02-first_steps.Rmd

# First Steps

```{r 02-load-libraries, include=FALSE}
library(ggplot2)
library(tidyverse)
```

## General Housekeeping Items

- This is a learning opportunity so feel free to ask any question at any time.
- Take time to learn the theory, in particular Grammar of Graphics.
- Please do the chapter exercises.  Second-best learning opportunity!
- Please plan to facilitate one of the discussions.  Best learning opportunity!

---

## Learning Objectives

- Brief introduction to ggplot's capabilities
- Learn about key components of every plot: data, aesthetics, geoms
- Learn about faceting
- See a few different geoms
- Modify the axes
- Save the plot to disk

---

## Introduction

```{r,echo=FALSE,warning=FALSE,message=FALSE}
library(png)
library(grid)
library(gridExtra)
img1 <- rasterGrob(as.raster(readPNG("images/grammar-of-graphics.png")),interpolate = FALSE)

img2 <- rasterGrob(as.raster(readPNG("images/ggplot2_logo.png")),interpolate = FALSE)


grid.arrange(img1,img2,ncol=2)
```

Leland Wilkinson (Grammar of Graphics, 1999) formalized two main principles in his plotting framework:

- Graphics = distinct layers of grammatical elements

- Meaningful plots through aesthetic mappings


- The essential grammatical elements to create any visualization with {ggplot2} are:

![](images/ge_all.png)


## Main data set

For this chapter, we'll mainly use the `mpg` dataset that comes with `ggplot`.

```{r 02-inspect-mpg-dataset}
mpg
```

- `cty` and `hwy` are miles per gallon measures
- `displ` is engine displacement in litres
- `drv` is front wheel (f), rear wheel (r) or four wheel (4)
- `model` is the model of the car
- `class` is two-seater, SUV, compact, etc.

---

## Components of every plot
![Three components](images/components.png)

```{r 02-plot-mpg}
ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point()
```

It's allowable to omit the `x =` and `y =` arguments of `aes`.  In other words, `aes(displ, hwy)` would be valid for this plot.

---

## Other aesthetic attributes

- color, shape and size can be mapped to variables in the data


The `class` variable of the `mpg` dataset has seven unique values.  The plot can assign a specific color to each value by mapping `class` to color _within_ the aesthetic function.

```{r 02-distinct-class, echo= FALSE}
mpg %>% distinct(class)
```


```{r 02-scatterplot-with-color}
ggplot(mpg, aes(displ, hwy, color = class)) +
  geom_point()
```


Including a color assignment _outside_ the aesthetic of the _geometry_ layer will make all of the points that color.

```{r 02-color-outside-aes}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(color = "blue")
```

Mapping a variable to `shape` and `color` adds some diversity and information to the plot.

```{r 02-mapping-shape-and-color}
ggplot(mpg, aes(displ, hwy, shape = drv, color = drv)) + 
  geom_point()
```


Mapping a variable to `size` can also add some new insights.

```{r 02-mapping-size}
ggplot(mpg, aes(manufacturer, drv, size = displ)) +
  geom_point() +
  theme(axis.text.x = element_text(angle = 90))
```

---

## Faceting

Faceting creates graphics by splitting the data into subsets and displaying the same graph for each subset.  Really helpful if there are lots of values, making color/shape less meaningful.

```{r 02-faceting-intro}
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  facet_wrap(~class)
```

- Exercise: Use faceting to explore the three-way relationship between fuel economy, engine size and number of cylinders.  How does faceting by number of cylinders change your assessment of the relationship between engine size and fuel economy?


## Geoms

The `geom_point()` geom gives a familiar scatterplot.

Other `geoms` include:


- `geom_smooth()` which fits a smooth line to the data

  - check `help` to see `geom_smooth`'s arguments like `method`, `se` or `span`.
```{r 02-geom-smooth-example, message=FALSE}
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_smooth()
```

- `geom_boxplot()` which generates a box-and-whisker plot

  - check `help` to see `geom_boxplot`'s arguments like `outlier` arguments, and `coef` which adjusts the whisker length.
  
```{r 02-geom-boxplot-example}
ggplot(mpg, aes(drv, hwy)) +
  geom_boxplot()
```

- consider boxplot variants like `geom_jitter` and `geom_violin`
```{r 02-geom-jitter-and-geom-violing-examples}
ggplot(mpg, aes(drv, hwy)) +
  geom_jitter()

ggplot(mpg, aes(drv, hwy)) +
  geom_violin()
```

- `geom_histogram` which generates a histogram and `geom_freqpoly` which generates a frequency polygon

- check `help` to see `geom_histogram`'s arguments like `position` and `binwidth`.
  
```{r 02-geom-histogram-and-geom-freqpoly-example}
ggplot(mpg, aes(hwy)) +
  geom_histogram()

ggplot(mpg, aes(hwy)) +
  geom_freqpoly()
```
  
- `geom_bar` which generates a bar chart

- check `help` to see `geom_bar`'s arguments like `position` and `width`

```{r 02-geom-bar-example}
ggplot(diamonds, aes(cut)) +
  geom_bar()
```

![](images/visualization-stat-bar.png)


This graph below uses `displ` for `y` in the aesthetic and uses the `stat` of `identity` so that it sums the total displacement for each manufacturer.
```{r 02-stat-identity}
ggplot(mpg, aes(manufacturer, displ)) +
  geom_bar(stat = "identity")
```

This plot now shows the total displacement.

```{r 02-stat-identity-table}
mpg %>% group_by(manufacturer) %>% summarize(sum(displ))
```


- `geom_line` and `geom_path` which generates a line chart or path chart (useful for time series data)
  - check `help` to see `geom_line`'s arguments like `lineend` and `arrow`
  
```{r 02-geom-line-and-geom-path-examples}
ggplot(economics, aes(date, unemploy / pop)) +
  geom_line()

ggplot(economics, aes(date, uempmed)) +
  geom_line()
```
  
- To investigate these plots further, we can draw them on the same plot.
  
```{r 02-same-plot}

year <- function(x) as.POSIXlt(x)$year + 1900


ggplot(economics, aes(unemploy / pop, uempmed)) +
  geom_path(color = "grey50") +
  geom_point(aes(color = year(date)))
```
  
## Modifying the Axes

- `xlab()` and `ylab()` modify the axis labels

```{r 02-modifying-axes}
ggplot(mpg, aes(cty, hwy)) +
  geom_point(alpha = 1/3)

ggplot(mpg, aes(cty, hwy)) +
  geom_point(alpha = 1/3) +
  xlab("city driving (mpg)") +
  ylab("highway driving (mpg)")

# remove labels with NULL
ggplot(mpg, aes(cty, hwy)) +
  geom_point(alpha = 1/3) +
  xlab(NULL) +
  ylab(NULL)
```


- `xlim()` and `ylim()` modify the limits of the axes (boundaries)
```{r 02-modifying-limits}
ggplot(mpg, aes(drv, hwy)) +
  geom_jitter(width = 0.25)

ggplot(mpg, aes(drv, hwy)) +
  geom_jitter(width = 0.25) +
  xlim("f", "r") +
  ylim(20, 30)
```

##  Output

- Save the plot to a variable
```{r 02-output-to-variable}
p <- ggplot(mpg, aes(displ, hwy, color = factor(cyl))) +
  geom_point()
```

- Then print it
```{r 02-printing-plot}
print(p)
```

- Save it to disk
```{r 02-saving-plot-to-disk, eval = FALSE}
ggsave("plot.png", p, width = 5, height = 5)
```

- Describe its structure
```{r 02-plot-structure}
summary(p)
```


## Meeting Videos

### Cohort 1

`r knitr::include_url("https://www.youtube.com/embed/fIaJLQfQx6o")`

<details>
  <summary> Meeting chat log </summary>
```
00:04:11	Lydia Gibson:	Hello! I missed last week but hoping to join weekly moving forward.
00:37:49	June Choe:	there's a good cheatsheet for this -- https://ggplot2tor.com/aesthetics
00:54:29	Michael Haugen:	One can use geom_col() as well which will work similar to stats = identity
00:58:12	Michael Haugen:	section 3.8 in R4DS may be relevant here as well: https://r4ds.had.co.nz/data-visualisation.html
01:07:43	Federica Gazzelloni:	thn
01:07:48	June Choe:	thanks!
```
</details>