Search Results for "install from git"

The purpose of this add-in is to let you explore your data quickly to extract the information they hold. You can create visualization with {ggplot2}, filter data with {dplyr} and retrieve generated code. This addin allows you to interactively explore your data by visualizing it with the ggplot2 package. It allows you to draw bar plots, curves, scatter plots, histograms, boxplot and sf objects, then export the graph or retrieve the code to reproduce the graph. This addin allows you to...

devtools is an R package designed to simplify R package development by providing functions for creating, building, testing, and installing packages from various sources (e.g., CRAN, GitHub). It integrates with usethis, roxygen2, testthat, and simplifies workflows for developers and contributors to the R ecosystem.

Paginate the HTML Output of R Markdown with CSS for Print. You only need a modern web browser (e.g., Google Chrome or Microsoft Edge) to generate PDF. No need to install LaTeX to get beautiful PDFs. This R package stands on the shoulders of two giants to support typesetting with CSS for R Markdown documents: Paged.js and ReLaXed (we only borrowed some CSS from the ReLaXed repo and didn't really use the Node package).

For detailed requirements and install instructions see irkernel.github.io. Per default IRkernel::installspec() will install a kernel with the name “ir” and a display name of “R”. Multiple calls will overwrite the kernel with a kernel spec pointing to the last R interpreter you called that commands from. You can install kernels for multiple versions of R by supplying a name and display name argument to the install spec() call (You still need to install these packages in all interpreters you want to run as a Jupyter kernel!)...

tidyverse is a meta‑package that installs and loads a cohesive suite of R packages designed for data science, sharing underlying design principles, grammar, and data structures. Core components include ggplot2, dplyr, tidyr, readr, purrr, tibble, stringr, forcats, and more. It promotes tidy data workflows and consistency across tasks.

latex2exp is an R package that lets you use LaTeX in plots. It parses and converts LaTeX to R’s custom plotmath expressions. You can read the full documentation on the package’s website. Expressions returned by latex2exp can be used to create formatted text and mathematical formulas and symbols to be rendered as axis labels, annotations, legends, titles, etc. throughout R’s plotting system.

In this tutorial, we describe a workflow to ensure long-term reproducibility of R-based data analyses. The workflow leverages established tools and practices from software engineering. It combines the benefits of various open-source software tools including R Markdown, Git, Make, and Docker, whose interplay ensures seamless integration of version management, dynamic report generation conforming to various journal styles, and full cross-platform and long-term computational reproducibility. The workflow ensures meeting the primary goals that 1) the reporting of statistical results is consistent with the actual statistical results (dynamic report generation), 2) the analysis exactly reproduces at a later point in time even if the computing platform or software is changed (computational reproducibility), and 3) changes at any time (during development and post-publication) are tracked, tagged, and documented while earlier versions of both data and code remain accessible.

Welcome to Posterdown! This is my attempt to provide a semi-smooth workflow for those who wish to take their RMarkdown skills to the conference world. Many creature comforts from RMarkdown are available in this package such as Markdown section notation, figure captioning, and even citations like this one (Allaire, Xie, McPherson, et al. 2018). The rest of this example poster will show how you can insert typical conference poster features into your own document. Posterdown was created as a...

...The TeX folder contains the text files that are used to typeset the chapters in the textbook. In many cases, R code is supplied with figures to regenerate the figure. It will often be necessary to install the "openintro" R package that is available from GitHub (https://github.com/OpenIntroOrg) if you would like to regenerate a figure. Other packages may also occasionally be required.