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Convert Spatial Data Using Tidy Tables
Tools to convert from specific formats to more general forms of spatial data. Using tables to store the actual entities present in spatial data provides flexibility, and the functions here deliberately minimize the level of interpretation applied, leaving that for specific applications. Includes support for simple features, round-trip for 'Spatial' classes and long-form tables, analogous to 'ggplot2::fortify'. There is also a more 'normal form' representation that decomposes simple features and their kin to tables of objects, parts, and unique coordinates.
Tidy Handling and Navigation of the Student-Life Dataset
Download, navigate and analyse the Student-Life dataset. The Student-Life dataset contains passive and automatic sensing data from the phones of a class of 48 Dartmouth college students. It was collected over a 10 week term. Additionally, the dataset contains ecological momentary assessment results along with pre-study and post-study mental health surveys. The intended use is to assess mental health, academic performance and behavioral trends. The raw dataset and additional information is available at < https://studentlife.cs.dartmouth.edu/>.
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.
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)
Transform Microplate Data into Tidy Dataframes
The goal of 'tidyplate' is to help researchers convert different types of microplates into tidy dataframes which can be used in data analysis. It accepts xlsx and csv files formatted in a specific way as input. It supports all types of standard microplate formats such as 6-well, 12-well, 24-well, 48-well, 96-well, 384-well, and, 1536-well plates.
Tidy Estimation of Heterogeneous Treatment Effects
Estimates heterogeneous treatment effects using tidy semantics
on experimental or observational data. Methods are based on the doubly-robust
learner of Kennedy (n.d.)
Tidy Dataframes and Expressions with Statistical Details
Utilities for producing dataframes with rich details for the
most common types of statistical approaches and tests: parametric,
nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation
analyses, contingency table analyses, and meta-analyses. The
functions are pipe-friendly and provide a consistent syntax to work
with tidy data. These dataframes additionally contain expressions with
statistical details, and can be used in graphing packages. This
package also forms the statistical processing backend for
'ggstatsplot'. References: Patil (2021)
Explore 'Wikidata' Through Tidy Data Frames
Query 'Wikidata' API < https://www.wikidata.org/wiki/Wikidata:Main_Page> with ease, get tidy data frames in response, and cache data in a local database.
Tidy Common Workflow Language Tools and Workflows
The Common Workflow Language < https://www.commonwl.org/> is an open standard for describing data analysis workflows. This package takes the raw Common Workflow Language workflows encoded in JSON or 'YAML' and turns the workflow elements into tidy data frames or lists. A graph representation for the workflow can be constructed and visualized with the parsed workflow inputs, outputs, and steps. Users can embed the visualizations in their 'Shiny' applications, and export them as HTML files or static images.
Tidy Prediction and Plotting of Generalised Additive Models
Provides functions that compute predictions from Generalised Additive Models (GAMs) fitted with 'mgcv' and return them as a tibble. These can be plotted with a generic plot()-method that uses 'ggplot2' or plotted as any other data frame. The main function is predict_gam().