6.4 Mapas
Cargamos paquetes
library(tidyverse)
library(sf)library(georefar)Para obtener un listado de provincias:
get_provincias(orden = "id", max = 5)Para obtener un listado de departamentos:
get_departamentos(provincia = "Buenos Aires", orden = "id", max = 5)Para obtener un listado de municipios:
muni <- get_municipios(provincia = "Buenos Aires")Para obtener un listado de localidades:
get_localidades(provincia = "Buenos Aires", max = 5)Para obtener la ubicacion de un punto (reverse-geocoding):
get_ubicacion(-31.6515236,-64.4358794)Provincias de Argentina
theme_set(theme_bw())
arg = rnaturalearth::ne_states(country = 'argentina', returnclass = "sf")
arg %>%
ggplot() +
geom_sf() +
geom_text(aes(longitude, latitude, label = name_fr), colour = "black", size=2)ARG1 <- raster::getData(name = "GADM", country = "ARG", level = 1) %>%
st_as_sf() %>%
st_transform(crs = 4326)
glimpse(ARG1)
ARG1 %>%
ggplot()+
geom_sf()
sf_cent <- st_centroid(ARG1)
ARG1 %>%
ggplot() +
geom_sf() +
geom_sf(data = sf_cent, color = 'red', size=1) +
geom_sf_text(data = sf_cent, aes(label = NAME_1))
ARG2 <- raster::getData(name = "GADM", country = "ARG", level = 2) %>% st_as_sf()
ARG2 %>%
# filter(NAME_1 == "Misiones") %>%
ggplot()+
geom_sf() Random points
puntos <- st_sample(ARG1, 30) # arg[1:3, ], 6)
puntos_coords <- unlist(st_geometry(puntos)) %>%
matrix(ncol=2, byrow=TRUE) %>%
as_tibble() %>%
setNames(c("lon","lat"))
ARG1 %>%
ggplot()+
geom_sf()+
geom_point(data = puntos_coords,
aes(x = lon,
y = lat),
size = 1)Leyendo archivos kml
inta <- "https://github.com/juanchiem/agro_data/raw/master/sedes_inta.kml"
aer_inta <- st_read(inta, layer = "Unidades de extensión")
eea_inta <- st_read(inta, layer = "Estaciones Experimentales Agropecuarias")
ci_inta <- st_read(inta, layer="Centros de investigación")
ggplot(ARG1) +
geom_sf()+
geom_sf(data=eea_inta, size=0.5, col ="red", alpha = 0.5) +
geom_sf(data=aer_inta, size=0.5, col ="blue", alpha = 0.5)+
geom_sf(data=ci_inta, size=0.5, col ="green")Tunneo fino
# library(googlesheets4)
# googlesheets4::gs4_auth()
# dat <- gs4_find("localidades_map") %>% range_read()
dat <- tibble::tribble(
~state, ~name, ~lat, ~lon,
"Buenos Aires", "Junin", "-34.593922", "-60.946446",
"Santiago del Estero", "Santiago del Estero", "-27.784444", "-64.266944",
"Córdoba", "Córdoba", "-31.416667", "-64.183333",
"Córdoba", "San José de la Quintana", "-31.807335", "-64.416866",
"Mendoza", "San Rafael", "-34.6175", "-68.335556",
"Misiones", "Cerro Azul", "-27.633535", "-55.497152"
)
sites <- dat %>%
mutate_at(vars(lat:lon), as.character) %>%
mutate_at(vars(lat:lon), as.numeric) %>%
st_as_sf(coords = c("lon","lat")) %>%
st_set_crs(4326)
ARG1 %>%
ggplot() +
geom_sf(expand = F) +
geom_sf(data=sites, size=1.5, expand = TRUE)+
ggrepel::geom_label_repel(data=st_crop(sites, st_bbox(sites)),
aes(label = name,
geometry = geometry),
stat = "sf_coordinates",
min.segment.length = 0)+
ggspatial::annotation_scale(location = "bl",
width_hint = 0.3,
pad_x = unit(4, "cm"),
pad_y = unit(0.2, "cm")) +
ggspatial::annotation_north_arrow(pad_x = unit(5, "cm"),
pad_y = unit(1, "cm"),
style = ggspatial::north_arrow_fancy_orienteering) +
# coord_sf(crs = st_crs(4326),
# # xlim = c(-64, -56.5),
# ylim = c(-40, -22),
# expand = TRUE)+
labs(x="", y="")
box <- st_bbox(sites)
mini <- ARG1 %>%
ggplot() +
geom_sf() +
geom_rect(xmin= box$xmin, ymin= box$ymin, xmax= box$xmax, ymax= box$ymax,
fill = NA, colour = "black", size = 1) +
theme_void()
mini# cowplot::ggdraw(big) +
# cowplot::draw_plot(mini,
# width = 0.2, height = 0.5,
# x = 0.85, y = 0.5)
# ggsave(w=85, h=70, units="mm", scale=2, dpi=150, "map2.tiff")6.4.1 Mapa de prevalencia de enfermedades
ARG2 <- raster::getData(name = "GADM", country = "ARG", level = 2) %>% st_as_sf()
# https://datascience.blog.wzb.eu/2019/04/30/zooming-in-on-maps-with-sf-and-ggplot2/
BSAS <- ARG2 %>% filter(NAME_1 == "Buenos Aires")
SEBA <- st_crop(BSAS,
xmin = -60, xmax = -57,
ymin = -39, ymax = -37)
SEBA %>%
ggplot() +
geom_sf()
cancro <- SEBA %>%
as_tibble %>%
mutate(preval_2015 = rnorm(18, 30, 10),
preval_2016 = preval_2015*1.05 + rnorm(1, 3, 2) ,
preval_2017 = preval_2016*1.05 + rnorm(1, 3, 2),
preval_2018 = preval_2017*1.05 + rnorm(1, 3, 2)) %>%
pivot_longer(preval_2015:preval_2018,
names_to = "anio", values_to = "prevalencia",
names_prefix = "preval_")
cancro
SEBA_cancro <- SEBA %>%
left_join(cancro, by= "NAME_2") #%>%
SEBA_cancro %>%
ggplot() +
geom_sf(data=SEBA)+
geom_sf(aes(fill=prevalencia))+
scale_fill_gradient2(midpoint = 35,
low = 'green2',
mid = 'yellow',
high = 'red3',
na.value = 'gray95')+
facet_wrap("anio")+
labs(title = "Evolución de la prevalencia del cancro del tallo de girasol",
x = NULL, y = NULL, fill = "Prevalencia")+
theme_bw()Puntos de muestreo
library(basemapR)
fina2020 <- st_read("https://github.com/juanchiem/agro_data/raw/master/Sitios_experimentales_2020_Juan.kml")
bbox <- expand_bbox(st_bbox(st_centroid(fina2020)), X = 0, Y = 150000)
BSAS %>%
ggplot() +
base_map(bbox, increase_zoom = 2, basemap = 'google-terrain') +
geom_sf(alpha = 0.1, color=alpha("red",0.1) , show.legend = FALSE)+
geom_sf(data = fina2020[1], show.legend = FALSE)+
coord_sf(xlim= c(-61, -57),ylim= c(-39,-36.5))+
theme_void() # geom_sf_label(aes(label = coddepto), label.padding = unit(1, "mm"))Mapas Interactivos
arg1 <- ARG1 %>% rename(provincia = "NAME_1") %>%
left_join(huertas, by = c("provincia"))
arg1 %>% ggplot() + geom_sf()+
geom_sf(aes(fill= X2016), alpha = 0.8)+
scale_fill_viridis_c()
pacman::p_load(mapview)
# https://bookdown.org/nicohahn/making_maps_with_r5/docs/mapview.html
mapview::mapview(arg1, zcol="X2016", reverse = F)
library(leaflet)
# https://bookdown.org/nicohahn/making_maps_with_r5/docs/leaflet.html
pal <- colorQuantile("YlGn", NULL, n = 5)
arg1 %>%
leaflet() %>%
addTiles() %>%
addPolygons(fillColor = ~pal(arg1$X2016),
fillOpacity = 0.8,
color = "#BDBDC3",
weight = 1)