Data Visualization with R

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## Data Visualization with R
#### Jeremy Mack
#### Lehigh University - Digital Scholarship Team
<img src="./images/notes2.png" alt="RStudio" height=242/>

---
class: center, middle, inverse, title-slide

## Data Visualization with R
#### Jeremy Mack
#### Lehigh University - Digital Scholarship Team
<img src="./images/gganimate_hex.png" alt="RStudio" height=150/> &nbsp; <img src="./images/ggplot_hex.png" alt="RStudio" height=150/> &nbsp; <img src="./images/patchwork_hex.png" alt="RStudio" height=150/>

---
### About this presentation

* This seminar is the second in a series on R programming, with
   a focus on **data visualization**.
 
 * It is targeted at people that have experience programming in R.
   
 * It could be useful for people who learned R some time ago and
   forgot it, or who are not familiar with modern R programming
   (`tidyverse`).
   
 * Slides are available on [Lehigh's Research Computing site](https://confluence.cc.lehigh.edu/display/hpc/Seminars) and Github ([slides](https://jeremymack-lu.github.io/rviz/) and [raw code](https://github.com/jeremymack-LU/rviz))

---
### Structure of the presentation

The presentation is split into four topics:

* [**Topic 1:**](https://jeremymack-lu.github.io/rviz/#8) The (layered) Grammar of Graphics
 
 * [**Topic 2:**](https://jeremymack-lu.github.io/rviz/#22) Static visualizations
 
 * [**Topic 3:**](https://jeremymack-lu.github.io/rviz/#106) Dynamic visualizations
 
 * [**Topic 4:**](https://jeremymack-lu.github.io/rviz/#135) Shiny Apps - a preview

---
#### Data Visualization with R

---
#### Data Visualization with R

---
#### Data Visualization with R

<center>“The simple graph has brought more information to the data analyst’s mind than any other device.” - John Tukey</center>
 
<center><img src="./images/dinosaur2.gif" alt="gif" height=200</></center>

---
class: center, middle, inverse

#### Topic 1: The (layered) Grammar of Graphics

---
#### Topic 1: The (layered) Grammar of Graphics
.pull-left2[
* *The Grammar of Gaphics*

* Structure of data graphics.

* Foundation for modern applications.
  + **ggplot2**
  
  + Tableau
  
  + Vega-Lite]

.pull-right2[
<center><img src="./images/grammar.png" alt="RStudio" height=350/></center>]

---
#### Topic 1: The (layered) Grammar of Graphics
.pull-left2[
* Tidyverse - **ggplot2**

* Implementation of the grammar
]

.pull-right2[
<center><img src="./images/ggplot2a.png" alt="RStudio" height=500/></center>]

---
#### Topic 1: The (layered) Grammar of Graphics
.pull-left2[
* Tidyverse - **ggplot2**

* Implementation of the grammar

* Layered approach to building graphics
]

.pull-right2[
<center><img src="./images/ggplot1.png" alt="RStudio" width=500/></center>]

---
#### Topic 1: The (layered) Grammar of Graphics
.pull-left2[
* Tidyverse - **ggplot2**

* Implementation of the grammar

* Layered approach to building graphics

+ Data creates the base (tidy format)
]

.pull-right2[
<center><img src="./images/ggplot2.png" alt="RStudio" width=500/></center>]

---
#### Topic 1: The (layered) Grammar of Graphics
.pull-left2[
* Tidyverse - **ggplot2**

* Implementation of the grammar

* Layered approach to building graphics

+ Data creates the base (tidy format)

+ Link variables to graphical properties
]

.pull-right2[
<center><img src="./images/ggplot3.png" alt="RStudio" width=500/></center>]

---
#### Topic 1: The (layered) Grammar of Graphics
.pull-left2[
* Tidyverse - **ggplot2**

* Implementation of the grammar

* Layered approach to building graphics

+ Data creates the base (tidy format)
 
 + Link variables to graphical properties

+ Transform data to displayed values
]

.pull-right2[
<center><img src="./images/ggplot4.png" alt="RStudio" width=500/></center>]

---
#### Topic 1: The (layered) Grammar of Graphics
.pull-left2[
* Tidyverse - **ggplot2**

* Implementation of the grammar

* Layered approach to building graphics

+ Data creates the base (tidy format)

+ Link variables to graphical properties

+ Transform data to displayed values

+ Variable and property ranges
]

.pull-right2[
<center><img src="./images/ggplot5.png" alt="RStudio" width=500/></center>]

---
#### Topic 1: The (layered) Grammar of Graphics
.pull-left2[
* Tidyverse - **ggplot2**

* Implementation of the grammar

* Layered approach to building graphics

+ Data creates the base (tidy format)

+ Link variables to graphical properties

+ Transform data to displayed values

+ Variable and property ranges

+ Graphical representation of the data
]

.pull-right2[
<center><img src="./images/ggplot6.png" alt="RStudio" width=500/></center>]

---
#### Topic 1: The (layered) Grammar of Graphics
.pull-left2[
* Tidyverse - **ggplot2**

* Implementation of the grammar

* Layered approach to building graphics

+ Data creates the base (tidy format)

+ Link variables to graphical properties

+ Transform data to displayed values

+ Variable and property ranges

+ Graphical representation of the data

+ Creates "small multiples"
]

.pull-right2[
<center><img src="./images/ggplot7.png" alt="RStudio" width=500/></center>]

---
#### Topic 1: The (layered) Grammar of Graphics
.pull-left2[
* Tidyverse - **ggplot2**

* Implementation of the grammar

* Layered approach to building graphics

+ Data creates the base (tidy format)

+ Link variables to graphical properties

+ Transform data to displayed values

+ Variable and property ranges

+ Graphical representation of the data

+ Creates "small multiples"
 
 + Sets unique position
]

.pull-right2[
<center><img src="./images/ggplot8.png" alt="RStudio" width=500/></center>]

---
#### Topic 1: The (layered) Grammar of Graphics
.pull-left2[
* Tidyverse - **ggplot2**

* Implementation of the grammar

* Layered approach to building graphics

+ Data creates the base (tidy format)

+ Link variables to graphical properties

+ Transform data to displayed values

+ Variable and property ranges

+ Graphical representation of the data

+ Creates "small multiples"
 
 + Sets unique position
 
 + Defines the overall look of the plot
]

.pull-right2[
<center><img src="./images/ggplot9.png" alt="RStudio" width=500/></center>]

---
#### Topic 1: The (layered) Grammar of Graphics
.pull-left2[
* Tidyverse - **ggplot2**

* Implementation of the grammar

* Layered approach to building graphics

+ Required arguments
 
 + Everything else has **sensible defaults**
]

.pull-right2[
<center><img src="./images/ggplot10.png" alt="RStudio" width=500/></center>]

---
#### Topic 1: The (layered) Grammar of Graphics
.pull-left2[
* Tidyverse - **ggplot2**

* Implementation of the grammar

* Layered approach to building graphics
 + Required arguments
 
 + Everything else has **sensible defaults**
 
* Basic template has three functions:
  + **ggplot** - creates a blank canvas
  
  + **geom** - adds a layer
  
  + **aes** - visual properties of an object
]

.pull-right2[
<center><img src="./images/ggplot10.png" alt="RStudio" width=500/></center>
<center><img src="./images/ggplot.png" alt="RStudio" width=500/></center>
]

---
class: center, middle, inverse

#### Topic 2: Static visualizations
#### "Old Faithful"

---
#### Topic 2: Static visualizations

.pull-right2[
<center><img src="./images/OldFaithfulGeyser_Yellowstone.jpg" alt="RStudio" width=500/></center>
]

.tiny2.pull-left2[
* Old Faithful dataset:

+ Geyser in Yellowstone National Park
  
  + Eruption time in minutes (*eruptions*)
  
  + Time until next eruption (*waiting*)
  {{content}}
]

```r
head(faithful, 5)
```

```
  eruptions waiting
1     3.600      79
2     1.800      54
3     3.333      74
4     2.283      62
5     4.533      85
```
{{content}}

```r
str(faithful)
```

```
'data.frame':	272 obs. of  2 variables:
 $ eruptions: num  3.6 1.8 3.33 2.28 4.53 ...
 $ waiting  : num  79 54 74 62 85 55 88 85 51 85 ...
```
{{content}}

---
#### Topic 2: Static visualizations
.right-column2[
<center><img src="./images/ggplot10.png" alt="RStudio" width=500/></center>
<center><img src="./images/ggplot.png" alt="RStudio" width=500/></center>
]

.left-column2[
* Layered approach to building graphics

.tiny[

```r
# Create a blank ggplot canvas
ggplot(data=faithful)
```
]
]

---
#### Topic 2: Static visualizations

.left-column2[
* Layered approach to building graphics

.tiny[

```r
# Create a blank ggplot canvas
ggplot(data=faithful)
```

```r
# Add a geometric (geom) layer and map variables to aesthetics (aes) function
ggplot(data=faithful) +
geom_point(mapping=aes(x=eruptions,
                       y=waiting))
```
]
]

---
#### Topic 2: Static visualizations

.left-column2[
* Layered approach to building graphics

* Some things to remember:

+ Multiple ways to map variables
]

.tiny.left-column2[
* Split mappings:

```r
ggplot(data=faithful) +
geom_point(mapping=aes(x=eruptions,
                       y=waiting))
```
{{content}}
]

* Mappings in **ggplot** function:

```r
ggplot(data=faithful,
       mapping=aes(x=eruptions,
                   y=waiting)) +
geom_point()
```
{{content}}

* Mappings in **geom** function:

```r
ggplot() +
geom_point(data=faithful,
           mapping=aes(x=eruptions,
                       y=waiting))
```
{{content}}

.left-column2[
* Layered approach to building graphics

.tiny[

```r
# Create a blank ggplot canvas
ggplot(data=faithful)
```

```r
# Add a geometric (geom) layer and map variables to aesthetics (aes) function
ggplot(data=faithful) +
geom_point(mapping=aes(x=eruptions,
                       y=waiting))
```

```r
# Add another aesthetic
ggplot(data=faithful) +
geom_point(mapping=aes(x=eruptions,
 y=waiting,
 color=eruptions < 3))
```
]
]

---
#### Topic 2: Static visualizations

.left-column2[
* Layered approach to building graphics

* Some things to remember:

+ Multiple ways to map variables

+ Multiple ways to change aesthetics
]

.tiny.left-column2[
* Mapping a color:

```r
ggplot(data=faithful) +
geom_point(mapping=aes(x=eruptions,
 y=waiting,
 color=eruptions < 3))
```
{{content}}
]

* Setting a color:

```r
ggplot(data=faithful) +
geom_point(mapping=aes(x=eruptions,
                       y=waiting),
           color='blue')
```
{{content}}

---
#### Topic 2: Static visualizations

.tiny.left-column2[
* Mapping a color:

```r
ggplot(data=faithful) +
geom_point(mapping=aes(x=eruptions,
 y=waiting,
 color=eruptions < 3))
```
* Setting a color:

```r
ggplot(data=faithful) +
geom_point(mapping=aes(x=eruptions,
                       y=waiting),
           color='blue')
```
* Setting inside aes function?

```r
ggplot(data=faithful) +
geom_point(mapping=aes(x=eruptions,
                       y=waiting,
                       color='blue'))
```
]

---
#### Topic 2: Static visualizations

.left-column2[
* Layered approach to building graphics

* Some things to remember:

+ Multiple ways to map variables

+ Multiple ways to change aesthetics
 
 + Aesthetics vary by geometry
]

---
#### Topic 2: Static visualizations

.tiny.left-column2[
* Mapping a color:

```r
ggplot(data=faithful) +
geom_point(mapping=aes(x=eruptions,
 y=waiting,
 color=eruptions < 3))
```
{{content}}
]

* Adding aesthetics:

```r
ggplot(data=faithful) +
 geom_point(mapping=aes(x=eruptions,
 y=waiting,
 color=eruptions < 3,
 size=waiting),
 alpha=0.5)
```
{{content}}

---
#### Topic 2: Static visualizations

.left-column2[
* Layered approach to building graphics

* Some things to remember:

---
#### Topic 2: Static visualizations

.right-column2[
<center><img src="./images/ggplot10.png" alt="RStudio" width=500/></center>
<center><img src="./images/ggplot.png" alt="RStudio" width=500/></center>
]

.tiny.left-column2[
* Add another geom ("stacking layers"):

```r
ggplot(data=faithful,
       mapping=aes(x=eruptions,
                   y=waiting)) +
geom_point() +
geom_smooth(method="lm")
```
]

---
#### Topic 2: Static visualizations

---
#### Topic 2: Static visualizations

.right-column2[
<center><img src="./images/ggplot10.png" alt="RStudio" width=500/></center>
<center><img src="./images/ggplot.png" alt="RStudio" width=500/></center>
]

.tiny.left-column2[
* Add another geom ("stacking layers"):

```r
ggplot(data=faithful,
       mapping=aes(x=eruptions,
                   y=waiting)) +
geom_point() +
geom_smooth(method="lm")
```
{{content}}
]

* Order matters

```r
ggplot(data=faithful,
       mapping=aes(x=eruptions,
                   y=waiting)) +
geom_smooth(method="lm") +
geom_point()
```
{{content}}

---
#### Topic 2: Static visualizations

.left-column2[
* Layered approach to building graphics

* Some things to remember:

+ Multiple ways to map variables
 
 + Multiple ways to change aesthetics
 
 + Aesthetics vary by geometry
 
 + When stacking layers, order matters
 
 + Many types of geoms, different mapping requirements
]

---
#### Topic 2: Static visualizations

---
#### Topic 2: Static visualizations
.right-column2[
<center><img src="./images/ggplot9.png" alt="RStudio" width=500/></center>
<center><img src="./images/ggplot.png" alt="RStudio" width=500/></center>
]

.left-column2[
* Layered approach to building graphics

* Some things to remember:

+ Multiple ways to map variables
 
 + Multiple ways to change aesthetics
 
 + Aesthetics vary by geometry
 
 + When stacking layers, order matters
 
 + Many types of geoms, different mapping requirements
 
 + Many (many!) theme arguments
]

---
#### Topic 2: Static visualizations

.right-column2[
<center><img src="./images/ggplot9.png" alt="RStudio" width=500/></center>
<center><img src="./images/ggplot.png" alt="RStudio" width=500/></center>
]

.tiny2.left-column2[
* Layered approach to building graphics

```r
# Create overall plot object
plot <- ggplot(data=faithful,
 mapping=aes(x=eruptions,
 y=waiting)) +
geom_smooth(method="lm") +
geom_point(size=0.25)
```
{{content}}
]

```r
# Clean up overall plot theme
plot <- plot +
 theme(plot.background=element_blank(),
 panel.border=element_blank(),
 panel.background=element_blank(),
 axis.ticks=element_line(size=0.25),
 axis.text=element_text(size = 7),
 axis.line=element_line(size=0.25),
 axis.title=element_text(size = 7),
 legend.text=element_text(size = 7),
 legend.title=element_text(size = 7),
 legend.key=element_blank(),
 plot.title=element_text(hjust=.5, size=8)) +
 labs(title="Old Faithful Waiting Time",
 x="Length of eruption (minutes)",
 y="Waiting time to next eruption (minutes)")
```
{{content}}

---
#### Topic 2: Static visualizations

---
class: center, middle, inverse

#### Topic 2: Static visualizations
#### ggplot2 extensions

---
#### Topic 2: Static visualizations
.right-column2[
<center><img src="./images/ggplot_hex.png" height=250 alt="RStudio" </center>
]

.left-column2[
* **ggplot2** extensions

* Extends the grammar of graphics

+ Developed by R users
 
 + Provides additional [functionality](<http://exts.ggplot2.tidyverse.org/gallery/>)
]

---
#### Topic 2: Static visualizations
.right-column2[
<center><img src="./images/patchwork_hex.png" height=250 alt="RStudio" </center>
]

.left-column2[
* **ggplot2** extensions - **patchwork**

* Extends the grammar of graphics

+ Arrange multiple plots, using mathematical operators
  
 + Many approaches
     - Facets
     - gridExtra::grid.arrange()
     - ggpubr::ggarrange()
     - cowplot::plot_grid()
     - patchwork
]

---
#### Topic 2: Static visualizations
.tiny.left-column3[
* Establish plots:

```r
library(patchwork)

pw1 <- ggplot(data=faithful,
 mapping=aes(x=eruptions,
 y=waiting)) +
 geom_point()

pw2 <- ggplot(data=faithful,
 mapping=aes(x=eruptions,
 y=waiting)) +
 geom_point() +
 geom_smooth(method="lm")
```
]

.tiny.right-column3[
* Patchwork together:

```r
# Side by side
pw1 + pw2
```
{{content}}
]

---
#### Topic 2: Static visualizations

---
#### Topic 2: Static visualizations
.tiny.left-column3[
* Establish plots:

```r
library(patchwork)

pw1 <- ggplot(data=faithful,
 mapping=aes(x=eruptions,
 y=waiting)) +
 geom_point()

pw2 <- ggplot(data=faithful,
 mapping=aes(x=eruptions,
 y=waiting)) +
 geom_point() +
 geom_smooth(method="lm")
```
]

.tiny.right-column3[
* Patchwork together:

```r
# Side by side
pw1 + pw2

# Stacked
pw1 / pw2
```
{{content}}
]

---
#### Topic 2: Static visualizations

---
#### Topic 2: Static visualizations
.tiny.left-column3[
* Establish plots:

```r
library(patchwork)

pw1 <- ggplot(data=faithful,
 mapping=aes(x=eruptions,
 y=waiting)) +
 geom_point()

pw2 <- ggplot(data=faithful,
 mapping=aes(x=eruptions,
 y=waiting)) +
 geom_point() +
 geom_smooth(method="lm")

pw3 <- ggplot(data=faithful,
 mapping=aes(x=eruptions)) +
 geom_histogram()
```
]

.tiny.right-column3[
* Patchwork together:

```r
# Side by side
pw1 + pw2

# Stacked
pw1 / pw2

# Three figures
(pw1 + pw2) / pw3
```
{{content}}
]

---
#### Topic 2: Static visualizations

---
#### Topic 2: Static visualizations

---
#### Topic 2: Static visualizations
.tiny.left-column3[
* Establish plots:

```r
library(patchwork)

pw1 <- ggplot(data=faithful,
 mapping=aes(x=eruptions,
 y=waiting)) +
 geom_point()

pw2 <- ggplot(data=faithful,
 mapping=aes(x=eruptions,
 y=waiting)) +
 geom_point() +
 geom_smooth(method="lm")
```
]

.tiny.right-column3[
* Patchwork together:

```r
# Side by side
pw1 + pw2
```
]

---
#### Topic 2: Static visualizations
.tiny.left-column3[
* Establish plots:

```r
library(patchwork)

pw1 <- ggplot(data=faithful,
 mapping=aes(x=eruptions,
 y=waiting)) +
 geom_point()

pw2 <- ggplot(data=faithful,
 mapping=aes(x=eruptions,
 y=waiting)) +
 geom_point() +
 geom_smooth(method="lm")
```
]

.tiny.right-column3[
* Patchwork together:

```r
# Side by side
pw1 + pw2 + plot_layout(widths=c(2,1))
```
]

---
#### Topic 2: Static visualizations

---
#### Topic 2: Static visualizations
.tiny.left-column3[
* Establish plots:

```r
library(patchwork)

pw1 <- ggplot(data=faithful,
 mapping=aes(x=eruptions,
 y=waiting)) +
 geom_point()

pw2 <- ggplot(data=faithful,
 mapping=aes(x=eruptions,
 y=waiting)) +
 geom_point() +
 geom_smooth(method="lm")
```
]

.tiny.right-column3[
* Patchwork together:

```r
# Side by side
pw1 + pw2
```
{{content}}
]

```r
library(grid); library(gridExtra)

# Remove axis title
pw1 <- pw1 +
 theme(axis.title=element_blank())
pw2 <- pw2 +
 theme(axis.title=element_blank())

# Patch together and create a 'grob' object
patch <- pw1 + pw2
patch.gr <- patchworkGrob(patch)

# Add grid text
grid.arrange(patch.gr, 
  left=textGrob("Time until next eruption",
       gp=gpar(fontsize=8), rot=90),
  bottom=textGrob("Length of eruption",
         gp=gpar(fontsize=8)))
```
{{content}}

---
#### Topic 2: Static visualizations

---
#### Topic 2: Static visualizations
.right-column2[
<center><img src="./images/ggforce_hex.png" height=250 alt="RStudio" </center>
]

.left-column2[
* **ggplot2** extensions - **ggforce/ggthemes**

* Extends the grammar of graphics

+ Provides extra geoms, scales, and themes
]
 
---
#### Topic 2: Static visualizations
.right-column2[
<center><img src="./images/ggforce_hex.png" height=250 alt="RStudio" </center>
]

.tiny.left-column2[
* **ggplot2** extensions - **ggforce/ggthemes**

* Extends the grammar of graphics

+ Provides extra geoms, scales, and themes
 
 + Microsoft Excel theme

```r
library(ggthemes)

ggplot(data=faithful) +
geom_point(mapping=aes(
             x=eruptions,
             y=waiting),
             color='blue') +
theme_excel()
```
]

---
#### Topic 2: Static visualizations

---
#### Topic 2: Static visualizations
.right-column2[
<center><img src="./images/ggforce_hex.png" height=250 alt="RStudio" </center>
]

.tiny.left-column2[
* **ggplot2** extensions - **ggforce/ggthemes**

* Extends the grammar of graphics

+ Provides extra geoms, scales, and themes
 
 + Microsoft Excel theme
 
 + Wall Street Journal theme

```r
library(ggthemes)

ggplot(data=faithful) +
geom_point(mapping=aes(
             x=eruptions,
             y=waiting),
             color='blue') +
#theme_excel()
theme_wsj()
```
]

---
#### Topic 2: Static visualizations

---
#### Topic 2: Static visualizations
.right-column2[
<center><img src="./images/ggforce_hex.png" height=250 alt="RStudio" </center>
]

.tiny.left-column2[
* **ggplot2** extensions - **ggforce/ggthemes**

* Extends the grammar of graphics

+ Provides extra geoms, scales, and themes
 
 + Microsoft Excel theme
 
 + Wall Street Journal theme
 
 + Google Docs theme

```r
library(ggthemes)

ggplot(data=faithful) +
geom_point(mapping=aes(
             x=eruptions,
             y=waiting),
             color='blue') +
#theme_excel()
#theme_wsj()
theme_gdocs()
```
]

---
#### Topic 2: Static visualizations

---
#### Topic 2: Static visualizations
.right-column2[
<center><img src="./images/ggstats_hex.png" height=250 alt="RStudio" </center>
]

.tiny.left-column2[
* **ggplot2** extensions - **ggstatsplot**

* Extends the grammar of graphics

+ Quickly combine plots with statistics
 {{content}}
]

```r
ggscatterstats(
  data=faithful,
  x=eruptions,
  y=waiting,
  xlab="Length of eruption",
  ylab="Time until next eruption",
  title="How long until Old Faithful erupts?")
```

---
#### Topic 2: Static visualizations

---
#### Topic 2: Static visualizations

[<center><img src="./images/ggstatsplot.png" alt="ggplot"</></center>](https://indrajeetpatil.github.io/ggstatsplot/)

---
class: center, middle, inverse

#### Topic 2: Static visualizations
#### ggplot2 and simple features (sf) data

---
#### Topic 2: Static visualizations
.right-column2[
<center><img src="./images/sf_hex.png" height=250 alt="RStudio" </center>
]

.left-column2[
* Simple features (**sf**) data

* Introduces a new type of object to work with spatial data
{{content}}
]
 
---
#### Topic 2: Static visualizations
<style>
 pre {
 background-color: lightyellow;
 white-space: pre-wrap;
 line-height: 90%;
 }
</style>
.right-column2[
<center><img src="./images/sf.jpg" height=250 alt="RStudio" </center>
]

.left-column2[
* Simple features (**sf**) data

* Introduces a new type of object to work with spatial data

+ Non-spatial data + Spatial data

+ Attributes + Geometries
]

---
class: center, middle, inverse

#### Comparison of sf and non-sf spatial data

---
#### Topic 2: Static visualizations
.left-column5b[
* Spatial data in a data frame

.tiny7[

```r
library(usmap) # State and county data non-sf
non_sf <- us_map(regions="states"); head(non_sf, 5); str(non_sf)
```

```
##         x        y order  hole piece group fips abbr    full
## 1 1091779 -1380695     1 FALSE     1  01.1   01   AL Alabama
## 2 1091268 -1376372     2 FALSE     1  01.1   01   AL Alabama
## 3 1091140 -1362998     3 FALSE     1  01.1   01   AL Alabama
## 4 1090940 -1343517     4 FALSE     1  01.1   01   AL Alabama
## 5 1090913 -1341006     5 FALSE     1  01.1   01   AL Alabama
```

```
## 'data.frame':	12999 obs. of  9 variables:
##  $ x    : num  1091779 1091268 1091140 1090940 1090913 ...
##  $ y    : num  -1380695 -1376372 -1362998 -1343517 -1341006 ...
##  $ order: int  1 2 3 4 5 6 7 8 9 10 ...
##  $ hole : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
##  $ piece: int  1 1 1 1 1 1 1 1 1 1 ...
##  $ group: chr  "01.1" "01.1" "01.1" "01.1" ...
##  $ fips : chr  "01" "01" "01" "01" ...
##  $ abbr : chr  "AL" "AL" "AL" "AL" ...
##  $ full : chr  "Alabama" "Alabama" "Alabama" "Alabama" ...
```
]
]

.right-column5b[
* Plot of Pennsylvania

.tiny7[

```r
non_sf %>% 
 filter(abbr=="PA") %>%
 ggplot() +
 geom_point(aes(x=x,y=y,group=group)) +
 geom_path(aes(x=x,y=y,group=group)) +
 coord_fixed() +
 ggthemes::theme_map()
```
]
<center><img src="./images/pa_state.png" alt="ggplot"</></center>
]

---
#### Topic 2: Static visualizations
.left-column5b[
* Spatial data in an sf object

.tiny7[

```r
library(urbnmapr) # State and county shapefiles in sf
states_sf <- get_urbn_map("states", sf = TRUE); print(states_sf)
```

```
## Simple feature collection with 51 features and 3 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -2600000 ymin: -2363000 xmax: 2516374 ymax: 732352.2
## Projected CRS: NAD27 / US National Atlas Equal Area
## First 10 features:
##    state_fips state_abbv  state_name                       geometry
## 1          01         AL     Alabama MULTIPOLYGON (((1150023 -15...
## 2          04         AZ     Arizona MULTIPOLYGON (((-1386136 -1...
## 3          08         CO    Colorado MULTIPOLYGON (((-786661.9 -...
## 4          09         CT Connecticut MULTIPOLYGON (((2156197 -83...
## 5          12         FL     Florida MULTIPOLYGON (((1953691 -20...
## 6          13         GA     Georgia MULTIPOLYGON (((1308636 -10...
## 7          16         ID       Idaho MULTIPOLYGON (((-1357097 78...
## 8          18         IN     Indiana MULTIPOLYGON (((1042064 -71...
## 9          20         KS      Kansas MULTIPOLYGON (((-174904.2 -...
## 10         22         LA   Louisiana MULTIPOLYGON (((1075669 -15...
```
]
]

.right-column5b[
* Plot of Pennsylvania

.tiny7[

```r
states_sf %>%
 filter(state_abbv=='PA') %>%
 ggplot() +
 geom_sf() +
 ggthemes::theme_map()
```
]
<center><img src="./images/pa_state2.png" alt="ggplot"</></center>
]

---
#### Topic 2: Static visualizations
.left-column5[
* Spatial data in a data frame

.tiny7[

```r
library(usmap) # State and county data non-sf
non_sf <- us_map(regions="states"); head(non_sf, 5); str(non_sf)
```

.right-column5[
* Spatial data in an sf object

.tiny7[

```r
library(urbnmapr) # State and county shapefiles in sf
states_sf <- get_urbn_map("states", sf = TRUE); print(states_sf)
```

---
#### Topic 2: Static visualizations

---
#### Topic 2: Static visualizations

.pull-right2[
<center><img src="./images/sf.jpg" height=250 alt="RStudio" </center>
]

.tiny2.pull-left2[
Necessary datasets:

* Spatial data: state and county boundaries
  
 * Non-spatial data: CDC COVID-19 metrics
]

---
#### Topic 2: Static visualizations
.right-column2[
<center><img src="./images/map2.png" width=300 alt="RStudio" </center>
 
<center><img src="./images/map1.png" width=300 alt="RStudio" </center>
 
<center><img src="./images/map3.png" width=300 alt="RStudio" </center>
]

.left-column2[
* Spatial data: state and county boundaries

.tiny3[

```r
# Install albersusa package if needed
#remotes::install_github("hrbrmstr/albersusa")

# Load required packages
library(albersusa, sf, ggtext)

# County boundary sf object
cty.sf <- counties_sf("longlat")
sf::st_crs(cty.sf) <- sf::st_crs(cty.sf)

# State boundary sf object
usa.sf <- usa_sf("longlat")
sf::st_crs(usa.sf) <- sf::st_crs(usa.sf)

# County boundary filtered for Pennsylvania
pa.sf <- cty.sf %>%
 filter(name=="Pennsylvania")
```
]
]

---
#### Topic 2: Static visualizations
.tiny3.left-column8[
* Non-spatial data: CDC COVID-19 metrics

```r
# Read cdc covid-19 community levels from cdc website
url <- "https://www.cdc.gov/coronavirus/2019-ncov/json/covid-community-level-structure-2.csv"
cdc <- read_csv(url, skip=1)
cdc <- cdc %>%
 #select(-c(10:12)) %>%
 rename(fips=2,
 hosp_beds=3,
 hosp_admin=4,
 cases_per_100k=5,
 comm_level=6,
 cases=7,
 positivity=8,
 comm_trans_level=9)
```

```
## # A tibble: 6 × 9
## County fips hosp_beds hosp_admin cases_per_100k comm_level cases positivity
## <chr> <dbl> <chr> <dbl> <dbl> <chr> <lgl> <lgl> 
## 1 Autauga… 1001 1.90% 2.6 35.8 Low NA NA 
## 2 Bullock… 1011 1.90% 2.6 0 Low NA NA 
## 3 Covingt… 1039 1.90% 2.6 32.4 Low NA NA 
## 4 Crensha… 1041 1.90% 2.6 36.3 Low NA NA 
## 5 Lowndes… 1085 1.90% 2.6 20.6 Low NA NA 
## 6 Montgom… 1101 1.90% 2.6 25.6 Low NA NA 
## # … with 1 more variable: comm_trans_level <lgl>
```
]

---
#### Topic 2: Static visualizations
.tiny3.left-column8[
* Combine spatial and non-spatial data

```r
# Join cdc covid-19 community levels to county boundary sf object
cty.sf <- cty.sf %>%
 select(fips,state) %>%
 mutate(fips=as.numeric(as.character(fips))) %>%
 left_join(cdc,by='fips') %>%
 mutate(comm_level=case_when(is.na(comm_level) ~ "No Data",
 TRUE ~ comm_level)) %>%
 select(fips,state,comm_level)
```

```
## Simple feature collection with 6 features and 3 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -88.13999 ymin: 31.19518 xmax: -85.12342 ymax: 34.8922
## Geodetic CRS:  WGS 84
##   fips   state comm_level                       geometry
## 1 1001 Alabama        Low MULTIPOLYGON (((-86.49677 3...
## 2 1009 Alabama        Low MULTIPOLYGON (((-86.5778 33...
## 3 1017 Alabama        Low MULTIPOLYGON (((-85.18413 3...
## 4 1021 Alabama     Medium MULTIPOLYGON (((-86.51734 3...
## 5 1033 Alabama        Low MULTIPOLYGON (((-88.13999 3...
## 6 1045 Alabama        Low MULTIPOLYGON (((-85.41644 3...
```
]

---
#### Topic 2: Static visualizations
.tiny3.left-column8[
* Set colors and specify elements of the plot's theme

```r
library(ggtext)
```

```r
# Specify color scheme
cols <- c("Low"="#197d7d",
 "Medium"="#dbc037",
 "High"="#e08f38",
 "No Data"="lightgray")

# Specify plot theme
plot.theme <- theme(
 # Add extra space to the right margin for inset map
 plot.margin = unit(c(0, 2.5, 0, 0), "cm"),
 legend.position=c(0.5,0.98),
 legend.direction="horizontal",
 legend.title=element_blank(),
 legend.key.height=unit(0.25, 'cm'),
 legend.key.width=unit(0.25, 'cm'),
 legend.text=element_text(size=6),
 legend.margin=margin(b=5.5),
 text=element_text(color="#22211d"),
 plot.background=element_rect(fill="white", color=NA),
 panel.background=element_rect(fill="white", color=NA),
 plot.title=element_textbox_simple(size=8, color="#4e4d47", padding=margin(5.5, 5.5, 5.5, 5.5)),
 plot.subtitle=element_textbox_simple(size=6, color="#4e4d47", face="italic", padding=margin(0, 5.5, 5.5, 5.5)))
```
]

---
#### Topic 2: Static visualizations
.tiny3.left-column8[
* Create the main plot

```r
plot <- ggplot() +
 geom_sf(data=cty.sf, aes(fill=comm_level),
 size=0.1, color="white", alpha=0.9) +
 geom_sf(data=usa.sf, size=0.1, color="black", fill=NA, alpha=0.9) +
 scale_fill_manual(values=cols) +
 labs(title="COVID-19 CDC Community Levels",
 subtitle=paste(
 "CDC metric that factors in new COVID-19 hospitalizations, existing hospital capacity, and new cases in an area. Data as of", Sys.Date(),"\n")) +
 theme_void() +
 plot.theme
```
]

---
#### Topic 2: Static visualizations

---
#### Topic 2: Static visualizations

.tiny3.left-column8[
* Add inset map of PA with Cowplot package

```r
library(cowplot)
mask <- ggdraw(plot) +
 draw_plot(
 {
 cty.sf %>%
 filter(state=="Pennsylvania") %>%
 ggplot() +
 geom_sf(aes(fill=comm_level),
 size=0.1, color="white", alpha=0.9) +
 geom_sf(data=pa.sf,size=0.1,color="black",fill=NA,alpha=0.9) +
 scale_fill_manual(values=cols) +
 theme_void() +
 theme(
 legend.position="none",
 plot.background=element_rect(fill="white", color=NA), 
 panel.background=element_rect(fill="white", color=NA))},
 x = 0.68, 
 y = 0.2,
 width = 0.3, 
 height = 0.3)
```
]

---
#### Topic 2: Static visualizations

---
class: inverse

#### Review - Grammar of Graphics & Static Visualizations:
* Grammar of graphics - basis for many data visualization programs.

* **ggplot2** is a popular implementation.

* Layered approach with sensible defaults.
 
 
 <center><img src="./images/ggplot.png" alt="RStudio" width=400/></center>

* Extension packages expand **ggplot2** capabilities.

* **sf** data makes working with spatial data more efficient.
 
.pull-right3[
<img src="./images/patchwork_hex.png" alt="RStudio" height=150/ align="right"> 
<img src="./images/ggstats_hex.png" alt="RStudio" height=150/ align="right" style="vertical-align:middle;margin:0px 10px">
<img src="./images/ggplot_hex.png" alt="RStudio" height=150/ align="right">
]

---
class: center, middle, inverse

#### Topic 3: Dynamic visualizations
#### "Life expectency"

---
#### Topic 3: Dynamic visualizations
.pull-right2[
<center><img src="./images/gapminder2.png" alt="RStudio" width=250/></center>
]

.tiny2.pull-left2[
* Life expectency (**gapminder**) dataset:

+ Data from the Gapminder foundation
  
  + Geography (*country* and *continent*)
  
  + Life expectency (*lifeExp*)
  
  + Economic data (*gdpPercap*)
  
  + Time (*year*)
]

---
#### Topic 3: Dynamic visualizations
.pull-right2[
<center><img src="./images/gapminder2.png" alt="RStudio" width=250/></center>
]

.pull-left2[
* Life expectency (**gapminder**) dataset:

+ Data from the Gapminder foundation
  
  + Geography (*country* and *continent*)
  
  + Life expectency (*lifeExp*)
  
  + Economic data (*gdpPercap*)
  
  + Time (*year*)
]

.tiny3[

```r
str(gapminder)
```

```
tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
 $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
 $ year     : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
 $ lifeExp  : num [1:1704] 28.8 30.3 32 34 36.1 ...
 $ pop      : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
 $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
```
]

---
#### Topic 3: Dynamic visualizations

---
#### Topic 3: Dynamic visualizations
.right-column2[
<center><img src="./images/gganimate_hex.png" height=250 alt="RStudio" </center>
]

.left-column2[
* **ggplot2** extensions - **gganimate**

* Extends the grammar of graphics
{{content}}
]

+ New grammar classes (functions)
  
  + How a plot should change with time
  {{content}}

---
#### Topic 3: Dynamic visualizations
.tiny[

```r
# Use the gapminder dataset in ggplot
ggplot(data=gapminder,
       aes(x=gdpPercap, y=lifeExp, size=pop, color=country)) +
# Add a point geom
 geom_point(alpha=0.7, show.legend=FALSE)
```
]

---
#### Topic 3: Dynamic visualizations

---
#### Topic 3: Dynamic visualizations
.tiny[

```r
# Use the gapminder dataset in ggplot
ggplot(data=gapminder,
       aes(x=gdpPercap, y=lifeExp, size=pop, color=country)) +
  # Add a point geom
 geom_point(alpha=0.7, show.legend=FALSE) +
```

```r
# Add some manual scaling and facets 
 scale_colour_manual(values=country_colors) +
 scale_size(range=c(2, 12)) +
 scale_x_log10() +
 facet_wrap(~continent, nrow=1)
```
]

---
#### Topic 3: Dynamic visualizations

---
#### Topic 3: Dynamic visualizations
.tiny[

```r
# Use the gapminder dataset in ggplot
ggplot(data=gapminder,
       aes(x=gdpPercap, y=lifeExp, size=pop, color=country)) +
# Add a point geom
 geom_point(alpha=0.7, show.legend=FALSE) +
```

```r
# Add some manual scaling and facets
 scale_colour_manual(values=country_colors) +
 scale_size(range=c(2, 12)) +
 scale_x_log10() +
 facet_wrap(~continent, nrow=1) +
```

```r
# Animate figure with gganimate package
 transition_time(year) +
 ease_aes('linear') +
 labs(title='Year: {frame_time}', 
      x='GDP per capita', 
      y='Life expectancy')
```
]

---
#### Topic 3: Dynamic visualizations

---
class: center, middle, inverse

#### Topic 3: Dynamic visualizations
#### ggplot2 integrations

---
#### Topic 3: Dynamic visualizations
.right-column2[
<center><img src="./images/ggplot_hex.png" height=250 alt="RStudio" </center>
]

.left-column2[
* **ggplot2** integrations

* Integrate with ggplot2 objects

+ Used as required arguments

]

---
#### Topic 3: Dynamic visualizations
.right-column2[
<center><img src="./images/plotly_logo.png" height=250 alt="RStudio" </center>
]

.left-column2[
* **ggplot2** integrations - **plotly**

* Extends the grammar of graphics

+ Creates interactive web graphics
  
  + Customizable user interface
]

---
#### Topic 3: Dynamic visualizations

.tiny2[
<div id="htmlwidget-a8df7415a9a6d1d9b116" style="width:750px;height:500px;" class="widgetframe html-widget"></div>
<script type="application/json" data-for="htmlwidget-a8df7415a9a6d1d9b116">{"x":{"url":"index_files/figure-html//widgets/widget_unnamed-chunk-76.html","options":{"xdomain":"*","allowfullscreen":false,"lazyload":false}},"evals":[],"jsHooks":[]}</script>
]

---
#### Topic 3: Dynamic visualizations

.tiny2.left-column5b[
* Create ggplot object

```r
pacman::p_load(gapminder,plotly)
plot <- ggplot(data=gapminder, 
 aes(x=gdpPercap, y=lifeExp, size=pop,
 color=continent)) +
 # Add a point geom
 geom_point(alpha=0.7, show.legend=FALSE,
 aes(frame = year, ids = country)) +
 # Add some manual scaling
 scale_colour_manual(values=continent_colors) +
 scale_size(range=c(2, 12)) +
 scale_x_log10() +
 labs(x='GDP per capita', 
 y='Life expectancy at birth')
```
]

.tiny2.right-column5b[]

---
#### Topic 3: Dynamic visualizations

---
#### Topic 3: Dynamic visualizations

.tiny2.left-column5b[
* Create ggplot object

.tiny2.right-column5b[
* Pass to plotly

```r
fig <- ggplotly(plot, height=500, width=750) %>%
 animation_opts(
 1000, easing = "linear", redraw = FALSE) %>%
 animation_slider(
 currentvalue=list(prefix="YEAR ",
 font=list(color="red"),
 xanchor='left',
 yanchor='top')) %>%
 animation_button(x=1,
 xanchor="right",
 y=-0.2,
 yanchor="bottom"
 )
```
]

---
#### Topic 3: Dynamic visualizations

<div id="htmlwidget-b6d683908d8d320de773" style="width:750px;height:500px;" class="widgetframe html-widget"></div>
<script type="application/json" data-for="htmlwidget-b6d683908d8d320de773">{"x":{"url":"index_files/figure-html//widgets/widget_unnamed-chunk-80.html","options":{"xdomain":"*","allowfullscreen":false,"lazyload":false}},"evals":[],"jsHooks":[]}</script>

---
#### Topic 3: Dynamic visualizations

.right-column2[
<center><img src="./images/rayshader_hex.png" height=250 alt="RStudio" </center>
]

.left-column2[
* **ggplot2** integrations - **rayshader**

* Extends the grammar of graphics

+ Translates ggplot2 objects into 3D
  
  + Models can be rotated and examined interactively
]

---
#### Topic 3: Dynamic visualizations

---
#### Topic 3: Dynamic visualizations

.tiny2[
* Create ggplot object with raster data (digital elevation model)

```r
# Load necessary packages
pacman::p_load(tidyverse, raster, ggthemes, sp, sf, rasterVis, rayshader)

# Read raster dataset from url and convert to data frame
url <- "https://github.com/jeremymack-LU/rviz/blob/master/data/smtn_dem_clip.tif?raw=true"
r.dem <- raster::raster(url)
r.df <- raster::as.data.frame(r.dem, xy=TRUE)
# Drop NAs and rename raster grid cell variable
r.df <- r.df %>% drop_na() %>% rename(elevation=3)

# Create ggplot2 object
r.plot <- ggplot() +
 geom_raster(data=r.df,
 aes(x=x, y=y, fill=elevation),
 interpolate=TRUE,
 show.legend=TRUE) +
 scale_fill_gradientn(name = "Elevation", colors=terrain.colors(10)) +
 coord_quickmap() +
 theme_dark(base_size=6) +
 theme(axis.title=element_blank(),
 legend.position='top',
 legend.direction='horizontal')
```
]

---
#### Topic 3: Dynamic visualizations

---
#### Topic 3: Dynamic visualizations

.tiny2[
* Pass ggplot object to rayshader

```r
# Pass ggplot2 object to rayshader function
plot_gg(r.plot,
        multicore=TRUE,
        width=5,
        height=5,
        scale=200,
        windowsize=c(1400,866),
        zoom=0.55,
        phi=30)
# Capture snapshot of the rgl window
render_snapshot(filename = "smtn_3D", clear=TRUE)
```
]

---
#### Topic 3: Dynamic visualizations

---
#### Topic 3: Dynamic visualizations

.tiny2.left-column7[
* Create ggplot object

```r
# Load viridis package
library(viridis)
# Plot using mtcars dataset
gg_cars <- ggplot(mtcars) + 
 geom_point(aes(x=disp, y=mpg, color=wt), size=2) +
 scale_fill_viridis("Weight") +
 ggtitle("mtcars: Displacement vs mpg vs # of cylinders") +
 theme_dark() +
 theme(title = element_text(size=8),
 text = element_text(size=12),
 legend.position = 'bottom') 
```
]

.tiny2.right-column7[
* Pass to rayshader

```r
# Pass ggplot2 object to rayshader function
plot_gg(gg_cars,
        multicore=TRUE,
        width=6,
        height=5,
        scale=250,
        windowsize=c(1400,866),
        zoom=0.55,
        phi=30)
# Capture snapshot of the rgl window
render_snapshot(filename = "mtcars",
                clear=TRUE)
```
]

---
#### Topic 3: Dynamic visualizations

---
class: inverse

#### Review - Dynamic visualizations:
* Dynamic visualization created with **ggplot2** extensions and integrations

* **gganimate** is a popular extension for animating graphs

* Adds new grammar to ggplot2

* **rayshader** is a popular integration for creating 3D objects

* Uses ggplot objects as required arguments
 
.pull-right3[
<img src="./images/rayshader.png" height=220 alt="ggplot" align="right">
<img src="./images/gganimate_hex.png" height=220 alt="RStudio" align="right" style="vertical-align:middle;margin:0px 10px">
]

---
class: center, middle, inverse

#### Topic 4: Shiny Apps - a preview

---
#### Topic 4: Shiny Apps - a preview
.right-column2[
<center><img src="./images/shiny_hex.png" height=250 alt="RStudio" </center>
 
<center><img src="./images/master_shiny.jpg" height=250 alt="RStudio" </center>
]

.left-column2[
* [RStudio](<https://shiny.rstudio.com/>) product - **shiny**

* Build interactive web applications, reports, and dashboards with R

* Reactive Programming
]

---
#### Topic 4: Shiny Apps - a preview

[<center><img src="./images/purpleair_shiny.png" alt="ggplot"</></center>](https://jeremymack.shinyapps.io/purpleair/)

---
class: inverse

#### Review - Data Visualization Resources:
* RStudio (and user contributed) cheatsheets - [link](https://rstudio.com/resources/cheatsheets/)

* Data visualization (**ggplot**)

* *R for Data Science* by Hadley Wickham and Garret Grolemund - [link](https://r4ds.had.co.nz/index.html)
 
--
 
 * Thomas Lin Pedersen ggplot2 presentation (YouTube), Part 1 - [link](https://www.youtube.com/watch?v=h29g21z0a68)
 
 * Thomas Lin Pedersen ggplot2 presentation (YouTube), Part 2 - [link](https://www.youtube.com/watch?v=0m4yywqNPVY)
 
--

* Shiny Apps

* *Mastering Shiny* by Hadley Wickham - [link](https://mastering-shiny.org)
 
 * RStudio tutortials - [link](https://shiny.rstudio.com/tutorial/)
 
---
class: center, middle, inverse, title-slide

## Questions?
<img src="./images/contact.png" alt="RStudio" height=400/>