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playground.qmd
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playground.qmd
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---
webr:
packages: ["ggplot2", "tidyverse", "palmerpenguins", "GGally"]
filters:
- webr
---
# `R` Playground
```{webr-r}
#| context: setup
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" }
## 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")
```
:::