---
title: "07: Summarise"
embed-resources: true
editor: visual
---

## Open the Tutorial

Use the following code chunks to open the accompanying tutorial.

### Open in RStudio Viewer

```{r}
#| eval: false
rstudioapi::viewer('https://r-training.netlify.app/tutorials/docs/07_summarise')
```

### Open in a New Browser Tab

```{r}
#| eval: false
utils::browseURL('https://r-training.netlify.app/tutorials/docs/07_summarise')
```

## Overview

## Setup

### Packages

Load the necessary packages.

```{r}

```

### Data

Read in the dataset and save it in a new object, `anx_scores`.

On the Cloud, you can read in this dataset from the `data` folder using `here::here()`.

Elsewhere, you can download the dataset, or copy the dataset URL, from the [Data and Workbooks page](../../../data_workbooks.qmd).

```{r}

```

#### Codebook

## Summarising

### Overall

#### Internal Calculations

### By Group

### Multiple Variables

#### Use an Existing Function

#### Iteration

## Exercises

Starting with the example code from previously:

```{r}
anx_scores |>   # <1>
  dplyr::group_by(mcq) |>   # <2>
  dplyr::summarise(  # <3>
    n = dplyr::n(), #<4>
    sticsa_trait_mean = mean(sticsa_trait_score, na.rm = TRUE),   # <4>
    sticsa_trait_sd = sd(sticsa_trait_score, na.rm = TRUE),
    sticsa_trait_se = sticsa_trait_sd/sqrt(n)  # <4>
  )
```

Make the following changes:

-   Add the min and max of `sticsa_trait_score` to the summary.
-   Split up the summary by gender as well as MCQ group.

Save your final summary table as `anx_scores_sum`.

```{r}

```

**CHALLENGE**: Instead of MCQ and gender, split up the summary information of `sticsa_trait_score` by whether each case scored higher, or lower than/equal to, the median value of the STARS test anxiety score.

```{r}

```

## Formatting Tables

### Quarto to HTML/PDF

Follow along with the instructions to create your own beautiful table.

```{r}

```

### Exporting

#### Creating a Folder

#### Exporting

## Quick Test: Correlation

### Visualisation

### Testing Correlation

## Exercises

Perform pairwise correlations on all the STARS subscales in this dataset.

```{r}

```

**CHALLENGE**: Pick a single pair from the pairwise correlations, generate the correlation output, and report the result.

```{r}

```

**CHALLENGE**: Use `GGally::ggpairs()` on the same numeric variables, but split up all the plots by `mcq` as well. Here we're getting at a key question of the original study: are maths and stats anxiety actually different?

*Hint*: There's an example of this code in [the introduction documentation for the lovely `palmerpenguins::penguins` dataset](https://allisonhorst.github.io/palmerpenguins/articles/intro.html)!

```{r}

```