RECICLADO1.Rmd

---
title: "Caracterización de cada cluster"
author: "Diego Medina"
date: "21-03-2022"
output: rmdformats::readthedown
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r,message=FALSE,warning=FALSE,include=FALSE}
library(sp)
library(sf)
library(dplyr)
library(ggplot2)
library(cowplot)

SANTIAGO           <- st_read('outputs/ISMT/SANTIAGO/SANTIAGO_ISMT.shp')
SANTIAGO_RECICLADO <- st_read('RECICLADO/inputs/SANTIAGO_JERARQUICO1.shp') %>% 
                      as.data.frame()                                      %>%
                      select(GEOCODIGO,WS_05m,WS_10m,WS_15m,WS_20m,CAD_P300,CAD_P450,CAD_P600,CAD_MS300,CAD_MS450,CAD_MS600)

SANTIAGO_FUSION <- merge(SANTIAGO,SANTIAGO_RECICLADO, by.x="GEOCODIGO",by.y="GEOCODIGO")
SANTIAGO <- SANTIAGO_FUSION

SANTIAGO$W_score5  <- as.numeric(SANTIAGO$WS_05m)
SANTIAGO$W_score10 <- as.numeric(SANTIAGO$WS_10m)
SANTIAGO$W_score15 <- as.numeric(SANTIAGO$WS_15m)
SANTIAGO$W_score20 <- as.numeric(SANTIAGO$WS_20m)

```

# Respecto a la caminabilidad

Antes de analizar los boxplots de caminabilidad, vale primero la pena analizar los histogramas de la variable para cada una de las solicitudes hechas.

```{r,fig.width=12,fig.height=7,message=FALSE,warning=FALSE}
hist(SANTIAGO$W_score5)
hist(SANTIAGO$W_score10)
hist(SANTIAGO$W_score15)
hist(SANTIAGO$W_score20)
```

Lo que nos indica esto, como es lógico, es que a medida que aumenta el tiempo de caminata, el puntaje mejora. Estos gráficos explican por qué los boxplots posteriores se ven tan sesgados a la media muchas de las veces.

# Santiago

En este apartado se mostrarán los resultados de la caracterización para el caso de Santiago para distintos cortes en el dendrograma (para k entre 2 y 8)

## para k = 2

```{r,message=FALSE,warning=FALSE,include=FALSE}

PORC_ADM2 <- ggplot(SANTIAGO,aes(x = cluster2,y = PORC_ADM)) + geom_boxplot() + labs(title = "Porcentaje adultos mayores por zona"           , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PORC_ADM)),linetype="dotted",size=1.5,color="deeppink1")
PJHSEM2   <- ggplot(SANTIAGO,aes(x = cluster2,y = PJHSEM))   + geom_boxplot() + labs(title = "Porcentaje Jefes de Hogar sin ensenanza media" , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PJHSEM))  ,linetype="dotted",size=1.5,color="deeppink1")
TIM2      <- ggplot(SANTIAGO,aes(x = cluster2,y = log(TIM))) + geom_boxplot() + labs(title = "Tasa de inmigracion"                           , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(log(TIM))),linetype="dotted",size=1.5,color="deeppink1")
EMP2      <- ggplot(SANTIAGO,aes(x = cluster2,y = EMP))      + geom_boxplot() + labs(title = "Porcentaje empleabilidad mayor a 15 anos"      , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(EMP))     ,linetype="dotted",size=1.5,color="deeppink1")
HAC2      <- ggplot(SANTIAGO,aes(x = cluster2,y = HAC))      + geom_boxplot() + labs(title = "Porcentaje hacinamiento por zona"              , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(HAC))     ,linetype="dotted",size=1.5,color="deeppink1")
PIMVZ2    <- ggplot(SANTIAGO,aes(x = cluster2,y = PIMVZ))    + geom_boxplot() + labs(title = "Materialidad de la vivienda"                   , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PIMVZ))   ,linetype="dotted",size=1.5,color="deeppink1")
```

```{r,message=FALSE,warning=FALSE,include=FALSE}

NCASA2    <- ggplot(SANTIAGO,aes(x = cluster2,y = NCASA))    + geom_boxplot() + labs(title = "Numero de casas  por zona"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(NCASA))   ,linetype="dotted",size=1.5,color="deeppink1")
NDEPA2    <- ggplot(SANTIAGO,aes(x = cluster2,y = NDEPA))    + geom_boxplot() + labs(title = "Numero de deptos por zona"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(NDEPA))   ,linetype="dotted",size=1.5,color="deeppink1")
UNI2      <- ggplot(SANTIAGO,aes(x = cluster2,y = UNI))      + geom_boxplot() + labs(title = "Numero hogares UNI "                                  , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(UNI))      ,linetype="dotted",size=1.5,color="deeppink1")
MONO2     <- ggplot(SANTIAGO,aes(x = cluster2,y = MONO))     + geom_boxplot() + labs(title = "Numero hogares MONO"                                  , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(MONO))     ,linetype="dotted",size=1.5,color="deeppink1")
BI_SHIJ2  <- ggplot(SANTIAGO,aes(x = cluster2,y = BI_SHIJ))  + geom_boxplot() + labs(title = "Numero hogares BI sin hijos"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(BI_SHIJ))  ,linetype="dotted",size=1.5,color="deeppink1")
BI_CHIJ2  <- ggplot(SANTIAGO,aes(x = cluster2,y = BI_CHIJ))  + geom_boxplot() + labs(title = "Numero hogares BI con hijos"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(BI_CHIJ))  ,linetype="dotted",size=1.5,color="deeppink1")
COMPUES2  <- ggplot(SANTIAGO,aes(x = cluster2,y = COMPUES))  + geom_boxplot() + labs(title = "Numero hogares Compuesto"                             , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(COMPUES))  ,linetype="dotted",size=1.5,color="deeppink1")
EXTENSO2  <- ggplot(SANTIAGO,aes(x = cluster2,y = EXTENSO))  + geom_boxplot() + labs(title = "Numero hogares Extenso"                               , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(EXTENSO))  ,linetype="dotted",size=1.5,color="deeppink1")
SIN_NUC2  <- ggplot(SANTIAGO,aes(x = cluster2,y = SIN_NUC))  + geom_boxplot() + labs(title = "Numero hogares Sin nucleo"                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(SIN_NUC))  ,linetype="dotted",size=1.5,color="deeppink1")
```

```{r,message=FALSE,warning=FALSE,include=FALSE}

CAMAY2    <- ggplot(SANTIAGO,aes(x = cluster2,y = CAMAY))        + geom_boxplot() + labs(title = "Numero AD"                                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(CAMAY))    ,linetype="dotted",size=1.5,color="deeppink1")
AMQVS2    <- ggplot(SANTIAGO,aes(x = cluster2,y = AMQVS))        + geom_boxplot() + labs(title = "Numero AD que viven solos"                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(AMQVS))    ,linetype="dotted",size=1.5,color="deeppink1")
PAMQVS2   <- ggplot(SANTIAGO,aes(x = cluster2,y = PAMQVS))       + geom_boxplot() + labs(title = "Porcentaje AD que viven solos"                        , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PAMQVS))   ,linetype="dotted",size=1.5,color="deeppink1")
PAMQTRA2  <- ggplot(SANTIAGO,aes(x = cluster2,y = PAMQTRA))      + geom_boxplot() + labs(title = "Porcentaje AD que trabajan"                           , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(PAMQTRA))  ,linetype="dotted",size=1.5,color="deeppink1")
PAMJUBI2  <- ggplot(SANTIAGO,aes(x = cluster2,y = PAMJUBI))      + geom_boxplot() + labs(title = "Porcentaje AD jubilados"                              , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(PAMJUBI))  ,linetype="dotted",size=1.5,color="deeppink1")
```

```{r,message=FALSE,warning=FALSE,include=FALSE}

SCORE05_2   <- ggplot(SANTIAGO,aes(x = cluster2,y = W_score5))   + geom_boxplot() + labs(title = "Caminabilidad  5 min a 2.4km/h", x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score5,na.rm=TRUE)),linetype="dotted",size=1.5,color="deeppink1")
SCORE10_2   <- ggplot(SANTIAGO,aes(x = cluster2,y = W_score10))  + geom_boxplot() + labs(title = "Caminabilidad 10 min a 2.4km/h", x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score10,na.rm=TRUE)), linetype="dotted",size=1.5,color="deeppink1")
SCORE15_2   <- ggplot(SANTIAGO,aes(x = cluster2,y = W_score15))  + geom_boxplot() + labs(title = "Caminabilidad 15 min a 2.4km/h", x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score15,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")
SCORE20_2   <- ggplot(SANTIAGO,aes(x = cluster2,y = W_score20))  + geom_boxplot() + labs(title = "Caminabilidad 20 min a 2.4km/h", x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score20,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")
```

```{r,message=FALSE,warning=FALSE,include=FALSE}

ISMT_2      <- ggplot(SANTIAGO,aes(x = cluster2,y = ISMT)) + geom_boxplot() + labs(title = "Indice sociomaterial territorial" , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(ISMT)) ,linetype="dotted",size=1.5,color="deeppink1")
```

```{r,message=FALSE,warning=FALSE,include=FALSE}
a <- plot_grid(PORC_ADM2,PJHSEM2,TIM2,EMP2,HAC2,PIMVZ2)
b <- plot_grid(NCASA2,NDEPA2)
c <- plot_grid(PAMQTRA2,PAMJUBI2)
d <- plot_grid(UNI2,MONO2,BI_SHIJ2,BI_CHIJ2,COMPUES2,EXTENSO2,SIN_NUC2)
e <- plot_grid(CAMAY2,AMQVS2,PAMQVS2)
f <- plot_grid(SCORE05_2,SCORE10_2,SCORE15_2,SCORE20_2)
```

### Indicadores clasificatorios

```{r,fig.width=12,fig.height=7}
a
```

### Tipo de vivienda

```{r,fig.width=12,fig.height=7}
b
```

### Porcentaje en trabajo o jubilo

```{r,fig.width=12,fig.height=7}
c
```

### Composicion familiar

```{r,fig.width=12,fig.height=7}
d
```

### Vive acompanado o solo

```{r,fig.width=12,fig.height=7}
e
```

### Caminabilidad del AD

```{r,fig.width=12,fig.height=7}
f
```

### Indice sociomaterial territorial

```{r,fig.width=12,fig.height=7}
ISMT_2
```

## para k = 3

```{r,message=FALSE,warning=FALSE,include=FALSE}

PORC_ADM3 <- ggplot(SANTIAGO,aes(x = cluster3,y = PORC_ADM)) + geom_boxplot() + labs(title = "Porcentaje adultos mayores por zona"                , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PORC_ADM)),linetype="dotted",size=1.5,color="deeppink1")
PJHSEM3   <- ggplot(SANTIAGO,aes(x = cluster3,y = PJHSEM))   + geom_boxplot() + labs(title = "Porcentaje Jefes de Hogar sin ensenanza media"      , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PJHSEM))  ,linetype="dotted",size=1.5,color="deeppink1")
TIM3      <- ggplot(SANTIAGO,aes(x = cluster3,y = log(TIM))) + geom_boxplot() + labs(title = "Tasa de inmigracion"                                , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(log(TIM))),linetype="dotted",size=1.5,color="deeppink1")
EMP3      <- ggplot(SANTIAGO,aes(x = cluster3,y = EMP))      + geom_boxplot() + labs(title = "Porcentaje empleabilidad mayor a 15 anos"           , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(EMP))     ,linetype="dotted",size=1.5,color="deeppink1")
HAC3      <- ggplot(SANTIAGO,aes(x = cluster3,y = HAC))      + geom_boxplot() + labs(title = "Porcentaje hacinamiento por zona"                   , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(HAC))     ,linetype="dotted",size=1.5,color="deeppink1")
PIMVZ3    <- ggplot(SANTIAGO,aes(x = cluster3,y = PIMVZ))    + geom_boxplot() + labs(title = "Materialidad de la vivienda"                        , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PIMVZ))   ,linetype="dotted",size=1.5,color="deeppink1")

NCASA3    <- ggplot(SANTIAGO,aes(x = cluster3,y = NCASA))    + geom_boxplot() + labs(title = "Numero de casas  por zona"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(NCASA))   ,linetype="dotted",size=1.5,color="deeppink1")
NDEPA3    <- ggplot(SANTIAGO,aes(x = cluster3,y = NDEPA))    + geom_boxplot() + labs(title = "Numero de deptos por zona"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(NDEPA))   ,linetype="dotted",size=1.5,color="deeppink1")


UNI3      <- ggplot(SANTIAGO,aes(x = cluster3,y = UNI))      + geom_boxplot() + labs(title = "Numero hogares UNI "                                  , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(UNI))      ,linetype="dotted",size=1.5,color="deeppink1")
MONO3     <- ggplot(SANTIAGO,aes(x = cluster3,y = MONO))     + geom_boxplot() + labs(title = "Numero hogares MONO"                                  , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(MONO))     ,linetype="dotted",size=1.5,color="deeppink1")
BI_SHIJ3  <- ggplot(SANTIAGO,aes(x = cluster3,y = BI_SHIJ))  + geom_boxplot() + labs(title = "Numero hogares BI sin hijos"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(BI_SHIJ))  ,linetype="dotted",size=1.5,color="deeppink1")
BI_CHIJ3  <- ggplot(SANTIAGO,aes(x = cluster3,y = BI_CHIJ))  + geom_boxplot() + labs(title = "Numero hogares BI con hijos"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(BI_CHIJ))  ,linetype="dotted",size=1.5,color="deeppink1")
COMPUES3  <- ggplot(SANTIAGO,aes(x = cluster3,y = COMPUES))  + geom_boxplot() + labs(title = "Numero hogares Compuesto"                             , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(COMPUES))  ,linetype="dotted",size=1.5,color="deeppink1")
EXTENSO3  <- ggplot(SANTIAGO,aes(x = cluster3,y = EXTENSO))  + geom_boxplot() + labs(title = "Numero hogares Extenso"                               , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(EXTENSO))  ,linetype="dotted",size=1.5,color="deeppink1")
SIN_NUC3  <- ggplot(SANTIAGO,aes(x = cluster3,y = SIN_NUC))  + geom_boxplot() + labs(title = "Numero hogares Sin nucleo"                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(SIN_NUC))  ,linetype="dotted",size=1.5,color="deeppink1")

CAMAY3    <- ggplot(SANTIAGO,aes(x = cluster3,y = CAMAY))        + geom_boxplot() + labs(title = "Numero AD"                                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(CAMAY))    ,linetype="dotted",size=1.5,color="deeppink1")
AMQVS3    <- ggplot(SANTIAGO,aes(x = cluster3,y = AMQVS))        + geom_boxplot() + labs(title = "Numero AD que viven solos"                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(AMQVS))    ,linetype="dotted",size=1.5,color="deeppink1")
PAMQVS3   <- ggplot(SANTIAGO,aes(x = cluster3,y = PAMQVS))       + geom_boxplot() + labs(title = "Porcentaje AD que viven solos"                        , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PAMQVS))   ,linetype="dotted",size=1.5,color="deeppink1")
PAMQTRA3  <- ggplot(SANTIAGO,aes(x = cluster3,y = PAMQTRA))      + geom_boxplot() + labs(title = "Porcentaje AD que trabajan"                           , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(PAMQTRA))  ,linetype="dotted",size=1.5,color="deeppink1")
PAMJUBI3  <- ggplot(SANTIAGO,aes(x = cluster3,y = PAMJUBI))      + geom_boxplot() + labs(title = "Porcentaje AD jubilados"                              , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(PAMJUBI))  ,linetype="dotted",size=1.5,color="deeppink1")

SCORE05_3   <- ggplot(SANTIAGO,aes(x = cluster3,y = W_score5))   + geom_boxplot() + labs(title = "Caminabilidad  5 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score5,na.rm=TRUE))  ,linetype="dotted",size=1.5,color="deeppink1")
SCORE10_3   <- ggplot(SANTIAGO,aes(x = cluster3,y = W_score10))  + geom_boxplot() + labs(title = "Caminabilidad 10 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score10,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")
SCORE15_3   <- ggplot(SANTIAGO,aes(x = cluster3,y = W_score15))  + geom_boxplot() + labs(title = "Caminabilidad 15 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score15,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")
SCORE20_3   <- ggplot(SANTIAGO,aes(x = cluster3,y = W_score20))  + geom_boxplot() + labs(title = "Caminabilidad 20 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score20,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")

ISMT_3      <- ggplot(SANTIAGO,aes(x = cluster3,y = ISMT)) + geom_boxplot() + labs(title = "Indice sociomaterial territorial", x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(ISMT)) ,linetype="dotted",size=1.5,color="deeppink1")


```

```{r,message=FALSE,warning=FALSE,include=FALSE}
a <- plot_grid(PORC_ADM3,PJHSEM3,TIM3,EMP3,HAC3,PIMVZ3)
b <- plot_grid(NCASA3,NDEPA3)
c <- plot_grid(PAMQTRA3,PAMJUBI3)
d <- plot_grid(UNI3,MONO3,BI_SHIJ3,BI_CHIJ3,COMPUES3,EXTENSO3,SIN_NUC3)
e <- plot_grid(CAMAY3,AMQVS3,PAMQVS3)
f <- plot_grid(SCORE05_3,SCORE10_3,SCORE15_3,SCORE20_3)
```


### Indicadores clasificatorios

```{r,fig.width=12,fig.height=7}
a
```

### Tipo de vivienda

```{r,fig.width=12,fig.height=7}
b
```

### Porcentaje en trabajo o jubilo

```{r,fig.width=12,fig.height=7}
c
```

### Composicion familiar

```{r,fig.width=12,fig.height=7}
d
```

### Vive acompanado o solo

```{r,fig.width=12,fig.height=7}
e
```

### Caminabilidad del AD

```{r,fig.width=12,fig.height=7}
f
```


### Indice sociomaterial territorial

```{r,fig.width=12,fig.height=7}
ISMT_3
```


## para k = 4

```{r,message=FALSE,warning=FALSE,include=FALSE}

PORC_ADM4 <- ggplot(SANTIAGO,aes(x = cluster4,y = PORC_ADM)) + geom_boxplot() + labs(title = "Porcentaje adultos mayores por zona"                , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PORC_ADM)),linetype="dotted",size=1.5,color="deeppink1")
PJHSEM4   <- ggplot(SANTIAGO,aes(x = cluster4,y = PJHSEM))   + geom_boxplot() + labs(title = "Porcentaje Jefes de Hogar sin ensenanza media"      , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PJHSEM))  ,linetype="dotted",size=1.5,color="deeppink1")
TIM4      <- ggplot(SANTIAGO,aes(x = cluster4,y = log(TIM))) + geom_boxplot() + labs(title = "Tasa de inmigracion"                                , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(log(TIM))),linetype="dotted",size=1.5,color="deeppink1")
EMP4      <- ggplot(SANTIAGO,aes(x = cluster4,y = EMP))      + geom_boxplot() + labs(title = "Porcentaje empleabilidad mayor a 15 anos"           , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(EMP))     ,linetype="dotted",size=1.5,color="deeppink1")
HAC4      <- ggplot(SANTIAGO,aes(x = cluster4,y = HAC))      + geom_boxplot() + labs(title = "Porcentaje hacinamiento por zona"                   , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(HAC))     ,linetype="dotted",size=1.5,color="deeppink1")
PIMVZ4    <- ggplot(SANTIAGO,aes(x = cluster4,y = PIMVZ))    + geom_boxplot() + labs(title = "Materialidad de la vivienda"                        , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PIMVZ))   ,linetype="dotted",size=1.5,color="deeppink1")

NCASA4    <- ggplot(SANTIAGO,aes(x = cluster4,y = NCASA))    + geom_boxplot() + labs(title = "Numero de casas  por zona"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(NCASA))   ,linetype="dotted",size=1.5,color="deeppink1")
NDEPA4    <- ggplot(SANTIAGO,aes(x = cluster4,y = NDEPA))    + geom_boxplot() + labs(title = "Numero de deptos por zona"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(NDEPA))   ,linetype="dotted",size=1.5,color="deeppink1")


UNI4      <- ggplot(SANTIAGO,aes(x = cluster4,y = UNI))      + geom_boxplot() + labs(title = "Numero hogares UNI "                                  , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(UNI))      ,linetype="dotted",size=1.5,color="deeppink1")
MONO4     <- ggplot(SANTIAGO,aes(x = cluster4,y = MONO))     + geom_boxplot() + labs(title = "Numero hogares MONO"                                  , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(MONO))     ,linetype="dotted",size=1.5,color="deeppink1")
BI_SHIJ4  <- ggplot(SANTIAGO,aes(x = cluster4,y = BI_SHIJ))  + geom_boxplot() + labs(title = "Numero hogares BI sin hijos"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(BI_SHIJ))  ,linetype="dotted",size=1.5,color="deeppink1")
BI_CHIJ4  <- ggplot(SANTIAGO,aes(x = cluster4,y = BI_CHIJ))  + geom_boxplot() + labs(title = "Numero hogares BI con hijos"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(BI_CHIJ))  ,linetype="dotted",size=1.5,color="deeppink1")
COMPUES4  <- ggplot(SANTIAGO,aes(x = cluster4,y = COMPUES))  + geom_boxplot() + labs(title = "Numero hogares Compuesto"                             , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(COMPUES))  ,linetype="dotted",size=1.5,color="deeppink1")
EXTENSO4  <- ggplot(SANTIAGO,aes(x = cluster4,y = EXTENSO))  + geom_boxplot() + labs(title = "Numero hogares Extenso"                               , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(EXTENSO))  ,linetype="dotted",size=1.5,color="deeppink1")
SIN_NUC4  <- ggplot(SANTIAGO,aes(x = cluster4,y = SIN_NUC))  + geom_boxplot() + labs(title = "Numero hogares Sin nucleo"                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(SIN_NUC))  ,linetype="dotted",size=1.5,color="deeppink1")

CAMAY4    <- ggplot(SANTIAGO,aes(x = cluster4,y = CAMAY))        + geom_boxplot() + labs(title = "Numero AD"                                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(CAMAY))    ,linetype="dotted",size=1.5,color="deeppink1")
AMQVS4    <- ggplot(SANTIAGO,aes(x = cluster4,y = AMQVS))        + geom_boxplot() + labs(title = "Numero AD que viven solos"                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(AMQVS))    ,linetype="dotted",size=1.5,color="deeppink1")
PAMQVS4   <- ggplot(SANTIAGO,aes(x = cluster4,y = PAMQVS))       + geom_boxplot() + labs(title = "Porcentaje AD que viven solos"                        , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PAMQVS))   ,linetype="dotted",size=1.5,color="deeppink1")
PAMQTRA4  <- ggplot(SANTIAGO,aes(x = cluster4,y = PAMQTRA))      + geom_boxplot() + labs(title = "Porcentaje AD que trabajan"                           , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(PAMQTRA))  ,linetype="dotted",size=1.5,color="deeppink1")
PAMJUBI4  <- ggplot(SANTIAGO,aes(x = cluster4,y = PAMJUBI))      + geom_boxplot() + labs(title = "Porcentaje AD jubilados"                              , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(PAMJUBI))  ,linetype="dotted",size=1.5,color="deeppink1")

SCORE05_4   <- ggplot(SANTIAGO,aes(x = cluster4,y = W_score5))   + geom_boxplot() + labs(title = "Caminabilidad  5 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score5,na.rm=TRUE))  ,linetype="dotted",size=1.5,color="deeppink1")
SCORE10_4   <- ggplot(SANTIAGO,aes(x = cluster4,y = W_score10))  + geom_boxplot() + labs(title = "Caminabilidad 10 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score10,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")
SCORE15_4   <- ggplot(SANTIAGO,aes(x = cluster4,y = W_score15))  + geom_boxplot() + labs(title = "Caminabilidad 15 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score15,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")
SCORE20_4   <- ggplot(SANTIAGO,aes(x = cluster4,y = W_score20))  + geom_boxplot() + labs(title = "Caminabilidad 20 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score20,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")

ISMT_4      <- ggplot(SANTIAGO,aes(x = cluster4,y = ISMT)) + geom_boxplot() + labs(title = "Indice sociomaterial territorial"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(ISMT)) ,linetype="dotted",size=1.5,color="deeppink1")

```


```{r,message=FALSE,warning=FALSE,include=FALSE}
a <- plot_grid(PORC_ADM4,PJHSEM4,TIM4,EMP4,HAC4,PIMVZ4)
b <- plot_grid(NCASA4,NDEPA4)
c <- plot_grid(PAMQTRA4,PAMJUBI4)
d <- plot_grid(UNI4,MONO4,BI_SHIJ4,BI_CHIJ4,COMPUES4,EXTENSO4,SIN_NUC4)
e <- plot_grid(CAMAY4,AMQVS4,PAMQVS4)
f <- plot_grid(SCORE05_4,SCORE10_4,SCORE15_4,SCORE20_4)
```


### Indicadores clasificatorios

```{r,fig.width=12,fig.height=7}
a
```

### Tipo de vivienda

```{r,fig.width=12,fig.height=7}
b
```

### Porcentaje en trabajo o jubilo

```{r,fig.width=12,fig.height=7}
c
```

### Composicion familiar

```{r,fig.width=12,fig.height=7}
d
```

### Vive acompanado o solo

```{r,fig.width=12,fig.height=7}
e
```

### Caminabilidad del AD

```{r,fig.width=12,fig.height=7}
f
```


### Indice sociomaterial territorial

```{r,fig.width=12,fig.height=7}
ISMT_4
```

## para k = 5

```{r,message=FALSE,warning=FALSE,include=FALSE}

PORC_ADM5 <- ggplot(SANTIAGO,aes(x = cluster5,y = PORC_ADM)) + geom_boxplot() + labs(title = "Porcentaje adultos mayores por zona"                , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PORC_ADM)),linetype="dotted",size=1.5,color="deeppink1")
PJHSEM5   <- ggplot(SANTIAGO,aes(x = cluster5,y = PJHSEM))   + geom_boxplot() + labs(title = "Porcentaje Jefes de Hogar sin ensenanza media"      , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PJHSEM))  ,linetype="dotted",size=1.5,color="deeppink1")
TIM5      <- ggplot(SANTIAGO,aes(x = cluster5,y = log(TIM))) + geom_boxplot() + labs(title = "Tasa de inmigracion"                                , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(log(TIM))),linetype="dotted",size=1.5,color="deeppink1")
EMP5      <- ggplot(SANTIAGO,aes(x = cluster5,y = EMP))      + geom_boxplot() + labs(title = "Porcentaje empleabilidad mayor a 15 anos"           , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(EMP))     ,linetype="dotted",size=1.5,color="deeppink1")
HAC5      <- ggplot(SANTIAGO,aes(x = cluster5,y = HAC))      + geom_boxplot() + labs(title = "Porcentaje hacinamiento por zona"                   , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(HAC))     ,linetype="dotted",size=1.5,color="deeppink1")
PIMVZ5    <- ggplot(SANTIAGO,aes(x = cluster5,y = PIMVZ))    + geom_boxplot() + labs(title = "Materialidad de la vivienda"                        , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PIMVZ))   ,linetype="dotted",size=1.5,color="deeppink1")

NCASA5    <- ggplot(SANTIAGO,aes(x = cluster5,y = NCASA))    + geom_boxplot() + labs(title = "Numero de casas  por zona"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(NCASA))   ,linetype="dotted",size=1.5,color="deeppink1")
NDEPA5    <- ggplot(SANTIAGO,aes(x = cluster5,y = NDEPA))    + geom_boxplot() + labs(title = "Numero de deptos por zona"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(NDEPA))   ,linetype="dotted",size=1.5,color="deeppink1")


UNI5      <- ggplot(SANTIAGO,aes(x = cluster5,y = UNI))      + geom_boxplot() + labs(title = "Numero hogares UNI "                                  , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(UNI))      ,linetype="dotted",size=1.5,color="deeppink1")
MONO5     <- ggplot(SANTIAGO,aes(x = cluster5,y = MONO))     + geom_boxplot() + labs(title = "Numero hogares MONO"                                  , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(MONO))     ,linetype="dotted",size=1.5,color="deeppink1")
BI_SHIJ5  <- ggplot(SANTIAGO,aes(x = cluster5,y = BI_SHIJ))  + geom_boxplot() + labs(title = "Numero hogares BI sin hijos"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(BI_SHIJ))  ,linetype="dotted",size=1.5,color="deeppink1")
BI_CHIJ5  <- ggplot(SANTIAGO,aes(x = cluster5,y = BI_CHIJ))  + geom_boxplot() + labs(title = "Numero hogares BI con hijos"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(BI_CHIJ))  ,linetype="dotted",size=1.5,color="deeppink1")
COMPUES5  <- ggplot(SANTIAGO,aes(x = cluster5,y = COMPUES))  + geom_boxplot() + labs(title = "Numero hogares Compuesto"                             , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(COMPUES))  ,linetype="dotted",size=1.5,color="deeppink1")
EXTENSO5  <- ggplot(SANTIAGO,aes(x = cluster5,y = EXTENSO))  + geom_boxplot() + labs(title = "Numero hogares Extenso"                               , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(EXTENSO))  ,linetype="dotted",size=1.5,color="deeppink1")
SIN_NUC5  <- ggplot(SANTIAGO,aes(x = cluster5,y = SIN_NUC))  + geom_boxplot() + labs(title = "Numero hogares Sin nucleo"                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(SIN_NUC))  ,linetype="dotted",size=1.5,color="deeppink1")

CAMAY5    <- ggplot(SANTIAGO,aes(x = cluster5,y = CAMAY))        + geom_boxplot() + labs(title = "Numero AD"                                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(CAMAY))    ,linetype="dotted",size=1.5,color="deeppink1")
AMQVS5    <- ggplot(SANTIAGO,aes(x = cluster5,y = AMQVS))        + geom_boxplot() + labs(title = "Numero AD que viven solos"                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(AMQVS))    ,linetype="dotted",size=1.5,color="deeppink1")
PAMQVS5   <- ggplot(SANTIAGO,aes(x = cluster5,y = PAMQVS))       + geom_boxplot() + labs(title = "Porcentaje AD que viven solos"                        , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PAMQVS))   ,linetype="dotted",size=1.5,color="deeppink1")
PAMQTRA5  <- ggplot(SANTIAGO,aes(x = cluster5,y = PAMQTRA))      + geom_boxplot() + labs(title = "Porcentaje AD que trabajan"                           , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(PAMQTRA))  ,linetype="dotted",size=1.5,color="deeppink1")
PAMJUBI5  <- ggplot(SANTIAGO,aes(x = cluster5,y = PAMJUBI))      + geom_boxplot() + labs(title = "Porcentaje AD jubilados"                              , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(PAMJUBI))  ,linetype="dotted",size=1.5,color="deeppink1")

SCORE05_5   <- ggplot(SANTIAGO,aes(x = cluster5,y = W_score5))   + geom_boxplot() + labs(title = "Caminabilidad  5 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score5,na.rm=TRUE))  ,linetype="dotted",size=1.5,color="deeppink1")
SCORE10_5   <- ggplot(SANTIAGO,aes(x = cluster5,y = W_score10))  + geom_boxplot() + labs(title = "Caminabilidad 10 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score10,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")
SCORE15_5   <- ggplot(SANTIAGO,aes(x = cluster5,y = W_score15))  + geom_boxplot() + labs(title = "Caminabilidad 15 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score15,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")
SCORE20_5   <- ggplot(SANTIAGO,aes(x = cluster5,y = W_score20))  + geom_boxplot() + labs(title = "Caminabilidad 20 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score20,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")

ISMT_5      <- ggplot(SANTIAGO,aes(x = cluster5,y = ISMT)) + geom_boxplot() + labs(title = "Indice sociomaterial territorial", x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(ISMT)) ,linetype="dotted",size=1.5,color="deeppink1")


```

```{r,message=FALSE,warning=FALSE,include=FALSE}
a <- plot_grid(PORC_ADM5,PJHSEM5,TIM5,EMP5,HAC5,PIMVZ5)
b <- plot_grid(NCASA5,NDEPA5)
c <- plot_grid(PAMQTRA5,PAMJUBI5)
d <- plot_grid(UNI5,MONO5,BI_SHIJ5,BI_CHIJ5,COMPUES5,EXTENSO5,SIN_NUC5)
e <- plot_grid(CAMAY5,AMQVS5,PAMQVS5)
f <- plot_grid(SCORE05_5,SCORE10_5,SCORE15_5,SCORE20_5)
```


### Indicadores clasificatorios

```{r,fig.width=12,fig.height=7}
a
```

### Tipo de vivienda

```{r,fig.width=12,fig.height=7}
b
```

### Porcentaje en trabajo o jubilo

```{r,fig.width=12,fig.height=7}
c
```

### Composicion familiar

```{r,fig.width=12,fig.height=7}
d
```

### Vive acompanado o solo

```{r,fig.width=12,fig.height=7}
e
```

### Caminabilidad del AD

```{r,fig.width=12,fig.height=7}
f
```


### Indice sociomaterial territorial

```{r,fig.width=12,fig.height=7}
ISMT_5
```

## para k = 6

```{r,message=FALSE,warning=FALSE,include=FALSE}

PORC_ADM6 <- ggplot(SANTIAGO,aes(x = cluster6,y = PORC_ADM)) + geom_boxplot() + labs(title = "Porcentaje adultos mayores por zona"                , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PORC_ADM)),linetype="dotted",size=1.5,color="deeppink1")
PJHSEM6   <- ggplot(SANTIAGO,aes(x = cluster6,y = PJHSEM))   + geom_boxplot() + labs(title = "Porcentaje Jefes de Hogar sin ensenanza media"      , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PJHSEM))  ,linetype="dotted",size=1.5,color="deeppink1")
TIM6      <- ggplot(SANTIAGO,aes(x = cluster6,y = log(TIM))) + geom_boxplot() + labs(title = "Tasa de inmigracion"                                , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(log(TIM))),linetype="dotted",size=1.5,color="deeppink1")
EMP6      <- ggplot(SANTIAGO,aes(x = cluster6,y = EMP))      + geom_boxplot() + labs(title = "Porcentaje empleabilidad mayor a 15 anos"           , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(EMP))     ,linetype="dotted",size=1.5,color="deeppink1")
HAC6      <- ggplot(SANTIAGO,aes(x = cluster6,y = HAC))      + geom_boxplot() + labs(title = "Porcentaje hacinamiento por zona"                   , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(HAC))     ,linetype="dotted",size=1.5,color="deeppink1")
PIMVZ6    <- ggplot(SANTIAGO,aes(x = cluster6,y = PIMVZ))    + geom_boxplot() + labs(title = "Materialidad de la vivienda"                        , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PIMVZ))   ,linetype="dotted",size=1.5,color="deeppink1")

NCASA6    <- ggplot(SANTIAGO,aes(x = cluster6,y = NCASA))    + geom_boxplot() + labs(title = "Numero de casas  por zona"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(NCASA))   ,linetype="dotted",size=1.5,color="deeppink1")
NDEPA6    <- ggplot(SANTIAGO,aes(x = cluster6,y = NDEPA))    + geom_boxplot() + labs(title = "Numero de deptos por zona"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(NDEPA))   ,linetype="dotted",size=1.5,color="deeppink1")


UNI6      <- ggplot(SANTIAGO,aes(x = cluster6,y = UNI))      + geom_boxplot() + labs(title = "Numero hogares UNI "                                  , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(UNI))      ,linetype="dotted",size=1.5,color="deeppink1")
MONO6     <- ggplot(SANTIAGO,aes(x = cluster6,y = MONO))     + geom_boxplot() + labs(title = "Numero hogares MONO"                                  , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(MONO))     ,linetype="dotted",size=1.5,color="deeppink1")
BI_SHIJ6  <- ggplot(SANTIAGO,aes(x = cluster6,y = BI_SHIJ))  + geom_boxplot() + labs(title = "Numero hogares BI sin hijos"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(BI_SHIJ))  ,linetype="dotted",size=1.5,color="deeppink1")
BI_CHIJ6  <- ggplot(SANTIAGO,aes(x = cluster6,y = BI_CHIJ))  + geom_boxplot() + labs(title = "Numero hogares BI con hijos"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(BI_CHIJ))  ,linetype="dotted",size=1.5,color="deeppink1")
COMPUES6  <- ggplot(SANTIAGO,aes(x = cluster6,y = COMPUES))  + geom_boxplot() + labs(title = "Numero hogares Compuesto"                             , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(COMPUES))  ,linetype="dotted",size=1.5,color="deeppink1")
EXTENSO6  <- ggplot(SANTIAGO,aes(x = cluster6,y = EXTENSO))  + geom_boxplot() + labs(title = "Numero hogares Extenso"                               , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(EXTENSO))  ,linetype="dotted",size=1.5,color="deeppink1")
SIN_NUC6  <- ggplot(SANTIAGO,aes(x = cluster6,y = SIN_NUC))  + geom_boxplot() + labs(title = "Numero hogares Sin nucleo"                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(SIN_NUC))  ,linetype="dotted",size=1.5,color="deeppink1")

CAMAY6    <- ggplot(SANTIAGO,aes(x = cluster6,y = CAMAY))        + geom_boxplot() + labs(title = "Numero AD"                                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(CAMAY))    ,linetype="dotted",size=1.5,color="deeppink1")
AMQVS6    <- ggplot(SANTIAGO,aes(x = cluster6,y = AMQVS))        + geom_boxplot() + labs(title = "Numero AD que viven solos"                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(AMQVS))    ,linetype="dotted",size=1.5,color="deeppink1")
PAMQVS6   <- ggplot(SANTIAGO,aes(x = cluster6,y = PAMQVS))       + geom_boxplot() + labs(title = "Porcentaje AD que viven solos"                        , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PAMQVS))   ,linetype="dotted",size=1.5,color="deeppink1")
PAMQTRA6  <- ggplot(SANTIAGO,aes(x = cluster6,y = PAMQTRA))      + geom_boxplot() + labs(title = "Porcentaje AD que trabajan"                           , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(PAMQTRA))  ,linetype="dotted",size=1.5,color="deeppink1")
PAMJUBI6  <- ggplot(SANTIAGO,aes(x = cluster6,y = PAMJUBI))      + geom_boxplot() + labs(title = "Porcentaje AD jubilados"                              , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(PAMJUBI))  ,linetype="dotted",size=1.5,color="deeppink1")

SCORE05_6   <- ggplot(SANTIAGO,aes(x = cluster6,y = W_score5))   + geom_boxplot() + labs(title = "Caminabilidad  5 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score5,na.rm=TRUE))  ,linetype="dotted",size=1.5,color="deeppink1")
SCORE10_6   <- ggplot(SANTIAGO,aes(x = cluster6,y = W_score10))  + geom_boxplot() + labs(title = "Caminabilidad 10 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score10,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")
SCORE15_6   <- ggplot(SANTIAGO,aes(x = cluster6,y = W_score15))  + geom_boxplot() + labs(title = "Caminabilidad 15 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score15,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")
SCORE20_6   <- ggplot(SANTIAGO,aes(x = cluster6,y = W_score20))  + geom_boxplot() + labs(title = "Caminabilidad 20 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score20,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")

ISMT_6      <- ggplot(SANTIAGO,aes(x = cluster6,y = ISMT)) + geom_boxplot() + labs(title = "Indice sociomaterial territorial", x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(ISMT)) ,linetype="dotted",size=1.5,color="deeppink1")

```

```{r,message=FALSE,warning=FALSE,include=FALSE}
a <- plot_grid(PORC_ADM6,PJHSEM6,TIM3,EMP6,HAC6,PIMVZ6)
b <- plot_grid(NCASA6,NDEPA6)
c <- plot_grid(PAMQTRA6,PAMJUBI6)
d <- plot_grid(UNI6,MONO6,BI_SHIJ6,BI_CHIJ6,COMPUES6,EXTENSO6,SIN_NUC6)
e <- plot_grid(CAMAY6,AMQVS6,PAMQVS6)
f <- plot_grid(SCORE05_6,SCORE10_6,SCORE15_6,SCORE20_6)
```


### Indicadores clasificatorios

```{r,fig.width=12,fig.height=7}
a
```

### Tipo de vivienda

```{r,fig.width=12,fig.height=7}
b
```

### Porcentaje en trabajo o jubilo

```{r,fig.width=12,fig.height=7}
c
```

### Composicion familiar

```{r,fig.width=12,fig.height=7}
d
```

### Vive acompanado o solo

```{r,fig.width=12,fig.height=7}
e
```

### Caminabilidad del AD

```{r,fig.width=12,fig.height=7}
f
```


### Indice sociomaterial territorial

```{r,fig.width=12,fig.height=7}
ISMT_6
```


## para k = 7

```{r,message=FALSE,warning=FALSE,include=FALSE}

PORC_ADM7 <- ggplot(SANTIAGO,aes(x = cluster7,y = PORC_ADM)) + geom_boxplot() + labs(title = "Porcentaje adultos mayores por zona"                , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PORC_ADM)),linetype="dotted",size=1.5,color="deeppink1")
PJHSEM7   <- ggplot(SANTIAGO,aes(x = cluster7,y = PJHSEM))   + geom_boxplot() + labs(title = "Porcentaje Jefes de Hogar sin ensenanza media"      , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PJHSEM))  ,linetype="dotted",size=1.5,color="deeppink1")
TIM7      <- ggplot(SANTIAGO,aes(x = cluster7,y = log(TIM))) + geom_boxplot() + labs(title = "Tasa de inmigracion"                                , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(log(TIM))),linetype="dotted",size=1.5,color="deeppink1")
EMP7      <- ggplot(SANTIAGO,aes(x = cluster7,y = EMP))      + geom_boxplot() + labs(title = "Porcentaje empleabilidad mayor a 15 anos"           , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(EMP))     ,linetype="dotted",size=1.5,color="deeppink1")
HAC7      <- ggplot(SANTIAGO,aes(x = cluster7,y = HAC))      + geom_boxplot() + labs(title = "Porcentaje hacinamiento por zona"                   , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(HAC))     ,linetype="dotted",size=1.5,color="deeppink1")
PIMVZ7    <- ggplot(SANTIAGO,aes(x = cluster7,y = PIMVZ))    + geom_boxplot() + labs(title = "Materialidad de la vivienda"                        , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PIMVZ))   ,linetype="dotted",size=1.5,color="deeppink1")

NCASA7    <- ggplot(SANTIAGO,aes(x = cluster7,y = NCASA))    + geom_boxplot() + labs(title = "Numero de casas  por zona"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(NCASA))   ,linetype="dotted",size=1.5,color="deeppink1")
NDEPA7    <- ggplot(SANTIAGO,aes(x = cluster7,y = NDEPA))    + geom_boxplot() + labs(title = "Numero de deptos por zona"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(NDEPA))   ,linetype="dotted",size=1.5,color="deeppink1")


UNI7      <- ggplot(SANTIAGO,aes(x = cluster7,y = UNI))      + geom_boxplot() + labs(title = "Numero hogares UNI "                                  , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(UNI))      ,linetype="dotted",size=1.5,color="deeppink1")
MONO7     <- ggplot(SANTIAGO,aes(x = cluster7,y = MONO))     + geom_boxplot() + labs(title = "Numero hogares MONO"                                  , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(MONO))     ,linetype="dotted",size=1.5,color="deeppink1")
BI_SHIJ7  <- ggplot(SANTIAGO,aes(x = cluster7,y = BI_SHIJ))  + geom_boxplot() + labs(title = "Numero hogares BI sin hijos"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(BI_SHIJ))  ,linetype="dotted",size=1.5,color="deeppink1")
BI_CHIJ7  <- ggplot(SANTIAGO,aes(x = cluster7,y = BI_CHIJ))  + geom_boxplot() + labs(title = "Numero hogares BI con hijos"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(BI_CHIJ))  ,linetype="dotted",size=1.5,color="deeppink1")
COMPUES7  <- ggplot(SANTIAGO,aes(x = cluster7,y = COMPUES))  + geom_boxplot() + labs(title = "Numero hogares Compuesto"                             , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(COMPUES))  ,linetype="dotted",size=1.5,color="deeppink1")
EXTENSO7  <- ggplot(SANTIAGO,aes(x = cluster7,y = EXTENSO))  + geom_boxplot() + labs(title = "Numero hogares Extenso"                               , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(EXTENSO))  ,linetype="dotted",size=1.5,color="deeppink1")
SIN_NUC7  <- ggplot(SANTIAGO,aes(x = cluster7,y = SIN_NUC))  + geom_boxplot() + labs(title = "Numero hogares Sin nucleo"                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(SIN_NUC))  ,linetype="dotted",size=1.5,color="deeppink1")

CAMAY7    <- ggplot(SANTIAGO,aes(x = cluster7,y = CAMAY))        + geom_boxplot() + labs(title = "Numero AD"                                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(CAMAY))    ,linetype="dotted",size=1.5,color="deeppink1")
AMQVS7    <- ggplot(SANTIAGO,aes(x = cluster7,y = AMQVS))        + geom_boxplot() + labs(title = "Numero AD que viven solos"                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(AMQVS))    ,linetype="dotted",size=1.5,color="deeppink1")
PAMQVS7   <- ggplot(SANTIAGO,aes(x = cluster7,y = PAMQVS))       + geom_boxplot() + labs(title = "Porcentaje AD que viven solos"                        , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PAMQVS))   ,linetype="dotted",size=1.5,color="deeppink1")
PAMQTRA7  <- ggplot(SANTIAGO,aes(x = cluster7,y = PAMQTRA))      + geom_boxplot() + labs(title = "Porcentaje AD que trabajan"                           , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(PAMQTRA))  ,linetype="dotted",size=1.5,color="deeppink1")
PAMJUBI7  <- ggplot(SANTIAGO,aes(x = cluster7,y = PAMJUBI))      + geom_boxplot() + labs(title = "Porcentaje AD jubilados"                              , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(PAMJUBI))  ,linetype="dotted",size=1.5,color="deeppink1")

SCORE05_7   <- ggplot(SANTIAGO,aes(x = cluster7,y = W_score5))   + geom_boxplot() + labs(title = "Caminabilidad  5 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score5,na.rm=TRUE))  ,linetype="dotted",size=1.5,color="deeppink1")
SCORE10_7   <- ggplot(SANTIAGO,aes(x = cluster7,y = W_score10))  + geom_boxplot() + labs(title = "Caminabilidad 10 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score10,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")
SCORE15_7   <- ggplot(SANTIAGO,aes(x = cluster7,y = W_score15))  + geom_boxplot() + labs(title = "Caminabilidad 15 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score15,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")
SCORE20_7   <- ggplot(SANTIAGO,aes(x = cluster7,y = W_score20))  + geom_boxplot() + labs(title = "Caminabilidad 20 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score20,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")

ISMT_7      <- ggplot(SANTIAGO,aes(x = cluster7,y = ISMT)) + geom_boxplot() + labs(title = "Indice sociomaterial territorial", x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(ISMT)) ,linetype="dotted",size=1.5,color="deeppink1")

```


```{r,message=FALSE,warning=FALSE,include=FALSE}
a <- plot_grid(PORC_ADM7,PJHSEM7,TIM7,EMP7,HAC7,PIMVZ7)
b <- plot_grid(NCASA7,NDEPA7)
c <- plot_grid(PAMQTRA7,PAMJUBI7)
d <- plot_grid(UNI7,MONO7,BI_SHIJ7,BI_CHIJ7,COMPUES7,EXTENSO7,SIN_NUC7)
e <- plot_grid(CAMAY7,AMQVS7,PAMQVS7)
f <- plot_grid(SCORE05_7,SCORE10_7,SCORE15_7,SCORE20_7)
```


### Indicadores clasificatorios

```{r,fig.width=12,fig.height=7}
a
```

### Tipo de vivienda

```{r,fig.width=12,fig.height=7}
b
```

### Porcentaje en trabajo o jubilo

```{r,fig.width=12,fig.height=7}
c
```

### Composicion familiar

```{r,fig.width=12,fig.height=7}
d
```

### Vive acompanado o solo

```{r,fig.width=12,fig.height=7}
e
```

### Caminabilidad del AD

```{r,fig.width=12,fig.height=7}
f
```


### Indice sociomaterial territorial

```{r,fig.width=12,fig.height=7}
ISMT_7
```

## para k = 8

```{r,message=FALSE,warning=FALSE,include=FALSE}

PORC_ADM8 <- ggplot(SANTIAGO,aes(x = cluster8,y = PORC_ADM)) + geom_boxplot() + labs(title = "Porcentaje adultos mayores por zona"                , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PORC_ADM)),linetype="dotted",size=1.5,color="deeppink1")
PJHSEM8   <- ggplot(SANTIAGO,aes(x = cluster8,y = PJHSEM))   + geom_boxplot() + labs(title = "Porcentaje Jefes de Hogar sin ensenanza media"      , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PJHSEM))  ,linetype="dotted",size=1.5,color="deeppink1")
TIM8      <- ggplot(SANTIAGO,aes(x = cluster8,y = log(TIM))) + geom_boxplot() + labs(title = "Tasa de inmigracion"                                , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(log(TIM))),linetype="dotted",size=1.5,color="deeppink1")
EMP8      <- ggplot(SANTIAGO,aes(x = cluster8,y = EMP))      + geom_boxplot() + labs(title = "Porcentaje empleabilidad mayor a 15 anos"           , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(EMP))     ,linetype="dotted",size=1.5,color="deeppink1")
HAC8      <- ggplot(SANTIAGO,aes(x = cluster8,y = HAC))      + geom_boxplot() + labs(title = "Porcentaje hacinamiento por zona"                   , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(HAC))     ,linetype="dotted",size=1.5,color="deeppink1")
PIMVZ8    <- ggplot(SANTIAGO,aes(x = cluster8,y = PIMVZ))    + geom_boxplot() + labs(title = "Materialidad de la vivienda"                        , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PIMVZ))   ,linetype="dotted",size=1.5,color="deeppink1")

NCASA8    <- ggplot(SANTIAGO,aes(x = cluster8,y = NCASA))    + geom_boxplot() + labs(title = "Numero de casas  por zona"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(NCASA))   ,linetype="dotted",size=1.5,color="deeppink1")
NDEPA8    <- ggplot(SANTIAGO,aes(x = cluster8,y = NDEPA))    + geom_boxplot() + labs(title = "Numero de deptos por zona"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(NDEPA))   ,linetype="dotted",size=1.5,color="deeppink1")

UNI8      <- ggplot(SANTIAGO,aes(x = cluster8,y = UNI))      + geom_boxplot() + labs(title = "Numero hogares UNI "                                  , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(UNI))      ,linetype="dotted",size=1.5,color="deeppink1")
MONO8     <- ggplot(SANTIAGO,aes(x = cluster8,y = MONO))     + geom_boxplot() + labs(title = "Numero hogares MONO"                                  , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(MONO))     ,linetype="dotted",size=1.5,color="deeppink1")
BI_SHIJ8  <- ggplot(SANTIAGO,aes(x = cluster8,y = BI_SHIJ))  + geom_boxplot() + labs(title = "Numero hogares BI sin hijos"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(BI_SHIJ))  ,linetype="dotted",size=1.5,color="deeppink1")
BI_CHIJ8  <- ggplot(SANTIAGO,aes(x = cluster8,y = BI_CHIJ))  + geom_boxplot() + labs(title = "Numero hogares BI con hijos"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(BI_CHIJ))  ,linetype="dotted",size=1.5,color="deeppink1")
COMPUES8  <- ggplot(SANTIAGO,aes(x = cluster8,y = COMPUES))  + geom_boxplot() + labs(title = "Numero hogares Compuesto"                             , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(COMPUES))  ,linetype="dotted",size=1.5,color="deeppink1")
EXTENSO8  <- ggplot(SANTIAGO,aes(x = cluster8,y = EXTENSO))  + geom_boxplot() + labs(title = "Numero hogares Extenso"                               , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(EXTENSO))  ,linetype="dotted",size=1.5,color="deeppink1")
SIN_NUC8  <- ggplot(SANTIAGO,aes(x = cluster8,y = SIN_NUC))  + geom_boxplot() + labs(title = "Numero hogares Sin nucleo"                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(SIN_NUC))  ,linetype="dotted",size=1.5,color="deeppink1")

CAMAY8    <- ggplot(SANTIAGO,aes(x = cluster8,y = CAMAY))        + geom_boxplot() + labs(title = "Numero AD"                                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(CAMAY))    ,linetype="dotted",size=1.5,color="deeppink1")
AMQVS8    <- ggplot(SANTIAGO,aes(x = cluster8,y = AMQVS))        + geom_boxplot() + labs(title = "Numero AD que viven solos"                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(AMQVS))    ,linetype="dotted",size=1.5,color="deeppink1")
PAMQVS8   <- ggplot(SANTIAGO,aes(x = cluster8,y = PAMQVS))       + geom_boxplot() + labs(title = "Porcentaje AD que viven solos"                        , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PAMQVS))   ,linetype="dotted",size=1.5,color="deeppink1")
PAMQTRA8  <- ggplot(SANTIAGO,aes(x = cluster8,y = PAMQTRA))      + geom_boxplot() + labs(title = "Porcentaje AD que trabajan"                           , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(PAMQTRA))  ,linetype="dotted",size=1.5,color="deeppink1")
PAMJUBI8  <- ggplot(SANTIAGO,aes(x = cluster8,y = PAMJUBI))      + geom_boxplot() + labs(title = "Porcentaje AD jubilados"                              , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(PAMJUBI))  ,linetype="dotted",size=1.5,color="deeppink1")

SCORE05_8   <- ggplot(SANTIAGO,aes(x = cluster8,y = W_score5))   + geom_boxplot() + labs(title = "Caminabilidad  5 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score5,na.rm=TRUE))  ,linetype="dotted",size=1.5,color="deeppink1")
SCORE10_8   <- ggplot(SANTIAGO,aes(x = cluster8,y = W_score10))  + geom_boxplot() + labs(title = "Caminabilidad 10 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score10,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")
SCORE15_8   <- ggplot(SANTIAGO,aes(x = cluster8,y = W_score15))  + geom_boxplot() + labs(title = "Caminabilidad 15 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score15,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")
SCORE20_8   <- ggplot(SANTIAGO,aes(x = cluster8,y = W_score20))  + geom_boxplot() + labs(title = "Caminabilidad 20 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score20,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")

ISMT_8      <- ggplot(SANTIAGO,aes(x = cluster8,y = ISMT)) + geom_boxplot() + labs(title = "Indice sociomaterial territorial", x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(ISMT)) ,linetype="dotted",size=1.5,color="deeppink1")
```


```{r,message=FALSE,warning=FALSE,include=FALSE}
a <- plot_grid(PORC_ADM8,PJHSEM8,TIM8,EMP8,HAC8,PIMVZ8)
b <- plot_grid(NCASA8,NDEPA8)
c <- plot_grid(PAMQTRA8,PAMJUBI8)
d <- plot_grid(UNI8,MONO8,BI_SHIJ8,BI_CHIJ8,COMPUES8,EXTENSO8,SIN_NUC8)
e <- plot_grid(CAMAY8,AMQVS8,PAMQVS8)
f <- plot_grid(SCORE05_8,SCORE10_8,SCORE15_8,SCORE20_8)
```


### Indicadores clasificatorios

```{r,fig.width=12,fig.height=7}
a
```

### Tipo de vivienda

```{r,fig.width=12,fig.height=7}
b
```

### Porcentaje en trabajo o jubilo

```{r,fig.width=12,fig.height=7}
c
```

### Composicion familiar

```{r,fig.width=12,fig.height=7}
d
```

### Vive acompanado o solo

```{r,fig.width=12,fig.height=7}
e
```

### Caminabilidad del AD

```{r,fig.width=12,fig.height=7}
f
```


### Indice sociomaterial territorial

```{r,fig.width=12,fig.height=7}
ISMT_8
```











## Casos especial

### Caso 1

En el primer caso se toman las categorías de cluster **más avejentadas** con la categoría **a** (categorías 1,5,6,8) y **menos avejentadas** como **b**

```{r,message=FALSE,warning=FALSE,include=FALSE}

PORC_ADM8 <- ggplot(SANTIAGO,aes(x = k8I,y = PORC_ADM)) + geom_boxplot() + labs(title = "Porcentaje adultos mayores por zona"                , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PORC_ADM)),linetype="dotted",size=1.5,color="deeppink1")
PJHSEM8   <- ggplot(SANTIAGO,aes(x = k8I,y = PJHSEM))   + geom_boxplot() + labs(title = "Porcentaje Jefes de Hogar sin ensenanza media"      , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PJHSEM))  ,linetype="dotted",size=1.5,color="deeppink1")
TIM8      <- ggplot(SANTIAGO,aes(x = k8I,y = log(TIM))) + geom_boxplot() + labs(title = "Tasa de inmigracion"                                , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(log(TIM))),linetype="dotted",size=1.5,color="deeppink1")
EMP8      <- ggplot(SANTIAGO,aes(x = k8I,y = EMP))      + geom_boxplot() + labs(title = "Porcentaje empleabilidad mayor a 15 anos"           , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(EMP))     ,linetype="dotted",size=1.5,color="deeppink1")
HAC8      <- ggplot(SANTIAGO,aes(x = k8I,y = HAC))      + geom_boxplot() + labs(title = "Porcentaje hacinamiento por zona"                   , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(HAC))     ,linetype="dotted",size=1.5,color="deeppink1")
PIMVZ8    <- ggplot(SANTIAGO,aes(x = k8I,y = PIMVZ))    + geom_boxplot() + labs(title = "Materialidad de la vivienda"                        , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PIMVZ))   ,linetype="dotted",size=1.5,color="deeppink1")

NCASA8    <- ggplot(SANTIAGO,aes(x = k8I,y = NCASA))    + geom_boxplot() + labs(title = "Numero de casas  por zona"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(NCASA))   ,linetype="dotted",size=1.5,color="deeppink1")
NDEPA8    <- ggplot(SANTIAGO,aes(x = k8I,y = NDEPA))    + geom_boxplot() + labs(title = "Numero de deptos por zona"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(NDEPA))   ,linetype="dotted",size=1.5,color="deeppink1")

UNI8      <- ggplot(SANTIAGO,aes(x = k8I,y = UNI))      + geom_boxplot() + labs(title = "Numero hogares UNI "                                  , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(UNI))      ,linetype="dotted",size=1.5,color="deeppink1")
MONO8     <- ggplot(SANTIAGO,aes(x = k8I,y = MONO))     + geom_boxplot() + labs(title = "Numero hogares MONO"                                  , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(MONO))     ,linetype="dotted",size=1.5,color="deeppink1")
BI_SHIJ8  <- ggplot(SANTIAGO,aes(x = k8I,y = BI_SHIJ))  + geom_boxplot() + labs(title = "Numero hogares BI sin hijos"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(BI_SHIJ))  ,linetype="dotted",size=1.5,color="deeppink1")
BI_CHIJ8  <- ggplot(SANTIAGO,aes(x = k8I,y = BI_CHIJ))  + geom_boxplot() + labs(title = "Numero hogares BI con hijos"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(BI_CHIJ))  ,linetype="dotted",size=1.5,color="deeppink1")
COMPUES8  <- ggplot(SANTIAGO,aes(x = k8I,y = COMPUES))  + geom_boxplot() + labs(title = "Numero hogares Compuesto"                             , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(COMPUES))  ,linetype="dotted",size=1.5,color="deeppink1")
EXTENSO8  <- ggplot(SANTIAGO,aes(x = k8I,y = EXTENSO))  + geom_boxplot() + labs(title = "Numero hogares Extenso"                               , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(EXTENSO))  ,linetype="dotted",size=1.5,color="deeppink1")
SIN_NUC8  <- ggplot(SANTIAGO,aes(x = k8I,y = SIN_NUC))  + geom_boxplot() + labs(title = "Numero hogares Sin nucleo"                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(SIN_NUC))  ,linetype="dotted",size=1.5,color="deeppink1")

CAMAY8    <- ggplot(SANTIAGO,aes(x = k8I,y = CAMAY))        + geom_boxplot() + labs(title = "Numero AD"                                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(CAMAY))    ,linetype="dotted",size=1.5,color="deeppink1")
AMQVS8    <- ggplot(SANTIAGO,aes(x = k8I,y = AMQVS))        + geom_boxplot() + labs(title = "Numero AD que viven solos"                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(AMQVS))    ,linetype="dotted",size=1.5,color="deeppink1")
PAMQVS8   <- ggplot(SANTIAGO,aes(x = k8I,y = PAMQVS))       + geom_boxplot() + labs(title = "Porcentaje AD que viven solos"                        , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PAMQVS))   ,linetype="dotted",size=1.5,color="deeppink1")
PAMQTRA8  <- ggplot(SANTIAGO,aes(x = k8I,y = PAMQTRA))      + geom_boxplot() + labs(title = "Porcentaje AD que trabajan"                           , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(PAMQTRA))  ,linetype="dotted",size=1.5,color="deeppink1")
PAMJUBI8  <- ggplot(SANTIAGO,aes(x = k8I,y = PAMJUBI))      + geom_boxplot() + labs(title = "Porcentaje AD jubilados"                              , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(PAMJUBI))  ,linetype="dotted",size=1.5,color="deeppink1")

SCORE05_8   <- ggplot(SANTIAGO,aes(x = k8I,y = W_score5))   + geom_boxplot() + labs(title = "Caminabilidad  5 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score5,na.rm=TRUE))  ,linetype="dotted",size=1.5,color="deeppink1")
SCORE10_8   <- ggplot(SANTIAGO,aes(x = k8I,y = W_score10))  + geom_boxplot() + labs(title = "Caminabilidad 10 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score10,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")
SCORE15_8   <- ggplot(SANTIAGO,aes(x = k8I,y = W_score15))  + geom_boxplot() + labs(title = "Caminabilidad 15 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score15,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")
SCORE20_8   <- ggplot(SANTIAGO,aes(x = k8I,y = W_score20))  + geom_boxplot() + labs(title = "Caminabilidad 20 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score20,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")

ISMT_8      <- ggplot(SANTIAGO,aes(x = k8I,y = ISMT)) + geom_boxplot() + labs(title = "Indice sociomaterial territorial", x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(ISMT)) ,linetype="dotted",size=1.5,color="deeppink1")
```


```{r,message=FALSE,warning=FALSE,include=FALSE}
a <- plot_grid(PORC_ADM8,PJHSEM8,TIM8,EMP8,HAC8,PIMVZ8)
b <- plot_grid(NCASA8,NDEPA8)
c <- plot_grid(PAMQTRA8,PAMJUBI8)
d <- plot_grid(UNI8,MONO8,BI_SHIJ8,BI_CHIJ8,COMPUES8,EXTENSO8,SIN_NUC8)
e <- plot_grid(CAMAY8,AMQVS8,PAMQVS8)
f <- plot_grid(SCORE05_8,SCORE10_8,SCORE15_8,SCORE20_8)
```


### Indicadores clasificatorios

```{r,fig.width=12,fig.height=7}
a
```

### Tipo de vivienda

```{r,fig.width=12,fig.height=7}
b
```

### Porcentaje en trabajo o jubilo

```{r,fig.width=12,fig.height=7}
c
```

### Composicion familiar

```{r,fig.width=12,fig.height=7}
d
```

### Vive acompanado o solo

```{r,fig.width=12,fig.height=7}
e
```

### Caminabilidad del AD

```{r,fig.width=12,fig.height=7}
f
```


### Indice sociomaterial territorial

```{r,fig.width=12,fig.height=7}
ISMT_8
```







### Caso 2

En el segundo caso se toma la categoría de cluster **más avejentada y acomodada** con la categoría **a** perteneciente al grupo (8). En el extremo opuesto se recategorizan **más aventajadas, pero menos acomodadas** (5,6) como **b**. El resto se recategoriza como **c**

```{r,message=FALSE,warning=FALSE,include=FALSE}

PORC_ADM8 <- ggplot(SANTIAGO,aes(x = k8II,y = PORC_ADM)) + geom_boxplot() + labs(title = "Porcentaje adultos mayores por zona"                , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PORC_ADM)),linetype="dotted",size=1.5,color="deeppink1")
PJHSEM8   <- ggplot(SANTIAGO,aes(x = k8II,y = PJHSEM))   + geom_boxplot() + labs(title = "Porcentaje Jefes de Hogar sin ensenanza media"      , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PJHSEM))  ,linetype="dotted",size=1.5,color="deeppink1")
TIM8      <- ggplot(SANTIAGO,aes(x = k8II,y = log(TIM))) + geom_boxplot() + labs(title = "Tasa de inmigracion"                                , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(log(TIM))),linetype="dotted",size=1.5,color="deeppink1")
EMP8      <- ggplot(SANTIAGO,aes(x = k8II,y = EMP))      + geom_boxplot() + labs(title = "Porcentaje empleabilidad mayor a 15 anos"           , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(EMP))     ,linetype="dotted",size=1.5,color="deeppink1")
HAC8      <- ggplot(SANTIAGO,aes(x = k8II,y = HAC))      + geom_boxplot() + labs(title = "Porcentaje hacinamiento por zona"                   , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(HAC))     ,linetype="dotted",size=1.5,color="deeppink1")
PIMVZ8    <- ggplot(SANTIAGO,aes(x = k8II,y = PIMVZ))    + geom_boxplot() + labs(title = "Materialidad de la vivienda"                        , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PIMVZ))   ,linetype="dotted",size=1.5,color="deeppink1")

NCASA8    <- ggplot(SANTIAGO,aes(x = k8II,y = NCASA))    + geom_boxplot() + labs(title = "Numero de casas  por zona"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(NCASA))   ,linetype="dotted",size=1.5,color="deeppink1")
NDEPA8    <- ggplot(SANTIAGO,aes(x = k8II,y = NDEPA))    + geom_boxplot() + labs(title = "Numero de deptos por zona"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(NDEPA))   ,linetype="dotted",size=1.5,color="deeppink1")

UNI8      <- ggplot(SANTIAGO,aes(x = k8II,y = UNI))      + geom_boxplot() + labs(title = "Numero hogares UNI "                                  , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(UNI))      ,linetype="dotted",size=1.5,color="deeppink1")
MONO8     <- ggplot(SANTIAGO,aes(x = k8II,y = MONO))     + geom_boxplot() + labs(title = "Numero hogares MONO"                                  , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(MONO))     ,linetype="dotted",size=1.5,color="deeppink1")
BI_SHIJ8  <- ggplot(SANTIAGO,aes(x = k8II,y = BI_SHIJ))  + geom_boxplot() + labs(title = "Numero hogares BI sin hijos"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(BI_SHIJ))  ,linetype="dotted",size=1.5,color="deeppink1")
BI_CHIJ8  <- ggplot(SANTIAGO,aes(x = k8II,y = BI_CHIJ))  + geom_boxplot() + labs(title = "Numero hogares BI con hijos"                          , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(BI_CHIJ))  ,linetype="dotted",size=1.5,color="deeppink1")
COMPUES8  <- ggplot(SANTIAGO,aes(x = k8II,y = COMPUES))  + geom_boxplot() + labs(title = "Numero hogares Compuesto"                             , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(COMPUES))  ,linetype="dotted",size=1.5,color="deeppink1")
EXTENSO8  <- ggplot(SANTIAGO,aes(x = k8II,y = EXTENSO))  + geom_boxplot() + labs(title = "Numero hogares Extenso"                               , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(EXTENSO))  ,linetype="dotted",size=1.5,color="deeppink1")
SIN_NUC8  <- ggplot(SANTIAGO,aes(x = k8II,y = SIN_NUC))  + geom_boxplot() + labs(title = "Numero hogares Sin nucleo"                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(SIN_NUC))  ,linetype="dotted",size=1.5,color="deeppink1")

CAMAY8    <- ggplot(SANTIAGO,aes(x = k8II,y = CAMAY))        + geom_boxplot() + labs(title = "Numero AD"                                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(CAMAY))    ,linetype="dotted",size=1.5,color="deeppink1")
AMQVS8    <- ggplot(SANTIAGO,aes(x = k8II,y = AMQVS))        + geom_boxplot() + labs(title = "Numero AD que viven solos"                            , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(AMQVS))    ,linetype="dotted",size=1.5,color="deeppink1")
PAMQVS8   <- ggplot(SANTIAGO,aes(x = k8II,y = PAMQVS))       + geom_boxplot() + labs(title = "Porcentaje AD que viven solos"                        , x = "Cluster", y = "Porcentaje") + geom_hline(aes(yintercept = mean(PAMQVS))   ,linetype="dotted",size=1.5,color="deeppink1")
PAMQTRA8  <- ggplot(SANTIAGO,aes(x = k8II,y = PAMQTRA))      + geom_boxplot() + labs(title = "Porcentaje AD que trabajan"                           , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(PAMQTRA))  ,linetype="dotted",size=1.5,color="deeppink1")
PAMJUBI8  <- ggplot(SANTIAGO,aes(x = k8II,y = PAMJUBI))      + geom_boxplot() + labs(title = "Porcentaje AD jubilados"                              , x = "Cluster", y = "Cantidad")   + geom_hline(aes(yintercept = mean(PAMJUBI))  ,linetype="dotted",size=1.5,color="deeppink1")

SCORE05_8   <- ggplot(SANTIAGO,aes(x = k8II,y = W_score5))   + geom_boxplot() + labs(title = "Caminabilidad  5 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score5,na.rm=TRUE))  ,linetype="dotted",size=1.5,color="deeppink1")
SCORE10_8   <- ggplot(SANTIAGO,aes(x = k8II,y = W_score10))  + geom_boxplot() + labs(title = "Caminabilidad 10 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score10,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")
SCORE15_8   <- ggplot(SANTIAGO,aes(x = k8II,y = W_score15))  + geom_boxplot() + labs(title = "Caminabilidad 15 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score15,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")
SCORE20_8   <- ggplot(SANTIAGO,aes(x = k8II,y = W_score20))  + geom_boxplot() + labs(title = "Caminabilidad 20 min a 2.4km/h"                    , x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(W_score20,na.rm=TRUE)) ,linetype="dotted",size=1.5,color="deeppink1")

ISMT_8      <- ggplot(SANTIAGO,aes(x = k8II,y = ISMT)) + geom_boxplot() + labs(title = "Indice sociomaterial territorial", x = "Cluster", y = "Puntaje")   + geom_hline(aes(yintercept = mean(ISMT)) ,linetype="dotted",size=1.5,color="deeppink1")
```


```{r,message=FALSE,warning=FALSE,include=FALSE}
a <- plot_grid(PORC_ADM8,PJHSEM8,TIM8,EMP8,HAC8,PIMVZ8)
b <- plot_grid(NCASA8,NDEPA8)
c <- plot_grid(PAMQTRA8,PAMJUBI8)
d <- plot_grid(UNI8,MONO8,BI_SHIJ8,BI_CHIJ8,COMPUES8,EXTENSO8,SIN_NUC8)
e <- plot_grid(CAMAY8,AMQVS8,PAMQVS8)
f <- plot_grid(SCORE05_8,SCORE10_8,SCORE15_8,SCORE20_8)
```


### Indicadores clasificatorios

```{r,fig.width=12,fig.height=7}
a
```

### Tipo de vivienda

```{r,fig.width=12,fig.height=7}
b
```

### Porcentaje en trabajo o jubilo

```{r,fig.width=12,fig.height=7}
c
```

### Composicion familiar

```{r,fig.width=12,fig.height=7}
d
```

### Vive acompanado o solo

```{r,fig.width=12,fig.height=7}
e
```

### Caminabilidad del AD

```{r,fig.width=12,fig.height=7}
f
```


### Indice sociomaterial territorial

```{r,fig.width=12,fig.height=7}
ISMT_8
```