By default, R installs a set of packages during installation. Ensembling h2o models got me second place in the 2015 Actuaries Institute Kaggle competition, so I can attest to its usefulness. [Rdoc](http://www.rdocumentation.org/badges/version/stats)](http://www.rdocumentation.org/packages/stats), Compute Theoretical ACF for an ARMA Process, Self-Starting Nls Weibull Growth Curve Model, Distribution of the Wilcoxon Signed Rank Statistic, The (non-central) Chi-Squared Distribution, Convert ARMA Process to Infinite MA Process, Self-Starting Nls Asymptotic Regression Model, SSD Matrix and Estimated Variance Matrix in Multivariate Models, Self-Starting Nls Four-Parameter Logistic Model, Compute Tukey Honest Significant Differences, Compute Summary Statistics of Data Subsets, Puts Arbitrary Margins on Multidimensional Tables or Arrays, Self-Starting Nls Asymptotic Regression Model through the Origin, Self-Starting Nls Asymptotic Regression Model with an Offset, Comparisons between Multivariate Linear Models, Self-Starting Nls First-order Compartment Model, Pearson's Chi-squared Test for Count Data, Auto- and Cross- Covariance and -Correlation Function Estimation, Distribution of the Wilcoxon Rank Sum Statistic, Compute an AR Process Exactly Fitting an ACF, Classical (Metric) Multidimensional Scaling, Add or Drop All Possible Single Terms to a Model, Analysis of Deviance for Generalized Linear Model Fits, Fit Autoregressive Models to Time Series by OLS, Group Averages Over Level Combinations of Factors, Bandwidth Selectors for Kernel Density Estimation, Bartlett Test of Homogeneity of Variances, Cophenetic Distances for a Hierarchical Clustering, ARIMA Modelling of Time Series -- Preliminary Version, Functions to Check the Type of Variables passed to Model Frames, Confidence Intervals for Model Parameters, Discrete Integration: Inverse of Differencing, Classical Seasonal Decomposition by Moving Averages, Compute Allowed Changes in Adding to or Dropping from a Formula, Correlation, Variance and Covariance (Matrices), Test for Association/Correlation Between Paired Samples, Extracting the Model Frame from a Formula or Fit, Symbolic and Algorithmic Derivatives of Simple Expressions, Empirical Cumulative Distribution Function, Compute Efficiencies of Multistratum Analysis of Variance, Fligner-Killeen Test of Homogeneity of Variances, Apply a Function to All Nodes of a Dendrogram, Formula Notation for Flat Contingency Tables, Median Polish (Robust Twoway Decomposition) of a Matrix, Find Longest Contiguous Stretch of non-NAs, Power Calculations for Balanced One-Way Analysis of Variance Tests, Ordering or Labels of the Leaves in a Dendrogram, A Class for Lists of (Parts of) Model Fits, Compute Diagnostics for lsfit Regression Results, McNemar's Chi-squared Test for Count Data, Compute Tables of Results from an Aov Model Fit, Cochran-Mantel-Haenszel Chi-Squared Test for Count Data, Plot Autocovariance and Autocorrelation Functions, Standard Errors for Contrasts in Model Terms, Plot a Seasonal or other Subseries from a Time Series, End Points Smoothing (for Running Medians), Plot Method for Kernel Density Estimation. This field is for validation purposes and should be left unchanged. However, thanks to Dirk’s CRANberries service I occasionally spot a new gem, such as wbstats, which appeared on CRAN last week.. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Like mlr above, there is feature importance, actual vs model predictions, partial dependence plots: Yep, that looks like it needs a bit of cleaning - check out the course materials... but the key use of DALEX in addition to mlr is individual prediction explanations. RStudio is an open source integrated development environment (IDE) for creating and running R code. More packages are added later, … You can list the data sets by their names and then load a data set into memory to be used in your statistical analysis. The table below shows my favorite go-to R packages for data import, wrangling, visualization and analysis -- plus a few miscellaneous tasks tossed in. However, the dplyr syntax may more familiar for those who use SQL heavily, and personally I find it more intuitive. They are stored under a directory called "library" in the R environment. My top 10 Python packages for data science. tidyr. Similarly to the WDI package, wbstats offers an interface to the World Bank database.. With the functions of wbstats the World Bank data can be searched and data … This extends R Markdown to use Markdown headings and code to signpost the panels of your dashboard. Data Visualization bayesplot: An R package providing an extensive library of plotting functions for use after fitting Bayesian models (typically with MCMC). This page shows a list of useful R packages and libraries. LightGBM has become my favourite now in Python. Power Calculations for Two-Sample Test for Proportions, Prediction Function for Fitted Holt-Winters Models, Tabulate p values for pairwise comparisons, Power calculations for one and two sample t tests, Summarizing Non-Linear Least-Squares Model Fits, Printing and Formatting of Time-Series Objects, Print Methods for Hypothesis Tests and Power Calculation Objects, Summary Method for Multivariate Analysis of Variance, Running Medians -- Robust Scatter Plot Smoothing, Predicting from Nonlinear Least Squares Fits, Summary method for Principal Components Analysis, Scatter Plot with Smooth Curve Fitted by Loess, Extract Residual Standard Deviation 'Sigma', Plot Ridge Functions for Projection Pursuit Regression Fit, Tsp Attribute of Time-Series-like Objects, Draw Rectangles Around Hierarchical Clusters, Seasonal Decomposition of Time Series by Loess, Calculate Variance-Covariance Matrix for a Fitted Model Object, Estimate Spectral Density of a Time Series by a Smoothed For example, if you are usually working with data frames, probably you will have heard about dplyr or data.table, two of the most popular R packages. Rpart. There are even R packages for specific functions, including credit risk scoring, scraping data from websites, econometrics, etc. dtplyr. Previously with the YAP-YDAWG R Workshop video presentation, we included an example of flexdashboard usage as a take-home exercise. Rpart stands for recursive partitioning and regression training. Many useful R function come in packages, free libraries of code written by R's active user community. tidycensus. Like him, my preferred way of doing data analysis has shifted away from proprietary tools to these amazing freely available packages. Staying on top of new CRAN packages is quite a challenge nowadays. R provides the ggplot package for this … The data contained in this package is derived from U. S. Census data and is in the public domain. However, installation in R remains tricky as at time of writing and involves downloading Rtools, Git for Windows, CMake, VS Build Tools and running the following: If that looks too hard, that is why I would still recommend xgboost for R users at the present time. Although you don’t need an IDE in order […] All packages share an underlying philosophy and common APIs. This package downloads data from the U.S. 10-year census and American Community Survey in R-ready format. It’s a tool for doing the computation and number-crunching that set the stage for statistical analysis and decision-making. But for those with a habit of exploding the data warehouse or those with cloud solutions being blocked by IT policy, disk.frame is an exciting new alternative. It does require some additional planning with respect to data chunks, but maintains a familiar syntax – check out the examples on the page. The Rstudio team were also incredibly responsive when I filed a bug report and had it fixed within a day. To action insights from modelling analysis generally involves some kind of report or presentation. Just an extra note for those coming to this later - there's some recurring display issues with the code on the website from time to time which breaks some of the symbols and line breaks. stats-package: The R Stats Package: ts-methods: Methods for Time Series Objects: update: Update and Re-fit a Model Call: uniroot: One Dimensional Root (Zero) Finding: wilcox.test: Wilcoxon Rank Sum and Signed Rank Tests: weighted.residuals: Compute Weighted Residuals: Exponential: The Exponential Distribution: No Results! We have taken a journey with ten amazing packages covering the full data analysis cycle, from data preparation, with a few solutions for managing “medium” data, then to models - with crowd favourites for gradient boosting and neural network prediction, and finally to actioning business change - through dashboard and explanatory visualisations - and most of the runners up too… I would recommend exploring the resources in the many links as well, there is a lot of content that I have found to be quite informative. This and more can be found on our knowledge bank page. Polls, data mining surveys, and studies of scholarly literature databases show substantial increases … I’d like to share some of my old-time favourites and exciting new packages for R. Whether you are an experienced R user or new to the game, I think there may be something here for you to take away. They increase the power of R by improving existing base R functionalities, or by adding new ones. 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