Making Maps with R

# Making Maps with R
### Courtney Lee
### Public Policy Institute of California
### 2019-10-16

---

# What do we need before building our maps?

* working knowledge of R  
--

* the data we want to visualize  
--

* an idea of what we want as our result

---

## A Glimpse of the Data

```
## # A tibble: 10 x 3
##    ctemacro                        unwgt_n eduattain_aa_pct
##    <chr>                             <dbl>            <dbl>
##  1 Northern                           376.            0.566
##  2 Sacramento Area                    651.            0.546
##  3 SF Bay Area                       1222.            0.518
##  4 Santa Clara/Santa Cruz/Monterey    469.            0.465
##  5 Central Valley/Mother Lode        1059.            0.623
##  6 Central Coast                      396.            0.552
##  7 Los Angeles                       2140.            0.482
##  8 Inland Empire                     1186.            0.555
##  9 Orange County                      541.            0.400
## 10 San Diego/Imperial                 810.            0.529
```
---

### One way to visualize the data

---

### Map version

---

# Libraries we need

```r
library(ggplot2) #plotting engine
library(maps) #contains outlines
library(mapdata) #higher resolution outlines
library(mapproj) #map projections collection
```
---

# USA, as a country, in a dataframe

```r
usa <- map_data("usa")
dim(usa)
```

```
# [1] 7243    6
```

```r
head(usa)
```

```
#        long      lat group order region subregion
# 1 -101.4078 29.74224     1     1   main      <NA>
# 2 -101.3906 29.74224     1     2   main      <NA>
# 3 -101.3620 29.65056     1     3   main      <NA>
# 4 -101.3505 29.63911     1     4   main      <NA>
# 5 -101.3219 29.63338     1     5   main      <NA>
# 6 -101.3047 29.64484     1     6   main      <NA>
```

---

# USA, dataframe, visualized

```r
library(ggplot2)
usa <- map_data("usa")
usa_base <- ggplot() +
  geom_polygon(data = usa, 
               aes(x = long, 
                   y = lat, 
                   group = group
                   )) +
  coord_fixed(1.3)
usa_base
```
]

.pull-right[
<img src="makemapswithR_slidedeck_191015_files/figure-html/unnamed-chunk-4-1.png" width="504" style="display: block; margin: auto;" />
]

---

### Change the fill color and projection

```r
library(ggplot2)
usa <- map_data("usa")
ggplot() +
  geom_polygon(data = usa, 
               aes(x = long, y = lat, group = group), 
*              fill = "lightgreen", color = "darkgreen") +
  coord_fixed(1.3) +
  labs(title = "The 48 Contiguous States of the USA") + 
* coord_map("conic", lat0 = 30) +
  theme(panel.background = element_blank(), 
        axis.title = element_blank(), 
        axis.text = element_blank(), 
        axis.ticks = element_blank())
```

---

### Fixed colors and projections

---

# County Levels

```r
states <- map_data("state")
calif <- subset(states, region == "california") 
# just the lat long points for the state

# county lines
*ca_counties <- subset(map_data("county"), region == "california")

# plot the state itself 
ca_base <- ggplot(data = calif, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
  geom_polygon(color = "black", fill = "lightgreen") +
  theme(panel.background = element_blank(),
        axis.text = element_blank(), 
        axis.ticks = element_blank(), 
        axis.title = element_blank(), 
        panel.border = element_blank(), 
        panel.grid = element_blank())

# plot county lines 
ca_base + 
  geom_polygon(data = ca_counties, fill = NA, color = "white") + 
  geom_polygon(color = "black", fill = NA) 
```

```r
  # get state border drawn as top layer
```

---

### All 58 of California's counties

---

### Forming County Groups

```r
# attach our county group definition to the lat/longs
# provided by the `maps` library
*cts_macro <-  inner_join(x = counties, y = cty_macro)

ca_base + 
  geom_polygon(data = cts_macro, aes(fill = msaname), 
               color = "white") + 
  geom_polygon(color = "black", fill = NA) + 
   scale_fill_viridis_d(option = "C") +
  labs(fill = "MSA Regions") + 
  guides(fill = FALSE)
```

---

## 34 Metropolitan Statistical Areas
<img src="makemapswithR_slidedeck_191015_files/figure-html/unnamed-chunk-7-1.png" width="504" style="display: block; margin: auto;" />
Metropolitan Statistical Areas, according to US Bureau of Labor Statistics

---

### Another Definition of County Groups: Fifteen Groups
(formed by attaching this 15 county group definition to the dataframe found in `map_data::counties`)

Source: California Community Colleges Chancellor's Office

---

### Consolidating 34 MSAs and 15 CC Regions Together

--
... into ten regions!

Now we are able to talk about regional employment demand (via BLS) and supply (how community colleges could address).

---

### Which regions sees the greatest share of Californians with some college educational attainment, and are employed in the public and protective services sector?

---

### Change that color scheme

```r
# 33. public and protective services
occ33 <- occ2sr %>% filter(occsoc_2 == "33" 
                           & ctemacro != "Statewide")
cty_occ33 <- inner_join(x = cts_macro, y = occ33, 
                        by = c("ctemacro" = "ctemacro"))

ca_base + 
  geom_polygon(data = cty_occ33, aes(fill = eduattain_aa_pct), 
               color = "white") + 
* scale_fill_distiller(type = "div", direction = 1,
                      labels = scales::percent_format(accuracy = 1),
                      ) + 
  geom_polygon(color = "black", fill = NA) + #state outline
  labs(fill = "Percent Some College\nAttainment")
```

---

### Change that color scheme 
<img src="makemapswithR_slidedeck_191015_files/figure-html/unnamed-chunk-11-1.png" width="504" style="display: block; margin: auto;" />

---

### Change that color scheme, again

ca_base + 
  geom_polygon(data = cty_occ33, aes(fill = eduattain_aa_pct), 
               color = "white") + 
* scale_fill_viridis_c(option = "D",
                      labels = scales::percent_format(accuracy = 1),
                      ) + 
  geom_polygon(color = "black", fill = NA) + #state outline
  labs(fill = "Percent Some College\nAttainment")
```

---

### Change that color scheme, again

---

# Thanks!

Content adapted from [Reproducible Research/Making Maps with R](https://eriqande.github.io/rep-res-web/lectures/making-maps-with-R.html).

Slides are available on **GitHub**:  
[https://www.github.com/leecourt98/nacis-2019/](https://www.github.com/leecourt98/nacis-2019/).

Slides created via the R package [xaringan](https://github.com/yihui/xaringan).

-->