bruceR
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 where the current file is, print strings with rich formats and colors); (2) multivariate computation (e.g., compute scale sums/means/... with reverse scoring); (3) reliability and factor analyses; (4) descriptive statistics and correlation analyses; (5) multi-factor analysis of variance (ANOVA), simple-effect analysis, and post-hoc multiple comparison; (6) tidy report of regression models and other results (to R Console and MS Word); (7) mediation and moderation analyses (PROCESS); and (8) additional toolbox for statistics and graphics.
Author
E-mail: baohws@foxmail.com
Website: psychbruce.github.io
ResearchGate | GitHub | 知乎
Citation
- Bao, H.-W.-S. (2021). bruceR: Broadly useful convenient and efficient R functions. R package version 0.x.x. https://CRAN.R-project.org/package=bruceR or https://github.com/psychbruce/bruceR
User Guide
Installation
## Method 1: Install from CRAN
install.packages("bruceR")
## Method 2: Install from GitHub
install.packages("devtools")
devtools::install_github("psychbruce/bruceR", force=TRUE, upgrade=FALSE)Tips:
- Please restart (close and reopen) RStudio before installation.
- If you see a dialog asking "Do you want to install from sources the package which needs compilation?", it would be better to choose "No" (to save your time).
- If you fail to install, please read carefully the warning messages and find out the key R package(s) causing the failure, manually uninstall and reinstall these R package(s), and then retry the main installation.
- It would be better to update R to its latest version (v4.0+).
- It would be better to download and install Rtools.exe on Windows system.
Package Dependency
bruceR depends on many important R packages.
Loading bruceR by library(bruceR) will also load these R packages for you:
[Data]:
rio: Data import and export (for all file formats).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: Advanceddata.framewith higher efficiency.
[Stat]:
psych: Toolbox for psychological and psychometric research.emmeans: Toolbox for estimated marginal means and contrasts.effectsize: Indices of effect size and standardized parameters.performance: Assessment of regression models performance.
[Plot]:
Main Functions in bruceR
Basic R Programming
set.wd()pkg_depend(),pkg_install_suggested()formatF(),formatN()Print(),Glue(),Run()%^%%notin%%allin%,%anyin%,%nonein%,%partin%
Multivariate Computation
SUM(),MEAN(),STD(),MODE(),COUNT(),CONSEC()RECODE(),RESCALE()LOOKUP()
Reliability and Factor analyses
Alpha()EFA()CFA()
Descriptive Statistics and Correlation Analyses
Describe()Freq()Corr()cor_diff()
Multi-Factor ANOVA, Simple-Effect Analysis, and Post-Hoc Multiple Comparison
MANOVA()EMMEANS()
Tidy Report of Regression Models
model_summary()GLM_summary()HLM_summary()HLM_ICC_rWG()regress()
Mediation and Moderation Analyses
PROCESS()lavaan_summary()med_summary()
Additional Toolbox for Statistics and Graphics
grand_mean_center()group_mean_center()ccf_plot()granger_test()granger_causality()theme_bruce()show_colors()
Function Output
Some functions in bruceR allow table output to Microsoft Word (by setting file="xxx.doc" in the function).
| bruceR Function | Output: R Console | Output: MS Word |
|---|---|---|
print_table() | √ | √ (basic usage) |
Describe() | √ | √ |
Freq() | √ | √ |
Corr() | √ | √ (recommended) |
Alpha() | √ | |
EFA() | √ | |
CFA() | √ | |
MANOVA() | √ | √ |
EMMEANS() | √ | |
PROCESS() | √ | √ (only a part) |
model_summary() | √ | √ (recommended) |
med_summary() | √ | √ |
lavaan_summary() | √ | |
GLM_summary() | √ | |
HLM_summary() | √ | |
HLM_ICC_rWG() | √ | |
granger_test() | √ | |
granger_causality() | √ | √ |
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")Learn More From Help Pages
library(bruceR)
## Overview
help("bruceR")
help(bruceR)
?bruceR
## See help pages of functions
## (use `?function` or `help(function)`)
?set.wd
?Describe
?Freq
?Corr
?Alpha
?MEAN
?RECODE
?MANOVA
?EMMEANS
?PROCESS
?model_summary
?lavaan_summary
?GLM_summary
?HLM_summary
...