biostats.tutorials

The biostats.tutorials package includes

• tutorials to reach reproducible data analysis with R with the tidyverse package.
• shiny apps for teaching statistics concepts to biologists.

Installation

You can install biostats.tutorials from GitHub with:

# install.packages("remotes")
remotes::install_github("biostats-r/biostats.tutorials")

The tutorials

Importing and cleaning data usually take up the vast majority of time spent working on a project. The statistical analysis is often fairly quick and done in a few lines of code. To make an analysis reproducible, we need to do all the data processing with code (rather than editing files in Excel).

The tutorials are designed to help you process your raw data entirely with R so that it is ready for plotting and statistical analysis.

To access the tutorials, you first need to load the package.

The tutorials should appear in the tutorials tab. This is normally in the top right pane of Rstudio next to the Environment and History tabs.

You can also get a list of the available tutorials by running

learnr::available_tutorials(package = "biostats.tutorials")
#> Available tutorials:
#> * biostats.tutorials
#>   - Dates-and-times   : "Working with dates and times"
#>   - expressions       : "Mathematical notation for plots"
#>   - function-packages : "Functions and Packages"
#>   - importing-data    : "Importing data"
#>   - naming-objects    : "Naming objects"
#>   - text-manipulation : "Processing text with `stringr`"
#>   - tidying-data      : "Tidying data with tidyr"
#>   - using-dplyr       : "Using dplyr"

Tutorials can be run with

learnr::run_tutorial("Dates-and-times", package = "biostats.tutorials")

Running apps

To run the shiny apps, load the package then run one of the apps.

library(biostats.tutorials)

power_lm_app()

Available apps

• publication_bias_app() How much could publication bias, the tendency not to publish non-significant results, bias the literature?
• power_lm_app() Find out how much statistical power a linear model has for an experiment has given the effect size, standard deviation of residuals and number of observations.
• influence_leverage_app() Explore how the position of an point changes diagnostic plots.