setup.qmd

---
title: "Setup"
---

## Installation

::: {.panel-tabset group="language"}
## R

#### Required software

- [Download R](https://cran.r-project.org/) and install it using default options. (Note: choose the "base" version for Windows)
- [Download RStudio](https://www.rstudio.com/products/rstudio/download/#download) and install it using default options. 

#### Setting up RStudio

After installing RStudio, change some of its default options (you only need to 
do this once): 

- From the upper menu go to <kbd>Tools</kbd> > <kbd>Global Options...</kbd> 
- _Untick_ the option "Restore .RData to workspace on startup."
- Change "Save workspace to .RData on exit" option to "Never"
- Press <kbd>OK</kbd>

## Python

For this course we'll be using [Visual Studio Code](https://code.visualstudio.com). This provides support for various programming languages (including Python and R). It works on Windows, MacOS and Linux. It's also open-source and free.

Please refer to the [installation instructions](https://code.visualstudio.com/docs/python/python-tutorial) and make sure that you verify that Python code will run.

A brief sequence of events:

1. Install Visual Studio Code
2. Install the VS Code Python extension
3. Install a Python interpreter
    * Windows: install from [Python.org](https://www.python.org/downloads/) or use the Microsoft Store
    * MacOS: install the [Homebrew](https://brew.sh) package manager, then use this to install Python
    * Linux: comes with Python 3, but needs `pip` to install additional packages
:::


## Packages
We will be using the following packages throughout this course:

::: {.panel-tabset group="language"}
## R

Install the required packages. Run the following code in the console:

```{r}
#| eval: false
install.packages("tidyverse")
install.packages("rstatix")
install.packages("ggResidpanel")
```


#### Testing your installation
On the RStudio panel named "Console" type `library(tidyverse)` and press <kbd>Enter</kbd>

A message similar to this should print:

```
── Attaching packages ─────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.2.1     ✔ purrr   0.3.2
✔ tibble  2.1.3     ✔ dplyr   0.8.3
✔ tidyr   1.0.0     ✔ stringr 1.4.0
✔ readr   1.3.1     ✔ forcats 0.4.0
── Conflicts ────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
```
<p></p>

If instead you get the message: 

```
Error in library(tidyverse) : there is no package called ‘tidyverse’
```
 
then your package installation did not work. Please ask the instructors for assistance before the course. 

## Python

#### NumPy
The `numpy` package provides fundamental data science functionality to Python. For more information see: [https://numpy.org/doc/stable/#](https://numpy.org/doc/stable/#)

It can be installed via `pip`
```{python}
#| eval: false
pip install numpy
```

or `conda`
```{python}
#| eval: false
conda install -c conda-forge numpy
```

#### pandas
The `pandas` package provides data structures to Python. For more information see: [https://pandas.pydata.org/docs/getting_started/install.html](https://pandas.pydata.org/docs/getting_started/install.html).

It can be installed via `pip`
```{python}
#| eval: false
pip install pandas
```

or `conda`
```{python}
#| eval: false
conda install pandas
```

#### pingouin
The `pingouin` package provides statistical functionality to Python. For more information see: [https://pingouin-stats.org](https://pingouin-stats.org).

It can be installed via `pip`

```{python}
#| eval: false
pip install pingouin
```

or `conda`
```{python}
#| eval: false
conda install -c conda-forge pingouin
```

#### patchworklib
The `patchworklib` package provides an easy way for assembling figures. This package is required to run the course-specific `dgplots()` function. For more information see: [https://pypi.org/project/patchworklib/](https://pypi.org/project/patchworklib/).

It can be installed via `pip`
```{python}
#| eval: false
pip install patchworklib
```

#### plotnine

The `plotnine` packages provides a grammar of graphics to Python - an equivalent to the `ggplot2` package in R. For more information see: [https://plotnine.readthedocs.io/en/stable/#](https://plotnine.readthedocs.io/en/stable/#).

It can be installed via `pip`
```{python}
#| eval: false
pip install plotnine
```

or `conda`
```{python}
#| eval: false
conda install -c conda-forge plotnine
```

### scikit-posthocs

The `scikit-posthocs` package provides post-hoc functionality. For more information see: [https://scikit-posthocs.readthedocs.io/en/latest/](https://scikit-posthocs.readthedocs.io/en/latest/)

It can be installed via `pip`
```{python}
#| eval: false
pip install scikit-posthocs
```

### statsmodels

The `statsmodels` package provides statistical functionality. For more information see: [https://www.statsmodels.org/stable/index.html](https://www.statsmodels.org/stable/index.html).

It can be installed via `pip`
```{python}
#| eval: false
pip install statsmodels
```

or `conda`
```{python}
#| eval: false
conda install -c conda-forge statsmodels
```
:::