Datos espaciales con R

class: center, middle, inverse, title-slide

# Datos espaciales con R
## Principios básicos
### Stephanie Orellana
### R Ladies Chile para RLadies La Paz
### 25-06-2020

---

# Hoy hablaremos de:

- Introducción a R

- Conceptos básicos de datos espaciales

- Paquete sf

- Paquete raster

- Ejemplos prácticos

---

# Sobre mí

.right-column2[

]

- Stephanie Orellana (sporella@uc.cl)

- Ingeniera Agrónoma y Magíster en Recursos Naturales

- No aprendí a programar en ningún curso de la Universidad, sino que por necesidad.

- Partí trabajando con modelos mixtos de variables climáticas espacializadas, usando:
  + Arcgis
  + Excel
  + R

- Luego fui migrando completamente a R
  - Pero para "mirar" los rásters y vectores uso QGIS

---
# Básicos de R

--
- R es un lenguaje de **programación** y es un software libre

--
- RStudio es la interfaz de usuario más conocida (hace que R sea amigable e incluye herramientas para facilitar el trabajo)

--
- R es un lenguaje orientado a **objetos** en donde pueden aplicarse **funciones** a estos objetos.

---
# Por qué usar R

--
- Es un software libre y gratuito

--
- Permite hacer investigación reproducible:

--
  - El flujo de trabajo queda escrito en código
  - Los análisis se hacen dentro del mismo programa

--
- Es una herramienta potente y está en constante desarrollo de nuevas funcionalidades.

--
- Existe una gran comunidad alrededor del mundo que siempre está dispuesta a enseñar, responder preguntas y desarrollar nuevos paquetes.

--
  - Aprender a **buscar en Google** es primordial
  - **Twitter**
  
<div class="figure" style="text-align: right">
<img src="https://jules32.github.io/useR-2019-keynote/img/horst-welcome_to_rstats_twitter.png" alt="&lt;a href='https://twitter.com/allison_horst'&gt; @Allison Horst &lt;/a&gt;" width="35%" />
<p class="caption"><a href='https://twitter.com/allison_horst'> @Allison Horst </a></p>
</div>

---
# R Base v/s tidyverse

.pull-left[
<img src="https://i.ytimg.com/vi/MCB4gNVfysU/maxresdefault.jpg" width="90%" style="display: block; margin: auto;" />

- Programar con la sintaxis nativa de R
- Indices de objetos se realizan con: ` $ , [ ], [,] o [[]]`
- Funciones incluidas en el paquete base (base::)

]

.pull-right[
<img src="https://miro.medium.com/max/4032/1*B-cwhqnFgGIbd9lWnzi_mQ.png" width="90%" style="display: block; margin: auto;" />

- Grupo de paquetes diseñados para hacer ciencia de datos
- Busca una programación más intuitiva. Funciones como verbos.
- Todos los paquetes comparten una filosofía de diseño, gramática y estructuras de datos subyacentes.
- Operador "pipe" %>% 
]

---

# R Base v/s tidyverse

```r
# Seleccionar columnas
## R base

iris$Sepal.Length
iris[, c("Petal.Length", "Sepal.Length", "Species")]

# tidyverse
library(dplyr)

iris %>% 
  select(Petal.Length, Sepal.Length, Species)
```

.pull-center[

¡Para procesar datos espaciales necesitaremos las dos formas!

]

---
class: middle

# ¡Vamos a la parte espacial!

---

# R vs GIS tradicionales

|R | SIG Tradicionales
--------|---------|---------
1 | Código ilimitado, se pueden mezclar diferentes paquetes para diferentes operaciones y análisis (incluso puedes hacer tus reportes y presentaciones en R). | Algunos como ArcGis y QGis tienen una consola de código en Python.
--

2 | Visualización de datos espaciales no es tan detallada como en un SIG tradicional, aunque existen paquetes para explorar de forma dinámica. | Visualización rápida de grandes vectores y creación de estos con herramientas manuales especializadas.
--

3 | Se puede hacer todo tipo de análisis y gráficos de la información espacial (ej: `ggplot2`). | Sólo vistas de mapas e histogramas.
--

4 | Se pueden programar procesos iterativos para hacer la misma acción muchas veces (se puede programar todo). | Procesos en "batch" siguen siendo manuales.
--

5 | Fácil análisis de las tablas de atributos (se puede aplicar dplyr y contenidos del libro R for Data Science). | Análisis limitado de tablas de atributos.
--

6| Permite reproducibilidad, queda todo por escrito. | Poco reproducible, nada (o muy poco) queda por escrito.

---
# R spatial

[CRAN TASK VIEW](https://cran.r-project.org/web/views/Spatial.html)

---
# Paquetes más descargados de #rspatial

.footnote[
[*] 50 paquetes más descargados con la etiqueta RSpatial desde 2015 
  
]

---
class: middle

# Conceptos básicos a tomar en cuenta

---
# 1) Tipo de dato/archivo

.pull-left[

.h2[VECTORES]

.p-caption[
<div class="figure" style="text-align: center">
<img src="img/vector.png" alt="Fuente: &lt;a href='http://katiejolly.io/rnorth-19'&gt; @Katie Jolly &lt;/a&gt;" width="55%" />
<p class="caption">Fuente: <a href='http://katiejolly.io/rnorth-19'> @Katie Jolly </a></p>
</div>
]

<img src="img/vectorcat.png" width="40%" style="display: block; margin: auto;" />
.small[
- Mediciones de campo
- Estaciones meteorológicas
- Caminos
- Rutas GPS
- Comunas
- Regiones
- Áreas de estudio
]
]

.pull-right[
.h2[RÁSTER]
.p-caption[
<div class="figure" style="text-align: center">
<img src="img/raster.png" alt="Fuente: &lt;a href='http://katiejolly.io/rnorth-19'&gt; @Katie Jolly &lt;/a&gt;" width="62%" />
<p class="caption">Fuente: <a href='http://katiejolly.io/rnorth-19'> @Katie Jolly </a></p>
</div>
]

.small[
- Imágenes satelitales
- Imágenes aéreas
- Modelos de elevación digital
- Productos de tipos de usos de suelo
- Datos (numéricos o categóricos) en grillas regulares
]
]

---
# Vectores: paquete "sf"

--
- Es el paquete "más moderno" para trabajar con vectores.

--
- Actúa bajo la lógica de "simple features" en donde un objeto espacial se divide en dos componentes:
  - data.frame (tabla de atributos)
  - geometría **geom** (información espacial)

--
- Es compatible con **tidyverse**

<div class="figure" style="text-align: right">
<img src="https://user-images.githubusercontent.com/520851/50280460-e35c1880-044c-11e9-9ed7-cc46754e49db.jpg" alt="&lt;a href='https://twitter.com/allison_horst'&gt; @Allison Horst &lt;/a&gt;" width="45%" />
<p class="caption"><a href='https://twitter.com/allison_horst'> @Allison Horst </a></p>
</div>

---
# Cargar datos con sf

```r
library(sf)

# Cargar datos
## Poligonos
*provincias <- read_sf("data/provincias/provincias.shp")
provincias
```

```
## Simple feature collection with 123 features and 5 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -69.64483 ymin: -22.90657 xmax: -57.45443 ymax: -9.669633
## geographic CRS: WGS 84
## # A tibble: 123 x 6
##    gml_id   COD_DEP NOM_DEP COD_PROV NOM_PROV                           geometry
##    <chr>    <chr>   <chr>   <chr>    <chr>                    <MULTIPOLYGON [°]>
##  1 provinc~ <NA>    Potosi  14       Daniel C~ (((-67.77305 -19.95029, -67.7731~
##  2 provinc~ 0       LAGO    <NA>     Lago Poo~ (((-67.06019 -18.44845, -67.0602~
##  3 provinc~ 0       LAGO    <NA>     Lago Tit~ (((-69.32165 -15.54775, -69.3212~
##  4 provinc~ 0       LAGO    <NA>     Lago Uru~ (((-67.10087 -18.02564, -67.1007~
##  5 provinc~ 0       SALAR   <NA>     Salar de~ (((-67.98348 -19.03682, -67.9834~
##  6 provinc~ 0       SALAR   0        Salar de~ (((-67.48285 -19.53102, -67.4830~
##  7 provinc~ 01      Chuqui~ 07       Nor Cinti (((-64.71348 -19.7808, -64.71298~
##  8 provinc~ 01      Chuqui~ 09       Sud Cinti (((-64.26963 -20.52255, -64.2672~
##  9 provinc~ 02      La Paz  02       Omasuyos  (((-68.53956 -16.10705, -68.5297~
## 10 provinc~ 02      La Paz  12       Los Andes (((-68.39181 -16.01124, -68.3901~
## # ... with 113 more rows
```

---
# Cargar datos con sf

```r
## Puntos
*aeropuetos <- read_sf("data/aero1/aero1.shp")
aeropuetos
```

```
## Simple feature collection with 33 features and 4 fields
## geometry type:  POINT
## dimension:      XY
## bbox:           xmin: -69.40331 ymin: -22.6977 xmax: -57.84501 ymax: -10.82314
## geographic CRS: WGS 84
## # A tibble: 33 x 5
##    gml_id      ID AERO_INTER AEROPUERTO                                geometry
##    <chr>    <dbl> <chr>      <chr>                                  <POINT [°]>
##  1 aero1.1      0 INTER      VIRU VIRU                    (-63.10903 -17.80115)
##  2 aero1.2      0 NAL        ROBORE                       (-59.69179 -18.31267)
##  3 aero1.3      0 NAL        SALVADOR OGAYA (Pto. Suarez) (-57.84501 -18.93599)
##  4 aero1.4      0 NAL        YACUIBA                      (-63.63405 -21.97789)
##  5 aero1.5      0 NAL        CAMIRI                       (-63.46358 -20.04498)
##  6 aero1.6      0 INTER      JORGE WILSTERMAN             (-66.12517 -17.40998)
##  7 aero1.7      0 INTER      J. .F. KENNEDY               (-68.22983 -16.51672)
##  8 aero1.8      0 NAL        <NA>                         (-68.09211 -13.74058)
##  9 aero1.9      0 NAL        RURRENABAQUE                 (-67.44159 -14.43207)
## 10 aero1.10     0 NAL        APOLO                          (-68.38563 -14.712)
## # ... with 23 more rows
```

---
# Rásters: paquete "raster"

--
- Es el paquete más utilizado para el procesamiento de rásters, aunque se está desarrollando el paquete `stars` (del mismo creador de `sf`) el cual permite el procesamiento de ráster en tidyverse

--
- El paquete ráster actúa bajo la lógica de una matriz de datos en donde el inicio se encuentra en el extremo superior izquierdo.

--
- Si sabes cómo procesar matrices con R Base, es fácil acceder a los valores de un ráster.

--
- El paquete tiene funciones para leer, transformar, analizar y guardar rásters.

---
# Cargar datos con raster

```r
library(raster)

# Cargar datos
## Ráster individual
*lst_01 <- raster("data/raster/lst_01.tif")
lst_01
```

```
## class      : RasterLayer 
## dimensions : 1546, 1420, 2195320  (nrow, ncol, ncell)
## resolution : 0.008983153, 0.008983153  (x, y)
## extent     : -69.87096, -57.11489, -23.28433, -9.396378  (xmin, xmax, ymin, ymax)
## crs        : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 
## source     : C:/Users/PEZ/Desktop/git/datos_espaciales_lapaz/data/raster/lst_01.tif 
## names      : lst_01
```

---
# Cargar datos con raster

```r
library(raster)

# Cargar datos
## Grupos de rásters

l <- list.files("data/raster/", full.names = T)
*lst <- stack(l)
lst
```

```
## class      : RasterStack 
## dimensions : 1546, 1420, 2195320, 12  (nrow, ncol, ncell, nlayers)
## resolution : 0.008983153, 0.008983153  (x, y)
## extent     : -69.87096, -57.11489, -23.28433, -9.396378  (xmin, xmax, ymin, ymax)
## crs        : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 
## names      : lst_01, lst_02, lst_03, lst_04, lst_05, lst_06, lst_07, lst_08, lst_09, lst_10, lst_11, lst_12
```

---
# 2) Proyección Espacial (CRS)

- Es fundamental conocer en qué proyección espacial se encuentran nuestros datos. Las proyecciones están determinadas por un CRS (coordinate reference system).

- Cada CRS puede estar definido por un EPSG (sigla de European Petroleum Survey Group) el cual es un código que se encuentra registrado en un listado de parámetros geodésicos (https://spatialreference.org/ref/)

- Si quiero hacer operaciones espaciales, todas las capas deben tener el mismo sistema de proyección.

- En Chile, los sistemas de proyección más usados son:
  - EPSG:4326: WGS 84
  - EPSG:32719: WGS 84 / UTM zone 19S
  - EPSG:3857: WGS84 Web (Pseudo)Mercator (Auxiliary Sphere)
  - EPSG:24879: PSAD56 / UTM zone 19S

---
# 2) Proyección espacial

[Andy Woodruff: Projection Face!](http://bl.ocks.org/awoodruff/9216081?utm_campaign)

---
# Cambiar proyección de un vector

```r
# Cambiar proyección
## Proyección actual 
st_crs(provincias)$epsg
st_crs(provincias)$proj4string
```

```
## [1] 4326
## [1] "+proj=longlat +datum=WGS84 +no_defs"
```

```r
## Cambiar a Pseudo Mercator
*provincias_psm <- st_transform(provincias, crs = 3857)
st_crs(provincias_psm)$epsg
st_crs(provincias_psm)$proj4string
```

```
## [1] 3857
## [1] "+proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs"
```

---
# Cambiar proyección de un ráster

```r
# Conocer CRS actual
crs(lst_01)
```

```
## CRS arguments:
##  +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
```

```r
# Proyectar a UTM
*lst_01_psm <- projectRaster(lst_01, crs = crs("+init=epsg:3857"))
crs(lst_01_psm)
```

```
## CRS arguments:
##  +init=epsg:3857 +proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0
## +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null +no_defs
```

---
# 3) Extensión y resolución espacial

--
- Muy importante cuando trabajo con rásters.

--
- Si mis rásters no tienen la misma extensión (bbox) espacial, no pueden tratarse como stacks.
  - Tampoco si es que tienen diferente resolución

.pull-left[

```r
extent(lst_01)
```

```
## class      : Extent 
## xmin       : -69.87096 
## xmax       : -57.11489 
## ymin       : -23.28433 
## ymax       : -9.396378
```

```r
extent(lst_01_psm)
```

```
## class      : Extent 
## xmin       : -7783000 
## xmax       : -6353000 
## ymin       : -2672081 
## ymax       : -1045521
```
]

--
.pull-right[

```r
res(lst_01)
```

```
## [1] 0.008983153 0.008983153
```

```r
res(lst_01_psm)
```

```
## [1] 1000 1040
```
]
---
class:: middle

# Visualización

---
# Cortar Vectores por atributos y visualizar

```r
library(sf)
library(tidyverse)
# Seleccionar sólo comunas en el departamento de Cochabamba
provincias_coch <- provincias %>%
* filter(NOM_DEP == "Cochabamba")
```

```r
## Gráfico rápido
plot(provincias_coch)
```

---
# Vectores y ggplot2

```r
## Gráfico con ggplot
library(ggplot2)

ggplot(provincias_coch) +
* geom_sf(aes(fill = NOM_PROV))
```

---
# Cortar rásters por máscaras y visualizar

```r
# Creo máscara
provincias_lapaz_psm <- provincias %>%
  filter(NOM_DEP == "La Paz") %>%     
  st_transform(crs = 3857)

*m_lst_01 <- mask(lst_01_psm, provincias_lapaz_psm)
```

```r
plot(m_lst_01)
```

---
# Rásters y ggplot2

```r
# Convertir a tabla

rt <- data.frame(rasterToPoints(m_lst_01))

ggplot() +
 geom_raster(data = rt , aes(x = x, y = y, fill=lst_01))+
  scale_fill_gradientn(colours=rev(terrain.colors(15)))+
  coord_equal()
```

---
# leaflet

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---
class: middle, invert

# Recomendaciones

---
# Libros
.pull-left[
<div class="figure" style="text-align: center">
<img src="https://geocompr.robinlovelace.net/images/cover.png" alt=" &lt;a href='https://geocompr.robinlovelace.net/'&gt;Geocomputation with R&lt;/a&gt;"  />
<p class="caption"> <a href='https://geocompr.robinlovelace.net/'>Geocomputation with R</a></p>
</div>
]

.pull-right[
<div class="figure" style="text-align: center">
<img src="https://d33wubrfki0l68.cloudfront.net/b88ef926a004b0fce72b2526b0b5c4413666a4cb/24a30/cover.png" alt=" &lt;a href='https://r4ds.had.co.nz/'&gt;R for data science&lt;/a&gt;"  />
<p class="caption"> <a href='https://r4ds.had.co.nz/'>R for data science</a></p>
</div>
]

---
# A quién seguir

- https://twitter.com/CivicAngela  `#rspatialchat`

- https://twitter.com/Paula_Moraga_

- https://twitter.com/hadleywickham

- https://twitter.com/edzerpebesma

- https://twitter.com/RogerBivand

- https://twitter.com/jakub_nowosad

- `#rspatial`

- `#rstats`

---
# Recomendaciones generales

--
- Si se demoró mucho en cargar, mejor no visualizar.

- Cuando se trabaja con datos muy grandes, mejor hacer todas las pruebas con una selección pequeña.

- R tampoco es mágico, si le damos las intrucciones incorrectas, no obtendremos el resultado esperado.

- R es rápido, pero hay procesos que se demoran. Esperar.