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.

Bruce H. W. S. Bao 包寒吴霜

📬 [email protected]

📋 psychbruce.github.io

• Bao, H. W. S. (2021). bruceR: Broadly useful convenient and efficient R functions. https://doi.org/10.32614/CRAN.package.bruceR

NEWS (Changelog)

Chinese Documentation for bruceR: I. Overview

Chinese Documentation for bruceR: II. FAQ

Please always set dep=TRUE to install ALL package dependencies for FULL features!

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

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

Tips:

• Good practices:
  • Restart RStudio before installation.
  • Update R to the latest version (v4.0+).
  • Install Rtools.exe (it is not an R package) on Windows system.
• If you see "Do you want to restart R prior to install?", choose "Yes" for the first time and then choose "No".
• 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.

bruceR depends on many important R packages.

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

• [Data]:

  • data.table: Advanced data.frame with higher efficiency.
  • dplyr: Data manipulation and processing.
  • tidyr: Data cleaning and reshaping.
  • stringr: Toolbox for string operation (with regular expressions).
  • ggplot2: Data visualization.

• [Stat]:

  • emmeans: Estimates of marginal means and multiple contrasts.
  • lmerTest: Linear mixed effects modeling (multilevel modeling).
  • effectsize: Effect sizes and standardized parameters.
  • performance: Performance of regression models.
  • interactions: Interaction and simple effect analyses.

1. Basic R Programming

   • cc() (suggested)
   • set.wd() (alias: set_wd()) (suggested)
   • import(), export() (suggested)
   • pkg_depend()
   • formatF(), formatN()
   • print_table()
   • Print(), Glue(), Run()
   • %^%
   • %notin%
   • %allin%, %anyin%, %nonein%, %partin%

2. Multivariate Computation

   • add(), added() (suggested)
   • .sum(), .mean() (suggested)
   • SUM(), MEAN(), STD(), MODE(), COUNT(), CONSEC()
   • RECODE(), RESCALE()
   • LOOKUP()

3. Reliability and Factor Analyses

   • Alpha()
   • EFA() / PCA()
   • CFA()

4. Descriptive Statistics and Correlation Analyses

   • Describe()
   • Freq()
   • Corr()
   • cor_diff()
   • cor_multilevel()

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

   • TTEST()
   • MANOVA()
   • EMMEANS()

6. Tidy Report of Regression Models

   • model_summary() (suggested)
   • lavaan_summary()
   • GLM_summary()
   • HLM_summary()
   • HLM_ICC_rWG()
   • regress()

7. Mediation and Moderation Analyses

   • PROCESS() (suggested)
   • med_summary()
   • lavaan_summary()

8. Additional Toolbox for Statistics and Graphics

   • grand_mean_center()
   • group_mean_center()
   • ccf_plot()
   • granger_test()
   • granger_causality()
   • theme_bruce()
   • show_colors()

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

Examples:

## 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")

library(bruceR)

## Overview
help("bruceR")
help(bruceR)
?bruceR

## See help pages of functions
## (use `?function` or `help(function)`)
?cc
?add
?.mean
?set.wd
?import
?export
?Describe
?Freq
?Corr
?Alpha
?MEAN
?RECODE
?TTEST
?MANOVA
?EMMEANS
?PROCESS
?model_summary
?lavaan_summary
?GLM_summary
?HLM_summary
...