Installed #R Libraries

After upgrading to WIN10 I have decided to make clean R Environment for R Base engine 3.1.2. and 3.2.2.

Most of R libraries was installed under 3.2.2. base. And here is the list of all:

abind, abn, acepack, adabag, ade4, ADGofTest, ahaz, akima, amap, Amelia, animation, aod, ape, aplpack, arm, arules, arulesViz, ash, assertthat, base64, bayesm, bayesmix, BayesTree, BBmisc, BDgraph, bdsmatrix, betareg, BH, bibtex, biclust, biglm, bigRR, bit, bit64,  bitops, BMA, bmrm, bnlearn, boa, boilerpipeR, bootstrap, Boruta, brew, brglm, BRugs, bst, C50, ca, Cairo, cairoDevice, calibrate, car,  caret, catnet, caTools, catspec, cba, checkmate, chron, circlize, CircStats, clue, cluster, clusterGeneration, clusterSim, clustvarsel,  clv, cmprsk, cobs, cocorresp, coda, codetools, coin, colorspace, combinat, concor, copula, CORElearn, corpcor, corpora, covRobust,  CoxBoost, cramer, cubature, Cubist, curl, cwhmisc, data.table, DatABEL, DBI, dclone, deal, delt, demography, denpro, DEoptimR,  desirability, devtools, diagram, DiagrammeR, dichromat, digest, diptest, dispmod, distrom, doParallel, dplyr, dr, dse, DSL, dynamicGraph,  e1071, earth, effects, elasticnet, ElemStatLearn, ellipse, elrm, emoa, energy, entropy, Epi, ergm, ergm.count, eRm, estimability, etm,  EvalEst, evaluate, evtree, exactLoglinTest, expm, FactoMineR, FAiR, fastcluster, fastICA, fBasics, FBFsearch, FCNN4R, fda, fdrtool,
feature, fgac, flashClust, flexclust, flexmix, flsa, foreach, forecast, foreign, formatR, Formula, forward, fpc, fracdiff, frbs, fso,
ftsa, gam, GAMBoost, gamboostLSS, gamlr, gbm, gclus, gdata, gee, geepack, gender, GeneNet, GenKern, geometry, geozoo, GGally, ggm,   ggplot2, ggvis, giRaph, git2r, glmnet, glmpath, GlobalOptions, GMMBoost, gmodels, gmp, gnm, googleVis, GPArotation, gplots, gRain, gRapHD,   gRbase, gRc, gridBase, gridExtra, gRim, grplasso, grpreg, gsl, gss, gsubfn, gtable, gtools, gWidgets, gWidgetsRGtk2, hash, hda, hddplot,   hdi, hdrcde, hexbin, hglm, hglm.data, highr, Hmisc, homals, htmltools, htmlwidgets, httpuv, httr, huge, hybridHclust, ICS, ICSNP, IDPmisc,  igraph, influence.ME, inline, iplots, ipred, irlba, ISLR, ISOcodes, iterators, JADE, JavaGD, jpeg, jsonlite, kernlab, KernSmooth, kknn,   klaR, knitcitations, knitr, knncat, kohonen, KoNLP, koRpus, ks, labdsv, labeling, Lahman, languageR, lars, lasso2, latentnet, lattice,
latticeExtra, lava, lazyeval, lcd, lda, leaps, lhs, LiblineaR, linprog, lme4, lmeSplines, lmm, lmtest, locfit, LogicForest, LogicReg,   logistf, logmult, longitudinal, LowRankQP, lpSolve, lsa, lsmeans, ltm, lubridate, magic, magrittr, maps, maptools, maptree, mAr, markdown,  MASS, Matching, MatchIt, mathgraph, Matrix, matrixcalc, MatrixModels, maxent, maxLik, mboost, mclust, MCMCglmm, MCMCpack, mda, memoise,  mgcv, mi, mice, migest, mime, minqa, misc3d, miscTools, mitools, mix, mlbench, mlegp, mlogit, mlr, mnormt, MNP, modeltools, monomvn,  movMF, msm, multcomp, multgee, multicool, multinomRob, multiplex, munsell, mvnmle, mvnormtest, mvoutlier, mvtnorm, ncvreg, ndtv, network,  networkDynamic, nFactors, nlme, nloptr, NLP, nlstools, NMF, nnet, nnls, norm, np, numDeriv, oblique.tree, onion, openNLP, openNLPdata,
optmatch, PAFit, pamr, pan, parallelMap, ParamHelpers, paran, parcor, party, partykit, pbkrtest, pcalg, pcaPP, PearsonICA, penalized, penalizedLDA, penalizedSVM, permute, perturb, pkgmaker, playwith, plotmo, plotrix, pls, plsgenomics, plyr, png, poLCA, polspline, polycor, ppls, prabclus, prim, pROC, prodlim, profileModel, proto, proxy, PSAgraphics, pscl, pspline, psy, psych, PTAk, qdap, qdapDictionaries,  qdapRegex, qdapTools, quadprog, quantmod, quantreg, quantregForest, QUIC, qvcalc, R2HTML, R2OpenBUGS, R2WinBUGS, R6, rainbow,  randomForest, randomForestSRC, ranger, rattle, rbugs, Rcmdr, RcmdrMisc, RcmdrPlugin.temis, RColorBrewer, Rcpp, RcppArmadillo, RcppEigen,  RCurl, rda, rdetools, readxl, REEMtree, RefManageR, registry, relaimpo, relaxo, relimp, reshape, reshape2, rgdal, rgenoud, rggobi, rgl,
RgoogleMaps, rgp, RGraphics, RGtk2, RItools, rjags, rJava, RJDBC, rjson, RJSONIO, RKEA, RKEAjars, Rmalschains, rminer, rms, rngtools,  ROAuth, robCompositions, robustbase, ROCR, RODBC, RoughSets, roxygen2, rpart, RPMM, rrcov, RSiena, RSNNS, RSQLite, RSQLServer, Rstem,  rstudioapi, RTextTools, rversions, rvest, RWeka, RWekajars, RXKCD, RXshrink, sandwich, sca, scagnostics, scales, scalreg, scatterplot3d, sda, sde, Sejong, selectr, sem, SensoMineR, sentiment, seriation, setRNG, sfsmisc, sgeostat, shape, shiny, simba, simpleboot, SIN,  skmeans, slam, sm, smatr, sn, sna, SnowballC, sp, spam, SparseM, SpatialNP, sROC, stabledist, stabs, statmod, statnet, statnet.common,  stringdist, stringi, stringr, strucchange, superpc, survey, survival, svd, svmpath, tau, tcltk2, TeachingDemos, tensor, tensorA, tergm,
textcat, textir, tfplot, tframe, tgp, TH.data, tidyr, timeDate, timeSeries, tkrplot, tm, tm.plugin.alceste, tm.plugin.dc,
tm.plugin.europresse, tm.plugin.factiva, tm.plugin.lexisnexis, tm.plugin.mail, tm.plugin.sentiment, tm.plugin.webmining, topicmodels,  tree, trimcluster, trust, tseries, tsfa, TSP, TTR, TurtleGraphics, twitteR, ucminf, varSelRF, vcd, vcrpart, vegan, venneuler, VGAM, VIM, vioplot, visNetwork, visreg, whisker, wordcloud, wordnet, xgobi, XLConnect, XLConnectJars, xlsx, xlsxjars, XML, xml2, xtable, xts,  YaleToolkit, yaml, Zelig, zipfR, zoo, base, boot, class, cluster, codetools, compiler, datasets, foreign, graphics, grDevices, grid,  KernSmooth, lattice, MASS, Matrix, methods, mgcv, nlme, nnet, parallel, rpart, spatial, splines, stats, stats4, survival, tcltk, tools,  translations, utils.
Generated with following R Script:

setwd(“C:/DataTK”)
if(exists(c(“x.lib”, “x.lib2″,”lib_vector”, “sep_val”))){rm(x.lib2, x.lib, lib_vector, sep_val)}
x.lib <- data.frame(installed.packages())
x.lib2 <- cbind.data.frame(x.lib$Priority, x.lib$Package, x.lib$Built, x.lib$Version)
lib_vector <- as.vector(x.lib$Package)
#class(lib_vector)
sep_val <- paste(as.character(lib_vector), sep=”‘ ‘”, collapse=”, “)
write(sep_val)

And if you want to install all these libraries, you can download attached text file with all the install commands.

R script for getting all  library names and storing them in *.txt file is:

if(exists(c(“libname”,”part1″, “part2”, “part3”, “str”, “install_pack”))){rm(libname,part1, part2, part3, str, install_pack)}

for (i in 1:nrow(x.lib))
{
libname = as.character(x.lib[i,”Package”])
part1 <- “if(!is.element(\””
part2 <- “\”,installed.packages()[,1])){install.packages(\””
part3 <- “\”)}”
str <- (c(part1, libname, part2, libname, part3))
install_pack <- paste(as.character(str), sep=””, collapse=””)
write(install_pack, “installed_packages.txt”, append=TRUE)
i <- i +1
}

installed_packages.txt

Enjoy!

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Welcome to SQL Saturday Turin #SqlSatTurin

sqlsat454_web

A week from another awesome Italian Sql Saturday Turin (#SqlSatTurin  #sqlsat454) and we would all like to welcome you to join us.

Schedule can be found here. Four tracks covering from Azure, Power BI, SQL Server 2016 and my favorite BI & Analytics.

A great Italian SQL Community will be awaiting there. And of course, international speakers three from Slovenian Dejan, Mladen and me; (Matija, we will be missing you) and Kenneth (Denmark) , Jean-Pierre (France) and Cedric (Belgium).

And looking forward for Expo 2015 visit.

logoExpo1

See you in Turin!

Some stats submission for SqlSatSlo 2015

October 1st 2015 was deadline for submitting the sessions for SQL Saturday Slovenia, taking place in Ljubljana on December 12th, 2015.

After closing the Call for Sessions, the result was, 121 Sessions submitted from 50 speakers coming from 20 different countries.

Simple data set (courtesy of Dejan):

id,SpeakersCount,SessionCount,country
1,1,1,Austria
2,2,6,Bulgaria
3,1,2,Canada
4,3,9,Denmark
5,6,17,Germany
6,1,2,Hungary
7,8,17,Italy
8,1,1,Macedonia
9,1,3,Netherlands
10,1,1,Norway
11,1,4,Poland
12,1,3,Portugal
13,1,2,Romania
14,1,2,Russia
15,1,1,Serbia
16,1,1,Slovakia
17,3,3,Slovenia
18,1,1,Sweden
19,12,36,UK
20,3,9,USA

After Dejan Sarka created first visualization for #sqlsatslovenia I have decided to play and goof around with simple dataset as well.

First the Treemap representing the number of sessions per country:

Treemap_sessionCount

Following is dendrogram, based on euclidean distance between groups, presented with 3 clusters. Keep in mind that dendrogram is representation of only two variables (SpeakersCount,SessionCount) which are also highly (and positive) correlated (our UK, German and Italian colleagues are super outliers, but we love them and they were not normalized). 🙂

Clusters_SQLSatSlovenia2015

Adding some more visualizations:

Sankey_Flow_levels

circos_pie