Creating an Interactive Map

# Creating an Interactive Map
## Getting Started with tmap
### Conner Surrency, July 2020

---

# What Are We Doing?
--

- We are going to walk through the process of creating a simple interactive map using tmap.
--

- We will start by walking through the steps of creating a simple standard map. After that, we'll transform it into an interactive one.
--

- In this case, we will narrow the data down to only information related to Australia, and then display each city in the nation based on its population.

---

## Set-Up
--

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

- tidyverse
--

- tmap

---

```r
*library(tidyverse)
*library(tmap)

## We will be using data from "world" and "Metro"
*data(World, metro)

## We will filter the metro datasets down so that we only have
## incidences within Australia
*metro
```
]
 
.panel2-standard-user[

```
Simple feature collection with 436 features and 12 fields
geometry type:  POINT
dimension:      XY
bbox:           xmin: -123.1193 ymin: -37.814 xmax: 174.7667 ymax: 60.16925
geographic CRS: WGS 84
First 10 features:
           name             name_long iso_a3 pop1950 pop1960 pop1970 pop1980
2         Kabul                 Kabul    AFG  170784  285352  471891  977824
8       Algiers El Djazair  (Algiers)    DZA  516450  871636 1281127 1621442
13       Luanda                Luanda    AGO  138413  219427  459225  771349
16 Buenos Aires          Buenos Aires    ARG 5097612 6597634 8104621 9422362
17      Cordoba               Cordoba    ARG  429249  605309  809794 1009521
25      Rosario               Rosario    ARG  554483  671349  816230  953491
32      Yerevan               Yerevan    ARM  341432  537759  778158 1041587
33     Adelaide              Adelaide    AUS  429277  571822  850168  971856
34     Brisbane              Brisbane    AUS  441718  602999  904777 1134833
37    Melbourne             Melbourne    AUS 1331966 1851220 2499109 2839019
    pop1990  pop2000  pop2010  pop2020  pop2030                    geometry
2   1549320  2401109  3722320  5721697  8279607   POINT (69.17246 34.52889)
8   1797068  2140577  2432023  2835218  3404575     POINT (3.04197 36.7525)
13  1390240  2591388  4508434  6836849 10428756   POINT (13.23432 -8.83682)
16 10513284 12406780 14245871 15894307 16956491 POINT (-58.40037 -34.60508)
17  1200168  1347561  1459268  1562509  1718192  POINT (-64.18105 -31.4135)
25  1083819  1152387  1298073  1453814  1606993 POINT (-60.63932 -32.94682)
32  1174524  1111301  1065597  1023703  1057459     POINT (44.51462 40.182)
33  1081618  1141623  1217990  1320783  1505422  POINT (138.5986 -34.92866)
34  1381306  1666203  2033617  2388517  2721325  POINT (153.0281 -27.46794)
37  3154314  3460541  3951216  4500501  5070873    POINT (144.9633 -37.814)
```
]

---
count: false
 
## Building a Simple Map
.panel1-standard-user[

```r
library(tidyverse)  
library(tmap)

## We will be using data from "world" and "Metro"
data(World, metro)

## We will filter the metro datasets down so that we only have
## incidences within Australia
metro %>%  
* filter(iso_a3 == "AUS")
```
]
 
.panel2-standard-user[

```
Simple feature collection with 5 features and 12 fields
geometry type:  POINT
dimension:      XY
bbox:           xmin: 115.8525 ymin: -37.814 xmax: 153.0281 ymax: -27.46794
geographic CRS: WGS 84
       name name_long iso_a3 pop1950 pop1960 pop1970 pop1980 pop1990 pop2000
1  Adelaide  Adelaide    AUS  429277  571822  850168  971856 1081618 1141623
2  Brisbane  Brisbane    AUS  441718  602999  904777 1134833 1381306 1666203
3 Melbourne Melbourne    AUS 1331966 1851220 2499109 2839019 3154314 3460541
4     Perth     Perth    AUS  310759  409131  687856  921623 1194665 1432387
5    Sydney    Sydney    AUS 1689935 2134673 2892477 3252111 3631940 4052499
  pop2010 pop2020 pop2030                   geometry
1 1217990 1320783 1505422 POINT (138.5986 -34.92866)
2 2033617 2388517 2721325 POINT (153.0281 -27.46794)
3 3951216 4500501 5070873   POINT (144.9633 -37.814)
4 1697517 2036118 2329184 POINT (115.8525 -31.94784)
5 4364380 4729406 5300933 POINT (151.2073 -33.86785)
```
]

---
count: false
 
## Building a Simple Map
.panel1-standard-user[

```r
library(tidyverse)  
library(tmap)

## We will be using data from "world" and "Metro"
data(World, metro)

## We will filter the metro datasets down so that we only have
## incidences within Australia
metro %>%  
  filter(iso_a3 == "AUS") ->  
* metro_australia
```
]
 
.panel2-standard-user[

]

---
count: false
 
## Building a Simple Map
.panel1-standard-user[

```r
library(tidyverse)  
library(tmap)

## We will be using data from "world" and "Metro"
data(World, metro)

## We will filter the metro datasets down so that we only have
## incidences within Australia
metro %>%  
  filter(iso_a3 == "AUS") ->  
  metro_australia

## Setting the mode as "Plot" will allow us to create a static map
*tmap_mode("plot")

*World
```
]
 
.panel2-standard-user[

```
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...
```
]

---
count: false
 
## Building a Simple Map
.panel1-standard-user[

```r
library(tidyverse)  
library(tmap)

## We will be using data from "world" and "Metro"
data(World, metro)

## We will filter the metro datasets down so that we only have
## incidences within Australia
metro %>%  
  filter(iso_a3 == "AUS") ->  
  metro_australia

## Setting the mode as "Plot" will allow us to create a static map
tmap_mode("plot")

World %>%  
## Here we will filter the "World" set down to just Australia as well
* filter(name == "Australia")
```
]
 
.panel2-standard-user[

```
Simple feature collection with 1 feature and 15 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: 10046010 ymin: -5409508 xmax: 13812740 ymax: -1401394
CRS:            +proj=eck4 +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs
  iso_a3      name sovereignt continent           area  pop_est pop_est_dens
1    AUS Australia  Australia   Oceania 7682300 [km^2] 21262641     2.767744
                     economy           income_grp gdp_cap_est life_exp
1 2. Developed region: nonG7 1. High income: OECD    37634.08   82.052
  well_being footprint inequality      HPI                       geometry
1        7.2      9.31 0.08067825 21.22897 MULTIPOLYGON (((12288137 -5...
```
]

---
count: false
 
## Building a Simple Map
.panel1-standard-user[

```r
library(tidyverse)  
library(tmap)

## We will be using data from "world" and "Metro"
data(World, metro)

## We will filter the metro datasets down so that we only have
## incidences within Australia
metro %>%  
  filter(iso_a3 == "AUS") ->  
  metro_australia

## Setting the mode as "Plot" will allow us to create a static map
tmap_mode("plot")

---
count: false
 
## Building a Simple Map
.panel1-standard-user[

```r
library(tidyverse)  
library(tmap)

## We will be using data from "world" and "Metro"
data(World, metro)

## We will filter the metro datasets down so that we only have
## incidences within Australia
metro %>%  
  filter(iso_a3 == "AUS") ->  
  metro_australia

## Setting the mode as "Plot" will allow us to create a static map
tmap_mode("plot")

World %>%  
## Here we will filter the "World" set down to just Australia as well
  filter(name == "Australia") %>%  
## Here we decide the projection type and create polygon shape to
## represent the country
  tm_shape(shp = .,  
           projection = "+proj=merc") +  
    tm_polygons("name",  
                legend.show = F) +  
## Finally we add the datapoints representing cities in Australia
* tm_shape(metro_australia) +
*   tm_symbols(col = "orange",
*              size = "pop2020",
*              scale = 1,
*              legend.size.show = T)
```
]
 
.panel2-standard-user[
<img src="AUS_tmap_files/figure-html/standard_user_07_output-1.png" width="432" />
]

---

## Transforming into an Interactive Map
--

- Turning a static map into an interactive one is extremely easy with tmap.
--

- Simply change the tmap_mode() from "plot" to "view", that's all there is to it.
--

- After we've changed the map mode, you can go ahead and explore the new interactive map. Click-and-drag, scroll in-or-out, toggle layers, and click on data points for more information.

---

```r
library(tidyverse)  
library(tmap)

data(World, metro)

metro_australia <- metro %>% filter(iso_a3 == "AUS")

## Swapping "plot" for "view" transforms the map from static
## to interactive
*tmap_mode("plot")

## Simple as that. Try exploring the map now, drag, scroll, click

---
count: false
 
## Transforming to Interactive
.panel1-interactive-rotate[

```r
library(tidyverse)  
library(tmap)

data(World, metro)

metro_australia <- metro %>% filter(iso_a3 == "AUS")

## Swapping "plot" for "view" transforms the map from static
## to interactive
*tmap_mode("view")

## Simple as that. Try exploring the map now, drag, scroll, click

World %>%  
  filter(name == "Australia") %>%  
  tm_shape(shp = .,  
           projection = "+proj=merc") +  
    tm_polygons("name",  
                legend.show = F) +  
  tm_shape(metro_australia) +  
    tm_symbols(col = "orange",  
               size = "pop2020",  
               scale = 1,  
               legend.size.show = T)  
```
]
 
.panel2-interactive-rotate[
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]

---
## Bonus Content: Map Projections
--

- By default, the projection type will be defined by the projection used by the shp object itself. However, its easy to manually switch out projections in tmap as an argument of tm_shape.
--

- We will rotate through a few good projection types on our first static map.

---

```r
library(tidyverse)  
library(tmap)

data(World, rivers)

tmap_mode("plot")

World %>%  
  tm_shape(shp = .,  
## Mercator
*          projection = "+proj=merc") +
## Robinson
## Wagner I  
## Interrupted Goode Homolosine  
## Nicolosi Globular  
## Lagrange  
## Van Der Grinten I  
## Tobler-Mercator  
## American Polyconic  
## Orthographic  
## Lambert Equal Area Conic  
    tm_polygons("name",  
                legend.show = F) +  
  tm_shape(rivers) +  
    tm_lines("lightblue",  
             lwd = "strokelwd",  
             scale = 3,  
             legend.lwd.show = F)  
```
]
 
.panel2-worldmap-rotate[
<img src="AUS_tmap_files/figure-html/worldmap_rotate_01_output-1.png" width="432" />
]

---
count: false
 
## Projections
.panel1-worldmap-rotate[

```r
library(tidyverse)  
library(tmap)

data(World, rivers)

tmap_mode("plot")

World %>%  
  tm_shape(shp = .,  
## Mercator
## Robinson
*          projection = "+proj=robin") +  #ROTATE
## Wagner I  
## Interrupted Goode Homolosine  
## Nicolosi Globular  
## Lagrange  
## Van Der Grinten I  
## Tobler-Mercator  
## American Polyconic  
## Orthographic  
## Lambert Equal Area Conic  
    tm_polygons("name",  
                legend.show = F) +  
  tm_shape(rivers) +  
    tm_lines("lightblue",  
             lwd = "strokelwd",  
             scale = 3,  
             legend.lwd.show = F)  
```
]
 
.panel2-worldmap-rotate[
<img src="AUS_tmap_files/figure-html/worldmap_rotate_02_output-1.png" width="432" />
]

---
count: false
 
## Projections
.panel1-worldmap-rotate[

```r
library(tidyverse)  
library(tmap)

data(World, rivers)

tmap_mode("plot")

World %>%  
  tm_shape(shp = .,  
## Mercator
## Robinson
## Wagner I  
*          projection = "+proj=wag1") +  #ROTATE
## Interrupted Goode Homolosine  
## Nicolosi Globular  
## Lagrange  
## Van Der Grinten I  
## Tobler-Mercator  
## American Polyconic  
## Orthographic  
## Lambert Equal Area Conic  
    tm_polygons("name",  
                legend.show = F) +  
  tm_shape(rivers) +  
    tm_lines("lightblue",  
             lwd = "strokelwd",  
             scale = 3,  
             legend.lwd.show = F)  
```
]
 
.panel2-worldmap-rotate[
<img src="AUS_tmap_files/figure-html/worldmap_rotate_03_output-1.png" width="432" />
]

---
count: false
 
## Projections
.panel1-worldmap-rotate[

```r
library(tidyverse)  
library(tmap)

data(World, rivers)

tmap_mode("plot")

World %>%  
  tm_shape(shp = .,  
## Mercator
## Robinson
## Wagner I  
## Interrupted Goode Homolosine  
*          projection = "+proj=igh") +    #ROTATE
## Nicolosi Globular  
## Lagrange  
## Van Der Grinten I  
## Tobler-Mercator  
## American Polyconic  
## Orthographic  
## Lambert Equal Area Conic  
    tm_polygons("name",  
                legend.show = F) +  
  tm_shape(rivers) +  
    tm_lines("lightblue",  
             lwd = "strokelwd",  
             scale = 3,  
             legend.lwd.show = F)  
```
]
 
.panel2-worldmap-rotate[
<img src="AUS_tmap_files/figure-html/worldmap_rotate_04_output-1.png" width="432" />
]

---
count: false
 
## Projections
.panel1-worldmap-rotate[

```r
library(tidyverse)  
library(tmap)

data(World, rivers)

tmap_mode("plot")

World %>%  
  tm_shape(shp = .,  
## Mercator
## Robinson
## Wagner I  
## Interrupted Goode Homolosine  
## Nicolosi Globular  
*          projection = "+proj=nicol") +   #ROTATE
## Lagrange  
## Van Der Grinten I  
## Tobler-Mercator  
## American Polyconic  
## Orthographic  
## Lambert Equal Area Conic  
    tm_polygons("name",  
                legend.show = F) +  
  tm_shape(rivers) +  
    tm_lines("lightblue",  
             lwd = "strokelwd",  
             scale = 3,  
             legend.lwd.show = F)  
```
]
 
.panel2-worldmap-rotate[
<img src="AUS_tmap_files/figure-html/worldmap_rotate_05_output-1.png" width="432" />
]

---
count: false
 
## Projections
.panel1-worldmap-rotate[

```r
library(tidyverse)  
library(tmap)

data(World, rivers)

tmap_mode("plot")

World %>%  
  tm_shape(shp = .,  
## Mercator
## Robinson
## Wagner I  
## Interrupted Goode Homolosine  
## Nicolosi Globular  
## Lagrange  
*          projection = "+proj=lagrng") +  #ROTATE
## Van Der Grinten I  
## Tobler-Mercator  
## American Polyconic  
## Orthographic  
## Lambert Equal Area Conic  
    tm_polygons("name",  
                legend.show = F) +  
  tm_shape(rivers) +  
    tm_lines("lightblue",  
             lwd = "strokelwd",  
             scale = 3,  
             legend.lwd.show = F)  
```
]
 
.panel2-worldmap-rotate[
<img src="AUS_tmap_files/figure-html/worldmap_rotate_06_output-1.png" width="432" />
]

---
count: false
 
## Projections
.panel1-worldmap-rotate[

```r
library(tidyverse)  
library(tmap)

data(World, rivers)

tmap_mode("plot")

World %>%  
  tm_shape(shp = .,  
## Mercator
## Robinson
## Wagner I  
## Interrupted Goode Homolosine  
## Nicolosi Globular  
## Lagrange  
## Van Der Grinten I  
*          projection = "+proj=vandg") +    #ROTATE
## Tobler-Mercator  
## American Polyconic  
## Orthographic  
## Lambert Equal Area Conic  
    tm_polygons("name",  
                legend.show = F) +  
  tm_shape(rivers) +  
    tm_lines("lightblue",  
             lwd = "strokelwd",  
             scale = 3,  
             legend.lwd.show = F)  
```
]
 
.panel2-worldmap-rotate[
<img src="AUS_tmap_files/figure-html/worldmap_rotate_07_output-1.png" width="432" />
]

---
count: false
 
## Projections
.panel1-worldmap-rotate[

```r
library(tidyverse)  
library(tmap)

data(World, rivers)

tmap_mode("plot")

World %>%  
  tm_shape(shp = .,  
## Mercator
## Robinson
## Wagner I  
## Interrupted Goode Homolosine  
## Nicolosi Globular  
## Lagrange  
## Van Der Grinten I  
## Tobler-Mercator  
*          projection = "+proj=tobmerc") +  #ROTATE
## American Polyconic  
## Orthographic  
## Lambert Equal Area Conic  
    tm_polygons("name",  
                legend.show = F) +  
  tm_shape(rivers) +  
    tm_lines("lightblue",  
             lwd = "strokelwd",  
             scale = 3,  
             legend.lwd.show = F)  
```
]
 
.panel2-worldmap-rotate[
<img src="AUS_tmap_files/figure-html/worldmap_rotate_08_output-1.png" width="432" />
]

---
count: false
 
## Projections
.panel1-worldmap-rotate[

```r
library(tidyverse)  
library(tmap)

data(World, rivers)

tmap_mode("plot")

World %>%  
  tm_shape(shp = .,  
## Mercator
## Robinson
## Wagner I  
## Interrupted Goode Homolosine  
## Nicolosi Globular  
## Lagrange  
## Van Der Grinten I  
## Tobler-Mercator  
## American Polyconic  
*          projection = "+proj=poly") +   #ROTATE
## Orthographic  
## Lambert Equal Area Conic  
    tm_polygons("name",  
                legend.show = F) +  
  tm_shape(rivers) +  
    tm_lines("lightblue",  
             lwd = "strokelwd",  
             scale = 3,  
             legend.lwd.show = F)  
```
]
 
.panel2-worldmap-rotate[
<img src="AUS_tmap_files/figure-html/worldmap_rotate_09_output-1.png" width="432" />
]

---
count: false
 
## Projections
.panel1-worldmap-rotate[

```r
library(tidyverse)  
library(tmap)

data(World, rivers)

tmap_mode("plot")

World %>%  
  tm_shape(shp = .,  
## Mercator
## Robinson
## Wagner I  
## Interrupted Goode Homolosine  
## Nicolosi Globular  
## Lagrange  
## Van Der Grinten I  
## Tobler-Mercator  
## American Polyconic  
## Orthographic  
*          projection = "+proj=ortho") +  #ROTATE
## Lambert Equal Area Conic  
    tm_polygons("name",  
                legend.show = F) +  
  tm_shape(rivers) +  
    tm_lines("lightblue",  
             lwd = "strokelwd",  
             scale = 3,  
             legend.lwd.show = F)  
```
]
 
.panel2-worldmap-rotate[
<img src="AUS_tmap_files/figure-html/worldmap_rotate_10_output-1.png" width="432" />
]

---
count: false
 
## Projections
.panel1-worldmap-rotate[

```r
library(tidyverse)  
library(tmap)

data(World, rivers)

tmap_mode("plot")

World %>%  
  tm_shape(shp = .,  
## Mercator
## Robinson
## Wagner I  
## Interrupted Goode Homolosine  
## Nicolosi Globular  
## Lagrange  
## Van Der Grinten I  
## Tobler-Mercator  
## American Polyconic  
## Orthographic  
## Lambert Equal Area Conic  
*          projection = "+proj=leac") +     #ROTATE
    tm_polygons("name",  
                legend.show = F) +  
  tm_shape(rivers) +  
    tm_lines("lightblue",  
             lwd = "strokelwd",  
             scale = 3,  
             legend.lwd.show = F)  
```
]
 
.panel2-worldmap-rotate[
<img src="AUS_tmap_files/figure-html/worldmap_rotate_11_output-1.png" width="432" />
]

---

## Additional Information & Options
--

- Different projections are better suited to different maps. 
--

- For example, if producing a map of Alaska then the "Alaska Grid" or "Alaska Series E" projection type would be typical. 
--

- When presenting a large region or the world at large, however, "Mercator" or "Robinson" are more reliable. 
--

- Find more projection types at: https://proj.org/operations/projections/index.html