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Previous Chapter

Practical Data Science with R

List of Figures

Index

[SYMBOL][A][B][C][D][E][F][G][H][I][J][K][L][M][N][O][P][Q][R][S][T][U][V][W][X][Y][Z]

SYMBOL

; (semicolons) :: notation .. notation .() notation .BY list .RData .RDS .tsv (tab-separated values) [,] indexing operator, 2^nd [[]] operator, 2^nd, 3^rd @ operator, 2^nd & operator, 2^nd && operator #' marks %in% operation <- operator, 2^nd = operator, 2^nd -> operator, 2^nd | operator, 2^nd || operator $ operator

A

A/B tests evaluating Fisher's test for independence frequentist significance test setting up accuracy evaluation measure, 2^nd accuracyMeasures() function additive processes adjusted R-squared AIC (Akaike information criterion) Akismet, 2^nd all.equal() method alpha parameter, 2^nd, 3^rd Apgar test Apriori algorithm apriori() function arcsinh area under the curve (AUC), 2^nd arrange() command arules package apriori() function examining data inspecting and evaluating rules reading in the data restricting which items to mine as.factor() function as.numeric() command assign_cluster() function assignment operators assignments, left-hand sides of association rules, 2^nd example problem mining with arules package apriori() function examining data inspecting and evaluating rules reading in the data restricting which items to mine overview asw (average silhouette width), 2^nd AUC (area under the curve) automatic printing

B

bagging classifiers bar areas bar charts checking distributions for single variables checking relationships between two variables with faceting base error rate base package basic analytics baskets Batch method beta regression betas between sum of squares (BSS), 2^nd, 3^rd bias bias variance decomposition bids bimodal distribution binary variables BinaryYScatterPlot function binomial classification binomial distribution bootstrap evaluation BSS (between sum of squares), 2^nd, 3^rd Buzz dataset buzz scoring by keyword

C

C hyperparameter, 2^nd c() operator, 2^nd, 3^rd calibration set Calinski-Harabasz (CH) index, 2^nd, 3^rd call-by-value effect CamelCase CART trees categorical variables, 2^nd bar charts for comparing comparing continuous and missing data in using xgboost with cbind operator, 2^nd cboot$bootmean cdata::pivot_to_rowrecs() cdata::unpivot_to_blocks cdatapivot_to_rowrecs() function cdataunpivot_to_blocks() function center attribute, 2^nd centering and scaling centroid CH (Calinski-Harabasz) index, 2^nd char.freq.bang character class character types, 2^nd Character vector Characterizing task chi-squared test chunks class of interest class() method, 2^nd classification, 2^nd, 3^rd accuracy classification problems confusion matrix data preparation for building model mkCrossFrameCExperiment() properly using treatment plan variable score frame defined F1 precision and recall sensitivity and specificity cleaning data domain-specific data cleaning missing values in categorical variables in numeric or logical variables nature of treating as information vtreat package for automatic treatment of ClevelandDotPlot function client role cluster analysis, 2^nd assigning new points to clusters data preparation distances cosine similarity Euclidean distance Hamming distance Manhattan (city block) distance hierarchical clustering with hclust bootstrap evaluation picking number of clusters principal components analysis k-means algorithm clusterboot() kmeans() function kmeansruns() function cluster stability clusterboot() function, 2^nd, 3^rd clustering, 2^nd, 3^rd, 4^th coalescing Codd-style operators coding style coef() function coefficient of determination coefficients, 2^nd, 3^rd, 4^th coefficients(model) function, 2^nd coefs vector collinearity, 2^nd colnames() command git help comma-separated values (CSV) comments comment character (#) writing effective complete cases complete.cases() function Comprehensive R Archive Network (CRAN), 2^nd, 3^rd, 4^th concatenate operator c() conditional transforms confusion matrix, 2^nd continuous histograms continuous variables, comparing categorical and coord_flip() function copy by value semantics correlation cos() function cosine similarity coverage CRAN (Comprehensive R Archive Network), 2^nd, 3^rd, 4^th create_pruned_vocabulary() function, 2^nd CRISP-DM (cross-industry, standard, process, for, data mining) cross-frame, 2^nd dangers of naively reusing data safely reusing data crossFrame element cross-language linkage method, to deploy models cryptographic hash, SHA CSV (comma-separated values) cumsum() customer churn, 2^nd customer_data2 dataset cutree() function cv.glmnet() function, 2^nd cva.glmnet() function

D

data architect role data collection and management stage data coordinates data dictionary, 2^nd data directory data engineering data selection ordering rows removing records with incomplete data subsetting rows and columns data transforms adding new columns aggregating multitable transforms combining data from multiple tables combining two or more ordered data frames quickly reshaping transforms data coordinates moving data from tall to wide form moving data from wide to tall form data frames data provenance, 2^nd data range problems data refresh data science projects roles in client data architect data scientist operations project sponsor setting expectations for stages of data collection and management defining goal model deployment and maintenance model evaluation and critique modeling presentation and documentation data scientists data selection ordering rows removing records with incomplete data subsetting rows and columns data shaping, reshaping transforms data coordinates moving data from tall to wide form moving data from wide to tall form data transformations, 2^nd adding new columns aggregating centering and scaling log transformations multitable transforms combining data from multiple tables combining two or more ordered data frames quickly normalization reshaping data coordinates moving data from tall to wide form moving data from wide to tall form data tubing data.frame class data.frame() function data.frames, 2^nd, 3^rd, 4^th, 5^th data.table by argument data.table class adding new columns, 2^nd appending columns appending rows combining many rows into summary rows full join inner join left join ordering rows removing records with incomplete data splitting tables subsetting rows and columns data.table package, 2^nd, 3^rd, 4^th, 5^th, 6^th data.table::melt.data.table() databases, using with R running database queries using query generator thinking relationally about data datasets package, 2^nd datatable::dcast.data.table() datatable::melt.data.table() DBI package dbplyr package, 2^nd dcast.data.table() decision surface decision trees degrees of freedom delayed class delegation, to R dendrograms denormalized form denormalized tables density estimation density plots dependent variables, 2^nd deploying models, 2^nd as HTTP services by export using Shiny derived columns design*() methods design_missingness_treatment() function, 2^nd, 3^rd designTreatments*() function designTreatmentsC() function, 2^nd, 3^rd, 4^th designTreatmentsN() function, 2^nd, 3^rd, 4^th designTreatmentsZ() function, 2^nd, 3^rd deviance evaluation measure df.null - df.model dgCMatrix class, 2^nd dim() function, 2^nd disparity in units dissimilarity dist() function distributions binomial distribution lognormal distribution normal distribution other R tools for R's distribution naming conventions documentation comments predicting popularity R markdown documenting data and producing model example purpose of technical details version control to explore projects to record history to share work document-term matrix domain empathy domain-specific data cleaning dot arrow pipe, 2^nd dot notation, 2^nd dot pipe %.>% dotplots dot-product similarity double density plot double-precision floating-point dply::bind_rows dplyr adding new columns, 2^nd appending columns appending rows combining many rows into summary rows full join inner join left join ordering rows removing records with incomplete data splitting tables subsetting rows and columns dplyr::bind_cols dplyr::filter, 2^nd dplyr::full_join dplyr::group_by dplyr::select dplyr::summarize drop = FALSE argument, 2^nd dtest data frame dummy variables, 2^nd, 3^rd

E

edf (effective degrees of freedom) effects coding efficiency, statistical elastic net, 2^nd end user presentations showing how model fits user workflow showing how to use the model summarizing project goals end-of-statement markers enrichment rate ensemble learning errors, 2^nd Euclidean distance evalframe evaluating models classification models accuracy confusion matrix F1 score precision and recall sensitivity and specificity measures of model performance overfitting K-fold cross-validation testing on held-out data probability models Akaike information criterion deviance double density plot log likelihood receiver operating characteristic curve scoring models root mean square error R-squared Excel Spreadsheet (XLS) exchangeability experimental design columns explain() function, 2^nd explainers explanatory variables, 2^nd explicit dot notation exploring data for problems summary statistics data range invalid values and outliers missing values units visualization and graphics checking distributions for single variables checking relationships between two variables exporting models extend() method eXtensible Markup Language (XML), 2^nd extrapolation

F

F1 score facets, 2^nd facet_wrap layer facet_wrap() command factor class factor coding factor variables Factor vector factors, 2^nd false negatives (FN), 2^nd false positive rate, 2^nd false positives (FP) family function filled bar charts, 2^nd filter() function Fisher scoring iterations, 2^nd Fisher's test for independence Fisher’s exact test fit_imdb_model() function fit_iris_example() function, 2^nd fixed-width files (FWF) floating-point format, 2^nd FN (false negatives), 2^nd forecasting FP (false positives) fpc package, 2^nd frequentist significance test F-statistic F-test, 2^nd, 3^rd full joins function arguments FWF (fixed-width files)

G

gam package gam() function, 2^nd, 3^rd, 4^th, 5^th GAMs (generalized additive models) extracting non-linear relationships one-dimensional regression example overview using for logistic regression using on actual data gap statistic Gaussian distributions generalization error generalized additive models. See GAMs. generalized linear models geom_hex layer geom_histogram layer geom_histogram() command geom_line layer geom_point layer, 2^nd geom_smooth function, 2^nd ggplot() function, 2^nd ggplot2 package, 2^nd, 3^rd, 4^th, 5^th ggpubr package ggstatsplot package Git installing, 2^nd starting project using command line using git diff to compare files from different commits using git log and git status to view progress using git log to find last time file was around using through RStudio git blame command git clone command, 2^nd git commit command git diff command git help log command git log command git pull command git push command, 2^nd git rebase command git remote add command git remote command git status, 2^nd git tag command glm() function, 2^nd, 3^rd, 4^th, 5^th, 6^th, 7^th, 8^th, 9^th, 10^th, 11^th glmnet elastic net lasso regression ridge regression glmnet method glmnet package glmnet::cv.glmnet() function glmnet::glmnet() function glmnetUtils package, 2^nd, 3^rd goal defining stage gradient boosting models gradient-boosted trees, 2^nd gradient boosting for text classification iris example using xgboost with categorical variables GROUP BY queries group() function group_by() command, 2^nd grouped data

H

Hamming distance hash mark (#) hashes hclust() function, 2^nd, 3^rd bootstrap evaluation picking number of clusters Calinski-Harabasz index total within sum of squares principal components analysis head() command, 2^nd, 3^rd help() command, 2^nd, 3^rd, 4^th help(match) command help(model_support) command help(setwd) command, 2^nd hexbin plots, 2^nd, 3^rd HexBinPlot function hierarchical clustering, 2^nd bootstrap evaluation picking number of clusters Calinski-Harabasz index total within sum of squares principal components analysis histograms, 2^nd Homebrew homoscedastic errors horizontal offset HTML HTTP services, deploying models as hyperparameters, 2^nd hypothesis testing

I

IDE (integrated development environment) identical() method if statements impact impact coding implicit printing importance() function imputed value independent variables, 2^nd, 3^rd indicator variables, 2^nd, 3^rd infix scalar-valued operators inner joins input variables install.packages() command integrated development environment (IDE) interestMeasure() function intermediate values, organizing introducing indicators invalid values iris dataset, 2^nd, 3^rd, 4^th itemFrequency() function items

J

Jaccard coefficient join command JSON (JavaScript Object Notation)

K

k(,) function kernel functions, 2^nd defined support vectors kernel trick kernlab library k-fold cross-validation k-means algorithm, 2^nd clusterboot() kmeans() function kmeansruns() function k-means clustering, 2^nd kmeans() function, 2^nd kmeansruns() function knitr, 2^nd, 3^rd documenting data and producing model confirming data provenance recording performance of naive analysis using milestones to save time technical details block declaration format chunk options

L

L1 distance L1-regularized regression L2 distance L2-regularized regression Laplace smoothing lasso regression, 2^nd LaTeX lattice package layers lazy copying LearnR least squares method left joins length() function length-zero vector lhs() function library() command, 2^nd, 3^rd library(pkgname) command lift LIME (local interpretable model-agnostic explanations) automated sanity checking example for text classification explaining predictions representing documents for modeling training text classifier how LIME works lime package, 2^nd LIME variable importances lime() function line breaks line of perfect prediction line plots, 2^nd line wrapping linear combination linear regression building model finding relations and extracting advice making predictions PUMS dataset reading model summary and characterizing coefficient quality coefficients table original model call overall model quality summaries residuals summary when assumptions of are violated link function lists, 2^nd lm() command, 2^nd, 3^rd, 4^th, 5^th, 6^th, 7^th, 8^th load() command local interpretable model-agnostic explanations. See LIME. loess function log likelihood, 2^nd log transformations logarithmic scale logical vectors logistic regression, 2^nd, 3^rd, 4^th, 5^th building model finding relations and extracting advice making predictions overview reading model summary and characterizing coefficients deviance residuals summary Fisher scoring iterations original model call overall model quality summaries summary coefficients table using generalized additive models for logit function logit link, 2^nd logit space (link space) logit() lognormal distribution lognormally distributed monetary amounts log-odds, of probabilities log-scaled density plot

M

magrittr package magrittr pipe operator %>%, 2^nd, 3^rd Manhattan (city block) distance mapping problems to machine learning tasks classification problems grouping association rules clustering problem-to-method mapping scoring problems margins Markdown match() method, 2^nd matrices MAX() function m-dimensional linear model mean squared error measurement types Mercer’s theorem method chaining mgcv package, 2^nd Microsoft Excel workbooks Microsoft Word missing values, 2^nd missing-value imputation mixture of Gaussians mkCrossFrame*Experiment() method mkCrossFrame*Experiment() methods, vtreat’s mkCrossFrame*Experiment()/$crossFrame pattern mkCrossFrameCExperiment() function, 2^nd, 3^rd mkCrossFrameNExperiment() function, 2^nd, 3^rd, 4^th mkExperiment*() methods mk_formula() function, 2^nd mlogit package model deployment and maintenance stage model evaluation and critique stage model matrix, 2^nd model object model performance, determining lower bounds on model.matrix() function, 2^nd, 3^rd, 4^th modeling evaluating models classification models measures of model performance overfitting probability models scoring models local interpretable model-agnostic explanations automated sanity checking example for text classification how LIME works mapping problems to machine learning tasks classification problems grouping problem-to-method mapping scoring problems sampling for trade-offs modeling algorithm model_ridge$lambda.1se model_ridge$lambda.min MongoDB multicategory classification multimodal data multinomial classification multiple comparison bias multiple comparison problems multiplicative process multitable transforms combining data from multiple tables full joins inner joins left joins right joins rolling joins combining two or more ordered data frames quickly appending columns appending rows splitting tables multivariate data matrix mutable data types mutate() function, 2^nd, 3^rd, 4^th

N

NA (not available) values, 2^nd, 3^rd, 4^th in categorical variables in numeric or logical variables nature of treating as information vtreat package for automatic treatment of vtreat variable treatment package na.locf() function, 2^nd na.omit() function na_if() function naked repositories named arguments named lists, 2^nd named maps NaN (not a number) narrow data ranges natural key columns NB (nota bene or note well) nchar() function needsSplit NEGATIVE (non-spam). See truth mark. negative coefficients negative R-squareds newpt data point n_features parameter N-fold cross-validation no operation (no-op) non-linear combinations nonsignaling --no-restore command-line flag set normal distribution normalization (rescaling) normalized form not a number (NaN) not available values. See NA (not available) values. nota bene or note well (NB) nu hyperparameter, 2^nd null deviance null model, 2^nd NULL values, 2^nd, 3^rd NULL vector Numeric vector

O

one-hot encoding, 2^nd, 3^rd one-versus-rest classifier operations role operator, 2^nd, 3^rd, 4^th order() function outcome variable outliers out-of-bag samples overfitting, 2^nd, 3^rd, 4^th, 5^th K-fold cross-validation testing on held-out data overlaid density plot

P

package notation payload columns PCR (principal components regression) peer presentations discussing related work discussing results and future work discussing your approach introducing problem permTestAUC() function phi() function, 2^nd, 3^rd piped notation pipe-separated (vertical bar) files pivoting, 2^nd plot() function plot(gammodel) function plot_features() function, 2^nd plot_text_explanations() function plumber package PMML (Predictive Model Markup Language) Poisson regression POSITIVE (spam). See truth mark. prcomp() function precision, 2^nd, 3^rd, 4^th, 5^th predict() function, 2^nd, 3^rd, 4^th, 5^th, 6^th, 7^th predictions, 2^nd prepare() method, 2^nd, 3^rd, 4^th, 5^th, 6^th, 7^th presentations end user presentations showing how model fits user workflow showing how to use the model summarizing project goals peer presentations discussing related work discussing results and future work discussing your approach introducing problem project sponsor presentations filling in details making recommendations and discussing future work stating project results summarizing project goals principal components analysis principal components regression (PCR) print() function, 2^nd printing probability models Akaike information criterion deviance double density plot log likelihood receiver operating characteristic curve problem-to-method mapping project sponsor presentations filling in details making recommendations and discussing future work stating project results summarizing project goals project sponsors provenance columns PRTPlot() function pseudo distance pseudo R-squared, 2^nd pseudo-random sample PUMS (Public Use Microdata Sample) data curating data examining and conditioning data factor coding linear regression working with p-value (significance), 2^nd, 3^rd

Q

quasi-separation query generators Quick-R quotes

R

R, 2^nd, 3^rd installing, 2^nd installing tools book-support materials R package system, 2nd required packages primary data types data frames factors lists matrices NULL and NA slots vectors primary features of assignment object system share-by-value characteristics vectorized operations programming basics assignment operators comment character data.frame class delegating to R factors identifiers left-hand sides of assignments line breaks lists NA value named arguments NULL value organizing intermediate values package notation printing semicolons value semantics vectors relational databases curating data examining and conditioning data factor coding production-size example working with resources for installing R views online structured data less-structured data well-structured data using databases with running database queries using query generator thinking relationally about data R markdown documenting data and produce model confirming data provenance recording performance of naive analysis using milestones to save time example purpose of technical details block declaration format chunk options RAND command random forests, 2^nd, 3^rd exporting to SQL with tidypredict variable importance, 2^nd randomForest package randomForest() function, 2^nd ranking tasks raw (unscaled) variables rbind R-bloggers Rcpp package, 2^nd RDF triples reactive programming read.table() function, 2^nd read.transactions() function reader package readr package readRDS() command readxl package rebase recall, 2^nd, 3^rd, 4^th, 5^th, 6^th receiver operating characteristic curve (ROC) record grouping rect.hclust() function reference level, 2^nd, 3^rd, 4^th reference semantics regression modeling, data preparation for regression testing regression to the mean regressions, 2^nd regularization, 2^nd example of quasi-separation types of elastic net lasso regression ridge regression with glmnet elastic net solution lasso regression solution ridge regression solution relational databases curating data examining and conditioning data factor coding production-size example working with relations, finding relevel() function remote relation residual deviance residual standard error residuals, 2^nd reversion to mediocrity ridge regression, 2^nd right joins rm.duplicates = TRUE argument rm() command RMSE (root mean square error), 2^nd, 3^rd, 4^th, 5^th ROC (receiver operating characteristic curve) rolling joins root mean square error (RMSE), 2^nd, 3^rd, 4^th, 5^th rownames() function rows roxygen2 R package rquery package, 2^nd, 3^rd, 4^th R-squared, 2^nd adjusted multiple pseudo R-squared RStudio, 2^nd, 3^rd, 4^th community for installing, 2^nd using Git through RStudio Desktop RTools runif() function

S

S programming language s() function, 2^nd, 3^rd sample_frac() function sample_n() function sampling creating sample group columns data provenance record grouping splitting data into training and test sets save() command saveRDS() function scale attribute, 2^nd scale() function, 2^nd, 3^rd scaling scatter plots, 2^nd, 3^rd schema documentation scikit-learn, Python score frame scoring, 2^nd scoring models root mean square error R-squared scoring problems scoring residuals sdata data frame, 2^nd se = FALSE argument sensitivity, 2^nd sentinel values, 2^nd sepal measurements separable data separation Services method sessionInfo() command set.seed() command setorderv() function setosa, 2^nd setwd() function, 2^nd shadow graphs shadow histograms shadow plots, 2^nd ShadowHist() function ShadowPlot command Shiny tool side-by-side bar charts, 2^nd sigmoid function signed logarithm significance lack of testing vs. goodness of fit sigr package, 2^nd sigr::wrapFTest() function silhouette clustering similarity sin() function single-variable models slotNames() slots smoothing smoothing curves, 2^nd SOA (services oriented architecture) soft margin sort() function spam filters spam model spam proportion sparse matrix specificity, 2^nd split() function sponsor sign-off SQL (Structured Query Language), 2^nd SQL WHEN clause sqldf package sqr_edist() function Stack Overflow R section stack() notation stacked bar charts, 2^nd, 3^rd standard error ribbon statistical efficiency statistical theory A/B tests evaluating setting up power of tests specialized statistical tests statistical philosophy bias variance decomposition exchangeability statistical efficiency statistical view of data, examples of omitted variable bias example of overview spoiled analysis working around sampling bias statistically efficient estimators statistically significant thresholds stats package, 2^nd, 3^rd, 4^th str() function, 2^nd, 3^rd str(dTrain) command stringsAsFactors = FALSE argument string-valued (categorical) variable structured data less-structured tabular data examining transforming well-structured data in comma-separated values format examining loading well-structured data in other data formats structured values subset() function, 2^nd sum(error_sq) (sum squared error) summarize() function summary statistics, 2^nd typical problems revealed by data range issues invalid values missing values outliers units summary() function, 2^nd, 3^rd, 4^th, 5^th, 6^th, 7^th, 8^th summary(customer_data$marital.stat) command summary(dpus) command summary(dpus$COW) command summary(dTrain) command summary(model) command, 2^nd, 3^rd, 4^th summary(model)$coefficients supervised learning support vectors Surrogate key columns SVMs (support vector machines), 2^nd, 3^rd, 4^th kernel functions defined support vectors overview problem solving with spiral example with good kernels with oversimple kernels symbol names (identifiers) syntax error synthetic data points

T

table() command tab-separated values (.tsv) tall data form tapply() command target class test (holdout) set, 2^nd test data text classification gradient boosting for local interpretable model-agnostic explanations for explaining predictions representing documents for modeling training text classifier text2vec package the kernel trick The R Foundation theta angle thin data form tidypredict package tidyr solution tidyrgather() function tidyrspread() function Times attribute title column TN (true negatives) token column topics total variance TP (true positives) trades training error training set, 2^nd training_prepared data frame train_treated transactions object, 2^nd transform() function, 2^nd treat package for automatic treatment of treatment plans, 2^nd, 3^rd, 4^th tree-based methods, 2^nd bagging basic decision tree gradient-boosted trees gradient boosting for text classification iris example using xgboost with categorical variables random forests true negative rate true outcome true positive rate truth mark TSS (total sum of squares) TSV (tab-separated values) two-category classification two-dimensional histograms typeof() command

U

unbiased predictors unconditioned transform underscore notation underscore style ungroup() function ungrouped data unimodal distribution units cluster analysis unit problems unstack() notation unsupervised learning unsupervised methods association rules example problem mining with arules package overview cluster analysis assigning new points to clusters data preparation distances hierarchical clustering k-means algorithm unsystematic errors utils package, 2^nd utils::read.table() command

V

validation, sampling for value semantics, 2^nd value.var argument values values in categorical variables values in numeric or logical variables values nature of value_variables_C() method, 2^nd varImpPlot() function vectorized operations vectors, 2^nd version control to explore projects finding out who wrote what and when using git diff to compare files from different commits using git log to find last time file was around to record history choosing project directory structure starting Git project using command line using add/commit pairs to checkpoint work using git log and git status to view progress using Git through RStudio to share work setting up remote repository relations using push and pull to synchronize work with remote repositories versioning vertical offset View() command, 2^nd visualization and graphics checking distributions for single variables bar charts density plots dotplots histograms checking relationships between two variables bar charts for two categorical variables comparing continuous and categorical variables hexbin plots line plots scatter plots and smoothing curves overview vtreat variable treatment package, 2^nd cross-frame dangers of naively reusing data safely reusing data data preparation for classification building model properly using treatment plan variable score frame data preparation for regression modeling dataset bull-in-the-china-shop approach characterizing outcome impact coding indicator variables missing values phases of purpose of treatment plan

W

with() function, 2^nd wrapr package, 2^nd wrapr pipe wrapr::orderv() WSS (within sum of squares), 2^nd, 3^rd WVPlots library, 2^nd WVPlots package, 2^nd

X

xcenter attribute Xcode tools xgb.cv() function, 2^nd xgboost package, 2^nd, 3^rd, 4^th, 5^th, 6^th xgboost() function, 2^nd, 3^rd XLS (Excel Spreadsheet) XLSX XML (eXtensible Markup Language), 2^nd xscale attribute

Y

YAML (yet another markup language)

Z

zeallot package, 2^nd zoo package

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