Becoming Familiar with R, the Tidyverse, + Making Good Charts

# Becoming Familiar with R, the Tidyverse, + Making Good Charts
## Sep 10 Session of the LBJ Data Studio
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### <span style="color:#005f86; font-weight:500">Ethan Tenison</span> | Project Manager for Data Initiatives, UT Austin - RGK Center
### <span style="color:#005f86; font-weight:500">Matt Worthington</span> | Sr. Project Manager for Data Initiatives, UT Austin - LBJ
### <span style="color:#bf5700; font-weight:300">September 10, 2021</span>

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* .orange[**Personal Background**]: Born and raised in San Antonio, Texas. Live in Austin with my wife and three kids. 
* .orange[**Education Backgrounds**]: English Studies, Special Education, and Public Policy
* .orange[**Professional Backgrounds**]: Public School Teacher, School District Administrator, and Data Scientist. 
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# Tell Us About You

https://www.menti.com/w983jx692v

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## .blue[How to understand R if you are new...]

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# How I Hope We End Today

#### Anywhere from here..

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#### to here.

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# 🪄 Showcase

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# Census Spellbook: `tidycensus`

```r
library(tidycensus)

# Create A List of Variables
demvars <- c(White = "P005003",    
              Black = "P005004",    
              Asian = "P005006",
              Hispanic = "P004003")

# Pull The Census Data 
harris <- get_decennial(
             geography = "tract",             
             variables = demvars,
             state = "TX", 
             county = "Harris County", 
             geometry = TRUE,
             summary_var = "P001001"
             ) |>
  # Create pct column 
  mutate(pct = 100 * (value / summary_value))

# Draw A Chart 
harris  |>
  ggplot(aes(fill = pct)) +
  facet_wrap(~variable) +
  geom_sf(color = NA) +
  coord_sf(crs = 26915, datum=NA) +
  scale_fill_viridis_c() +
  theme_lbj() +
  labs(title="Population Demographics in Harris County",
       subtitle="US Census | 2010 Decennial Census")
```

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# Table Spellbook: `gt`

```r
library(gt)

# Create A List of Variables
demvars <- c(White = "P005003", Black = "P005004",
             Asian = "P005006", Hispanic = "P004003",
             AIAN = "P005005", NHOPI = "P005007",
             Other = "P003007",`Two or More` = "P003008")

# Pull The Census Data 
harris <- get_decennial(geography = "county", variables = demvars,
                        state = "TX", county = "Harris County", summary_var = "P001001") |> 
  # Create pct column 
  mutate(pct = (value / summary_value)) |>
  select(variable, value,  pct)

# Draw A Table 
harris |>
  gt() |> # Pass The Data To A gt table
  tab_header(title = md("**Demographics in Harris County**"),
             subtitle = "Harris County reported 4,092,459 residents in the 2010 Census. Here's how each demographic group compares to the total population.") |>
  tab_source_note(source_note = "Data: US Census | 2010 Decennial Census | pulled with {tidycensus} in R") |>
  fmt_number(columns = c("value")) |>
  fmt_percent(columns = c("pct")) |>
  cols_label(
    variable = md("**Group**"),
    value = md("**Population**"),
    pct = md("**Pct. of Total**")
  )
```
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    <tr>
      <th colspan="3" class="gt_heading gt_title gt_font_normal" style><strong>Demographics in Harris County</strong></th>
    </tr>
    <tr>
      <th colspan="3" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>Harris County reported 4,092,459 residents in the 2010 Census. Here's how each demographic group compares to the total population.</th>
    </tr>
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      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1"><strong>Group</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1"><strong>Population</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1"><strong>Pct. of Total</strong></th>
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  </thead>
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    <tr><td class="gt_row gt_left">White</td>
<td class="gt_row gt_right">1,349,646.00</td>
<td class="gt_row gt_right">32.98%</td></tr>
    <tr><td class="gt_row gt_left">Black</td>
<td class="gt_row gt_right">754,258.00</td>
<td class="gt_row gt_right">18.43%</td></tr>
    <tr><td class="gt_row gt_left">Asian</td>
<td class="gt_row gt_right">249,853.00</td>
<td class="gt_row gt_right">6.11%</td></tr>
    <tr><td class="gt_row gt_left">Hispanic</td>
<td class="gt_row gt_right">1,671,540.00</td>
<td class="gt_row gt_right">40.84%</td></tr>
    <tr><td class="gt_row gt_left">AIAN</td>
<td class="gt_row gt_right">8,150.00</td>
<td class="gt_row gt_right">0.20%</td></tr>
    <tr><td class="gt_row gt_left">NHOPI</td>
<td class="gt_row gt_right">2,260.00</td>
<td class="gt_row gt_right">0.06%</td></tr>
    <tr><td class="gt_row gt_left">Other</td>
<td class="gt_row gt_right">583,566.00</td>
<td class="gt_row gt_right">14.26%</td></tr>
    <tr><td class="gt_row gt_left">Two or More</td>
<td class="gt_row gt_right">131,332.00</td>
<td class="gt_row gt_right">3.21%</td></tr>
  </tbody>
  <tfoot class="gt_sourcenotes">
    <tr>
      <td class="gt_sourcenote" colspan="3">Data: US Census | 2010 Decennial Census (pulled with {tidycensus} in R)</td>
    </tr>
  </tfoot>
  
</table>
</div>
]

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# Static Charts: [`ggplot`]()

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# Interactive Charts: [`highcharter`](https://jkunst.com/highcharter/index.html)

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!important","color":"#2d2d2d"}},"title":{"style":{"color":"#fff","fontSize":"12px","color.1":"#fff","fontWeight":"500"}},"gridLineWidth":0.5},"plotOptions":{"line":{"marker":{"enabled":false}},"spline":{"marker":{"enabled":false}},"area":{"marker":{"enabled":false}},"areaspline":{"marker":{"enabled":false}},"arearange":{"marker":{"enabled":false}},"bubble":{"maxSize":"10%"}},"credits":{"style":{"color":"#fff"}}},"conf_opts":{"global":{"Date":null,"VMLRadialGradientURL":"http =//code.highcharts.com/list(version)/gfx/vml-radial-gradient.png","canvasToolsURL":"http =//code.highcharts.com/list(version)/modules/canvas-tools.js","getTimezoneOffset":null,"timezoneOffset":0,"useUTC":true},"lang":{"contextButtonTitle":"Chart context menu","decimalPoint":".","downloadJPEG":"Download JPEG image","downloadPDF":"Download PDF document","downloadPNG":"Download PNG image","downloadSVG":"Download SVG vector image","drillUpText":"Back to {series.name}","invalidDate":null,"loading":"Loading...","months":["January","February","March","April","May","June","July","August","September","October","November","December"],"noData":"No data to display","numericSymbols":["k","M","G","T","P","E"],"printChart":"Print chart","resetZoom":"Reset zoom","resetZoomTitle":"Reset zoom level 1:1","shortMonths":["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],"thousandsSep":" ","weekdays":["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"]}},"type":"chart","fonts":"-apple-system+!important","debug":false},"evals":[],"jsHooks":[]}</script>

---

# Dashboards: `shiny`

#### Texas Workforce Commission

![](assets/images/twc_dashboard.png)

]

#### National Center for Farmworker Health

![](assets/images/ncfh_dashboard.png)

]

---

# Websites: `blogdown` + `distill`

![](assets/images/blogdown.png)

]

![](assets/images/distill.png)

]

---

# Books: `bookdown`

---

# Documents: `rmarkdown`

---

# Like anything, this is a journey...

.rmd-img[![](https://github.com/allisonhorst/stats-illustrations/raw/master/rstats-artwork/r_rollercoaster.png
)]

---
class: journey-bg, section, center, middle, inverse

# Let's get started 👟

---

# Downloading Materials

### Today's Agenda

.b--gray.ba.bw2.ma2.shadow-1[

*	Navigating Rstudio
  *	Rstudio Interface
  *	R Projects
* Basics of ggplot2
  * `ggplot()`
  * `aes()`
  * `geom_*()`
  * `scale_*()`
  * `theme_*()`
  * `labs()`
* Exporting Data & Charts
  * `ggsave()`

]
]

### Today's Materials

.b--gray.ba.bw2.ma2.pa2.shadow-1[

#### What You Should Download

* [**ggplot2 Materials** ](https://tinyurl.com/making-ggplot2-charts): tinyurl.com/making-ggplot2-charts

#### What You Should Reference Later

* [**Today's Slides**](https://slides.lbjdata.org/data-studio/ggplot2_basics/index.html)
* [**The ggplot2 Website**](https://ggplot2.tidyverse.org)
* [**Chapter 1 of R For Data Science**](https://r4ds.had.co.nz/data-visualisation.html)
* [**ggplot2 Cheatsheets**]()
* [**ggplot2tor**](https://ggplot2tor.com)
]
]

---
background-image: url('assets/images/rstudio_hex.png')
background-size: cover
class: center, bottom

---

# Downloading The Materials

* [**Rmarkdown Analysis** ](tinyurl.com/TXVaxUptake): tinyurl.com/TXVaxUptake

![](assets/images/github_repo.png)

---

# Open the `.Rproj` file

---
background-image: url('https://utexas-lbjp-data.github.io/assets/rmarkdown/slide_17.jpeg')
background-size: cover
class: center, bottom, inverse

---
background-image: url('https://utexas-lbjp-data.github.io/assets/rmarkdown/slide_18.jpeg')
background-size: cover
class: center, bottom, inverse

---
background-image: url('assets/images/tidyverse_hex.png')
background-size: cover
class: center, bottom, inverse

---
background-image: url('assets/images/tidyverse_network.png')
background-size: cover
class: center, bottom, inverse animated fadeIn

---
background-image: url('assets/images/tidyverse_data_lifecycle_map.png')
background-size: cover
class: center, bottom, inverse animated fadeIn

---
background-image: url('assets/images/lifecycle_focus.png')
background-size: cover
class: center, bottom, inverse animated fadeIn

---
background-image: url('assets/images/what_todays_focus_will_be.png')
background-size: cover
class: center, bottom, inverse animated animate fadeIn

---
background-image: url('assets/images/marie_kondo.png')
background-size: cover
class: center, bottom, inverse animated fadeIn

---

# What is ggplot2?

.pull-left.b--gray.ba.bw2.ma2.pa4.shadow-1[
* ["A coherent system for describing and building graphs"](https://r4ds.had.co.nz/data-visualisation.html) (✔️)
* [A tool for implementing a Grammar of Graphics](https://ggplot2-book.org/introduction.html). (✔️)
* [An R package named ggplot2](https://rmarkdown.rstudio.com/docs/). (✔️)
* [A tool that people build on](https://exts.ggplot2.tidyverse.org) (✔️)
* [A tool for making artwork](https://www.data-imaginist.com/art) (✔️)
* [A tool for reproducible analysis](http://radar.oreilly.com/2011/07/wolframs-computational-documen.html) (✔️)
* Really fun (🚀️)
]

.pull-right[
.rmd-small[![](https://raw.githubusercontent.com/rstudio/hex-stickers/master/PNG/ggplot2.png)]
]

.tl.burntorange[
Courtesy of Alison Presmanes Hill ([@apreshill](https://twitter.com/apreshill))
]

---
class: center middle

---
class: center middle

.rmd-tiniest[![](https://images.squarespace-cdn.com/content/v1/4f3f61bae4b063b909445965/1622454363382-U130NBNWBZMVGI2PF0YH/employed.png)]

.tl.burntorange[
Credit: Georgios Karamanis ([@geokaramanis](https://twitter.com/geokaramanis))
]

---
class: center middle

.rmd-med[![](https://raw.githubusercontent.com/Z3tt/TidyTuesday/master/plots/2020_08/2020_08_CarbonFoodprint_alt.png)]

.tl.burntorange[
Credit: Cédric Scherer ([@CedScherer](https://twitter.com/CedScherer))
]

---
class: center middle

.rmd-small[![](https://raw.githubusercontent.com/Z3tt/TidyTuesday/master/plots/2020_27/2020_27_Xmen_annotated.png)]

.tl.burntorange[
Credit: Cédric Scherer ([@CedScherer](https://twitter.com/CedScherer))
]

---
class: center middle

.rmd-small[![](https://www.data-imaginist.com/art/011_yonder/yonder1831_hu827b7f9bcd74231a2b6a7030989792b6_784253_0x1000_resize_q75_box.jpg)]

.tl.burntorange[
Credit: Thomas Lin Pederson ([@thomasp85](https://twitter.com/thomasp85))
]

---

background-image: url('https://utexas-lbjp-data.github.io/assets/rmarkdown/slide_17.jpeg')
background-size: cover
class: center, bottom, inverse

---
background-image: url('https://utexas-lbjp-data.github.io/assets/rmarkdown/slide_18.jpeg')
background-size: cover
class: center, bottom, inverse

---

background-image: url('https://utexas-lbjp-data.github.io/assets/rmarkdown/slide_19.jpeg')
background-size: cover
class: center, bottom, inverse

---
class: title center pipe-page

# `|>` and `%>%` and `+`

<code class ='r hljs remark-code'><b>leave_house</b>(<b>get_dressed</b>(<b>get_out_of_bed</b>(<b>wake_up</b>(<span style='color:#bf5700'>me</span>, <span style='color:#005f86'>time</span> = <span style='color:#bf5700'>"8:00"</span>), <span style='color:#005f86'>side</span> = <span style='color:#bf5700'>"correct"</span>), <span style='color:#005f86'>pants</span> = <span style='color:#bf5700'>TRUE</span>, <span style='color:#005f86'>shirt</span> = <span style='color:#bf5700'>TRUE</span>), <span style='color:#005f86'>car</span> = <span style='color:#bf5700'>TRUE</span>, <span style='color:#005f86'>bike</span> = <span style='color:#bf5700'>FALSE</span>)</code>

<code class ='r hljs remark-code'>me <span style='background-color:#ffff7f'>|></span> <br>&nbsp;&nbsp;<b>wake_up</b>(<span style='color:#005f86'>time</span> = <span style='color:#bf5700'>"8:00"</span>) <span style='background-color:#ffff7f'>|></span> <br>&nbsp;&nbsp;<b>get_out_of_bed</b>(<span style='color:#005f86'>side</span> = <span style='color:#bf5700'>"correct"</span>) <span style='background-color:#ffff7f'>|></span> <br>&nbsp;&nbsp;<b>get_dressed</b>(<span style='color:#005f86'>pants</span> = <span style='color:#bf5700'>TRUE</span>, <span style='color:#005f86'>shirt</span> = <span style='color:#bf5700'>TRUE</span>) <span style='background-color:#ffff7f'>|></span> <br>&nbsp;&nbsp;<b>leave_house</b>(<span style='color:#005f86'>car</span> = <span style='color:#bf5700'>TRUE</span>, <span style='color:#005f86'>bike</span> = <span style='color:#bf5700'>FALSE</span>)</code>

.tl.footnote-small[
Courtesy of Andrew Heiss ([@andrewheiss](https://twitter.com/andrewheiss))
]

---
class: vaccine-bg, section, center, middle, inverse

# And now... making charts!

---
class: center middle

# The Grammar of Graphics

.tl.burntorange[
Credit: ([Dr Anna Krystalli](https://annakrystalli.me/rrresearchACCE20/plotting-with-ggplot2.html#the-grammar-of-grahics))
]

---
class: center middle

# The Grammar of Graphics

.tl.burntorange[
Credit: ([Dr Anna Krystalli](https://annakrystalli.me/rrresearchACCE20/plotting-with-ggplot2.html#the-grammar-of-grahics))
]

---

# ggplot2 Basics in Action

```r
library(ggplot2)
library(ggthemes)

diamonds |>  # Call On Your Dataset
  ggplot() +  # Draw A Canvas
  aes(cut, fill = cut) +  # Define How The Data Gets Mapped
  geom_bar(show.legend = FALSE) +  # Define What Kind of Chart to Draw
  labs(
    x = "Cut",  # Specify X-Axis Label                       
    y = "Count",  # Specify Y-Axis Label
    title = "A Fancy diamonds Plot", # Specify Title Label
    subtitle = "A compelling subtitle", # Specify Subtitle Label
    caption = "Source: ggplot2 package | Data: 'diamonds'"  # Specify Source/Caption Label
  ) +
  theme_minimal() + # Add the minimal Theme
  theme(plot.title = element_text(color = "#bf5700")) +   # Modify Title's Color
  ggthemes::scale_fill_tableau(palette = "Color Blind")   # Add A Color Blind Friendly Palette
```

]

<img src="index_files/figure-html/fancy_diamonds-1.png" width="504" />
]

---
class: center middle

## `ggplot()`

**What it does:** Loads the data and draws a ggplot2 canvas for your chart

---
class: center middle

## `aes()`

**What it does:** Lets you dictate what goes where on the chart, like what's on the x-axis versus the y-axis or how stuff gets colored.

---
class: center middle

## `geom_*()`

**What it does:** Lets you add shapes to your charts (bars vs. points vs. lines vs. maps, etc.)

---
class: center middle

## `labs()`

**What it does:** Lets you add or adjust labels on your chart, like the Title, Subtitle, X-Axis, Y-Axis, or Caption Labels.

---
class: center middle

## `theme_*()`

**What it does:** Lets you add style to the chart!

---
class: center middle

## `scale_*()`

**What it does:** Lets you change or adjust things about scales you've mapped using `aes()`.

---

```r
*library(ggplot2)
```
]
 
.panel2-fancy_diamonds-auto[

]

---
count: false
 
# A step by step view
.panel1-fancy_diamonds-auto[

```r
library(ggplot2)
*library(ggthemes)
```
]
 
.panel2-fancy_diamonds-auto[

]

---
count: false
 
# A step by step view
.panel1-fancy_diamonds-auto[

```r
library(ggplot2)
library(ggthemes)

*diamonds   # Call On Your Dataset
```
]
 
.panel2-fancy_diamonds-auto[

```
# A tibble: 53,940 × 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
 7  0.24 Very Good I     VVS1     62.3    57   336  3.95  3.98  2.47
 8  0.26 Very Good H     SI1      61.9    55   337  4.07  4.11  2.53
 9  0.22 Fair      E     VS2      65.1    61   337  3.87  3.78  2.49
10  0.23 Very Good H     VS1      59.4    61   338  4     4.05  2.39
# … with 53,930 more rows
```
]

---
count: false
 
# A step by step view
.panel1-fancy_diamonds-auto[

```r
library(ggplot2)
library(ggthemes)

diamonds %>%  # Call On Your Dataset
* ggplot()   # Draw A Canvas
```
]
 
.panel2-fancy_diamonds-auto[
<img src="index_files/figure-html/fancy_diamonds_auto_04_output-1.png" width="504" />
]

---
count: false
 
# A step by step view
.panel1-fancy_diamonds-auto[

```r
library(ggplot2)
library(ggthemes)

diamonds %>%  # Call On Your Dataset
  ggplot() +  # Draw A Canvas
* aes(cut, fill = cut)   # Define How The Data Gets Mapped
```
]
 
.panel2-fancy_diamonds-auto[
<img src="index_files/figure-html/fancy_diamonds_auto_05_output-1.png" width="504" />
]

---
count: false
 
# A step by step view
.panel1-fancy_diamonds-auto[

```r
library(ggplot2)
library(ggthemes)

diamonds %>%  # Call On Your Dataset
  ggplot() +  # Draw A Canvas
  aes(cut, fill = cut) +  # Define How The Data Gets Mapped
* geom_bar(show.legend = FALSE)   # Define What Kind of Chart to Draw
```
]
 
.panel2-fancy_diamonds-auto[
<img src="index_files/figure-html/fancy_diamonds_auto_06_output-1.png" width="504" />
]

---
count: false
 
# A step by step view
.panel1-fancy_diamonds-auto[

```r
library(ggplot2)
library(ggthemes)

diamonds %>%  # Call On Your Dataset
  ggplot() +  # Draw A Canvas
  aes(cut, fill = cut) +  # Define How The Data Gets Mapped
  geom_bar(show.legend = FALSE) +  # Define What Kind of Chart to Draw
* labs(
*   x = "Cut",  # Specify X-Axis Label
*   y = "Count",  # Specify Y-Axis Label
*   title = "A Fancy diamonds Plot",  # Specify Title Label
*   subtitle = "A compelling subtitle",  # Specify Subtitle Label
*   caption = "Source: ggplot2 package | Data: 'diamonds'"  # Specify Source/Caption Label
* )
```
]
 
.panel2-fancy_diamonds-auto[
<img src="index_files/figure-html/fancy_diamonds_auto_07_output-1.png" width="504" />
]

---
count: false
 
# A step by step view
.panel1-fancy_diamonds-auto[

```r
library(ggplot2)
library(ggthemes)

diamonds %>%  # Call On Your Dataset
  ggplot() +  # Draw A Canvas
  aes(cut, fill = cut) +  # Define How The Data Gets Mapped
  geom_bar(show.legend = FALSE) +  # Define What Kind of Chart to Draw
  labs(
    x = "Cut",  # Specify X-Axis Label
    y = "Count",  # Specify Y-Axis Label
    title = "A Fancy diamonds Plot",  # Specify Title Label
    subtitle = "A compelling subtitle",  # Specify Subtitle Label
    caption = "Source: ggplot2 package | Data: 'diamonds'"  # Specify Source/Caption Label
  ) +
* theme_minimal()   # Add the minimal Theme
```
]
 
.panel2-fancy_diamonds-auto[
<img src="index_files/figure-html/fancy_diamonds_auto_08_output-1.png" width="504" />
]

---
count: false
 
# A step by step view
.panel1-fancy_diamonds-auto[

```r
library(ggplot2)
library(ggthemes)

diamonds %>%  # Call On Your Dataset
  ggplot() +  # Draw A Canvas
  aes(cut, fill = cut) +  # Define How The Data Gets Mapped
  geom_bar(show.legend = FALSE) +  # Define What Kind of Chart to Draw
  labs(
    x = "Cut",  # Specify X-Axis Label
    y = "Count",  # Specify Y-Axis Label
    title = "A Fancy diamonds Plot",  # Specify Title Label
    subtitle = "A compelling subtitle",  # Specify Subtitle Label
    caption = "Source: ggplot2 package | Data: 'diamonds'"  # Specify Source/Caption Label
  ) +
  theme_minimal() +  # Add the minimal Theme
* theme(plot.title = element_text(color = "#bf5700"))   # Modify Title's Color
```
]
 
.panel2-fancy_diamonds-auto[
<img src="index_files/figure-html/fancy_diamonds_auto_09_output-1.png" width="504" />
]

---
count: false
 
# A step by step view
.panel1-fancy_diamonds-auto[

```r
library(ggplot2)
library(ggthemes)

diamonds %>%  # Call On Your Dataset
  ggplot() +  # Draw A Canvas
  aes(cut, fill = cut) +  # Define How The Data Gets Mapped
  geom_bar(show.legend = FALSE) +  # Define What Kind of Chart to Draw
  labs(
    x = "Cut",  # Specify X-Axis Label
    y = "Count",  # Specify Y-Axis Label
    title = "A Fancy diamonds Plot",  # Specify Title Label
    subtitle = "A compelling subtitle",  # Specify Subtitle Label
    caption = "Source: ggplot2 package | Data: 'diamonds'"  # Specify Source/Caption Label
  ) +
  theme_minimal() +  # Add the minimal Theme
  theme(plot.title = element_text(color = "#bf5700")) +  # Modify Title's Color
* scale_fill_tableau(palette = "Color Blind")  # Add A Color Blind Friendly Palette
```
]
 
.panel2-fancy_diamonds-auto[
<img src="index_files/figure-html/fancy_diamonds_auto_10_output-1.png" width="504" />
]

---
count: false
 
# A step by step view
.panel1-fancy_diamonds-auto[

```r
library(ggplot2)
library(ggthemes)

diamonds %>%  # Call On Your Dataset
  ggplot() +  # Draw A Canvas
  aes(cut, fill = cut) +  # Define How The Data Gets Mapped
  geom_bar(show.legend = FALSE) +  # Define What Kind of Chart to Draw
  labs(
    x = "Cut",  # Specify X-Axis Label
    y = "Count",  # Specify Y-Axis Label
    title = "A Fancy diamonds Plot",  # Specify Title Label
    subtitle = "A compelling subtitle",  # Specify Subtitle Label
    caption = "Source: ggplot2 package | Data: 'diamonds'"  # Specify Source/Caption Label
  ) +
  theme_minimal() +  # Add the minimal Theme
  theme(plot.title = element_text(color = "#bf5700")) +  # Modify Title's Color
  scale_fill_tableau(palette = "Color Blind")  # Add A Color Blind Friendly Palette
```
]
 
.panel2-fancy_diamonds-auto[
<img src="index_files/figure-html/fancy_diamonds_auto_11_output-1.png" width="504" />
]

---
class: code50

# Our Data

```r
library(tidyverse)    # Core Set of R Data Science Tools (dplyr, ggplot2, tidyr, readr, etc.)
library(ggthemes)     # ggplot2 extension package that has themes + functions

# Import Texas County Data ------------------------------------------------

tx_cnty_data <- read_rds("raw_data/texas_cnty_data.rds")

# Review Data Available ---------------------------------------------------

glimpse(tx_cnty_data) ## Preview The Data
```

```
Rows: 254
Columns: 28
$ county                                        <chr> "Anderson", "Andrews", "…
$ public_health_region_phr                      <chr> "4/5N", "9/10", "4/5N", …
$ total_doses_allocated                         <dbl> 17870, 6100, 58100, 6840…
$ vaccine_doses_administered                    <chr> "39286", "14009", "65694…
$ people_vaccinated_with_at_least_one_dose      <chr> "22765", "7770", "37278"…
$ people_fully_vaccinated                       <dbl> 18919, 6525, 31129, 1199…
$ population_12                                 <dbl> 50661, 14863, 72486, 208…
$ population_16                                 <chr> "48045", "13503", "67056…
$ population_65                                 <dbl> 8658, 1833, 14422, 6791,…
$ population_phase_1a_healthcare_workers        <chr> "2051", "309", "6534", "…
$ population_phase_1a_long_term_care_residents  <chr> "833", "106", "1752", "2…
$ population_16_64_any_medical_condition        <chr> "20206", "5450", "28054"…
$ population_education_and_child_care_personnel <chr> "1574", "41", "3266", "3…
$ confirmed_cases                               <dbl> 4837, 2280, 6086, 1740, …
$ probable_cases                                <dbl> 2089, 0, 5970, 511, 130,…
$ fatalities                                    <dbl> 142, 49, 323, 52, 13, 7,…
$ prez_candidate_2020                           <chr> "Donald J Trump", "Donal…
$ vote_share_2020                               <dbl> 0.786, 0.843, 0.725, 0.7…
$ tot_pop                                       <dbl> 57810, 18036, 87322, 244…
$ med_inc                                       <dbl> 43455, 76158, 50453, 451…
$ broadband                                     <dbl> 83.6, 91.6, 89.5, 90.9, …
$ labor_part_16                                 <dbl> 41.4, 64.9, 58.2, 51.6, …
$ unemployment                                  <dbl> 3.2, 3.7, 6.6, 7.5, 2.8,…
$ w_health_insurance                            <dbl> 85.5, 80.2, 81.2, 80.0, …
$ geometry                                      <MULTIPOLYGON [°]> MULTIPOLYGO…
$ pct_vaccinated_eligible                       <dbl> 0.3734431, 0.4390096, 0.…
$ pct_vaccinated_all                            <dbl> 0.3272617, 0.3617764, 0.…
$ share_above_65                                <dbl> 0.14976648, 0.10163007, …
```

---

# 1. Top 10 Chart

```r
# 1. Draw A Bar Chart -----------------------------------------------------

## Subset the Top 10 Counties for Vaccine Uptake

top_10_vax <- tx_cnty_data |> 
  arrange(desc(pct_vaccinated_eligible)) |> 
  slice(1:10)

## Draw Bar Chart of Top 10 Counties

top_10_vax |> 
  ggplot() + 
  aes(x=reorder(county, pct_vaccinated_eligible), y = pct_vaccinated_eligible, fill = pct_vaccinated_eligible) +
  geom_col() +
  scale_y_continuous(labels = scales::percent) +
  theme_minimal() +
  theme(legend.position  = "none",
        plot.title = element_text(face = "bold")) +
  labs(title = "Top 10 Counties for Vacine Uptake",
       subtitle = "As of September 9th, 2021",
       caption = "Data: Texas Department of State Health Services",
       x = "Counties",
       y = "% Fully Vaccinated (Population 12+)")
```

]

]

---

```r
# 1. Draw A Bar Chart -----------------------------------------------------

## Subset the Top 10 Counties for Vaccine Uptake

*top_10_vax <- tx_cnty_data
```
]
 
.panel2-draw-viz-1-auto[

]

---
count: false
 
# A step by step view
.panel1-draw-viz-1-auto[

```r
# 1. Draw A Bar Chart -----------------------------------------------------

## Subset the Top 10 Counties for Vaccine Uptake

top_10_vax <- tx_cnty_data %>%
* arrange(desc(pct_vaccinated_eligible))
```
]
 
.panel2-draw-viz-1-auto[

]

---
count: false
 
# A step by step view
.panel1-draw-viz-1-auto[

```r
# 1. Draw A Bar Chart -----------------------------------------------------

## Subset the Top 10 Counties for Vaccine Uptake

top_10_vax <- tx_cnty_data %>%
  arrange(desc(pct_vaccinated_eligible)) %>%
* slice(1:10)
```
]
 
.panel2-draw-viz-1-auto[

]

---
count: false
 
# A step by step view
.panel1-draw-viz-1-auto[

```r
# 1. Draw A Bar Chart -----------------------------------------------------

## Subset the Top 10 Counties for Vaccine Uptake

top_10_vax <- tx_cnty_data %>%
  arrange(desc(pct_vaccinated_eligible)) %>%
  slice(1:10)

## Draw Bar Chart of Top 10 Counties

*top_10_vax
```
]
 
.panel2-draw-viz-1-auto[

```
Simple feature collection with 10 features and 27 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -106.6456 ymin: 25.83738 xmax: -95.42412 ymax: 32.00253
Geodetic CRS:  NAD83
# A tibble: 10 × 28
   county    public_health_r… total_doses_all… vaccine_doses_a… people_vaccinat…
   <chr>     <chr>                       <dbl> <chr>            <chr>           
 1 Presidio  9/10                         5300 10802            6053            
 2 Webb      11                         206550 383755           222300          
 3 Starr     11                          43780 82960            47909           
 4 Cameron   11                         377955 548307           307452          
 5 Maverick  8                           46700 77596            48312           
 6 Hidalgo   11                         641140 1082649          610221          
 7 El Paso   9/10                       713000 1074306          595876          
 8 Fort Bend 6/5S                       528850 1004811          553677          
 9 Travis    7                         2165090 1551208          864715          
10 Hudspeth  9/10                         2000 5415             3166            
# … with 23 more variables: people_fully_vaccinated <dbl>, population_12 <dbl>,
#   population_16 <chr>, population_65 <dbl>,
#   population_phase_1a_healthcare_workers <chr>,
#   population_phase_1a_long_term_care_residents <chr>,
#   population_16_64_any_medical_condition <chr>,
#   population_education_and_child_care_personnel <chr>, confirmed_cases <dbl>,
#   probable_cases <dbl>, fatalities <dbl>, prez_candidate_2020 <chr>, …
```
]

---
count: false
 
# A step by step view
.panel1-draw-viz-1-auto[

```r
# 1. Draw A Bar Chart -----------------------------------------------------

## Subset the Top 10 Counties for Vaccine Uptake

top_10_vax <- tx_cnty_data %>%
  arrange(desc(pct_vaccinated_eligible)) %>%
  slice(1:10)

## Draw Bar Chart of Top 10 Counties

top_10_vax %>%
* ggplot()
```
]
 
.panel2-draw-viz-1-auto[
<img src="index_files/figure-html/draw-viz-1_auto_05_output-1.png" width="504" />
]

---
count: false
 
# A step by step view
.panel1-draw-viz-1-auto[

```r
# 1. Draw A Bar Chart -----------------------------------------------------

## Subset the Top 10 Counties for Vaccine Uptake

top_10_vax <- tx_cnty_data %>%
  arrange(desc(pct_vaccinated_eligible)) %>%
  slice(1:10)

## Draw Bar Chart of Top 10 Counties

top_10_vax %>%
  ggplot() +
* aes(x=reorder(county, pct_vaccinated_eligible), y = pct_vaccinated_eligible, fill = pct_vaccinated_eligible)
```
]
 
.panel2-draw-viz-1-auto[
<img src="index_files/figure-html/draw-viz-1_auto_06_output-1.png" width="504" />
]

---
count: false
 
# A step by step view
.panel1-draw-viz-1-auto[

```r
# 1. Draw A Bar Chart -----------------------------------------------------

## Subset the Top 10 Counties for Vaccine Uptake

top_10_vax <- tx_cnty_data %>%
  arrange(desc(pct_vaccinated_eligible)) %>%
  slice(1:10)

## Draw Bar Chart of Top 10 Counties

top_10_vax %>%
  ggplot() +
  aes(x=reorder(county, pct_vaccinated_eligible), y = pct_vaccinated_eligible, fill = pct_vaccinated_eligible) +
* geom_col()
```
]
 
.panel2-draw-viz-1-auto[
<img src="index_files/figure-html/draw-viz-1_auto_07_output-1.png" width="504" />
]

---
count: false
 
# A step by step view
.panel1-draw-viz-1-auto[

```r
# 1. Draw A Bar Chart -----------------------------------------------------

## Subset the Top 10 Counties for Vaccine Uptake

top_10_vax <- tx_cnty_data %>%
  arrange(desc(pct_vaccinated_eligible)) %>%
  slice(1:10)

## Draw Bar Chart of Top 10 Counties

top_10_vax %>%
  ggplot() +
  aes(x=reorder(county, pct_vaccinated_eligible), y = pct_vaccinated_eligible, fill = pct_vaccinated_eligible) +
  geom_col() +
* scale_y_continuous(labels = scales::percent)
```
]
 
.panel2-draw-viz-1-auto[
<img src="index_files/figure-html/draw-viz-1_auto_08_output-1.png" width="504" />
]

---
count: false
 
# A step by step view
.panel1-draw-viz-1-auto[

```r
# 1. Draw A Bar Chart -----------------------------------------------------

## Subset the Top 10 Counties for Vaccine Uptake

top_10_vax <- tx_cnty_data %>%
  arrange(desc(pct_vaccinated_eligible)) %>%
  slice(1:10)

## Draw Bar Chart of Top 10 Counties

top_10_vax %>%
  ggplot() +
  aes(x=reorder(county, pct_vaccinated_eligible), y = pct_vaccinated_eligible, fill = pct_vaccinated_eligible) +
  geom_col() +
  scale_y_continuous(labels = scales::percent) +
* theme_minimal()
```
]
 
.panel2-draw-viz-1-auto[
<img src="index_files/figure-html/draw-viz-1_auto_09_output-1.png" width="504" />
]

---
count: false
 
# A step by step view
.panel1-draw-viz-1-auto[

```r
# 1. Draw A Bar Chart -----------------------------------------------------

## Subset the Top 10 Counties for Vaccine Uptake

top_10_vax <- tx_cnty_data %>%
  arrange(desc(pct_vaccinated_eligible)) %>%
  slice(1:10)

## Draw Bar Chart of Top 10 Counties

---
count: false
 
# A step by step view
.panel1-draw-viz-1-auto[

```r
# 1. Draw A Bar Chart -----------------------------------------------------

## Subset the Top 10 Counties for Vaccine Uptake

top_10_vax <- tx_cnty_data %>%
  arrange(desc(pct_vaccinated_eligible)) %>%
  slice(1:10)

## Draw Bar Chart of Top 10 Counties

top_10_vax %>%
  ggplot() +
  aes(x=reorder(county, pct_vaccinated_eligible), y = pct_vaccinated_eligible, fill = pct_vaccinated_eligible) +
  geom_col() +
  scale_y_continuous(labels = scales::percent) +
  theme_minimal() +
  theme(legend.position  = "none",
        plot.title = element_text(face = "bold")) +
* labs(title = "Top 10 Counties for Vacine Uptake",
*      subtitle = "As of September 9th, 2021",
*      caption = "Data: Texas Department of State Health Services",
*      x = "Counties",
*      y = "% Fully Vaccinated (Population 12+)")
```
]
 
.panel2-draw-viz-1-auto[
<img src="index_files/figure-html/draw-viz-1_auto_11_output-1.png" width="504" />
]

---
count: false
 
# A step by step view
.panel1-draw-viz-1-auto[

```r
# 1. Draw A Bar Chart -----------------------------------------------------

## Subset the Top 10 Counties for Vaccine Uptake

top_10_vax <- tx_cnty_data %>%
  arrange(desc(pct_vaccinated_eligible)) %>%
  slice(1:10)

## Draw Bar Chart of Top 10 Counties

top_10_vax %>%
  ggplot() +
  aes(x=reorder(county, pct_vaccinated_eligible), y = pct_vaccinated_eligible, fill = pct_vaccinated_eligible) +
  geom_col() +
  scale_y_continuous(labels = scales::percent) +
  theme_minimal() +
  theme(legend.position  = "none",
        plot.title = element_text(face = "bold")) +
  labs(title = "Top 10 Counties for Vacine Uptake",
       subtitle = "As of September 9th, 2021",
       caption = "Data: Texas Department of State Health Services",
       x = "Counties",
       y = "% Fully Vaccinated (Population 12+)")
```
]
 
.panel2-draw-viz-1-auto[
<img src="index_files/figure-html/draw-viz-1_auto_12_output-1.png" width="504" />
]

---

# 2. Map 1

```r
# 1. Map of Vaccine Uptake 12+ --------------------------------------------

tx_cnty_data %>% 
  ggplot() + 
  aes(fill = pct_vaccinated_eligible, color = pct_vaccinated_eligible) +
  geom_sf(size = 0.15) +
  scale_color_gradient_tableau(palette = "Blue", labels = scales::percent) +
  scale_fill_gradient_tableau(palette = "Blue", labels = scales::percent) +
  theme_void() +
  theme(legend.position  = "right",
        plot.title = element_text(face = "bold")) +
  labs(title = "Vacine Uptake Among Eligible Residents",
       subtitle = "As of September 9th, 2021",
       caption = "Data: Texas Department of State Health Services",
       x = "Counties",
       y = "% Fully Vaccinated (Population 12+)",
       fill = "% Fully Vaccinated (12+)",
       color = "% Fully Vaccinated (12+)")
```

]

]

---

```r
*# 1. Map of Vaccine Uptake 12+ -------------------------------------------  # 1. Map of Vaccine Uptake 12+ --------------------------------------------
```
]
 
.panel2-draw-viz-2-auto[

]

---
count: false
 
# A step by step view
.panel1-draw-viz-2-auto[

```r
# 1. Map of Vaccine Uptake 12+ --------------------------------------------  # 1. Map of Vaccine Uptake 12+ --------------------------------------------

*tx_cnty_data
```
]
 
.panel2-draw-viz-2-auto[

```
Simple feature collection with 254 features and 27 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -106.6456 ymin: 25.83738 xmax: -93.50829 ymax: 36.5007
Geodetic CRS:  NAD83
# A tibble: 254 × 28
   county    public_health_r… total_doses_all… vaccine_doses_a… people_vaccinat…
   <chr>     <chr>                       <dbl> <chr>            <chr>           
 1 Anderson  4/5N                        17870 39286            22765           
 2 Andrews   9/10                         6100 14009            7770            
 3 Angelina  4/5N                        58100 65694            37278           
 4 Aransas   11                           6840 25402            14309           
 5 Archer    2/3                          3200 6928             3879            
 6 Armstrong 1                            2100 1266             711             
 7 Atascosa  8                           19000 42788            24286           
 8 Austin    6/5S                         8700 25127            14708           
 9 Bailey    1                           24135 4722             2730            
10 Bandera   8                            5100 17602            10067           
# … with 244 more rows, and 23 more variables: people_fully_vaccinated <dbl>,
#   population_12 <dbl>, population_16 <chr>, population_65 <dbl>,
#   population_phase_1a_healthcare_workers <chr>,
#   population_phase_1a_long_term_care_residents <chr>,
#   population_16_64_any_medical_condition <chr>,
#   population_education_and_child_care_personnel <chr>, confirmed_cases <dbl>,
#   probable_cases <dbl>, fatalities <dbl>, prez_candidate_2020 <chr>, …
```
]

---
count: false
 
# A step by step view
.panel1-draw-viz-2-auto[

```r
# 1. Map of Vaccine Uptake 12+ --------------------------------------------  # 1. Map of Vaccine Uptake 12+ --------------------------------------------

tx_cnty_data %>%
* ggplot()
```
]
 
.panel2-draw-viz-2-auto[
<img src="index_files/figure-html/draw-viz-2_auto_03_output-1.png" width="504" />
]

---
count: false
 
# A step by step view
.panel1-draw-viz-2-auto[

```r
# 1. Map of Vaccine Uptake 12+ --------------------------------------------  # 1. Map of Vaccine Uptake 12+ --------------------------------------------

tx_cnty_data %>%
  ggplot() +
* aes(fill = pct_vaccinated_eligible, color = pct_vaccinated_eligible)
```
]
 
.panel2-draw-viz-2-auto[
<img src="index_files/figure-html/draw-viz-2_auto_04_output-1.png" width="504" />
]

---
count: false
 
# A step by step view
.panel1-draw-viz-2-auto[

```r
# 1. Map of Vaccine Uptake 12+ --------------------------------------------  # 1. Map of Vaccine Uptake 12+ --------------------------------------------

tx_cnty_data %>%
  ggplot() +
  aes(fill = pct_vaccinated_eligible, color = pct_vaccinated_eligible) +
* geom_sf(size = 0.15)
```
]
 
.panel2-draw-viz-2-auto[
<img src="index_files/figure-html/draw-viz-2_auto_05_output-1.png" width="504" />
]

---
count: false
 
# A step by step view
.panel1-draw-viz-2-auto[

```r
# 1. Map of Vaccine Uptake 12+ --------------------------------------------  # 1. Map of Vaccine Uptake 12+ --------------------------------------------

tx_cnty_data %>%
  ggplot() +
  aes(fill = pct_vaccinated_eligible, color = pct_vaccinated_eligible) +
  geom_sf(size = 0.15) +
* scale_color_gradient_tableau(palette = "Blue", labels = scales::percent)
```
]
 
.panel2-draw-viz-2-auto[
<img src="index_files/figure-html/draw-viz-2_auto_06_output-1.png" width="504" />
]