Data Viz with ggplot2

class: center, middle, inverse, title-slide

.title[
# Data Viz with ggplot2
]
.author[
### DSC365: Introduction to Data Science
]
.date[
### September 1, 2026
]

---

### Announcements

- Lab 1 due **tonight**

- Start Lab 2 in class on Thursday
  + Due **next tuesday** 
  
- Will Discuss Mini-Project 1 on Thursday
  + Due **two weeks later, before class starts**

- Quiz #1
  + Last 30 Minutes next Thursday (September 10)
  + Quarto, ggplot2, dplyr

---

---
### Why visualize?

Our brain is good at processing large amounts of visual information quickly! Long lists of numbers, not so much.

**Useful at two point in Data Science Cycle**
+ Exploratory Data Analysis (EDA)
+ Communicating Information

---
class:primary
### Datasaurus Part 2: Summary Statistics

|dataset  | mean_x| mean_y| std_dev_x| std_dev_y| corr_x_y|
|:--------|------:|------:|---------:|---------:|--------:|
|away     | 54.266| 47.835|    16.770|    26.940|   -0.064|
|bullseye | 54.269| 47.831|    16.769|    26.936|   -0.069|
|dino     | 54.263| 47.832|    16.765|    26.935|   -0.064|
|dots     | 54.260| 47.840|    16.768|    26.930|   -0.060|
|star     | 54.267| 47.840|    16.769|    26.930|   -0.063|
|v_lines  | 54.270| 47.837|    16.770|    26.938|   -0.069|

---
class:primary
### Datasaurus: Plots

---
### Grammar of Graphics

Grammar of Graphics: grammatical rules for creating perceivable graphs
  
  - [Developed by Leland Wilkison](https://link.springer.com/book/10.1007/0-387-28695-0)

Instead of thinking about a limited set of graphs, think about graphical forms
  
  - Different types of graphs may appear completely distinct, but in actuality share many common elements

By making different visual choices, you can use graphs to highlight different aspects of the same data.

- For example, here are three ways of displaying the same data:

---
### ggplot

Based on The Grammar of Graphics where the components are independent, meaning we can generally change a component in isolation [(source)](https://www.tandfonline.com/doi/epdf/10.1198/jcgs.2009.07098?needAccess=true).

.pull-left[

<img src="images/ggplot-hex.png" alt="Hex logo for ggplot2 package." width="40%" style="display: block; margin: auto;" />
].pull-right[

]

[Help Information](https://rstudio.github.io/cheatsheets/html/data-visualization.html)

---
### ggplot 2: specifications

A plot consists of several mostly independent specifications:

1. **aesthetics** - links between data variables and graphical features (position, color, shape, size)
2. **geometries** - geometric elements (points, lines, rectangles, text,...)
3. **scales** - scales map values in data space values in the aesthetic space. Scales change the coordinate space of an aesthetic, but don't change the underlying value (change at visual level, not mathematical)
4. **coordinate system** 
5. **statistics**: mean, quantiles, etc
6. **faceting** - facets allow you to split plot by other variables to produce many sub-plots
7. **theme** - formating items, such as background color, fonts, margins...

**Limitation**: tells us what words make up our graphical “sentences,” but offers no advice on how to write well

---
### Let's Build a Graph!

``` r
library(tidyverse)
head(diamonds)
```

```
## # A tibble: 6 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.2 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
```

---
### Plot Plan

---
### We Begin with the Data

``` r
*ggplot(data=diamonds)
```

---
### Then we specify the aesthetic mappings

``` r
*ggplot(data=diamonds, aes(x=carat, y = price))
```

---
### Then we choose a geom

``` r
ggplot(data=diamonds, aes(x=carat, y = price)) +
* geom_point()
```

---
### We can then add an aesthetic

``` r
ggplot(data = diamonds, aes(x = carat, y = price)) +
*   geom_point(aes(colour = cut))
```

<img src="ggplot2-notes_files/figure-html/unnamed-chunk-13-1.png" alt="Identical to previous plot, except now each point is colored by the cut of the diamond for that observation." />
---
### And add another layer

``` r
ggplot(data = diamonds, aes(x = carat, y = price)) +
    geom_point(aes(colour = cut)) +
*   geom_smooth()
```

```
## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
```

---
### Mapping aesthetics vs setting aesthetics

``` r
ggplot(data = diamonds, aes(x = carat, y = price) +
*   geom_point(aes(colour = cut), size = 2, alpha = .5) +
*   geom_smooth(aes(fill = cut), colour = "lightgrey")
```

```
## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
```

---
### Facet

``` r
ggplot(data = diamonds, aes(x = carat, y = price)) +
    geom_point(aes(colour = cut), size = 2, alpha = .5) +
    geom_smooth(aes(fill = cut), colour = "lightgrey") +
*   facet_wrap(~cut)
```

```
## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
```

---
### What Type of Chart to Use?

- What type of variables are you working with? Numerical? Categorical?
- Number of observations?
- Important moderating variables?
- Use of small multiples?

[The R Graph Gallery](https://r-graph-gallery.com)

---
### Later: Graphical Perception

To visually display data, information is encoded into a graph. The viewer then visually decodes this information, known as graphical perception, to gain knowledge. A successful graphic allows the user to perform graphical perception accurately and efficiently

After we have chosen the appropriate plot to visualize the data, focus on these two principles:

- Keep it simple
- Show the data clearly

But for now, let's move to the .qmd document