ggplot_quick.qmd

---
title: "ggplot2 - quick and easy"

date-modified: 'today'
date-format: long

format: 
  html:
    footer: "CC BY 4.0 John R Little"

license: CC BY-NC
---

```{r}
#| echo=FALSE
htmltools::img(src = knitr::image_uri("images/Rlogo.png"), 
               alt = 'Rfun logo', 
               style = 'position:absolute; top:0; right:0; padding:10px;')
```

<!-- CSS style -->

```{css, echo=FALSE}
.myccfoot {
  font-size: 85%;
  text-align: right;
}

```

This code can be found at https://github.com/libjohn/rfun_flipped

## Load library packages

I only need `ggplot2` but I like to load `tidyverse` because it includes 8 complimentary packages, including `ggplot2`.

```{r}
#| message: false
#| warning: false
# library(ggplot2)
library(tidyverse)
```

Get more information from:

-   https://tidyverse.org
-   https://ggplot2.tidyverse.org

## ggplot2 template code

The ggplot2 template is used to identify the dataframe, identify the x and y axis, and define visualized layers

> `ggplot(data = ---, mapping = aes(x = ---, y = ---)) + geom_----()`

Note: `----` is meant to imply text (function names, dataframe names, variable names) you supply.

It is helpful to see the argument mapping, above. In practice, rather than typing the formal arguments, code is typically shorthanded to this:

> `dataframe %>%  ggplot(aes(xvar, yvar)) + geom_----()`

## Goal

Visualize a scatter plot showing the relationship of mass to height for *Star Wars* characters in the `dplyr::starwars` dataframe, excluding the heaviest character. Indicate a linear regression line.

```{r}
#| echo: false
#| message: false
#| warning: false
starwars %>% 
  filter(mass < 500) %>% 
  ggplot(aes(height, mass)) +
  geom_point() +
  geom_smooth(method = lm, se = FALSE)
```

## Import data

dplyr has an onboard dataset, `starwars`

```{r}
data(starwars)
starwars
```

## Steps to Visualization

### Draw the base layer

This feels like, and looks like, you drew an empty box.

```{r}
starwars %>% 
  ggplot() 
```

*But wait, there's more....*

### Map the aesthetics to variables in the dataframe

Still doesn't look like much. You will initialize the plot scales and labels based on the values of the variables in the dataframe.

```{r}
starwars %>% 
  filter(mass < 500) %>% 
  ggplot(aes(height, mass))
```

In the above, I subset the data, removing any *Star Wars* characters weighing more than 500 Kg -- `dplyr::filter()`. Then I initialized the base layer with the `height` as the x axis and `mass` as the y axis. ggplot drew the scales for me.

### Visualize a layer

Since I have two numeric variables, `height` and `mass`, I'll start with a scatter plot. Scatter plots are generated by the `geom_point()` function.

```{r}
#| message: false
#| warning: false
starwars %>% 
  filter(mass < 500) %>% 
  ggplot(aes(height, mass)) +
  geom_point() 
```

### Global v local arguments

So far, the **aesthetics** are **mapped** in the `aes()` function within the initial `ggplot` function. As such, these values are mapped globally and all layers are affected by this mapping. See the `aes()` function, above. Arguments can also be mapped locally, within a geom function layer, as as `geom_point(aes(height, mass))`.

```{r}
#| message: false
#| warning: false
starwars %>% 
  filter(mass < 500) %>% 
  ggplot() +
  geom_point(aes(height, mass)) 
```

### Mapping v Setting

Dataframe values can be *mapped* inside the aesthetic, `aes()`, to visualize variable dataframe values. Alternatively, data values can be *set* as an argument outside the `aes()` function but inside the geom\_ function. This is done to affect a visual quality that is manually assigned, as opposed to being derived from variable data values.

Aesthetic arguments include:

-   color
-   fill
-   size
-   linetype
-   opacity
-   shape
-   *and more* see documentation for each geom\_

> Mapping: `color` is **mapped** *inside* `aes()` function. In this case, `color = starwars$gender`

```{r}
#| message: false
#| warning: false
starwars %>% 
  filter(mass < 500) %>% 
  ggplot() +
  # geom_point(mapping = aes(x = height, y = mass, color = gender))
  geom_point(aes(height, mass, color = gender))
```

Notice the legend was drawn automatically, above, by mapping an aesthetic

> Setting: The `color` argument can be **set** outside the `aes()` function, but within the `geom_` function. In this case with `color = "goldenrod"`

```{r}
#| message: false
#| warning: false
starwars %>% 
  filter(mass < 500) %>% 
  ggplot() +
  geom_point(aes(height, mass), color = "goldenrod")
```

## Common geom\_ functions

| Type             | Geom                             |
|------------------|----------------------------------|
| Bar graph:       | `geom_bar()` `geom_col()`        |
| Histogram:       | `geom_histogram()`               |
| Scatter plot:    | `geom_point()` `geom_jitter()`   |
| Line graph:      | `geom_line()`                    |
| Box plot:        | `geom_boxplot()`                 |
| Density:         | `geom_density()` `geom_violin()` |
| Heat map:        | `geom_heatmap()`                 |
| Mapping:         | `geom_sf()`                      |
| Regression line: | `geom_smooth()`                  |

> A list of available geom\_ functions, or layers, can be found in the help or on the website: [https://ggplot2.tidyverse.org/reference/index.html#section-geoms](https://ggplot2.tidyverse.org/reference/index.html#section-geomshttps://ggplot2.tidyverse.org/reference/index.html#section-geoms)

### Boxplot

```{r}
#| message: false
#| warning: false
starwars %>% 
  mutate(species = fct_lump_min(species, 2)) %>% 
  ggplot(aes(species, height)) +
  geom_boxplot() 
```

### Line graph

```{r}
#| message: false
#| warning: false
babynames::babynames %>% 
  filter(name == "Watts") %>% 
  ggplot(aes(year, n)) +
  # geom_point() +
  geom_line()
```

### Overplotting

There are two simple approaches to visualizing overplotted data: `geom_jitter()` and decrease the opacity be setting the `alpha =` argument.

-   Adjust **opacity**. The `alpha` argument within the geom function affects the opacity of the points. In this way, overplotted data will appear as darker points on the plot

```{r}
starwars %>% 
  filter(mass < 500) %>% 
  ggplot() +
  geom_point(aes(height, mass), alpha = .3)
```

-   **Jitter** the data with `geom_jitter()`

`geom_jitter` will not change the values of the data but it will offset data points, making it easier to perceive the overplotting.

```{r}
starwars %>% 
  filter(mass < 500) %>% 
  ggplot() +
  geom_jitter(aes(height, mass))
```

### Multiple layers

Each layer, visualized by a geom\_ function, can support local arguments and draw from the global settings. Below we use the `geom_line()` function, followed by the `geom_point()` function.

    babynames %>%
      ggplot(aes(year, prop)) +
      geom_line(aes(color = sex)) +
      geom_point(alpha = 0.4, shape = "cross")

![](images/line_graph_je_names.png)

The full code for the above graph can be seen below.

    library(babynames)
    library(ggplot)

    babynames %>% 
      filter(name == "John" & sex == "M" | 
               name == "Elizabeth" & sex == "F") %>% 
      ggplot(aes(year, prop)) +
      geom_line(aes(color = sex)) +
      geom_point(alpha = 0.4, shape = "cross") +
      geom_text(data = . %>% filter(year == 1965), aes(label = name),
                nudge_y = .009) +
      labs(title = "Name Popularity") + 
      theme(legend.position = "none")

### Goal

Recall the goal mentioned in the beginning. We want a scatter plot and a regression line. The regression line is drawn with the `geom_smooth()` function.

```{r}
#| message: false
#| warning: false
starwars %>% 
  filter(mass < 500) %>% 
  ggplot(aes(height, mass)) +
  geom_point() +
  geom_smooth(method = lm, se = FALSE)
```

## Arrange order

Categorical values are most easily ordered with the `forcats` library. Part of the Tidyverse, the [forcats](https://forcats.tidyverse.org/) package is used to transform string data as a factor data type. Data types in R can be simple distinctions useful in efficient computation, such as calculating numeric outcomes versus manipulating character data (i.e. string or text data). R data types are rich and sometimes complex. Staying simple, text data consisting of categories, may be efficiently handled as a factor data type. For example, eye colors can be categorized. Brown, blue, and green are nominal categorical values for the factor variable `eye_color`. Among other things, treating `eye_color` as a factor data type enables visually ordering categorical values by frequency.

#### Before ordering

```{r}
msleep %>% 
  ggplot(aes(vore)) +
  geom_bar()
```

### Ordering with forcats

Change the order of the bars by the frequency of observations using `forcats::fct_infreq()`

```{r}
msleep %>% 
  ggplot(aes(fct_infreq(vore))) +
  geom_bar() 
```

Notice below, we use the `fill =` argument to set the color of an individual bar. In the scatter plot examples, above, we used the `color =` argument. In many `geoms_` you can use both `color` and `fill` arguments. How do these arguments differ? Where can you look to find out more about `fill` and `color`?

```{r}
starwars %>% 
  ggplot(aes(fct_rev(fct_infreq(eye_color)))) +
  geom_bar(fill = "grey70") +
  geom_bar(data = starwars %>% filter(eye_color == "orange"), fill = "darkorange") +
  coord_flip()
  
```

## Facet wrap

Faceting is great way to make subplots of the same dataframe. See both `facet_wrap()` and `facet_grid()`

```{r}
mpg %>% 
  ggplot(aes(displ, hwy)) +
  geom_point() +
  facet_wrap(~ class)
```

## Scales

Scales are used to affect the visual qualities of the data. I'll introduce scales to visualize discrete categories by associating each discrete value with a specific color. [Read more about scales](https://ggplot2.tidyverse.org/reference/index.html#section-scales).

Viridis scales apply color palettes to continuous, discrete, or binned data. For discrete data we can use the `scale_fill_viridis_d()` function.

> By using one the `scale_fill_` functions, we are able to affect the variable values associated in the `fill = conservation` argument.

```{r}
msleep %>% 
  ggplot(aes(fct_infreq(vore), sleep_total)) +
  geom_col(aes(fill = conservation)) +
  scale_fill_viridis_d(na.value = "grey80")
```

The color brewer palette is similar but has a wider array of palettes to choose from. Below we use `scale_fill_brewer()` and a default *qualitative* color palette by setting the `type =` argument to *qual* (for qualitative). Alternatively, or additionally, we could assign a `palette =` argument to choose a particular ColorBrewer palette, such as choosing the "Dark2" palette with the argument `palette = "Dark2"`

```{r}
msleep %>% 
  ggplot(aes(fct_infreq(vore), sleep_total)) +
  geom_col(aes(fill = conservation)) +
  scale_fill_brewer(type = "qual", na.value = "grey80") 
```

Sometimes a manual scale is preferred. Below we use `scale_fill_manual()` to associate a defined set of color names with my `fill = conservation` argument

```{r}
mycolors <- c("firebrick", "forestgreen", "navy", "darkorange", 
               "goldenrod", "sienna")

msleep %>% 
  ggplot(aes(fct_infreq(vore), sleep_total)) +
  geom_col(aes(fill = conservation)) +
  scale_fill_manual(values = mycolors, na.value = "grey80") 
```

To find available colors, I typically Google search "R color names." A more specific technique, within R, can be used to find the array of ColorBrewer palettes...

```{r}
#| fig-height: 8
RColorBrewer::display.brewer.all()
```

Scales are used to manipulate the visual properties of the data. Beyond using scales to modify colors, another example is logarithmic scales to account for data skew. In this way you can clarify the data pattern. For example, using the `ChickWeight` dataset, we visualize the weights of the chicks over time. Hint: You can visualize the data skew with a histogram, `geom_histogram()`.

```{r}
data("ChickWeight")

ChickWeight %>% 
  ggplot(aes(Time, weight, color = Diet)) +
  geom_line(aes(group = Chick))
```

Using `scale_y_log10` we can alter the scale to highlight a more understandable data pattern

```{r}
chicken_plot <- ChickWeight %>% 
  ggplot(aes(Time, weight, color = Diet)) +
  geom_line(aes(group = Chick)) +
  scale_y_log10()
chicken_plot

```

## Labels

The `labs()` function is a specialized scales function, used to apply labels. For example, use the `labs()` function to add a title, subtitle, legend title, modify axis labels, and set a caption. See more on [scales](https://ggplot2.tidyverse.org/reference/index.html#section-scales).

```{r}
plot_sleep <- msleep %>% 
  mutate(vore = case_when(
    vore == "herbi" ~ "Herbivore",
    vore == "omni"  ~ "Omnivore",
    vore == "carni" ~ "Carnivore",
    vore == "insecti" ~ "Insectivore"
  ))  %>%
  ggplot(aes(fct_infreq(vore), sleep_total)) +
  geom_col(aes(fill = conservation)) +
  scale_fill_brewer(type = "qual", na.value = "grey80") +
  labs(title = "Animal sleep times", 
       subtitle = "A practice dataset",
       fill = "Conservation\nType",
       x = "",
       y = "Sleep time in hours",
       caption = "Source: ggplot::msleep")

plot_sleep
```

## Themes

Themes are used to manipulate the stylistic characteristics of the non-data components of your plot, such as font faces, text sizes, and grid lines. **ProTip:** quickly manipulate a single plot with preset themes such as `theme_dark`, or use a specialized theme extension such as `theme_ipsum` from the `hrbrthemes` package.

-   https://ggplot2.tidyverse.org/reference/ggtheme.html

    -   for example... `theme_dark()`, `theme_light()`, `theme_classic()`

-   https://cinc.rud.is/web/packages/hrbrthemes/

-   https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/

See more on [themes](https://ggplot2.tidyverse.org/reference/index.html#section-themes)

### Example themes

![](https://r4ds.had.co.nz/images/visualization-themes.png){fig-alt="ggplot2 themes" width="683"}

### theme_dark()

```{r}
plot_sleep +
  theme_dark()
```

### theme_classic

```{r}
plot_sleep +
  theme_classic()
```

### hbrthemes

https://cinc.rud.is/web/packages/hrbrthemes/

```{r}
#| message: false
#| warning: false
plot_sleep +
  hrbrthemes::theme_ipsum(grid = "Y") +
  hrbrthemes::scale_fill_ipsum(na.value = "grey80",
                               labels = c("Critical", "Domesticated", 
                                          "Endangered", "Least Concern", 
                                          "Threatened", "Vulnerable")) +
  theme(plot.title.position = "plot")
```

## Combine plots

The `patchwork` package makes it "ridiculously simple to combine separate ggplot objects into the same graphic." The `/`will separate plots vertically. The `|` will separate plots horizontally. See more about [patchwork](https://patchwork.data-imaginist.com/)

> Try also: (plot_sleep \| chicken_plot)

```{r}
# https://patchwork.data-imaginist.com/
library(patchwork)

(plot_sleep / chicken_plot) 
```

## Interactive plots

Use the `ggplotly` function will transform your static ggplot object into an interactive plot. This interactive plot can be used in dashboards and web presentations.

See more at the [Plotly ggplot2 Library](https://plotly.com/ggplot2/) page, and the [*Interactive web-based data visualization with R, plotly, and shiny*](https://plotly-r.com/) book.

```{r}
#| message: false
#| warning: false
library(plotly)
ggplotly(plot_sleep)
```

## Annimate plots

Use the `gganimate` package to bring your plot to life through the wonders of animation. Learn more at the resource page for [gganimate](https://gganimate.com/)

For Example:

```{r example_gganimate}
#| message: false
#| warning: false
#| echo: false
library(htmltools)
img(src = knitr::include_graphics("images/gganmimate_example.gif"), alt = 'gganmimate example')
div(class="captxt", "Image source:  https://gganimate.com/index.html#yet-another-example")
```

## Reinforce your learning

On your own...

Interactive Exercises from [RStudio Primers -- Visualization](https://rstudio.cloud/learn/primers/3)

[Angela Zoss code exercises](https://github.com/amzoss/ggplot2-S20)

## Resources

[Data Visualization: A Practical Introduction.](https://socviz.co/lookatdata.html) Kieran Healy

### books

[ggplot2: Elegant Graphics for Data Analysis.](https://ggplot2-book.org/) Hadley Wickham

[Data Visualization with R.](https://rkabacoff.github.io/datavis/) Rob Kabacoff

[Interactive web-based data visualization with R, plotly, and shiny.](https://plotly-r.com/) Carson Sievert

```{r}
#| echo: false
htmltools::tagList(rmarkdown::html_dependency_font_awesome())
htmltools::div(class = "myccfoot", htmlTemplate("template/footer.html"))
```