Tidy Processing and Analysis of Biological Sequences

A tidy approach to analysis of biological sequences. All processing and data-storage functions are heavily optimized to allow the fastest and most efficient data storage.

Regularized Linear Modeling with Tidy Data

An extension to the 'R' tidy data environment for automated machine learning. The package allows fitting and cross validation of linear regression and classification algorithms on grouped data.

Download and Tidy Data from the 'OECD'

Provides clean, tidy access to key economic indicators published by the 'Organisation for Economic Co-operation and Development' ('OECD'), covering GDP, CPI inflation, unemployment, tax revenue, government deficit, health expenditure, education expenditure, income inequality, labour productivity, and current account balance across all 38 'OECD' member countries. Data is downloaded from the 'OECD Data Explorer' API < https://data-explorer.oecd.org> on first use and cached locally for subsequent calls. Returns tidy long-format data frames ready for analysis and visualisation.

Simple Scraping and Tidy Webpage Summaries

Simple tools for scraping webpages, extracting common html tags and parsing contents to a tidy, tabular format. Tools help with extraction of page titles, links, images, rss feeds, social media handles and page metadata.

Tidy Functional Data Wrangling and Visualization

Represent, visualize, describe and wrangle functional data in tidy data frames, building on the 'tf' package. Provides data types for functional observations that work as columns in data frames, enabling manipulation with 'dplyr' verbs and visualization with 'ggplot2' geoms designed for functional data.

Download and Tidy IPC and CH Data

Utilities to access Integrated Food Security Phase Classification (IPC) and Cadre Harmonisé (CH) food security data. Wrapper functions are available for all of the 'IPC-CH' Public API (< https://docs.api.ipcinfo.org>) simplified and advanced endpoints to easily download the data in a clean and tidy format.

A Tidy Framework for Changepoint Detection Analysis

Changepoint detection algorithms for R are widespread but have different interfaces and reporting conventions. This makes the comparative analysis of results difficult. We solve this problem by providing a tidy, unified interface for several different changepoint detection algorithms. We also provide consistent numerical and graphical reporting leveraging the 'broom' and 'ggplot2' packages.

Tidy Common R Statistical Functions

Provides functions to scale, log-transform and fit linear models within a 'tidyverse'-style R code framework. Intended to smooth over inconsistencies in output of base R statistical functions, allowing ease of teaching, learning and daily use. Inspired by the tidy principles used in 'broom' Robinson (2017) .

A Tidy Implementation of the Synthetic Control Method

A synthetic control offers a way of evaluating the effect of an intervention in comparative case studies. The package makes a number of improvements when implementing the method in R. These improvements allow users to inspect, visualize, and tune the synthetic control more easily. A key benefit of a tidy implementation is that the entire preparation process for building the synthetic control can be accomplished in a single pipe.

Simple Conjoint Tidying, Analysis, and Visualization

Simple tidying, analysis, and visualization of conjoint (factorial) experiments, including estimation and visualization of average marginal component effects ('AMCEs') and marginal means ('MMs') for weighted and un-weighted survey data, along with useful reference category diagnostics and statistical tests. Estimation of 'AMCEs' is based upon methods described by Hainmueller, Hopkins, and Yamamoto (2014) .