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.