Working Smarter, Not Harder

# Working Smarter, Not Harder
## Reproducible Analysis + Visualization with R
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### <strong>Matt Worthington</strong> | M.Ed./M.PAff
### Sr. Project Manager for Data Initiatives, LBJ School of Public Affairs
### August 19, 2021

---

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

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## .blue[Understanding R vs. RStudio...]

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.pull-right[
<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">src/ : folder for scripts<br>data/ : original data pulled from database<br>output/ : intermediate RDS data objects needed for Rmd<br>analysis/: Rmd files and HTML output<br>doc/ : any long-form notes to self or documentation<br>ext/: external images or other random files I want to keep in proj</p>&mdash; Emily Riederer (@EmilyRiederer) <a href="https://twitter.com/allison_horst/status/1304134105643118592?s=20">September 10, 2018</a></blockquote>
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
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# The Basics of a ggplot2 chart

```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_lbj() + # Add The CTP 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="lbj_r_skillshare_files/figure-html/fancy_diamonds-1.png" width="504" />
]
---

```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="lbj_r_skillshare_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="lbj_r_skillshare_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="lbj_r_skillshare_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="lbj_r_skillshare_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_lbj()   # Add The CTP Theme
```
]
 
.panel2-fancy_diamonds-auto[
<img src="lbj_r_skillshare_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_lbj() +  # Add The CTP Theme
* theme(plot.title = element_text(color = "#bf5700"))   # Modify Title's Color
```
]
 
.panel2-fancy_diamonds-auto[
<img src="lbj_r_skillshare_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_lbj() +  # Add The CTP 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="lbj_r_skillshare_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_lbj() +  # Add The CTP 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="lbj_r_skillshare_files/figure-html/fancy_diamonds_auto_11_output-1.png" width="504" />
]

---
# You Can Also Make Tables

```r
library(gt)

diamonds %>% 
  group_by(cut) %>% 
  summarise(mean_price=mean(price),
            median_price=median(price)) %>% 
  gt() %>% # Pass The Data To A gt table 
  tab_header(title = md("**Average Diamond Prices, by Cut**"),
             subtitle = "Averages calculated are the mean + median") %>%
  tab_source_note(
    source_note = "Data: diamonds | Source: ggplot2 package"
  ) %>% 
  fmt_currency(currency = "USD",
               columns=c("mean_price", "median_price")) %>% 
  cols_label(cut=md("**Cut**"),
             mean_price=md("**Mean Price**"),
             median_price=md("**Median Price"))
```
]

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</style>
<table class="gt_table">
  <thead class="gt_header">
    <tr>
      <th colspan="3" class="gt_heading gt_title gt_font_normal" style><strong>Average Diamond Prices, by Cut</strong></th>
    </tr>
    <tr>
      <th colspan="3" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>Averages calculated are the mean + median</th>
    </tr>
  </thead>
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Cut</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1"><strong>Mean Price</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1"><strong>Median Price</strong></th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_center">Fair</td>
<td class="gt_row gt_right">$4,358.76</td>
<td class="gt_row gt_right">$3,282.00</td></tr>
    <tr><td class="gt_row gt_center">Good</td>
<td class="gt_row gt_right">$3,928.86</td>
<td class="gt_row gt_right">$3,050.50</td></tr>
    <tr><td class="gt_row gt_center">Very Good</td>
<td class="gt_row gt_right">$3,981.76</td>
<td class="gt_row gt_right">$2,648.00</td></tr>
    <tr><td class="gt_row gt_center">Premium</td>
<td class="gt_row gt_right">$4,584.26</td>
<td class="gt_row gt_right">$3,185.00</td></tr>
    <tr><td class="gt_row gt_center">Ideal</td>
<td class="gt_row gt_right">$3,457.54</td>
<td class="gt_row gt_right">$1,810.00</td></tr>
  </tbody>
  <tfoot class="gt_sourcenotes">
    <tr>
      <td class="gt_sourcenote" colspan="3">Data: diamonds | Source: ggplot2 package</td>
    </tr>
  </tfoot>
  
</table>
</div>
]

---

# Spatial Work in R

```r
library(tidycensus)

racevars <- c(White = "P005003",
              Black = "P005004",
              Asian = "P005006",
              Hispanic = "P004003")

harris <- get_decennial(geography = "tract", variables = racevars,
                  state = "TX", county = "Harris County", geometry = TRUE,
                  summary_var = "P001001")

harris %>%
  mutate(pct = 100 * (value / summary_value)) %>%
  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="Demographics in Harris County",
       subtitle="US Census Bureau | 2018 ACS 5-Yr Series")
```
]

]

---

# Interactive Charts in R

<div id="htmlwidget-de849ec95b61a2bc3be3" style="width:100%;height:500px;" class="highchart html-widget"></div>
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---

# Load Packages

```r
options(htmltools.dir.version = FALSE)
library(tidyverse)        # What loads the core set of Modern R Packages, like ggplot2
library(tidycensus)       # Tidyverse-friendly connection to Census API
library(janitor)          # Really useful functions for cleaning data
library(lubridate)        # Makes Working With Dates Pretty Easy
library(ggtext)           # Incredibl text features for ggplot2 objects
library(cowplot)          # Helps with arranging and layering of ggplot2 objects 
readRenviron("~/.Renviron")
census_api_key <- Sys.getenv("CENSUS_API_KEY")
```

* [tidyverse](https://www.tidyverse.org)
* [tidycensus](https://walker-data.com/tidycensus/)
* [janitor](http://sfirke.github.io/janitor/)
* [lubridate](https://lubridate.tidyverse.org)
* [ggtext](https://wilkelab.org/ggtext/)           
* [cowplot](https://wilkelab.org/cowplot/index.html)

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

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

---

# Xaringan, xaringanExtra, + flipbookr

These slides are mostly made with three R packages called [`xaringan`](https://github.com/yihui/xaringan), [`xaringanExtra`](https://pkg.garrickadenbuie.com/xaringanExtra/#/), and [`flipbookr`](https://github.com/EvaMaeRey/flipbookr) (which generates the step-by-step code and organizes it alongside the output).

I'm not really going to go into all the things each of these packages can do, but I hope the slides speak to how great the packages are! If you have R + RStudio installed on your laptop, you can install these packages directly from Github by pasting this in your RStudio console:

```r
# install.packages("devtools")
devtools::install_github("yihui/xaringan")
devtools::install_github("gadenbuie/xaringanExtra")
devtools::install_github("EvaMaeRey/flipbookr")
```

Also, the interactive chart was made with one of my favorite R packages called [`highcharter`](https://jkunst.com/highcharter/) which is an amazing R wrapper for the highcharts.js library.