Maps with ggplot2 and sf

# Maps with ggplot2 and sf
### Data Visualization for Social Good <a href='https://correlaid.org/correlaid-x/switzerland/'> CorrelAid Switzerland </a> <a href='https://correlaidswitzerland.github.io/DataViz4Good/'> </a>  <a href='https://correlaid.org/correlaid-x/switzerland/'> </a>  <a href='switzerland@correlaid.org'> </a>  <a href='https://www.linkedin.com/company/correlaidswiss/'> </a>
### February 2021

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 </a>
 <a href="https://correlaid.org/correlaid-x/switzerland/">
 
 Data Visualization for Social Good | February 2021
 
 </a>
 
 </div>

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# Maps with <mono>ggplot</mono>/<mono>sf</mono>

<ul>
 <li class="m1">Maps require geometric shapes stored in <high>shapefiles</high>.</li> 
 <li class="m2">The <high>simple features</high> (<mono>sf</mono>) framework makes processing and visualizing maps with the <mono>tidyverse<mono> easy.</li>
</ul>

]

]

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# Shapefiles

<ul>
 <li class="m1">Geospatial vector data format for <high>geographic information system</high> (GIS) software.</li> 
 <li class="m2">Necessary files:</li>
 <ul class="level">
 <li><mono>.shp</mono> | actual shapefile</li>
 <li><mono>.shx</mono> | shape index format</li>
 <li><mono>.dbf</mono> | attribute format</li>
 </ul> 
 <li class="m3">Optional files:</li>
 <ul class="level">
 <li><mono>.prj</mono> | projection description</li>
 <li><mono>.cpg</mono> | code page specification</li>
 </ul>
</ul>

]

---

# <mono>sf</mono>

```r
read_sf('1_Data/quarters') 
```

```
Simple feature collection with 21 features and 5 fields
geometry type: POLYGON
dimension: XY
bbox: xmin: 2609000 ymin: 1263000 xmax: 2619000 ymax: 1272000
projected CRS: CH1903+ / LV95
# A tibble: 21 x 6
 OBJID OBJECTID TXT ZTXT TYPE geometry
 <chr> <dbl> <chr> <chr> <chr> <POLYGON [m]>
 1 17136 1 7 07 Bruderholz ((2612556 1264548, 2612561 1264514, 2612565 1264482, 261…
 2 17139 2 6 06 Gundeldingen ((2610887 1266551, 2610896 1266546, 2610918 1266533, 261…
 3 17142 3 5 05 St. Alban ((2612942 1267023, 2613000 1267019, 2613027 1267020, 261…
 4 17145 4 4 04 Breite ((2613684 1266891, 2613686 1266889, 2613689 1266887, 261…
 5 17148 5 8 08 Bachletten ((2610561 1266791, 2610571 1266781, 2610595 1266757, 261…
 6 17151 6 2 02 Vorstädte ((2610928 1268323, 2610934 1268293, 2610947 1268297, 261…
 7 17154 7 1 01 Altstadt Gr… ((2611366 1267578, 2611375 1267568, 2611376 1267569, 261…
 8 17157 8 3 03 Am Ring ((2610705 1267923, 2610680 1267844, 2610647 1267740, 261…
 9 17160 9 9 09 Gotthelf ((2609245 1266802, 2609236 1266801, 2609366 1267143, 260…
10 17163 10 10 10 Iselin ((2610234 1267656, 2610284 1267389, 2610234 1267403, 261…
# … with 11 more rows
```

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# <mono>geom_sf</mono>

<ul>
 <li class="m1">Since <mono>read_sf</mono> creates a <mono>tibble</tibble>, it can be plugged <high>straight into <mono>ggplot</mono></high>.</li> 
 <li class="m2">The dedicated geom <mono>geom_sf</mono> plots the geometric polygons.</li>
</ul>

```r
# read shapefiles
quarters_map <- read_sf('1_Data/quarters')

# plot quarters
quarters_map %>% 
  ggplot() +
  geom_sf()
```

]

]

---

# <mono>geom_sf</mono>

```r
# read shapefiles
quarters_map <- read_sf('1_Data/quarters')

# plot quarters
quarters_map %>% 
  ggplot() +
  geom_sf() + 
  
  # remove background
  theme_void()
```

]

]

---

# Wrangling

<ul>
 <li class="m1">Since <mono>read_sf</mono> creates a <mono>tibble</tibble>, one can easily <high>join additional data</high>.</li>
</ul>

```r
# join basel tax data
quarters_map <- quarters_map %>% 
 left_join(filter(basel, year == 2017), 
 by = c("TYPE" = "quarter"))

# plot quarters
quarters_map %>% 
  ggplot() +
  geom_sf() + 
  theme_void()
```

]

]

---

# Add color

<ul>
 <li class="m1">Colors are set using <mono>aes()</mono> just like in a regular <mono>ggplot</mono>.</li>
</ul>

```r
# join basel tax data
quarters_map <- quarters_map %>% 
 left_join(filter(basel, year == 2017), 
 by = c("TYPE" = "quarter"))

# plot quarters
quarters_map %>% 
  ggplot() +
  
  # fill color by income
  geom_sf(aes(fill = income_mean)) + 
  theme_void()
```

]

]

---

# Styling

<ul>
 <li class="m1">Styling can be adjusted just like in any other <mono>ggplot<mono>.</li>
</ul>

```r
# join basel tax data
quarters_map <- quarters_map %>% 
 left_join(filter(basel, year == 2017), 
 by = c("TYPE" = "quarter"))

# plot quarters
quarters_map %>% 
  ggplot() +
  
  # add white outlines
  geom_sf(aes(fill = income_mean),
              col = "white") + 
  theme_void()
```

]

]

---

# Styling

<ul>
 <li class="m1">Styling can be adjusted just like in any other <mono>ggplot<mono>.</li>
</ul>

```r
# join basel tax data
quarters_map <- quarters_map %>% 
 left_join(filter(basel, year == 2017), 
 by = c("TYPE" = "quarter"))

# plot quarters
quarters_map %>% 
  ggplot() +
  geom_sf(aes(fill = income_mean),
              col = "white") + 
  theme_void() + 
  
  # change legend title
  scale_fill_continuous(name = 'Income') 
```

]

]

---

# Styling

<ul>
 <li class="m1">Styling can be adjusted just like in any other <mono>ggplot<mono>.</li>
</ul>

```r
# join basel tax data
quarters_map <- quarters_map %>% 
 left_join(filter(basel, year == 2017), 
 by = c("TYPE" = "quarter"))

# plot quarters
quarters_map %>% 
  ggplot() +
  geom_sf(aes(fill = income_mean),
              col = "white") + 
  theme_void() + 
  scale_fill_continuous(name = 'Income') +
  
  # add annotion
  labs(title = "Inequality in Basel",
     subtitle = "Average income in Basel...",
     caption = "ource: Open Data Basel...")
```

]

]

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<h1><a href="https://correlaidswitzerland.github.io/DataViz4Good/">Schedule</a></h1>