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server.R
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# visualization
library(ggplot2)
library(spatstat)
library(plotrix)
library(fields)
library(leaflet)
library(maptools)
library(RColorBrewer)
library(lattice)
library(geoR)
library(plotrix)
library(RcppArmadillo)
# spatial data management and point process analysis
library(sp)
library(gtools)
# point processes
library(spatstat)
library(splancs) # K-function
library(smacpod) # Spatial scanning statistic
library(car) # contains a function for logistic transformation (log odds ratio) to make more normal
library(leaflet)
library(tidyverse)
library(shiny)
library(xts)
library(knitr)
# read in csv
acled <- read.csv("1900-01-01-2020-05-06-Sudan.csv", sep = ";") # Sudan
# select for year 2005+ (after CPA was signed), filter, and mutate
acled_sub <- acled %>% select(year, event_type, admin2, fatalities, longitude, latitude) %>%
filter(year >= 2005) %>%
mutate(i_extreme = ifelse(event_type %in% "Battles" |
event_type %in% "Explosions/Remote violence" |
event_type %in% "Violence against civilians",
1, 0),
i_extreme_label = ifelse(event_type %in% "Battles" |
event_type %in% "Explosions/Remote violence" |
event_type %in% "Violence against civilians",
'Extreme violence', 'Non-extreme violence'))
function(input, output, session) {
df <- reactive({
acled_sub %>%
filter(year %in% input$year)
})
output$mymap <- renderLeaflet({
pal = colorFactor("YlOrRd", acled_sub$i_extreme_label, reverse=T)
leaflet() %>%
setView(lat = 15.898457, lng = 30.392880, zoom = 5) %>%
addProviderTiles(providers$Stamen.Toner,
options = providerTileOptions(opacity = .4)) %>%
addCircleMarkers(data = df(), lng = ~longitude, lat = ~latitude,
fillOpacity=0.3, fillColor = ~pal(i_extreme_label),
radius=8, weight=0.1, stroke=TRUE
) %>%
leaflet::addLegend("bottomright", pal = pal,
values = acled_sub$i_extreme_label,
title = "Violence Type")
})
}