Trabajo final
bicis <- read.csv("C:/Users/Public/colectivo/bici.csv" ,fileEncoding="UTF-8",sep=",", stringsAsFactors =FALSE)estaciones <- read.csv("C:/Users/Public/colectivo/estaciones.csv" ,fileEncoding="UTF-8",sep=",", stringsAsFactors =FALSE)library(tidyverse)## Warning: package 'tidyverse' was built under R version 3.6.1## Registered S3 method overwritten by 'rvest':
## method from
## read_xml.response xml2## -- Attaching packages ---------------------------------------------------------------------- tidyverse 1.2.1 --## v ggplot2 3.2.0 v purrr 0.3.2
## v tibble 2.1.1 v dplyr 0.8.3
## v tidyr 0.8.3 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0## Warning: package 'purrr' was built under R version 3.6.1## Warning: package 'dplyr' was built under R version 3.6.1## -- Conflicts ------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(ggmap)## Warning: package 'ggmap' was built under R version 3.6.1## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.## Please cite ggmap if you use it! See citation("ggmap") for details.library(sf)## Warning: package 'sf' was built under R version 3.6.1## Linking to GEOS 3.6.1, GDAL 2.2.3, PROJ 4.9.3library(osrm)## Warning: package 'osrm' was built under R version 3.6.1## Data: (c) OpenStreetMap contributors, ODbL 1.0 - http://www.openstreetmap.org/copyright## Routing: OSRM - http://project-osrm.org/library(dplyr)
library(purrr)
library(osmdata)## Warning: package 'osmdata' was built under R version 3.6.1## Data (c) OpenStreetMap contributors, ODbL 1.0. http://www.openstreetmap.org/copyrightbicis <- bicis %>%
rename(origen="bici_nombre_estacion_origen") %>%
rename(destino="bici_nombre_estacion_destino")estaciones_viejas <- st_read("C:/Users/Public/colectivo/zip/estaciones_de_bicicletas.shp")## Reading layer `estaciones_de_bicicletas' from data source `C:\Users\Public\colectivo\zip\estaciones_de_bicicletas.shp' using driver `ESRI Shapefile'
## Simple feature collection with 199 features and 8 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 100755.7 ymin: 98616.85 xmax: 109815.6 ymax: 106787.5
## epsg (SRID): NA
## proj4string: +proj=tmerc +lat_0=-34.6297166 +lon_0=-58.4627 +k=0.9999980000000001 +x_0=100000 +y_0=100000 +ellps=intl +units=m +no_defsespacios_verdes <- st_read("C:/Users/Public/colectivo/everdes/espacios-verdes-catastrales.shp")## Reading layer `espacios-verdes-catastrales' from data source `C:\Users\Public\colectivo\everdes\espacios-verdes-catastrales.shp' using driver `ESRI Shapefile'
## Simple feature collection with 1358 features and 25 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -58.53175 ymin: -34.70481 xmax: -58.33981 ymax: -34.52654
## epsg (SRID): 4326
## proj4string: +proj=longlat +datum=WGS84 +no_defs- Ejecutamos un conteo de viajes según estación de origen a estación de destino.
conteo <- bicis %>%
group_by(origen, bici_estacion_origen, destino,bici_estacion_destino ) %>%
summarise(total=n())%>%
arrange(total)%>%
rename(id_estacion="bici_estacion_origen")%>%
rename(NRO_EST="bici_estacion_destino")- En esta oportunidad detectamos que los 21 viajes más utilizados durante 2019 fueron circulares y la mayoria de los viajes que se repitieron más de 1000 veces a lo largo del año fueron circulares. Por eso decidimos elaborar un top 10 de los viajes no circulares.
top10 <- conteo %>%
ungroup() %>%
filter(origen != destino) %>%
top_n(10)## Selecting by totaltop10_NO <- top10 %>%
mutate(id_estacion=ifelse(id_estacion==175,147,id_estacion))ggplot() +
geom_tile(data = conteo, aes(x = origen, y = destino, fill = total)) +
scale_fill_distiller(palette = "Spectral")
ggplot() +
geom_tile(data = top10,
aes(y = as.factor(origen),
x = as.factor(destino),
fill = total)) +
scale_fill_distiller(palette = "Spectral")
estaciones_viejas <- estaciones_viejas %>%
mutate(lat = unlist(map(estaciones_viejas$geometry,1)),
long = unlist(map(estaciones_viejas$geometry,2)))top10 <- top10 %>%
mutate(id_estacion=as.numeric(id_estacion)) %>%
mutate(NRO_EST=as.numeric(NRO_EST))Viajes <- left_join(top10,estaciones)%>%
mutate(lat=as.numeric(lat)) %>%
mutate(long=as.numeric(long)) %>%
rename(origen_lat = lat,
origen_long = long)## Joining, by = "id_estacion"Viajes <- Viajes %>%
left_join(estaciones,
by = c("NRO_EST"= "id_estacion")) recorridos <- Viajes %>%
mutate(ID = paste(origen, "-", destino))bbox <- make_bbox(Viajes$origen_long, Viajes$origen_lat)mapa_base <- get_stamenmap(bbox, color = "bw", zoom = 12)## Source : http://tile.stamen.com/terrain/12/1383/2467.png## Source : http://tile.stamen.com/terrain/12/1383/2468.pngggmap(mapa_base) +
geom_point(data = Viajes, aes(x = origen_long, y = origen_lat), color = "red")
ggplot()+
geom_sf(data=espacios_verdes, fill="green")
Consti <- c(name="Constitucion",long =-58.38093,lat=-34.62674)
Guemes <- c(name="Plaza Guemes", long= -58.41612,lat=-34.58943)
LasHeras <- c(name="Parque Las Heras", long= -58.40774,lat=-34.58544)
Ecuador<- c(name="Ecuador", long= -58.40461,lat=-34.59610)
EcoParque<- c(name="EcoParque", long= -58.41388,lat=-34.57551)
Italia<- c(name="Plaza Italia", long= -58.42095,lat=-34.58055)
Pena<- c(name="Pena", long= -58.39737,lat=-34.59082)
Congreso <- c(name="Plaza Congreso", long= -58.38934,lat=-34.6094218)
Lezama<- c(name="Parque Lezama", long= -58.36976,lat=-34.628526) Uno <- osrmRoute(src = Pena,
dst = LasHeras,
sp = TRUE,
overview = "full")## Warning: sp is deprecated; use returnclass instead.Nueve <- osrmRoute(src = Guemes,
dst = LasHeras,
sp = TRUE,
overview = "full")## Warning: sp is deprecated; use returnclass instead.Ocho <- osrmRoute(src = LasHeras,
dst = Guemes,
sp = TRUE,
overview = "full")## Warning: sp is deprecated; use returnclass instead.Siete <- osrmRoute(src = Ecuador,
dst = LasHeras,
sp = TRUE,
overview = "full")## Warning: sp is deprecated; use returnclass instead.Seis <- osrmRoute(src = Consti,
dst = Lezama,
sp = TRUE,
overview = "full")## Warning: sp is deprecated; use returnclass instead.Cinco <- osrmRoute(src = LasHeras,
dst = Ecuador,
sp = TRUE,
overview = "full")## Warning: sp is deprecated; use returnclass instead.Cuatro <- osrmRoute(src = EcoParque,
dst = Italia,
sp = TRUE, overview = "full")## Warning: sp is deprecated; use returnclass instead.Tres <- osrmRoute(src = Italia,
dst = EcoParque,
sp = TRUE, overview = "full")## Warning: sp is deprecated; use returnclass instead.Dos <- osrmRoute(src = LasHeras,
dst = Pena,
sp = TRUE,
overview = "full")## Warning: sp is deprecated; use returnclass instead.Diez <- osrmRoute(src = Consti,
dst = Congreso,
sp = TRUE,
overview = "full")## Warning: sp is deprecated; use returnclass instead.library(leaflet)## Warning: package 'leaflet' was built under R version 3.6.1leaflet(Uno) %>%
addTiles() %>%
addPolylines(color = "red")EspaciosVerdes <- opq(bbox) %>%
add_osm_feature(key = "landuse", value = "greenfield") %>%
osmdata_sf() Recorridos <- rbind(Uno,Dos,Tres,Cuatro,Cinco,Seis,Siete,Ocho,Nueve,Diez)Mapa del top 10 de recorridos no circulares
- A excepción del viaje de Constitución a Congreso que parece un viaje de trabajo, el resto tienen la particularidad de comenzar o terminar en una estación con espacios verdes cercanos. Por esto mismo, sumado a la cantidad de viajes circulares, se puede inferir que este medio de movilidad se utiliza de manera mayoritaria como medio de recreación en vez del uso intermodal impuesto como objetivo.
leaflet(Recorridos) %>%
addTiles() %>%
addPolylines(color = "red")ggmap(mapa_base,darken = 0.7) +
geom_path(data = Recorridos, aes(x = long, y = lat,color="red"), alpha = 0.7, size=1.5) +
theme_nothing()