rm(list = ls())
library(tidyverse)
## -- Attaching packages ---------------------------------------------------------------------------- tidyverse 1.2.0 --
## v ggplot2 2.2.1 v purrr 0.2.3
## v tibble 1.3.4 v dplyr 0.7.4
## v tidyr 0.7.1 v stringr 1.2.0
## v readr 1.1.1 v forcats 0.2.0
## -- Conflicts ------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(httr)
library(geojsonio)
##
## Attaching package: 'geojsonio'
## The following object is masked from 'package:base':
##
## pretty
library(highcharter)
## Highcharts (www.highcharts.com) is a Highsoft software product which is
## not free for commercial and Governmental use
mapa <- "https://raw.githubusercontent.com/juaneladio/peru-geojson/master/peru_departamental_simple.geojson" %>%
GET() %>%
content() %>%
jsonlite::fromJSON(simplifyVector = FALSE)
# Extraemos lo que tiene de información
data <- map_df(mapa$features, "properties")
data <- mutate(data, value = COUNT)
data
## # A tibble: 25 x 5
## NOMBDEP COUNT FIRST_IDDP HECTARES value
## <chr> <int> <chr> <dbl> <int>
## 1 AMAZONAS 84 01 3930646.57 84
## 2 ANCASH 166 02 3596224.60 166
## 3 APURIMAC 80 03 2111415.17 80
## 4 AREQUIPA 109 04 6325588.93 109
## 5 AYACUCHO 111 05 4350381.78 111
## 6 CAJAMARCA 127 06 3304465.55 127
## 7 CALLAO 6 07 14140.95 6
## 8 CUSCO 108 08 7207614.24 108
## 9 HUANCAVELICA 94 09 2206503.88 94
## 10 HUANUCO 76 10 3720052.60 76
## # ... with 15 more rows
highchart(type = "map") %>%
hc_add_series(mapData = mapa, showInLegend = TRUE, data = data,
joinBy = "FIRST_IDDP", name = "Hectareas") %>%
hc_colorAxis(enabled = TRUE) %>%
# acá accedes a los valores del data frame "data"
hc_tooltip(pointFormat = "{point.NOMBDEP}: {point.value}")