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Efecto Airbnb
Efecto Airbnb Valencia
Commits
2cf2dcdd
Commit
2cf2dcdd
authored
Apr 03, 2019
by
numeroteca
Browse files
añade mapas en VUT e inicia comparativa con datos de Airbnb
parent
7ab52cc0
Changes
1
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Inline
Side-by-side
analysis/vut-analysis.R
100644 → 100755
View file @
2cf2dcdd
# script para analizar las viviendas turísticas de la comunidad Valenciana
# Load libraries
library
(
tidyverse
)
# for maps and theme nothing
library
(
ggmap
)
# read geojson
library
(
rgdal
)
library
(
gsubfn
)
# load data ------
vut_valenciana
<-
read.csv
(
"data/original/190302_viviendas-turisticas-comunidad-valenciana.csv"
,
stringsAsFactors
=
FALSE
)
vut_valencia
<-
read.csv
(
"data/original/190302_viviendas-turisticas-comunidad-valenciana_valencia.csv"
,
stringsAsFactors
=
FALSE
)
# load data
vut_valencia
<-
read_csv
(
"data/original/190302_viviendas-turisticas-comunidad-valenciana.csv"
)
vut
<-
read_csv
(
"data/output/190302_viviendas-turisticas-comunidad-valenciana_valencia_geocoded_barrio-distrito.csv"
)
vut
<-
read_csv
(
"data/original/190302_viviendas-turisticas-comunidad-valenciana_valencia.csv"
)
# shapes
barrios
<-
readOGR
(
"data/original/shapes/barrios.valencia.wgs84.geojson"
)
distritos
<-
readOGR
(
"data/original/shapes/distritos.valencia.wgs84.geojson"
)
# municipios <- distritos
municipios
<-
readOGR
(
"data/original/shapes/municipios.provincia.valencia.geojson"
)
# Analisis comunidad valenciana -------------
vut.municipio
<-
group_by
(
vut_valencia
,
Municipio
)
%>%
summarise
(
n
=
n
()
)
%>%
arrange
(
desc
(
n
))
vut.municipio
<-
group_by
(
vut_valencia
na
,
Municipio
)
%>%
summarise
(
n
=
n
()
)
%>%
arrange
(
desc
(
n
))
vut.municipio
%>%
head
(
25
)
%>%
ggplot
(
aes
(
x
=
reorder
(
Municipio
,
n
),
y
=
n
))
+
...
...
@@ -23,21 +37,16 @@ vut.municipio %>% head(25) %>%
x
=
"tlf"
,
caption
=
"Datos: Comunidad Valenciana. Gráfico: lab.montera34.com/airbnb"
)
# analisis Valencia -----------------
table
(
vut
$
Signatura
)
names
(
vut
)
<-
c
(
"signatura"
,
"municipio"
,
"provincia"
,
"addres"
,
"tlf"
)
# names(vut) <- c("signatura","municipio","provincia","addres","tlf","lat","lon")
ggplot
(
data
=
vut
)
+
geom_bar
(
stat
=
'identity'
,
aes
(
x
=
signatura
,
y
=
tlf
))
ntlf
<-
group_by
(
vut
,
tlf
)
%>%
summarise
(
n
=
n
()
)
%>%
arrange
(
desc
(
n
))
ntlf
<-
group_by
(
vut
,
Teléfono
)
%>%
summarise
(
n
=
n
()
)
%>%
arrange
(
desc
(
n
))
ntlf
[
!
is.na
(
ntlf
$
tlf
),]
%>%
head
(
25
)
%>%
ggplot
(
aes
(
x
=
reorder
(
tlf
,
n
),
y
=
n
))
+
ntlf
[
!
is.na
(
ntlf
$
Teléfono
),]
%>%
head
(
25
)
%>%
ggplot
(
aes
(
x
=
reorder
(
Teléfono
,
n
),
y
=
n
))
+
geom_col
()
+
coord_flip
()
+
theme_minimal
(
base_family
=
"Roboto Condensed"
,
base_size
=
14
)
+
theme
(
...
...
@@ -50,8 +59,112 @@ theme_minimal(base_family = "Roboto Condensed", base_size = 14) +
x
=
"tlf"
,
caption
=
"Datos: Comunidad Valenciana. Gráfico: lab.montera34.com/airbnb"
)
select
(
vut
,
tlf
==
963356793
)
table
(
local_activo
$
room_type
)
# select(vut,Teléfono=="963356793")
#
# vut[vut$tlf=="963356793",]
# table(local_activo$room_type)
#
# tlf=="963356793"
vut
$
Teléfono
<-
as.factor
(
vut
$
Teléfono
)
# extract VUT ID --------
# vut_valencia$registro.number <- str_extract(vut_valencia$Signatura,"[:punctuation:]?[:blank:]?-\\d{5}")
vut_valencia
$
registro.num
<-
as.character
(
strapplyc
(
vut_valencia
$
Signatura
,
".*(\\d{5}).*"
,
simplify
=
TRUE
))
vut_valencia.airbnb
<-
merge
(
vut_valencia
,
datos2
,
by
=
"registro.num"
,
type
=
"left"
)
# agrupa por host_id de airbnb y add tlf de vut
x
<-
vut_valencia.airbnb
%>%
group_by
(
host_id
,
host_name
,
Teléfono
)
%>%
summarise
(
count
=
n
())
# Puntos en mapa ---------------
ggplot
()
+
geom_polygon
(
data
=
municipios
,
aes
(
x
=
long
,
y
=
lat
,
group
=
group
),
color
=
"grey"
,
fill
=
"white"
,
size
=
0.1
)
+
geom_polygon
(
data
=
barrios
,
aes
(
x
=
long
,
y
=
lat
,
group
=
group
),
color
=
"grey"
,
fill
=
"white"
,
size
=
0.1
)
+
geom_point
(
data
=
vut
,
aes
(
x
=
lon
,
y
=
lat
),
alpha
=
1
,
size
=
0.1
)
+
geom_jitter
(
data
=
vut
,
aes
(
x
=
lon
,
y
=
lat
),
alpha
=
0.6
,
size
=
0.6
,
color
=
"red"
,
width
=
0.0007
,
height
=
0.0007
)
+
# geom_point(data= local_activo[host_id %in% n_alojamientos[1:5,]$host_id,],
# aes(x=longitude, y=latitude, color=host_name),alpha=0.6,size = 1) + #color="blue"
coord_fixed
(
ratio
=
1.3
)
+
# coord_fixed(xlim= c(-0.4, -0.3),ylim=c(39.45,39.5),ratio=1.3 ) +
theme_nothing
(
legend
=
TRUE
)
+
theme_minimal
(
base_family
=
"Roboto Condensed"
,
base_size
=
12
)
+
theme
(
panel.grid
=
element_blank
(),
axis.title
=
element_blank
(),
axis.text
=
element_blank
(),
panel.background
=
element_rect
(
fill
=
"#EEEEFF"
,
color
=
"grey"
,
size
=
0.25
),
legend.position
=
"top"
)
+
labs
(
title
=
paste
(
"Cada punto es una vivienda turística"
,
sep
=
""
))
+
guides
(
colour
=
guide_legend
(
override.aes
=
list
(
size
=
3
)))
ggplot
()
+
geom_polygon
(
data
=
municipios
,
aes
(
x
=
long
,
y
=
lat
,
group
=
group
),
color
=
"grey"
,
fill
=
"white"
,
size
=
0.1
)
+
geom_polygon
(
data
=
barrios
,
aes
(
x
=
long
,
y
=
lat
,
group
=
group
),
color
=
"grey"
,
fill
=
"white"
,
size
=
0.1
)
+
geom_point
(
data
=
vut
,
aes
(
x
=
lon
,
y
=
lat
),
alpha
=
1
,
size
=
0.1
)
+
geom_jitter
(
data
=
vut
,
aes
(
x
=
lon
,
y
=
lat
),
alpha
=
0.6
,
size
=
0.6
,
color
=
"red"
,
width
=
0.0007
,
height
=
0.0007
)
+
# geom_point(data= local_activo[host_id %in% n_alojamientos[1:5,]$host_id,],
# aes(x=longitude, y=latitude, color=host_name),alpha=0.6,size = 1) + #color="blue"
coord_fixed
(
xlim
=
c
(
-0.4
,
-0.3
),
ylim
=
c
(
39.45
,
39.5
),
ratio
=
1.3
)
+
theme_nothing
(
legend
=
TRUE
)
+
theme_minimal
(
base_family
=
"Roboto Condensed"
,
base_size
=
12
)
+
theme
(
panel.grid
=
element_blank
(),
axis.title
=
element_blank
(),
axis.text
=
element_blank
(),
panel.background
=
element_rect
(
fill
=
"#EEEEFF"
,
color
=
"grey"
,
size
=
0.25
),
legend.position
=
"top"
)
+
labs
(
title
=
paste
(
"Valencia zoom. Cada punto es una vivienda turística"
,
sep
=
""
))
+
guides
(
colour
=
guide_legend
(
override.aes
=
list
(
size
=
3
)))
# por distrito en Valencia------------
vut.distrito
<-
group_by
(
vut
,
distrito
)
%>%
summarise
(
n
=
n
()
)
%>%
arrange
(
desc
(
n
))
vut.distrito
%>%
filter
(
!
is.na
(
distrito
))
%>%
ggplot
(
aes
(
x
=
reorder
(
distrito
,
n
),
y
=
n
))
+
geom_col
()
+
geom_text
(
data
=
vut.distrito
%>%
filter
(
!
is.na
(
distrito
)),
aes
(
label
=
n
,
y
=
n
+3
),
hjust
=
0
,
size
=
3
,
color
=
"#000000"
)
+
coord_flip
()
+
theme_minimal
(
base_family
=
"Roboto Condensed"
,
base_size
=
10
)
+
theme
(
panel.grid.minor.y
=
element_blank
(),
panel.grid.major.y
=
element_blank
(),
legend.position
=
"bottom"
)
+
labs
(
title
=
"Número de viviendas turísticas por distrito"
,
subtitle
=
"Valencia. Marzo 2019."
,
y
=
"nº anuncios"
,
x
=
"tlf"
,
caption
=
"Datos: Comunidad Valenciana. Gráfico: lab.montera34.com/airbnb"
)
# por barrio
vut.barrio
<-
group_by
(
vut
,
barrio
)
%>%
summarise
(
n
=
n
()
)
%>%
arrange
(
desc
(
n
))
vut.barrio
%>%
ggplot
(
aes
(
x
=
reorder
(
barrio
,
n
),
y
=
n
))
+
geom_col
()
+
coord_flip
()
+
theme_minimal
(
base_family
=
"Roboto Condensed"
,
base_size
=
10
)
+
theme
(
panel.grid.minor.y
=
element_blank
(),
panel.grid.major.y
=
element_blank
(),
legend.position
=
"bottom"
)
+
labs
(
title
=
"Número de viviendas turísticas por barrio"
,
subtitle
=
"Valencia. Marzo 2019."
,
y
=
"nº anuncios"
,
x
=
"tlf"
,
caption
=
"Datos: Comunidad Valenciana. Gráfico: lab.montera34.com/airbnb"
)
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