{tmap}

# {tmap}
### Gina Reynolds, July 2019

---

---
class: inverse, center, middle

# A simple build w filled polygons

---

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

]]
---
class: split-40
count: false

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

]]
---
class: split-40
count: false

```r
library(tidyverse)  
library(tmap)  
*data("World")
```
]]
.column[.content[

]]
---
class: split-40
count: false

```r
library(tidyverse)  
library(tmap)  
data("World")

*World
```
]]
.column[.content[

```
  Simple feature collection with 177 features and 15 fields
  geometry type:  MULTIPOLYGON
  dimension:      XY
  bbox:           xmin: -16656120 ymin: -8460601 xmax: 16656120 ymax: 8375779
  CRS:            +proj=eck4 +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs
  First 10 features:
     iso_a3                   name           sovereignt               continent
  1     AFG            Afghanistan          Afghanistan                    Asia
  2     AGO                 Angola               Angola                  Africa
  3     ALB                Albania              Albania                  Europe
  4     ARE   United Arab Emirates United Arab Emirates                    Asia
  5     ARG              Argentina            Argentina           South America
  6     ARM                Armenia              Armenia                    Asia
  7     ATA             Antarctica           Antarctica              Antarctica
  8     ATF Fr. S. Antarctic Lands               France Seven seas (open ocean)
  9     AUS              Australia            Australia                 Oceania
  10    AUT                Austria              Austria                  Europe
                    area  pop_est pop_est_dens                    economy
  1    652860.000 [km^2] 28400000 4.350090e+01  7. Least developed region
  2   1246700.000 [km^2] 12799293 1.026654e+01  7. Least developed region
  3     27400.000 [km^2]  3639453 1.328268e+02       6. Developing region
  4     71252.172 [km^2]  4798491 6.734519e+01       6. Developing region
  5   2736690.000 [km^2] 40913584 1.495003e+01    5. Emerging region: G20
  6     28470.000 [km^2]  2967004 1.042151e+02       6. Developing region
  7  12259213.973 [km^2]     3802 3.101341e-04       6. Developing region
  8      7257.455 [km^2]      140 1.929051e-02       6. Developing region
  9   7682300.000 [km^2] 21262641 2.767744e+00 2. Developed region: nonG7
  10    82523.000 [km^2]  8210281 9.949082e+01 2. Developed region: nonG7
                  income_grp gdp_cap_est life_exp well_being footprint inequality
  1            5. Low income    784.1549   59.668        3.8      0.79 0.42655744
  2   3. Upper middle income   8617.6635       NA         NA        NA         NA
  3   4. Lower middle income   5992.6588   77.347        5.5      2.21 0.16513372
  4  2. High income: nonOECD  38407.9078       NA         NA        NA         NA
  5   3. Upper middle income  14027.1261   75.927        6.5      3.14 0.16423830
  6   4. Lower middle income   6326.2469   74.446        4.3      2.23 0.21664810
  7  2. High income: nonOECD 200000.0000       NA         NA        NA         NA
  8  2. High income: nonOECD 114285.7143       NA         NA        NA         NA
  9     1. High income: OECD  37634.0832   82.052        7.2      9.31 0.08067825
  10    1. High income: OECD  40132.6093   81.004        7.4      6.06 0.07129351
          HPI                       geometry
  1  20.22535 MULTIPOLYGON (((5310471 451...
  2        NA MULTIPOLYGON (((1531585 -77...
  3  36.76687 MULTIPOLYGON (((1729835 521...
  4        NA MULTIPOLYGON (((4675864 313...
  5  35.19024 MULTIPOLYGON (((-5017766 -6...
  6  25.66642 MULTIPOLYGON (((3677241 513...
  7        NA MULTIPOLYGON (((-3403793 -8...
  8        NA MULTIPOLYGON (((5550200 -59...
  9  21.22897 MULTIPOLYGON (((12288137 -5...
  10 30.47822 MULTIPOLYGON (((1371802 588...
```
]]
---
class: split-40
count: false

```r
library(tidyverse)  
library(tmap)  
data("World")

World %>%  
* tm_shape(shp = .) +
*   tm_polygons("HPI")
```
]]
.column[.content[
<img src="tmap_files/figure-html/basic_user_5_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(tidyverse)  
library(tmap)  
data("World")

World %>%  
  tm_shape(shp = .) +  
    tm_polygons("HPI") +  
* tm_layout(bg.color = "skyblue",
*           inner.margins = c(0.15, 0.10, 0.15, 0.08),
*           title = "Happy Planet Index",
*           title.size = 1.1,
*           title.position = c("center", "top"))
```
]]
.column[.content[
<img src="tmap_files/figure-html/basic_user_6_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(tidyverse)  
library(tmap)  
data("World")

World %>%  
  tm_shape(shp = .) +  
    tm_polygons("HPI") +  
  tm_layout(bg.color = "skyblue",  
            inner.margins = c(0.15, 0.10, 0.15, 0.08),  
            title = "Happy Planet Index",  
            title.size = 1.1,  
            title.position = c("center", "top")) +  
* tm_scale_bar()
```
]]
.column[.content[
<img src="tmap_files/figure-html/basic_user_7_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(tidyverse)  
library(tmap)  
data("World")

World %>%  
  tm_shape(shp = .) +  
    tm_polygons("HPI") +  
  tm_layout(bg.color = "skyblue",  
            inner.margins = c(0.15, 0.10, 0.15, 0.08),  
            title = "Happy Planet Index",  
            title.size = 1.1,  
            title.position = c("center", "top")) +  
  tm_scale_bar() +  
* tm_compass(position = c("right", "top"))
```
]]
.column[.content[
<img src="tmap_files/figure-html/basic_user_8_output-1.png" width="100%" />
]]

---
class: inverse, center, middle

# projections

---

```r
library(tidyverse)  
library(tmap)  
data("World")

World %>%  
  tm_shape(shp = .,  
           projection = "+proj=eck4"  
           ) +  
    tm_polygons("HPI") + #BREAK  
  tm_layout(bg.color = "skyblue",  
            inner.margins = c(0.15, 0.10, 0.15, 0.08),  
            title = "Happy Planet Index",  
            title.size = 1.1,  
            title.position = c("center", "top")) + #BREAK  
  tm_scale_bar() + #BREAK  
  tm_compass(position = c("right", "top")) #BREAK  
```
]]
.column[.content[
<img src="tmap_files/figure-html/proj_rotate_1_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(tidyverse)  
library(tmap)  
data("World")

World %>%  
  tm_shape(shp = .,  
*          projection = 3857 #ROTATE
           ) +  
    tm_polygons("HPI") + #BREAK  
  tm_layout(bg.color = "skyblue",  
            inner.margins = c(0.15, 0.10, 0.15, 0.08),  
            title = "Happy Planet Index",  
            title.size = 1.1,  
            title.position = c("center", "top")) + #BREAK  
  tm_scale_bar() + #BREAK  
  tm_compass(position = c("right", "top")) #BREAK  
```
]]
.column[.content[
<img src="tmap_files/figure-html/proj_rotate_2_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(tidyverse)  
library(tmap)  
data("World")

World %>%  
  tm_shape(shp = .,  
*          projection = "+proj=merc" #ROTATE
           ) +  
    tm_polygons("HPI") + #BREAK  
  tm_layout(bg.color = "skyblue",  
            inner.margins = c(0.15, 0.10, 0.15, 0.08),  
            title = "Happy Planet Index",  
            title.size = 1.1,  
            title.position = c("center", "top")) + #BREAK  
  tm_scale_bar() + #BREAK  
  tm_compass(position = c("right", "top")) #BREAK  
```
]]
.column[.content[
<img src="tmap_files/figure-html/proj_rotate_3_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(tidyverse)  
library(tmap)  
data("World")

World %>%  
  tm_shape(shp = .,  
*          projection = "+proj=gall" #ROTATE
           ) +  
    tm_polygons("HPI") + #BREAK  
  tm_layout(bg.color = "skyblue",  
            inner.margins = c(0.15, 0.10, 0.15, 0.08),  
            title = "Happy Planet Index",  
            title.size = 1.1,  
            title.position = c("center", "top")) + #BREAK  
  tm_scale_bar() + #BREAK  
  tm_compass(position = c("right", "top")) #BREAK  
```
]]
.column[.content[
<img src="tmap_files/figure-html/proj_rotate_4_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(tidyverse)  
library(tmap)  
data("World")

World %>%  
  tm_shape(shp = .,  
*          projection = "+proj=robin" #ROTATE
           ) +  
    tm_polygons("HPI") + #BREAK  
  tm_layout(bg.color = "skyblue",  
            inner.margins = c(0.15, 0.10, 0.15, 0.08),  
            title = "Happy Planet Index",  
            title.size = 1.1,  
            title.position = c("center", "top")) + #BREAK  
  tm_scale_bar() + #BREAK  
  tm_compass(position = c("right", "top")) #BREAK  
```
]]
.column[.content[
<img src="tmap_files/figure-html/proj_rotate_5_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(tidyverse)  
library(tmap)  
data("World")

World %>%  
  tm_shape(shp = .,  
*          projection = "+proj=longlat" #ROTATE
           ) +  
    tm_polygons("HPI") + #BREAK  
  tm_layout(bg.color = "skyblue",  
            inner.margins = c(0.15, 0.10, 0.15, 0.08),  
            title = "Happy Planet Index",  
            title.size = 1.1,  
            title.position = c("center", "top")) + #BREAK  
  tm_scale_bar() + #BREAK  
  tm_compass(position = c("right", "top")) #BREAK  
```
]]
.column[.content[
<img src="tmap_files/figure-html/proj_rotate_6_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(tidyverse)  
library(tmap)  
data("World")

World %>%  
  tm_shape(shp = .,  
*          projection = "+proj=mill" #ROTATE
           ) +  
    tm_polygons("HPI") + #BREAK  
  tm_layout(bg.color = "skyblue",  
            inner.margins = c(0.15, 0.10, 0.15, 0.08),  
            title = "Happy Planet Index",  
            title.size = 1.1,  
            title.position = c("center", "top")) + #BREAK  
  tm_scale_bar() + #BREAK  
  tm_compass(position = c("right", "top")) #BREAK  
```
]]
.column[.content[
<img src="tmap_files/figure-html/proj_rotate_7_output-1.png" width="100%" />
]]

---
class: inverse, center, middle

# layering raster, polygon borders, text, symbols

---

```r
*data(World, metro, rivers, land)
```
]]
.column[.content[

]]
---
class: split-40
count: false

```r
data(World, metro, rivers, land)

*tmap_mode("plot")
```
]]
.column[.content[

]]
---
class: split-40
count: false

```r
data(World, metro, rivers, land)

tmap_mode("plot")

## tmap mode set to plotting
*land
```
]]
.column[.content[

```
  stars object with 2 dimensions and 4 attributes
  attribute(s):
                 cover                              cover_cls     
   Water bodies     :393060   Water                      :393060  
   Snow / Ice       : 61986   Snow/ice                   : 61986  
   Herbaceous       : 21377   Forest                     : 48851  
   Tree Open        : 16171   Other natural vegetation   : 32611  
   Sparse vegetation: 12247   Bare area/Sparse vegetation: 26904  
   Cropland         : 11658   Cropland                   : 17843  
   (Other)          : 66701   (Other)                    :  1945  
       trees          elevation     
   Min.   :  0.0    Min.   :-412    
   1st Qu.:  0.0    1st Qu.: 218    
   Median :  0.0    Median : 608    
   Mean   : 15.6    Mean   :1140    
   3rd Qu.: 19.0    3rd Qu.:1941    
   Max.   :100.0    Max.   :6410    
   NA's   :393060   NA's   :389580  
  dimension(s):
    from   to offset     delta                       refsys point values    
  x    1 1080   -180  0.333333 +proj=longlat +ellps=WGS8...  NULL   NULL [x]
  y    1  540     90 -0.333333 +proj=longlat +ellps=WGS8...  NULL   NULL [y]
```
]]
---
class: split-40
count: false

```r
data(World, metro, rivers, land)

tmap_mode("plot")

## tmap mode set to plotting
land %>%  
* tm_shape() +
*   tm_raster("elevation",
*             palette = terrain.colors(10))
```
]]
.column[.content[
<img src="tmap_files/figure-html/basic1_user_4_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
data(World, metro, rivers, land)

tmap_mode("plot")

## tmap mode set to plotting
land %>%  
  tm_shape() +  
    tm_raster("elevation",  
              palette = terrain.colors(10)) +  
* tm_shape(World) +
*   tm_borders("white", lwd = .5)
```
]]
.column[.content[
<img src="tmap_files/figure-html/basic1_user_5_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
data(World, metro, rivers, land)

tmap_mode("plot")

## tmap mode set to plotting
land %>%  
  tm_shape() +  
    tm_raster("elevation",  
              palette = terrain.colors(10)) +  
  tm_shape(World) +  
    tm_borders("white", lwd = .5) +  
*   tm_text("iso_a3", size = "AREA")
```
]]
.column[.content[
<img src="tmap_files/figure-html/basic1_user_6_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
data(World, metro, rivers, land)

tmap_mode("plot")

## tmap mode set to plotting
land %>%  
  tm_shape() +  
    tm_raster("elevation",  
              palette = terrain.colors(10)) +  
  tm_shape(World) +  
    tm_borders("white", lwd = .5) +  
    tm_text("iso_a3", size = "AREA") +  
* tm_shape(metro) +
*   tm_symbols(col = "red",
*              size = "pop2020",
*              scale = .5)
```
]]
.column[.content[
<img src="tmap_files/figure-html/basic1_user_7_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
data(World, metro, rivers, land)

tmap_mode("plot")

## tmap mode set to plotting
land %>%  
  tm_shape() +  
    tm_raster("elevation",  
              palette = terrain.colors(10)) +  
  tm_shape(World) +  
    tm_borders("white", lwd = .5) +  
    tm_text("iso_a3", size = "AREA") +  
  tm_shape(metro) +  
    tm_symbols(col = "red",  
               size = "pop2020",  
               scale = .5) +  
* tm_legend(show = FALSE)
```
]]
.column[.content[
<img src="tmap_files/figure-html/basic1_user_8_output-1.png" width="100%" />
]]

---
class: inverse, center, middle

# choropleth

---

```r
library(tidyverse)  
library(tmap)  
data("World")

World %>%  
  tm_shape(shp = .) +  
    tm_polygons("HPI", palette = "RdYlBu") +  
  tm_layout(bg.color = "aliceblue",  
            inner.margins = c(0.15, 0.10, 0.15, 0.08),  
            title = "Happy Planet Index",  
            title.size = 1.1,  
            title.position = c("center", "top")) +  
  tm_scale_bar() +  
  tm_compass(position = c("right", "top"))  
```
]]
.column[.content[
<img src="tmap_files/figure-html/choropleth_rotate_1_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(tidyverse)  
library(tmap)  
data("World")

World %>%  
  tm_shape(shp = .) +  
*   tm_polygons("HPI", palette = "Blues") +
  tm_layout(bg.color = "aliceblue",  
            inner.margins = c(0.15, 0.10, 0.15, 0.08),  
            title = "Happy Planet Index",  
            title.size = 1.1,  
            title.position = c("center", "top")) +  
  tm_scale_bar() +  
  tm_compass(position = c("right", "top"))  
```
]]
.column[.content[
<img src="tmap_files/figure-html/choropleth_rotate_2_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(tidyverse)  
library(tmap)  
data("World")

World %>%  
  tm_shape(shp = .) +  
*   tm_polygons("HPI", palette = paletteer_c("grDevices::Fall", 10)) +
  tm_layout(bg.color = "aliceblue",  
            inner.margins = c(0.15, 0.10, 0.15, 0.08),  
            title = "Happy Planet Index",  
            title.size = 1.1,  
            title.position = c("center", "top")) +  
  tm_scale_bar() +  
  tm_compass(position = c("right", "top"))  
```
]]
.column[.content[
<img src="tmap_files/figure-html/choropleth_rotate_3_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(tidyverse)  
library(tmap)  
data("World")

World %>%  
  tm_shape(shp = .) +  
*   tm_polygons("HPI", pal = paletteer_c("pals::jet", 10)) +
  tm_layout(bg.color = "aliceblue",  
            inner.margins = c(0.15, 0.10, 0.15, 0.08),  
            title = "Happy Planet Index",  
            title.size = 1.1,  
            title.position = c("center", "top")) +  
  tm_scale_bar() +  
  tm_compass(position = c("right", "top"))  
```
]]
.column[.content[
<img src="tmap_files/figure-html/choropleth_rotate_4_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(tidyverse)  
library(tmap)  
data("World")

World %>%  
  tm_shape(shp = .) +  
*   tm_polygons("HPI", pal = paletteer_c("gameofthrones::stark", 10)) +
  tm_layout(bg.color = "aliceblue",  
            inner.margins = c(0.15, 0.10, 0.15, 0.08),  
            title = "Happy Planet Index",  
            title.size = 1.1,  
            title.position = c("center", "top")) +  
  tm_scale_bar() +  
  tm_compass(position = c("right", "top"))  
```
]]
.column[.content[
<img src="tmap_files/figure-html/choropleth_rotate_5_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(tidyverse)  
library(tmap)  
data("World")

World %>%  
  tm_shape(shp = .) +  
*   tm_polygons("HPI", pal = viridisLite::plasma(10)) +
  tm_layout(bg.color = "aliceblue",  
            inner.margins = c(0.15, 0.10, 0.15, 0.08),  
            title = "Happy Planet Index",  
            title.size = 1.1,  
            title.position = c("center", "top")) +  
  tm_scale_bar() +  
  tm_compass(position = c("right", "top"))  
```
]]
.column[.content[
<img src="tmap_files/figure-html/choropleth_rotate_6_output-1.png" width="100%" />
]]

---
class: inverse, center, middle

# map ensembles

---

```r
*tmap_mode("plot")
```
]]
.column[.content[

]]
---
class: split-40
count: false

```r
tmap_mode("plot")

*data("World")
```
]]
.column[.content[

]]
---
class: split-40
count: false

```r
tmap_mode("plot")

data("World")

*World
```
]]
.column[.content[

```r
tmap_mode("plot")

data("World")

World %>%  
* filter(name != "Antarctica")
```
]]
.column[.content[

```
  Simple feature collection with 176 features and 15 fields
  geometry type:  MULTIPOLYGON
  dimension:      XY
  bbox:           xmin: -16656120 ymin: -6609565 xmax: 16656120 ymax: 8375779
  CRS:            +proj=eck4 +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs
  First 10 features:
     iso_a3                   name           sovereignt               continent
  1     AFG            Afghanistan          Afghanistan                    Asia
  2     AGO                 Angola               Angola                  Africa
  3     ALB                Albania              Albania                  Europe
  4     ARE   United Arab Emirates United Arab Emirates                    Asia
  5     ARG              Argentina            Argentina           South America
  6     ARM                Armenia              Armenia                    Asia
  7     ATF Fr. S. Antarctic Lands               France Seven seas (open ocean)
  8     AUS              Australia            Australia                 Oceania
  9     AUT                Austria              Austria                  Europe
  10    AZE             Azerbaijan           Azerbaijan                    Asia
                   area  pop_est pop_est_dens                    economy
  1   652860.000 [km^2] 28400000  43.50090372  7. Least developed region
  2  1246700.000 [km^2] 12799293  10.26653806  7. Least developed region
  3    27400.000 [km^2]  3639453 132.82675182       6. Developing region
  4    71252.172 [km^2]  4798491  67.34518909       6. Developing region
  5  2736690.000 [km^2] 40913584  14.95002503    5. Emerging region: G20
  6    28470.000 [km^2]  2967004 104.21510362       6. Developing region
  7     7257.455 [km^2]      140   0.01929051       6. Developing region
  8  7682300.000 [km^2] 21262641   2.76774417 2. Developed region: nonG7
  9    82523.000 [km^2]  8210281  99.49082074 2. Developed region: nonG7
  10   82663.000 [km^2]  8238672  99.66577550       6. Developing region
                  income_grp gdp_cap_est life_exp well_being footprint inequality
  1            5. Low income    784.1549   59.668        3.8      0.79 0.42655744
  2   3. Upper middle income   8617.6635       NA         NA        NA         NA
  3   4. Lower middle income   5992.6588   77.347        5.5      2.21 0.16513372
  4  2. High income: nonOECD  38407.9078       NA         NA        NA         NA
  5   3. Upper middle income  14027.1261   75.927        6.5      3.14 0.16423830
  6   4. Lower middle income   6326.2469   74.446        4.3      2.23 0.21664810
  7  2. High income: nonOECD 114285.7143       NA         NA        NA         NA
  8     1. High income: OECD  37634.0832   82.052        7.2      9.31 0.08067825
  9     1. High income: OECD  40132.6093   81.004        7.4      6.06 0.07129351
  10  3. Upper middle income   9420.2075       NA         NA        NA         NA
          HPI                       geometry
  1  20.22535 MULTIPOLYGON (((5310471 451...
  2        NA MULTIPOLYGON (((1531585 -77...
  3  36.76687 MULTIPOLYGON (((1729835 521...
  4        NA MULTIPOLYGON (((4675864 313...
  5  35.19024 MULTIPOLYGON (((-5017766 -6...
  6  25.66642 MULTIPOLYGON (((3677241 513...
  7        NA MULTIPOLYGON (((5550200 -59...
  8  21.22897 MULTIPOLYGON (((12288137 -5...
  9  30.47822 MULTIPOLYGON (((1371802 588...
  10       NA MULTIPOLYGON (((3825061 498...
```
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

data("World")

World %>%  
  filter(name != "Antarctica") %>%  
* tm_shape() +
*   tm_polygons(c("HPI", "economy"))
```
]]
.column[.content[
<img src="tmap_files/figure-html/ensemble_user_5_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

data("World")

World %>%  
  filter(name != "Antarctica") %>%  
  tm_shape() +  
    tm_polygons(c("HPI", "economy")) +  
*   tm_facets(sync = TRUE, ncol = 2)
```
]]
.column[.content[
<img src="tmap_files/figure-html/ensemble_user_6_output-1.png" width="100%" />
]]

---
class: inverse, center, middle

# filtering sf objects using dplyr

---

```r
tmap_mode("plot")

data("World")

World %>%  
  filter(continent == "Asia") %>%  
  tm_shape(shp = .) +  
    tm_polygons(c("HPI"))  
```
]]
.column[.content[
<img src="tmap_files/figure-html/region_filter_rotate_1_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

data("World")

World %>%  
* filter(continent == "Africa") %>%
  tm_shape(shp = .) +  
    tm_polygons(c("HPI"))  
```
]]
.column[.content[
<img src="tmap_files/figure-html/region_filter_rotate_2_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

data("World")

World %>%  
* filter(continent == "Europe") %>%
  tm_shape(shp = .) +  
    tm_polygons(c("HPI"))  
```
]]
.column[.content[
<img src="tmap_files/figure-html/region_filter_rotate_3_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

data("World")

World %>%  
* filter(continent == "South America") %>%
  tm_shape(shp = .) +  
    tm_polygons(c("HPI"))  
```
]]
.column[.content[
<img src="tmap_files/figure-html/region_filter_rotate_4_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

data("World")

World %>%  
* filter(continent == "Oceania") %>%
  tm_shape(shp = .) +  
    tm_polygons(c("HPI"))  
```
]]
.column[.content[
<img src="tmap_files/figure-html/region_filter_rotate_5_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

data("World")

World %>%  
* filter(name != "Antarctica") %>%
  tm_shape(shp = .) +  
    tm_polygons(c("HPI"))  
```
]]
.column[.content[
<img src="tmap_files/figure-html/region_filter_rotate_6_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

data("World")

World %>%  
* filter(name %in% c("Australia", "New Zealand")) %>%
  tm_shape(shp = .) +  
    tm_polygons(c("HPI"))  
```
]]
.column[.content[
<img src="tmap_files/figure-html/region_filter_rotate_7_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

data("World")

World %>%  
* filter(continent == "Europe") %>%
  tm_shape(shp = .) +  
    tm_polygons(c("HPI"))  
```
]]
.column[.content[
<img src="tmap_files/figure-html/region_filter_rotate_8_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

data("World")

World %>%  
* filter(continent == "Europe" & name != "Russia") %>%
  tm_shape(shp = .) +  
    tm_polygons(c("HPI"))  
```
]]
.column[.content[
<img src="tmap_files/figure-html/region_filter_rotate_9_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

data("World")

World %>%  
* filter(continent == "North America") %>%
  tm_shape(shp = .) +  
    tm_polygons(c("HPI"))  
```
]]
.column[.content[
<img src="tmap_files/figure-html/region_filter_rotate_10_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

data("World")

World %>%  
* filter(continent == "North America" & name != "Greenland") %>%
  tm_shape(shp = .) +  
    tm_polygons(c("HPI"))  
```
]]
.column[.content[
<img src="tmap_files/figure-html/region_filter_rotate_11_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

data("World")

World %>%  
* filter(!is.na("HPI")) %>%
  tm_shape(shp = .) +  
    tm_polygons(c("HPI"))  
```
]]
.column[.content[
<img src="tmap_files/figure-html/region_filter_rotate_12_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

data("World")

World %>%  
* filter(economy == "5. Emerging region: G20") %>%
  tm_shape(shp = .) +  
    tm_polygons(c("HPI"))  
```
]]
.column[.content[
<img src="tmap_files/figure-html/region_filter_rotate_13_output-1.png" width="100%" />
]]

---
class: inverse, center, middle

# raster data

---

```r
library(paletteer)  
data(World, metro, rivers, land)

tmap_mode("plot")

## tmap mode set to plotting
land %>%  
  tm_shape() +  
    tm_raster("elevation", palette = terrain.colors(10)) +  
  tm_shape(World %>% filter(name != "Antarctica")) +  
    tm_borders("white", lwd = .5) +  
    tm_text("iso_a3", size = "AREA") +  
  tm_shape(metro) +  
    tm_symbols(col = "red",  
               size = "pop2020",  
               scale = .5) +  
  tm_legend(show = FALSE)  
```
]]
.column[.content[
<img src="tmap_files/figure-html/raster_rotate_1_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(paletteer)  
data(World, metro, rivers, land)

tmap_mode("plot")

## tmap mode set to plotting
land %>%  
  tm_shape() +  
*   tm_raster("elevation", pal = paletteer_c("gameofthrones::lannister", n = 10)) +
  tm_shape(World %>% filter(name != "Antarctica")) +  
    tm_borders("white", lwd = .5) +  
    tm_text("iso_a3", size = "AREA") +  
  tm_shape(metro) +  
    tm_symbols(col = "red",  
               size = "pop2020",  
               scale = .5) +  
  tm_legend(show = FALSE)  
```
]]
.column[.content[
<img src="tmap_files/figure-html/raster_rotate_2_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(paletteer)  
data(World, metro, rivers, land)

tmap_mode("plot")

## tmap mode set to plotting
land %>%  
  tm_shape() +  
*   tm_raster("trees", palette = terrain.colors(10)) +
  tm_shape(World %>% filter(name != "Antarctica")) +  
    tm_borders("white", lwd = .5) +  
    tm_text("iso_a3", size = "AREA") +  
  tm_shape(metro) +  
    tm_symbols(col = "red",  
               size = "pop2020",  
               scale = .5) +  
  tm_legend(show = FALSE)  
```
]]
.column[.content[
<img src="tmap_files/figure-html/raster_rotate_3_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(paletteer)  
data(World, metro, rivers, land)

tmap_mode("plot")

## tmap mode set to plotting
land %>%  
  tm_shape() +  
*   tm_raster("trees", pal = paletteer_c("gameofthrones::jon_snow", n = 5)) +
  tm_shape(World %>% filter(name != "Antarctica")) +  
    tm_borders("white", lwd = .5) +  
    tm_text("iso_a3", size = "AREA") +  
  tm_shape(metro) +  
    tm_symbols(col = "red",  
               size = "pop2020",  
               scale = .5) +  
  tm_legend(show = FALSE)  
```
]]
.column[.content[
<img src="tmap_files/figure-html/raster_rotate_4_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(paletteer)  
data(World, metro, rivers, land)

tmap_mode("plot")

## tmap mode set to plotting
land %>%  
  tm_shape() +  
*   tm_raster("trees", pal = viridisLite::magma(7)) +
  tm_shape(World %>% filter(name != "Antarctica")) +  
    tm_borders("white", lwd = .5) +  
    tm_text("iso_a3", size = "AREA") +  
  tm_shape(metro) +  
    tm_symbols(col = "red",  
               size = "pop2020",  
               scale = .5) +  
  tm_legend(show = FALSE)  
```
]]
.column[.content[
<img src="tmap_files/figure-html/raster_rotate_5_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(paletteer)  
data(World, metro, rivers, land)

tmap_mode("plot")

## tmap mode set to plotting
land %>%  
  tm_shape() +  
*   tm_raster("trees", pal = viridisLite::viridis(7)) +
  tm_shape(World %>% filter(name != "Antarctica")) +  
    tm_borders("white", lwd = .5) +  
    tm_text("iso_a3", size = "AREA") +  
  tm_shape(metro) +  
    tm_symbols(col = "red",  
               size = "pop2020",  
               scale = .5) +  
  tm_legend(show = FALSE)  
```
]]
.column[.content[
<img src="tmap_files/figure-html/raster_rotate_6_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(paletteer)  
data(World, metro, rivers, land)

tmap_mode("plot")

## tmap mode set to plotting
land %>%  
  tm_shape() +  
*   tm_raster("trees", pal = viridisLite::cividis(7)) +
  tm_shape(World %>% filter(name != "Antarctica")) +  
    tm_borders("white", lwd = .5) +  
    tm_text("iso_a3", size = "AREA") +  
  tm_shape(metro) +  
    tm_symbols(col = "red",  
               size = "pop2020",  
               scale = .5) +  
  tm_legend(show = FALSE)  
```
]]
.column[.content[
<img src="tmap_files/figure-html/raster_rotate_7_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(paletteer)  
data(World, metro, rivers, land)

tmap_mode("plot")

## tmap mode set to plotting
land %>%  
  tm_shape() +  
*   tm_raster("trees", pal = viridisLite::inferno(7)) +
  tm_shape(World %>% filter(name != "Antarctica")) +  
    tm_borders("white", lwd = .5) +  
    tm_text("iso_a3", size = "AREA") +  
  tm_shape(metro) +  
    tm_symbols(col = "red",  
               size = "pop2020",  
               scale = .5) +  
  tm_legend(show = FALSE)  
```
]]
.column[.content[
<img src="tmap_files/figure-html/raster_rotate_8_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(paletteer)  
data(World, metro, rivers, land)

tmap_mode("plot")

## tmap mode set to plotting
land %>%  
  tm_shape() +  
*   tm_raster("cover_cls", palette = terrain.colors(10)) +
  tm_shape(World %>% filter(name != "Antarctica")) +  
    tm_borders("white", lwd = .5) +  
    tm_text("iso_a3", size = "AREA") +  
  tm_shape(metro) +  
    tm_symbols(col = "red",  
               size = "pop2020",  
               scale = .5) +  
  tm_legend(show = FALSE)  
```
]]
.column[.content[
<img src="tmap_files/figure-html/raster_rotate_9_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
library(paletteer)  
data(World, metro, rivers, land)

tmap_mode("plot")

## tmap mode set to plotting
land %>%  
  tm_shape() +  
*   tm_raster("cover", palette = terrain.colors(10)) +
  tm_shape(World %>% filter(name != "Antarctica")) +  
    tm_borders("white", lwd = .5) +  
    tm_text("iso_a3", size = "AREA") +  
  tm_shape(metro) +  
    tm_symbols(col = "red",  
               size = "pop2020",  
               scale = .5) +  
  tm_legend(show = FALSE)  
```
]]
.column[.content[
<img src="tmap_files/figure-html/raster_rotate_10_output-1.png" width="100%" />
]]

---
class: inverse, center, middle

# facets

---

```r
*tmap_mode("plot")
```
]]
.column[.content[

]]
---
class: split-40
count: false

```r
tmap_mode("plot")  
## tmap mode set to plotting

*data(NLD_muni)  # Netherlands
```
]]
.column[.content[

]]
---
class: split-40
count: false

```r
tmap_mode("plot")  
## tmap mode set to plotting

data(NLD_muni)  # Netherlands

*NLD_muni
```
]]
.column[.content[

```
  Simple feature collection with 403 features and 14 fields
  geometry type:  MULTIPOLYGON
  dimension:      XY
  bbox:           xmin: 13565.4 ymin: 306846.9 xmax: 277992.8 ymax: 619292
  CRS:            EPSG:28992
  First 10 features:
    code                name  province population pop_men pop_women pop_0_14
  0 0003          Appingedam Groningen      12065    5845      6215       15
  1 0005               Bedum Groningen      10495    5245      5245       17
  2 0007        Bellingwedde Groningen       8920    4450      4470       14
  3 0009            Ten Boer Groningen       7480    3730      3750       20
  4 0010            Delfzijl Groningen      25695   12860     12835       15
  5 0014           Groningen Groningen     198315   98425     99890       12
  6 0015          Grootegast Groningen      12165    6210      5950       19
  7 0017               Haren Groningen      18780    8925      9855       17
  8 0018 Hoogezand-Sappemeer Groningen      34305   16950     17350       16
  9 0022                Leek Groningen      19595    9690      9905       18
    pop_15_24 pop_25_44 pop_45_64 pop_65plus origin_native origin_west
  0        11        22        30         22            87           8
  1        12        21        31         19            94           4
  2        10        19        34         24            90           8
  3        11        22        29         18            93           4
  4        11        21        30         23            83           8
  5        24        30        21         12            78          11
  6        12        24        28         16            96           3
  7        10        18        30         26            88           8
  8        11        24        29         21            82           7
  9        11        22        30         20            91           6
    origin_non_west                       geometry
  0               5 MULTIPOLYGON (((251260.5 59...
  1               2 MULTIPOLYGON (((235657.7 59...
  2               2 MULTIPOLYGON (((269992.6 56...
  3               3 MULTIPOLYGON (((245788.1 59...
  4               9 MULTIPOLYGON (((262016.6 58...
  5              11 MULTIPOLYGON (((239007.3 57...
  6               1 MULTIPOLYGON (((217219.8 58...
  7               4 MULTIPOLYGON (((241309.5 57...
  8              11 MULTIPOLYGON (((238747.6 57...
  9               3 MULTIPOLYGON (((226022.4 57...
```
]]
---
class: split-40
count: false

```r
tmap_mode("plot")  
## tmap mode set to plotting

data(NLD_muni)  # Netherlands

NLD_muni %>%  
* mutate(perc_men = pop_men / population * 100)
```
]]
.column[.content[

```
  Simple feature collection with 403 features and 15 fields
  geometry type:  MULTIPOLYGON
  dimension:      XY
  bbox:           xmin: 13565.4 ymin: 306846.9 xmax: 277992.8 ymax: 619292
  CRS:            EPSG:28992
  First 10 features:
     code                name  province population pop_men pop_women pop_0_14
  1  0003          Appingedam Groningen      12065    5845      6215       15
  2  0005               Bedum Groningen      10495    5245      5245       17
  3  0007        Bellingwedde Groningen       8920    4450      4470       14
  4  0009            Ten Boer Groningen       7480    3730      3750       20
  5  0010            Delfzijl Groningen      25695   12860     12835       15
  6  0014           Groningen Groningen     198315   98425     99890       12
  7  0015          Grootegast Groningen      12165    6210      5950       19
  8  0017               Haren Groningen      18780    8925      9855       17
  9  0018 Hoogezand-Sappemeer Groningen      34305   16950     17350       16
  10 0022                Leek Groningen      19595    9690      9905       18
     pop_15_24 pop_25_44 pop_45_64 pop_65plus origin_native origin_west
  1         11        22        30         22            87           8
  2         12        21        31         19            94           4
  3         10        19        34         24            90           8
  4         11        22        29         18            93           4
  5         11        21        30         23            83           8
  6         24        30        21         12            78          11
  7         12        24        28         16            96           3
  8         10        18        30         26            88           8
  9         11        24        29         21            82           7
  10        11        22        30         20            91           6
     origin_non_west                       geometry perc_men
  1                5 MULTIPOLYGON (((251260.5 59... 48.44592
  2                2 MULTIPOLYGON (((235657.7 59... 49.97618
  3                2 MULTIPOLYGON (((269992.6 56... 49.88789
  4                3 MULTIPOLYGON (((245788.1 59... 49.86631
  5                9 MULTIPOLYGON (((262016.6 58... 50.04865
  6               11 MULTIPOLYGON (((239007.3 57... 49.63064
  7                1 MULTIPOLYGON (((217219.8 58... 51.04809
  8                4 MULTIPOLYGON (((241309.5 57... 47.52396
  9               11 MULTIPOLYGON (((238747.6 57... 49.40971
  10               3 MULTIPOLYGON (((226022.4 57... 49.45139
```
]]
---
class: split-40
count: false

```r
tmap_mode("plot")  
## tmap mode set to plotting

data(NLD_muni)  # Netherlands

NLD_muni %>%  
  mutate(perc_men = pop_men / population * 100)  %>%  
* tm_shape() +
*   tm_polygons("perc_men",
*               palette = "RdYlBu")
```
]]
.column[.content[
<img src="tmap_files/figure-html/facets_user_5_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")  
## tmap mode set to plotting

data(NLD_muni)  # Netherlands

NLD_muni %>%  
  mutate(perc_men = pop_men / population * 100)  %>%  
  tm_shape() +  
    tm_polygons("perc_men",  
                palette = "RdYlBu") +  
*   tm_facets(by = "province")
```
]]
.column[.content[
<img src="tmap_files/figure-html/facets_user_6_output-1.png" width="100%" />
]]

---

```r
*tmap_mode("plot")
```
]]
.column[.content[

]]
---
class: split-40
count: false

```r
tmap_mode("plot")  
## tmap mode set to plotting

*data("World")  # Netherlands
```
]]
.column[.content[

]]
---
class: split-40
count: false

```r
tmap_mode("plot")  
## tmap mode set to plotting

data("World")  # Netherlands

*World
```
]]
.column[.content[

```r
tmap_mode("plot")  
## tmap mode set to plotting

data("World")  # Netherlands

World %>%  
* tm_shape() +
*   tm_polygons("HPI",
*               palette = "RdYlBu")
```
]]
.column[.content[
<img src="tmap_files/figure-html/facets_world_user_4_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")  
## tmap mode set to plotting

data("World")  # Netherlands

World %>%  
  tm_shape() +  
    tm_polygons("HPI",  
                palette = "RdYlBu") +  
*   tm_facets(by = "continent")
```
]]
.column[.content[
<img src="tmap_files/figure-html/facets_world_user_5_output-1.png" width="100%" />
]]

---

```r
*tmap_mode("plot")
```
]]
.column[.content[

]]
---
class: split-40
count: false

```r
tmap_mode("plot")  
## tmap mode set to plotting

*data("World")  # Netherlands
```
]]
.column[.content[

]]
---
class: split-40
count: false

```r
tmap_mode("plot")  
## tmap mode set to plotting

data("World")  # Netherlands

*World %>%
* filter(name != "Anarctica")
```
]]
.column[.content[

```r
tmap_mode("plot")  
## tmap mode set to plotting

data("World")  # Netherlands

World %>%  
  filter(name != "Anarctica") %>%  
* tm_shape() +
*   tm_polygons()
```
]]
.column[.content[
<img src="tmap_files/figure-html/facets_economy_user_4_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")  
## tmap mode set to plotting

data("World")  # Netherlands

World %>%  
  filter(name != "Anarctica") %>%  
  tm_shape() +  
    tm_polygons() +  
*   tm_facets(by = "economy",
*             free.coords = FALSE)
```
]]
.column[.content[
<img src="tmap_files/figure-html/facets_economy_user_5_output-1.png" width="100%" />
]]

---
class: inverse, center, middle

# {tmap} styles

---

```r
tmap_mode("plot")

tmap_style("classic")

data("World")

tm_shape(shp = World) +  
    tm_polygons("HPI",  
                legend.title = "Happy Planet Index")  
```
]]
.column[.content[
<img src="tmap_files/figure-html/style_rotate_1_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

*tmap_style("white")

data("World")

tm_shape(shp = World) +  
    tm_polygons("HPI",  
                legend.title = "Happy Planet Index")  
```
]]
.column[.content[
<img src="tmap_files/figure-html/style_rotate_2_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

*tmap_style("gray")

data("World")

tm_shape(shp = World) +  
    tm_polygons("HPI",  
                legend.title = "Happy Planet Index")  
```
]]
.column[.content[
<img src="tmap_files/figure-html/style_rotate_3_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

*tmap_style("natural")

data("World")

tm_shape(shp = World) +  
    tm_polygons("HPI",  
                legend.title = "Happy Planet Index")  
```
]]
.column[.content[
<img src="tmap_files/figure-html/style_rotate_4_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

*tmap_style("cobalt")

data("World")

tm_shape(shp = World) +  
    tm_polygons("HPI",  
                legend.title = "Happy Planet Index")  
```
]]
.column[.content[
<img src="tmap_files/figure-html/style_rotate_5_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

*tmap_style("col_blind")

data("World")

tm_shape(shp = World) +  
    tm_polygons("HPI",  
                legend.title = "Happy Planet Index")  
```
]]
.column[.content[
<img src="tmap_files/figure-html/style_rotate_6_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

*tmap_style("albatross")

data("World")

tm_shape(shp = World) +  
    tm_polygons("HPI",  
                legend.title = "Happy Planet Index")  
```
]]
.column[.content[
<img src="tmap_files/figure-html/style_rotate_7_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

*tmap_style("beaver")

data("World")

tm_shape(shp = World) +  
    tm_polygons("HPI",  
                legend.title = "Happy Planet Index")  
```
]]
.column[.content[
<img src="tmap_files/figure-html/style_rotate_8_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

*tmap_style("bw")

data("World")

tm_shape(shp = World) +  
    tm_polygons("HPI",  
                legend.title = "Happy Planet Index")  
```
]]
.column[.content[
<img src="tmap_files/figure-html/style_rotate_9_output-1.png" width="100%" />
]]
---
class: split-40
count: false

```r
tmap_mode("plot")

*tmap_style("watercolor")

data("World")

tm_shape(shp = World) +  
    tm_polygons("HPI",  
                legend.title = "Happy Planet Index")  
```
]]
.column[.content[
<img src="tmap_files/figure-html/style_rotate_10_output-1.png" width="100%" />
]]

---

r chunk_reveal("watercolor", break_type = "user")`