shinystats

The goal of shinystats is to make shiny apps that explain statistical concepts. The apps are designed to be light-weight (no extra packages) and fast so that they can be used in quarto presentations with shinylive.

Installation and Use

You can install shinystats from GitHub with:

# install.packages("pak")
pak::pak("biostats-r/shinystats")

To use shinystats in a quarto document with shinylive, you also need to do the following:

1. Install shinylive from CRAN with install.packages("shinylive")

2. Install shinylive assets with shinylive::assets_ensure()

3. Add the following to the YAML header of your quarto document:

format: 
  html:
    resources: 
      - shinylive-sw.js
filters:
  - shinylive

Shinylive also works with revealjs presentations. Simply substitute html with revealjs in the YAML header.

4. Add a shinylive-r code chunk that runs the app:

```{shinylive-r}
#| label: power-app
#| standalone: true
#| viewerHeight: 600

shinystats::ar_app()
```

5. Render the document

Example

Here is a sample quarto document that uses shinystats.

---
title: "shinystats demo"
format:
  html:
    resources:
      - shinylive-sw.js
filters:
  - shinylive
---


App should appear below. Might take a while to load.

```{shinylive-r}
#| label: ar-app
#| standalone: true
#| viewerHeight: 600

shinystats::ar_app()
```

Available Apps

Power analysis

• climate_effect_app() - visualise effect sizes with climate data
• one_sided_z_app() - power of a z-test
• t_test_power_app() - power of a t-test
• t_test_sim_app() - power of a t-test by simulation
• misreported_cohens_d_app() - effect of misreporting Cohen’s d

Linear regression

• coef_explain_app() - how to interpret the coefficients of a two-way ANOVA
• f_test_app() - interpreting the F statistic
• regression_line_ss_app() - fit a regression and find the sum of squares
• sample_app() - in development
• x_distribution_app() - shows that it is the distribution of residuals that matter

Autocorrelation

• ar_app() - effect of autocorrelation on models
• acf_app() - how ACF is calculated
• acf_pacf_app() - using ACF and PACF to determine number of AR terms