EJERCICIO MORTALIDAD 2024

ggplot(comunas) +
    geom_sf()

ggplot(comunas) +
    geom_sf(aes(fill = comunas))

dim(mortalidad2024)

## [1] 15  2

str(mortalidad2024)

## 'data.frame':    15 obs. of  2 variables:
##  $ comuna: int  1 2 3 4 5 6 7 8 9 10 ...
##  $ tasa  : num  3.4 0 6 7 5.5 7.5 5.2 8.9 4 5.6 ...

summary(mortalidad2024)

##      comuna          tasa      
##  Min.   : 1.0   Min.   :0.000  
##  1st Qu.: 4.5   1st Qu.:3.200  
##  Median : 8.0   Median :5.500  
##  Mean   : 8.0   Mean   :4.833  
##  3rd Qu.:11.5   3rd Qu.:6.300  
##  Max.   :15.0   Max.   :8.900

names(mortalidad2024)

## [1] "comuna" "tasa"

ggplot(mortalidad2024) +
geom_col(aes(x = factor(comuna), y = tasa))

library(sf)
comunas <- st_read('https://bitsandbricks.github.io/data/CABA_comunas.geojson')

## Reading layer `CABA_comunas' from data source 
##   `https://bitsandbricks.github.io/data/CABA_comunas.geojson' 
##   using driver `GeoJSON'
## Simple feature collection with 15 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -58.53152 ymin: -34.70529 xmax: -58.33514 ymax: -34.52754
## Geodetic CRS:  WGS 84

dim(comunas)

## [1] 15  5

names(comunas)

## [1] "barrios"   "perimetro" "area"      "comunas"   "geometry"

head(comunas)

## Simple feature collection with 6 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -58.4627 ymin: -34.6625 xmax: -58.33514 ymax: -34.56935
## Geodetic CRS:  WGS 84
##                                                                        barrios
## 1 CONSTITUCION - MONSERRAT - PUERTO MADERO -  RETIRO - SAN NICOLAS - SAN TELMO
## 2                                                                     RECOLETA
## 3                                                    BALVANERA - SAN CRISTOBAL
## 4                           BARRACAS - BOCA - NUEVA POMPEYA - PARQUE PATRICIOS
## 5                                                              ALMAGRO - BOEDO
## 6                                                                    CABALLITO
##   perimetro     area comunas                       geometry
## 1  35572.65 17802807       1 MULTIPOLYGON (((-58.36854 -...
## 2  21246.61  6140873       2 MULTIPOLYGON (((-58.39521 -...
## 3  10486.26  6385991       3 MULTIPOLYGON (((-58.41192 -...
## 4  36277.44 21701236       4 MULTIPOLYGON (((-58.3552 -3...
## 5  12323.47  6660526       5 MULTIPOLYGON (((-58.41287 -...
## 6  10990.96  6851029       6 MULTIPOLYGON (((-58.43061 -...

ggplot(comunas) +
    geom_sf()

ggplot(comunas) +
    geom_sf(aes(fill = comunas))

rivadavia <- st_read('https://bitsandbricks.github.io/data/avenida_rivadavia.geojson')

## Reading layer `avenida_rivadavia' from data source 
##   `https://bitsandbricks.github.io/data/avenida_rivadavia.geojson' 
##   using driver `GeoJSON'
## Simple feature collection with 1 feature and 1 field
## Geometry type: LINESTRING
## Dimension:     XY
## Bounding box:  xmin: -58.53014 ymin: -34.63946 xmax: -58.37017 ymax: -34.60711
## Geodetic CRS:  WGS 84

ggplot(comunas) +
    geom_sf(aes(fill = comunas)) +
    geom_sf(data = rivadavia, color = "red")

nueva_columna <- c("Sur", "Norte", "Sur", "Sur", "Sur", "Norte", "Sur", "Sur", 
                   "Sur", "Norte", "Norte", "Norte", "Norte", "Norte", "Norte")
comunas <- mutate(comunas, ubicacion = nueva_columna)
head(comunas)

## Simple feature collection with 6 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -58.4627 ymin: -34.6625 xmax: -58.33514 ymax: -34.56935
## Geodetic CRS:  WGS 84
##                                                                        barrios
## 1 CONSTITUCION - MONSERRAT - PUERTO MADERO -  RETIRO - SAN NICOLAS - SAN TELMO
## 2                                                                     RECOLETA
## 3                                                    BALVANERA - SAN CRISTOBAL
## 4                           BARRACAS - BOCA - NUEVA POMPEYA - PARQUE PATRICIOS
## 5                                                              ALMAGRO - BOEDO
## 6                                                                    CABALLITO
##   perimetro     area comunas                       geometry ubicacion
## 1  35572.65 17802807       1 MULTIPOLYGON (((-58.36854 -...       Sur
## 2  21246.61  6140873       2 MULTIPOLYGON (((-58.39521 -...     Norte
## 3  10486.26  6385991       3 MULTIPOLYGON (((-58.41192 -...       Sur
## 4  36277.44 21701236       4 MULTIPOLYGON (((-58.3552 -3...       Sur
## 5  12323.47  6660526       5 MULTIPOLYGON (((-58.41287 -...       Sur
## 6  10990.96  6851029       6 MULTIPOLYGON (((-58.43061 -...     Norte

ggplot(comunas) +
    geom_sf(aes(fill = ubicacion)) +
    geom_sf(data = rivadavia, color = "red")

mortalidad2024 <- mutate(mortalidad2024, ubicación = nueva_columna)
head(mortalidad2024)

##   comuna tasa ubicación
## 1      1  3.4       Sur
## 2      2  0.0     Norte
## 3      3  6.0       Sur
## 4      4  7.0       Sur
## 5      5  5.5       Sur
## 6      6  7.5     Norte

```