Tidy RSS for R

With the objective of including data from RSS feeds into your analysis, 'tidyRSS' parses RSS, Atom and JSON feeds and returns a tidy data frame.

Create Tidy Data Frames of Marginal Effects for 'ggplot' from Model Outputs

Compute marginal effects and adjusted predictions from statistical models and returns the result as tidy data frames. These data frames are ready to use with the 'ggplot2'-package. Effects and predictions can be calculated for many different models. Interaction terms, splines and polynomial terms are also supported. The main functions are ggpredict(), ggemmeans() and ggeffect(). There is a generic plot()-method to plot the results using 'ggplot2'.

Format R Code Automatically

Provides a function tidy_source() to format R source code. Spaces and indent will be added to the code automatically, and comments will be preserved under certain conditions, so that R code will be more human-readable and tidy. There is also a Shiny app as a user interface in this package (see tidy_app()).

Tidy Structural Equation Modeling

A tidy workflow for generating, estimating, reporting, and plotting structural equation models using 'lavaan', 'OpenMx', or 'Mplus'. Throughout this workflow, elements of syntax, results, and graphs are represented as 'tidy' data, making them easy to customize. Includes functionality to estimate latent class analyses.

Extract and Tidy Canadian 'Hydrometric' Data

Provides functions to access historical and real-time national 'hydrometric' data from Water Survey of Canada data sources (< https://dd.weather.gc.ca/hydrometric/csv/> and < https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/>) and then applies tidy data principles.

Tidy, Type-Safe 'prediction()' Methods

A one-function package containing prediction(), a type-safe alternative to predict() that always returns a data frame. The summary() method provides a data frame with average predictions, possibly over counterfactual versions of the data (à la the margins command in 'Stata'). Marginal effect estimation is provided by the related package, 'margins' < https://cran.r-project.org/package=margins>. The package currently supports common model types (e.g., lm, glm) from the 'stats' package, as well as numerous other model classes from other add-on packages. See the README file or main package documentation page for a complete listing.

Tidy Anomaly Detection

The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data. The main functions are time_decompose(), anomalize(), and time_recompose(). When combined, it's quite simple to decompose time series, detect anomalies, and create bands separating the "normal" data from the anomalous data at scale (i.e. for multiple time series). Time series decomposition is used to remove trend and seasonal components via the time_decompose() function and methods include seasonal decomposition of time series by Loess ("stl") and seasonal decomposition by piecewise medians ("twitter"). The anomalize() function implements two methods for anomaly detection of residuals including using an inner quartile range ("iqr") and generalized extreme studentized deviation ("gesd"). These methods are based on those used in the 'forecast' package and the Twitter 'AnomalyDetection' package. Refer to the associated functions for specific references for these methods.

A Tidy Approach to 'NetCDF' Data Exploration and Extraction

Tidy tools for 'NetCDF' data sources. Explore the contents of a 'NetCDF' source (file or URL) presented as variables organized by grid with a database-like interface. The hyper_filter() interactive function translates the filter value or index expressions to array-slicing form. No data is read until explicitly requested, as a data frame or list of arrays via hyper_tibble() or hyper_array().

S3 Classes and Methods for Tidy Functional Data

Defines S3 vector data types for vectors of functional data (grid-based, spline-based or functional principal components-based) with all arithmetic and summary methods, derivation, integration and smoothing, plotting, data import and export, and data wrangling, such as re-evaluating, subsetting, sub-assigning, zooming into sub-domains, or extracting functional features like minima/maxima and their locations. The implementation allows including such vectors in data frames for joint analysis of functional and scalar variables.

Tidy GeoRSS

In order to easily integrate geoRSS data into analysis, 'tidygeoRSS' parses 'geo' feeds and returns tidy simple features data frames.