library("tidyverse")
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library("ggplot2")
library("shiny")
library("sf")
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library("randomForest")
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library("osmdata")
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library("rgeos")
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library("rgdal")
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library("readxl")
library("ggmap")
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library("rgdal")
library("haven")
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library("mapview")
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library("hrbrthemes")
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calles <-read_sf ("https://cdn.buenosaires.gob.ar/datosabiertos/datasets/jefatura-de-gabinete-de-ministros/calles/callejero.geojson")
avenidas = calles %>% filter(tipo_c =="AVENIDA")
radios = read_sf("https://cdn.buenosaires.gob.ar/datosabiertos/datasets/informacion-censal-por-radio/caba_radios_censales.geojson")
radios = radios %>% mutate (femeneidad = as.numeric(T_MUJER) /as.numeric(TOTAL_POB) )
Graficamos la femeneidad y las avenidas
ggplot() +
geom_sf(data = radios, aes(fill = femeneidad), color = NA) +
scale_fill_viridis_c() +
labs(title = "Femenización de los radios censales",
subtitle = "Ciudad Autónoma de Buenos Aires",
fill = "% de poblacion femenina por radio censal") + geom_sf(data= avenidas)
Calculamos la distancia de los radios censales a las avenidas
start.time <- Sys.time()
radios = radios %>% mutate(dist_avenida = apply(st_distance(radios, avenidas), 1, function(x) min(x)))
radios = radios %>% filter( femeneidad >0.3)
end.time <- Sys.time()
time.taken <- end.time - start.time
time.taken
## Time difference of 6.425079 mins
p3 <- ggplot(radios, aes(y=radios$femeneidad, x=radios$dist_avenida)) +
geom_point() +
geom_smooth(method=lm , color="red", fill="#69b3a2", se=TRUE) +
theme_ipsum()
cor(x =radios$femeneidad, y= radios$dist_avenida)
## [1] -0.02011164
subtes = read_sf("https://cdn.buenosaires.gob.ar/datosabiertos/datasets/sbase/subte-estaciones/subte_estaciones.geojson")
radios = radios %>% mutate(dist_subte = apply(st_distance(radios, subtes), 1, function(x) min(x)))
p4 <- ggplot(radios, aes(y=radios$femeneidad, x=radios$dist_subte)) +
geom_point() +
geom_smooth(method=lm , color="red", fill="#69b3a2", se=TRUE) +
theme_ipsum()
cor(radios$femeneidad,radios$dist_subte)
## [1] -0.2040477
ggplot() +
geom_sf(data = radios, aes(fill = femeneidad), color = NA) +
scale_fill_viridis_c() +
labs(title = "Femenización de los radios censales",
subtitle = "Ciudad Autónoma de Buenos Aires",
fill = "% de poblacion femenina por radio censal") + geom_sf(data= subtes)
ggplot() +
geom_sf(data = radios, aes(fill = dist_subte), color = NA) +
scale_fill_viridis_c() +
labs(title = "Femenización de los radios censales",
subtitle = "Ciudad Autónoma de Buenos Aires",
fill = "Distancia a una estacion de subterráneo") + geom_sf(data= subtes)
radios_cerca = radios %>% filter(dist_subte >500)
radios_lejos = radios %>% filter(dist_subte <500)
summary(radios_lejos$femeneidad)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.3530 0.5333 0.5501 0.5481 0.5660 0.6667
summary(radios_cerca$femeneidad)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.3200 0.5196 0.5345 0.5348 0.5502 0.6223