Essential Documentation for the sjPlot Package in R

R Package Documentation

Explore valuable documentation and insights to make the most of the sjPlot package in R. Get ready to unlock the full potential of the sjPlot package!

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Basic details of the sjPlot package in R

At R Basics, our goal is to make it easy for you to find documentation about the sjPlot package. That’s why we’ve compiled all the key information in one place. Dive into the details below to understand how this package can improve your work.

Title: Data Visualization for Statistics in Social Science
Description: Collection of plotting and table output functions for data visualization. Results of various statistical analyses (that are commonly used in social sciences) can be visualized using this package, including simple and cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, principal component analysis and correlation matrices, cluster analyses, scatter plots, stacked scales, effects plots of regression models (including interaction terms) and much more. This package supports labelled data.
Author: Daniel Lüdecke [aut, cre] (), Alexander Bartel [ctb] (), Carsten Schwemmer [ctb], Chuck Powell [ctb] (), Amir Djalovski [ctb], Johannes Titz [ctb] ()
Maintainer: Daniel Lüdecke
License type: GPL-3

Technical details of the sjPlot package in R

R documentation also provides technical details of a package, including its dependencies, enhancements, and compilation requirements. Learning this information will help you master the sjPlot package.

Version: In R, a version refers to a specific release of a package and can be used to track changes. To find the version number of the sjPlot package in the R console, you can use the packageVersion("sjPlot") function.
Compilation requirements: Some R packages include internal code that must be compiled for them to function correctly. The sjPlot package does not have compilation requirements.
Required dependencies: A required dependency refers to another package that is essential for the functioning of the main package. The sjPlot package has the following required dependencies: R (>= 3.6).
Suggested dependencies: A suggested dependency adds extra features to the main package, but the main package can work without it. The sjPlot package has the following suggested dependencies: brms, car, clubSandwich, cluster, cowplot, effects, haven, GPArotation, ggrepel, glmmTMB, gridExtra, ggridges, httr, lme4, nFactors, pscl, psych, rmarkdown, rstanarm, sandwich, splines, survey, TMB, testthat.
External dependencies: External dependencies are other packages that the main package depends on for linking at compile time. The sjPlot package does not use any external sources.
Imported packages: Importing packages allows developers to leverage existing code and functionalities without having to reinvent the wheel. The sjPlot package has the following imported packages: graphics, grDevices, stats, utils, bayestestR, datawizard, dplyr, ggeffects, ggplot2 (>= 3.2.0), knitr, insight, MASS, parameters, performance, purrr, rlang, scales, sjlabelled (>= 1.1.2), sjmisc (>= 2.8.2), sjstats (>= 0.17.8), tidyr (>= 1.0.0).
Enhancements: Enhancements help developers expand the capabilities of their packages without starting from scratch. The sjPlot package has no enhancements.

Three data scientists looking at a laptop

Capabilities of the sjPlot package in R

R documentation provides comprehensive information about a package, including its functions, datasets, and vignettes. Exploring this information will help you make the most of the sjPlot package’s functionalities.

Functions: Functions play a crucial role in R packages. They allow you to perform specific tasks and computations efficiently. To identify the functions in the sjPlot package, you can use the ls("package:sjPlot") function.
Datasets: Many R packages include built-in datasets that you can use to familiarize yourself with their functionalities. To identify any built-in datasets in the sjPlot package, you can use the data(package = "sjPlot") function.
Vignettes: R vignettes are documents that include examples for using a package. To view the list of available vignettes you can use the vignette(package = "sjPlot") function. This code will display a list of available vignettes you can read.
Citation information: Citing R packages in your publications is important as it recognizes the contributions of the developers. To find the citation information for the sjPlot package in the R console, you can use the citation("sjPlot") function.

Getting help with the sjPlot package in R

If you have trouble using the sjPlot package in R, there are several ways to get help.

The help() function: R’s built-in help system is a handy tool to find documentation. You can use the help("sjPlot") function to retrieve detailed information, examples, and usage instructions.
The ? operator: The ? operator is a shortcut for the help() function. You can simply type ?sjPlot to quickly find documentation.
Discussion forums: Online forums are excellent platforms to ask questions, share knowledge, and troubleshoot issues. The most popular forums for R programmers are StackOverflow and Posit Community.

Generate R documentation code

Use R Basics’ code generator tool to effortlessly access essential documentation for the sjPlot package within your R environment.



Please enter a package name and click "Generate Code" above.

### Install the {Package} package if not already installed.
if (!require("{Package}")) {
install.packages("{Package}")
}

### Load the {Package} package.
library("{Package}")

### Find details of the {Package} package.
packageDescription("{Package}")

### Explore the capabilities of the {Package} package.
# List of functions
ls("package:{Package}")
# List of datasets
data(package = "{Package}")
# List of vignettes
vignette(package = "{Package}")
# Citation information
citation("{Package}")

### Get help with the {Package} package.
help(package = "{Package}")

Additional R package documentation resources

Explore our comprehensive resource guides related to various types of R documentation. These guides are valuable resources for accessing a wide range of information, making it easier to navigate R documentation in one place.

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