# An Introduction to Biostatistics

## Introduction to R

These are the solutions to the exercises contained within the handout to Introduction to R which walks you through the basics of the `R` machinery. `R` is a coding language that can be highly individualised and hence there are often multiple solutions to the same problem. Within these solutions, I shall only present you with one solution for every given task. However, do keep in mind that there is probably a myriad of other ways to achieve your goal.

## A Primer For Statistical Tests

These are the solutions to the exercises contained within the handout to A Primer For Statistical Tests which walks you through the basics of variables, their scales and distributions. Keep in mind that there is probably a myriad of other ways to reach the same conclusions as presented in these solutions.

## Descriptive Statistics

These are the solutions to the exercises contained within the handout to Descriptive Statistics which walks you through the basics of descriptive statistics and its parameters. The analyses presented here are using data from the `StarWars` data set supplied through the `dplyr` package that have been saved as a .csv file.

## Data Visualisation

These are the solutions to the exercises contained within the handout to Data Visualisation which walks you through the basics of data visualisation in `R`using `ggplot2`. The plots presented here are using data from the `iris` data set supplied through the `datasets` package.

## Data Handling and Data Mining

Welcome to our first "real" practical experience in `R`. The following notes present you with an example of how data handling (also known as data cleaning) can be done. Obviously, the possibility for flaws to occur in any given data set are seemingly endless and so the following, tedious procedure should be thought of as less of an recipe of how to fix common flaws in biological data sets but make you aware of how important proper data collection and data entry is.

## Nominal Tests

Welcome to our second practical experience in `R`. Throughout the following notes, I will introduce you to a couple nominal statistical test approaches that might be useful to you and are often used in biology. To do so, I will enlist the sparrow data set we handled in our last exercise.

## Correlation Tests

Welcome to our third practical experience in R. Throughout the following notes, I will introduce you to a couple statistical correlation approaches that might be useful to you and are, to varying degrees, often used in biology. To do so, I will enlist the sparrow data set we handled in our first exercise.

## Ordinal & Metric Tests (Two-Sample Situations)

Welcome to our fourth practical experience in R. Throughout the following notes, I will introduce you to a couple statistical approaches for metric or ordinal data when wanting to compare two samples/populations that might be useful to you and are, to varying degrees, often used in biology. To do so, I will enlist the sparrow data set we handled in our first exercise.

## Ordinal & Metric Tests (More-Than-Two-Sample Situations)

Welcome to our fifth practical experience in R. Throughout the following notes, I will introduce you to a couple statistical approaches for metric or ordinal data when wanting to compare more than two samples/populations that might be useful to you and are, to varying degrees, often used in biology. To do so, I will enlist the sparrow data set we handled in our first exercise.

## Simple Parametric Tests

Welcome to our sixth practical experience in R. Throughout the following notes, I will introduce you to a couple of simple parametric test. Whilst parametric tests are used extremely often in biological statistics, they can be somewhat challenging to fit to your data as you will see soon. To do so, I will enlist the sparrow data set we handled in our first exercise. Additionally, todays seminar is showing plotting via base plot instead of `ggplot2` to highlight the usefulness of base plot and show you the base notation.