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BRoadly Useful Convenient and Efficient R functions that BRing Users Concise and Elegant R data analyses.

This package includes easy-to-use functions for:

  1. Basic R programming (e.g., set working directory to the path of currently opened file; import/export data from/to files in any format; print tables to Microsoft Word);
  2. Multivariate computation (e.g., compute scale sums/means/… with reverse scoring);
  3. Reliability analyses and factor analyses (PCA, EFA, CFA);
  4. Descriptive statistics and correlation analyses;
  5. t-test, multi-factor analysis of variance (ANOVA), simple-effect analysis, and post-hoc multiple comparison;
  6. Tidy report of statistical models (to R Console and Microsoft Word);
  7. Mediation and moderation analyses (PROCESS);
  8. Additional toolbox for statistics and graphics.


Han-Wu-Shuang (Bruce) Bao 包寒吴霜




User Guide

NEWS (Changelog)

Chinese Documentation for bruceR: I. Overview

Chinese Documentation for bruceR: II. FAQ


## Method 1: Install from CRAN
install.packages("bruceR", dep=TRUE)  # dependencies=TRUE

## Method 2: Install from GitHub
devtools::install_github("psychbruce/bruceR", dep=TRUE, force=TRUE)


  1. Good practice: restart RStudio before installation.
  2. Good practice: update R to the latest version (v4.0+).
  3. Good practice: install Rtools.exe (it is not an R package) on Windows system.
  4. If you see “Do you want to restart R prior to install?”, choose “Yes” at the first time and then choose “No”.
  5. If you see “Do you want to install from sources the package which needs compilation?”, just choose “No”.
  6. If you fail to install, please carefully read the warning messages and find out the R package(s) causing the failure, manually uninstall and reinstall these R package(s), and then retry the main installation.

Package Dependency

bruceR depends on many important R packages.

Loading bruceR by library(bruceR) will also load these R packages for you:

  • [Data]:

    • dplyr: Data manipulation and processing.
    • tidyr: Data cleaning and reshaping.
    • stringr: Toolbox for string operation (with regular expressions).
    • forcats: Toolbox for factor manipulation (for categorical variables).
    • data.table: Advanced data.frame with higher efficiency.
  • [Stat]:

    • emmeans: Estimates of marginal means and multiple contrasts.
    • effectsize: Estimates of effect sizes and standardized parameters.
    • performance: Estimates of regression models performance.
    • lmerTest: Tests of linear mixed effects models (LMM, also known as HLM and multilevel models).
  • [Plot]:

    • ggplot2: Data visualization.
    • ggtext: Markdown/HTML rich text format for ggplot2 (geoms and themes).
    • cowplot: Advanced toolbox for ggplot2 (arrange multiple plots and add labels).
    • see: Advanced toolbox for ggplot2 (geoms, scales, themes, and color palettes).

Main Functions in bruceR

  1. Basic R Programming

  2. Multivariate Computation

  3. Reliability and Factor Analyses

  4. Descriptive Statistics and Correlation Analyses

  5. T-Test, Multi-Factor ANOVA, Simple-Effect Analysis, and Post-Hoc Multiple Comparison

  6. Tidy Report of Regression Models

  7. Mediation and Moderation Analyses

  8. Additional Toolbox for Statistics and Graphics

Function Output

For some functions, the results can be saved to Microsoft Word using the file argument.

bruceR Function Output: R Console Output: MS Word
print_table() √ (basic usage)
Corr() √ (suggested)
Alpha() (unnecessary)
EFA() / PCA()
PROCESS() √ (partial)
model_summary() √ (suggested)
HLM_ICC_rWG() (unnecessary)


## Correlation analysis (and descriptive statistics)
Corr(airquality, file="cor.doc")

## Regression analysis
lm1 = lm(Temp ~ Month + Day, data=airquality)
lm2 = lm(Temp ~ Month + Day + Wind + Solar.R, data=airquality)
model_summary(list(lm1, lm2), file="reg.doc")
model_summary(list(lm1, lm2), std=TRUE, file="reg_std.doc")

Learn More From Help Pages


## Overview

## See help pages of functions
## (use `?function` or `help(function)`)