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This vignette can be cited as: To cite package 'statsExpressions' in publications use: Patil, I., (2021). statsExpressions: R Package for Tidy Dataframes and Expressions with Statistical Details. Journal of Open Source Software, 6(61), 3236, https://doi.org/10.21105/joss.03236 A BibTeX entry for LaTeX users is @Article{, doi = {10.21105/joss.03236}, url = {https://doi.org/10.21105/joss.03236}, yea
broom: let’s tidy up a bit The broom package takes the messy output of built-in functions in R, such as lm, nls, or t.test, and turns them into tidy tibbles. The concept of “tidy data”, as introduced by Hadley Wickham, offers a powerful framework for data manipulation and analysis. That paper makes a convincing statement of the problem this package tries to solve (emphasis mine): While model input
Laying out multiple plots on a page Baptiste Auguié 2019-07-13 An individual ggplot object contains multiple pieces – axes, plot panel(s), titles, legends –, and their layout is defined and enforced via the gtable package, itself built around the lower-level grid package. Plots themselves become graphical objects, which can be arranged on a page using e.g. the gridExtra or egg packages, which prov
Convert plot to grob and ggplot object Guangchuang Yu School of Basic Medical Sciences, Southern Medical University 2023-08-08 library("grid") library("ggplotify") p1 <- as.grob(~barplot(1:10)) p2 <- as.grob(expression(plot(rnorm(10)))) p3 <- as.grob(function() plot(sin)) library("vcd") data(Titanic) p4 <- as.grob(~mosaic(Titanic)) library("lattice") data(mtcars) p5 <- as.grob(densityplot(~mpg|cyl
Alluvial Plots in ggplot2 Jason Cory Brunson 2023-02-13 The {ggalluvial} package is a {ggplot2} extension for producing alluvial plots in a {tidyverse} framework. The design and functionality were originally inspired by the {alluvial} package and have benefitted from the feedback of many users. This vignette defines the essential components of alluvial plots as used in the naming schemes and docum
Introduction Most dplyr verbs use tidy evaluation in some way. Tidy evaluation is a special type of non-standard evaluation used throughout the tidyverse. There are two basic forms found in dplyr: arrange(), count(), filter(), group_by(), mutate(), and summarise() use data masking so that you can use data variables as if they were variables in the environment (i.e. you write my_variable not df$my_
Introduction to ggjoy Claus O. Wilke 2018-05-17 The ggjoy package has been deprecated. Please switch over to ggridges. To port from ggjoy to ggridges, make the following substitutions in your code: ggjoy function ggridges function
Overview ggrepel provides geoms for ggplot2 to repel overlapping text labels: geom_text_repel() geom_label_repel() Text labels repel away from each other, away from data points, and away from edges of the plotting area (panel). Let’s compare geom_text() and geom_text_repel(): library(ggrepel) set.seed(42) dat <- subset(mtcars, wt > 2.75 & wt < 3.45) dat$car <- rownames(dat) p <- ggplot(dat, aes(wt
Introducing magrittr Stefan Milton Bache November, 2014 Abstract The magrittr (to be pronounced with a sophisticated french accent) package has two aims: decrease development time and improve readability and maintainability of code. Or even shortr: make your code smokin’ (puff puff)! To achieve its humble aims, magrittr (remember the accent) provides a new “pipe”-like operator, %>%, with which you
This document explains PCA, clustering, LFDA and MDS related plotting using {ggplot2} and {ggfortify}. Plotting PCA (Principal Component Analysis) {ggfortify} let {ggplot2} know how to interpret PCA objects. After loading {ggfortify}, you can use ggplot2::autoplot function for stats::prcomp and stats::princomp objects. library(ggfortify) df <- iris[1:4] pca_res <- prcomp(df, scale. = TRUE) autoplo
Documenting functions This vignette introduces the basics of roxygen2 for documenting functions. See vignette("rd-other") for documenting other types of objects and vignette("reuse") for reusing documentation across topics. Basics A roxygen block is a sequence of lines starting with #' (optionally preceded by white space). The first lines of the block is called the introduction and forms the title
Introduction to ggthemes Jeffrey B. Arnold 2018-05-07 Themes theme_base: a theme resembling the default base graphics in R. See also theme_par. theme_calc: a theme based on LibreOffice Calc. theme_economist: a theme based on the plots in the The Economist magazine. theme_excel: a theme replicating the classic ugly gray charts in Excel theme_few: theme from Stephen Few’s “Practical Rules for Using
A Brief Introduction to caretEnsemble Zach Deane-Mayer 2024-09-12 caretEnsemble is a package for making ensembles of caret models. You should already be somewhat familiar with the caret package before trying out caretEnsemble. caretEnsemble has 3 primary functions: caretList, caretEnsemble and caretStack. caretList is used to build lists of caret models on the same training data, with the same re-
Introduction to the viridis color maps Bob Rudis, Noam Ross and Simon Garnier 2024-01-28 tl;dr Use the color scales in this package to make plots that are pretty, better represent your data, easier to read by those with colorblindness, and print well in gray scale. Install viridis like any R package: install.packages("viridis") library(viridis) For base plots, use the viridis() function to generat
The tableplot is a powerful visualization method to explore and analyse large multivariate datasets. In this vignette, the implementation of tableplots in R is described, and illustrated with the diamonds dataset from the ggplot2 package. Introduction The tableplot is a visualization method that is used to explore and analyse large datasets. Tableplots are used to explore the relationships between
Tidy data (This is an informal and code heavy version of the full tidy data paper. Please refer to that for more details.) Data tidying It is often said that 80% of data analysis is spent on the cleaning and preparing data. And it’s not just a first step, but it must be repeated many times over the course of analysis as new problems come to light or new data is collected. To get a handle on the pr
The R Manuals edited by the R Development Core Team. The following manuals for R were created on Debian Linux and may differ from the manuals for Mac or Windows on platform-specific pages, but most parts will be identical for all platforms. The correct version of the manuals for each platform are part of the respective R installations. The manuals change with R, hence we provide versions for the m
Extending ggplot2 This vignette documents the official extension mechanism provided in ggplot2 2.0.0. This vignette is a high-level adjunct to the low-level details found in ?Stat, ?Geom and ?theme. You’ll learn how to extend ggplot2 by creating a new stat, geom, or theme. As you read this document, you’ll see many things that will make you scratch your head and wonder why on earth is it designed
R Internals ¶ This is a guide to the internal structures of R and coding standards for the core team working on R itself. This manual is for R, version 4.4.2 (2024-10-31). Copyright © 1999–2024 R Core Team Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy an
As well as working with local in-memory data like data frames and data tables, dplyr also works with remote on-disk data stored in databases. Generally, if your data fits in memory there is no advantage to putting it in a database: it will only be slower and more hassle. The reason you’d want to use dplyr with a database is because either your data is already in a database (and you don’t want to w
This directory contains binaries for the base distribution and of R and packages to run on macOS. R and package binaries for R versions older than 4.0.0 are only available from the CRAN archive so users of such versions should adjust the CRAN mirror setting (https://cran-archive.r-project.org) accordingly. Note: Although we take precautions when assembling binaries, please use the normal precautio
<h1>The Comprehensive R Archive Network</h1> Your browser seems not to support frames, here is the <A href="navbar.html">contents page</A> of CRAN.
Devtools dependencies Jim Hester, Hadley Wickham 2022-10-11 Package remotes Devtools version 1.9 supports package dependency installation for packages not yet in a standard package repository such as CRAN or Bioconductor. You can mark any regular dependency defined in the Depends, Imports, Suggests or Enhances fields as being installed from a remote location by adding the remote location to Remote
CRAN task views aim to provide guidance which packages on CRAN are relevant for tasks related to a certain topic. They give a brief overview of the included packages which can also be automatically installed using the ctv package. The views are intended to have a sharp focus so that it is sufficiently clear which packages should be included (or excluded) - and they are not meant to endorse the "be
flexmix: Flexible Mixture Modeling A general framework for finite mixtures of regression models using the EM algorithm is implemented. The E-step and all data handling are provided, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering.
RTools: Toolchains for building R and R packages from source on Windows
googleVis examples Markus Gesmann and Diego de Castillo 2024-05-25 1 Demonstration of googleVis It may take a little while to load all charts. Please be patient. All charts require an Internet connection. These examples are taken from the googleVis demo. You can execute the demo via library(googleVis) demo(googleVis) For more details about the charts and further examples see the help files of the
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