In altaf-ali/phoenix_tracker: Phoenix Tracker

Activity

library(plyr)
library(cshapes)
library(countrycode)
library(tidyverse)
library(lubridate)
library(broom)
library(yaml)
library(ggrepel)

Loading Dataset

Load Phoenix events and few other things we need for plotting.

source('R/phoenix.R')

config <- yaml.load_file("config.yml")
events <- phoenix_load(config, "2017-01-01")
country_centroids <- read_csv(config$google$centroids)
world_map <- tidy(cshp(as.Date("2016-06-30")), region = "COWCODE")

Function for summarizing events. Return value contains a list of nodes and edges reprensenting dyadic events.

get_event_summary <- function(events, centroids, period = 0) {
  events <- events %>%
    filter(Date >= (max(Date) - days(period))) %>%
    filter(SourceActorRole == "GOV" & TargetActorRole == "GOV")

  nodes <- events %>%
    mutate(TargetActorEntity = ifelse(SourceActorEntity == TargetActorEntity, NA, TargetActorEntity)) %>%
    gather(ActorType, ActorEntity, SourceActorEntity, TargetActorEntity) %>%
    filter(!is.na(ActorEntity), !(ActorEntity == "")) %>%
    group_by(ActorEntity) %>%
    summarize(EventCount = n()) %>%
    mutate(CountryCode = countrycode(ActorEntity, "iso3c", "cown"), EventCount) %>%
    select(CountryCode, Country = ActorEntity, EventCount) %>%
    left_join(centroids, by = "Country") %>%
    arrange(desc(EventCount)) 

  edges <- events %>%
    filter(!is.na(SourceActorEntity), !(SourceActorEntity == ""),
           !is.na(TargetActorEntity), !(TargetActorEntity == ""),
           SourceActorEntity != TargetActorEntity) %>%
    rowwise() %>%
    mutate(Dyad = paste(sort(c(SourceActorEntity, TargetActorEntity)), collapse = "-")) %>%
    ungroup() %>%
    group_by(Dyad) %>%
    summarize(EventCount = n()) %>%
    ungroup() %>%
    separate(Dyad, c("SideA", "SideB"), "-", remove = FALSE) %>%
    mutate(CountryA = countrycode(SideA, "iso3c", "country.name"),
           CountryB = countrycode(SideB, "iso3c", "country.name")) %>%
    left_join(centroids, by = c("SideA" = "Country")) %>%
    select(Dyad, SideA, SideB, CountryA, CountryB, EventCount, SideA_Latitude = Latitude, SideA_Longitude = Longitude) %>%
    left_join(centroids, by = c("SideB" = "Country")) %>%
    select(Dyad, SideA, SideB, CountryA, CountryB, EventCount, SideA_Latitude, SideA_Longitude, SideB_Latitude = Latitude, SideB_Longitude = Longitude) %>%
    arrange(desc(EventCount))

  return(list(nodes = nodes, edges = edges))
}

Function for plotting activity on a map.

plot_activity <- function(map, event_summary) {
  map <- map %>%
    mutate(id = as.numeric(id)) %>%
    left_join(event_summary$nodes, by = c("id" = "CountryCode"))

  ggplot(map) +
    geom_map(map = map, aes(map_id = id, fill = EventCount), color = "gray", size = 0.5) +
    scale_fill_distiller(name = "Event Count", palette = "Blues", direction = 1, na.value = "white") +
    expand_limits(x = map$long, y = map$lat) +
    coord_cartesian() +
    geom_point(aes(x = Longitude, y = Latitude), 
               data = event_summary$nodes, 
               size = 0.5) +
    geom_curve(aes(x = SideA_Longitude,
                   y = SideA_Latitude,
                   xend = SideB_Longitude,
                   yend = SideB_Latitude),
               data = event_summary$edges,
               size = 0.2,
               alpha = 0.5,
               color = "red") +
    geom_text_repel(aes(x = Longitude, y = Latitude, label = CountryName), 
                    data = head(event_summary$nodes, 10), 
                    force = 0.1, 
                    size = 3,
                    fontface = "bold") +
    theme_minimal() +
    theme(legend.position = "bottom",
          legend.key.width = unit(5, "line"),
          axis.title = element_blank(),
          axis.text = element_blank(),
          panel.grid = element_blank())
}

This function simply formats the top 10 rows from the dataset in a pretty table.

show_top10 <- function(x) {
  x %>%
    head(n = 10) %>%
    knitr::kable()
}

Most Active on `r max(events$Date)`

event_summary <- get_event_summary(events, country_centroids)
show_top10(select(event_summary$nodes, CountryName, EventCount))
show_top10(select(event_summary$edges, CountryA, CountryB, EventCount))
plot_activity(world_map, event_summary)

Most Active in Last 7 Days

event_summary <- get_event_summary(events, country_centroids, period = 7)
show_top10(select(event_summary$nodes, CountryName, EventCount))
show_top10(select(event_summary$edges, CountryA, CountryB, EventCount))
plot_activity(world_map, event_summary)

Most Active in Last 30 Days

event_summary <- get_event_summary(events, country_centroids, period = 30)
show_top10(select(event_summary$nodes, CountryName, EventCount))
show_top10(select(event_summary$edges, CountryA, CountryB, EventCount))
plot_activity(world_map, event_summary)