LEADER 05037nam 2200649 450 001 9910788049803321 005 20200520144314.0 010 $a1-78439-265-0 035 $a(CKB)2670000000590759 035 $a(EBL)1910211 035 $a(SSID)ssj0001436055 035 $a(PQKBManifestationID)11746541 035 $a(PQKBTitleCode)TC0001436055 035 $a(PQKBWorkID)11436345 035 $a(PQKB)10794509 035 $a(MiAaPQ)EBC1910211 035 $a(Au-PeEL)EBL1910211 035 $a(CaPaEBR)ebr11001835 035 $a(CaONFJC)MIL687817 035 $a(OCoLC)900882999 035 $a(PPN)228020018 035 $a(EXLCZ)992670000000590759 100 $a20150116h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aR for data science $elearn and explore the fundamentals of data science with R /$fDan Toomey 210 1$aBirmingham, England :$cPackt Publishing,$d2014. 210 4$dİ2014 215 $a1 online resource (364 p.) 225 1 $aCommunity Experience Distilled 300 $aIncludes index. 311 $a1-78439-086-0 311 $a1-322-56535-X 327 $aCover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Data Mining Patterns; Cluster analysis; K-means clustering; Usage; Example; K-medoids clustering; Usage; Example; Hierarchical clustering; Usage; Example; Expectation-maximization; Usage; List of model names; Example; Density estimation; Usage; Example; Anomaly detection; Show outliers; Example; Example; Another anomaly detection example; Calculating anomalies; Usage; Example 1; Example 2; Association rules; Mine for associations; Usage; Example; Questions; Summary 327 $aChapter 2: Data Mining SequencesPatterns; Eclat; Usage; Using eclat to find similarities in adult behavior; Finding frequent items in a dataset; An example focusing on highest frequency; arulesNBMiner; Usage; Mining the Agrawal data for frequent sets; Apriori; Usage; Evaluating associations in a shopping basket; Determining sequences using TraMineR; Usage; Determining sequences in training and careers; Similarities in the sequence; Sequence metrics; Usage; Example; Questions; Summary; Chapter 3: Text Mining; Packages; Text processing; Example; Creating a corpus; Text clusters; Word graphics 327 $aAnalyzing the XML textQuestions; Summary; Chapter 4: Data Analysis - Regression Analysis; Packages; Simple regression; Multiple regression; Multivariate regression analysis; Robust regression; Questions; Summary; Chapter 5: Data Analysis - Correlation; Packages; Correlation; Example; Visualizing correlations; Covariance; Pearson correlation; Polychoric correlation; Tetrachoric correlation; A heterogeneous correlation matrix; Partial correlation; Questions; Summary; Chapter 6: Data Analysis - Clustering; Packages; K-means clustering; Example; Optimal number of clusters; Medoids clusters 327 $aThe cascadeKM functionSelecting clusters based on Bayesian information; Affinity propagation clustering; Gap statistic to estimate the number of clusters; Hierarchical clustering; Questions; Summary; Chapter 7: Data Visualization - R Graphics; Packages; Interactive graphics; The latticist package; Bivariate binning display; Mapping; Plotting points on a map; Plotting points on a world map; Google Maps; The ggplot2 package; Questions; Summary; Chapter 8: Data Visualization - Plotting; Packages; Scatter plots; Regression line; A lowess line; scatterplot; Scatterplot matrices 327 $asplom - display matrix datacpairs - plot matrix data; Density scatter plots; Bar charts and plots; Bar plot; Usage; Bar chart; ggplot2; Word cloud; Questions; Summary; Chapter 9: Data Visualization - 3D; Packages; Generating 3D graphics; Lattice Cloud - 3D scatterplot; scatterplot3d; scatter3d; cloud3d; RgoogleMaps; vrmlgenbar3D; Big Data; pbdR; bigmemory; Research areas; Rcpp; parallel; microbenchmark; pqR; SAP integration; roxygen2; bioconductor; swirl; pipes; Questions; Summary; Chapter 10: Machine Learning in Action; Packages; Dataset; Data partitioning; Model; Linear model; Prediction 327 $aLogistic regression 330 $aIf you are a data analyst who has a firm grip on some advanced data analysis techniques and wants to learn how to leverage the features of R, this is the book for you. You should have some basic knowledge of the R language and should know about some data science topics. 410 0$aCommunity experience distilled. 606 $aR (Computer program language) 606 $aMathematical statistics$xData processing 615 0$aR (Computer program language) 615 0$aMathematical statistics$xData processing. 676 $a519.502855133 700 $aToomey$b Dan$0480564 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910788049803321 996 $aR for data science$9256294 997 $aUNINA