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código.R
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# Librerías utilizadas
library(tidyverse) # Paquete para manipulación y consulta.
library(lubridate)
library(highcharter)
library(readxl) #Paquete para lectura de datos.
library(ggplot2)
library(htmlwidgets)
library(leaflet)
library(rgdal)
library(rjson)
# source("functions.R", encoding = 'UTF-8')
#
#
# tipovar <- c("text", "text", "date" ,"date"
) # Especificar tipo de variables del Dataset
#colores de las series
col <- c( "#8cc63f", # verde
"#f15a24", # naranja
"#0071bc", # azul vivo
"#6d6666", # gris
"#fbb03b", # amarillo
"#93278f", # morado
"#29abe2", # azul claro
"#c1272d", # rojo
"#8b7355", # cafe
"#855b5b", # vinotinto
"#ed1e79") # rosado
#Lectura de bases de datos
cartas <- read_excel("cartas.xlsx",
sheet = 1, col_types = tipovar)
#
# cartas$YEAR <- as.character(year(cartas$Fecha)) #Año
# cartas$MONTH <- as.character(month(cartas$Fecha)) #meses
#
#
# for (i in 1:9){
# cartas$MONTH[cartas$MONTH==i]= paste0("0",i)
# }
#
# conteo <- function(varc){
# cartas %>% group_by_(.dots = list("YEAR",varc)) %>%
# summarise(Total = n()) %>% rename_(.dots=list("Clase"=varc)) %>%
# mutate_(Variable = "varc") %>% select(Variable, YEAR, Clase, Total)
# }
#
# conteo2 <- function(varc){
# cartas %>% group_by_(.dots = list("MONTH",varc)) %>%
# summarise(Total = n()) %>% rename_(.dots=list("Clase"=varc)) %>%
# mutate_(Variable = "varc") %>% select(Variable, MONTH, Clase, Total)
# }
#
# meses <- conteo2("YEAR")
# meses <- na.omit(meses[meses$Clase != "20173" & meses$Clase != "2010",,])
#
# serie_global <- series3(
# datos = meses,
# categoria = "YEAR",
# colores = col,
# titulo = "Evolución por meses del número de cartas en cada año",
# eje = "Número de cartas"
# ); serie_global
#
# saveWidget(serie_global, file = file.path(getwd(), "gráficos", "serie_mensual.html") , selfcontained = F , libdir = "libraryjs")
#
# Total <- cartas %>% group_by(YEAR) %>% summarise(Total = n()) %>% ungroup() %>%
# mutate(Variable="TOTAL", YEAR=YEAR, Clase = "Total", Total=Total) %>%
# select(Variable, YEAR, Clase, Total)
#
# Total <- na.omit(Total[Total$YEAR != "20173" & Total$YEAR != "2010",,])
#
#
# serie_global <- series2(
# datos = Total,
# categoria = "TOTAL",
# colores = col,
# titulo = "Evolución histórica anual",
# eje = "Número de cartas"
# ); serie_global
#
# saveWidget(serie_global, file = file.path(getwd(), "gráficos", paste0("serie_anual.html")) , selfcontained = F , libdir = "libraryjs")
#
# paises <- conteo("País")
#
# #paises <- paises[order(paises$Total, decreasing = T),]
# paises <- paises[paises$YEAR != "20173",]
# paises <- paises[paises$Clase == "Colombia" | paises$Clase == "Estados Unidos" | paises$Clase == "Francia" | paises$Clase == "Alemania" | paises$Clase == "España" & paises$Clase == "México" | paises$Clase == "Argentina" | paises$Clase == "Brasil" | paises$Clase == "Chile" | paises$Clase == "Venezuela" | paises$Clase == "Perú" | paises$Clase == "Italia",]
# paises <- na.omit(paises)
# serie_global <- series2(
# datos = paises,
# categoria = "País",
# colores = col,
# titulo = "Evolución por meses del número de cartas por país de orígen",
# eje = "Número de cartas"
# ); serie_global
#
# saveWidget(serie_global, file = file.path(getwd(), "gráficos", "serie_paises.html") , selfcontained = F , libdir = "libraryjs")
#
# Conteo3 <- function(varc){
# cartas %>% group_by_(.dots = list("YEAR",varc)) %>%
# summarise(Total = n()) %>% rename_(.dots=list("Clase"=varc)) %>%
# mutate_(Variable = "varc") %>% select(Variable, YEAR, Clase, Total)
# }
#
#
#
# paises <- Conteo3("tramite")
# paises <- na.omit(paises[paises$YEAR != "20173",])
#
# serie_global <- series2(
# datos = paises,
# categoria = "tramite",
# colores = col,
# titulo = "Evolución por meses del número de cartas según tipo de trámite",
# eje = "Número de cartas"
# ); serie_global
#
# saveWidget(serie_global, file = file.path(getwd(), "gráficos", "serie_tramites.html") , selfcontained = F , libdir = "libraryjs")
#
# ##### Mapa
#
# divipola.R <- read.table("DIVIPOLA_20160930.csv", sep=";", header=T)
#
#
# #### Lectura de datos
#
# # Base de datos con solo cabeceras municipales
#
# cabeceras <- divipola.R %>% select(code_dept=Código.Departamento,
# code_mun=Código.Municipio,
# departamentos=Nombre.Departamento,
# municipios=Nombre.Municipio,
# tipo_centro=Tipo.Centro.Poblado,
# longitud=Longitud,
# latitud=Latitud) %>%
# filter(tipo_centro == "CABECERA MUNICIPAL (CM)")
centroids <- read_xlsx("centroids.xlsx")
# Lista con los nombres ISO en español de los diferentes países.
spanish <- read_xlsx("centroids.xlsx", sheet = "Spanish")
spanish <- na.omit(spanish)
#### Manipulación
# Cuenta el número de docentes por país
cant_cartas <- cartas %>% group_by(País) %>% summarise(Total=n())
cant_cartas <- na.omit(cant_cartas)
cant_cartasPIP <- cartas[cartas$tramite== "PIP",] %>% group_by(País) %>% summarise(Total=n())
cant_cartasPIP <- na.omit(cant_cartasPIP)
cant_cartasAPC <- cartas[cartas$tramite== "APC",] %>% group_by(País) %>% summarise(Total=n())
cant_cartasAPC <- na.omit(cant_cartasAPC)
cant_cartasPIP5 <- cartas[cartas$tramite== "PIP5",] %>% group_by(País) %>% summarise(Total=n())
cant_cartasPIP5 <- na.omit(cant_cartasPIP5)
cant_cartasTP12 <- cartas[cartas$tramite== "TP12",] %>% group_by(País) %>% summarise(Total=n())
cant_cartasTP12 <- na.omit(cant_cartasTP12)
cant_cartasTP7 <- cartas[cartas$tramite== "TP7",] %>% group_by(País) %>% summarise(Total=n())
cant_cartasTP7 <- na.omit(cant_cartasTP7)
cant_cartasAUIP <- cartas[cartas$tramite== "PIP6",] %>% group_by(País) %>% summarise(Total=n())
cant_cartasAUIP <- na.omit(cant_cartasAUIP)
cant_cartasTP4 <- cartas[cartas$tramite== "TP4",] %>% group_by(País) %>% summarise(Total=n())
cant_cartasTP4 <- na.omit(cant_cartasTP4)
cant_cartasVISA <- cartas[cartas$tramite== "VISA",] %>% group_by(País) %>% summarise(Total=n())
cant_cartasVISA <- na.omit(cant_cartasVISA)
cant_cartasPIP7 <- cartas[cartas$tramite== "PIP7",] %>% group_by(País) %>% summarise(Total=n())
cant_cartasPIP7 <- na.omit(cant_cartasPIP7)
cant_cartasTP5 <- cartas[cartas$tramite== "TP5",] %>% group_by(País) %>% summarise(Total=n())
cant_cartasTP5 <- na.omit(cant_cartasTP5)
cant_cartasPIP2 <- cartas[cartas$tramite== "PIP2",] %>% group_by(País) %>% summarise(Total=n())
cant_cartasPIP2 <- na.omit(cant_cartasPIP2)
cant_cartasTP1 <- cartas[cartas$tramite== "TP1",] %>% group_by(País) %>% summarise(Total=n())
cant_cartasTP1 <- na.omit(cant_cartasTP1)
cant_cartasTP6 <- cartas[cartas$tramite== "TP6",] %>% group_by(País) %>% summarise(Total=n())
cant_cartasTP6 <- na.omit(cant_cartasTP6)
countries <- rgdal::readOGR("countriesgeo.json")
codigos <- matrix(0, nrow = 180, ncol = 18)
for(i in 1:180){
codigos[i,1] = as.character(countries@data$id[i])
}
for(i in spanish$ISO){
codigos[codigos[,1] == i, 2] = spanish$Nombre[spanish$ISO== i]
}
for(i in na.omit(cant_cartas$País)){
codigos[codigos[,2] == i, 3] = cant_cartas$Total[cant_cartas$País == i]
}
for(i in codigos[-c(40,148, 69, 91, 140, 143, 176),1]){
codigos[codigos[,1] == i, 4] = centroids$Longitude[centroids$CODS_PAISU == i]
}
for(i in codigos[-c(40,148, 69, 91, 140, 143, 176),1]){
codigos[codigos[,1] == i, 5] = centroids$Latitude[centroids$CODS_PAISU == i]
}
for(i in na.omit(cant_cartasPIP$País)){
codigos[codigos[,2] == i, 6] = cant_cartasPIP$Total[cant_cartasPIP$País == i]
}
for(i in na.omit(cant_cartasAPC$País)){
codigos[codigos[,2] == i, 7] = cant_cartasAPC$Total[cant_cartasAPC$País == i]
}
for(i in na.omit(cant_cartasAUIP$País)){
codigos[codigos[,2] == i, 8] = cant_cartasAUIP$Total[cant_cartasAUIP$País == i]
}
for(i in na.omit(cant_cartasPIP2$País)){
codigos[codigos[,2] == i, 9] = cant_cartasPIP2$Total[cant_cartasPIP2$País == i]
}
for(i in na.omit(cant_cartasPIP5$País)){
codigos[codigos[,2] == i, 10] = cant_cartasPIP5$Total[cant_cartasPIP5$País == i]
}
for(i in na.omit(cant_cartasPIP7$País)){
codigos[codigos[,2] == i, 11] = cant_cartasPIP7$Total[cant_cartasPIP7$País == i]
}
for(i in na.omit(cant_cartasTP1$País)){
codigos[codigos[,2] == i, 12] = cant_cartasTP1$Total[cant_cartasTP1$País == i]
}
for(i in na.omit(cant_cartasTP12$País)){
codigos[codigos[,2] == i, 13] = cant_cartasTP12$Total[cant_cartasTP12$País == i]
}
for(i in na.omit(cant_cartasTP4$País)){
codigos[codigos[,2] == i, 14] = cant_cartasTP4$Total[cant_cartasTP4$País == i]
}
for(i in na.omit(cant_cartasTP5$País)){
codigos[codigos[,2] == i, 15] = cant_cartasTP5$Total[cant_cartasTP5$País == i]
}
for(i in na.omit(cant_cartasTP6$País)){
codigos[codigos[,2] == i, 16] = cant_cartasTP6$Total[cant_cartasTP6$País == i]
}
for(i in na.omit(cant_cartasTP7$País)){
codigos[codigos[,2] == i, 17] = cant_cartasTP7$Total[cant_cartasTP7$País == i]
}
for(i in na.omit(cant_cartasVISA$País)){
codigos[codigos[,2] == i, 18] = cant_cartasVISA$Total[cant_cartasVISA$País == i]
}
countries@data$CANT_CARTAS <- as.numeric(codigos[,3])
countries@data$LONGITUD <- as.numeric(codigos[,4])
countries@data$LATITUD <- as.numeric(codigos[,5])
countries@data$NOMBRE <- codigos[,2]
countries@data$CANT_CARTASPIP <- as.numeric(codigos[,6])
countries@data$CANT_CARTASAPC <- as.numeric(codigos[,7])
countries@data$CANT_CARTASAUIP <- as.numeric(codigos[,8])
countries@data$CANT_CARTASPIP2 <- as.numeric(codigos[,9])
countries@data$CANT_CARTASPIP5 <- as.numeric(codigos[,10])
countries@data$CANT_CARTASPIP7 <- as.numeric(codigos[,11])
countries@data$CANT_CARTASTP1 <- as.numeric(codigos[,12])
countries@data$CANT_CARTASTP12 <- as.numeric(codigos[,13])
countries@data$CANT_CARTASTP4 <- as.numeric(codigos[,14])
countries@data$CANT_CARTASTP5 <- as.numeric(codigos[,15])
countries@data$CANT_CARTASTP6 <- as.numeric(codigos[,16])
countries@data$CANT_CARTASTP7 <- as.numeric(codigos[,17])
countries@data$CANT_CARTASVISA <- as.numeric(codigos[,18])
### Cambio para Francia de centroide afectado por isla en el Meditarraneo
countries@data[57,]$LONGITUD <- 2.413913
countries@data[57,]$LATITUD <- 46.766583
#### Base de mapa
#ESRI es un proveedor de bases de mapas con licencia pública
esri <- grep("^Esri", providers, value = T)
esri<- esri[c(11, 2,5)]
esri
names.esri <- c("SimpleMap","StreetMap", "SateliteMap")
# Etiquetas con información para cada país
labels_countries <- sprintf(
"<strong> %s </strong> <br/> %s %g cartas <br/> %s %g   %s %g <br/> %s %g   %s %g <br/> %s %g   %s %g <br/> %s %g   %s %g <br/> %s %g   %s %g <br/> %s %g   %s %g <br/> %s %g " ,
countries@data$NOMBRE, "Total: ", countries@data$CANT_CARTAS,
"PIP: ", countries@data$CANT_CARTASPIP,
"PIP2: ", countries@data$CANT_CARTASPIP2,
"PIP5: ", countries@data$CANT_CARTASPIP5,
"PIP7: ", countries@data$CANT_CARTASPIP7,
"APC: ", countries@data$CANT_CARTASAPC,
"AUIP: ", countries@data$CANT_CARTASAUIP,
"TP1: ", countries@data$CANT_CARTASTP1,
"TP4: ", countries@data$CANT_CARTASTP4,
"TP5: ", countries@data$CANT_CARTASTP5,
"TP6: ", countries@data$CANT_CARTASTP6,
"TP7: ", countries@data$CANT_CARTASTP7,
"TP12: ", countries@data$CANT_CARTASTP12,
"VISA: ", countries@data$CANT_CARTASVISA
) %>% lapply(htmltools::HTML)
# Paleta de colores
colores <- c('#ffffcc','#c2e699','#78c679','#31a354','#006837')
binpal <- colorBin("Blues", bins=c(0,1, 11, 51, 101, Inf), palette = colores)
# código de mapa con leaflet
countriesmap2 <- leaflet(countries)
for (k in c(1 , 2 , 3)) {
countriesmap2 <- countriesmap2 %>% addProviderTiles(names(esri[k]) , group = names.esri[k] , options = providerTileOptions(minZoom = 2))
}
countriesmap2 <- countriesmap2 %>%
addLayersControl(baseGroups = names.esri ,
options = layersControlOptions(collapsed = T)) %>%
setView(lat = 0, lng = 0, zoom = 2)%>%
addPolygons(stroke = T , smoothFactor = 0.05 , color = "gray" , weight = 1 ,
fillColor = ~binpal(CANT_CARTAS) ,
label = labels_countries , labelOptions = labelOptions( style = list("font-weight" = "normal" , padding = "3px 8px") , textsize = "12px" , direction = "auto" ) , dashArray = "3" ,
fillOpacity = 0.6 ,
highlight = highlightOptions(
weight = 5 ,
color = "#666" ,
dashArray = "" ,
fillOpacity = 0.7 ,
bringToFront = F)) %>%
addLegend("bottomright" , values = ~CANT_CARTAS, colors = colores ,
title = "Cartas", opacity = 1, bins=c(0,1, 11, 51, 101, Inf), labels = c("0","1 - 10 ", "11 - 50","51 - 100","Más de 100"))%>%
addMiniMap(position = "bottomleft" , zoomAnimation = T , toggleDisplay = T , autoToggleDisplay = T)%>%
addEasyButton(easyButton(
icon = "glyphicon-screenshot" , title = "Retornar" ,
onClick = JS("function(btn , map){ map.setView(L.latLng(0 , 0) , 2); }")))%>%
addLabelOnlyMarkers(lat = ~LATITUD, lng = ~LONGITUD , label = ~paste0(countries$NOMBRE) , labelOptions = labelOptions(noHide = T , direction = 'top' , textOnly = T , textsize = "10px") )%>%
addScaleBar(position = "bottomleft" , scaleBarOptions(metric = T , imperial = F))