Introduction

# Introduction
### Derek Ogle, April 2020

---

# What is a Graphic?

---

# What is a Graphic?

- Most simply ... a "data visualization."

&nbsp;

--
- Attempting to convey the "story" found in the data.
  - Patterns, signals, variability.

&nbsp;

--
- Simple, clear, effective, and, possibly, elegant.

&nbsp;

--
- LOTS of opinions about graphing principles.

---

# Tufte's Broad Guidelines

.pull-left[
- Graphical excellence.
- Visual integrity.
- Maximize data-ink ratio.
- Aesthetic elegance.
]

---

# Graphical Excellence

- Graphic should provide the user with “*the greatest number of ideas, in the shortest time, using the least amount of ink, in the smallest space.*”

&nbsp;

--
- Can be simple or complex, but whichever it is it fits the data and the narrative.

---

# Graphical Excellence

.center[
<img src="https://derekogle.com/NCGraphing/modules/zimgs/polarization-feat3.jpg" width="90%" />
<a href="http://thehigherlearning.com/2015/05/03/this-visualization-shows-how-ridiculously-divided-our-congress-has-become/">The Higher Learning</a>
]

---

# Visual Integrity

- Numerical scales should be properly proportionate (and not made to exaggerate (usually) or obfuscate the pattern).

&nbsp;

--
- Variations should relate to the data rather than the artistic interpretation of the data.

---

# Visual Integrity

.center[
<img src="https://derekogle.com/NCGraphing/modules/zimgs/Bad_Graph_1.JPG" width="90%" />
<a href="https://flowingdata.com/category/statistics/mistaken-data/">FLOWINGDATA</a>
]

---

# Maximize data-ink ratio

- Ratio of ink *required* to present data to ink *used* to present the data.

&nbsp;

--
- The closer this ratio is to 1 the less distracted the user will be and the easier it will be to visualize the data.

&nbsp;

--
- Borders, backgrounds, 3-D effects, etc. tend to decrease this ratio.

---

# Maximize data-ink ratio

.center[
<img src="https://derekogle.com/NCGraphing/modules/zimgs/Tufte_figure2.png" width="90%" />
From Tufte (1983)</a>
]

---

# Aesthetic elegance

- Not in the sense of "physical beauty."

--
- More in the sense of simplicity used to evoke the complexity of the data.

---

# Aesthetic elegance

.center[
<img src="https://derekogle.com/NCGraphing/modules/zimgs/Savings_By_Income.JPG" width="80%" />
<a href="https://flowingdata.com/category/statistics/mistaken-data/">FLOWINGDATA</a>
]

---

# Further Guidelines
From <a href="https://mschermann.github.io/data_viz_reader/fundamentals.html#best-practices">A Reader on Data Visualization</a>

- **Five Second Rule**
 - The average modern attention span for viewing anything online is <5 s.
 - If you can not grab attention within 5 s, you have likely lost your audience.
 - Include clear titles and instructions; tell people what the visualization shows and how to interact with it.

&nbsp;

--
- **Design and Layout Matter**
  - Design and layout should ease understanding your message.
  - Incorporate principles of graphic design to present a compelling story.

---

# Further Guidelines

- **Keep it Simple**
  - Keep graphs simple and easy to interpret.
  - Keep only necessary elements in the graph.
  - Help your audience understand quickly what is going on.

&nbsp;

--
- **Pretty Does Not Mean Effective**
  - Aesthetically pleasing visualizations are not necessarily more effective.
  - Pretty and eye-catching may be nice, but communicating the data properly is paramount.

&nbsp;

--
- **Use Color Purposefully**
  - Use of color may be attractive, but it can also be distracting.
  - Color should be used only if it assists in conveying your message.
  - Be consistent with the color scheme.

---

Our focus will be on the mechanics of making graphs in R with enough flexibility that you can follow any principles you desire.

---

# What is ggplot2?

---

# What is ggplot2?

- A package for making graphics in R.
  - R is now one of the most used programs for data analysis.
  - `ggplot2` is one of most used packages for data visualization in R.

--
- Originally based on Leland Wilkinson's *The Grammar of Graphics*.

--
- Extended and brought to R by Hadley Wickham.

&nbsp;

--
- `ggplot2` describes itself as

> "a system for declaratively creating graphics. You provide the data, tell `ggplot2` how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details."

---

# ggplot2 in the Wild 1

.center[
<img src="https://derekogle.com/NCGraphing/modules/zimgs/Ex_ggplot2_1.JPG" width="600px" />
From <a href="https://www.int-res.com/articles/esr2020/41/n041p319.pdf">Roos NC, Taylor BM, Carvalho AR, Longo GO. 2020. Demography of the largest and most endangered Brazilian Parrotfish, Scarus trispinosus, reveals overfishing. Endangered Species Research 41:319-327.</a>
]

---

# ggplot2 in the Wild 2

.center[
<img src="https://derekogle.com/NCGraphing/modules/zimgs/Ex_ggplot2_2.JPG" width="700px" />
From <a href="https://agupubs-onlinelibrary-wiley-com.ezproxy.uwsp.edu/doi/full/10.1002/2015WR017519">Lessels JS et al. 2016. Water sources and mixing in riparian wetlands revealed by tracers and geospatial analysis. Water Resources Research. 52:456-470</a>
]

---

# ggplot2 in the Wild 3

.center[
<img src="https://derekogle.com/NCGraphing/modules/zimgs/Ex_ggplot2_3.JPG" width="500px" />
From <a href="https://fivethirtyeight.com/features/the-midwest-is-getting-drenched-and-its-causing-big-problems/">538.com</a>
]

---

# ggplot2 in the Wild 4

.center[
<img src="https://derekogle.com/NCGraphing/modules/zimgs/Ex_ggplot2_4.JPG" width="800px" />
From <a href="https://www.ft.com/coronavirus-latest">Financial Times</a>
]

---

# Very Basics of ggplot2

---

# `tidyverse` Package

- A package that loads many other packages.
  - `ggplot2`: What we need to use the "Grammar of Graphics" in R.
  - `dplyr`: For manipulating data.
  - `tidyr`: Also for manipulating data.
  - `lubridate`: For manipulating dates.

--
- Load packages with `library()`.
  - Must be done every time you start coding in R.

```r
library(tidyverse)
```

---

# Data Format

- All `ggplot2` plots need data, usually in a "tidy format."
  - **Rows**: Contain one **individual**/subject/unit.
  - **Columns**: Contain one **variable** (characteristic recorded about an individual).

&nbsp;

--
- Example from data on Isle Royale Wolves and Moose

```
   year wolves moose winter_temp ice_bridges
1  1959     20   538        1.40          no
2  1960     22   564        8.45          no
3  1961     22   572        9.75         yes
4  1962     23   579        2.15         yes
5  1963     20   596       -0.35         yes
6  1964     26   620       12.40          no
7  1965     28   634        1.25         yes
8  1966     26   661        1.70         yes
9  1967     22   766        2.75         yes
10 1968     22   848        5.85         yes
```

---

# Simple Data

- For purposes of this introduction, we will use the simple data entered below.

```r
dfobj <- data.frame(var1=c(3,1,5),
 var2=c(2,4,6),
 lbls=c("a","b","c"))
dfobj
```

```
  var1 var2 lbls
1    3    2    a
2    1    4    b
3    5    6    c
```

---

# ggplot Base Plot

```r
p <- ggplot(data=dfobj,
 mapping=aes(x=var1,y=var2))
```

- All ggplots begin with `ggplot()`.

--
  - *Usually* has `data=` that declares a *global* data.frame for use.

--
  - *Usually* has `mapping=` that declares how 'aesthetics' are mapped to 'variables'.

--
  - Aesthetics are wrapped in `aes()` and *usually* require variables mapped to `x` and `y`.

--
- Result is assigned to an object (here `p`).

--
  - Items ('layers') will be added to this base plot.

--
- Plot not shown until object is "evaluated" (i.e., type `p`).

---

# ggplot Base Plot

```r
p
```

---

# Geometric Objects

- Called 'geoms' for short.

--
- Declare the geometric object or shape that should be plotted.

--
- A 'layer' that is 'added' to the base plot with `+`.

--
- Functions are like `geom_XXXX()`.

--
- Some common 'geoms' are:
  - `geom_point()`: Shows data as points (think *scatterplot*).
  - `geom_line()`: Shows data as a connected line (think *line plot*).
  - `geom_boxplot()`: shows data as a boxplot.
  - `geom_bar()`: Shows data as a vertical bar (think *bar chart*).
  - `geom_histogram()`: Show data as a histogram.
  - `geom_rug()`: Shows values as 'ticks' along the axis.

---

# Adding geoms to Base Plot

---

```r
*dfobj
```
]]
.column[.content[

```
  var1 var2 lbls
1    3    2    a
2    1    4    b
3    5    6    c
```
]]
---
class: split-50
count: false

```r
dfobj

*p 
```
]]
.column[.content[

```
  var1 var2 lbls
1    3    2    a
2    1    4    b
3    5    6    c
```

<img src="Lecture_Intro_ggplot2_files/figure-html/scatter_user_2_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
dfobj

p +  
* geom_point()
```
]]
.column[.content[

```
  var1 var2 lbls
1    3    2    a
2    1    4    b
3    5    6    c
```

<img src="Lecture_Intro_ggplot2_files/figure-html/scatter_user_3_output-1.png" width="100%" />
]]

---

```r
*dfobj

*p 
```
]]
.column[.content[

```
  var1 var2 lbls
1    3    2    a
2    1    4    b
3    5    6    c
```

<img src="Lecture_Intro_ggplot2_files/figure-html/line_user_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
dfobj

p +  
* geom_line()
```
]]
.column[.content[

```
  var1 var2 lbls
1    3    2    a
2    1    4    b
3    5    6    c
```

<img src="Lecture_Intro_ggplot2_files/figure-html/line_user_2_output-1.png" width="100%" />
]]

---

```r
dfobj

p +  
  geom_path()  
```
]]
.column[.content[

```
  var1 var2 lbls
1    3    2    a
2    1    4    b
3    5    6    c
```

<img src="Lecture_Intro_ggplot2_files/figure-html/path_1_1_output-1.png" width="100%" />
]]

---

```r
dfobj

p +  
  geom_area()  
```
]]
.column[.content[

```
  var1 var2 lbls
1    3    2    a
2    1    4    b
3    5    6    c
```

<img src="Lecture_Intro_ggplot2_files/figure-html/area_1_1_output-1.png" width="100%" />
]]

---

```r
dfobj

p +  
  geom_polygon()  
```
]]
.column[.content[

```
  var1 var2 lbls
1    3    2    a
2    1    4    b
3    5    6    c
```

<img src="Lecture_Intro_ggplot2_files/figure-html/polygon_1_1_output-1.png" width="100%" />
]]

---

```r
dfobj

p +  
  geom_tile()  
```
]]
.column[.content[

```
  var1 var2 lbls
1    3    2    a
2    1    4    b
3    5    6    c
```

<img src="Lecture_Intro_ggplot2_files/figure-html/tile_1_1_output-1.png" width="100%" />
]]

---

```r
dfobj

p +  
  geom_bar(stat="identity")  
```
]]
.column[.content[

```
  var1 var2 lbls
1    3    2    a
2    1    4    b
3    5    6    c
```

<img src="Lecture_Intro_ggplot2_files/figure-html/bar_1_1_output-1.png" width="100%" />
]]

---

```r
dfobj

p +  
  geom_text(aes(label=lbls))  
```
]]
.column[.content[

```
  var1 var2 lbls
1    3    2    a
2    1    4    b
3    5    6    c
```

<img src="Lecture_Intro_ggplot2_files/figure-html/text_1_1_output-1.png" width="100%" />
]]

---

# Adding geoms to geoms

---

```r
*p 
```
]]
.column[.content[
<img src="Lecture_Intro_ggplot2_files/figure-html/forFun_auto_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p + 
* geom_bar(stat="identity")
```
]]
.column[.content[
<img src="Lecture_Intro_ggplot2_files/figure-html/forFun_auto_2_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p + 
 geom_bar(stat="identity") + 
* geom_line()
```
]]
.column[.content[
<img src="Lecture_Intro_ggplot2_files/figure-html/forFun_auto_3_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p + 
 geom_bar(stat="identity") + 
 geom_line() + 
* geom_point()
```
]]
.column[.content[
<img src="Lecture_Intro_ggplot2_files/figure-html/forFun_auto_4_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p + 
 geom_bar(stat="identity") + 
 geom_line() + 
 geom_point() + 
* geom_text(aes(label=lbls),vjust=2)
```
]]
.column[.content[
<img src="Lecture_Intro_ggplot2_files/figure-html/forFun_auto_5_output-1.png" width="100%" />
]]

---

# Next Time

We will start adding more control over layers in the graph.