cm008.Rmd

# Intro to plotting with `ggplot2`, Part II

## Orientation

### Worksheet

You can find a worksheet template for today [here](https://raw.githubusercontent.com/STAT545-UBC/Classroom/master/tutorials/cm008-exercise.Rmd).

### Announcements

From Assignment 3 onwards, whenever you produce an HTML file, you must link to a rendered version of the file. We'll cover this today.

### Today 

- GitHub Pages (15 min)
- Continue with `ggplot2`: a tour of some important `geom`s (20 min)
- `ggplot2` exercises from the worksheet (40 min)


## Participation repository and GitHub Pages (15 min)

### GitHub Pages

You can turn your GitHub repository into a website, by enabling __GitHub Pages__ on that repo. This is useful for something as small as being able to display HTML files without getting a local copy of the repository, to something as big as making a full fledged website like the [stat545.stat.ubc.ca](https://stat545.stat.ubc.ca) website. 

- If you make a repo called `yourusername.github.io`, and enable GitHub Pages on that repo, then the URL of the website will be `https://yourusername.github.io/`.
- If you enable GitHub pages on any other repo, the URL for that repo will be `https://yourusername.github.io/name_of_other_repo`.

Learn more with GitHub's [GitHub Pages](https://pages.github.com/) tutorial. 

### Practice with HTML file linking

We'll practice linking to an HTML file for today's exercise, by following the instructions on the (new!) ["Viewing and Linking to HTML Files"](https://stat545.stat.ubc.ca/evaluation/assignments/#viewing-and-linking-to-html-files) on the assignments home page.

## A tour of some important `geom`s (20 min)

Here, we'll explore some common plot types, and how to produce them with `ggplot2`.

### Histograms: `geom_histogram()`

Useful for depicting the distribution of a continuous random variable. Partitions the number line into bins of certain width, counts the number of observations falling into each bin, and erects a bar of that height for each bin.

Required aesthetics:

- `x`: A numeric vector.

By default, a histogram plots the _count_ on the y-axis. If you want to use proportion, specify the `y = ..prop..` aesthetic. 

You can change the smoothness of the plot via two arguments (your choice):

- `bins`: the number of bins/bars shown in the plot.
- `binwidth`: the with of the bins shown on the plot.

Example:

```{r}
ggplot(gapminder, aes(lifeExp)) +
  geom_histogram(bins = 50)
```


### Density: `geom_density()`

Essentially, a "smooth" version of a histogram. Uses [kernels](https://en.wikipedia.org/wiki/Kernel_density_estimation) to produce the curve.

Required aesthetics:

- `x`: A numeric vector.

Good to know:

- `bw` argument controls the smoothness: Smaller = rougher.

Example:

```{r}
ggplot(gapminder, aes(lifeExp)) +
  geom_density()
```

### Jitter plots: `geom_jitter()`

A scatterplot, but with minor random perturbations of each point. Useful for scatterplots where points are overlaying, or when one variable is categorical.

Required aesthetics:

- `x`: any vector
- `y`: any vector

Example:

```{r}
ggplot(gapminder, aes(continent, lifeExp)) +
  geom_jitter()
```

### Box plots: `geom_boxplot()`

This geom makes a boxplot for a numeric variable in each of a category. Useful for visualizing probability distributions across different categories.

Required aesthetics:

- `x`: A factor (categorical variable)
- `y`: A numeric variable

Example:

```{r}
ggplot(gapminder, aes(continent, lifeExp)) +
  geom_boxplot()
```


### Ridge plots: `ggridges::geom_density_ridges()`

A (superior?) alternative to the boxplot, the ridge plot (also known as the joy plot) places a kernel density for each group, instead of the box. 

You'll need to install the `ggridges` package. You can do lots more with ridges -- check out [the ggridges intro vignette](https://cran.r-project.org/web/packages/ggridges/vignettes/introduction.html).

Required aesthetics (reversed from boxplots!)

- `x`: A numeric variable
- `y`: A factor (categorical variable) 

Example:

```{r}
ggplot(gapminder, aes(lifeExp, continent)) +
  ggridges::geom_density_ridges()
```

### Bar plots: `geom_bar()` or `geom_col()`

These geom's erect a bar over each category.

`geom_bar()` automatically determines the height of the bar according to the count of each category.

`geom_col()` requires a manual specification of the bar heights.

Required aesthetics:

- `x`: A categorical variable
- `y`: A numeric variable (only required for `geom_col()`!)
  - To use proportion in `geom_bar()` instead of count, set `y = ..prop..`

Example: number of 4-, 6-, and 8- cylinder cars in the `mtcars` dataset:

```{r}
ggplot(mtcars, aes(cyl)) +
  geom_bar()
```

### Line charts: `geom_line()`

A line plot connects points with straight lines, from left-to-right. Especially useful if time is on the x-axis.

Required aesthetics:

- `x`: a variable having some ordering to it.
- `y`: a numeric variable.

Although not required, the `group` aesthetic will come in handy here. This aesthetic produces a plot independently for each group, and overlays the results.

```{r}
tsibble::as_tsibble(co2) %>% 
  rename(yearmonth = index,
         conc = value) %>% 
  mutate(month = lubridate::month(yearmonth, label = TRUE),
         year  = lubridate::year(yearmonth)) %>% 
  ggplot(aes(month, conc)) +
  geom_line(aes(group = year), alpha = 0.5) +
  ylab("CO2 Concentration")
```



### Path plots: `geom_path()`

Like `geom_line()`, except connects points in the order that they appear in the dataset.



## Activity: Fix the Plots (40 min)

Fill out the [worksheet](https://raw.githubusercontent.com/STAT545-UBC/Classroom/master/tutorials/cm008-exercise.Rmd) together.


## Time remaining?

If so, let's make tibbles with `tibble()`, and make a list column while we're at it. Maybe even `nest()` and `unnest()`.