Mapping Data in R with ggplot2 and geom

# Mapping Data in R with ggplot2 and geom_sf()
## a ‘flipbookr’ tutorial by Conner J. Surrency, January 2020
### advice/editing Gina Reynolds

---

# What Are We Doing?
--

- Here we will walk through the process of creating maps in RStudio.
--

- This will include the packages required and how to code a map.
--

- We will be looking at data from two cases: Wetherspoon restaurants in UK, and defense expenditure of NATO member states.

---

## Set-Up
--

We will need the following packages (and data) installed:  
--

- tidyverse
--

- rnaturalearth

---
#Example #1
--

We know the location of restaurants in the UK and the price of the empire burgur.

The map we make will be a simple static map that shows those points.

---

```r
*library(tidyverse)
```
]]
.column[.content[

]]
---
class: split-40
count: false
.column[.content[

```r
library(tidyverse)  
*library(rnaturalearth)
```
]]
.column[.content[

]]
---
class: split-40
count: false
.column[.content[

```r
library(tidyverse)  
library(rnaturalearth)  
*library(readxl)
```
]]
.column[.content[

]]
---
class: split-40
count: false
.column[.content[

```r
library(tidyverse)  
library(rnaturalearth)  
library(readxl)  
*read_excel("wetherspoon.xlsx")
```
]]
.column[.content[

```
# A tibble: 213 x 19
   Name  Location Latitude Longitude `Empire State B… `Chicken Tikka`
   <chr> <chr>       <dbl>     <dbl>            <dbl>           <dbl>
 1 The … Leicest…     51.5   -0.129             11.2             8.39
 2 The … Whiteha…     51.5   -0.127             11.0             8.39
 3 Pend… Holborn…     51.5   -0.115             11.0             8.39
 4 The … City of…     51.5   -0.0847            11.0             8.39
 5 The … Manches…     53.5   -2.35              13.6             9.15
 6 The … Windsor      51.5   -0.609             10.6             8.99
 7 The … Beacons…     51.6   -0.627              9.75            8.99
 8 Good… Tower H…     51.5   -0.0728            11.0             8.39
 9 Cast… Manches…     53.5   -2.36              11.5             8.4 
10 The … Camden,…     51.5   -0.145             11.2             8.39
# … with 203 more rows, and 13 more variables: `Gammon afternoon deal` <chr>,
#   `Chocolate Brownie` <dbl>, `Doom Bar` <dbl>, `Birra Moretti` <dbl>, `Porn
#   Star at 2 x £12` <chr>, `Porn Star (glass)` <chr>, MEAL <dbl>, Notes <chr>,
#   `Moretti as a % of a tikka` <dbl>, `Moretti as % of burger` <dbl>, `PRICE
#   BUCKETS` <chr>, `Food cost £` <dbl>, `Drink cost £` <dbl>
```
]]
---
class: split-40
count: false
.column[.content[

```r
library(tidyverse)  
library(rnaturalearth)  
library(readxl)  
read_excel("wetherspoon.xlsx") ->  
*wetherspoon_raw
```
]]
.column[.content[

]]
---
class: split-40
count: false
.column[.content[

```r
library(tidyverse)  
library(rnaturalearth)  
library(readxl)  
read_excel("wetherspoon.xlsx") ->  
wetherspoon_raw  
* #
*wetherspoon_raw
```
]]
.column[.content[

```r
library(tidyverse)  
library(rnaturalearth)  
library(readxl)  
read_excel("wetherspoon.xlsx") ->  
wetherspoon_raw  
  #
wetherspoon_raw %>%  
* janitor::clean_names()
```
]]
.column[.content[

```
# A tibble: 213 x 19
   name  location latitude longitude empire_state_bu… chicken_tikka
   <chr> <chr>       <dbl>     <dbl>            <dbl>         <dbl>
 1 The … Leicest…     51.5   -0.129             11.2           8.39
 2 The … Whiteha…     51.5   -0.127             11.0           8.39
 3 Pend… Holborn…     51.5   -0.115             11.0           8.39
 4 The … City of…     51.5   -0.0847            11.0           8.39
 5 The … Manches…     53.5   -2.35              13.6           9.15
 6 The … Windsor      51.5   -0.609             10.6           8.99
 7 The … Beacons…     51.6   -0.627              9.75          8.99
 8 Good… Tower H…     51.5   -0.0728            11.0           8.39
 9 Cast… Manches…     53.5   -2.36              11.5           8.4 
10 The … Camden,…     51.5   -0.145             11.2           8.39
# … with 203 more rows, and 13 more variables: gammon_afternoon_deal <chr>,
#   chocolate_brownie <dbl>, doom_bar <dbl>, birra_moretti <dbl>,
#   porn_star_at_2_x_12 <chr>, porn_star_glass <chr>, meal <dbl>, notes <chr>,
#   moretti_as_a_percent_of_a_tikka <dbl>, moretti_as_percent_of_burger <dbl>,
#   price_buckets <chr>, food_cost <dbl>, drink_cost <dbl>
```
]]
---
class: split-40
count: false
.column[.content[

```r
library(tidyverse)  
library(rnaturalearth)  
library(readxl)  
read_excel("wetherspoon.xlsx") ->  
wetherspoon_raw  
  #
wetherspoon_raw %>%  
  janitor::clean_names() %>%  
* select(1:5)
```
]]
.column[.content[

```
# A tibble: 213 x 5
   name                location            latitude longitude empire_state_burg…
   <chr>               <chr>                  <dbl>     <dbl>              <dbl>
 1 The Moon Under Wat… Leicester Square, …     51.5   -0.129               11.2 
 2 The Lord Moon of t… Whitehall, London       51.5   -0.127               11.0 
 3 Pendrel's Oak       Holborn, London         51.5   -0.115               11.0 
 4 The Cross Keyes     City of London, Lo…     51.5   -0.0847              11.0 
 5 The Mardi Gras      Manchester              53.5   -2.35                13.6 
 6 The King and Castle Windsor                 51.5   -0.609               10.6 
 7 The Hope & Champion Beaconsfield Servi…     51.6   -0.627                9.75
 8 Goodman's Field     Tower Hamlets, Lon…     51.5   -0.0728              11.0 
 9 Castle In the Air   Manchester              53.5   -2.36                11.5 
10 The Ice Wharf       Camden, London          51.5   -0.145               11.2 
# … with 203 more rows
```
]]
---
class: split-40
count: false
.column[.content[

]]

---

```r
*rnaturalearth::ne_countries(
* scale = "medium", returnclass = "sf")
```
]]
.column[.content[

```
Simple feature collection with 241 features and 63 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -180 ymin: -89.99893 xmax: 180 ymax: 83.59961
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs
First 10 features:
  scalerank      featurecla labelrank           sovereignt sov_a3 adm0_dif
0         3 Admin-0 country         5          Netherlands    NL1        1
1         1 Admin-0 country         3          Afghanistan    AFG        0
2         1 Admin-0 country         3               Angola    AGO        0
3         1 Admin-0 country         6       United Kingdom    GB1        1
4         1 Admin-0 country         6              Albania    ALB        0
5         3 Admin-0 country         6              Finland    FI1        1
6         3 Admin-0 country         6              Andorra    AND        0
7         1 Admin-0 country         4 United Arab Emirates    ARE        0
8         1 Admin-0 country         2            Argentina    ARG        0
9         1 Admin-0 country         6              Armenia    ARM        0
  level              type                admin adm0_a3 geou_dif
0     2           Country                Aruba     ABW        0
1     2 Sovereign country          Afghanistan     AFG        0
2     2 Sovereign country               Angola     AGO        0
3     2        Dependency             Anguilla     AIA        0
4     2 Sovereign country              Albania     ALB        0
5     2           Country                Aland     ALD        0
6     2 Sovereign country              Andorra     AND        0
7     2 Sovereign country United Arab Emirates     ARE        0
8     2 Sovereign country            Argentina     ARG        0
9     2 Sovereign country              Armenia     ARM        0
               geounit gu_a3 su_dif              subunit su_a3 brk_diff
0                Aruba   ABW      0                Aruba   ABW        0
1          Afghanistan   AFG      0          Afghanistan   AFG        0
2               Angola   AGO      0               Angola   AGO        0
3             Anguilla   AIA      0             Anguilla   AIA        0
4              Albania   ALB      0              Albania   ALB        0
5                Aland   ALD      0                Aland   ALD        0
6              Andorra   AND      0              Andorra   AND        0
7 United Arab Emirates   ARE      0 United Arab Emirates   ARE        0
8            Argentina   ARG      0            Argentina   ARG        0
9              Armenia   ARM      0              Armenia   ARM        0
                  name            name_long brk_a3             brk_name
0                Aruba                Aruba    ABW                Aruba
1          Afghanistan          Afghanistan    AFG          Afghanistan
2               Angola               Angola    AGO               Angola
3             Anguilla             Anguilla    AIA             Anguilla
4              Albania              Albania    ALB              Albania
5                Aland        Aland Islands    ALD                Aland
6              Andorra              Andorra    AND              Andorra
7 United Arab Emirates United Arab Emirates    ARE United Arab Emirates
8            Argentina            Argentina    ARG            Argentina
9              Armenia              Armenia    ARM              Armenia
  brk_group abbrev postal                    formal_en formal_fr note_adm0
0      <NA>  Aruba     AW                        Aruba      <NA>     Neth.
1      <NA>   Afg.     AF Islamic State of Afghanistan      <NA>      <NA>
2      <NA>   Ang.     AO  People's Republic of Angola      <NA>      <NA>
3      <NA>   Ang.     AI                         <NA>      <NA>      U.K.
4      <NA>   Alb.     AL          Republic of Albania      <NA>      <NA>
5      <NA>  Aland     AI                Åland Islands      <NA>      Fin.
6      <NA>   And.    AND      Principality of Andorra      <NA>      <NA>
7      <NA> U.A.E.     AE         United Arab Emirates      <NA>      <NA>
8      <NA>   Arg.     AR           Argentine Republic      <NA>      <NA>
9      <NA>   Arm.    ARM          Republic of Armenia      <NA>      <NA>
  note_brk            name_sort name_alt mapcolor7 mapcolor8 mapcolor9
0     <NA>                Aruba     <NA>         4         2         2
1     <NA>          Afghanistan     <NA>         5         6         8
2     <NA>               Angola     <NA>         3         2         6
3     <NA>             Anguilla     <NA>         6         6         6
4     <NA>              Albania     <NA>         1         4         1
5     <NA>                Aland     <NA>         4         1         4
6     <NA>              Andorra     <NA>         1         4         1
7     <NA> United Arab Emirates     <NA>         2         1         3
8     <NA>            Argentina     <NA>         3         1         3
9     <NA>              Armenia     <NA>         3         1         2
  mapcolor13  pop_est gdp_md_est pop_year lastcensus gdp_year
0          9   103065     2258.0       NA       2010       NA
1          7 28400000    22270.0       NA       1979       NA
2          1 12799293   110300.0       NA       1970       NA
3          3    14436      108.9       NA         NA       NA
4          6  3639453    21810.0       NA       2001       NA
5          6    27153     1563.0       NA         NA       NA
6          8    83888     3660.0       NA       1989       NA
7          3  4798491   184300.0       NA       2010       NA
8         13 40913584   573900.0       NA       2010       NA
9         10  2967004    18770.0       NA       2001       NA
                     economy              income_grp wikipedia fips_10 iso_a2
0       6. Developing region 2. High income: nonOECD        NA    <NA>     AW
1  7. Least developed region           5. Low income        NA    <NA>     AF
2  7. Least developed region  3. Upper middle income        NA    <NA>     AO
3       6. Developing region  3. Upper middle income        NA    <NA>     AI
4       6. Developing region  4. Lower middle income        NA    <NA>     AL
5 2. Developed region: nonG7    1. High income: OECD        NA    <NA>     AX
6 2. Developed region: nonG7 2. High income: nonOECD        NA    <NA>     AD
7       6. Developing region 2. High income: nonOECD        NA    <NA>     AE
8    5. Emerging region: G20  3. Upper middle income        NA    <NA>     AR
9       6. Developing region  4. Lower middle income        NA    <NA>     AM
  iso_a3 iso_n3 un_a3 wb_a2 wb_a3 woe_id adm0_a3_is adm0_a3_us adm0_a3_un
0    ABW    533   533    AW   ABW     NA        ABW        ABW         NA
1    AFG    004   004    AF   AFG     NA        AFG        AFG         NA
2    AGO    024   024    AO   AGO     NA        AGO        AGO         NA
3    AIA    660   660  <NA>  <NA>     NA        AIA        AIA         NA
4    ALB    008   008    AL   ALB     NA        ALB        ALB         NA
5    ALA    248   248  <NA>  <NA>     NA        ALA        ALD         NA
6    AND    020   020    AD   ADO     NA        AND        AND         NA
7    ARE    784   784    AE   ARE     NA        ARE        ARE         NA
8    ARG    032   032    AR   ARG     NA        ARG        ARG         NA
9    ARM    051   051    AM   ARM     NA        ARM        ARM         NA
  adm0_a3_wb     continent region_un       subregion                  region_wb
0         NA North America  Americas       Caribbean  Latin America & Caribbean
1         NA          Asia      Asia   Southern Asia                 South Asia
2         NA        Africa    Africa   Middle Africa         Sub-Saharan Africa
3         NA North America  Americas       Caribbean  Latin America & Caribbean
4         NA        Europe    Europe Southern Europe      Europe & Central Asia
5         NA        Europe    Europe Northern Europe      Europe & Central Asia
6         NA        Europe    Europe Southern Europe      Europe & Central Asia
7         NA          Asia      Asia    Western Asia Middle East & North Africa
8         NA South America  Americas   South America  Latin America & Caribbean
9         NA          Asia      Asia    Western Asia      Europe & Central Asia
  name_len long_len abbrev_len tiny homepart                       geometry
0        5        5          5    4       NA MULTIPOLYGON (((-69.89912 1...
1       11       11          4   NA        1 MULTIPOLYGON (((74.89131 37...
2        6        6          4   NA        1 MULTIPOLYGON (((14.19082 -5...
3        8        8          4   NA       NA MULTIPOLYGON (((-63.00122 1...
4        7        7          4   NA        1 MULTIPOLYGON (((20.06396 42...
5        5       13          5    5       NA MULTIPOLYGON (((20.61133 60...
6        7        7          4    5        1 MULTIPOLYGON (((1.706055 42...
7       20       20          6   NA        1 MULTIPOLYGON (((53.92783 24...
8        9        9          4   NA        1 MULTIPOLYGON (((-64.54917 -...
9        7        7          4   NA        1 MULTIPOLYGON (((45.55234 40...
```
]]
---
class: split-40
count: false
.column[.content[

```r
rnaturalearth::ne_countries(  
  scale = "medium", returnclass = "sf") %>%  
* select(name, continent, geometry)
```
]]
.column[.content[

```
Simple feature collection with 241 features and 2 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -180 ymin: -89.99893 xmax: 180 ymax: 83.59961
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs
First 10 features:
                  name     continent                       geometry
0                Aruba North America MULTIPOLYGON (((-69.89912 1...
1          Afghanistan          Asia MULTIPOLYGON (((74.89131 37...
2               Angola        Africa MULTIPOLYGON (((14.19082 -5...
3             Anguilla North America MULTIPOLYGON (((-63.00122 1...
4              Albania        Europe MULTIPOLYGON (((20.06396 42...
5                Aland        Europe MULTIPOLYGON (((20.61133 60...
6              Andorra        Europe MULTIPOLYGON (((1.706055 42...
7 United Arab Emirates          Asia MULTIPOLYGON (((53.92783 24...
8            Argentina South America MULTIPOLYGON (((-64.54917 -...
9              Armenia          Asia MULTIPOLYGON (((45.55234 40...
```
]]
---
class: split-40
count: false
.column[.content[

```r
rnaturalearth::ne_countries(  
  scale = "medium", returnclass = "sf") %>%  
  select(name, continent, geometry) %>%  
* filter(name == 'United Kingdom')
```
]]
.column[.content[

```
Simple feature collection with 1 feature and 2 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -8.144824 ymin: 50.02139 xmax: 1.746582 ymax: 60.83188
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs
            name continent                       geometry
1 United Kingdom    Europe MULTIPOLYGON (((-1.065576 5...
```
]]
---
class: split-40
count: false
.column[.content[

```r
rnaturalearth::ne_countries(  
  scale = "medium", returnclass = "sf") %>%  
  select(name, continent, geometry) %>%  
  filter(name == 'United Kingdom') ->  
*uk
```
]]
.column[.content[

]]
---
class: split-40
count: false
.column[.content[

```r
rnaturalearth::ne_countries(  
  scale = "medium", returnclass = "sf") %>%  
  select(name, continent, geometry) %>%  
  filter(name == 'United Kingdom') ->  
uk  
* #
*ggplot(data = wetherspoon)
```
]]
.column[.content[
<img src="geom_sf_files/figure-html/wether_auto_5_output-1.png" width="864" />
]]
---
class: split-40
count: false
.column[.content[

```r
rnaturalearth::ne_countries(  
  scale = "medium", returnclass = "sf") %>%  
  select(name, continent, geometry) %>%  
  filter(name == 'United Kingdom') ->  
uk  
  #
ggplot(data = wetherspoon) +  
* ggplot2::geom_sf(data = uk)
```
]]
.column[.content[
<img src="geom_sf_files/figure-html/wether_auto_6_output-1.png" width="864" />
]]
---
class: split-40
count: false
.column[.content[

```r
rnaturalearth::ne_countries(  
  scale = "medium", returnclass = "sf") %>%  
  select(name, continent, geometry) %>%  
  filter(name == 'United Kingdom') ->  
uk  
  #
ggplot(data = wetherspoon) +  
  ggplot2::geom_sf(data = uk) +  
* geom_point(aes(x = longitude,
*                  y = latitude,
*                  color = empire_state_burger),
*              alpha = .3)
```
]]
.column[.content[
<img src="geom_sf_files/figure-html/wether_auto_7_output-1.png" width="864" />
]]
---
class: split-40
count: false
.column[.content[

```r
rnaturalearth::ne_countries(  
  scale = "medium", returnclass = "sf") %>%  
  select(name, continent, geometry) %>%  
  filter(name == 'United Kingdom') ->  
uk  
  #
ggplot(data = wetherspoon) +  
  ggplot2::geom_sf(data = uk) +  
  geom_point(aes(x = longitude,  
                   y = latitude,  
                   color = empire_state_burger),  
               alpha = .3) +  
*   scale_color_viridis_c()
```
]]
.column[.content[
<img src="geom_sf_files/figure-html/wether_auto_8_output-1.png" width="864" />
]]
---
class: split-40
count: false
.column[.content[

```r
rnaturalearth::ne_countries(  
  scale = "medium", returnclass = "sf") %>%  
  select(name, continent, geometry) %>%  
  filter(name == 'United Kingdom') ->  
uk  
  #
ggplot(data = wetherspoon) +  
  ggplot2::geom_sf(data = uk) +  
  geom_point(aes(x = longitude,  
                   y = latitude,  
                   color = empire_state_burger),  
               alpha = .3) +  
    scale_color_viridis_c() +  
*   labs(color = "Empire State Burger\nCost (pounds)")
```
]]
.column[.content[
<img src="geom_sf_files/figure-html/wether_auto_9_output-1.png" width="864" />
]]
---
class: split-40
count: false
.column[.content[

```r
rnaturalearth::ne_countries(  
  scale = "medium", returnclass = "sf") %>%  
  select(name, continent, geometry) %>%  
  filter(name == 'United Kingdom') ->  
uk  
  #
ggplot(data = wetherspoon) +  
  ggplot2::geom_sf(data = uk) +  
  geom_point(aes(x = longitude,  
                   y = latitude,  
                   color = empire_state_burger),  
               alpha = .3) +  
    scale_color_viridis_c() +  
    labs(color = "Empire State Burger\nCost (pounds)") +  
*   theme_bw()
```
]]
.column[.content[
<img src="geom_sf_files/figure-html/wether_auto_10_output-1.png" width="864" />
]]
---
class: split-40
count: false
.column[.content[

```r
rnaturalearth::ne_countries(  
  scale = "medium", returnclass = "sf") %>%  
  select(name, continent, geometry) %>%  
  filter(name == 'United Kingdom') ->  
uk  
  #
ggplot(data = wetherspoon) +  
  ggplot2::geom_sf(data = uk) +  
  geom_point(aes(x = longitude,  
                   y = latitude,  
                   color = empire_state_burger),  
               alpha = .3) +  
    scale_color_viridis_c() +  
    labs(color = "Empire State Burger\nCost (pounds)") +  
    theme_bw() +  
*   theme(plot.title = element_text(hjust = 0.5))
```
]]
.column[.content[
<img src="geom_sf_files/figure-html/wether_auto_11_output-1.png" width="864" />
]]
---
class: split-40
count: false
.column[.content[

```r
rnaturalearth::ne_countries(  
  scale = "medium", returnclass = "sf") %>%  
  select(name, continent, geometry) %>%  
  filter(name == 'United Kingdom') ->  
uk  
  #
ggplot(data = wetherspoon) +  
  ggplot2::geom_sf(data = uk) +  
  geom_point(aes(x = longitude,  
                   y = latitude,  
                   color = empire_state_burger),  
               alpha = .3) +  
    scale_color_viridis_c() +  
    labs(color = "Empire State Burger\nCost (pounds)") +  
    theme_bw() +  
    theme(plot.title = element_text(hjust = 0.5)) +  
*   theme(axis.title = element_blank())
```
]]
.column[.content[
<img src="geom_sf_files/figure-html/wether_auto_12_output-1.png" width="864" />
]]

---
#Example #2
--

The second example will also draw its shapefiles from the rnaturalearth package. We will also use a dataset containing the defense expenditure of NATO member states.

We will make a colored map that allows for easy visual comparison of spending levels. This type is called a choropleth map.

---

```r
library(readxl)
nato_expenditure <- read_excel("nato_expenditure.xlsx")

nato_expenditure
```

```
# A tibble: 29 x 2
   name           spendingPercap
   <chr>                   <dbl>
 1 Albania                  1.26
 2 Belgium                  0.93
 3 Bulgaria                 1.61
 4 Canada                   1.27
 5 Croatia                  1.75
 6 Czech Republic           1.19
 7 Denmark                  1.35
 8 Estonia                  2.13
 9 France                   1.84
10 Germany                  1.36
# … with 19 more rows
```
---

```r
*nato_names <- c("Albania", "Belgium", "Bulgaria", "Canada", "Croatia", "Czech Republic", "Denmark", "Estonia", "France", "Germany", "Greece", "Hungary",
*               "Iceland", "Italy", "Latvia", "Lithuania", "Luxembourg", "Montenegro", "Netherlands", "Norway", "Poland", "Portugal", "Romania", "Slovakia", "Slovenia", "Spain", "Turkey", "United Kingdom", "United States")
```
]]
.column[.content[

]]
---
class: split-40
count: false
.column[.content[

```
Simple feature collection with 241 features and 4 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -180 ymin: -89.99893 xmax: 180 ymax: 83.59961
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs
First 10 features:
                  name  pop_est gdp_md_est     continent
0                Aruba   103065     2258.0 North America
1          Afghanistan 28400000    22270.0          Asia
2               Angola 12799293   110300.0        Africa
3             Anguilla    14436      108.9 North America
4              Albania  3639453    21810.0        Europe
5                Aland    27153     1563.0        Europe
6              Andorra    83888     3660.0        Europe
7 United Arab Emirates  4798491   184300.0          Asia
8            Argentina 40913584   573900.0 South America
9              Armenia  2967004    18770.0          Asia
                        geometry
0 MULTIPOLYGON (((-69.89912 1...
1 MULTIPOLYGON (((74.89131 37...
2 MULTIPOLYGON (((14.19082 -5...
3 MULTIPOLYGON (((-63.00122 1...
4 MULTIPOLYGON (((20.06396 42...
5 MULTIPOLYGON (((20.61133 60...
6 MULTIPOLYGON (((1.706055 42...
7 MULTIPOLYGON (((53.92783 24...
8 MULTIPOLYGON (((-64.54917 -...
9 MULTIPOLYGON (((45.55234 40...
```
]]
---
class: split-40
count: false
.column[.content[

```
Simple feature collection with 28 features and 4 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -178.1945 ymin: -21.36904 xmax: 179.78 ymax: 83.11611
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs
First 10 features:
             name  pop_est gdp_md_est     continent
1         Albania  3639453      21810        Europe
2         Belgium 10414336     389300        Europe
3        Bulgaria  7204687      93750        Europe
4          Canada 33487208    1300000 North America
5         Germany 82329758    2918000        Europe
6         Denmark  5500510     203600        Europe
7           Spain 40525002    1403000        Europe
8         Estonia  1299371      27410        Europe
9          France 64057792    2128000        Europe
10 United Kingdom 62262000    1977704        Europe
                         geometry
1  MULTIPOLYGON (((20.06396 42...
2  MULTIPOLYGON (((4.816016 51...
3  MULTIPOLYGON (((28.58535 43...
4  MULTIPOLYGON (((-59.7876 43...
5  MULTIPOLYGON (((14.19824 53...
6  MULTIPOLYGON (((11.36143 54...
7  MULTIPOLYGON (((-17.88794 2...
8  MULTIPOLYGON (((22.61738 58...
9  MULTIPOLYGON (((55.79736 -2...
10 MULTIPOLYGON (((-1.065576 5...
```
]]
---
class: split-40
count: false
.column[.content[

```
Simple feature collection with 28 features and 5 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -178.1945 ymin: -21.36904 xmax: 179.78 ymax: 83.11611
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs
First 10 features:
             name  pop_est gdp_md_est     continent spendingPercap
1         Albania  3639453      21810        Europe           1.26
2         Belgium 10414336     389300        Europe           0.93
3        Bulgaria  7204687      93750        Europe           1.61
4          Canada 33487208    1300000 North America           1.27
5         Germany 82329758    2918000        Europe           1.36
6         Denmark  5500510     203600        Europe           1.35
7           Spain 40525002    1403000        Europe           0.92
8         Estonia  1299371      27410        Europe           2.13
9          France 64057792    2128000        Europe           1.84
10 United Kingdom 62262000    1977704        Europe           2.13
                         geometry
1  MULTIPOLYGON (((20.06396 42...
2  MULTIPOLYGON (((4.816016 51...
3  MULTIPOLYGON (((28.58535 43...
4  MULTIPOLYGON (((-59.7876 43...
5  MULTIPOLYGON (((14.19824 53...
6  MULTIPOLYGON (((11.36143 54...
7  MULTIPOLYGON (((-17.88794 2...
8  MULTIPOLYGON (((22.61738 58...
9  MULTIPOLYGON (((55.79736 -2...
10 MULTIPOLYGON (((-1.065576 5...
```
]]
---
class: split-40
count: false
.column[.content[

]]
---
class: split-40
count: false
.column[.content[

```r
nato_names <- c("Albania", "Belgium", "Bulgaria", "Canada", "Croatia", "Czech Republic", "Denmark", "Estonia", "France", "Germany", "Greece", "Hungary",  
                "Iceland", "Italy", "Latvia", "Lithuania", "Luxembourg", "Montenegro", "Netherlands", "Norway", "Poland", "Portugal", "Romania", "Slovakia", "Slovenia", "Spain", "Turkey", "United Kingdom", "United States")  
  #
rnaturalearth::ne_countries(  
  scale = "medium", returnclass = "sf") %>%  
  select(name, pop_est, gdp_md_est,  
         continent, geometry) %>%  
  filter(name %in% nato_names) %>%  
  left_join(nato_expenditure, by = "name") ->  
  nato_countries  
  #
ggplot(data = nato_countries) +  
  ggplot2::geom_sf(data = nato_countries) +  
    aes(fill = spendingPercap) +  
*   scale_fill_viridis_c(option = "magma")
```
]]
.column[.content[
<img src="geom_sf_files/figure-html/nato_auto_10_output-1.png" width="864" />
]]
---
class: split-40
count: false
.column[.content[

```r
nato_names <- c("Albania", "Belgium", "Bulgaria", "Canada", "Croatia", "Czech Republic", "Denmark", "Estonia", "France", "Germany", "Greece", "Hungary",  
                "Iceland", "Italy", "Latvia", "Lithuania", "Luxembourg", "Montenegro", "Netherlands", "Norway", "Poland", "Portugal", "Romania", "Slovakia", "Slovenia", "Spain", "Turkey", "United Kingdom", "United States")  
  #
rnaturalearth::ne_countries(  
  scale = "medium", returnclass = "sf") %>%  
  select(name, pop_est, gdp_md_est,  
         continent, geometry) %>%  
  filter(name %in% nato_names) %>%  
  left_join(nato_expenditure, by = "name") ->  
  nato_countries  
  #
ggplot(data = nato_countries) +  
  ggplot2::geom_sf(data = nato_countries) +  
    aes(fill = spendingPercap) +  
    scale_fill_viridis_c(option = "magma") +  
* ggplot2::coord_sf(xlim = c(-175, 47.5),
*          ylim = c(23, 85),
*          expand = FALSE)
```
]]
.column[.content[
<img src="geom_sf_files/figure-html/nato_auto_11_output-1.png" width="864" />
]]
---
class: split-40
count: false
.column[.content[

```r
nato_names <- c("Albania", "Belgium", "Bulgaria", "Canada", "Croatia", "Czech Republic", "Denmark", "Estonia", "France", "Germany", "Greece", "Hungary",  
                "Iceland", "Italy", "Latvia", "Lithuania", "Luxembourg", "Montenegro", "Netherlands", "Norway", "Poland", "Portugal", "Romania", "Slovakia", "Slovenia", "Spain", "Turkey", "United Kingdom", "United States")  
  #
rnaturalearth::ne_countries(  
  scale = "medium", returnclass = "sf") %>%  
  select(name, pop_est, gdp_md_est,  
         continent, geometry) %>%  
  filter(name %in% nato_names) %>%  
  left_join(nato_expenditure, by = "name") ->  
  nato_countries  
  #
ggplot(data = nato_countries) +  
  ggplot2::geom_sf(data = nato_countries) +  
    aes(fill = spendingPercap) +  
    scale_fill_viridis_c(option = "magma") +  
  ggplot2::coord_sf(xlim = c(-175, 47.5),  
           ylim = c(23, 85),  
           expand = FALSE) +  
* labs(title = "Defense Expenditure by Share of GDP (%)",
*        subtitle = "NATO Member States",
*        fill = "Share of GDP (%)")
```
]]
.column[.content[
<img src="geom_sf_files/figure-html/nato_auto_12_output-1.png" width="864" />
]]
---
class: split-40
count: false
.column[.content[

```r
nato_names <- c("Albania", "Belgium", "Bulgaria", "Canada", "Croatia", "Czech Republic", "Denmark", "Estonia", "France", "Germany", "Greece", "Hungary",  
                "Iceland", "Italy", "Latvia", "Lithuania", "Luxembourg", "Montenegro", "Netherlands", "Norway", "Poland", "Portugal", "Romania", "Slovakia", "Slovenia", "Spain", "Turkey", "United Kingdom", "United States")  
  #
rnaturalearth::ne_countries(  
  scale = "medium", returnclass = "sf") %>%  
  select(name, pop_est, gdp_md_est,  
         continent, geometry) %>%  
  filter(name %in% nato_names) %>%  
  left_join(nato_expenditure, by = "name") ->  
  nato_countries  
  #
ggplot(data = nato_countries) +  
  ggplot2::geom_sf(data = nato_countries) +  
    aes(fill = spendingPercap) +  
    scale_fill_viridis_c(option = "magma") +  
  ggplot2::coord_sf(xlim = c(-175, 47.5),  
           ylim = c(23, 85),  
           expand = FALSE) +  
  labs(title = "Defense Expenditure by Share of GDP (%)",  
         subtitle = "NATO Member States",  
         fill = "Share of GDP (%)") +  
* theme_bw()
```
]]
.column[.content[
<img src="geom_sf_files/figure-html/nato_auto_13_output-1.png" width="864" />
]]
---
class: split-40
count: false
.column[.content[

```r
nato_names <- c("Albania", "Belgium", "Bulgaria", "Canada", "Croatia", "Czech Republic", "Denmark", "Estonia", "France", "Germany", "Greece", "Hungary",  
                "Iceland", "Italy", "Latvia", "Lithuania", "Luxembourg", "Montenegro", "Netherlands", "Norway", "Poland", "Portugal", "Romania", "Slovakia", "Slovenia", "Spain", "Turkey", "United Kingdom", "United States")  
  #
rnaturalearth::ne_countries(  
  scale = "medium", returnclass = "sf") %>%  
  select(name, pop_est, gdp_md_est,  
         continent, geometry) %>%  
  filter(name %in% nato_names) %>%  
  left_join(nato_expenditure, by = "name") ->  
  nato_countries  
  #
ggplot(data = nato_countries) +  
  ggplot2::geom_sf(data = nato_countries) +  
    aes(fill = spendingPercap) +  
    scale_fill_viridis_c(option = "magma") +  
  ggplot2::coord_sf(xlim = c(-175, 47.5),  
           ylim = c(23, 85),  
           expand = FALSE) +  
  labs(title = "Defense Expenditure by Share of GDP (%)",  
         subtitle = "NATO Member States",  
         fill = "Share of GDP (%)") +  
  theme_bw() +  
* theme(plot.title =
*           element_text(hjust = 0.5))
```
]]
.column[.content[
<img src="geom_sf_files/figure-html/nato_auto_14_output-1.png" width="864" />
]]
---
class: split-40
count: false
.column[.content[

```r
nato_names <- c("Albania", "Belgium", "Bulgaria", "Canada", "Croatia", "Czech Republic", "Denmark", "Estonia", "France", "Germany", "Greece", "Hungary",  
                "Iceland", "Italy", "Latvia", "Lithuania", "Luxembourg", "Montenegro", "Netherlands", "Norway", "Poland", "Portugal", "Romania", "Slovakia", "Slovenia", "Spain", "Turkey", "United Kingdom", "United States")  
  #
rnaturalearth::ne_countries(  
  scale = "medium", returnclass = "sf") %>%  
  select(name, pop_est, gdp_md_est,  
         continent, geometry) %>%  
  filter(name %in% nato_names) %>%  
  left_join(nato_expenditure, by = "name") ->  
  nato_countries  
  #
ggplot(data = nato_countries) +  
  ggplot2::geom_sf(data = nato_countries) +  
    aes(fill = spendingPercap) +  
    scale_fill_viridis_c(option = "magma") +  
  ggplot2::coord_sf(xlim = c(-175, 47.5),  
           ylim = c(23, 85),  
           expand = FALSE) +  
  labs(title = "Defense Expenditure by Share of GDP (%)",  
         subtitle = "NATO Member States",  
         fill = "Share of GDP (%)") +  
  theme_bw() +  
  theme(plot.title =  
            element_text(hjust = 0.5)) +  
* theme(plot.subtitle =
*           element_text(hjust = 0.5))
```
]]
.column[.content[
<img src="geom_sf_files/figure-html/nato_auto_15_output-1.png" width="864" />
]]