CS&SS 569 Visualizing Data and Models

.title[
# CS&SS 569 Visualizing Data and Models
]
.subtitle[
## Lab 6: Visualizing spatial data
]
.author[
### Brian Leung
]
.institute[
### Department of Political Science, UW
]
.date[
### 2024-02-09
]

---

## Introduction

- A long rant on space: What do we mean when we say visualizing "spatial data"?

1. Physical world

- Physical, spatial features: geographical area, features, contours, etc

2. Social world

- Some socio-economic-political variables that could be mapped onto physical, geographical features 
  
  - E.g., district-level income level, crime rate, education spending, etc
  
--

3. Graphical space as dimension(s) to express some variables

- E.g., x and y coordinates on our 2D canvas

---

## Introduction

- We can project (1) physical space onto (3) graphical space
  
  - Traditional understanding of "map"
  
--

- We can project (2) social variables directly onto (3) graphical space

- Bubble plot uses size/area to express magnitude; bar plot uses length to express magnitude

- Problems start to occur when we project (2) social variables onto (1) physical space, then onto (3) graphical space

- Let's say we want to visualize some district-level covariates

- Problem 1: incongruity b/w social space and physical space 
  
  - We visualize district-level variables onto the whole WA state; too much extrapolation
    
--
  
  - Problem 2: why do we prioritize physical space? 
  
  - E.g., why a vast piece of land w/ little people should be prioritized over a small but densely populated area?
  
  - Can we use variables other than physical space to mediate (2) and (3)?

---

## (In)famous US election map...

---

## ... inspires many advances in visualizing spatial data

---

## But the problem is more common than you think...

---

## Popular ways to visualize spatial data: Choropleth map

---

## Popular ways to visualize spatial data: Choropleth map w/ small multiples

---

## Popular ways to visualize spatial data: Geo-faceted small multiples

---

## Popular ways to visualize spatial data: Cartogram heatmap

---

## Popular ways to visualize spatial data: Tiled cartogram

---

## Roadmap

- Cover different ways to visualize spatial data:

- Geo-faceted small multiples w/ package **`geofacet`**

- Cartogram heatmap w/ package **`statebins`**

- Tiled cartogram w/ package **`tilegramsR`**

- Choropleth maps (a.k.a. "maps'')

- Brief introduction to spatial data file (e.g., shapefile; `.shp`)

- package **`sf`** to wrangle spatial data and **`tmap`** to visualize map

- Interactive maps; small multiples in choropleth maps

- Be mindful of the issues of space(s) and the curse of dimensionality

- How to incorporate time series or extra covariates when visualizing spatial data?

---

## Prerequisite

```r
# download packages 
packages <- c("tidyverse", "geofacet", "statebins", "tilegramsR", "sf", "tmap", "tigris")
not_installed <- setdiff(packages, rownames(installed.packages()))
if (length(not_installed)) install.packages(not_installed)

# load packages
library(tidyverse)
library(geofacet)
library(statebins)
library(tilegramsR)
library(sf)
library(tmap)
library(tigris)

# load theme
source("https://students.washington.edu/kpleung/vis/theme_cavis.R")
```

---

```r
*WaPo_data <- read_csv("data/WaPo_data.csv")
```
]
 
.panel2-geofacet1-auto[

]

---
count: false
 
## Geo-faceted small multiples
.panel1-geofacet1-auto[

```r
WaPo_data <- read_csv("data/WaPo_data.csv")

*print(WaPo_data, n = 5)
```
]
 
.panel2-geofacet1-auto[

```
# A tibble: 51 × 23
  stab  state          share_cut1994 share_cut1995 share_cut1996 share_cut1997
  <chr> <chr>                  <dbl>         <dbl>         <dbl>         <dbl>
1 NC    North Carolina           1.4           0.9           3             4.1
2 AL    Alabama                  1.1           2.4           5.2           4.6
3 MS    Mississippi              2             3.9           5.1           2.2
4 AR    Arkansas                 1.6           1.3           1.6           2.7
5 TN    Tennessee                1.6           2             3.4           3.5
# ℹ 46 more rows
# ℹ 17 more variables: share_cut1998 <dbl>, share_cut1999 <dbl>,
#   share_cut2000 <dbl>, share_cut2001 <dbl>, share_cut2002 <dbl>,
#   share_cut2003 <dbl>, share_cut2004 <dbl>, share_cut2005 <dbl>,
#   share_cut2006 <dbl>, share_cut2007 <dbl>, share_cut2008 <dbl>,
#   share_cut2009 <dbl>, share_cut2010 <dbl>, share_cut2011 <dbl>,
#   share_cut2012 <dbl>, share_cut2013 <dbl>, avgshare <dbl>
```
]

---

```r
# convert the data into long format
*WaPo_data
```
]
 
.panel2-geofacet2-auto[

```
# A tibble: 51 × 23
   stab  state          share_cut1994 share_cut1995 share_cut1996 share_cut1997
   <chr> <chr>                  <dbl>         <dbl>         <dbl>         <dbl>
 1 NC    North Carolina           1.4           0.9           3             4.1
 2 AL    Alabama                  1.1           2.4           5.2           4.6
 3 MS    Mississippi              2             3.9           5.1           2.2
 4 AR    Arkansas                 1.6           1.3           1.6           2.7
 5 TN    Tennessee                1.6           2             3.4           3.5
 6 SC    South Carolina           1.5           1             2.5           0.7
 7 ME    Maine                    2.3           1.9           3.5           1.7
 8 MI    Michigan                 0.3           0.4           1.1           0.9
 9 OR    Oregon                   0.4           0.7           1.3           0.8
10 KY    Kentucky                 2.5           1             3.1           1.8
# ℹ 41 more rows
# ℹ 17 more variables: share_cut1998 <dbl>, share_cut1999 <dbl>,
#   share_cut2000 <dbl>, share_cut2001 <dbl>, share_cut2002 <dbl>,
#   share_cut2003 <dbl>, share_cut2004 <dbl>, share_cut2005 <dbl>,
#   share_cut2006 <dbl>, share_cut2007 <dbl>, share_cut2008 <dbl>,
#   share_cut2009 <dbl>, share_cut2010 <dbl>, share_cut2011 <dbl>,
#   share_cut2012 <dbl>, share_cut2013 <dbl>, avgshare <dbl>
```
]

---
count: false
 
## Geo-faceted small multiples
.panel1-geofacet2-auto[

```r
# convert the data into long format
WaPo_data %>%
* select(-avgshare)
```
]
 
.panel2-geofacet2-auto[

```
# A tibble: 51 × 22
   stab  state          share_cut1994 share_cut1995 share_cut1996 share_cut1997
   <chr> <chr>                  <dbl>         <dbl>         <dbl>         <dbl>
 1 NC    North Carolina           1.4           0.9           3             4.1
 2 AL    Alabama                  1.1           2.4           5.2           4.6
 3 MS    Mississippi              2             3.9           5.1           2.2
 4 AR    Arkansas                 1.6           1.3           1.6           2.7
 5 TN    Tennessee                1.6           2             3.4           3.5
 6 SC    South Carolina           1.5           1             2.5           0.7
 7 ME    Maine                    2.3           1.9           3.5           1.7
 8 MI    Michigan                 0.3           0.4           1.1           0.9
 9 OR    Oregon                   0.4           0.7           1.3           0.8
10 KY    Kentucky                 2.5           1             3.1           1.8
# ℹ 41 more rows
# ℹ 16 more variables: share_cut1998 <dbl>, share_cut1999 <dbl>,
#   share_cut2000 <dbl>, share_cut2001 <dbl>, share_cut2002 <dbl>,
#   share_cut2003 <dbl>, share_cut2004 <dbl>, share_cut2005 <dbl>,
#   share_cut2006 <dbl>, share_cut2007 <dbl>, share_cut2008 <dbl>,
#   share_cut2009 <dbl>, share_cut2010 <dbl>, share_cut2011 <dbl>,
#   share_cut2012 <dbl>, share_cut2013 <dbl>
```
]

---
count: false
 
## Geo-faceted small multiples
.panel1-geofacet2-auto[

```r
# convert the data into long format
WaPo_data %>%
  select(-avgshare) %>%
* pivot_longer(contains("share_cut"), names_to = "year", values_to = "share_cut")
```
]
 
.panel2-geofacet2-auto[

```
# A tibble: 1,020 × 4
   stab  state          year          share_cut
   <chr> <chr>          <chr>             <dbl>
 1 NC    North Carolina share_cut1994       1.4
 2 NC    North Carolina share_cut1995       0.9
 3 NC    North Carolina share_cut1996       3  
 4 NC    North Carolina share_cut1997       4.1
 5 NC    North Carolina share_cut1998       3  
 6 NC    North Carolina share_cut1999       3.9
 7 NC    North Carolina share_cut2000       1.5
 8 NC    North Carolina share_cut2001       3.8
 9 NC    North Carolina share_cut2002       6.1
10 NC    North Carolina share_cut2003       6.7
# ℹ 1,010 more rows
```
]

---
count: false
 
## Geo-faceted small multiples
.panel1-geofacet2-auto[

```r
# convert the data into long format
WaPo_data %>%
  select(-avgshare) %>%
  pivot_longer(contains("share_cut"), names_to = "year", values_to = "share_cut") %>%
* mutate(year = str_extract(year, "\\d+"),
*        year = as.numeric(year))
```
]
 
.panel2-geofacet2-auto[

```
# A tibble: 1,020 × 4
   stab  state           year share_cut
   <chr> <chr>          <dbl>     <dbl>
 1 NC    North Carolina  1994       1.4
 2 NC    North Carolina  1995       0.9
 3 NC    North Carolina  1996       3  
 4 NC    North Carolina  1997       4.1
 5 NC    North Carolina  1998       3  
 6 NC    North Carolina  1999       3.9
 7 NC    North Carolina  2000       1.5
 8 NC    North Carolina  2001       3.8
 9 NC    North Carolina  2002       6.1
10 NC    North Carolina  2003       6.7
# ℹ 1,010 more rows
```
]

---
count: false
 
## Geo-faceted small multiples
.panel1-geofacet2-auto[

```r
# convert the data into long format
WaPo_data %>%
  select(-avgshare) %>%
  pivot_longer(contains("share_cut"), names_to = "year", values_to = "share_cut") %>%
  mutate(year = str_extract(year, "\\d+"),
         year = as.numeric(year)) ->
* WaPo_data_long
```
]
 
.panel2-geofacet2-auto[

]

---

```r
*ggplot(WaPo_data_long, aes(x = year , y = share_cut))
```
]
 
.panel2-geofacet3-auto[
![](lab6_slides_files/figure-html/geofacet3_auto_01_output-1.png)
]

---
count: false
 
## Geo-faceted small multiples
.panel1-geofacet3-auto[

```r
ggplot(WaPo_data_long, aes(x = year , y = share_cut)) +
* theme_cavis_hgrid
```
]
 
.panel2-geofacet3-auto[
![](lab6_slides_files/figure-html/geofacet3_auto_02_output-1.png)
]

---
count: false
 
## Geo-faceted small multiples
.panel1-geofacet3-auto[

```r
ggplot(WaPo_data_long, aes(x = year , y = share_cut)) +
  theme_cavis_hgrid +
* geom_line()
```
]
 
.panel2-geofacet3-auto[
![](lab6_slides_files/figure-html/geofacet3_auto_03_output-1.png)
]

---
count: false
 
## Geo-faceted small multiples
.panel1-geofacet3-auto[

```r
ggplot(WaPo_data_long, aes(x = year , y = share_cut)) +
  theme_cavis_hgrid +
  geom_line() +
* facet_wrap(~ state)
```
]
 
.panel2-geofacet3-auto[
![](lab6_slides_files/figure-html/geofacet3_auto_04_output-1.png)
]

---
count: false
 
## Geo-faceted small multiples
.panel1-geofacet3-auto[

```r
ggplot(WaPo_data_long, aes(x = year , y = share_cut)) +
  theme_cavis_hgrid +
  geom_line() +
  facet_wrap(~ state) +
* scale_x_continuous(
*   labels = function(x) paste0("'", str_sub(x, 3, 4))
* )
```
]
 
.panel2-geofacet3-auto[
![](lab6_slides_files/figure-html/geofacet3_auto_05_output-1.png)
]

---
count: false
 
## Geo-faceted small multiples
.panel1-geofacet3-auto[

```r
ggplot(WaPo_data_long, aes(x = year , y = share_cut)) +
  theme_cavis_hgrid +
  geom_line() +
  facet_wrap(~ state) +
  scale_x_continuous(
    labels = function(x) paste0("'", str_sub(x, 3, 4))
  ) +
* scale_y_continuous(
*   labels = function(x) paste0(x, "%")
* )
```
]
 
.panel2-geofacet3-auto[
![](lab6_slides_files/figure-html/geofacet3_auto_06_output-1.png)
]

---
count: false
 
## Geo-faceted small multiples
.panel1-geofacet3-auto[

```r
ggplot(WaPo_data_long, aes(x = year , y = share_cut)) +
  theme_cavis_hgrid +
  geom_line() +
  facet_wrap(~ state) +
  scale_x_continuous(
    labels = function(x) paste0("'", str_sub(x, 3, 4))
  ) +
  scale_y_continuous(
    labels = function(x) paste0(x, "%")
  ) +
* theme(
*   strip.text.x = element_text(size = 7),
*   axis.text.x = element_text(size = 7),
*   axis.text.y = element_text(size = 7)
* )
```
]
 
.panel2-geofacet3-auto[
![](lab6_slides_files/figure-html/geofacet3_auto_07_output-1.png)
]

---
count: false
 
## Geo-faceted small multiples
.panel1-geofacet3-auto[

```r
ggplot(WaPo_data_long, aes(x = year , y = share_cut)) +
  theme_cavis_hgrid +
  geom_line() +
  facet_wrap(~ state) +
  scale_x_continuous(
    labels = function(x) paste0("'", str_sub(x, 3, 4))
  ) +
  scale_y_continuous(
    labels = function(x) paste0(x, "%")
  ) +
  theme(
    strip.text.x = element_text(size = 7),
    axis.text.x = element_text(size = 7),
    axis.text.y = element_text(size = 7)
  ) +
* labs(x = NULL, y = "Share of job loss due to trade")
```
]
 
.panel2-geofacet3-auto[
![](lab6_slides_files/figure-html/geofacet3_auto_08_output-1.png)
]

---

```r
ggplot(WaPo_data_long, aes(x = year , y = share_cut)) +
  theme_cavis_hgrid +
  geom_line() +
* facet_geo(~ state, grid = "us_state_grid2") +
  scale_x_continuous(
    labels = function(x) paste0("'", str_sub(x, 3, 4))
  ) +
  scale_y_continuous(
    labels = function(x) paste0(x, "%")
  ) +
  theme(
    strip.text.x = element_text(size = 7),
    axis.text.x = element_text(size = 7),
    axis.text.y = element_text(size = 7)
  ) +
  labs(x = NULL, y = "Share of job loss due to trade")
```
]
 
.panel2-geofacet3-rotate[
![](lab6_slides_files/figure-html/geofacet3_rotate_01_output-1.png)
]

---
count: false
 
## Geo-faceted small multiples
.panel1-geofacet3-rotate[

```r
ggplot(WaPo_data_long, aes(x = year , y = share_cut)) +
  theme_cavis_hgrid +
  geom_line() +
* facet_geo(~ state, grid = "us_state_grid1") +
  scale_x_continuous(
    labels = function(x) paste0("'", str_sub(x, 3, 4))
  ) +
  scale_y_continuous(
    labels = function(x) paste0(x, "%")
  ) +
  theme(
    strip.text.x = element_text(size = 7),
    axis.text.x = element_text(size = 7),
    axis.text.y = element_text(size = 7)
  ) +
  labs(x = NULL, y = "Share of job loss due to trade")
```
]
 
.panel2-geofacet3-rotate[
![](lab6_slides_files/figure-html/geofacet3_rotate_02_output-1.png)
]

---

## Geo-faceted small multiples

- Check out the [available grids](https://hafen.github.io/geofacet/articles/geofacet.html) (users submitted)

- You can build your own grid using [Geo Grid Designer](https://hafen.github.io/grid-designer/) and make it a public good

---

## Cartogram Heatmaps

---

count: false
 
## Cartogram heatmaps with statebins package
.panel1-statebins1-auto[

```r
# coerce avgshare into a factor
*WaPo_data
```
]
 
.panel2-statebins1-auto[

```
# A tibble: 51 × 23
   stab  state          share_cut1994 share_cut1995 share_cut1996 share_cut1997
   <chr> <chr>                  <dbl>         <dbl>         <dbl>         <dbl>
 1 NC    North Carolina           1.4           0.9           3             4.1
 2 AL    Alabama                  1.1           2.4           5.2           4.6
 3 MS    Mississippi              2             3.9           5.1           2.2
 4 AR    Arkansas                 1.6           1.3           1.6           2.7
 5 TN    Tennessee                1.6           2             3.4           3.5
 6 SC    South Carolina           1.5           1             2.5           0.7
 7 ME    Maine                    2.3           1.9           3.5           1.7
 8 MI    Michigan                 0.3           0.4           1.1           0.9
 9 OR    Oregon                   0.4           0.7           1.3           0.8
10 KY    Kentucky                 2.5           1             3.1           1.8
# ℹ 41 more rows
# ℹ 17 more variables: share_cut1998 <dbl>, share_cut1999 <dbl>,
#   share_cut2000 <dbl>, share_cut2001 <dbl>, share_cut2002 <dbl>,
#   share_cut2003 <dbl>, share_cut2004 <dbl>, share_cut2005 <dbl>,
#   share_cut2006 <dbl>, share_cut2007 <dbl>, share_cut2008 <dbl>,
#   share_cut2009 <dbl>, share_cut2010 <dbl>, share_cut2011 <dbl>,
#   share_cut2012 <dbl>, share_cut2013 <dbl>, avgshare <dbl>
```
]

---
count: false
 
## Cartogram heatmaps with statebins package
.panel1-statebins1-auto[

```r
# coerce avgshare into a factor
WaPo_data %>%
* select(stab, state, avgshare)
```
]
 
.panel2-statebins1-auto[

```
# A tibble: 51 × 3
   stab  state          avgshare
   <chr> <chr>             <dbl>
 1 NC    North Carolina     3.56
 2 AL    Alabama            3.22
 3 MS    Mississippi        2.94
 4 AR    Arkansas           2.89
 5 TN    Tennessee          2.86
 6 SC    South Carolina     2.61
 7 ME    Maine              2.58
 8 MI    Michigan           2.55
 9 OR    Oregon             2.38
10 KY    Kentucky           2.24
# ℹ 41 more rows
```
]

---
count: false
 
## Cartogram heatmaps with statebins package
.panel1-statebins1-auto[

```r
# coerce avgshare into a factor
WaPo_data %>%
  select(stab, state, avgshare) %>%
* mutate(avgshare2 = cut(avgshare,
*                        breaks = 4,
*                        labels = c("0-1", "1-2", "2-3", "3-4")))
```
]
 
.panel2-statebins1-auto[

```
# A tibble: 51 × 4
   stab  state          avgshare avgshare2
   <chr> <chr>             <dbl> <fct>    
 1 NC    North Carolina     3.56 3-4      
 2 AL    Alabama            3.22 3-4      
 3 MS    Mississippi        2.94 3-4      
 4 AR    Arkansas           2.89 3-4      
 5 TN    Tennessee          2.86 3-4      
 6 SC    South Carolina     2.61 2-3      
 7 ME    Maine              2.58 2-3      
 8 MI    Michigan           2.55 2-3      
 9 OR    Oregon             2.38 2-3      
10 KY    Kentucky           2.24 2-3      
# ℹ 41 more rows
```
]

---
count: false
 
## Cartogram heatmaps with statebins package
.panel1-statebins1-auto[

```r
# coerce avgshare into a factor
WaPo_data %>%
  select(stab, state, avgshare) %>%
  mutate(avgshare2 = cut(avgshare,
                         breaks = 4,
                         labels = c("0-1", "1-2", "2-3", "3-4"))) ->
* WaPo_data2
```
]
 
.panel2-statebins1-auto[

]

---

count: false
 
## Cartogram heatmaps with statebins package
.panel1-statebins2-non_seq[

```r
WaPo_data2
```
]
 
.panel2-statebins2-non_seq[

---
count: false
 
## Cartogram heatmaps with statebins package
.panel1-statebins2-non_seq[

```r
WaPo_data2 %>%
* statebins(
*   value_col = "avgshare2",
*   ggplot2_scale_function = scale_fill_brewer,
* )
```
]
 
.panel2-statebins2-non_seq[
![](lab6_slides_files/figure-html/statebins2_non_seq_02_output-1.png)
]

---
count: false
 
## Cartogram heatmaps with statebins package
.panel1-statebins2-non_seq[

```r
WaPo_data2 %>%
  statebins(
    value_col = "avgshare2",
    ggplot2_scale_function = scale_fill_brewer,
  ) +
* theme_statebins()
```
]
 
.panel2-statebins2-non_seq[
![](lab6_slides_files/figure-html/statebins2_non_seq_03_output-1.png)
]

---
count: false
 
## Cartogram heatmaps with statebins package
.panel1-statebins2-non_seq[

```r
WaPo_data2 %>%
  statebins(
    value_col = "avgshare2",
    ggplot2_scale_function = scale_fill_brewer,
*   round = TRUE
  ) +
  theme_statebins()
```
]
 
.panel2-statebins2-non_seq[
![](lab6_slides_files/figure-html/statebins2_non_seq_04_output-1.png)
]

---
count: false
 
## Cartogram heatmaps with statebins package
.panel1-statebins2-non_seq[

```r
WaPo_data2 %>%
  statebins(
    value_col = "avgshare2",
    ggplot2_scale_function = scale_fill_brewer,
    round = TRUE
  ) +
  theme_statebins() +
* scale_fill_brewer(
*   palette = "Blues",
*   labels = c("Smallest\nshare", "", "", "Largest"),
*   name = "Share of job loss due to trade")
```
]
 
.panel2-statebins2-non_seq[
![](lab6_slides_files/figure-html/statebins2_non_seq_05_output-1.png)
]

---
count: false
 
## Cartogram heatmaps with statebins package
.panel1-statebins2-non_seq[

```r
WaPo_data2 %>%
  statebins(
    value_col = "avgshare2",
    ggplot2_scale_function = scale_fill_brewer,
    round = TRUE
  ) +
  theme_statebins() +
  scale_fill_brewer(
    palette = "Blues",
    labels = c("Smallest\nshare", "", "", "Largest"),
    name = "Share of job loss due to trade") +
* guides(fill = guide_legend(title.position = "top",
*                            label.position = "bottom"))
```
]
 
.panel2-statebins2-non_seq[
![](lab6_slides_files/figure-html/statebins2_non_seq_06_output-1.png)
]

---
count: false
 
## Cartogram heatmaps with statebins package
.panel1-statebins2-non_seq[

```r
WaPo_data2 %>%
  statebins(
    value_col = "avgshare2",
    ggplot2_scale_function = scale_fill_brewer,
    round = TRUE
  ) +
  theme_statebins() +
  scale_fill_brewer(
    palette = "Blues",
    labels = c("Smallest\nshare", "", "", "Largest"),
    name = "Share of job loss due to trade") +
  guides(fill = guide_legend(title.position = "top",
                             label.position = "bottom")) +
* theme(legend.position = c(0, 0.85),
*       legend.direction = "horizontal",
*       )
```
]
 
.panel2-statebins2-non_seq[
![](lab6_slides_files/figure-html/statebins2_non_seq_07_output-1.png)
]

---
count: false
 
## Cartogram heatmaps with statebins package
.panel1-statebins2-non_seq[

```r
WaPo_data2 %>%
  statebins(
    value_col = "avgshare2",
    ggplot2_scale_function = scale_fill_brewer,
    round = TRUE
  ) +
  theme_statebins() +
  scale_fill_brewer(
    palette = "Blues",
    labels = c("Smallest\nshare", "", "", "Largest"),
    name = "Share of job loss due to trade") +
  guides(fill = guide_legend(title.position = "top",
                             label.position = "bottom")) +
  theme(legend.position = c(0, 0.85),
        legend.direction = "horizontal",
*       legend.key.height = unit(0.2, "cm"),
*       legend.key.width = unit(1.6, "cm")
        )
```
]
 
.panel2-statebins2-non_seq[
![](lab6_slides_files/figure-html/statebins2_non_seq_08_output-1.png)
]

---

## Cartogram heatmaps with statebins package

- The **`statebins`** package only supports the United States data at the moment

- But customizing grid should be easy to implement, like **`geofacet`** package?

---

## Tiled cartogram with tilegramsR package

---

count: false
 
## Tiled cartogram with tilegramsR package
.panel1-tileCartogram1-auto[

```r
*ec_tiles <- tilegramsR::sf_FiveThirtyEightElectoralCollege
```
]
 
.panel2-tileCartogram1-auto[

]

---
count: false
 
## Tiled cartogram with tilegramsR package
.panel1-tileCartogram1-auto[

```r
ec_tiles <- tilegramsR::sf_FiveThirtyEightElectoralCollege
*class(ec_tiles)  # more on sf obejct soon
```
]
 
.panel2-tileCartogram1-auto[

```
[1] "sf"         "data.frame"
```
]

---
count: false
 
## Tiled cartogram with tilegramsR package
.panel1-tileCartogram1-auto[

```r
ec_tiles <- tilegramsR::sf_FiveThirtyEightElectoralCollege
class(ec_tiles)  # more on sf obejct soon

*ec_tiles
```
]
 
.panel2-tileCartogram1-auto[

```
[1] "sf"         "data.frame"
```

```
Simple feature collection with 538 features and 3 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 293.6239 ymin: 16.95238 xmax: 2495.803 ymax: 1661.333
CRS:           NA
First 10 features:
   FID state tilegramVa                       geometry
1   02    AK          3 POLYGON ((293.6239 237.3333...
2   06    CA         55 POLYGON ((352.3486 847.619,...
3   06    CA         55 POLYGON ((322.9862 796.7619...
4   06    CA         55 POLYGON ((352.3486 745.9048...
5   06    CA         55 POLYGON ((322.9862 695.0476...
6   06    CA         55 POLYGON ((352.3486 644.1905...
7   02    AK          3 POLYGON ((322.9862 288.1905...
8   02    AK          3 POLYGON ((352.3486 237.3333...
9   06    CA         55 POLYGON ((411.0734 949.3333...
10  06    CA         55 POLYGON ((381.711 898.4762,...
```
]

---
count: false
 
## Tiled cartogram with tilegramsR package
.panel1-tileCartogram1-auto[

```r
ec_tiles <- tilegramsR::sf_FiveThirtyEightElectoralCollege
class(ec_tiles)  # more on sf obejct soon

ec_tiles %>%
* left_join(WaPo_data2, by = c("state" = "stab"))
```
]
 
.panel2-tileCartogram1-auto[

```
[1] "sf"         "data.frame"
```

```
Simple feature collection with 538 features and 6 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 293.6239 ymin: 16.95238 xmax: 2495.803 ymax: 1661.333
CRS:           NA
First 10 features:
   FID state tilegramVa    state.y avgshare avgshare2
1   02    AK          3     Alaska     1.88       2-3
2   06    CA         55 California     0.72       0-1
3   06    CA         55 California     0.72       0-1
4   06    CA         55 California     0.72       0-1
5   06    CA         55 California     0.72       0-1
6   06    CA         55 California     0.72       0-1
7   02    AK          3     Alaska     1.88       2-3
8   02    AK          3     Alaska     1.88       2-3
9   06    CA         55 California     0.72       0-1
10  06    CA         55 California     0.72       0-1
                         geometry
1  POLYGON ((293.6239 237.3333...
2  POLYGON ((352.3486 847.619,...
3  POLYGON ((322.9862 796.7619...
4  POLYGON ((352.3486 745.9048...
5  POLYGON ((322.9862 695.0476...
6  POLYGON ((352.3486 644.1905...
7  POLYGON ((322.9862 288.1905...
8  POLYGON ((352.3486 237.3333...
9  POLYGON ((411.0734 949.3333...
10 POLYGON ((381.711 898.4762,...
```
]

---
count: false
 
## Tiled cartogram with tilegramsR package
.panel1-tileCartogram1-auto[

```r
ec_tiles <- tilegramsR::sf_FiveThirtyEightElectoralCollege
class(ec_tiles)  # more on sf obejct soon

ec_tiles %>%
  left_join(WaPo_data2, by = c("state" = "stab")) ->
* ec_tiles
```
]
 
.panel2-tileCartogram1-auto[

```
[1] "sf"         "data.frame"
```
]

---
count: false
 
## Tiled cartogram with tilegramsR package
.panel1-tileCartogram1-auto[

```r
ec_tiles <- tilegramsR::sf_FiveThirtyEightElectoralCollege
class(ec_tiles)  # more on sf obejct soon

ec_tiles %>%
  left_join(WaPo_data2, by = c("state" = "stab")) ->
  ec_tiles

*state_tiles <- sf_FiveThirtyEightElectoralCollege.states  # state polygons for drawing borders
```
]
 
.panel2-tileCartogram1-auto[

```
[1] "sf"         "data.frame"
```
]

---
count: false
 
## Tiled cartogram with tilegramsR package
.panel1-tileCartogram1-auto[

```r
ec_tiles <- tilegramsR::sf_FiveThirtyEightElectoralCollege
class(ec_tiles)  # more on sf obejct soon

ec_tiles %>%
  left_join(WaPo_data2, by = c("state" = "stab")) ->
  ec_tiles

state_tiles <- sf_FiveThirtyEightElectoralCollege.states  # state polygons for drawing borders
*ec_centers <- sf_FiveThirtyEightElectoralCollege.centers  # state centroids for drawing labels
```
]
 
.panel2-tileCartogram1-auto[

```
[1] "sf"         "data.frame"
```
]

---

count: false
 
## Tiled cartogram with tilegramsR package
.panel1-tileCartogram2-non_seq[

```r
ggplot() +
  theme_void()
```
]
 
.panel2-tileCartogram2-non_seq[
![](lab6_slides_files/figure-html/tileCartogram2_non_seq_01_output-1.png)
]