R refcard-data-mining
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This document provides a summary of data mining and text mining packages and functions available in R. It lists popular packages and functions for tasks such as association rule mining, classification/prediction using decision trees and random forests, clustering, outlier detection, time series analysis, text cleaning/preparation, topic modeling, and social network analysis. It also includes packages and functions for evaluating model performance and visualizing results.
R refcard-data-mining
- 1. R Reference Card for Data Mining Yanchang Zhao, RDataMining.com, February 6, 2014 - See the latest version at http://www.RDataMining.com - The package names are in parentheses. - Recommended packages and functions are shown in bold. - Click a package in this PDF file to find it on CRAN. Association Rules and Sequential Patterns Functions apriori() mine associations with APRIORI algorithm – a level-wise, breadth-first algorithm which counts transactions to find frequent item- sets (arules) eclat() mine frequent itemsets with the Eclat algorithm, which employs equivalence classes, depth-first search and set intersection instead of counting (arules) cspade() mine frequent sequential patterns with the cSPADE algorithm (aru- lesSequences) seqefsub() search for frequent subsequences (TraMineR) Packages arules mine frequent itemsets, maximal frequent itemsets, closed frequent item- sets and association rules. It includes two algorithms, Apriori and Eclat. arulesViz visualizing association rules arulesSequences add-on for arules to handle and mine frequent sequences TraMineR mining, describing and visualizing sequences of states or events Classification & Prediction Decision Trees ctree() conditional inference trees, recursive partitioning for continuous, cen- sored, ordered, nominal and multivariate response variables in a condi- tional inference framework (party) rpart() recursive partitioning and regression trees (rpart) mob() model-based recursive partitioning, yielding a tree with fitted models as- sociated with each terminal node (party) Random Forest cforest() random forest and bagging ensemble (party) randomForest() random forest (randomForest) importance() variable importance (randomForest) varimp() variable importance (party) Neural Networks nnet() fit single-hidden-layer neural network (nnet) mlp(), dlvq(), rbf(), rbfDDA(), elman(), jordan(), som(), art1(), art2(), artmap(), assoz() various types of neural networks (RSNNS) neuralnet training of neural networks (neuralnet) Support Vector Machine (SVM) svm() train a support vector machine for regression, classification or density- estimation (e1071) ksvm() support vector machines (kernlab) Bayes Classifiers naiveBayes() naive Bayes classifier (e1071) Performance Evaluation performance() provide various measures for evaluating performance of pre- diction and classification models (ROCR) PRcurve() precision-recall curves (DMwR) CRchart() cumulative recall charts (DMwR) roc() build a ROC curve (pROC) auc() compute the area under the ROC curve (pROC) ROC() draw a ROC curve (DiagnosisMed) Packages party recursive partitioning rpart recursive partitioning and regression trees randomForest classification and regression based on a forest of trees using ran- dom inputs ROCR visualize the performance of scoring classifiers caret classification and regression models r1071 functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged cluster- ing, naive Bayes classifier, ... rpartOrdinal ordinal classification trees, deriving a classification tree when the response to be predicted is ordinal rpart.plot plots rpart models pROC display and analyze ROC curves nnet feed-forward neural networks and multinomial log-linear models RSNNS neural networks in R using the Stuttgart Neural Network Simulator (SNNS) neuralnet training of neural networks using backpropagation, resilient backprop- agation with or without weight backtracking Regression Functions lm() linear regression glm() generalized linear regression gbm() generalized boosted regression models (gbm) predict() predict with models residuals() residuals, the difference between observed values and fitted val- ues nls() non-linear regression gls() fit a linear model using generalized least squares (nlme) gnls() fit a nonlinear model using generalized least squares (nlme) Packages nlme linear and nonlinear mixed effects models gbm generalized boosted regression models Clustering Partitioning based Clustering partition the data into k groups first and then try to improve the quality of clus- tering by moving objects from one group to another kmeans() perform k-means clustering on a data matrix kmeansruns() call kmeans for the k-means clustering method and includes estimation of the number of clusters and finding an optimal solution from several starting points (fpc) pam() the Partitioning Around Medoids (PAM) clustering method (cluster) pamk() the Partitioning Around Medoids (PAM) clustering method with esti- mation of number of clusters (fpc) kmeansCBI() interface function for kmeans (fpc) cluster.optimal() search for the optimal k-clustering of the dataset (bayesclust) clara() Clustering Large Applications (cluster) fanny(x,k,...) compute a fuzzy clustering of the data into k clusters (cluster) kcca() k-centroids clustering (flexclust) ccfkms() clustering with Conjugate Convex Functions (cba) apcluster() affinity propagation clustering for a given similarity matrix (ap- cluster) apclusterK() affinity propagation clustering to get K clusters (apcluster) cclust() Convex Clustering, incl. k-means and two other clustering algorithms (cclust) KMeansSparseCluster() sparse k-means clustering (sparcl) tclust(x,k,alpha,...) trimmed k-means with which a proportion alpha of observations may be trimmed (tclust) Hierarchical Clustering a hierarchical decomposition of data in either bottom-up (agglomerative) or top- down (divisive) way hclust() hierarchical cluster analysis on a set of dissimilarities birch() the BIRCH algorithm that clusters very large data with a CF-tree (birch) pvclust() hierarchical clustering with p-values via multi-scale bootstrap re- sampling (pvclust) agnes() agglomerative hierarchical clustering (cluster) diana() divisive hierarchical clustering (cluster) mona() divisive hierarchical clustering of a dataset with binary variables only (cluster) rockCluster() cluster a data matrix using the Rock algorithm (cba) proximus() cluster the rows of a logical matrix using the Proximus algorithm (cba) isopam() Isopam clustering algorithm (isopam) flashClust() optimal hierarchical clustering (flashClust) fastcluster() fast hierarchical clustering (fastcluster) cutreeDynamic(), cutreeHybrid() detection of clusters in hierarchical clus- tering dendrograms (dynamicTreeCut) HierarchicalSparseCluster() hierarchical sparse clustering (sparcl) Model based Clustering Mclust() model-based clustering (mclust) HDDC() a model-based method for high dimensional data clustering (HDclassif) fixmahal() Mahalanobis Fixed Point Clustering (fpc) fixreg() Regression Fixed Point Clustering (fpc) mergenormals() clustering by merging Gaussian mixture components (fpc) Density based Clustering generate clusters by connecting dense regions dbscan(data,eps,MinPts,...) generate a density based clustering of arbitrary shapes, with neighborhood radius set as eps and density thresh- old as MinPts (fpc) pdfCluster() clustering via kernel density estimation (pdfCluster) Other Clustering Techniques mixer() random graph clustering (mixer) nncluster() fast clustering with restarted minimum spanning tree (nnclust) orclus() ORCLUS subspace clustering (orclus) 1
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Plotting Clustering Solutions plotcluster() visualisation of a clustering or grouping in data (fpc) bannerplot() a horizontal barplot visualizing a hierarchical clustering (cluster) Cluster Validation silhouette() compute or extract silhouette information (cluster) cluster.stats() compute several cluster validity statistics from a clustering and a dissimilarity matrix (fpc) clValid() calculate validation measures for a given set of clustering algorithms and number of clusters (clValid) clustIndex() calculate the values of several clustering indexes, which can be independently used to determine the number of clusters existing in a data set (cclust) NbClust() provide 30 indices for cluster validation and determining the number of clusters (NbClust) Packages cluster cluster analysis fpc various methods for clustering and cluster validation mclust model-based clustering and normal mixture modeling birch clustering very large datasets using the BIRCH algorithm pvclust hierarchical clustering with p-values apcluster Affinity Propagation Clustering cclust Convex Clustering methods, including k-means algorithm, On-line Up- date algorithm and Neural Gas algorithm and calculation of indexes for finding the number of clusters in a data set cba Clustering for Business Analytics, including clustering techniques such as Proximus and Rock bclust Bayesian clustering using spike-and-slab hierarchical model, suitable for clustering high-dimensional data biclust algorithms to find bi-clusters in two-dimensional data clue cluster ensembles clues clustering method based on local shrinking clValid validation of clustering results clv cluster validation techniques, contains popular internal and external cluster validation methods for outputs produced by package cluster bayesclust tests/searches for significant clusters in genetic data clustsig significant cluster analysis, tests to see which (if any) clusters are statis- tically different clusterSim search for optimal clustering procedure for a data set clusterGeneration random cluster generation gcExplorer graphical cluster explorer hybridHclust hybrid hierarchical clustering via mutual clusters Modalclust hierarchical modal Clustering iCluster integrative clustering of multiple genomic data types EMCC evolutionary Monte Carlo (EMC) methods for clustering rEMM extensible Markov Model (EMM) for data stream clustering Outlier Detection Functions boxplot.stats()$out list data points lying beyond the extremes of the whiskers lofactor() calculate local outlier factors using the LOF algorithm (DMwR or dprep) lof() a parallel implementation of the LOF algorithm (Rlof) Packages Rlof a parallel implementation of the LOF algorithm extremevalues detect extreme values in one-dimensional data mvoutlier multivariate outlier detection based on robust methods outliers some tests commonly used for identifying outliers Time Series Analysis Construction & Plot ts() create time-series objects plot.ts() plot time-series objects smoothts() time series smoothing (ast ) sfilter() remove seasonal fluctuation using moving average (ast ) Decomposition decomp() time series decomposition by square-root filter (timsac) decompose() classical seasonal decomposition by moving averages stl() seasonal decomposition of time series by loess tsr() time series decomposition (ast) ardec() time series autoregressive decomposition (ArDec) Forecasting arima() fit an ARIMA model to a univariate time series predict.Arima() forecast from models fitted by arima auto.arima() fit best ARIMA model to univariate time series (forecast) forecast.stl(), forecast.ets(), forecast.Arima() forecast time series using stl, ets and arima models (forecast) Correlation and Covariance acf() autocovariance or autocorrelation of a time series ccf() cross-correlation or cross-covariance of two univariate series Packages forecast displaying and analysing univariate time series forecasts TSclust time series clustering utilities dtw Dynamic Time Warping (DTW) timsac time series analysis and control program ast time series analysis ArDec time series autoregressive-based decomposition dse tools for multivariate, linear, time-invariant, time series models Text Mining Text Cleaning and Preparation Corpus() build a corpus, which is a collection of text documents (tm) tm map() transform text documents, e.g., stemming, stopword removal (tm) tm filter() filtering out documents (tm) TermDocumentMatrix(), DocumentTermMatrix() construct a term-document matrix or a document-term matrix (tm) Dictionary() construct a dictionary from a character vector or a term- document matrix (tm) stemDocument() stem words in a text document (tm) stemCompletion() complete stemmed words (tm) SnowballStemmer() Snowball word stemmers (Snowball) stopwords(language) return stopwords in different languages (tm) removeNumbers(), removePunctuation(), removeWords() re- move numbers, punctuation marks, or a set of words from a text docu- ment (tm) removeSparseTerms() remove sparse terms from a term-document matrix (tm) Frequent Terms and Association findAssocs() find associations in a term-document matrix (tm) findFreqTerms() find frequent terms in a term-document matrix (tm) termFreq() generate a term frequency vector from a text document (tm) Topic Modelling LDA() fit a LDA (latent Dirichlet allocation) model (topicmodels) CTM() fit a CTM (correlated topics model) model (topicmodels) terms() extract the most likely terms for each topic (topicmodels) topics() extract the most likely topics for each document (topicmodels) Sentiment Analysis polarity() polarity score (sentiment analysis) (qdap) Text Categorization textcat() n-gram based text categorization (textcat) Text Visualizatoin wordcloud() plot a word cloud (wordcloud) comparison.cloud() plot a cloud comparing the frequencies of words across documents (wordcloud) commonality.cloud() plot a cloud of words shared across documents (wordcloud) Packages tm a framework for text mining applications topicmodels fit topic models with LDA and CTM wordcloud various word clouds lda fit topic models with LDA wordnet an interface to the WordNet RTextTools automatic text classification via supervised learning qdap transcript analysis, text mining and natural language processing tm.plugin.dc a plug-in for package tm to support distributed text mining tm.plugin.mail a plug-in for package tm to handle mail textir a suite of tools for inference about text documents and associated sentiment tau utilities for text analysis textcat n-gram based text categorization Social Network Analysis and Graph Mining Functions graph(), graph.edgelist(), graph.adjacency(), graph.incidence() create graph objects respectively from edges, an edge list, an adjacency matrix and an incidence matrix (igraph) plot(), tkplot(), rglplot() static, interactive and 3D plotting of graphs (igraph) gplot(), gplot3d() plot graphs (sna) vcount(), ecount() number of vertices/edges (igraph) V(), E() vertex/edge sequence of igraph (igraph) is.directed() whether the graph is directed (igraph) are.connected() check whether two nodes are connected (igraph) degree(), betweenness(), closeness(), transitivity() various cen- trality scores (igraph, sna) add.edges(), add.vertices(), delete.edges(), delete.vertices() add and delete edges and vertices (igraph) neighborhood() neighborhood of graph vertices (igraph, sna) get.adjlist() adjacency lists for edges or vertices (igraph) nei(), adj(), from(), to() vertex/edge sequence indexing (igraph) 2
- 3. cliques(), largest.cliques(), maximal.cliques(), clique.number() find cliques, ie. complete subgraphs (igraph) clusters(), no.clusters() maximal connected components of a graph and the number of them (igraph) fastgreedy.community(), spinglass.community() community detection (igraph) cohesive.blocks() calculate cohesive blocks (igraph) induced.subgraph() create a subgraph of a graph (igraph) %->%, %<-%, %--% edge sequence indexing (igraph) get.edgelist() return an edge list in a two-column matrix (igraph) read.graph(), write.graph() read and writ graphs from and to files of various formats (igraph) Packages igraph network analysis and visualization sna social network analysis statnet a set of tools for the representation, visualization, analysis and simulation of network data egonet ego-centric measures in social network analysis snort social network-analysis on relational tables network tools to create and modify network objects bipartite visualising bipartite networks and calculating some (ecological) indices blockmodelinggeneralized and classical blockmodeling of valued networks diagram visualising simple graphs (networks), plotting flow diagrams NetCluster clustering for networks NetData network data for McFarland’s SNA R labs NetIndices estimating network indices, including trophic structure of foodwebs in R NetworkAnalysis statistical inference on populations of weighted or unweighted networks tnet analysis of weighted, two-mode, and longitudinal networks Spatial Data Analysis Functions geocode() geocodes a location using Google Maps (ggmap) plotGoogleMaps() create a plot of spatial data on Google Maps (plot- GoogleMaps) qmap() quick map plot (ggmap) get map() queries the Google Maps, OpenStreetMap, or Stamen Maps server for a map at a certain location (ggmap) gvisGeoChart(), gvisGeoMap(), gvisIntensityMap(), gvisMap() Google geo charts and maps (googleVis) GetMap() download a static map from the Google server (RgoogleMaps) ColorMap() plot levels of a variable in a colour-coded map (RgoogleMaps) PlotOnStaticMap() overlay plot on background image of map tile (RgoogleMaps) TextOnStaticMap() plot text on map (RgoogleMaps) Packages plotGoogleMaps plot spatial data as HTML map mushup over Google Maps RgoogleMaps overlay on Google map tiles in R ggmap Spatial visualization with Google Maps and OpenStreetMap plotKML visualization of spatial and spatio-temporal objects in Google Earth SGCS Spatial Graph based Clustering Summaries for spatial point patterns spdep spatial dependence: weighting schemes, statistics and models Statistics Summarization summary() summarize data describe() concise statistical description of data (Hmisc) boxplot.stats() box plot statistics Analysis of Variance aov() fit an analysis of variance model anova() compute analysis of variance (or deviance) tables for one or more fitted model objects Statistical Tests chisq.test() chi-squared contingency table tests and goodness-of-fit tests ks.test() Kolmogorov-Smirnov tests t.test() student’s t-test prop.test() test of equal or given proportions binom.test() exact binomial test Mixed Effects Models lme() fit a linear mixed-effects model (nlme) nlme() fit a nonlinear mixed-effects model (nlme) Principal Components and Factor Analysis princomp() principal components analysis prcomp() principal components analysis Other Functions var(), cov(), cor() variance, covariance, and correlation density() compute kernel density estimates Packages nlme linear and nonlinear mixed effects models Graphics Functions plot() generic function for plotting barplot(), pie(), hist() bar chart, pie chart and histogram boxplot() box-and-whisker plot stripchart() one dimensional scatter plot dotchart() Cleveland dot plot qqnorm(), qqplot(), qqline() QQ (quantile-quantile) plot coplot() conditioning plot splom() conditional scatter plot matrices (lattice) pairs() a matrix of scatterplots cpairs() enhanced scatterplot matrix (gclus) parcoord() parallel coordinate plot (MASS) cparcoord() enhanced parallel coordinate plot (gclus) parallelplot() parallel coordinates plot (lattice) densityplot() kernel density plot (lattice) contour(), filled.contour() contour plot levelplot(), contourplot() level plots and contour plots (lattice) smoothScatter() scatterplots with smoothed densities color representation; capable of visualizing large datasets sunflowerplot() a sunflower scatter plot assocplot() association plot mosaicplot() mosaic plot matplot() plot the columns of one matrix against the columns of another fourfoldplot() a fourfold display of a 2×2×k contingency table persp() perspective plots of surfaces over the x?y plane cloud(), wireframe() 3d scatter plots and surfaces (lattice) interaction.plot() two-way interaction plot iplot(), ihist(), ibar(), ipcp() interactive scatter plot, histogram, bar plot, and parallel coordinates plot (iplots) pdf(), postscript(), win.metafile(), jpeg(), bmp(), png(), tiff() save graphs into files of various formats gvisAnnotatedTimeLine(), gvisAreaChart(), gvisBarChart(), gvisBubbleChart(), gvisCandlestickChart(), gvisColumnChart(), gvisComboChart(), gvisGauge(), gvisGeoChart(), gvisGeoMap(), gvisIntensityMap(), gvisLineChart(), gvisMap(), gvisMerge(), gvisMotionChart(), gvisOrgChart(), gvisPieChart(), gvisScatterChart(), gvisSteppedAreaChart(), gvisTable(), gvisTreeMap() various interactive charts produced with the Google Visualisation API (googleVis) gvisMerge() merge two googleVis charts into one (googleVis) Packages ggplot2 an implementation of the Grammar of Graphics googleVis an interface between R and the Google Visualisation API to create interactive charts rCharts interactive javascript visualizations from R lattice a powerful high-level data visualization system, with an emphasis on mul- tivariate data vcd visualizing categorical data iplots interactive graphics Data Manipulation Functions transform() transform a data frame scale() scaling and centering of matrix-like objects t() matrix transpose aperm() array transpose sample() sampling table(), tabulate(), xtabs() cross tabulation stack(), unstack() stacking vectors split(), unsplit() divide data into groups and reassemble reshape() reshape a data frame between “wide” and “long” format merge() merge two data frames; similar to database join operations aggregate() compute summary statistics of data subsets by() apply a function to a data frame split by factors melt(), cast() melt and then cast data into the reshaped or aggregated form you want (reshape) complete.cases() find complete cases, i.e., cases without missing values na.fail, na.omit, na.exclude, na.pass handle missing values Packages reshape flexibly restructure and aggregate data data.table extension of data.frame for fast indexing, ordered joins, assignment, and grouping and list columns gdata various tools for data manipulation 3
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Data Access Functions save(), load() save and load R data objects read.csv(), write.csv() import from and export to .CSV files read.table(), write.table(), scan(), write() read and write data read.xlsx(), write.xlsx() read and write Excel files (xlsx) read.fwf() read fixed width format files write.matrix() write a matrix or data frame (MASS) readLines(), writeLines() read/write text lines from/to a connection, such as a text file sqlQuery() submit an SQL query to an ODBC database (RODBC) sqlFetch() read a table from an ODBC database (RODBC) sqlSave(), sqlUpdate() write or update a table in an ODBC database (RODBC) sqlColumns() enquire about the column structure of tables (RODBC) sqlTables() list tables on an ODBC connection (RODBC) odbcConnect(), odbcClose(), odbcCloseAll() open/close con- nections to ODBC databases (RODBC) dbSendQuery execute an SQL statement on a given database connection (DBI) dbConnect(), dbDisconnect() create/close a connection to a DBMS (DBI) Packages RODBC ODBC database access foreign read and write data in other formats, such as Minitab, S, SAS, SPSS, Stata, Systat, ... DBI a database interface (DBI) between R and relational DBMS RMySQL interface to the MySQL database RJDBC access to databases through the JDBC interface RSQLite SQLite interface for R ROracle Oracle database interface (DBI) driver RpgSQL DBI/RJDBC interface to PostgreSQL database RODM interface to Oracle Data Mining xlsx read, write, format Excel 2007 and Excel 97/2000/XP/2003 files xlsReadWrite read and write Excel files WriteXLS create Excel 2003 (XLS) files from data frames Web Data Access Functions download.file() download a file from the Internet xmlParse(), htmlParse() parse an XML or HTML file (XML) userTimeline(), homeTimeline(), mentions(), retweetsOfMe() retrieve various timelines within the Twitter uni- verse (twitteR) searchTwitter() a search of Twitter based on a supplied search string (twit- teR) getUser(), lookupUsers() get information of Twitter users (twitteR) getFollowers(), getFollowerIDs(), getFriends(), getFriendIDs() get a list of followers/friends or their IDs of a Twitter user (twitteR) twListToDF() convert twitteR lists to data frames (twitteR) Packages twitteR an interface to the Twitter web API RCurl general network (HTTP/FTP/...) client interface for R XML reading and creating XML and HTML documents MapReduce and Hadoop Functions mapreduce() define and execute a MapReduce job (rmr2) keyval() create a key-value object (rmr2) from.dfs(), to.dfs() read/write R objects from/to file system (rmr2) Packages rmr2 perform data analysis with R via MapReduce on a Hadoop cluster rhdfs connect to the Hadoop Distributed File System (HDFS) rhbase connect to the NoSQL HBase database Rhipe R and Hadoop Integrated Processing Environment RHive distributed computing via HIVE query Segue Parallel R in the cloud using Amazon’s Elastic Map Reduce (EMR) engine HadoopStreaming Utilities for using R scripts in Hadoop streaming hive distributed computing via the MapReduce paradigm rHadoopClient Hadoop client interface for R Large Data Functions as.ffdf() coerce a dataframe to an ffdf (ff) read.table.ffdf(), read.csv.ffdf() read data from a flat file to an ffdf object (ff) write.table.ffdf(), write.csv.ffdf() write an ffdf object to a flat file (ff) ffdfappend() append a dataframe or an ffdf to an existing ffdf (ff) big.matrix() create a standard big.matrix, which is constrained to available RAM (bigmemory) read.big.matrix() create a big.matrix by reading from an ASCII file (big- memory) write.big.matrix() write a big.matrix to a file (bigmemory) filebacked.big.matrix() create a file-backed big.matrix, which may ex- ceed available RAM by using hard drive space (bigmemory) mwhich() expanded “which”-like functionality (bigmemory) Packages ff memory-efficient storage of large data on disk and fast access functions ffbase basic statistical functions for package ff filehash a simple key-value database for handling large data g.data create and maintain delayed-data packages BufferedMatrix a matrix data storage object held in temporary files biglm regression for data too large to fit in memory bigmemory manage massive matrices with shared memory and memory-mapped files biganalytics extend the bigmemory package with various analytics bigtabulate table-, tapply-, and split-like functionality for matrix and big.matrix objects Parallel Computing Functions sfInit(), sfStop() initialize and stop the cluster (snowfall) sfLapply(), sfSapply(), sfApply() parallel versions of lapply(), sapply(), apply() (snowfall) foreach(...) %dopar% looping in parallel (foreach) registerDoSEQ(), registerDoSNOW(), registerDoMC() register respec- tively the sequential, SNOW and multicore parallel backend with the foreach package (foreach, doSNOW, doMC) Packages snowfall usability wrapper around snow for easier development of parallel R programs snow simple parallel computing in R multicore parallel processing of R code on machines with multiple cores or CPUs snowFT extension of snow supporting fault tolerant and reproducible applica- tions, and easy-to-use parallel programming Rmpi interface (Wrapper) to MPI (Message-Passing Interface) rpvm R interface to PVM (Parallel Virtual Machine) nws provide coordination and parallel execution facilities foreach foreach looping construct for R doMC foreach parallel adaptor for the multicore package doSNOW foreach parallel adaptor for the snow package doMPI foreach parallel adaptor for the Rmpi package doParallel foreach parallel adaptor for the multicore package doRNG generic reproducible parallel backend for foreach Loops GridR execute functions on remote hosts, clusters or grids fork R functions for handling multiple processes Interface to Weka Package RWeka is an R interface to Weka, and enables to use the following Weka functions in R. Association rules: Apriori(), Tertius() Regression and classification: LinearRegression(), Logistic(), SMO() Lazy classifiers: IBk(), LBR() Meta classifiers: AdaBoostM1(), Bagging(), LogitBoost(), MultiBoostAB(), Stacking(), CostSensitiveClassifier() Rule classifiers: JRip(), M5Rules(), OneR(), PART() Regression and classification trees: J48(), LMT(), M5P(), DecisionStump() Clustering: Cobweb(), FarthestFirst(), SimpleKMeans(), XMeans(), DBScan() Filters: Normalize(), Discretize() Word stemmers: IteratedLovinsStemmer(), LovinsStemmer() Tokenizers: AlphabeticTokenizer(), NGramTokenizer(), WordTokenizer() Interface to Other Programming Languages Functions .jcall() call a Java method (rJava) .jnew() create a new Java object (rJava) .jinit() initialize the Java Virtual Machine (JVM) (rJava) 4
- 5. .jaddClassPath() adds directories or JAR files to the class path (rJava) Packages rJava low-level R to Java interface Generating Reports Functions Sweave() mixing text and R/S code for automatic report generation Packages knitr a general-purpose package for dynamic report generation in R R2HTML making HTML reports R2PPT generating Microsoft PowerPoint presentations Building GUIs and Web Applications shiny web application framework for R svDialogs dialog boxes gWidgets a toolkit-independent API for building interactive GUIs R Editors/GUIs RStudio a free integrated development environment (IDE) for R Tinn-R a free GUI for R language and environment rattle graphical user interface for data mining in R Rpad workbook-style, web-based interface to R RPMG graphical user interface (GUI) for interactive R analysis sessions Red-R An open source visual programming GUI interface for R R AnalyticFlow a software which enables data analysis by drawing analysis flowcharts latticist a graphical user interface for exploratory visualisation Other R Reference Cards R Reference Card, by Tom Short http://rpad.googlecode.com/svn-history/r76/Rpad_homepage/ R-refcard.pdf or http://cran.r-project.org/doc/contrib/Short-refcard.pdf R Reference Card, by Jonathan Baron http://cran.r-project.org/doc/contrib/refcard.pdf R Functions for Regression Analysis, by Vito Ricci http://cran.r-project.org/doc/contrib/Ricci-refcard-regression. pdf R Functions for Time Series Analysis, by Vito Ricci http://cran.r-project.org/doc/contrib/Ricci-refcard-ts.pdf RDataMining Books R and Data Mining: Examples and Case Studies introduces into using R for data mining with examples and case studies. http://www.rdatamining.com/books/rdm Data Mining Applications with R presents 15 real-world applications on data mining with R. http://www.rdatamining.com/books/dmar RDataMining Website, Group, Twitter & Package RDataMining Website http://www.rdatamining.com RDataMining Group on LinkedIn (4000+ members) http://group.rdatamining.com RDataMining on Twitter (1400+ followers) http://twitter.com/rdatamining RDataMining Project on R-Forge http://www.rdatamining.com/package http://package.rdatamining.com Comments & Feedback If you have any comments, or would like to suggest any relevant R pack- ages/functions, please feel free to email me <yanchang@rdatamining.com>. Thanks. 5