predictivemodeling.html while the free version and code is at rattle.togaware.com)
John- I feel that the R Commander GUI has been generally positively received, both by members of R Core who have said something about it to me and by others in the R community. Of course, a nice feature of the R package system is that people can simply ignore packages in which they have no interest. I noticed recently that a Journal of Statistical Software paper that I wrote several years ago on the Rcmdr package has been downloaded nearly 35,000 times.
Because I wouldn’t expect many students using the Rcmdr package in a course to read that paper, I expect that the package is being used fairly widely.
Ajay- What does John Fox do for fun or as a hobby?
John- I’m tempted to say that much of my work is fun — particularly doing research, writing programs, and writing papers and books. I used to be quite a serious photographer, but I haven’t done that in years, and the technology of photography has changed a great deal. I run and swim for exercise, but that’s not really fun. I like to read and to travel, but who doesn’t?
Prof John Fox is a giant in his chosen fields and has edited/authored 13 books and written chapters for 12 more books. He has also written and been published in almost 49 Journal articles. He is also editor in chief for R News newsletter. You can read more about Dr Fox at http://socserv.mcmaster.ca/jfox/
On R Cmdr-
R Cmdr has substantially decreased the hygiene factor for people wanting to learn R- they begin with the GUI and then later transition to customization using command line. It is so simple in its design that even under graduates have started basic data analysis with R Cmdr after just a class.You can read more on it here at http://socserv.mcmaster.ca/jfox/Misc/Rcmdr/Getting-Started-with-the-Rcmdr.pdf