README.md
In vvdoctor: Statistical Test App with R 'shiny'

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/

## 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.

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 |

Based on the characteristics of the input data, the vvdoctor app uses the following decision tree to select the appropriate statistical test:

Decision Tree Flowchart

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.

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() |

Below is a screenshot of an example in vvdoctor.

example