UP 494-AG: Neighborhood Analysis

name: Visualizing Data
class: right, top, inverse
background-image: url(images/01_VisualizingData.jpg)
background-size: cover

# Neighborhood Analysis
.font-35[Session 5: Visualizing Data]

???

---
name: Why Visualization?
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# Why do we visualize our data?
---
name: DuBois
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background-image: url(images/02_DuBois_1.png)
background-size: contain
---
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.pull-left[
![](images/03_Du_Bois.jpg)
]

.pull-right[
“I got a couple of my best students and put a series of facts into charts: the size and growth of the Negro American group; its division by age and sex; its distribution, education and occupations; its books and periodicals. We made a most interesting set of drawings, limned on pasteboard cards about a yard square and mounted on a number of moveable standards. The details of finishing these 50 or more charts, in colors, with accuracy, was terribly difficult with little money, limited time and not much encouragement.”
]
---
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.pull-left[
![](images/03_Du_Bois.jpg)
]

.pull-right[
![](images/04_Du_Bois.jpg)
]
---
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.pull-left[
![](images/05_GA.png)
]

.pull-right[
### How do craft visualizations that tell a story today?
]
---
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.pull-left[.font-35[
 
![](images/06_GGplot.png)
 
In this week's labs you'll start to learn the *grammar of graphics* in GGplot]
]
---
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.pull-left[.font-35[
 
![](images/06_GGplot.png)
 
In this week's labs you'll start to learn the *grammar of graphics* in GGplot]
]

.pull-right[
![](images/07_layers.png)
 
Data: Your source data

Aesthetics: What variables are visualized and how

Geometry: The specific plot type we would like to make

Facets: Facets allow us to create small multiples

Statistics: summaries of data distribution

Coordinates: Coordinate system for the plot

Themes: Collections of plot styles and options
]
---
class: left, top

#   Basic template

- ggplot: "Initialize" a map object (data frame, aesthetics, columns...)
- geom: add layers (use the ** + ** operator)
- The **+** sign used to add new layers must be placed at the end of the line

```r
ggplot(data = <DATA>, mapping = aes(<MAPPINGS>)) + <GEOM_FUNCTION>()
```
---
# Aesthetics

```r
ggplpt(data = <DATA>, mapping = aes(<mappings)) + <GEOM_FUNCTION>()
```

Item | Description
---------| -------------
x	| Position on x-axis
y	| Position on y-axis
shape	| Shape
color |	Color of border of elements
fill | 	Color of inside of elements (changes the color of the graph by group/facor)
size |	Size
alpha | 	Transparency (1: opaque; 0: transparent)
line type  |	Type of line (e.g., solid, dashed)

---
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# Geometries

```r
ggplpt(data = <DATA>, mapping = aes(<mappings)) + <GEOM_FUNCTION>()
```
Item | Description
---------| -------------
geom_point | Scatter plots, dot plots, etc. 
geom_boxplot | Boxplots 
geom_line | Trend lines, time series, etc. 
geom_path |Connects the observations in the order in which they appear in the data
geom_histogram | Visualise the distribution of a single continuous variable 
geom_hline | Add horizontal reference lines
geom_vline | Add vertical reference lines
geom_smooth | Add a smoothing line in order to see what the trends look like
geom_sf| Visualize simple feature (sf) objects(maps)

---
name: pause for covid
class: top, left

# A Tweet sparks a question...
.pull-left[
<img src="images/CDPH_Tweet.png" width="83%" />
]

.pull-right[
.font-35[
Sorry to bring COVID into this...

What demographic factors help to explain the difference between where people are dying and where vaccinations are occurring?
]
]
---
name: Get Data
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<img src="images/chi_portal_covid.png" width="100%" />
---
name: Get Data 2
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<img src="images/chi_portal_covid_2.png" width="100%" />
---
name: Get Data 3
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<img src="images/chi_portal_covid_3.png" width="100%" />
---
name: Scatterplot COVID Infection Rate
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.font-35[What's the relationship between COVID *cases* and vaccinations?]
.pull-left[

.panelset[
   .panel[.panel-name[Output]

![](Session-5_files/figure-html/scatterplot-1.png)
 ]
 .panel[.panel-name[Code]

```r
ggplot(dataset %>% filter(percent_tested_positive_cumulative !=0)) +
  geom_point(aes(x=percent_tested_positive_cumulative, y=X_1st_dose_percent_population))+
  labs(x="Pop. Positive (%)", y="Pop. Vaccinated (%)")+
  scale_x_continuous(labels=scales::percent)+
  scale_y_continuous(labels=scales::percent)+
  theme_minimal()
```
   ]
]
]
.pull-right[
How would you describe this in your own words?

- What does each point represent?

- Are the scales for X and Y reasonable? What do they tell you?

- Is there a particular region on this plot that might spark questions for further exploration?

- Are you ready to write an op-ed for the Chicago Tribune or Sun Times (the two major newspapers)?
]
---
name: Scatterplot death rate
class: top, left

.font-35[What's the relationship between COVID *deaths* and vaccinations?]
.pull-left[
.panelset[
.panel[.panel-name[Output]
![](Session-5_files/figure-html/scatterplot_death-1.png)
]

.panel[.panel-name[Code]

```r
ggplot(dataset %>% filter(percent_tested_positive_cumulative !=0)) +
  geom_point(aes(x=death_rate_cumulative, y=X_1st_dose_percent_population))+
  labs(x="Death Rate (per 1,000)", y="Pop. Vaccinated (%)")+
  scale_x_continuous(labels=scales::percent)+
  scale_y_continuous(labels=scales::percent)+
  theme_minimal()
```
]

]
]
.pull-right[
How would you describe this in your own words?

- What does each point represent?

- Are the scales for X and Y reasonable? What do they tell you?

- Is there a particular region on this plot that might spark questions for further exploration?

- Are you ready to write an op-ed for the Chicago Tribune or Sun Times (the two major newspapers)?
]
---
name: Maps
class: top, left

.pull-left[
.panelset[
.panel[.panel-name[Output]
![](Session-5_files/figure-html/map_tested-1.png)
]
.panel[.panel-name[Code]

```r
ggplot() +
  geom_sf(data=dataset, aes(fill=percent_tested_positive_cumulative))+
  scale_fill_continuous(trans = 'reverse')+
  geom_sf(data=chi, colour="gray40", fill=NA)+
  labs(fill="Pop. Positive (%)")+
  theme_minimal()
```
]
]]

.pull-right[
.panelset[
.panel[.panel-name[Output]
![](Session-5_files/figure-html/map_vaccinated-1.png)
]
.panel[.panel-name[Code]

```r
ggplot() +
  geom_sf(data=dataset, aes(fill=X_1st_dose_percent_population))+
  scale_fill_continuous(trans = 'reverse')+
  geom_sf(data=chi, colour="gray40", fill=NA)+
  labs(fill="Pop. Vaccinated (%)")+
  theme_minimal()
```
]
]

]

---
name: Maps2
class: top, left

.pull-left[
.panelset[
.panel[.panel-name[Output]
![](Session-5_files/figure-html/map_died-1.png)
]
.panel[.panel-name[Code]

```r
ggplot() +
  geom_sf(data=dataset, aes(fill=death_rate_cumulative))+
  scale_fill_continuous(trans = 'reverse')+
  geom_sf(data=chi, colour="gray40", fill=NA)+
  labs(fill="Pop. Died (%)")+
  theme_minimal()
```
]
]]

.pull-right[
.panelset[
.panel[.panel-name[Output]
![](Session-5_files/figure-html/map_vaccinated2-1.png)
]
.panel[.panel-name[Code]

]
---
name: What Next?
class: bottom, left
background-image: url(images/kelly-sikkema-cXkrqY2wFyc-unsplash.jpg)
background-size: cover

### .salt[What Next?]
.font-35[
What are the types of things you would want to explore to understand what's going on here?

Where might the information come from?
]
---
class: middle

.font-35[With Data , 
Aesthetics, and 
Geometry, you can make a plot - in this week's lab you'll learn how to start layering plot elements together and take an iterative approach to refining plots.]
---
name: Next Class
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# Thursday's Class
.font-35[On Thursday, we'll continue working on our manipulation of basic data visualizations]