alramadona

library(dplyr)
library(rio)
library(data.table)


pst_20152016 <- import("D:/Projects/Data Sampel BPJS Kesehatan Edisi August2019/01 KEPESERTAAN 260819.dta")
kunFKL_20152016 <- import("D:/Projects/Data Sampel BPJS Kesehatan Edisi August2019/04 FKRTL 260819.dta")
kunFKP_20152016 <- import("D:/Projects/Data Sampel BPJS Kesehatan Edisi August2019/02 FKTP Kapitasi 260819.dta")
kunPNK_20152016 <- import("D:/Projects/Data Sampel BPJS Kesehatan Edisi August2019/03 FKTP Non Kapitasi 260819.dta")
# FKLdx2nd <- import("D:/Projects/Data Sampel BPJS Kesehatan Edisi August2019/05 FKRTL Diagnosis Sekunder 260819.dta")

alramadona

# An example of spatial analysis with R that involves grabbing data
# from a PostGIS database, joining to tabular data, projecting
# simplifying and creating an interactive map.

# This data is publicly available but requires some processing
# if you want the county boundaries you can find them here:

# cftp://ftp2.census.gov/geo/tiger/TIGER2016/COUNTY/

# The tabular data requires some processing but the raw data

alramadona

max_plots <- 5

shinyServer(function(input, output) {

  # Insert the right number of plot output objects into the web page
  output$plots <- renderUI({
    plot_output_list <- lapply(1:input$n, function(i) {
      plotname <- paste("plot", i, sep="")
      plotOutput(plotname, height = 280, width = 250)
    })