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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

## 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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.