<p><div><div><div><div data-show-faces="false" data-height="21" data-width="100" data-layout="button_count" data-send="true" data-href="https://www.r-bloggers.com/july-2017-new-package-picks/"></div></div><div></div></div><div></div></div>
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(This article was first published on <strong><a href="https://rviews.rstudio.com/2017/08/28/july-2017-new-package-picks/"> R Views</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>Two hundred and twenty-four new packages were added to CRAN in July. Below are my picks for the “Top 40” packages arranged in eight categories: Machine Learning, Science, Statistics, Numerical Methods, Statistics, Time Series, Utilities and Visualizations. Science and Numerical Methods are categories that I have not used before. The idea behind the Science category is to find a place for packages that appear to have been created with some particular scientific investigation or problem in mind. The Numerical Methods category is reserved for packages that, while they may be targeted to some general form of statistical analysis, emphasize numerical considerations and carefully constructed algorithms.</p>
<p>As always, my selections are heavily weighted by the availability of documentation beyond what is included in the package PDF. I rarely select packages that do not have a vignette or some other source of documentation about how the package can be used, for example, README files or a referenced URL. I almost never select “professional” packages, which I define as packages that are devoted to esoteric topics that either include no documentation beyond the PDF, or exclusively refer to papers that are protected by a paywall. While these packages usually comprise serious, valuable contributions to R, they also appear to have been written for very small audiences.</p>
<p>Finally, before listing this month’s Top 40, I would like to call attention to an awesome display of productivity by Kevin R. Coombes, who had fourteen packages on various topics accepted by CRAN in July: <a rel="nofollow" href="https://cran.r-project.org/package=BimodalIndex" target="_blank">BimodalIndex</a>, <a rel="nofollow" href="https://cran.r-project.org/package=ClassComparison" target="_blank">ClassComparison</a>, <a rel="nofollow" href="https://cran.r-project.org/package=ClassDiscovery" target="_blank">ClassDiscovery</a>, <a rel="nofollow" href="https://cran.r-project.org/package=CrossValidate" target="_blank">CrossValidate</a>, <a rel="nofollow" href="https://cran.r-project.org/package=GenAlgo" target="_blank">GeneAlgo</a>, <a rel="nofollow" href="https://cran.r-project.org/package=integIRTy" target="_blank">IntegIRTy</a>, <a rel="nofollow" href="https://cran.r-project.org/package=Modeler" target="_blank">Modeler</a>, <a rel="nofollow" href="https://cran.r-project.org/package=NameNeedle" target="_blank">NameNeedle</a>, <a rel="nofollow" href="https://cran.r-project.org/package=oompaBase" target="_blank">oompaBase</a>, <a rel="nofollow" href="https://cran.r-project.org/package=oompaData" target="_blank">oompaData</a>, <a rel="nofollow" href="https://cran.r-project.org/package=PreProcess" target="_blank">PreProcess</a>, <a rel="nofollow" href="https://cran.r-project.org/package=SIBERG" target="_blank">SIBERG</a>, <a rel="nofollow" href="https://cran.r-project.org/package=TailRank" target="_blank">TailRank</a> and <a rel="nofollow" href="https://cran.r-project.org/package=Umpire" target="_blank">Umpire</a>.</p>
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<h2>The July 2017 Top 40</h2>
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<h3>Machine Learning</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=autoBagging" target="_blank">autoBagging</a> v0.1.0: Implements a framework for automated machine learning that focuses on the optimization of bagging workflows. See the <a rel="nofollow" href="https://arxiv.org/pdf/1706.09367.pdf" target="_blank">paper</a> by Pinto et al. for details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=grf" target="_blank">grf</a> v0.9.3: Provides methods for non-parametric least-squares regression, quantile regression, and treatment effect estimation (optionally using instrumental variables).</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=iRF" target="_blank">iRF</a> v2.0.0: Provides functions to iteratively grow feature-weighted random forests and finds high-order feature interactions in a stable fashion. Look <a rel="nofollow" href="https://arxiv.org/pdf/1706.08457.pdf" target="_blank">here</a> for the details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=keras" target="_blank">keras</a> v2.0.5: Implements an interface to <a rel="nofollow" href="https://keras.io/" target="_blank">Keras</a>, a high-level neural networks API that runs on top of TensorFlow. There is an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/keras/vignettes/backend.html" target="_blank">Overview</a> of the Keras backend, and a number of vignettes including <a rel="nofollow" href="https://cran.rstudio.com/web/packages/keras/vignettes/about_keras_layers.html" target="_blank">Keras Layers</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/keras/vignettes/custom_layers.html" target="_blank">Writing Custom Keras Layers</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/keras/vignettes/about_keras_models.html" target="_blank">Keras Models</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/keras/vignettes/applications.html" target="_blank">Using Pre-Trained Models</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/keras/vignettes/faq.html" target="_blank">Sequential Models</a> and more.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=randomForestExplainer" target="_blank">randomForestExplainer</a> v0.9: Provide set of tools to help explain which variables are most important in a random forests. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/randomForestExplainer/vignettes/randomForestExplainer.html" target="_blank">vignette</a> provides examples of visualizing multiple performance measures.</p>
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<img src="https://i2.wp.com/rviews.rstudio.com/post/2017-08-21-Rickert-July-Pkgs_files/randomForestExplainer.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=sgmcmc" target="_blank">sgmcmc</a> v0.1.0: Provides functions to implement stochastic gradient Markov chain Monte Carlo (SGMCMC) methods for user-specified models. <a rel="nofollow" href="https://www.tensorflow.org/" target="_blank">TensorFlow</a> is used to calculate the gradients. There is a <a rel="nofollow" href="https://cran.rstudio.com/web/packages/sgmcmc/vignettes/sgmcmc.html" target="_blank">Getting Started Guide</a> and vignettes for <a rel="nofollow" href="https://cran.rstudio.com/web/packages/sgmcmc/vignettes/gaussMixture.html" target="_blank">Simulating from a Gaussian Mixture</a>, a <a rel="nofollow" href="https://cran.rstudio.com/web/packages/sgmcmc/vignettes/mvGauss.html" target="_blank">Multivariate Gaussian Mixture</a> and <a rel="nofollow" href="https://cran.rstudio.com/web/packages/sgmcmc/vignettes/logisticRegression.html" target="_blank">Logistic Regression</a>.</p>
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<h3>Numerical Methods</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=mcMST" target="_blank">mcMST</a> v1.0.0: Provides algorithms to approximate the Pareto-front of multi-criteria minimum spanning tree problems, along with a toolbox for generating multi-objective benchmark graph problems. There is an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/mcMST/vignettes/introduction.html" target="_blank">Introduction</a> and a vignette on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/mcMST/vignettes/Generation.html" target="_blank">benchmarking optimization problems</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=mize" target="_blank">mize</a> v0.1.1: Provides optimization algorithms, including conjugate gradient (<a rel="nofollow" href="https://en.wikipedia.org/wiki/Conjugate_gradient_method" target="_blank">CG</a>), Broyden-Fletcher-Goldfarb-Shanno (<a rel="nofollow" href="https://en.wikipedia.org/wiki/Broyden%E2%80%93Fletcher%E2%80%93Goldfarb%E2%80%93Shanno_algorithm" target="_blank">BFGS</a>), and the limited memory BFGS (<a rel="nofollow" href="https://en.wikipedia.org/wiki/Limited-memory_BFGS" target="_blank">L-BFGS</a>) methods. There is an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/mize/vignettes/mize.html" target="_blank">introduction</a> and vignettes on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/mize/vignettes/convergence.html" target="_blank">Convergence</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/mize/vignettes/mmds.html" target="_blank">Metric MDS</a>, and <a rel="nofollow" href="https://cran.rstudio.com/web/packages/mize/vignettes/stateful.html" target="_blank">Stateful Optimization</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=SuperGauss" target="_blank">SuperGauss</a> v1.0: Provides a fast C++ based algorithm for the evaluation of Gaussian time series, along with efficient implementations of the score and Hessian functions. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/SuperGauss/vignettes/SuperGauss-quicktut.html" target="_blank">vignette</a> shows an example of inference for the Hurst parameter.</p>
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<h3>Science</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=GROAN" target="_blank">GROAN</a> v1.0.0: Is a workbench for testing genomic regression accuracy on noisy phenotypes. It contains a noise-generator function. There is a vignette on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/GROAN/vignettes/GROAN.vignette.html" target="_blank">Genomic Regression in Noisy Scenarios</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=noaastormevents" target="_blank">noaastormevents</a> v0.1.0: Allows users to explore and plot data from the National Oceanic and Atmospheric Administration (NOAA) Storm Events database for United States counties through R. There is an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/noaastormevents/vignettes/noaastormevents.html" target="_blank">Overview</a> and a vignette providing <a rel="nofollow" href="https://cran.rstudio.com/web/packages/noaastormevents/vignettes/details.html" target="_blank">details</a>.</p>
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<img src="https://i2.wp.com/rviews.rstudio.com/post/2017-08-21-Rickert-July-Pkgs_files/noaastormevents.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=swmmr" target="_blank">swmmr</a> v0.7.0: Provides functions to connect the <a rel="nofollow" href="https://www.epa.gov/water-research/storm-water-management-model-swmm" target="_blank">Storm Water Management Model</a> (SWMM) of the United States Environmental Protection Agency (US EPA).</p>
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<div>
<h3>Statistics</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=blandr" target="_blank">blandr</a> v0.4.3: Contains functions to carry out Bland Altman analyses (also known as a Tukey mean-difference plot) as described by <a rel="nofollow" href="http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(86)90837-8/abstract" target="_blank">JM Bland and DG Altman</a>. See the <a rel="nofollow" href="https://cran.rstudio.com/web/packages/blandr/vignettes/introduction.html" target="_blank">vignette</a> for details.</p>
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<img src="https://i2.wp.com/rviews.rstudio.com/post/2017-08-21-Rickert-July-Pkgs_files/blandr.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=cnbdistr" target="_blank">cnbdistr</a> v1.0.1: Provides the distribution functions for the Conditional Negative Binomial distribution. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/cnbdistr/vignettes/my-vignette.html" target="_blank">vignette</a> shows the math.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=diffpriv" target="_blank">diffpriv</a> v0.4.2: Provides an implementation of major general-purpose mechanisms for privatizing statistics, models, and machine learners, within the framework of differential privacy of <a rel="nofollow" href="https://link.springer.com/chapter/10.1007%2F11681878_14" target="_blank">Dwork et al. (2006)</a>. There is a vignette on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/diffpriv/vignettes/bernstein.pdf" target="_blank">The Bernstein Mechanism</a> and an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/diffpriv/vignettes/diffpriv.pdf" target="_blank">Introduction</a></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=fence" target="_blank">fence</a> v1.0: Implements a new class of model-selection strategies for mixed-model selection, which includes linear and generalized linear mixed models. The idea involves a procedure to isolate a subgroup of what are known as correct models (of which the optimal model is a member). The package points to several references in the literature including papers by <a rel="nofollow" href="https://projecteuclid.org/euclid.aos/1216237296" target="_blank">Jiang et al. 2008</a>, <a rel="nofollow" href="http://publications.gc.ca/collections/collection_2010/statcan/12-001-X/12-001-x2010001-eng.pdf" target="_blank">Jiang et al. 2010</a>, <a rel="nofollow" href="http://www.intlpress.com/site/pub/files/_fulltext/journals/sii/2011/0004/0003/SII-2011-0004-0003-a014.pdf" target="_blank">Jiang et al. 2011</a>, <a rel="nofollow" href="https://academic.oup.com/biostatistics/article-lookup/doi/10.1093/biostatistics/kxr046" target="_blank">Nguyen et al. 2012</a>, and <a rel="nofollow" href="https://www.hindawi.com/archive/2014/830821/" target="_blank">Jiang 2014</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=llogistic" target="_blank">llogistic</a> v1.0.0: Provides density, distribution, quantile and random generation functions for the L-Logistic distribution with parameters <code>m</code> (median) and <code>b</code>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=metaBMA" target="_blank">metaBMA</a> v0.3.9: Provides functions to compute the posterior model probabilities the meta-analysis models assuming either fixed or random effects. See the paper by <a rel="nofollow" href="http://www.tandfonline.com/doi/full/10.1080/23743603.2017.1326760" target="_blank">Gronau et al.</a> and the <a rel="nofollow" href="https://cran.rstudio.com/web/packages/metaBMA/vignettes/metaBMA.html" target="_blank">vignette</a> for details.</p>
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<img src="https://i1.wp.com/rviews.rstudio.com/post/2017-08-21-Rickert-July-Pkgs_files/metaBMA.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=MFKnockoffs" target="_blank">MFKnockoffs</a> v0.9: Provides functions to create model-free knockoffs, a general procedure for controlling the false discovery rate <a rel="nofollow" href="https://en.wikipedia.org/wiki/False_discovery_rate" target="_blank">FDR</a> when performing variable selection. There are vignettes on using the the Model-Free Knockoff Filter <a rel="nofollow" href="https://cran.rstudio.com/web/packages/MFKnockoffs/vignettes/MFKnockoffs.html" target="_blank">Basic</a> and <a rel="nofollow" href="https://cran.rstudio.com/web/packages/MFKnockoffs/vignettes/advanced.html" target="_blank">Advanced</a>, and <a rel="nofollow" href="https://cran.rstudio.com/web/packages/MFKnockoffs/vignettes/fixed.html" target="_blank">Using the Filter with a Fixed Design Matrix</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=Modeler" target="_blank">Modeler</a> v3.4.2: Provides tools to define classes and methods to learn models and use them to predict binary outcomes. There is a vignette on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/Modeler/vignettes/Modeler.pdf" target="_blank">Learning and Predicting</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=msde" target="_blank">msde</a> v1.0: Implements an MCMC sampler for the posterior distribution of arbitrary, time-homogeneous, multivariate stochastic differential equation (SDE) models with possibly latent components. There is a vignette with <a rel="nofollow" href="https://cran.rstudio.com/web/packages/msde/vignettes/msde-exmodels.html" target="_blank">Sample Models</a> and another for <a rel="nofollow" href="https://cran.rstudio.com/web/packages/msde/vignettes/msde-quicktut.html" target="_blank">Inference</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=RBesT" target="_blank">RBesT</a> v1.2-3: Provides a tool set to support Bayesian evidence synthesis, including meta-analysis, prior derivation from historical data, operating characteristics, and analysis. There is an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/RBesT/vignettes/introduction.html" target="_blank">Introduction</a> and vignettes on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/RBesT/vignettes/customizing_plots.html" target="_blank">Customizing Plots</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/RBesT/vignettes/introduction_normal.html" target="_blank">Normal Endpoints</a>, and <a rel="nofollow" href="https://cran.rstudio.com/web/packages/RBesT/vignettes/robustMAP.html" target="_blank">Robust Priors</a>.</p>
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<img src="https://i1.wp.com/rviews.rstudio.com/post/2017-08-21-Rickert-July-Pkgs_files/RBesT.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=RcppTN" target="_blank">RcppTN</a> v0.2-1: Provides R and C++ functions to generate random deviates from and calculate moments of a Truncated Normal distribution using the <a rel="nofollow" href="https://link.springer.com/article/10.1007%2FBF00143942" target="_blank">algorithm of Robert (1995)</a>. There is a <a rel="nofollow" href="https://cran.rstudio.com/web/packages/RcppTN/vignettes/using.pdf" target="_blank">vignette</a> showing how to use the package, and one for <a rel="nofollow" href="https://cran.rstudio.com/web/packages/RcppTN/vignettes/speed.pdf" target="_blank">Performance</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=rsample" target="_blank">rsample</a> v0.0.1: Provides classes and functions to create and summarize different kinds of resampling objects. There is a vignette on the <a rel="nofollow" href="https://cran.rstudio.com/web/packages/rsample/vignettes/Basics.html" target="_blank">Basics</a>, and another for <a rel="nofollow" href="https://cran.rstudio.com/web/packages/rsample/vignettes/Working_with_rsets.html" target="_blank">Working with rsets</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=SMM" target="_blank">SMM</a> v1.0: Provides functions to simulate and estimate of Multi-State Discrete-Time Semi-Markov and Markov Models. The implementation details are described in two papers by Barbu, Limnios <a rel="nofollow" href="http://www.tandfonline.com/doi/abs/10.1080/10485250701261913" target="_blank">one</a> and <a rel="nofollow" href="http://www.tandfonline.com/doi/abs/10.1080/10485250701261913" target="_blank">two</a>, and one <a rel="nofollow" href="http://www.tandfonline.com/doi/abs/10.1080/10485252.2011.555543" target="_blank">paper</a> by Trevezas and Limnios. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/SMM/vignettes/SMM.pdf" target="_blank">vignette</a> also provides considerable detail.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=treeDA" target="_blank">treeDA</a> v0.02: Provides functions to perform sparse discriminant analysis on a combination of node and leaf predictors, when the predictor variables are structured according to a tree. There is a <a rel="nofollow" href="https://cran.rstudio.com/web/packages/treeDA/vignettes/treeda-vignette.html" target="_blank">vignette</a>.</p>
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<p><a rel="nofollow" href="https://cran.r-project.org/package=vennLasso" target="_blank">vennLasso</a> v0.1: Provides variable selection and estimation routines for models stratified by binary factors. There is a <a rel="nofollow" href="https://cran.rstudio.com/web/packages/vennLasso/vignettes/using_the_vennLasso_package.pdf" target="_blank">vignette</a>.</p>
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<div>
<h3>Time Series</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=sweep" target="_blank">sweep</a> v0.2.0: Provides tools for bringing <code>tidyverse</code> organization to time series forecasting. There is an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/sweep/vignettes/SW00_Introduction_to_sweep.html" target="_blank">Introduction</a>, as well as vignettes on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/sweep/vignettes/SW01_Forecasting_Time_Series_Groups.html" target="_blank">Forecasting</a> and <a rel="nofollow" href="https://cran.rstudio.com/web/packages/sweep/vignettes/SW02_Forecasting_Multiple_Models.html" target="_blank">Forecasting with Mutiple Models</a>.</p>
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<img src="https://i2.wp.com/rviews.rstudio.com/post/2017-08-21-Rickert-July-Pkgs_files/sweep.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=timetk" target="_blank">timetk</a> v0.1.0: Implements a toolkit for working with time series, including functions to interrogate time series objects and tibbles, and coerce between time-based tibbles (‘tbl’) and ‘xts’, ‘zoo’, and ‘ts’. There is an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/timetk/vignettes/TK00_Time_Series_Coercion.html" target="_blank">Introduction</a> and vignettes on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/timetk/vignettes/TK01_Working_With_Time_Series_Index.html" target="_blank">Working with time series index</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/timetk/vignettes/TK02_Making_A_Future_Time_Series_Index.html" target="_blank">Making a Future Index</a>, and <a rel="nofollow" href="https://cran.rstudio.com/web/packages/timetk/vignettes/TK03_Forecasting_Using_Time_Series_Signature.html" target="_blank">Forecasting</a>.</p>
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<h3>Utilities</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=dataCompareR" target="_blank">dataCompareR</a> v0.1.0: Contains functions to compare two tabular data objects with the specific intent of showing differences in a way that should make it easier to understand the differences. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/dataCompareR/vignettes/dataCompareR.html" target="_blank">vignette</a> shows how to use the package.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=dataPreparation" target="_blank">dataPreparation</a> v0.2: Provides functions for data preparation that take advantage of <code>data.table</code> efficiencies. There is a <a rel="nofollow" href="https://cran.rstudio.com/web/packages/dataPreparation/vignettes/dataPreparation.html" target="_blank">tutorial</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=datastructures" target="_blank">datastructures</a> v0.2.0: Provides implementations of advanced data structures such as hashmaps, heaps, and queues. There is a <a rel="nofollow" href="https://cran.rstudio.com/web/packages/datastructures/vignettes/datastructures.html" target="_blank">Tutorial</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=docker" target="_blank">docker</a> v0.0.2: Provides access to the Docker SDK from R via python, using the <a rel="nofollow" href="https://cran.r-project.org/package=reticulate" target="_blank">reticulate</a> package. There is a <a rel="nofollow" href="https://cran.rstudio.com/web/packages/docker/vignettes/Getting_Started_with_Docker.html" target="_blank">vignette</a> to get you started.</p>
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<img src="https://i0.wp.com/rviews.rstudio.com/post/2017-08-21-Rickert-July-Pkgs_files/vennLasso.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=seplyr" target="_blank">seplyr</a> v0.1.4: Supplies standard evaluation adapter methods for important common <code>dplyr</code> methods that currently have a non-standard programming interface. There is an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/seplyr/vignettes/seplyr.html" target="_blank">Introduction</a>, as well as vignettes for <a rel="nofollow" href="https://cran.rstudio.com/web/packages/seplyr/vignettes/using_seplyr.html" target="_blank">Using seplyr with dplyr</a>, the operator <a rel="nofollow" href="https://cran.rstudio.com/web/packages/seplyr/vignettes/named_map_builder.html" target="_blank">named map builder</a>, and the operator <a rel="nofollow" href="https://cran.rstudio.com/web/packages/seplyr/vignettes/rename_se.html" target="_blank">rename_se</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=vetr" target="_blank">vetr</a> v0.1.0: Provides a declarative template-based framework for verifying that objects meet structural requirements, and auto-composing error messages when they do not. There is a vignette on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/vetr/vignettes/alike.html" target="_blank">Alikeness</a> and one on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/vetr/vignettes/vetr.html" target="_blank">Trust, but Verify</a>.</p>
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<h3>Visualization</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=ggjoy" target="_blank">ggjoy</a> v0.3.0: Joyplots provide a convenient way of visualizing changes in distributions over time or space. <code>ggjoy</code> enables the creation of such plots in <code>ggplot2</code>. There is an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/ggjoy/vignettes/introduction.html" target="_blank">Introduction</a> and a <a rel="nofollow" href="https://cran.r-project.org/web/packages/ggjoy/vignettes/gallery.html" target="_blank">Gallery</a> of examples.</p>
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<img src="https://i0.wp.com/rviews.rstudio.com/post/2017-08-21-Rickert-July-Pkgs_files/ggjoy.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=ggplotgui" target="_blank">ggplotgui</a> v1.0.0: Implements a <code>shiny</code> app for creating and exploring <code>ggplot2</code> graphs that also generates the required R code. Look <a rel="nofollow" href="https://github.com/gertstulp/ggplotgui/" target="_blank">here</a> for examples.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=loon" target="_blank">loon</a> v1.1.0: Is an extensible toolkit for interactive data visualization and exploration. There are two vignettes containing examples: <a rel="nofollow" href="https://cran.rstudio.com/web/packages/loon/vignettes/minorities.html" target="_blank">Visible minorities in Canadian cities</a> and <a rel="nofollow" href="https://cran.rstudio.com/web/packages/loon/vignettes/teaching-example-smoothing.html" target="_blank">Smoothers and Bone Mineral Density</a></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=sugrrants" target="_blank">sugrrants</a> v0.1.0: Provides <code>ggplot2</code> graphics for analyzing time series data with the goal of fitting into the <code>tidyverse</code> and grammar of graphics framework. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/sugrrants/vignettes/frame-calendar.html" target="_blank">vignette</a> provides examples.</p>
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<img src="https://i0.wp.com/rviews.rstudio.com/post/2017-08-21-Rickert-July-Pkgs_files/sugrrants.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=tidygraph" target="_blank">tidygraph</a> v1.0.0: A graph, while not “tidy” in itself, can be thought of as two tidy data frames describing node and edge data respectively. <code>tidygraph</code> provides functions to manipulate these virtual data frames using the <code>dplyr</code> package. Look <a rel="nofollow" href="https://cran.rstudio.com/web/packages/tidygraph/README.html" target="_blank">here</a> for some details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=visdat" target="_blank">visdat</a> v0.1.0: provides functions to create preliminary exploratory data visualizations of an entire dataset using <code>ggplot2</code>. There is a <a rel="nofollow" href="https://cran.rstudio.com/web/packages/visdat/vignettes/using_visdat.html" target="_blank">vignette</a> to get you started.</p>
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