https://github.com/vusaverse/vvdoctor
R Shiny app / package to automate statistical testing
https://github.com/vusaverse/vvdoctor
hypothesis-testing r- r-shiny r-stats shiny-apps shiny-r statistical-tests statistics stats
Last synced: about 1 month ago
JSON representation
R Shiny app / package to automate statistical testing
- Host: GitHub
- URL: https://github.com/vusaverse/vvdoctor
- Owner: vusaverse
- License: other
- Created: 2023-07-10T08:25:00.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-08-20T12:17:07.000Z (2 months ago)
- Last Synced: 2025-08-20T12:53:18.869Z (2 months ago)
- Topics: hypothesis-testing, r-, r-shiny, r-stats, shiny-apps, shiny-r, statistical-tests, statistics, stats
- Language: R
- Homepage: https://edulytics.shinyapps.io/vvdoctor/
- Size: 1.14 MB
- Stars: 8
- Watchers: 1
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- License: LICENSE
Awesome Lists containing this project
README
# vvdoctor 
[](https://github.com/vusaverse/vvdoctor/actions/workflows/check-standard.yaml)
[](https://www.codefactor.io/repository/github/vusaverse/vvdoctor)
[](https://edulytics.shinyapps.io/vvdoctor/)
[](https://CRAN.R-project.org/package=vvdoctor/)
[](https://cran.r-project.org/package=vvdoctor/)
[](https://cran.r-project.org/package=vvdoctor/)
`vvdoctor` is an R package/Shiny app that provides a user-friendly interface for data analysis. It allows users to upload data files, visualize the data, perform statistical tests, and interpret the results.
The app is currently live on shinyapps.io, see: https://edulytics.shinyapps.io/vvdoctor/
## Usage
``` r
## Install the app
devtools::install_github("vusaverse/vvdoctor")
library(vvdoctor)
## run the app
vvdoctor::run_vvdoctor()
```
- Uploading Data Files: Click on the "Browse" button to select and upload your data file in CSV or Excel format.
- Displaying the Dataframe: Once the data file is uploaded, the app will display the data as a dataframe. You can explore the data by scrolling through the table or using the search and filter options.
- Generating a Histogram: To generate a histogram of a specific variable, select a numeric dependent variable from the dropdown menu. The histogram will be displayed, allowing you to visualize the distribution of the data.
- Choosing Dependent and Independent Variables: To perform statistical tests, select the dependent and independent variables from the respective dropdown menus. The available variables will be automatically populated based on the uploaded data.
- Statistical Test Options: Once the variables are selected, the app will provide a list of statistical test options, such as t-tests, ANOVA, or correlation analysis. Choose the desired test and click on the "Run Test" button. The output of the test will be displayed, including the test statistic, p-value, and any additional relevant information.
## Supported File Extensions
Currently, the following file types are supported:
| Full File Type Name | Full Extension Name | Package | Read Function | Parsable Arguments |
|----------------------|---------------------|---------|---------------|--------------------|
| R Data File | .RData | base | readRDS | None |
| ASCII Text File | .asc | utils | read.table | None |
| Comma Separated Values File | .csv | utils | read.csv | sep, header |
| Apache Feather File | .feather | feather | read_feather | None |
| Fixed-Size File | .fst | fst | read_fst | None |
| Apache Parquet File | .parquet | arrow | read_parquet | None |
| R Data File | .rda | base | readRDS | None |
| R Data File | .rds | base | readRDS | None |
| SPSS Data File | .sav | haven | read_sav | None |
| Tab Separated Values File | .tsv | utils | read.delim | sep, header |
| Text File | .txt | utils | read.delim | sep, header |
| Microsoft Excel File | .xlsx | readxl | read_excel | None |
## Decision Tree for Statistical Test Selection
Based on the characteristics of the input data, the `vvdoctor` app uses the following decision tree to select the appropriate statistical test:
This flowchart illustrates the process of selecting a statistical test based on the class of independent/dependent variables, whether the test is paired or unpaired, and whether the data is normally distributed.
## Overview of Implemented Statistical Test
The table below serves as a reference for understanding the logic behind the app's functionality,
showcasing how different statistical tests are executed through various R packages and functions.
| Statistical Test Name | R Package | R Function (from the package) |
|------------------------------------------------------------|------------|---------------------------------|
| Sign Test | DescTools | SignTest() |
| Wilcoxon Signed Rank Test | stats | wilcox.test() |
| Mann-Whitney U Test | stats | wilcox.test() |
| Kruskal-Wallis Test | stats | kruskal.test() |
| One Sample t-test | stats | t.test() |
| Paired t-test | stats | t.test() |
| Independent Samples t-test | stats | t.test() |
| Repeated Measures ANOVA | ez | ezANOVA() |
| One-way ANOVA | stats | aov() |
| Chi-Square Goodness-of-Fit and Binomial Test | stats | chisq.test() |
| McNemar's Test | exact2x2 | exact2x2() |
| Chi-Square Test for Independence and Fisher's Exact Test | stats | chisq.test() |
| Bhapkar's Test | irr | bhapkar() |
## Example
Below is a screenshot of an example in `vvdoctor`.
