library(dplyr)
##
## Adjuntando el paquete: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(readxl)
library(ggplot2)
library(plotly)
##
## Adjuntando el paquete: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
#file.choose()
Sinies_nacional<-read_excel("C:\\Users\\ADMIN.PC_ADMIN\\Downloads\\Copia de Siniestralidad_2018_2025p(1).xlsx")
names(Sinies_nacional)
## [1] "CodigoDepartamentoHecho" "CodigoMunicipioHecho"
## [3] "Departamento" "Municipio"
## [5] "EstadoVictima" "AnoHecho"
## [7] "Mes" "MesOCurrencia"
## [9] "Dia" "DiaOcurrencia"
## [11] "TipoVehiculo" "TipoVehiculoGrupo"
## [13] "TipoServicio" "CondicionVictima"
## [15] "UsuarioVia" "Zona"
## [17] "ObjetoColision" "Rango3horas"
## [19] "Rango6horas" "Sexo"
## [21] "RangoEdad" "ClaseAccidente"
## [23] "Hipotesis" "DireccionHecho"
#---------------------
mun_ten_died= Sinies_nacional%>%
group_by(Departamento,Municipio)%>%
summarise(Total_sinister=n(),.groups ="drop" )%>%
arrange(desc(Total_sinister))%>%
top_n(10,Total_sinister)
plot_ly(
mun_ten_died,
x = ~Municipio,
y = ~Departamento,
z = ~Total_sinister,
type = "scatter3d",
marker = list(
color = ~Total_sinister,
colorscale = "Viridis"
)
) %>%
layout(
scene = list(
xaxis = list(title = "Municipio"),
yaxis = list(title = "Departamento"),
zaxis = list(title = "Total de Siniestros")
),
title = "Top 5 de Siniestros Fallecidos acumulado por Departamento y Municipio (2018-2025)"
)
## No scatter3d mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode