Welcome to the world of practical Machine Learning With R! This marks the beginning of your journey with the book, Hands-On Machine Learning with R, a comprehensive resource meticulously crafted to guide you through the most impactful machine learning methodologies using the R programming language.
This book is designed to be your hands-on companion, offering practical modules covering a wide array of machine learning techniques. You’ll delve into:
- Generalized low rank models
- Clustering algorithms for data segmentation
- Autoencoders for dimensionality reduction and feature learning
- Regularized models to prevent overfitting
- Random forests for robust predictions
- Gradient boosting machines for high accuracy
- Deep neural networks for complex patterns
- Stacking and super learners for ensemble modeling
- And much more!
Our primary goal is to empower you with the ability to not only build and implement these models using R packages known for their scalability and reliability but also to cultivate a deep intuition for the strengths and weaknesses inherent in each technique. We prioritize clarity and understanding, minimizing complex mathematical jargon where possible, while providing ample resources for those who wish to explore the theoretical underpinnings in greater depth.
Who Will Benefit from This Book?
This book serves as an indispensable guide for practitioners seeking to navigate the machine learning landscape and for anyone eager to grasp the intuition behind modern and powerful methods widely adopted in the machine learning community. Even if you possess a strong foundation in analytic methodologies, this book offers significant value as a practical reference for implementing these techniques using various R packages.
While numerous online resources such as videos, blog posts, and tutorials exist, we recognized a gap in consistent, comprehensive, and unbiased guidance across different implementation packages. This book was born out of the need to address this very inconsistency.
It’s important to note that this book is not intended as an introductory text to R or general programming. We assume readers have a working familiarity with the R language, including function definitions, object management, program flow control, and other fundamental tasks. For those new to R, we recommend exploring “R for Data Science” by Wickham and Grolemund 2016 to master the essentials of data science with R, such as data importing, cleaning, transformation, visualization, and exploration. To further enhance your R programming expertise, “Advanced R” by Wickham 2014 is an excellent resource. Furthermore, this book does not aim to be a deep theoretical exploration of machine learning algorithms. For in-depth theoretical understanding, resources like “Elements of Statistical Learning” (J. Friedman, Hastie, and Tibshirani 2001), “Computer Age Statistical Inference” (Efron and Hastie 2016), and “Deep Learning” (Goodfellow, Bengio, and Courville 2016) are highly recommended.
Instead, our focus is to equip R users with the skills to effectively utilize the machine learning ecosystem within R. This involves leveraging diverse R packages such as glmnet, h2o, ranger, xgboost, lime, and others to build models and extract valuable insights from your data. We champion a hands-on learning approach, fostering intuitive understanding through practical examples and just-enough theory. While reading this book without R is possible, we strongly encourage you to actively engage with the code examples provided throughout to maximize your learning experience.
Why R for Machine Learning?
Over the past two decades, R has solidified its position as a leading tool for scientific computing and statistical analysis. Its prominence in data science stems from a vibrant and expanding ecosystem of third-party packages. Key packages include tidyverse for data manipulation and exploration, high-performance machine learning packages like h2o, ranger, and xgboost, interpretability tools like iml, pdp, and vip, and a wealth of other specialized tools that we will introduce in the following chapters. R’s comprehensive ecosystem makes it an ideal choice for tackling machine learning challenges, offering both breadth and depth for practitioners of all levels.
Conventions Used
Throughout this book, we employ specific formatting conventions to enhance readability and clarity:
- strong italic: Denotes new terms as they are introduced.
- bold: Indicates package names and file names for easy identification.
inline code: Highlights functions or commands that you can directly type into your R console.- code chunk: Represents blocks of code that are intended for execution in R.
<span>1</span> <span>+</span><span>2</span>
<span>## [1] 3</span>Furthermore, you will encounter special code blocks accompanied by images to signify important information:
Signifies a valuable tip or suggestion to improve your workflow or understanding.
Signifies a general note providing additional context or information.
Signifies a warning or caution about potential pitfalls or considerations.
Expand Your Learning
To further enrich your machine learning knowledge, we encourage you to explore the numerous excellent resources available. Throughout the book, we point to resources that we have personally found invaluable for deeper methodological understanding and practical application. Due to space limitations in the printed version, the online supplementary material at https://koalaverse.github.io/homlr/ offers extended chapter content (e.g., random forest package benchmarking) and new material we couldn’t include in the book (e.g., random hyperparameter search). This online resource also provides access to the datasets used in the book, teaching materials (slides and exercises), and more to support your learning journey in machine learning with R.
We Value Your Feedback
Your feedback is essential to improving this resource. If you encounter any errors or bugs, please report them by creating an issue at https://github.com/koalaverse/homlr/issues. Your contributions will help us make Hands-On Machine Learning with R even better for future learners.
Acknowledgements
We extend our sincere gratitude to the many individuals who contributed their valuable feedback, typo corrections, and insightful discussions during the writing process. Our GitHub contributors include (@)agailloty, (@)asimumba, (@)benprew, (@)bfgray3, (@)bragks, (@)cunningjames, (@)DesmondChoy, (@)erickeniuk, (@)j-ryanhart, (@)lcreteig, (@)liangwu82, (@)Lianta, (@)mccurcio, (@)mmelcher76, (@)MMonterosso89, (@)nsharkey, (@)raycblai, (@)schoonees, (@)tpristavec and (@)william3031. We also thank Alex Gutman, Greg Anderson, Jay Cunningham, Joe Keller, Mike Pane, Scott Crawford, and numerous other colleagues for their invaluable input on the machine learning content.
Software Environment
This book was developed using the following software packages and R version. All code was executed on a 2017 MacBook Pro with specific hardware configurations as detailed below:
<span># packages used</span>
pkgs <-<span>c</span>( <span>"AmesHousing"</span>, <span>"AppliedPredictiveModeling"</span>, <span>"bookdown"</span>, <span>"broom"</span>, <span>"caret"</span>, <span>"caretEnsemble"</span>, <span>"cluster"</span>, <span>"cowplot"</span>, <span>"DALEX"</span>, <span>"data.table"</span>, <span>"doParallel"</span>, <span>"dplyr"</span>, <span>"dslabs"</span>, <span>"e1071"</span>, <span>"earth"</span>, <span>"emo"</span>, <span>"extracat"</span>, <span>"factoextra"</span>, <span>"foreach"</span>, <span>"forecast"</span>, <span>"ggbeeswarm"</span>, <span>"ggmap"</span>, <span>"ggplot2"</span>, <span>"ggplotify"</span>, <span>"gbm"</span>, <span>"glmnet"</span>, <span>"gridExtra"</span>, <span>"h2o"</span>, <span>"HDclassif"</span>, <span>"iml"</span>, <span>"ipred"</span>, <span>"kableExtra"</span>, <span>"keras"</span>, <span>"kernlab"</span>, <span>"knitr"</span>, <span>"lime"</span>, <span>"markdown"</span>, <span>"MASS"</span>, <span>"Matrix"</span>, <span>"mclust"</span>, <span>"mlbench"</span>, <span>"NbClust"</span>, <span>"pBrackets"</span>, <span>"pcadapt"</span>, <span>"pdp"</span>, <span>"plotROC"</span>, <span>"pls"</span>, <span>"pROC"</span>, <span>"purrr"</span>, <span>"ranger"</span>, <span>"readr"</span>, <span>"recipes"</span>, <span>"reshape2"</span>, <span>"ROCR"</span>, <span>"rpart"</span>, <span>"rpart.plot"</span>, <span>"rsample"</span>, <span>"scales"</span>, <span>"sparsepca"</span>, <span>"stringr"</span>, <span>"subsemble"</span>, <span>"SuperLearner"</span>, <span>"tfruns"</span>, <span>"tfestimators"</span>, <span>"tidyr"</span>, <span>"vip"</span>, <span>"visdat"</span>, <span>"xgboost"</span>, <span>"yardstick"</span> )
<span># package & session info</span>
sessioninfo<span>::</span><span>session_info</span>(pkgs)
<span>#> ─ Session info ──────────────────────────────────────────────────────────</span>
<span>#> setting value</span>
<span>#> version R version 3.6.2 (2019-12-12)</span>
<span>#> os macOS Mojave 10.14.6</span>
<span>#> system x86_64, darwin15.6.0</span>
<span>#> ui X11</span>
<span>#> language (EN)</span>
<span>#> collate en_US.UTF-8</span>
<span>#> ctype en_US.UTF-8</span>
<span>#> tz America/New_York</span>
<span>#> date 2020-02-01</span>
<span>#></span>
<span>#> ─ Packages ──────────────────────────────────────────────────────────────</span>
<span>#> ! package * version date lib</span>
<span>#> abind 1.4-5 2016-07-21 [1]</span>
<span>#> AmesHousing 0.0.3 2017-12-17 [1]</span>
<span>#> ape 5.3 2019-03-17 [1]</span>
<span>#> AppliedPredictiveModeling 1.1-7 2018-05-22 [1]</span>
<span>#> askpass 1.1 2019-01-13 [1]</span>
<span>#> assertthat 0.2.1 2019-03-21 [1]</span>
<span>#> backports 1.1.5 2019-10-02 [1]</span>
<span>#> base64enc 0.1-3 2015-07-28 [1]</span>
<span>#> beeswarm 0.2.3 2016-04-25 [1]</span>
<span>#> BH 1.69.0-1 2019-01-07 [1]</span>
<span>#> bitops 1.0-6 2013-08-17 [1]</span>
<span>#> bookdown 0.11 2019-05-28 [1]</span>
<span>#> boot 1.3-23 2019-07-05 [1]</span>
<span>#> broom 0.5.2 2019-04-07 [1]</span>
<span>#> callr 3.3.2 2019-09-22 [1]</span>
<span>#> car 3.0-3 2019-05-27 [1]</span>
<span>#> carData 3.0-2 2018-09-30 [1]</span>
<span>#> caret 6.0-84 2019-04-27 [1]</span>
<span>#> caretEnsemble 2.0.0 2016-02-07 [1]</span>
<span>#> caTools 1.17.1.2 2019-03-06 [1]</span>
<span>#> cellranger 1.1.0 2016-07-27 [1]</span>
<span>#> checkmate 1.9.3 2019-05-03 [1]</span>
<span>#> class 7.3-15 2019-01-01 [1]</span>
<span>#> cli 2.0.1 2020-01-08 [1]</span>
<span>#> clipr 0.7.0 2019-07-23 [1]</span>
<span>#> cluster 2.1.0 2019-06-19 [1]</span>
<span>#> codetools 0.2-16 2018-12-24 [1]</span>
<span>#> colorspace 1.4-1 2019-03-18 [1]</span>
<span>#> config 0.3 2018-03-27 [1]</span>
<span>#> CORElearn 1.53.1 2018-09-29 [1]</span>
<span>#> cowplot 0.9.4 2019-01-08 [1]</span>
<span>#> crayon 1.3.4 2017-09-16 [1]</span>
<span>#> crosstalk 1.0.0 2016-12-21 [1]</span>
<span>#> curl 4.3 2019-12-02 [1]</span>
<span>#> cvAUC 1.1.0 2014-12-09 [1]</span>
<span>#> DALEX 0.4 2019-05-17 [1]</span>
<span>#> data.table 1.12.6 2019-10-18 [1]</span>
<span>#> dendextend 1.12.0 2019-05-11 [1]</span>
<span>#> DEoptimR 1.0-8 2016-11-19 [1]</span>
<span>#> digest 0.6.22 2019-10-21 [1]</span>
<span>#> doParallel 1.0.14 2018-09-24 [1]</span>
<span>#> dplyr 0.8.3 2019-07-04 [1]</span>
<span>#> dslabs 0.5.2 2018-12-19 [1]</span>
<span>#> e1071 1.7-2 2019-06-05 [1]</span>
<span>#> earth 5.1.1 2019-04-12 [1]</span>
<span>#> ellipse 0.4.1 2018-01-05 [1]</span>
<span>#> ellipsis 0.3.0 2019-09-20 [1]</span>
<span>#> emo 0.0.0.9000 2019-05-03 [1]</span>
<span>#> evaluate 0.14 2019-05-28 [1]</span>
<span>#> R extracat <NA> <NA> [?]</span>
<span>#> factoextra 1.0.5 2017-08-22 [1]</span>
<span>#> FactoMineR 1.41 2018-05-04 [1]</span>
<span>#> fansi 0.4.1 2020-01-08 [1]</span>
<span>#> fit.models 0.5-14 2017-04-06 [1]</span>
<span>#> flashClust 1.01-2 2012-08-21 [1]</span>
<span>#> forcats 0.4.0 2019-02-17 [1]</span>
<span>#> foreach 1.4.4 2017-12-12 [1]</span>
<span>#> forecast 8.7 2019-04-29 [1]</span>
<span>#> foreign 0.8-72 2019-08-02 [1]</span>
<span>#> forge 0.2.0 2019-02-26 [1]</span>
<span>#> Formula 1.2-3 2018-05-03 [1]</span>
<span>#> fracdiff 1.4-2 2012-12-02 [1]</span>
<span>#> furrr 0.1.0 2018-05-16 [1]</span>
<span>#> future 1.13.0 2019-05-08 [1]</span>
<span>#> gbm 2.1.5 2019-01-14 [1]</span>
<span>#> gdata 2.18.0 2017-06-06 [1]</span>
<span>#> generics 0.0.2 2018-11-29 [1]</span>
<span>#> ggbeeswarm 0.6.0 2017-08-07 [1]</span>
<span>#> ggmap 3.0.0 2019-02-05 [1]</span>
<span>#> ggplot2 3.2.1 2019-08-10 [1]</span>
<span>#> ggplotify 0.0.3 2018-08-03 [1]</span>
<span>#> ggpubr 0.2 2018-11-15 [1]</span>
<span>#> ggrepel 0.8.1 2019-05-07 [1]</span>
<span>#> ggsci 2.9 2018-05-14 [1]</span>
<span>#> ggsignif 0.5.0 2019-02-20 [1]</span>
<span>#> glmnet 3.0 2019-11-09 [1]</span>
<span>#> globals 0.12.4 2018-10-11 [1]</span>
<span>#> glue 1.3.1 2019-03-12 [1]</span>
<span>#> gower 0.2.0 2019-03-07 [1]</span>
<span>#> gplots 3.0.1.1 2019-01-27 [1]</span>
<span>#> gridExtra 2.3 2017-09-09 [1]</span>
<span>#> gridGraphics 0.4-1 2019-05-20 [1]</span>
<span>#> gridSVG 1.7-0 2019-02-12 [1]</span>
<span>#> gtable 0.3.0 2019-03-25 [1]</span>
<span>#> gtools 3.8.1 2018-06-26 [1]</span>
<span>#> h2o 3.22.1.1 2019-01-10 [1]</span>
<span>#> haven 2.2.0 2019-11-08 [1]</span>
<span>#> HDclassif 2.1.0 2018-05-11 [1]</span>
<span>#> hexbin 1.27.3 2019-05-14 [1]</span>
<span>#> highr 0.8 2019-03-20 [1]</span>
<span>#> hms 0.5.2 2019-10-30 [1]</span>
<span>#> htmltools 0.3.6 2017-04-28 [1]</span>
<span>#> htmlwidgets 1.3 2018-09-30 [1]</span>
<span>#> httpuv 1.5.1 2019-04-05 [1]</span>
<span>#> httr 1.4.1 2019-08-05 [1]</span>
<span>#> iml 0.9.0 2019-02-05 [1]</span>
<span>#> inum 1.0-1 2019-04-25 [1]</span>
<span>#> ipred 0.9-9 2019-04-28 [1]</span>
<span>#> iterators 1.0.10 2018-07-13 [1]</span>
<span>#> jpeg 0.1-8.1 2019-10-24 [1]</span>
<span>#> jsonlite 1.6 2018-12-07 [1]</span>
<span>#> kableExtra 1.1.0 2019-03-16 [1]</span>
<span>#> keras 2.2.5.0 2019-10-08 [1]</span>
<span>#> kernlab 0.9-27 2018-08-10 [1]</span>
<span>#> KernSmooth 2.23-16 2019-10-15 [1]</span>
<span>#> knitr 1.25 2019-09-18 [1]</span>
<span>#> labeling 0.3 2014-08-23 [1]</span>
<span>#> later 0.8.0 2019-02-11 [1]</span>
<span>#> lattice 0.20-38 2018-11-04 [1]</span>
<span>#> lava 1.6.5 2019-02-12 [1]</span>
<span>#> lazyeval 0.2.2 2019-03-15 [1]</span>
<span>#> leaps 3.0 2017-01-10 [1]</span>
<span>#> libcoin 1.0-4 2019-02-28 [1]</span>
<span>#> lifecycle 0.1.0 2019-08-01 [1]</span>
<span>#> lime 0.5.1 2019-11-12 [1]</span>
<span>#> listenv 0.7.0 2018-01-21 [1]</span>
<span>#> lme4 1.1-21 2019-03-05 [1]</span>
<span>#> lmtest 0.9-37 2019-04-30 [1]</span>
<span>#> lubridate 1.7.4 2018-04-11 [1]</span>
<span>#> magrittr 1.5 2014-11-22 [1]</span>
<span>#> maptools 0.9-5 2019-02-18 [1]</span>
<span>#> markdown 1.1 2019-08-07 [1]</span>
<span>#> MASS 7.3-51.4 2019-03-31 [1]</span>
<span>#> Matrix 1.2-18 2019-11-27 [1]</span>
<span>#> MatrixModels 0.4-1 2015-08-22 [1]</span>
<span>#> mclust 5.4.3 2019-03-14 [1]</span>
<span>#> memuse 4.0-0 2017-11-10 [1]</span>
<span>#> Metrics 0.1.4 2018-07-09 [1]</span>
<span>#> mgcv 1.8-31 2019-11-09 [1]</span>
<span>#> mime 0.8 2019-12-19 [1]</span>
<span>#> minqa 1.2.4 2014-10-09 [1]</span>
<span>#> mlbench 2.1-1 2012-07-10 [1]</span>
<span>#> mmapcharr 0.3.0 2019-02-26 [1]</span>
<span>#> ModelMetrics 1.2.2 2018-11-03 [1]</span>
<span>#> munsell 0.5.0 2018-06-12 [1]</span>
<span>#> mvtnorm 1.0-10 2019-03-05 [1]</span>
<span>#> NbClust 3.0 2015-04-13 [1]</span>
<span>#> nlme 3.1-142 2019-11-07 [1]</span>
<span>#> nloptr 1.2.1 2018-10-03 [1]</span>
<span>#> nnet 7.3-12 2016-02-02 [1]</span>
<span>#> nnls 1.4 2012-03-19 [1]</span>
<span>#> numDeriv 2016.8-1 2016-08-27 [1]</span>
<span>#> openssl 1.4.1 2019-07-18 [1]</span>
<span>#> openxlsx 4.1.0.1 2019-05-28 [1]</span>
<span>#> partykit 1.2-3 2019-01-31 [1]</span>
<span>#> pbapply 1.4-2 2019-08-31 [1]</span>
<span>#> pbkrtest 0.4-7 2017-03-15 [1]</span>
<span>#> pBrackets 1.0 2014-10-17 [1]</span>
<span>#> pcadapt 4.1.0 2019-02-27 [1]</span>
<span>#> pcaPP 1.9-73 2018-01-14 [1]</span>
<span>#> pdp 0.7.0 2018-08-27 [1]</span>
<span>#> permute 0.9-5 2019-03-12 [1]</span>
<span>#> pillar 1.4.2 2019-06-29 [1]</span>
<span>#> pinfsc50 1.1.0 2016-12-02 [1]</span>
<span>#> pkgconfig 2.0.3 2019-09-22 [1]</span>
<span>#> plogr 0.2.0 2018-03-25 [1]</span>
<span>#> plotly 4.9.1 2019-11-07 [1]</span>
<span>#> plotmo 3.5.4 2019-04-06 [1]</span>
<span>#> plotrix 3.7-5 2019-04-07 [1]</span>
<span>#> plotROC 2.2.1 2018-06-23 [1]</span>
<span>#> pls 2.7-1 2019-03-23 [1]</span>
<span>#> plyr 1.8.4 2016-06-08 [1]</span>
<span>#> png 0.1-7 2013-12-03 [1]</span>
<span>#> polynom 1.4-0 2019-03-22 [1]</span>
<span>#> prediction 0.3.6.2 2019-01-31 [1]</span>
<span>#> prettyunits 1.0.2 2015-07-13 [1]</span>
<span>#> pROC 1.14.0 2019-03-12 [1]</span>
<span>#> processx 3.4.1 2019-07-18 [1]</span>
<span>#> prodlim 2018.04.18 2018-04-18 [1]</span>
<span>#> progress 1.2.2 2019-05-16 [1]</span>
<span>#> promises 1.0.1 2018-04-13 [1]</span>
<span>#> ps 1.3.0 2018-12-21 [1]</span>
<span>#> purrr 0.3.3 2019-10-18 [1]</span>
<span>#> quadprog 1.5-7 2019-05-06 [1]</span>
<span>#> quantmod 0.4-15 2019-06-17 [1]</span>
<span>#> quantreg 5.38 2018-12-18 [1]</span>
<span>#> R6 2.4.1 2019-11-12 [1]</span>
<span>#> ranger 0.11.2 2019-03-07 [1]</span>
<span>#> rARPACK 0.11-0 2016-03-10 [1]</span>
<span>#> RColorBrewer 1.1-2 2014-12-07 [1]</span>
<span>#> Rcpp 1.0.3 2019-11-08 [1]</span>
<span>#> RcppArmadillo 0.9.500.2.0 2019-06-12 [1]</span>
<span>#> RcppEigen 0.3.3.5.0 2018-11-24 [1]</span>
<span>#> RCurl 1.95-4.12 2019-03-04 [1]</span>
<span>#> readr 1.3.1 2018-12-21 [1]</span>
<span>#> readxl 1.3.1 2019-03-13 [1]</span>
<span>#> recipes 0.1.7 2019-09-15 [1]</span>
<span>#> rematch 1.0.1 2016-04-21 [1]</span>
<span>#> reshape2 1.4.3 2017-12-11 [1]</span>
<span>#> reticulate 1.13 2019-07-24 [1]</span>
<span>#> RgoogleMaps 1.4.3 2018-11-07 [1]</span>
<span>#> rio 0.5.16 2018-11-26 [1]</span>
<span>#> rjson 0.2.20 2018-06-08 [1]</span>
<span>#> rlang 0.4.4 2020-01-28 [1]</span>
<span>#> rmarkdown 1.15.1 2019-09-09 [1]</span>
<span>#> rmio 0.1.2 2019-02-22 [1]</span>
<span>#> robust 0.4-18 2017-04-27 [1]</span>
<span>#> robustbase 0.93-5 2019-05-12 [1]</span>
<span>#> ROCR 1.0-7 2015-03-26 [1]</span>
<span>#> rpart 4.1-15 2019-04-12 [1]</span>
<span>#> rpart.plot 3.0.7 2019-04-12 [1]</span>
<span>#> rrcov 1.4-7 2018-11-15 [1]</span>
<span>#> rsample 0.0.5 2019-07-12 [1]</span>
<span>#> RSpectra 0.14-0 2019-04-04 [1]</span>
<span>#> rstudioapi 0.10 2019-03-19 [1]</span>
<span>#> rsvd 1.0.0 2018-11-06 [1]</span>
<span>#> rvcheck 0.1.3 2018-12-06 [1]</span>
<span>#> rvest 0.3.5 2019-11-08 [1]</span>
<span>#> scales 1.0.0 2018-08-09 [1]</span>
<span>#> scatterplot3d 0.3-41 2018-03-14 [1]</span>
<span>#> selectr 0.4-1 2018-04-06 [1]</span>
<span>#> shape 1.4.4 2018-02-07 [1]</span>
<span>#> shiny 1.3.2 2019-04-22 [1]</span>
<span>#> shinythemes 1.1.2 2018-11-06 [1]</span>
<span>#> sourcetools 0.1.7 2018-04-25 [1]</span>
<span>#> sp 1.3-1 2018-06-05 [1]</span>
<span>#> SparseM 1.77 2017-04-23 [1]</span>
<span>#> sparsepca 0.1.2 2018-04-11 [1]</span>
<span>#> SQUAREM 2017.10-1 2017-10-07 [1]</span>
<span>#> stringi 1.4.3 2019-03-12 [1]</span>
<span>#> stringr * 1.4.0.9000 2019-11-12 [1]</span>
<span>#> R subsemble <NA> <NA> [?]</span>
<span>#> SuperLearner 2.0-25 2019-08-09 [1]</span>
<span>#> survival 3.1-8 2019-12-03 [1]</span>
<span>#> sys 3.3 2019-08-21 [1]</span>
<span>#> TeachingDemos 2.10 2016-02-12 [1]</span>
<span>#> tensorflow 2.0.0 2019-10-02 [1]</span>
<span>#> tfestimators 1.9.1 2018-11-07 [1]</span>
<span>#> tfruns 1.4 2018-08-25 [1]</span>
<span>#> tibble 2.1.3 2019-06-06 [1]</span>
<span>#> tidyr 1.0.0 2019-09-11 [1]</span>
<span>#> tidyselect 0.2.5 2018-10-11 [1]</span>
<span>#> timeDate 3043.102 2018-02-21 [1]</span>
<span>#> tinytex 0.15 2019-08-07 [1]</span>
<span>#> tseries 0.10-47 2019-06-05 [1]</span>
<span>#> TTR 0.23-4 2018-09-20 [1]</span>
<span>#> urca 1.3-0 2016-09-06 [1]</span>
<span>#> utf8 1.1.4 2018-05-24 [1]</span>
<span>#> vcfR 1.8.0 2018-04-17 [1]</span>
<span>#> vctrs 0.2.0 2019-07-05 [1]</span>
<span>#> vegan 2.5-5 2019-05-12 [1]</span>
<span>#> vip * 0.2.0 2020-01-20 [1]</span>
<span>#> vipor 0.4.5 2017-03-22 [1]</span>
<span>#> viridis 0.5.1 2018-03-29 [1]</span>
<span>#> viridisLite 0.3.0 2018-02-01 [1]</span>
<span>#> visdat 0.5.3 2019-02-15 [1]</span>
<span>#> webshot 0.5.1 2018-09-28 [1]</span>
<span>#> whisker 0.4 2019-08-28 [1]</span>
<span>#> withr 2.1.2 2018-03-15 [1]</span>
<span>#> xfun 0.10 2019-10-01 [1]</span>
<span>#> xgboost 0.90.0.2 2019-08-01 [1]</span>
<span>#> XML 3.98-1.19 2019-03-06 [1]</span>
<span>#> xml2 1.2.2 2019-08-09 [1]</span>
<span>#> xtable 1.8-4 2019-04-21 [1]</span>
<span>#> xts 0.11-2 2018-11-05 [1]</span>
<span>#> yaImpute 1.0-31 2019-01-09 [1]</span>
<span>#> yaml 2.2.0 2018-07-25 [1]</span>
<span>#> yardstick 0.0.3 2019-03-08 [1]</span>
<span>#> zeallot 0.1.0 2018-01-28 [1]</span>
<span>#> zip 2.0.4 2019-09-01 [1]</span>
<span>#> zoo 1.8-6 2019-05-28 [1]</span>
<span>#></span>
<span>#> [1] /Library/Frameworks/R.framework/Versions/3.6/Resources/library</span>
<span>#></span>
<span>#> R ── Package was removed from disk.</span>