- List of packages I’ve found useful in my workflow during 2024 (so far)
Plot
spiralize: Visualize Data on Spirals
tags: #plot
[cran package link] https://CRAN.R-project.org/package=spiralize
description from the author/vignette
It visualizes data along an Archimedean spiral https://en.wikipedia.org/wiki/Archimedean_spiral, makes so-called spiral graph or spiral chart. It has two major advantages for visualization: 1. It is able to >visualize data with very long axis with high resolution. 2. It is efficient for time series data to reveal periodic patterns.
panelView: Visualizing Panel Data
tags: #plot
[cran package link] https://CRAN.R-project.org/package=panelView
description from the author/vignette
Visualizes panel data. It has three main functionalities: (1) it plots the treatment status and missing values in a panel dataset; (2) it visualizes the temporal dynamics of a main variable of interest; (3) it depicts the bivariate relationships between a treatment variable and an outcome variable either by unit or in aggregate. For details, see doi:10.18637/jss.v107.i07.
Spectroscopy
plsVarSel: Variable Selection in Partial Least Squares
tags: #pls #partial least squares #regression
[cran package link] https://CRAN.R-project.org/package=plsVarSel
description from the author/vignette
Interfaces and methods for variable selection in Partial Least Squares. The methods include filter methods, wrapper methods and embedded methods. Both regression and classification is supported.
Statistics
qreport: Statistical Reporting with ‘Quarto’
tags: #statistics
[cran package link] https://CRAN.R-project.org/package=qreport
description from the author/vignette
Provides statistical components, tables, and graphs that are useful in ‘Quarto’ and ‘RMarkdown’ reports and that produce ‘Quarto’ elements for special formatting such as tabs and marginal notes and graphs. Some of the functions produce entire report sections with tabs, e.g., the missing data report created by missChk(). Functions for inserting variables and tables inside ‘graphviz’ and ‘mermaid’ diagrams are included, and so are special clinical trial graphics for adverse event reporting.
MVET: Multivariate Estimates and Tests
tags: #statistics
[cran package link] https://CRAN.R-project.org/package=MVET
description from the author/vignette
Multivariate estimation and testing, currently a package for testing parametric data. To deal with parametric data, various multivariate normality tests and outlier detection are performed and visualized using the ‘ggplot2’ package. Homogeneity tests for covariance matrices are also possible, as well as the Hotelling’s T-square test and the multivariate analysis of variance test. We are exploring additional tests and visualization techniques, such as profile analysis and randomized complete block design, to be made available in the future and making them easily accessible to users.
pbox: Exploring Multivariate Spaces with Probability Boxes
tags: #statistics
[cran package link] https://CRAN.R-project.org/package=pbox
description from the author/vignette
Advanced statistical library offering a method to encapsulate and query the probability space of a dataset effortlessly using Probability Boxes (p-boxes). Its distinctive feature lies in the ease with which users can navigate and analyze marginal, joint, and conditional probabilities while taking into account the underlying correlation structure inherent in the data using copula theory and models. A comprehensive explanation is available in the paper “pbox: Exploring Multivariate Spaces with Probability Boxes” to be published in the Journal of Statistical Software.
equatiomatic: Transform Models into ‘LaTeX’ Equations
tags: #statistics #latex #regression #models
[cran package link] https://CRAN.R-project.org/package=equatiomatic
description from the author/vignette
The goal of ‘equatiomatic’ is to reduce the pain associated with writing ‘LaTeX’ formulas from fitted models. The primary function of the package, extract_eq(), takes a fitted model object as its input and returns the corresponding ‘LaTeX’ code for the model.
bulkreadr: The Ultimate Tool for Reading Data in Bulk
tags: #bulk import
[cran package link] https://CRAN.R-project.org/package=bulkreadr
description from the author/vignette
Designed to simplify and streamline the process of reading and processing large volumes of data in R, this package offers a collection of functions tailored for bulk data operations. It enables users to efficiently read multiple sheets from Microsoft Excel and Google Sheets workbooks, as well as various CSV files from a directory. The data is returned as organized data frames, facilitating further analysis and manipulation. Ideal for handling extensive data sets or batch processing tasks, bulkreadr empowers users to manage data in bulk effortlessly, saving time and effort in data preparation workflows. Additionally, the package seamlessly works with labelled data from SPSS and Stata.
Simulated data
rsurv: Random Generation of Survival Data
tags: #rsurv
[cran package link] https://CRAN.R-project.org/package=rsurv
description from the author/vignette
Random generation of survival data from a wide range of regression models, including accelerated failure time (AFT), proportional hazards (PH), proportional odds (PO), accelerated hazard (AH), Yang and Prentice (YP), and extended hazard (EH) models. The package ‘rsurv’ also stands out by its ability to generate survival data from an unlimited number of baseline distributions provided that an implementation of the quantile function of the chosen baseline distribution is available in R. Another nice feature of the package ‘rsurv’ lies in the fact that linear predictors are specified via a formula-based approach, facilitating the inclusion of categorical variables and interaction terms. The functions implemented in the package ‘rsurv’ can also be employed to simulate survival data with more complex structures, such as survival data with different types of censoring mechanisms, survival data with cure fraction, survival data with random effects (frailties), multivariate survival data, and competing risks survival data. Details about the R package ‘rsurv’ can be found in Demarqui (2024) doi:10.48550/arXiv.2406.01750.
Reporting and Formatting
ftExtra: Extensions for ‘Flextable’
tags: #tables #flextables
[cran package link] https://CRAN.R-project.org/package=ftExtra
description from the author/vignette
Build display tables easily by extending the functionality of the ‘flextable’ package. Features include spanning header, grouping rows, parsing markdown and so on.
Fun
PlayerChart: Generate Pizza Chart: Player Stats 0-100
tags: #statistics [cran package link] https://CRAN.R-project.org/package=PlayerChart
description from the author/vignette
Create an interactive pizza chart visualizing a specific player’s statistics across various attributes in a sports dataset. The chart is constructed based on input parameters: ‘data’, a dataframe containing player data for any sports; ‘player_stats_col’, a vector specifying the names of the columns from the dataframe that will be used to create slices in the pizza chart, with statistics ranging between 0 and 100; ‘name_col’, specifying the name of the column in the dataframe that contains the player names; and ‘player_name’, representing the specific player whose statistics will be visualized in the chart, serving as the chart title.
gameR: Color Palettes Inspired by Video Games
tags: #statistics [cran package link] https://CRAN.R-project.org/package=gameR
description from the author/vignette
Palettes based on video games.