Visualisation

# Visualisation
## Drawing maps
### Marie-Pierre Etienne
### <a href="https://marieetienne.github.io/" class="uri">https://marieetienne.github.io/</a>
### 2020/09/11 (updated: 2022-10-28)

---

---
# Des sources d'inspirations

- [Drawing beautiful maps programmatically](https://www.r-spatial.org/r/2018/10/25/ggplot2-sf.html)
- [StanMap](https://statnmap.com/fr/gallery/) (un ancien d'Agrocampus Ouest)

- [Geocomputation with R](https://geocompr.robinlovelace.net/)

- [Leaflet introduction](https://rstudio.github.io/leaflet/)

---

# Drawing a simple map with ggmap

Get free maps thanks to  OpenStreetMap through the `osmdata` package  ( [vignette available](https://cran.r-project.org/web/packages/osmdata/vignettes/osmdata.html) )

```r
library('tidyverse')
library('ggplot2')
##remotes::install_github("ropensci/osmdata")
rennes_bb <- osmdata::getbb('Rennes') %>% 
  as.numeric() ##get bounding box
rennes_bb
```

```
## [1] -1.752588 48.076915 -1.624405 48.154970
```

```r
bat24 <- data.frame(lat= 48.114072, lon = -1.710184, 
                    text = 'Batiment 24')
zone_map <- ggmap::get_stamenmap(bbox = rennes_bb, 
                                 zoom = 13) 
```

```
## Source : http://tile.stamen.com/terrain/13/4056/2842.png
```

```
## Source : http://tile.stamen.com/terrain/13/4057/2842.png
```

```
## Source : http://tile.stamen.com/terrain/13/4058/2842.png
```

```
## Source : http://tile.stamen.com/terrain/13/4059/2842.png
```

```
## Source : http://tile.stamen.com/terrain/13/4056/2843.png
```

```
## Source : http://tile.stamen.com/terrain/13/4057/2843.png
```

```
## Source : http://tile.stamen.com/terrain/13/4058/2843.png
```

```
## Source : http://tile.stamen.com/terrain/13/4059/2843.png
```

```
## Source : http://tile.stamen.com/terrain/13/4056/2844.png
```

```
## Source : http://tile.stamen.com/terrain/13/4057/2844.png
```

```
## Source : http://tile.stamen.com/terrain/13/4058/2844.png
```

```
## Source : http://tile.stamen.com/terrain/13/4059/2844.png
```

```
## Source : http://tile.stamen.com/terrain/13/4056/2845.png
```

```
## Source : http://tile.stamen.com/terrain/13/4057/2845.png
```

```
## Source : http://tile.stamen.com/terrain/13/4058/2845.png
```

```
## Source : http://tile.stamen.com/terrain/13/4059/2845.png
```

```r
zone_map %>%
  ggmap::ggmap() + 
  geom_point(data = bat24, aes(x= lon, y = lat), col = 'red') + 
  geom_text(data = bat24, aes(x= lon, y = lat, label = text), hjust = 0.1, nudge_x = 0.004, col = 'red' ) 
```

```r
##remotes::install_github("ropensci/osmdata")
rennes_bb <- osmdata::getbb('Rennes') %>% 
  as.numeric() ##get bounding box
bat24 <- data.frame(lat= 48.114072, lon = -1.710184, 
                    text = 'Batiment 24')
zone_map <- ggmap::get_stamenmap(bbox = rennes_bb, 
                                 zoom = 13) 
zone_map %>%
  ggmap::ggmap() + 
  geom_point(data = bat24, aes(x= lon, y = lat), col = 'red') + 
  geom_text(data = bat24, aes(x= lon, y = lat, label = text), hjust = 0.1, nudge_x = 0.004, col = 'red' ) 
```

---

```r
*library('tidyverse')
```
]
 
.panel2-openstreetmap_package-auto[

]

---
count: false

```r
library('tidyverse')
*library('ggplot2')
```
]
 
.panel2-openstreetmap_package-auto[

]

---
count: false

```r
library('tidyverse')
library('ggplot2')
*##remotes::install_github("ropensci/osmdata")  ##remotes::install_github("ropensci/osmdata")
```
]
 
.panel2-openstreetmap_package-auto[

]

---
count: false

```r
library('tidyverse')
library('ggplot2')
##remotes::install_github("ropensci/osmdata")  ##remotes::install_github("ropensci/osmdata")
*rennes_bb <- osmdata::getbb('Rennes')
```
]
 
.panel2-openstreetmap_package-auto[

]

---
count: false

]

---
count: false

```
## [1] -1.752588 48.076915 -1.624405 48.154970
```
]

---
count: false

```
## [1] -1.752588 48.076915 -1.624405 48.154970
```
]

---
count: false

```
## [1] -1.752588 48.076915 -1.624405 48.154970
```
]

---
count: false

```r
library('tidyverse')
library('ggplot2')
##remotes::install_github("ropensci/osmdata")  ##remotes::install_github("ropensci/osmdata")
rennes_bb <- osmdata::getbb('Rennes') %>%
  as.numeric()  ##get bounding box
rennes_bb
bat24 <- data.frame(lat= 48.114072, lon = -1.710184,
                    text = 'Batiment 24')
zone_map <- ggmap::get_stamenmap(bbox = rennes_bb,
                                 zoom = 13)
*zone_map
```
]
 
.panel2-openstreetmap_package-auto[

```
## [1] -1.752588 48.076915 -1.624405 48.154970
```

```
## 682x746 terrain map image from Stamen Maps. 
## See ?ggmap to plot it.
```
]

---
count: false

```r
library('tidyverse')
library('ggplot2')
##remotes::install_github("ropensci/osmdata")  ##remotes::install_github("ropensci/osmdata")
rennes_bb <- osmdata::getbb('Rennes') %>%
  as.numeric()  ##get bounding box
rennes_bb
bat24 <- data.frame(lat= 48.114072, lon = -1.710184,
                    text = 'Batiment 24')
zone_map <- ggmap::get_stamenmap(bbox = rennes_bb,
                                 zoom = 13)
zone_map %>%
* ggmap::ggmap()
```
]
 
.panel2-openstreetmap_package-auto[

```
## [1] -1.752588 48.076915 -1.624405 48.154970
```

![](maps_files/figure-html/openstreetmap_package_auto_10_output-1.png)
]

---
count: false

```r
library('tidyverse')
library('ggplot2')
##remotes::install_github("ropensci/osmdata")  ##remotes::install_github("ropensci/osmdata")
rennes_bb <- osmdata::getbb('Rennes') %>%
  as.numeric()  ##get bounding box
rennes_bb
bat24 <- data.frame(lat= 48.114072, lon = -1.710184,
                    text = 'Batiment 24')
zone_map <- ggmap::get_stamenmap(bbox = rennes_bb,
                                 zoom = 13)
zone_map %>%
  ggmap::ggmap() +
* geom_point(data = bat24, aes(x= lon, y = lat), col = 'red')
```
]
 
.panel2-openstreetmap_package-auto[

```
## [1] -1.752588 48.076915 -1.624405 48.154970
```

![](maps_files/figure-html/openstreetmap_package_auto_11_output-1.png)
]

---
count: false

```r
library('tidyverse')
library('ggplot2')
##remotes::install_github("ropensci/osmdata")  ##remotes::install_github("ropensci/osmdata")
rennes_bb <- osmdata::getbb('Rennes') %>%
  as.numeric()  ##get bounding box
rennes_bb
bat24 <- data.frame(lat= 48.114072, lon = -1.710184,
                    text = 'Batiment 24')
zone_map <- ggmap::get_stamenmap(bbox = rennes_bb,
                                 zoom = 13)
zone_map %>%
  ggmap::ggmap() +
  geom_point(data = bat24, aes(x= lon, y = lat), col = 'red') +
* geom_text(data = bat24, aes(x= lon, y = lat, label = text), hjust = 0.1, nudge_x = 0.004, col = 'red' )
```
]
 
.panel2-openstreetmap_package-auto[

```
## [1] -1.752588 48.076915 -1.624405 48.154970
```

![](maps_files/figure-html/openstreetmap_package_auto_12_output-1.png)
]

---
name: data_type
# Data types

---
template: data_type

## Raster data

Basically image ( a matrix where a cell represents a pixel)

```r
class(zone_map)
```

```
## [1] "ggmap"  "raster"
```

For simple plots only, difficult to transform

## Vector data

- Vector data are collections of points,  lines, polygons (potentially connected). 
- Many different objects in `R` deals with spatial data .highlight[focus on ` sf`  objects]

Many possible operation (intersection, union, buffer, etc ....)

Two main formats in R : [sp](https://cran.r-project.org/web/packages/sp/sp.pdf) and [sf](https://cran.r-project.org/web/packages/sf/sf.pdf)

---
template: data_type
## Vector data

### sf package

Simple features (`sf`) is an [open standard](https://www.ogc.org/standards/sfa) for spatial object with hierarchical structure. The package extends the data.frame format to account for a geometry column. The `sf` package provides
- efficient access to data (writing and reading)
- easy and fancy plotting
- `sf` objects can be used as `data.frame`

---
template: data_type

## Vector data

### `sf` objects example

```r
library("sf")
```

```
## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1
```

```r
library("rnaturalearth")
library("rnaturalearthdata")

world <- ne_countries(scale = "medium", returnclass = "sf")
```
---
template: data_type

```r
world %>% select(admin, income_grp, wikipedia, economy, geometry) %>% print(n=5)
```

```
## 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
## CRS:            +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
## First 5 features:
##         admin              income_grp wikipedia                   economy
## 0       Aruba 2. High income: nonOECD        NA      6. Developing region
## 1 Afghanistan           5. Low income        NA 7. Least developed region
## 2      Angola  3. Upper middle income        NA 7. Least developed region
## 3    Anguilla  3. Upper middle income        NA      6. Developing region
## 4     Albania  4. Lower middle income        NA      6. Developing region
##                         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...
```

---
template: data_type

```r
st_crs(world )
```

```
## Coordinate Reference System:
##   User input: +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 
##   wkt:
## BOUNDCRS[
##     SOURCECRS[
##         GEOGCRS["unknown",
##             DATUM["World Geodetic System 1984",
##                 ELLIPSOID["WGS 84",6378137,298.257223563,
##                     LENGTHUNIT["metre",1]],
##                 ID["EPSG",6326]],
##             PRIMEM["Greenwich",0,
##                 ANGLEUNIT["degree",0.0174532925199433],
##                 ID["EPSG",8901]],
##             CS[ellipsoidal,2],
##                 AXIS["longitude",east,
##                     ORDER[1],
##                     ANGLEUNIT["degree",0.0174532925199433,
##                         ID["EPSG",9122]]],
##                 AXIS["latitude",north,
##                     ORDER[2],
##                     ANGLEUNIT["degree",0.0174532925199433,
##                         ID["EPSG",9122]]]]],
##     TARGETCRS[
##         GEOGCRS["WGS 84",
##             DATUM["World Geodetic System 1984",
##                 ELLIPSOID["WGS 84",6378137,298.257223563,
##                     LENGTHUNIT["metre",1]]],
##             PRIMEM["Greenwich",0,
##                 ANGLEUNIT["degree",0.0174532925199433]],
##             CS[ellipsoidal,2],
##                 AXIS["latitude",north,
##                     ORDER[1],
##                     ANGLEUNIT["degree",0.0174532925199433]],
##                 AXIS["longitude",east,
##                     ORDER[2],
##                     ANGLEUNIT["degree",0.0174532925199433]],
##             ID["EPSG",4326]]],
##     ABRIDGEDTRANSFORMATION["Transformation from unknown to WGS84",
##         METHOD["Geocentric translations (geog2D domain)",
##             ID["EPSG",9603]],
##         PARAMETER["X-axis translation",0,
##             ID["EPSG",8605]],
##         PARAMETER["Y-axis translation",0,
##             ID["EPSG",8606]],
##         PARAMETER["Z-axis translation",0,
##             ID["EPSG",8607]]]]
```

---
name: coordinate_header
# Coordinate Reference system (CRS)

---
template: coordinate_header

From spherical object to maps

## Geodetic datum (or geodetic system)

An  approximation of sea level on Earth contains:

- the definition of the chosen ellipsoide
- a geodetic datum on Earth

```r
knitr::include_graphics("1280px-ECEF.png")
```

<div class="figure">
<img src="1280px-ECEF.png" alt="Geodetic datum (source https://commons.wikimedia.org/wiki/File:Azimutalprojektion-schief_kl.jpg, author:	Stefan Kühn,  Fotograf )" width="40%" />
<p class="caption">Geodetic datum (source https://commons.wikimedia.org/wiki/File:Azimutalprojektion-schief_kl.jpg, author:	Stefan Kühn,  Fotograf )</p>
</div>

At global scale  *World Geodetic System* (WGS84) is the reference geodetic datum for GPS

---
template: coordinate_header

## Projected CRS

- Cartesian coordinates on  flat surface,
- based on a geodetic datum
- rely on on map projection to convert the 3d surface on Earth into Eastings and Northings (x and y)

---
template: coordinate_header

## Effects of projection

### WGS84  representation

```r
world %>%  filter(brk_name == "United States") %>% ggplot() + geom_sf()
```

![](maps_files/figure-html/sf_world_example_map-1.png)

---
template: coordinate_header

## Effects of projection

### US National Atlas Equal Area

```r
world %>%   filter(brk_name == "United States") %>% st_transform(crs = 2163) %>% ggplot() + geom_sf()
```

![](maps_files/figure-html/sf_world_example_map_2163-1.png)

---
template: coordinate_header

### Focus on France

```r
france_dta <-   world  %>% filter(name == 'France') 
st_crs(france_dta)
```

```r
france_dta%>% ggplot() + geom_sf()
```

![](maps_files/figure-html/sf_france_example_map-1.png)

---

```r
*france_dta <-   world  %>% filter(name == 'France')
```
]
 
.panel2-sf_france_example_map-auto[

]

---
count: false

```r
france_dta <-   world  %>% filter(name == 'France')
*st_crs(france_dta)
```
]
 
.panel2-sf_france_example_map-auto[

---
count: false

```r
france_dta <-   world  %>% filter(name == 'France')
st_crs(france_dta)
*france_dta%>% ggplot() + geom_sf()
```
]
 
.panel2-sf_france_example_map-auto[

![](maps_files/figure-html/sf_france_example_map_auto_03_output-1.png)
]

---
template: coordinate_header

### Focus on Metropolitan France

Change of WGS84

```r
metro_dta <- france_dta %>% st_crop( xmin = -5, xmax = 11, ymin= 40.6, ymax = 52) 
```

```
## although coordinates are longitude/latitude, st_intersection assumes that they are planar
```

```
## Warning: attribute variables are assumed to be spatially constant throughout all
## geometries
```

```r
metro_dta %>% ggplot() + geom_sf()
```

![](maps_files/figure-html/sf_metro_example_map-1.png)

---
template: coordinate_header

### Focus on Metropolitan France

Change of CRS for Lambert 93

```r
metro_dta %>% st_transform( crs = 2154 ) %>%
  ggplot() + geom_sf() 
```

![](maps_files/figure-html/sf_metro_example_map_lambert-1.png)

---
template: coordinate_header

### Focus on Metropolitan France

Change of CRS for UTM31N

```r
metro_dta %>% st_transform( crs = 32631 ) %>%
  ggplot() + geom_sf() 
```

![](maps_files/figure-html/sf_metro_example_map_utm-1.png)

---
name: sf

# Simple features geometry

---
template: sf

## Geometry types

- Vector data are collections of points,  lines, polygons (potentially connected). 
- Many different objects in `R` deals with spatial data. focus on ` sf`  objects}

Simple features (`sf`) is an [open standard](https://www.ogc.org/standards/sfa) for spatial object with hierarchical structure. The `sf` package provides
- efficient access to data (writing and reading)
- easy and fancy plotting
- `sf` objects can be used as `data.frame`

---
template: sf

## What is feature ?
*A feature is thought of as a thing, or an object in the real world, such as a building or a tree. As is the case with objects, they often consist of other objects. This is the case with features too: a set of features can form a single feature. A forest stand can be a feature, a forest can be a feature, a city can be a feature. A satellite image pixel can be a feature, a complete image can be a feature too.* [Peb18].

All geometries are composed by a set points.

---
template: sf

*A feature is thought of as a thing, or an object in the real world, such as a building or a tree. As is the case with objects, they often consist of other objects. This is the case with features too: a set of features can form a single feature. A forest stand can be a feature, a forest can be a feature, a city can be a feature. A satellite image pixel can be a feature, a complete image can be a feature too.* [Peb18].

---
template: sf

### Point

A two dimensionnal point refer to Esating and northing or longitude and latitude (for extension to more than 2D see the [sf vignette](https://r-spatial.github.io/sf/articles/sf1.html))

```r
x <- st_point(c(1,2))
x
```

```
## POINT (1 2)
```

```r
class(x)
```

```
## [1] "XY"    "POINT" "sfg"
```

```r
x %>% ggplot() + geom_sf()
```

---
template: sf
count: false

### Multi Point

```r
p <- rbind( c(3.2,4), c(3,4.6), c(3.8,4.4), c(3.5,3.8), c(3.4,3.6), c(3.9,4.5))
(mp <- st_multipoint(p))
```

```
## MULTIPOINT ((3.2 4), (3 4.6), (3.8 4.4), (3.5 3.8), (3.4 3.6), (3.9 4.5))
```

```r
mp
```

```
## MULTIPOINT ((3.2 4), (3 4.6), (3.8 4.4), (3.5 3.8), (3.4 3.6), (3.9 4.5))
```

```r
class(mp)
```

```
## [1] "XY"         "MULTIPOINT" "sfg"
```

```r
mp %>% ggplot() + geom_sf()
```

---
template: sf
count: false

### Line

```r
line_sfg <- st_linestring(p)
line_sfg
```

```
## LINESTRING (3.2 4, 3 4.6, 3.8 4.4, 3.5 3.8, 3.4 3.6, 3.9 4.5)
```

```r
class(line_sfg)
```

```
## [1] "XY"         "LINESTRING" "sfg"
```

```r
line_sfg%>% ggplot() + geom_sf()
```

---
template: sf
count: false

### Polygon

```r
p1 <- rbind(c(0,0), c(1,0), c(3,2), c(2,4), c(1,4), c(0,0))
p2 <- rbind(c(1,1), c(1,2), c(2,2), c(1,1))
poly_sfg <- st_polygon(list(p1, p2))
poly_sfg 
```

```
## POLYGON ((0 0, 1 0, 3 2, 2 4, 1 4, 0 0), (1 1, 1 2, 2 2, 1 1))
```

```r
class(poly_sfg )
```

```
## [1] "XY"      "POLYGON" "sfg"
```

```r
poly_sfg %>% ggplot() + geom_sf()
```

---
template: sf
count: false

### Point, Line Polygon

### MultiPoint, MultiLine MultiPolygon

### Geometry collection

---
template: sf

## Reading vector spatial data

The shapefile of the cities in Ile de France is available [here](https://www.data.gouv.fr/en/datasets/r/aad5c727-290e-4bc3-bc7e-d1e6b020f5c1)

```r
idf_shape <- st_read(dsn = '../ile_de_france_shape/',)
```

```
## Reading layer `donnees-communales-sur-la-population-dile-de-france' from data source `/__w/Vizualisation/Vizualisation/ile_de_france_shape' using driver `ESRI Shapefile'
## Simple feature collection with 1287 features and 24 fields
## geometry type:  POINT
## dimension:      XY
## bbox:           xmin: 1.480775 ymin: 48.1406 xmax: 3.489334 ymax: 49.21325
## geographic CRS: WGS 84
```

```r
idf_shape %>% print(n=10)
```

```
## Simple feature collection with 1287 features and 24 fields
## geometry type:  POINT
## dimension:      XY
## bbox:           xmin: 1.480775 ymin: 48.1406 xmax: 3.489334 ymax: 49.21325
## geographic CRS: WGS 84
## First 10 features:
##    insee pop1946 psdc1962 psdc1968 psdc1975 psdc1982 psdc1990 popexh1999
## 1  93070   45465    51956    48886    43588    43606    42343      39719
## 2  91573     571      602      611     1360     1849     3342       5797
## 3  77434     123      105       69       98      172      214        289
## 4  94070     688      701      774     2242     2621     2810       3138
## 5  78586   17494    31267    40277    42253    46197    50329      50222
## 6  77450     405      822     1174     1163     1526     1924       2791
## 7  95607    5899     8606    12902    16963    21299    25151      25906
## 8  77463    1378     1949     1906     2249     2647     3025       3200
## 9  95610     163      180      173      188      201      229        229
## 10 92076    3283     6690     8065     9257     8313     8118       8130
##    psdc82df99 psdc90df99 popmun2006 popmun2007 popmun2008 popmun2009 popmun2010
## 1       43606      42343      42950   43954.30      45595      46510      47189
## 2        1849       3342       7733    7818.00       7844       8083       8350
## 3         172        214        312     316.00        320        322        330
## 4        2621       2810       3550    3610.00       3675       3676       3724
## 5       46197      50329      51600   51601.15      51447      51459      51150
## 6        1526       1924       2851    2880.68       2910       2940       2974
## 7       21299      25151      26436   26228.12      26349      26134      26144
## 8        2647       3025       3290    3345.36       3385       3425       3436
## 9         201        229        259     263.00        266        259        264
## 10       8313       8118       8547    8670.01       8636       8618       8615
##    popmun2011 objectid popmun2012 popmun2013 popmun2014 popmun2015   tcam1217
## 1       47783     1037      47499      47534      47432      48431 1.47542617
## 2        8618     1042       9370       9758      10151      10501 2.94798840
## 3         339     1048        347        350        348        352 0.68226909
## 4        3688     1070       3651       3640       3644       3640 0.67022160
## 5       51431     1073      51713      51599      51747      52538 0.09804205
## 6        3104     1088       3121       3138       3154       3133 0.81952342
## 7       26195     1113      26094      25998      25875      26077 0.26455800
## 8        3429     1120       3443       3468       3493       3502 0.13902620
## 9         268     1123        273        278        278        284 1.69948658
## 10       8612     1156       8611       8816       8747       8688 0.12972916
##    popmun2016 popmun2017                  geometry
## 1       49664      51108 POINT (2.333478 48.90959)
## 2       10851      10835 POINT (2.517426 48.60545)
## 3         355        359 POINT (3.205181 48.43767)
## 4        3708       3775 POINT (2.576295 48.73522)
## 5       52648      51967 POINT (2.174487 48.93982)
## 6        3233       3251 POINT (2.591731 48.71473)
## 7       26296      26441 POINT (2.220662 49.02636)
## 8        3493       3467 POINT (2.781282 48.40434)
## 9         291        297 POINT (1.905006 49.09092)
## 10       8628       8667 POINT (2.160643 48.84209)
```

---
template: sf

## Metropolitan French departments

The limit of the French department are descibed in [this archive](http://www.infosig.net/telechargements/IGN_GEOFLA/GEOFLA-Dept-FR-Corse-TAB-L93.zip).

```r
dpt_shape <-  st_read(dsn = '../depts/', layer = 'DEPARTEMENT') 
```

```
## Reading layer `DEPARTEMENT' from data source `/__w/Vizualisation/Vizualisation/depts' using driver `MapInfo File'
## Simple feature collection with 96 features and 11 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 99226 ymin: 6049647 xmax: 1242375 ymax: 7110524
## projected CRS:  RGF93 / Lambert-93
```

```r
dpt_shape %>% print(n=10)
```

```
## Simple feature collection with 96 features and 11 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 99226 ymin: 6049647 xmax: 1242375 ymax: 7110524
## projected CRS:  RGF93 / Lambert-93
## First 10 features:
##    ID_GEOFLA CODE_DEPT                NOM_DEPT CODE_CHF              NOM_CHF
## 1          1        01                     AIN      053      BOURG-EN-BRESSE
## 2          2        02                   AISNE      408                 LAON
## 3          3        03                  ALLIER      190              MOULINS
## 4          4        04 ALPES-DE-HAUTE-PROVENCE      070      DIGNE-LES-BAINS
## 5          5        05            HAUTES-ALPES      061                  GAP
## 6          6        06         ALPES-MARITIMES      088                 NICE
## 7          7        07                 ARDECHE      186               PRIVAS
## 8          8        08                ARDENNES      105 CHARLEVILLE-MEZIERES
## 9          9        09                  ARIEGE      122                 FOIX
## 10        10        10                    AUBE      387               TROYES
##    X_CHF_LIEU Y_CHF_LIEU X_CENTROID Y_CENTROID CODE_REG
## 1        8717      65696       8814      65582       82
## 2        7451      69406       7404      69401       22
## 3        7254      66072       7144      65882       83
## 4        9590      63379       9596      63394       93
## 5        9443      63891       9585      64014       93
## 6       10439      62985      10303      63240       93
## 7        8266      64052       8127      64068       82
## 8        8239      69649       8186      69475       21
## 9        5862      62083       5777      62035       73
## 10       7799      68003       7861      68010       21
##                    NOM_REGION                       geometry
## 1                 RHONE-ALPES MULTIPOLYGON (((919195 6541...
## 2                    PICARDIE MULTIPOLYGON (((735603 6861...
## 3                    AUVERGNE MULTIPOLYGON (((753769 6537...
## 4  PROVENCE-ALPES-COTE-D'AZUR MULTIPOLYGON (((992638 6305...
## 5  PROVENCE-ALPES-COTE-D'AZUR MULTIPOLYGON (((1012913 640...
## 6  PROVENCE-ALPES-COTE-D'AZUR MULTIPOLYGON (((1018256 627...
## 7                 RHONE-ALPES MULTIPOLYGON (((831641 6353...
## 8           CHAMPAGNE-ARDENNE MULTIPOLYGON (((842092 6905...
## 9               MIDI-PYRENEES MULTIPOLYGON (((631545 6174...
## 10          CHAMPAGNE-ARDENNE MULTIPOLYGON (((796594 6759...
```

---
template: sf
count: false

## Metropolitan French departments

```r
dpt_shape %>% 
  ggplot() + geom_sf()
```

![](maps_files/figure-html/plot_shp_dpt-1.png)

---
template: sf
count: false

## Metropolitan French departments

The population by department is available [here](https://www.insee.fr/fr/statistiques/fichier/2012713/TCRD_004.xls) or [here in csv format]()

```r
population <- read_csv("../population.csv",
    skip = 3) %>%   rename(p2020 = '2020 (p)', p2017 = '2017', p2012 = '2012',
          p2007 = '2007', p1999 = '1999')
```

```
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   CODE_DEPT = col_character(),
##   NAME_DEPT = col_character(),
##   `2020 (p)` = col_double(),
##   `Part dans la France (p) (en %)` = col_double(),
##   `2017` = col_double(),
##   `2012` = col_double(),
##   `2007` = col_double(),
##   `1999` = col_double()
## )
```

Joining the two dataset

```r
dpt_complete <- dpt_shape %>% inner_join( y = population, by = "CODE_DEPT")
```

---
template: sf
count: false

## Metropolitan French departments

```r
dpt_complete %>% ggplot2::ggplot() + 
  geom_sf( aes(fill = p2017) ) 
```

![](maps_files/figure-html/plot_data_dpt1-1.png)

---

```r
*dpt_complete %>% ggplot2::ggplot()
```
]
 
.panel2-plot_data_dpt1-auto[
![](maps_files/figure-html/plot_data_dpt1_auto_01_output-1.png)
]

---
count: false

```r
dpt_complete %>% ggplot2::ggplot() +
* geom_sf( aes(fill = p2017) )
```
]
 
.panel2-plot_data_dpt1-auto[
![](maps_files/figure-html/plot_data_dpt1_auto_02_output-1.png)
]

![](maps_files/figure-html/plot_data_dpt2-1.png)

---

```r
*dpt_complete
```
]
 
.panel2-plot_data_dpt2-auto[

```
## Simple feature collection with 87 features and 18 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 99226 ymin: 6049647 xmax: 1242375 ymax: 7110524
## projected CRS:  RGF93 / Lambert-93
## First 10 features:
##    ID_GEOFLA CODE_DEPT          NOM_DEPT CODE_CHF     NOM_CHF X_CHF_LIEU
## 1         10        10              AUBE      387      TROYES       7799
## 2         11        11              AUDE      069 CARCASSONNE       6472
## 3         12        12           AVEYRON      202       RODEZ       6660
## 4         13        13  BOUCHES-DU-RHONE      055   MARSEILLE       8929
## 5         14        14          CALVADOS      118        CAEN       4543
## 6         15        15            CANTAL      014    AURILLAC       6557
## 7         16        16          CHARENTE      015   ANGOULEME       4788
## 8         17        17 CHARENTE-MARITIME      300 LA ROCHELLE       3797
## 9         18        18              CHER      033     BOURGES       6541
## 10        19        19           CORREZE      272       TULLE       6037
##    Y_CHF_LIEU X_CENTROID Y_CENTROID CODE_REG                 NOM_REGION
## 1       68003       7861      68010       21          CHAMPAGNE-ARDENNE
## 2       62353       6522      62228       91       LANGUEDOC-ROUSSILLON
## 3       63613       6744      63535       73              MIDI-PYRENEES
## 4       62470       8686      62740       93 PROVENCE-ALPES-COTE-D'AZUR
## 5       69032       4544      68940       25            BASSE-NORMANDIE
## 6       64252       6739      64391       83                   AUVERGNE
## 7       65095       4823      65170       54           POITOU-CHARENTES
## 8       65705       4165      65256       54           POITOU-CHARENTES
## 9       66649       6613      66628       24                     CENTRE
## 10      64636       6120      64736       74                   LIMOUSIN
##            NAME_DEPT   p2020 Part dans la France (p) (en %)   p2017   p2012
## 1               Aube  309907                            0.5  310020  305606
## 2               Aude  372705                            0.6  370260  362339
## 3            Aveyron  278360                            0.4  279206  276229
## 4   Bouches-du-Rhône 2034469                            3.0 2024162 1984784
## 5           Calvados  691453                            1.0  694002  687854
## 6             Cantal  142811                            0.2  145143  147415
## 7           Charente  348180                            0.5  352335  353657
## 8  Charente-Maritime  647080                            1.0  644303  628733
## 9               Cher  296404                            0.4  304256  311897
## 10           Corrèze  240336                            0.4  241464  241247
##      p2007   p1999                       geometry
## 1   300840  292317 MULTIPOLYGON (((796594 6759...
## 2   345779  309463 MULTIPOLYGON (((703561 6193...
## 3   274425  264048 MULTIPOLYGON (((728810 6312...
## 4  1958926 1833982 MULTIPOLYGON (((917346 6234...
## 5   673667  647951 MULTIPOLYGON (((510545 6875...
## 6   149057  150977 MULTIPOLYGON (((637122 6391...
## 7   349535  339828 MULTIPOLYGON (((464807 6459...
## 8   605410  556419 MULTIPOLYGON (((460931 6449...
## 9   314599  314603 MULTIPOLYGON (((644784 6591...
## 10  242038  232819 MULTIPOLYGON (((626129 6431...
```
]

---
count: false

```r
dpt_complete %>%
* mutate(area = st_area(geometry), dens = p2017/area)
```
]
 
.panel2-plot_data_dpt2-auto[

```
## Simple feature collection with 87 features and 20 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 99226 ymin: 6049647 xmax: 1242375 ymax: 7110524
## projected CRS:  RGF93 / Lambert-93
## First 10 features:
##    ID_GEOFLA CODE_DEPT          NOM_DEPT CODE_CHF     NOM_CHF X_CHF_LIEU
## 1         10        10              AUBE      387      TROYES       7799
## 2         11        11              AUDE      069 CARCASSONNE       6472
## 3         12        12           AVEYRON      202       RODEZ       6660
## 4         13        13  BOUCHES-DU-RHONE      055   MARSEILLE       8929
## 5         14        14          CALVADOS      118        CAEN       4543
## 6         15        15            CANTAL      014    AURILLAC       6557
## 7         16        16          CHARENTE      015   ANGOULEME       4788
## 8         17        17 CHARENTE-MARITIME      300 LA ROCHELLE       3797
## 9         18        18              CHER      033     BOURGES       6541
## 10        19        19           CORREZE      272       TULLE       6037
##    Y_CHF_LIEU X_CENTROID Y_CENTROID CODE_REG                 NOM_REGION
## 1       68003       7861      68010       21          CHAMPAGNE-ARDENNE
## 2       62353       6522      62228       91       LANGUEDOC-ROUSSILLON
## 3       63613       6744      63535       73              MIDI-PYRENEES
## 4       62470       8686      62740       93 PROVENCE-ALPES-COTE-D'AZUR
## 5       69032       4544      68940       25            BASSE-NORMANDIE
## 6       64252       6739      64391       83                   AUVERGNE
## 7       65095       4823      65170       54           POITOU-CHARENTES
## 8       65705       4165      65256       54           POITOU-CHARENTES
## 9       66649       6613      66628       24                     CENTRE
## 10      64636       6120      64736       74                   LIMOUSIN
##            NAME_DEPT   p2020 Part dans la France (p) (en %)   p2017   p2012
## 1               Aube  309907                            0.5  310020  305606
## 2               Aude  372705                            0.6  370260  362339
## 3            Aveyron  278360                            0.4  279206  276229
## 4   Bouches-du-Rhône 2034469                            3.0 2024162 1984784
## 5           Calvados  691453                            1.0  694002  687854
## 6             Cantal  142811                            0.2  145143  147415
## 7           Charente  348180                            0.5  352335  353657
## 8  Charente-Maritime  647080                            1.0  644303  628733
## 9               Cher  296404                            0.4  304256  311897
## 10           Corrèze  240336                            0.4  241464  241247
##      p2007   p1999                       geometry             area
## 1   300840  292317 MULTIPOLYGON (((796594 6759... 6020293118 [m^2]
## 2   345779  309463 MULTIPOLYGON (((703561 6193... 6356214401 [m^2]
## 3   274425  264048 MULTIPOLYGON (((728810 6312... 8770954522 [m^2]
## 4  1958926 1833982 MULTIPOLYGON (((917346 6234... 5093290260 [m^2]
## 5   673667  647951 MULTIPOLYGON (((510545 6875... 5605649712 [m^2]
## 6   149057  150977 MULTIPOLYGON (((637122 6391... 5768091192 [m^2]
## 7   349535  339828 MULTIPOLYGON (((464807 6459... 5963483218 [m^2]
## 8   605410  556419 MULTIPOLYGON (((460931 6449... 6901566358 [m^2]
## 9   314599  314603 MULTIPOLYGON (((644784 6591... 7295949293 [m^2]
## 10  242038  232819 MULTIPOLYGON (((626129 6431... 5891105885 [m^2]
##                    dens
## 1  5.149583e-05 [1/m^2]
## 2  5.825165e-05 [1/m^2]
## 3  3.183302e-05 [1/m^2]
## 4  3.974174e-04 [1/m^2]
## 5  1.238040e-04 [1/m^2]
## 6  2.516309e-05 [1/m^2]
## 7  5.908208e-05 [1/m^2]
## 8  9.335605e-05 [1/m^2]
## 9  4.170204e-05 [1/m^2]
## 10 4.098789e-05 [1/m^2]
```
]

---
count: false

```r
dpt_complete %>%
  mutate(area = st_area(geometry), dens = p2017/area) %>%
* ggplot()
```
]
 
.panel2-plot_data_dpt2-auto[
![](maps_files/figure-html/plot_data_dpt2_auto_03_output-1.png)
]

---
count: false

```r
dpt_complete %>%
  mutate(area = st_area(geometry), dens = p2017/area) %>%
  ggplot() +
* geom_sf(aes(fill = as.numeric(dens)))
```
]
 
.panel2-plot_data_dpt2-auto[
![](maps_files/figure-html/plot_data_dpt2_auto_04_output-1.png)
]

---
count: false

```r
dpt_complete %>%
  mutate(area = st_area(geometry), dens = p2017/area) %>%
  ggplot() +
  geom_sf(aes(fill = as.numeric(dens))) +
* scale_fill_gradient(low = "#75c9c5", high = "#fb1c05", trans = "log")
```
]
 
.panel2-plot_data_dpt2-auto[
![](maps_files/figure-html/plot_data_dpt2_auto_05_output-1.png)
]

---
count: false

```r
dpt_complete %>%
  mutate(area = st_area(geometry), dens = p2017/area) %>%
  ggplot() +
  geom_sf(aes(fill = as.numeric(dens))) +
  scale_fill_gradient(low = "#75c9c5", high = "#fb1c05", trans = "log") +
* labs(fill = 'Density')
```
]
 
.panel2-plot_data_dpt2-auto[
![](maps_files/figure-html/plot_data_dpt2_auto_06_output-1.png)
]

---

## From departments to regions

### Tidying the dataset

```r
dpt_complete %>%  print(n=5)
```

```r
dpt_complete %>%
  select(ID_GEOFLA, CODE_DEPT, NOM_DEPT, NOM_REGION,
         p2020, p2017, p2012, p2007, p1999) %>%
  pivot_longer(cols = starts_with("p"), names_to = 'Year', values_to = 'Population'  ) %>%
  group_by(NOM_REGION, Year) %>%
  summarise(pop = sum(Population),  geometry= st_union(geometry) ,
            area = units::set_units(st_area(geometry), km^2), dens = (pop/area)) %>% st_as_sf() -> 
  region_complete 
```

```
## Warning in val_cols[col_id] <- unname(as.list(data[cols])): number of items to
## replace is not a multiple of replacement length
```

```
## `summarise()` has grouped output by 'NOM_REGION'. You can override using the
## `.groups` argument.
```

```r
region_complete %>% 
  ggplot() + facet_wrap(~Year) + geom_sf(aes(fill = as.numeric(dens), geometry = geometry)) +
  scale_fill_gradient(low = "#75c9c5", high = "#fb1c05", trans = "log", breaks = c(50, 150, 450),
                      name = 'Density')
```

![](maps_files/figure-html/tidying_eval2-1.png)

---

```r
*dpt_complete
```
]
 
.panel2-tidying_eval2-auto[

---
count: false

```r
dpt_complete %>%
* select(ID_GEOFLA, CODE_DEPT, NOM_DEPT, NOM_REGION,
*        p2020, p2017, p2012, p2007, p1999)
```
]
 
.panel2-tidying_eval2-auto[

```
## Simple feature collection with 87 features and 9 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 99226 ymin: 6049647 xmax: 1242375 ymax: 7110524
## projected CRS:  RGF93 / Lambert-93
## First 10 features:
##    ID_GEOFLA CODE_DEPT          NOM_DEPT                 NOM_REGION   p2020
## 1         10        10              AUBE          CHAMPAGNE-ARDENNE  309907
## 2         11        11              AUDE       LANGUEDOC-ROUSSILLON  372705
## 3         12        12           AVEYRON              MIDI-PYRENEES  278360
## 4         13        13  BOUCHES-DU-RHONE PROVENCE-ALPES-COTE-D'AZUR 2034469
## 5         14        14          CALVADOS            BASSE-NORMANDIE  691453
## 6         15        15            CANTAL                   AUVERGNE  142811
## 7         16        16          CHARENTE           POITOU-CHARENTES  348180
## 8         17        17 CHARENTE-MARITIME           POITOU-CHARENTES  647080
## 9         18        18              CHER                     CENTRE  296404
## 10        19        19           CORREZE                   LIMOUSIN  240336
##      p2017   p2012   p2007   p1999                       geometry
## 1   310020  305606  300840  292317 MULTIPOLYGON (((796594 6759...
## 2   370260  362339  345779  309463 MULTIPOLYGON (((703561 6193...
## 3   279206  276229  274425  264048 MULTIPOLYGON (((728810 6312...
## 4  2024162 1984784 1958926 1833982 MULTIPOLYGON (((917346 6234...
## 5   694002  687854  673667  647951 MULTIPOLYGON (((510545 6875...
## 6   145143  147415  149057  150977 MULTIPOLYGON (((637122 6391...
## 7   352335  353657  349535  339828 MULTIPOLYGON (((464807 6459...
## 8   644303  628733  605410  556419 MULTIPOLYGON (((460931 6449...
## 9   304256  311897  314599  314603 MULTIPOLYGON (((644784 6591...
## 10  241464  241247  242038  232819 MULTIPOLYGON (((626129 6431...
```
]

---
count: false

```r
dpt_complete %>%
  select(ID_GEOFLA, CODE_DEPT, NOM_DEPT, NOM_REGION,
         p2020, p2017, p2012, p2007, p1999) %>%
* pivot_longer(cols = starts_with("p"), names_to = 'Year', values_to = 'Population'  )
```
]
 
.panel2-tidying_eval2-auto[

```
## Warning in val_cols[col_id] <- unname(as.list(data[cols])): number of items to
## replace is not a multiple of replacement length
```

```
## # A tibble: 435 × 7
##    ID_GEOFLA CODE_DEPT NOM_DEPT NOM_RE…¹                  geometry Year  Popul…²
##        <dbl> <chr>     <chr>    <chr>           <MULTIPOLYGON [m]> <chr>   <dbl>
##  1        10 10        AUBE     CHAMPAG… (((796594 6759156, 79623… p2020  309907
##  2        10 10        AUBE     CHAMPAG… (((796594 6759156, 79623… p2017  310020
##  3        10 10        AUBE     CHAMPAG… (((796594 6759156, 79623… p2012  305606
##  4        10 10        AUBE     CHAMPAG… (((796594 6759156, 79623… p2007  300840
##  5        10 10        AUBE     CHAMPAG… (((796594 6759156, 79623… p1999  292317
##  6        11 11        AUDE     LANGUED… (((703561 6193187, 70323… p2020  372705
##  7        11 11        AUDE     LANGUED… (((703561 6193187, 70323… p2017  370260
##  8        11 11        AUDE     LANGUED… (((703561 6193187, 70323… p2012  362339
##  9        11 11        AUDE     LANGUED… (((703561 6193187, 70323… p2007  345779
## 10        11 11        AUDE     LANGUED… (((703561 6193187, 70323… p1999  309463
## # … with 425 more rows, and abbreviated variable names ¹NOM_REGION, ²Population
```
]

---
count: false

```
## Warning in val_cols[col_id] <- unname(as.list(data[cols])): number of items to
## replace is not a multiple of replacement length
```

```
## # A tibble: 435 × 7
## # Groups:   NOM_REGION, Year [110]
##    ID_GEOFLA CODE_DEPT NOM_DEPT NOM_RE…¹                  geometry Year  Popul…²
##        <dbl> <chr>     <chr>    <chr>           <MULTIPOLYGON [m]> <chr>   <dbl>
##  1        10 10        AUBE     CHAMPAG… (((796594 6759156, 79623… p2020  309907
##  2        10 10        AUBE     CHAMPAG… (((796594 6759156, 79623… p2017  310020
##  3        10 10        AUBE     CHAMPAG… (((796594 6759156, 79623… p2012  305606
##  4        10 10        AUBE     CHAMPAG… (((796594 6759156, 79623… p2007  300840
##  5        10 10        AUBE     CHAMPAG… (((796594 6759156, 79623… p1999  292317
##  6        11 11        AUDE     LANGUED… (((703561 6193187, 70323… p2020  372705
##  7        11 11        AUDE     LANGUED… (((703561 6193187, 70323… p2017  370260
##  8        11 11        AUDE     LANGUED… (((703561 6193187, 70323… p2012  362339
##  9        11 11        AUDE     LANGUED… (((703561 6193187, 70323… p2007  345779
## 10        11 11        AUDE     LANGUED… (((703561 6193187, 70323… p1999  309463
## # … with 425 more rows, and abbreviated variable names ¹NOM_REGION, ²Population
```
]

---
count: false

```r
dpt_complete %>%
  select(ID_GEOFLA, CODE_DEPT, NOM_DEPT, NOM_REGION,
         p2020, p2017, p2012, p2007, p1999) %>%
  pivot_longer(cols = starts_with("p"), names_to = 'Year', values_to = 'Population'  ) %>%
  group_by(NOM_REGION, Year) %>%
* summarise(pop = sum(Population),  geometry= st_union(geometry) ,
*           area = units::set_units(st_area(geometry), km^2), dens = (pop/area)) %>% st_as_sf()
```
]
 
.panel2-tidying_eval2-auto[

```
## Warning in val_cols[col_id] <- unname(as.list(data[cols])): number of items to
## replace is not a multiple of replacement length
```

```
## Simple feature collection with 110 features and 5 fields
## geometry type:  GEOMETRY
## dimension:      XY
## bbox:           xmin: 99226 ymin: 6049647 xmax: 1242375 ymax: 7110524
## projected CRS:  RGF93 / Lambert-93
## # A tibble: 110 × 6
## # Groups:   NOM_REGION [22]
##    NOM_REGION Year      pop                           geometry     area     dens
##    <chr>      <chr>   <dbl>                     <GEOMETRY [m]>   [km^2] [1/km^2]
##  1 ALSACE     p1999 1732588 POLYGON ((1040442 6789985, 104040…  8319.64 208.252…
##  2 ALSACE     p2007 1827248 POLYGON ((1040442 6789985, 104040…  8319.64 219.630…
##  3 ALSACE     p2012 1859869 POLYGON ((1040442 6789985, 104040…  8319.64 223.551…
##  4 ALSACE     p2017 1889589 POLYGON ((1040442 6789985, 104040…  8319.64 227.123…
##  5 ALSACE     p2020 1895811 POLYGON ((1040442 6789985, 104040…  8319.64 227.871…
##  6 AQUITAINE  p1999 2906748 MULTIPOLYGON (((428954 6200122, 4… 41783.65  69.566…
##  7 AQUITAINE  p2007 3150890 MULTIPOLYGON (((428954 6200122, 4… 41783.65  75.409…
##  8 AQUITAINE  p2012 3285970 MULTIPOLYGON (((428954 6200122, 4… 41783.65  78.642…
##  9 AQUITAINE  p2017 3414585 MULTIPOLYGON (((428954 6200122, 4… 41783.65  81.720…
## 10 AQUITAINE  p2020 3467317 MULTIPOLYGON (((428954 6200122, 4… 41783.65  82.982…
## # … with 100 more rows
```
]

---
count: false

```
## Warning in val_cols[col_id] <- unname(as.list(data[cols])): number of items to
## replace is not a multiple of replacement length
```
]

---
count: false

*region_complete
```
]
 
.panel2-tidying_eval2-auto[

```
## Warning in val_cols[col_id] <- unname(as.list(data[cols])): number of items to
## replace is not a multiple of replacement length
```

---
count: false

region_complete %>%
* ggplot() + facet_wrap(~Year) + geom_sf(aes(fill = as.numeric(dens), geometry = geometry))
```
]
 
.panel2-tidying_eval2-auto[

```
## Warning in val_cols[col_id] <- unname(as.list(data[cols])): number of items to
## replace is not a multiple of replacement length
```

![](maps_files/figure-html/tidying_eval2_auto_08_output-1.png)
]

---
count: false

region_complete %>%
  ggplot() + facet_wrap(~Year) + geom_sf(aes(fill = as.numeric(dens), geometry = geometry)) +
* scale_fill_gradient(low = "#75c9c5", high = "#fb1c05", trans = "log", breaks = c(50, 150, 450),
*                     name = 'Density')
```
]
 
.panel2-tidying_eval2-auto[

```
## Warning in val_cols[col_id] <- unname(as.list(data[cols])): number of items to
## replace is not a multiple of replacement length
```

![](maps_files/figure-html/tidying_eval2_auto_09_output-1.png)
]

---
name: leaflet

# Leaflet to build interactive maps

---
template: leaflet

## Presentation of the fisheries example

```r
longline_dta <- read.table('https://raw.githubusercontent.com/MarieEtienne/MarieEtienne.github.io/master/datasets/LonglineExample.csv', 
           sep = ";", 
           header= TRUE, 
           skip = 3) %>% 
  as_tibble() %>% 
  rename(Nyellow = Nyelloweye_caught, Area = DFO_STAT_AREA_CODE,
         Lat = Lat_start , Long = Lon_start,
         soaktime = soaktime_mn) %>%
  select(Year, Area, Lat, Long, soaktime, Nyellow)
longline_dta %>% print(n=5)
```

```
## # A tibble: 210 × 6
##    Year  Area   Lat  Long soaktime Nyellow
##   <int> <int> <dbl> <dbl>    <int>   <int>
## 1  2003    12  50.8 -128.      127       6
## 2  2003    12  50.9 -128.      128       0
## 3  2003    12  50.9 -128.      127       3
## 4  2003    12  50.9 -127.      122       0
## 5  2003    12  50.9 -127.      134       2
## # … with 205 more rows
```

---
template: leaflet

## Mapping the area of interest

```r
library('leaflet')
longline_map <- leaflet() %>% 
  setView(lng = mean(longline_dta$Long), lat = mean(longline_dta$Lat), zoom = 4) %>% 
  addTiles()
longline_map
```

<div id="htmlwidget-db0bc91875cdfaf0d8ad" style="width:504px;height:504px;" class="leaflet html-widget"></div>
<script type="application/json" data-for="htmlwidget-db0bc91875cdfaf0d8ad">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"setView":[[50.5450476190476,-126.216],4,[]],"calls":[{"method":"addTiles","args":["//{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"© <a href=\"http://openstreetmap.org\">OpenStreetMap<\/a> contributors, <a href=\"http://creativecommons.org/licenses/by-sa/2.0/\">CC-BY-SA<\/a>"}]}]},"evals":[],"jsHooks":[]}</script>

---
template: leaflet

## Adding information

```r
longline_map %>% addCircles(lng = longline_dta$Long, lat = longline_dta$Lat)
```

<div id="htmlwidget-3ad0cfe3f5dc4c29eb86" style="width:504px;height:504px;" class="leaflet html-widget"></div>
<script type="application/json" data-for="htmlwidget-3ad0cfe3f5dc4c29eb86">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"setView":[[50.5450476190476,-126.216],4,[]],"calls":[{"method":"addTiles","args":["//{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"© <a href=\"http://openstreetmap.org\">OpenStreetMap<\/a> contributors, <a href=\"http://creativecommons.org/licenses/by-sa/2.0/\">CC-BY-SA<\/a>"}]},{"method":"addCircles","args":[[50.81,50.88,50.93,50.87,50.89,50.88,50.88,50.84,50.73,50.9,50.87,50.87,50.85,50.8,50.77,50.67,50.72,50.75,50.78,50.8,50.82,50.82,50.77,50.72,50.65,50.61,50.63,50.61,50.67,50.67,50.78,50.8,50.76,50.63,50.71,50.64,50.63,50.63,50.61,50.59,50.7,50.64,50.56,50.54,50.47,50.48,50.61,50.6,50.68,50.73,50.77,50.82,50.84,50.82,50.9,50.87,50.89,50.85,50.81,50.76,50.93,50.91,50.76,50.79,50.77,50.9,50.88,50.84,50.56,50.53,50.51,50.66,50.79,50.78,50.61,50.59,50.61,50.68,50.65,50.46,50.46,50.78,50.8,50.82,50.82,50.89,50.92,50.94,50.95,50.92,50.83,50.87,50.89,50.87,50.76,50.73,50.73,50.68,50.8,50.71,50.7,50.68,50.57,50.59,50.62,50.6,50.62,50.78,50.77,50.83,50.74,50.73,50.72,50.71,50.67,50.6,50.59,50.5,50.54,50.52,50.49,50.5,50.47,50.82,50.81,50.78,50.77,50.62,50.67,50.66,50.45,50.37,50.39,50.46,50.48,50.01,50.04,49.99,50.11,50.43,50.44,50.36,50.59,50.5,50.47,50.47,50.38,50.37,50.58,50.48,50.51,50.48,50.49,50.62,50.44,50.49,50.43,50.46,50.48,50.63,50.63,50.37,50.32,50.35,50.38,50.32,50.45,50.51,50.53,50.44,50.4,50.39,50.5,50.46,50.52,50.51,50.43,50.37,50.32,50.3,50.13,50.11,50.04,50.03,49.99,50.39,50.45,50.4,50.42,50.05,50.26,50.3,50.31,50.33,50.34,50.01,49.94,49.44,49.45,49.5,49.54,49.57,49.74,49.92,49.94,49.91,49.87,49.58,49.52,49.45],[-127.66,-127.64,-127.77,-127.46,-127.42,-127.39,-127.24,-127.09,-127.41,-127.01,-126.68,-126.65,-126.61,-126.81,-126.53,-126.69,-126.78,-126.64,-126.69,-126.57,-126.38,-126.36,-126.21,-126.18,-126.25,-126.33,-126.43,-126.73,-127.15,-126.97,-126.9,-126.88,-126.88,-127.19,-127.21,-127.2,-127.07,-127.05,-126.93,-126.76,-126.86,-126.85,-126.8,-126.77,-126.29,-126.01,-127.08,-126.91,-126.93,-126.98,-126.9,-126.96,-127.09,-127.01,-127,-127.29,-127.39,-127.45,-127.48,-127.41,-127.76,-127.7,-126.8,-126.86,-126.66,-126.78,-126.57,-126.6,-126.76,-126.62,-126.65,-126.39,-126.2,-126.22,-126.34,-126.42,-126.74,-126.74,-126.54,-126.17,-126.13,-127.55,-127.6,-127.58,-127.63,-127.97,-127.64,-127.57,-127.51,-127.45,-127.48,-127.42,-127.36,-127.21,-126.9,-126.98,-127.03,-127.01,-127.13,-127.21,-127.32,-127.22,-126.87,-126.81,-126.81,-126.89,-127.11,-126.71,-126.66,-126.58,-126.56,-126.58,-126.64,-126.73,-126.68,-126.74,-126.73,-126.63,-126.77,-126.5,-126.4,-126.22,-126.04,-126.23,-126.2,-126.22,-126.2,-126.28,-126.19,-126.41,-125.95,-125.78,-125.54,-125.4,-125.35,-125.02,-125.05,-125.11,-125.21,-124.95,-125.01,-125.09,-124.87,-125.11,-125.11,-125.05,-125.06,-125.11,-125.56,-125.57,-125.83,-125.57,-125.58,-125.55,-125.43,-125.38,-125.3,-125.27,-125.05,-124.9,-124.88,-125.06,-125.07,-124.99,-124.97,-124.98,-125.93,-125.59,-125.55,-125.45,-125.34,-125.33,-125.26,-125.27,-125.02,-124.97,-125.06,-125.1,-125.08,-125.04,-125.12,-125.09,-125.07,-125.01,-125.11,-125.54,-126,-125.85,-125.15,-125.17,-125.04,-125.1,-125.11,-124.99,-125.01,-124.99,-125.15,-124.18,-124.21,-124.7,-124.63,-124.7,-124.87,-125.01,-125.15,-125.12,-124.83,-124.76,-124.73,-124.2],10,null,null,{"interactive":true,"className":"","stroke":true,"color":"#03F","weight":5,"opacity":0.5,"fill":true,"fillColor":"#03F","fillOpacity":0.2},null,null,null,{"interactive":false,"permanent":false,"direction":"auto","opacity":1,"offset":[0,0],"textsize":"10px","textOnly":false,"className":"","sticky":true},null,null]}],"limits":{"lat":[49.44,50.95],"lng":[-127.97,-124.18]}},"evals":[],"jsHooks":[]}</script>

---
template: leaflet

## Adding color

```r
pal <- colorNumeric(
    palette = as.character(wesanderson::wes_palette(name = "Zissou1", type = "continuous") ),
    domain = longline_dta$Nyellow)

longline_map %>% addCircles(lng = longline_dta$Long, lat = longline_dta$Lat, color = pal(longline_dta$Nyellow))
```

<div id="htmlwidget-c95cdc071eb6c567e7c1" style="width:504px;height:504px;" class="leaflet html-widget"></div>
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---
template: leaflet

## Adapting circles size

```r
pal <- colorNumeric(
    palette = as.character(wesanderson::wes_palette(name = "Zissou1", type = "continuous") ),
    domain = longline_dta$Nyellow)

longline_map %>% 
  addCircleMarkers(lng = longline_dta$Long, lat = longline_dta$Lat,
                   color = pal(longline_dta$Nyellow),
                  radius = longline_dta$Nyellow,
                  stroke = FALSE, fillOpacity = 0.5,
                  popup = longline_dta$Nyellow)
```

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---

### Exercise

Get the data the Coronavirus data available [here](https://raw.githubusercontent.com/opencovid19-fr/data/master/dist/chiffres-cles.csv)

By group ate the regio scale, propose the map of a region  to illustarte 5 key moemnts of the pandemy.

---
# References

Pebesma, E. (2018). "Simple Features for R: Standardized Support for
Spatial Vector Data". In: _The R Journal_ 10.1, pp. 439-446. DOI:
[10.32614/RJ-2018-009](https://doi.org/10.32614%2FRJ-2018-009). URL:
[https://doi.org/10.32614/RJ-2018-009](https://doi.org/10.32614/RJ-2018-009).