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There are lots of general R cheat sheets, but is there a cheat sheet for ggplot2?
Here are several options:
A very polished ggplot2 cheat sheet from RStudio (Dec 2015). This version is updated for ggplot2 2.0.
Another ggplot cheat sheet.
A ggplot2 tutorial (October 2014).
A 14-page quick reference/tutorial for ggplot2 published in September 2011:
ggplot2 tutorial by Ramon Saccilotto.
A detailed tutorial on a wide range of plot types and customization options, published in 2012.
Winston Chang's Cookbook for R website has a section with ggplot2 recipes. Also see his R Graphics Cookbook from O'Reilly.
A website with brief tutorials on the major aspects of ggplot2.
The ggplot2 transition guide from February 2012 is longer, but systematically goes through all the features that were changed or added in going from the original version of ggplot2 to the (at the time) latest version.
If you're interested in drawing maps with ggplot2, there's Hadley's article on spatial visualization with ggplot2. Also see this detailed tutorial on maps and spatial analysis with R at rPubs (2014).
Detailed examples on scatterplots, visualizing distributions (density and box plots), and barplots with ggplot2 (late 2013 - early 2014).
A more detailed task based ggplot2 cheat sheet, or rather a quick reference.
Not exactly a cheat sheet, but also helpful in figuring out the right command, is Jeroen Ooms' web interface to ggplot2
To me ggplot2 has one of the best and compressed documentation of any R package.
I think it would be really difficult pack everything to an even smaller document.
This holds in particular because some of the magic of ggplot2 is closely related to functions from plyr or reshape. Though I have suggested via feedback form that some .pdf reference card would be really nice. Besides I recommend the ggplot2 book.
This might be what you are looking for: http://had.co.nz/stat480/r/graphics.html
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I have a quick question on the conversion from R to Latex. I did a lot of calculations in R to produce various tables. These are not standard, but rather custom objects. Next I'd like to convert the results to my Latex document which is generally created by Texstudio.
Some of the R-packages I have checked here: Tools for making latex tables in R
allow for standard objects to be converted into Latex tables.
I think the question is interesting to a lot of people: What is the usual package you would use for this purpose? There are a few packages out there which could be used, but I am asking for experience and recommendations in the described context. Some of my tables are quite big, others rather small. Also someone recommended me Knitr/Sweave, but it doesn't seem to produce tables in Latex style that I can copy into my Latex document; instead it produces directly pdf-tables. Am I missing something here? But that was only a sidenote, I am just looking for the best method to accomplish conversion from non-standard R-tables into Latex.
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I have a map of France with the contour of French departments in an SVG file. I would like to import the data in R to draw a choropleth map. Is it possible ? What would be the best solution ?
There's SVGMapping::loadSVG (at CRAN) -- I haven't tried it so can't speak to its quality.
Edit: might as well mention the sos package here, as it's really an indispensable tool for finding R packages and functions for the user.
I guess it is possible. SVG is nothing more than a XML document. But why don't you use maps library?
library(maps)
map('france')
You can check:
> map('france')$names
[1] "Nord"
[2] "Pas-de-Calais"
[3] "Somme"
...
for departments names, and paint individual departments like this:
colors[c(112, 114)] <- 'blue'
map('france', fill=T, col=colors)
See maps documentation for more.
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I am looking for a free software for mathematical modeling.
Here is a list of things I might be willing to achieve with this software: Integrating functions, solving differential equations, graph theory, analyzing infinite series, local stability analysis, Taylor series, get eigenvectors, compute the long term behaviour of a system of equations, etc...
Here is a related SE post. I am surprised that nobody is suggesting R. I am currently a R user and already use R for graph theory. Therefore I would appreciate to use R also to make other mathematical modeling. Is R less efficient that Sage, SimPy, Mathematica and others for mathematical modeling? Why? Do you know a manual providing exaplanation for how to make mathematical modeling with R?
Thank you
Sounds like R is your first way to go. It does not make to good sense to compare R with any other tool in such a braod way you are asking for. R packages differ largely in efficiency, some are in fact C tools while others are written in the R language. As a start R can hardly be any wrong and is free.
Matlab might be a stable alternative, Julia is rising but still pre alpha.
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I just searched on google and r-project.org but couldn't find anything.
Is there a package in R for statistical relational learning?
Link to wikipedia:
http://en.wikipedia.org/wiki/Statistical_relational_learning
Yes, there are quite a few actually.
On CRAN, look at the gRaphical Models Task View.
Under each header is a listing of R Packages subsumed under that subject header--the first being Representation, manipulation and display of graphs.
Scroll down this page to the last three section headers:
Miscellaneous: Model search, specialized types of models etc.,
Bayesian Networks/Probabilistic expert systems; and
BUGS models, just below it.
Under these three headers are a total of 16 R Packages (seven, three and six packages, respectively). Within these nine, you should have no trouble finding a couple most suited for your project.
The only one I have personally used is bnlearn, a Package for Bayesian network structure learning. This is not my field, so I recall that the Package documentation (Manual and Vignette) are excellent and include working code examples.
Finally, for visualizing these models, I recommend highly the R bindings to the excellent Graphviz Library; the R bindings are gathered in the R Package, RGraphviz. RGraphviz is not available on CRAN but rather on Bioconductor; it depends on GraphViz.
Not yet, actually. Doug's answer is inherently misleading. If someone's looking for non-R implementation of the requested models, I may provide this link.
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I am simply looking for a good tutorial that will walk me through how to create a SOM in R. I am reading Kohonen and Kaski's paper on using the maps to identify the structure of Welfare, and want to try the technique my self.
I think many of the examples in R have a lot to be desired. I have looked on the web and didn't find anything that I thought made the process easy to understand.
Any help you can provide will be greatly appreciated.
A few pointers:
SOM package
Kohonen package
Most of the SOM related packages are from the Chemometrics and Computational Physics area, but you also have a look at the cluster view on CRAN.
Edit: Incidentally, I'm not aware of any tutorials, but one good way to find examples is to google with ext:r. There are several "som" scripts that show up that way, including this example which shows how to use it with GGobi.
Shane already mentioned the kohonen package. An introduction and tutorial can be found in the Journal of Statistical Software:
Self- and Super-organizing Maps in R: The kohonen Package
Did you look at the CRAN package SOM ?
GeoSpatial Data Mining course from edugi has pretty good materials.