webr-example.qmd

---
webr:
  packages: ["ggplot2", "tidyverse", "palmerpenguins", "GGally"]
filters:
  - webr
---

# `R` Playground

```{webr-r}
#| context: setup

# Download reporter data
download.file('https://raw.githubusercontent.com/sipbs-compbiol/BM214-Workshop-3/main/assets/data/reporter_curves.csv', 'reporter_curves.csv')

library(ggplot2)
library(palmerpenguins)
library(tidyverse)
```

## Introduction

This page provides a `WebR` cell for you to use as a playground to experiment with some example datasets. You can use this page to explore data management and visualisation in `R`.

## Playground

```{webr-r}
# Use this WebR cell to experiment with some practice biological datasets
```

## Things you can do

This `WebR` instance has three packages installed:

- `ggplot2`
- `GGally`
- `tidyverse`
- `palmerpenguins`

Open the callout boxes below to see some examples you can try in the code cell above.

::: { .callout-tip collapse="true }
## Play with data from a GitHub repository

One of our [BM214 workshops](https://sipbs-compbiol.github.io/BM214-Workshop-3/) involves a `WebR`-supported interactive exercise involving simulated reporter curves. We preload this data in the `setup` cell (see source code), and you can interact with it below with the code:

```r
data <- read.csv("reporter_curves.csv")
glimpse(data)
```
:::

::: { .callout-tip collapse="true" }
## Investigate Palmer's Penguins

The `penguins` dataset contains data about three different species of penguins. Take a look at the format of the dataset:

```r
glimpse(penguins)
```

You'll see there are eight variables, including `species`, `weight`, `sex`, etc. - some of these variables are _categorical_ (i.e. a category, like `species`), and others are _continuous_ (i.e. numerical). You can see a visual overview of how the data is related using the `plot()` function:

```r
plot(penguins)
```

We can visualise the number of penguins of each species in a bar chart:

```r
fig <- ggplot(penguins, aes(species, fill=species)) +
         geom_bar()
fig
```

And break this down in a facet plot, by sex:

```r
fig <- ggplot(penguins, aes(species, fill=species)) +
         geom_bar() +
         facet_wrap(~sex)
fig
```

We can make a box and whisker plot of penguin body mass by species:

```r
fig <- ggplot(penguins, aes(x=species, y=body_mass_g, fill=species)) +
         geom_boxplot()
fig
```

And plot the body mass for each sex side-by-side

```r
fig <- ggplot(penguins, aes(x=species, y=body_mass_g, fill=sex)) +
         geom_boxplot()
fig
```

We can investigate correlations, such as between body mass and flipper length:

```r
fig <- ggplot(penguins, aes(x=body_mass_g, y=flipper_length_mm)) +
         geom_point()
fig
```

We can colour datapoints by species:

```r
fig <- ggplot(penguins, aes(x=body_mass_g, y=flipper_length_mm, colour=species)) +
         geom_point()
fig
```

And fit a linear regression to each species separately:

```r
fig <- ggplot(penguins, aes(x=body_mass_g, y=flipper_length_mm, colour=species)) +
         geom_point() +
         geom_smooth(method="lm")
fig
```

:::

::: { .callout-note }
`R` comes with a number of example datasets you can practice with, including:

- `mtcars`: fuel consumption and other statistic for 32 automobiles
- `Titanic`: information on the fate of passengers on the fatal maiden voyage of the ocean liner _Titanic_
 
You can see a full list by running the command

```r
library(help = "datasets")
```


:::