---
title: "Biostatistics vignette"
author: "Rob Knell"
date: "January 2021"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Biostatistics vignette}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup, echo = FALSE}
library(Biostatistics)
```

# The Biostatistics Package

This package consists of a series of learnr tutorials for use in teaching statistics to biologists. They were written for use in undergraduate and postgraduate teaching in the UK but they could also be used for individual, self-directed learning. The subjects covered range from basic data visualistion and description through to reasonably advanced linear modelling. There are obviously many subjects which are not currently covered such as generalised linear models, mixed effects models and multivariate statistics and it is hoped that these will be incorporated in the future.

There is a strong emphasis throughout the tutorial on analysing real data sets. This is much better for learning statistics than using synthetic example data because with real data comes all of the issues and uncertainty associated with real science. The data used here have mostly been made publicly available by the authors of papers published in the biological literature, mostly via the [Dryad data repository](https://datadryad.org/stash), and I would like to thank all of them for this.

The tutorials are written for the [learnr](https://rstudio.github.io/learnr/) package which uses an [rmarkdown](https://rmarkdown.rstudio.com/) framework to render tutorials into [shiny](https://shiny.rstudio.com/) webapps. The rmarkdown files for all of the tutorials are available on the [author's github page](https://github.com/rjknell).

## Running tutorials

There are two ways of running these tutorials. The easy way assumes you are using a recent version of RStudio. If this is the case then once you have installed the package the tutorials will show up in the 'Tutorial' tab in the RStudio pane that also includes the Environment and History tabs. Click the "Start Tutorial" button and the tutorial will render, which can take a few seconds, and then appear in the Tutorial tab. You'll probably want to maximise the pane within your RStudio window. If you want to finish the tutorial click on the 'Stop' sign button at the top left of the tab.

If you would rather run your tutorial in a separate browser window then you can use the `run_tutorial()` function from the learnr package. You need to specify the name of the tutorial and the package, so 

`learnr::run_tutorial("02_Descriptive_statistics", package = "Biostatistics")`

will run the Descriptive Statistics tutorial and 

`learnr::run_tutorial("17_Multiple_Regression", package = "Biostatistics")`

will run the Multiple Regression tutorial. In my experience the first method, with the Tutorial pane, seems more stable and sometimes tutorials won't render using `run_tutorial()` for reasons that are not clear.

## List of tutorials

The tutorials currently in the package are:

00_Introduction                                             
01_Frequency_histograms                                     
02_Descriptive_statistics                                   
03_Boxplots                                                 
04_Scatterplots                                             
05_Sampling_distributions                                   
06_Standard_errors                                          
07_Confidence_intervals                                     
08_CIs_comparing_two_means
09_Paired_sample_t_tests                                    
10_Two_sample_t_tests                                       
11_Chi_square_tests                                         
12_Correlation                                              
13_Single_factor_ANOVA                      
14_Linear_Regression                        
15_Model_assumptions        
16_Multi_factor_ANOVA                       
17_Multiple_regression                      
18_Factors_and_continuous_variables         
19_Model_selection

