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coropleto_barrios_2.R
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coropleto_barrios_2.R
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require(ggplot2)
require(maptools)
require(rgdal)
require(spatialEco)
require(spatial)
require(sp)
# # # # # # # # # # # # # # # # # # # # # # # # #
# Leemos los datos
P4S.latlon <- CRS("+proj=utm +zone=30 +ellps=WGS84 +datum=WGS84 +units=m +no_defs")
barrios.shp <- readShapePoly("SHP_ED50/Barrios.shp", verbose=TRUE, proj4string=P4S.latlon)
barrios <- spTransform(barrios.shp, CRS("+proj=longlat +datum=WGS84"))
atlas <- read.csv("../atlasTuristificacion/atlas.csv")
barrios.data.corregido <- read.csv("barrios.data.csv")
barrios.data.corregido
levels <- barrios$names[order(tabla$i1, decreasing=FALSE)]
## tabla$names <- factor(tabla$names, levels = levels)
barrios@data <- barrios.data.corregido
barrios.f <- fortify(barrios, region = "CODBARRIO")
# # # # # # # # # # # # # # # # # # # # # # # # #
# Calculamos el índice que queramos
# A.
indice <- as.data.frame(cbind(lat=atlas$lat, lon=atlas$lon,
i = atlas$rn_med_tramo,
i2 = (atlas$rn_fc_med_price /
atlas$rn_med_tramo)))
rescale <- function(x, ...){(x - min(x, ...)) / (max(x, ...) - min(x, ...))}
indice$i2r <- rescale(indice$i2, na.rm=TRUE)
#Sustituimos los NA por la media de la ciudad
indice$i2r[is.na(indice$i2r)] <- mean(indice$i2r, na.rm=TRUE)
indice$i[is.na(indice$i)] <- mean(indice$i, na.rm=TRUE)
## indice$i2[is.na(indice$i2)] <- 0
## indice <- indice[!is.na(indice$i2),]
coordinates(indice) <- ~lat+lon
proj4string(indice) <- CRS("+proj=longlat +datum=WGS84")
# Asignamos cada punto a su barrio correspondiente
## pts.poly <- point.in.poly(indice, barrios)
## index <- as.data.frame(tapply(pts.poly@data$i2r,
## pts.poly@data$CODBARRIO, FUN=mean, na.rm=TRUE))
## index$names <- rownames(index)
## colnames(index) <- c("index", "id")
## index$index <- index$index*100
# Asignamos cada punto a su barrio correspondiente (otra forma)
index <- sp::over(barrios, indice, fn = mean, na.rm=TRUE)
index$names <- barrios@data$NOMBRE
index$id <- barrios@data$CODBARRIO
index$i1 <- barrios@data$precio / index$i
# # # # # # # # # # # # # # # # # # # # # # # # #
# Lo calculamos por barrios
datos <- merge(barrios.f, index, by="id", all.x=TRUE)
##barrios@data$index <- index$i1
# # # # # # # # # # # # # # # # # # # # # # # # #
# A dibujar...
titleGraph <- "Precio medio de alquiler dividido entre renta media"
p1 <- ggplot(datos) +
geom_polygon(aes(x = long, y = lat, group = group,
fill=i1))+
geom_polygon(data=barrios.f, aes(x=long, y=lat, group=group),
color="black", alpha=0, size=0.1)+
scale_fill_distiller(name="ratio",
palette="BrBG", direction=-1)+
labs(title="",
caption="Fuente: idealista.com, turistificacion.300000kms.net",
y="", x="") +
theme_minimal()+ # no backgroundcolor
ggthemes::theme_map() +
theme(legend.position=c(0.8,0.7),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
rect = element_blank()) +
coord_map()
p1
# Tabla para diagrama de barras
tabla <- unique(datos[,c("id","names","i1", "group")])
tabla$lab <- format(round(tabla$i1,2), nsmall=2)
tabla.ord <- tabla[order(tabla$i1),]
## levels <- tabla$names[order(tabla$i1, decreasing=FALSE)]
## tabla$names <- factor(tabla$names, levels = levels)
tabla.red <- tabla.ord[c(1:11,118:129),]
## levels(tabla.red$names) <- sub("^Berruguete$", "(...)",
## levels(tabla.red$names))
levels(tabla.red$names) <- sub("^Numancia$", "Madrid (media)",
levels(tabla.red$names))
## levels(tabla.red$names) <- sub("^Valdemarin$", "(...)",
## levels(tabla.red$names))
## tabla.red[11, "i1"] <- 100
tabla.red[12, "i1"] <- mean(tabla$i1)
## tabla.red[13, "i1"] <- 100
p2 <- ggplot(tabla.red) +
geom_col(aes(x=reorder(names, i1), y=i1, fill=i1)) +
scale_fill_distiller(palette="BrBG", direction=-1)+
## geom_text(size = 4, hjust = -0.1) +
labs(title=titleGraph, y="", x="") +
coord_flip() +
# scale_x_discrete(limits=c(1,20)) +
scale_y_continuous(limits = c(0, 10)) +
theme_minimal()+ # no backgroundcolor
theme(axis.text.y=element_text(size=14),
plot.title=element_text(size=14, face="bold"),
plot.caption=element_text(size=8),
legend.position="none",
panel.grid.major.y = element_blank(),
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank(),
axis.text.x = element_text(size=10))
p2
p3 <- grid.arrange(p2, p1, ncol=2)
ggsave(grid.arrange(p2, p1, ncol = 2),
filename="alquiler_entre_renta_media.png", device="png")
ggplot(final.plot) +
theme_minimal()+ # no backgroundcolor
geom_polygon(aes(x = long, y = lat, group = group,
fill=cut(indexF, c(0,0.12,1))))+
geom_polygon(data=barrios.f,aes(x=long, y=lat, group=group),
color="black", alpha=0, size=0.1)+
scale_fill_brewer(type="seq",
labels=c("<0.12",">0.12"))+
guides(fill=guide_legend(title="Ratio"))+
labs(title=titleGraph) +
coord_map()
ggsave("rentaMediaPrecioAlquiler_2c.png")
# # # # # # # # # # # # # # # # # # # # # # # # # # #
# # # # # # # # # # # # # # # # # # # # # # # # # # #
# Coropleto interactivo
library(leaflet)
library(htmlwidgets)
m <- leaflet(barrios) %>%
setView(-3.7, 40.5, 11) %>%
## fitBounds(-3.9, -3.5, 40.3, 40.7) %>%
addProviderTiles("OpenStreetMap.Mapnik")
# Create a continuous palette function
pal <- colorNumeric(
palette = "Blues",
domain = c(0.07,0.3))
labels <- sprintf(
"<strong>%s</strong><br/>ratio: %g",
barrios$NOMBRE, barrios$index
) %>% lapply(htmltools::HTML)
m %>% addPolygons(smoothFactor = 0,
fillOpacity = 0.9,
color = ~pal(index),
weight=1,
highlight = highlightOptions(
weight = 1,
color = "#666",
dashArray = "",
fillOpacity = 1.0,
bringToFront = TRUE),
label = labels,
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto")) %>%
addLegend(pal = pal, values = barrios$index,
opacity = 1, title = "Ratio entre<br/>salario medio y<br/>precio del alquiler",
position = "topleft") %>%
saveWidget(file="rentaMediaPrecioAlquiler.html",
selfcontained=TRUE)