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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.
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I have a df called crash_speed detailing the number of crashes, casualties and deaths for each speed interval (from 10-110). It looks like the follwing:
I'm just wondering how I'd go about in plotting a table of this, and what packages and fucntions to use ect. I pretty much want to just keep the same, but just plot it
I think knitr, kable , and kableExtra are the things you are looking for.
For more info check the docs:
https://cran.r-project.org/web/packages/kableExtra/vignettes/awesome_table_in_html.html
Before posting a question please do your own research on finding a topic as said in general terms of posting.
This answer took 1 minute of googling :)
One way is to use gridExtra
library(gridExtra)
df <- iris[1:10,]
grid.table(df)
This outputs to the plot window the following:
You can either try out the table functions of ggpubr or dive into numerous packages. The easiest way to get started is the gt package which also provides a list of alternatives like
knitr (GITHUB, WEBSITE)
kableExtra (GITHUB, WEBSITE)
formattable (GITHUB, WEBSITE)
DT (GITHUB, WEBSITE)
pander (GITHUB, WEBSITE)
huxtable (GITHUB, WEBSITE)
reactable (GITHUB, WEBSITE)
flextable (GITHUB, WEBSITE)
ftextra (GITHUB, WEBSITE)
pixiedust (GITHUB)
tangram (GITHUB)
ztable (GITHUB)
condformat (GITHUB)
stargazer (CRAN)
xtable (CRAN)
I do also know at least one blog writing about gt tables and the author of the blog additionally maintains a gt extension package.
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I am looking for an R implemention of the excellent Glicko-2 algorithm of Mark Glickman. Thusfar I found this one. Although this is a very nice piece of code I am particularly looking for a code that is able to deal with large data frames with match scores (meaning that it is capable of ranking all the players in the data frame in one go). A bit like the way the PlayerRatings package does the trick with e.g. Elo, Glicko. Unfortenately this package doesn't haven an implementation of the Glicko-2 algorithm.
Does anyone have an idea?
Glicko2 and few other algorithms are available in R package sport. Possible for two-player and multi-player matchups. Available on cran and github. Vignette included, standarized syntax, supported by C++.
Quick snippet
# install.packages("sport")
library(sport)
glicko2 <- glicko2_run(formula = rank|id ~ rider, data = gpheats)
# computation results
print(glicko2)
summary(glicko2)
tail(glicko2$r)
tail(glicko2$pairs)
If you had noticed the fine print at the bottom of Mark Glickman's page you would have seen (in tiny text admittedly)
PlayerRatings, an R package implementation of Glicko, as well as a
few other rating systems
with the link being: https://cran.r-project.org/web/packages/PlayerRatings/
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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'm interesting in evolutionary algorithms. I have tested genetic algorithms with R but has someone tried with genetic programming? Do you know, if there are code somewhere written in R.
I'm not aware of anything on CRAN for genetic programming in particular, although there is some work being done on the problem. In particular, have a look at the RGP package that is still in development.
Otherwise, have a look at the RFreak package, which is a wrapper for the "Free Evolutionary Algorithm Kit".
You might want to have a look at genalg, at GA and at galgo
There is a new package available:gramEvol: Grammatical Evolution for R
A native R implementation of grammatical evolution (GE). GE
facilitates the discovery of programs that can achieve a desired goal.
This is done by performing an evolutionary optimisation over a
population of R expressions generated via a user-defined context-free
grammar (CFG) and cost function.
You can find the vignette here:Grammatical Evolution: A Tutorial using gramEvol
UPDATE: I posted an article on my blog about it:
Symbolic Regression, Genetic Programming… or if Kepler had R.
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Are there any R-project packages that facilitate asynchronous network IO?
I'm think here along the lines of Ruby's Eventmachine or Python's Twisted.
If there are several such packages/libraries which is the best in terms of:
- performance
- features
First of all, R is single-threaded, so typically people try to use parallel computing approaches (see, for instance, the snow package). I don't think there's anything quite like Eventmachine or Twisted.
Check out the following:
The "State of the Art in Parallel Computing with R" paper describes most of the approaches to parallel computing in R (http://www.jstatsoft.org/v31/i01/paper). There are many useful packages in the HighPerformanceComputing view: http://cran.r-project.org/web/views/HighPerformanceComputing.html.
Check out svSocket: http://cran.r-project.org/web/packages/svSocket/
You can try using NetWorkSpaces with R: http://cran.r-project.org/web/packages/nws/.
There are several examples of R servers. RServe: http://www.rforge.net/Rserve/
The iBrokers packages is one of the only ones that I know which uses asynchonous requests. Have a look at the source code for that package (you can download it off R-Forge) and the related vignette: http://cran.r-project.org/web/packages/IBrokers/vignettes/RealTime.pdf
The biocep project also includes many relevant features: http://biocep-distrib.r-forge.r-project.org/