Awesome Shiny Apps for Statistics

A curated list of awesome Shiny Apps for statistics (ASAS) can

• help teachers teach basic statistics to their students.
• help self-learners to visualize statistics concepts.

Contents

• Resources
• Common Plots
• Common Statistic
• Common Distribution
• Random Samples
• Two Groups or Multiple Groups Comparison
• Hypothesis Testing
• Linear Regression
• Nonlinear Models for Continous Variables
• Categorical Models
• Survival Model
• Bayesian Analysis
• Longitudinal Analysis
• Test Analysis
• Complete Data Analysis
• Help Wanted

Resources

• Awesome R Shiny - A curated list of resources for R Shiny.

Common Plots

• Boxplots & Histograms
• List of R graphs

Common Statistic

• continuous variables
  • Stability of Mean & Median
  • Correlation
• p-value
  • When does a significant p-value indicate a true effect?
  • Hack p-value
  • the Vovk-Sellke maximum p-ratio - the maximum diagnosticity of a two-sided p-value.

Common Distribution

• Uniform
• Normal
• Binomial
• Student's T
• F
• Chi-square
• Shiny Apps including more than one distribution
  • Nomral, Binomial, Student's T, F, Chi-square

Random Samples

• Sampling and standard error
• Central Limit Theorem
  • Means
  • Proportions

Two groups or multiple groups comparison

• Cohen's d
• ANOVA
  • Sums of squares in ANOVA
  • BIC approximation for ANOVA designs

Hypothesis Testing

• Bootstrap resampling - Demonstrate hypothesis testing using bootstrap resampling.
• Power - Demonstrate the relationship of statistical power, effect size, and false positives
• Calculate power - Calculat the power of a statistical hypothesis test for a two-sided symmetrical test and show how statistical power is related to the p-value and the significance level.
• Trade Off - Visualize the trade off between type I and type II errors in a Null Hypothesis Significance Test (NHST).

Linear Regression

• Simple linear regression
• Sum of Square in simple linear regression | Code - Explore how sums of squares are calculated in simple linear regressions.
• Fit a simple linear regression model
• Diagnostics for simple linear regression
• Uncertainty
• Influence analysis - Demonstrates the leverage and influence of an adjustable point/outliers
• Graphs for linear regression with high orders
• Multicollinearity
• Model selection - Choose models between simple regression, additive regression, and interactive models.
• others
  • Residual error models
  • Meta analysis | Code

Nonlinear Models for Continous Variables

K-means Clustering

• Estimate K
• K-means Clustering
  • Iris dataset

Categorical Models

• Relationship between test accuracy, precision, sensitivity, and specificity

Survival Model

• Hazard model

Bayesian Analysis

• Bayes factors
• Robustness analysis for Bayes factors: Two sample t test
• Bayesian Inference
• Posterior distribution | Documentation - Calculate posterior distribution based on different priors
• Hypothesis Testing
  • Binomial & Normal Distribution

Longitudinal Analysis

• Longitudinal data exploration

Test Analysis

• Text analysis of an uploaded .txt file

Complete Data Analysis

• Linear regression
• shinyData | Demo

Help Wanted

Several ways you can help

1. Create a Shiny App that explains the statistics concept missing on the list
2. Add latest and greatest Shiny Apps that explain statistics concepts
3. Delete broken links to Shiny Apps
4. Delete links to low-quality Shiny Apps
5. Design the appearance of the website
6. Fix any typo
7. Rewrite the title or description of any Shiny App to make them more easily understood
8. Suggest different ways to categorize

Please adhere to the contribution guidelines.

License

This work is licensed under a Creative Commons Attribution 4.0 International License.