---
title: "Data Visualization With ggplot2"
author: "Dr. Hua Zhou @ UCLA"
date: "Jan 29, 2018"
subtitle: Biostat M280
output:
  # ioslides_presentation: default
  html_document:
    toc: true
    toc_depth: 4  
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(fig.align = 'center', cache = TRUE)
```

## Outline

We will spend next couple lectures studying some R packages for typical data science projects.

* Data wrangling (import, visualization, transformation, tidy).  
  [R for Data Science](http://r4ds.had.co.nz) by Garrett Grolemund and Hadley Wickham.

* Web applications by Shiny.

* Interface with databases, eg., SQL and Apache Spark.

A typical data science project:

<p align="center">
<img src="./data-science.png" height="300">
</p>

## Tidyverse

- `tidyverse` is a collection of R packages that make data wrangling easy.

- Install `tidyverse` from RStudio menu `Tools -> Install Packages...` or
    ```{r, eval = FALSE}
    install.packages("tidyverse")
    ```

- After installation, load `tidyverse` by
    ```{r}
    library("tidyverse")
    ```

## Data visualization

> “The simple graph has brought more information to the data analyst’s mind than any other device.”  
> 
> John Tukey

## `mpg` data

- `mpg` data is available from the `ggplot2` package:
    ```{r}
    mpg
    ```

- Tibbles are a generalized form of data frames, which are extensively used in tidyverse.
    
- `displ`: engine size, in litres.  
    `hwy`: highway fuel efficiency, in mile per gallen (mpg).

# Aesthetic mappings | r4ds chapter 3.3
    
## Scatter plot

- `hwy` vs `displ` 
    ```{r}
    ggplot(data = mpg) + 
      geom_point(mapping = aes(x = displ, y = hwy))
    ```
    
- An aesthetic maps data to a specifc feature of plot. 

- Check available aesthetics for a geometric object by `?geom_point`.

## Color of points

- Color points according to `class`:
    ```{r}
    ggplot(data = mpg) + 
      geom_point(mapping = aes(x = displ, y = hwy, color = class))
    ```

## Size of points 

- Assign different sizes to points according to `class`:
    ```{r, warning = FALSE}
    ggplot(data = mpg) + 
      geom_point(mapping = aes(x = displ, y = hwy, size = class))
    ```

## Transparency of points 

- Assign different transparency levels to points according to `class`:
    ```{r}
    ggplot(data = mpg) + 
      geom_point(mapping = aes(x = displ, y = hwy, alpha = class))
    ```

## Shape of points 

- Assign different shapes to points according to `class`:
    ```{r, warning = FALSE}
    ggplot(data = mpg) + 
      geom_point(mapping = aes(x = displ, y = hwy, shape = class))
    ```

- Maximum of 6 shapes at a time. By default, additional groups will go unplotted.

## Manual setting of an aesthetic 

- Set the color of all points to be blue:
    ```{r}
    ggplot(data = mpg) + 
      geom_point(mapping = aes(x = displ, y = hwy), color = "blue")
    ```

# Facets | r4ds chapter 3.5

## Facets 

- Facets divide a plot into subplots based on the values of one or more discrete variables.

- A subplot for each car type:
    ```{r}
    ggplot(data = mpg) + 
      geom_point(mapping = aes(x = displ, y = hwy)) + 
      facet_wrap(~ class, nrow = 2)
    ```
    
----

- A subplot for each car type and drive:
    ```{r}
    ggplot(data = mpg) + 
      geom_point(mapping = aes(x = displ, y = hwy)) + 
      facet_grid(drv ~ class)
    ```
    
# Geometric objects | r4ds chapter 3.6

## `geom_smooth()`: smooth line

- `hwy` vs `displ` line:
    ```{r, fig.width = 4.5, fig.height = 3, message = FALSE}
    ggplot(data = mpg) + 
      geom_smooth(mapping = aes(x = displ, y = hwy))
    ```
    
## Different line types

- Different line types according to `drv`:
    ```{r, fig.width = 4.5, fig.height = 3, , message = FALSE}
    ggplot(data = mpg) + 
      geom_smooth(mapping = aes(x = displ, y = hwy, linetype = drv))
    ```

## Different line colors

- Different line colors according to `drv`:
    ```{r, fig.width = 4.5, fig.height = 3, message = FALSE}
    ggplot(data = mpg) + 
      geom_smooth(mapping = aes(x = displ, y = hwy, color = drv))
    ```

## Points and lines

- Lines overlaid over scatter plot:
    ```{r, fig.width = 4.5, fig.height = 3, message = FALSE}
    ggplot(data = mpg) + 
      geom_point(mapping = aes(x = displ, y = hwy)) + 
      geom_smooth(mapping = aes(x = displ, y = hwy))
    ```

----

- Same as
    ```{r, fig.width = 4.5, fig.height = 3, message = FALSE}
    ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
      geom_point() + geom_smooth()
    ```

## Aesthetics for each geometric object

- Different aesthetics in different layers:
    ```{r, fig.width = 4.5, fig.height = 3, message = FALSE}
    ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
      geom_point(mapping = aes(color = class)) + 
      geom_smooth(data = filter(mpg, class == "subcompact"), se = FALSE)
    ```
    
# Bar charts | r4ds chapter 3.7

## `diamonds` data 

- `diamonds` data:
    ```{r}
    diamonds
    ```
    
## Bar chart

- `geom_bar()` creates bar chart:
    ```{r}
    ggplot(data = diamonds) + 
      geom_bar(mapping = aes(x = cut))
    ```

----

- Bar charts, like histograms, frequency polygons, smoothers, and boxplots, plot some computed variables instead of raw data.

- Check available computed variables for a geometric object via help:
    ```{r, eval = FALSE}
    ?geom_bar
    ```

----

- Use `stat_count()` directly:
    ```{r}
    ggplot(data = diamonds) + 
      stat_count(mapping = aes(x = cut))
    ```

- `stat_count()` has a default geom `geom_bar()`.

----

- Display frequency instead of counts:
    ```{r}
    ggplot(data = diamonds) + 
      geom_bar(mapping = aes(x = cut, y = ..prop.., group = 1))    
    ```
    Note the aesthetics mapping `group=1` overwrites the default grouping (by `cut`) by considering all observations as a group. Without this we get
    ```{r}
    ggplot(data = diamonds) + 
      geom_bar(mapping = aes(x = cut, y = ..prop..))    
    ```
    
----

- Color bar:
    ```{r, results = 'hold'}
    ggplot(data = diamonds) + 
      geom_bar(mapping = aes(x = cut, colour = cut))
    ```

----

- Fill color: 
    ```{r, results = 'hold'}
    ggplot(data = diamonds) + 
      geom_bar(mapping = aes(x = cut, fill = cut))
    ```

----

- Fill color according to another variable:
    ```{r}
    ggplot(data = diamonds) + 
      geom_bar(mapping = aes(x = cut, fill = clarity))
    ```

# Positional arguments | r4ds chapter 3.8

----

- `position_gitter()` add random noise to X and Y position of each
element to avoid overplotting:  
    ```{r}
    ggplot(data = mpg) + 
      geom_point(mapping = aes(x = displ, y = hwy), position = "jitter")
    ```

----

- `geom_jitter()` is similar:  
    ```{r}
    ggplot(data = mpg) + 
      geom_jitter(mapping = aes(x = displ, y = hwy))
    ```

----
    
- `position_fill()` stack elements on top of one another,
normalize height:  
    ```{r}
    ggplot(data = diamonds) + 
      geom_bar(mapping = aes(x = cut, fill = clarity), position = "fill")
    ```

----

- `position_dodge()` arrange elements side by side:  
    ```{r}
    ggplot(data = diamonds) + 
      geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge")
    ```

----

- `position_stack()` stack elements on top of each other:  
    ```{r}
    ggplot(data = diamonds) + 
      geom_bar(mapping = aes(x = cut, fill = clarity), position = "stack")
    ```


# Coordinate systems | r4ds chapter 3.9

----

- A boxplot:
    ```{r}
    ggplot(data = mpg, mapping = aes(x = class, y = hwy)) + 
      geom_boxplot()
    ```

----

- `coord_cartesian()` is the default cartesian coordinate system:
    ```{r}
    ggplot(data = mpg, mapping = aes(x = class, y = hwy)) + 
      geom_boxplot() + 
      coord_cartesian(xlim = c(0, 5))
    ```

----

- `coord_fixed()` specifies aspect ratio (x / y):
    ```{r}
    ggplot(data = mpg, mapping = aes(x = class, y = hwy)) + 
      geom_boxplot() + 
      coord_fixed(ratio = 1/2)
    ```

----

- `coord_flip()` flips x- and y- axis:
    ```{r}
    ggplot(data = mpg, mapping = aes(x = class, y = hwy)) + 
      geom_boxplot() + 
      coord_flip()
    ```

----

- A map:
    ```{r}
    library("maps")
    nz <- map_data("nz")
    head(nz, 20)
    ```
    
----    

```{r}
ggplot(nz, aes(x = long, y = lat, group = group)) +
  geom_polygon(fill = "white", colour = "black")
```

----

- `coord_quickmap()` puts maps in scale:
    ```{r}
    ggplot(nz, aes(long, lat, group = group)) +
      geom_polygon(fill = "white", colour = "black") +
      coord_quickmap()
    ```

# Graphics for communications | r4ds chapter 28

## Title 

- Figure title should be descriptive:
    ```{r, message = FALSE}
    ggplot(mpg, aes(x = displ, y = hwy)) +
      geom_point(aes(color = class)) +
      geom_smooth(se = FALSE) +
      labs(title = "Fuel efficiency generally decreases with engine size")
    ```

## Subtitle and caption 

- 
    ```{r, message = FALSE}
    ggplot(mpg, aes(displ, hwy)) +
      geom_point(aes(color = class)) +
      geom_smooth(se = FALSE) + 
      labs(
        title = "Fuel efficiency generally decreases with engine size",
        subtitle = "Two seaters (sports cars) are an exception because of their light weight",
        caption = "Data from fueleconomy.gov"
      )
    ```
    
## Axis labels 

- 
    ```{r, message = FALSE}
    ggplot(mpg, aes(displ, hwy)) +
    geom_point(aes(colour = class)) +
    geom_smooth(se = FALSE) +
    labs(
      x = "Engine displacement (L)",
      y = "Highway fuel economy (mpg)"
    )
    ```

## Math equations 

-
    ```{r}
    df <- tibble(x = runif(10), y = runif(10))
    ggplot(df, aes(x, y)) + geom_point() +
      labs(
        x = quote(sum(x[i] ^ 2, i == 1, n)),
        y = quote(alpha + beta + frac(delta, theta))
      )
    ```

- `?plotmath` 

## Annotations 

- Create labels
    ```{r}
    best_in_class <- mpg %>%
      group_by(class) %>%
      filter(row_number(desc(hwy)) == 1)
    best_in_class
    ```

---

- Annotate points
    ```{r}
    ggplot(mpg, aes(x = displ, y = hwy)) +
      geom_point(aes(colour = class)) +
      geom_text(aes(label = model), data = best_in_class)
    ```
    
----

- `ggrepel` package automatically adjust labels so that they don’t overlap:
    ```{r}
    library("ggrepel")
    ggplot(mpg, aes(displ, hwy)) +
      geom_point(aes(colour = class)) +
      geom_point(size = 3, shape = 1, data = best_in_class) +
      ggrepel::geom_label_repel(aes(label = model), data = best_in_class)
    ```
    
## Scales

- 
    ```{r, eval=FALSE}
    ggplot(mpg, aes(displ, hwy)) +
      geom_point(aes(colour = class))
    ```
automatically adds scales
    ```{r}
    ggplot(mpg, aes(displ, hwy)) +
      geom_point(aes(colour = class)) +
      scale_x_continuous() +
      scale_y_continuous() +
      scale_colour_discrete()
    ```

----

- `breaks`
    ```{r}
    ggplot(mpg, aes(displ, hwy)) +
      geom_point() +
      scale_y_continuous(breaks = seq(15, 40, by = 5))
    ```

----

- `labels`
    ```{r}
    ggplot(mpg, aes(displ, hwy)) +
      geom_point() +
      scale_x_continuous(labels = NULL) +
      scale_y_continuous(labels = NULL)
    ```

----

- Plot y-axis at log scale:
    ```{r}
    ggplot(mpg, aes(x = displ, y = hwy)) +
      geom_point() +
      scale_y_log10()
    ```

----

- Plot x-axis in reverse order:
    ```{r}
    ggplot(mpg, aes(x = displ, y = hwy)) +
      geom_point() +
      scale_x_reverse()
    ```

    
## Legends

- Set legend position: `"left"`, `"right"`, `"top"`, `"bottom"`, `none`:
    ```{r, collapse = TRUE}
    ggplot(mpg, aes(displ, hwy)) +
      geom_point(aes(colour = class)) + 
      theme(legend.position = "left")
    ```

----

- See following link for more details on how to change title, labels, ... of a legend.

    <http://www.sthda.com/english/wiki/ggplot2-legend-easy-steps-to-change-the-position-and-the-appearance-of-a-graph-legend-in-r-software> 


## Zooming

- Without clipping (removes unseen data points)
    ```{r, message = FALSE}
    ggplot(mpg, mapping = aes(displ, hwy)) +
      geom_point(aes(color = class)) +
      geom_smooth() +
      coord_cartesian(xlim = c(5, 7), ylim = c(10, 30))
    ```

----

- With clipping (removes unseen data points)
    ```{r, message = FALSE, warning = FALSE}
    ggplot(mpg, mapping = aes(displ, hwy)) +
      geom_point(aes(color = class)) +
      geom_smooth() +
      xlim(5, 7) + ylim(10, 30)
    ```

----

-
    ```{r, message = FALSE, warning = FALSE}
    ggplot(mpg, mapping = aes(displ, hwy)) +
      geom_point(aes(color = class)) +
      geom_smooth() +
      scale_x_continuous(limits = c(5, 7)) +
      scale_y_continuous(limits = c(10, 30))
    ```

----

- 
    ```{r, message = FALSE}
    mpg %>%
      filter(displ >= 5, displ <= 7, hwy >= 10, hwy <= 30) %>%
      ggplot(aes(displ, hwy)) +
      geom_point(aes(color = class)) +
      geom_smooth()
    ```

## Themes

- 
    ```{r, message = FALSE}
    ggplot(mpg, aes(displ, hwy)) +
      geom_point(aes(color = class)) +
      geom_smooth(se = FALSE) +
      theme_bw()
    ```

----

<p align="center">
<img src="./visualization-themes.png" height="550">
</p>

## Saving plots

```{r, collapse = TRUE}
ggplot(mpg, aes(displ, hwy)) + geom_point()
ggsave("my-plot.pdf")
```

## Cheat sheet  

[RStudio cheat sheet](https://github.com/rstudio/cheatsheets/raw/master/data-visualization-2.1.pdf) is extremely helpful.