.Rhistory

shiny::runApp()
rsconnect::setAccountInfo(
name = 'odrr2p-captnemomar',
token = 'CCF748BD4E11509F41BA9E73E83CF421',
secret = 'myOnb/DsTZZpsnJSdOAggcAWMA6DvkVLM3izuVXX'
)
setwd("~/GitHub/Bombing-Death-Tracker")
rsconnect::deployApp()
rsconnect::deployApp()
#package installation
install.packages("rsconnect")
install.packages("tidyverse")
install.packages("ggplot2")
install.packages("httr")
install.packages("jsonlite")
install.packages("dplyr")
library(rsconnect)
library(tidyverse)
library(ggplot2)
library(httr)  # For handling API requests
library(jsonlite) # For handling the response of the API
library(dplyr) # For handling data
# Base URL
base_url <- "https://api.acleddata.com/acled/read"
# Set up the list of parameters
params <- list(
email = "oansari@uchicago.edu",
key = "IbF5sT9lJbY-7eqg7LFf",  # Replace with your actual API key
country = "Israel|Palestine|Syria|Lebanon",
fields = "event_date|year|latitude|longitude|event_type|sub_event_type|country|fatalities|actor1|actor2",
limit = 5000,  # Maximum records per page
page = 1       # Starting page
)
# Initialize an empty list to store all records
all_data <- list()
# Paginated request loop for 2023 and 2024
for (yr in c(2023, 2024)) {
cat("Fetching data for year", yr, "...\n")
params$year <- yr  # Add the year filter to the parameters
params$page <- 1   # Reset the page to 1 for each year
repeat {
cat("Fetching page", params$page, "for year", yr, "...\n")
# Make the API request
response <- GET(url = base_url, query = params)
# Check response status
if (status_code(response) != 200) {
stop("API request failed with status:", status_code(response))
}
# Parse the JSON response
response_json <- jsonlite::fromJSON(content(response, "text"), simplifyVector = TRUE)
# Append data to the list
if (!is.null(response_json$data) && length(response_json$data) > 0) {
# Convert to a DataFrame and add the year column
df <- as.data.frame(response_json$data)
df$year <- yr
all_data <- append(all_data, list(df))
params$page <- params$page + 1  # Increment the page number
} else {
cat("No more data available for year", yr, ".\n")
break  # Exit loop if no more data
}
}
}
install.packages("dplyr")
install.packages("jsonlite")
install.packages("httr")
install.packages("ggplot2")
install.packages("tidyverse")
# Combine all pages into a single data frame
final_data <- bind_rows(all_data)
# Save the data to a CSV file
write.csv(final_data, "acled_2023_2024_data.csv", row.names = FALSE)
cat("Data saved to 'acled_2023_2024_data.csv'.\n")
install.packages("tidyverse")
install.packages("shiny")       # For creating interactive web applications
install.packages("leaflet")     # For creating interactive maps
install.packages("dplyr")       # For data manipulation
install.packages("lubridate")   # For handling dates
install.packages("sf")          # For spatial data manipulation
install.packages("fastmap")
install.packages("bslib")
library(shiny)
library(leaflet)
library(dplyr)
library(lubridate)
library(sf)
library(fastmap)
library(bslib)
install.packages("leaflet.extras")
install.packages("ggplot2")
install.packages("shinyWidgets")
library(leaflet.extras)
library(ggplot2)
library(shinyWidgets)
# Load your ACLED data
acled_data <- read.csv("acled_2023_2024_data.csv")
acled_data$event_date <- as.Date(acled_data$event_date)
ui <- fluidPage(
install.packages("shiny")
titlePanel("Dynamic Event Density Map for Israel, Palestine, Lebanon, and Syria"),
sidebarLayout(
install.packages("shinyWidgets")
install.packages("ggplot2")
install.packages("leaflet.extras")
install.packages("sf")
install.packages("fastmap")
install.packages("lubridate")
install.packages("bslib")
install.packages("dplyr")
install.packages("leaflet")
install.packages("ggplot2")
install.packages("httr")
install.packages("jsonlite")
# Load your ACLED data
acled_data <- read.csv("acled_2023_2024_data.csv")
acled_data$event_date <- as.Date(acled_data$event_date)
ui <- fluidPage(
titlePanel("Dynamic Event Density Map for Israel, Palestine, Lebanon, and Syria"),
sidebarLayout(
sidebarPanel(
switchInput(
"mode",
label = "Mode",
onLabel = "Animate",
offLabel = "Manual",
value = FALSE,  # Default to manual
inline = TRUE
),
conditionalPanel(
condition = "input.mode == false",  # Manual mode
sliderInput(
"date_range",
"Select Date Range:",
min = as.Date("2023-10-01"),
max = max(acled_data$event_date),
value = c(as.Date("2023-10-01"), max(acled_data$event_date)),
timeFormat = "%Y-%m-%d"
)
),
conditionalPanel(
condition = "input.mode == true",  # Animation mode
sliderInput(
"animation_date",
"Animate Through Dates:",
min = as.Date("2023-10-01"),
max = max(acled_data$event_date),
value = as.Date("2023-10-01"),
timeFormat = "%Y-%m-%d",
animate = animationOptions(interval = 167, loop = TRUE)  # Animation settings
)
)
),
mainPanel(
leafletOutput("event_map", height = "630px"),
htmlOutput("fatality_counters")
)
)
)
server <- function(input, output, session) {
# Reactive filtered data for manual mode
manual_data <- reactive({
acled_data %>%
filter(event_date >= input$date_range[1] & event_date <= input$date_range[2]) %>%
filter(event_type == "Explosions/Remote violence" & actor1 == "Military Forces of Israel (2022-)") %>%
filter(country %in% c("Israel", "Palestine", "Syria", "Lebanon"))
})
# Reactive filtered data for animation mode
animation_data <- reactive({
acled_data %>%
filter(event_date == input$animation_date) %>%
filter(event_type == "Explosions/Remote violence" & actor1 == "Military Forces of Israel (2022-)") %>%
filter(country %in% c("Israel", "Palestine", "Syria", "Lebanon"))
})
# Reactive fatalities for counters
cumulative_data <- reactive({
if (input$mode == FALSE) {  # Manual mode
acled_data %>%
filter(event_date >= input$date_range[1] & event_date <= input$date_range[2]) %>%
filter(event_type == "Explosions/Remote violence" & actor1 == "Military Forces of Israel (2022-)") %>%
filter(country %in% c("Israel", "Palestine", "Syria", "Lebanon")) %>%
group_by(country) %>%
summarize(cumulative_fatalities = sum(fatalities, na.rm = TRUE))
} else {  # Animation mode
acled_data %>%
filter(event_date <= input$animation_date) %>%
filter(event_type == "Explosions/Remote violence" & actor1 == "Military Forces of Israel (2022-)") %>%
filter(country %in% c("Israel", "Palestine", "Syria", "Lebanon")) %>%
group_by(country) %>%
summarize(cumulative_fatalities = sum(fatalities, na.rm = TRUE))
}
})
# Render leaflet map
output$event_map <- renderLeaflet({
leaflet() %>%
setView(lng = 35.2137, lat = 31.7683, zoom = 8) %>%
addProviderTiles("CartoDB.Positron")
})
# Update heatmap based on selected mode
observe({
filtered_data <- if (input$mode == FALSE) manual_data() else animation_data()
leafletProxy("event_map", data = filtered_data) %>%
clearHeatmap() %>%
addHeatmap(
lat = ~latitude,
lng = ~longitude,
intensity = ~1,
blur = 20,
max = 0.05,
radius = 15
)
})
# Update fatality counters
output$fatality_counters <- renderUI({
cumulative <- cumulative_data()
israel_fatalities <- if ("Israel" %in% cumulative$country) cumulative %>% filter(country == "Israel") %>% pull(cumulative_fatalities) else 0
palestine_fatalities <- if ("Palestine" %in% cumulative$country) cumulative %>% filter(country == "Palestine") %>% pull(cumulative_fatalities) else 0
syria_fatalities <- if ("Syria" %in% cumulative$country) cumulative %>% filter(country == "Syria") %>% pull(cumulative_fatalities) else 0
lebanon_fatalities <- if ("Lebanon" %in% cumulative$country) cumulative %>% filter(country == "Lebanon") %>% pull(cumulative_fatalities) else 0
tags$div(
style = "text-align: center; margin-top: 20px;",
tags$div(style = "font-size: 20px; font-weight: bold; color: #333;", "Fatalities by Country Caused by Israel:"),
tags$div(style = "font-size: 18px; color: #333;", paste("Israel: ", israel_fatalities)),
tags$div(style = "font-size: 18px; color: #333;", paste("Palestine: ", palestine_fatalities)),
tags$div(style = "font-size: 18px; color: #333;", paste("Syria: ", syria_fatalities)),
tags$div(style = "font-size: 18px; color: #333;", paste("Lebanon: ", lebanon_fatalities))
)
})
}
shinyApp(ui = ui, server = server)
rsconnect::deployApp()
# Load your ACLED data
acled_data <- read.csv("acled_2023_2024_data.csv")
acled_data$event_date <- as.Date(acled_data$event_date)
ui <- fluidPage(
titlePanel("Dynamic Event Density Map for Israel, Palestine, Lebanon, and Syria"),
sidebarLayout(
sidebarPanel(
switchInput(
"mode",
label = "Mode",
onLabel = "Animate",
offLabel = "Manual",
value = FALSE,  # Default to manual
inline = TRUE
),
conditionalPanel(
condition = "input.mode == false",  # Manual mode
sliderInput(
"date_range",
"Select Date Range:",
min = as.Date("2023-10-01"),
max = max(acled_data$event_date),
value = c(as.Date("2023-10-01"), max(acled_data$event_date)),
timeFormat = "%Y-%m-%d"
)
),
conditionalPanel(
condition = "input.mode == true",  # Animation mode
sliderInput(
"animation_date",
"Animate Through Dates:",
min = as.Date("2023-10-01"),
max = max(acled_data$event_date),
value = as.Date("2023-10-01"),
timeFormat = "%Y-%m-%d",
animate = animationOptions(interval = 167, loop = TRUE)  # Animation settings
)
)
),
mainPanel(
leafletOutput("event_map", height = "630px"),
htmlOutput("fatality_counters")
)
)
)
server <- function(input, output, session) {
# Reactive filtered data for manual mode
manual_data <- reactive({
acled_data %>%
filter(event_date >= input$date_range[1] & event_date <= input$date_range[2]) %>%
filter(event_type == "Explosions/Remote violence" & actor1 == "Military Forces of Israel (2022-)") %>%
filter(country %in% c("Israel", "Palestine", "Syria", "Lebanon"))
})
# Reactive filtered data for animation mode
animation_data <- reactive({
acled_data %>%
filter(event_date == input$animation_date) %>%
filter(event_type == "Explosions/Remote violence" & actor1 == "Military Forces of Israel (2022-)") %>%
filter(country %in% c("Israel", "Palestine", "Syria", "Lebanon"))
})
# Reactive fatalities for counters
cumulative_data <- reactive({
if (input$mode == FALSE) {  # Manual mode
acled_data %>%
filter(event_date >= input$date_range[1] & event_date <= input$date_range[2]) %>%
filter(event_type == "Explosions/Remote violence" & actor1 == "Military Forces of Israel (2022-)") %>%
filter(country %in% c("Israel", "Palestine", "Syria", "Lebanon")) %>%
group_by(country) %>%
summarize(cumulative_fatalities = sum(fatalities, na.rm = TRUE))
} else {  # Animation mode
acled_data %>%
filter(event_date <= input$animation_date) %>%
filter(event_type == "Explosions/Remote violence" & actor1 == "Military Forces of Israel (2022-)") %>%
filter(country %in% c("Israel", "Palestine", "Syria", "Lebanon")) %>%
group_by(country) %>%
summarize(cumulative_fatalities = sum(fatalities, na.rm = TRUE))
}
})
# Render leaflet map
output$event_map <- renderLeaflet({
leaflet() %>%
setView(lng = 35.2137, lat = 31.7683, zoom = 8) %>%
addProviderTiles("CartoDB.Positron")
})
# Update heatmap based on selected mode
observe({
filtered_data <- if (input$mode == FALSE) manual_data() else animation_data()
leafletProxy("event_map", data = filtered_data) %>%
clearHeatmap() %>%
addHeatmap(
lat = ~latitude,
lng = ~longitude,
intensity = ~1,
blur = 20,
max = 0.05,
radius = 15
)
})
# Update fatality counters
output$fatality_counters <- renderUI({
cumulative <- cumulative_data()
israel_fatalities <- if ("Israel" %in% cumulative$country) cumulative %>% filter(country == "Israel") %>% pull(cumulative_fatalities) else 0
palestine_fatalities <- if ("Palestine" %in% cumulative$country) cumulative %>% filter(country == "Palestine") %>% pull(cumulative_fatalities) else 0
syria_fatalities <- if ("Syria" %in% cumulative$country) cumulative %>% filter(country == "Syria") %>% pull(cumulative_fatalities) else 0
lebanon_fatalities <- if ("Lebanon" %in% cumulative$country) cumulative %>% filter(country == "Lebanon") %>% pull(cumulative_fatalities) else 0
tags$div(
style = "text-align: center; margin-top: 20px;",
tags$div(style = "font-size: 20px; font-weight: bold; color: #333;", "Fatalities by Country Caused by Israel:"),
tags$div(style = "font-size: 18px; color: #333;", paste("Israel: ", israel_fatalities)),
tags$div(style = "font-size: 18px; color: #333;", paste("Palestine: ", palestine_fatalities)),
tags$div(style = "font-size: 18px; color: #333;", paste("Syria: ", syria_fatalities)),
tags$div(style = "font-size: 18px; color: #333;", paste("Lebanon: ", lebanon_fatalities))
)
})
}
shinyApp(ui = ui, server = server)
rsconnect::deployApp()
# Base URL
base_url <- "https://api.acleddata.com/acled/read"
# Set up the list of parameters
params <- list(
email = "oansari@uchicago.edu",
key = "IbF5sT9lJbY-7eqg7LFf",  # Replace with your actual API key
country = "Israel|Palestine|Syria|Lebanon",
fields = "event_date|year|latitude|longitude|event_type|sub_event_type|country|fatalities|actor1|actor2",
limit = 5000,  # Maximum records per page
page = 1       # Starting page
)
# Initialize an empty list to store all records
all_data <- list()
# Paginated request loop for 2023 and 2024
for (yr in c(2023, 2024)) {
cat("Fetching data for year", yr, "...\n")
params$year <- yr  # Add the year filter to the parameters
params$page <- 1   # Reset the page to 1 for each year
repeat {
cat("Fetching page", params$page, "for year", yr, "...\n")
# Make the API request
response <- GET(url = base_url, query = params)
# Check response status
if (status_code(response) != 200) {
stop("API request failed with status:", status_code(response))
}
# Parse the JSON response
response_json <- jsonlite::fromJSON(content(response, "text"), simplifyVector = TRUE)
# Append data to the list
if (!is.null(response_json$data) && length(response_json$data) > 0) {
# Convert to a DataFrame and add the year column
df <- as.data.frame(response_json$data)
df$year <- yr
all_data <- append(all_data, list(df))
params$page <- params$page + 1  # Increment the page number
} else {
cat("No more data available for year", yr, ".\n")
break  # Exit loop if no more data
}
}
}
# Load your ACLED data
acled_data <- read.csv("acled_2023_2024_data.csv")
acled_data$event_date <- as.Date(acled_data$event_date)
ui <- fluidPage(
titlePanel("Dynamic Event Density Map for Israel, Palestine, Lebanon, and Syria"),
sidebarLayout(
sidebarPanel(
switchInput(
"mode",
label = "Mode",
onLabel = "Animate",
offLabel = "Manual",
value = FALSE,  # Default to manual
inline = TRUE
),
conditionalPanel(
condition = "input.mode == false",  # Manual mode
sliderInput(
"date_range",
"Select Date Range:",
min = as.Date("2023-10-01"),
max = max(acled_data$event_date),
value = c(as.Date("2023-10-01"), max(acled_data$event_date)),
timeFormat = "%Y-%m-%d"
)
),
conditionalPanel(
condition = "input.mode == true",  # Animation mode
sliderInput(
"animation_date",
"Animate Through Dates:",
min = as.Date("2023-10-01"),
max = max(acled_data$event_date),
value = as.Date("2023-10-01"),
timeFormat = "%Y-%m-%d",
animate = animationOptions(interval = 167, loop = TRUE)  # Animation settings
)
)
),
mainPanel(
leafletOutput("event_map", height = "630px"),
htmlOutput("fatality_counters")
)
)
)
server <- function(input, output, session) {
# Reactive filtered data for manual mode
manual_data <- reactive({
acled_data %>%
filter(event_date >= input$date_range[1] & event_date <= input$date_range[2]) %>%
filter(event_type == "Explosions/Remote violence" & actor1 == "Military Forces of Israel (2022-)") %>%
filter(country %in% c("Israel", "Palestine", "Syria", "Lebanon"))
})
# Reactive filtered data for animation mode
animation_data <- reactive({
acled_data %>%
filter(event_date == input$animation_date) %>%
filter(event_type == "Explosions/Remote violence" & actor1 == "Military Forces of Israel (2022-)") %>%
filter(country %in% c("Israel", "Palestine", "Syria", "Lebanon"))
})
# Reactive fatalities for counters
cumulative_data <- reactive({
if (input$mode == FALSE) {  # Manual mode
acled_data %>%
filter(event_date >= input$date_range[1] & event_date <= input$date_range[2]) %>%
filter(event_type == "Explosions/Remote violence" & actor1 == "Military Forces of Israel (2022-)") %>%
filter(country %in% c("Israel", "Palestine", "Syria", "Lebanon")) %>%
group_by(country) %>%
summarize(cumulative_fatalities = sum(fatalities, na.rm = TRUE))
} else {  # Animation mode
acled_data %>%
filter(event_date <= input$animation_date) %>%
filter(event_type == "Explosions/Remote violence" & actor1 == "Military Forces of Israel (2022-)") %>%
filter(country %in% c("Israel", "Palestine", "Syria", "Lebanon")) %>%
group_by(country) %>%
summarize(cumulative_fatalities = sum(fatalities, na.rm = TRUE))
}
})
# Render leaflet map
output$event_map <- renderLeaflet({
leaflet() %>%
setView(lng = 35.2137, lat = 31.7683, zoom = 8) %>%
addProviderTiles("CartoDB.Positron")
})
# Update heatmap based on selected mode
observe({
filtered_data <- if (input$mode == FALSE) manual_data() else animation_data()
leafletProxy("event_map", data = filtered_data) %>%
clearHeatmap() %>%
addHeatmap(
lat = ~latitude,
lng = ~longitude,
intensity = ~1,
blur = 20,
max = 0.05,
radius = 15
)
})
# Update fatality counters
output$fatality_counters <- renderUI({
cumulative <- cumulative_data()
israel_fatalities <- if ("Israel" %in% cumulative$country) cumulative %>% filter(country == "Israel") %>% pull(cumulative_fatalities) else 0
palestine_fatalities <- if ("Palestine" %in% cumulative$country) cumulative %>% filter(country == "Palestine") %>% pull(cumulative_fatalities) else 0
syria_fatalities <- if ("Syria" %in% cumulative$country) cumulative %>% filter(country == "Syria") %>% pull(cumulative_fatalities) else 0
lebanon_fatalities <- if ("Lebanon" %in% cumulative$country) cumulative %>% filter(country == "Lebanon") %>% pull(cumulative_fatalities) else 0
tags$div(
style = "text-align: center; margin-top: 20px;",
tags$div(style = "font-size: 20px; font-weight: bold; color: #333;", "Fatalities by Country Caused by Israel:"),
tags$div(style = "font-size: 18px; color: #333;", paste("Israel: ", israel_fatalities)),
tags$div(style = "font-size: 18px; color: #333;", paste("Palestine: ", palestine_fatalities)),
tags$div(style = "font-size: 18px; color: #333;", paste("Syria: ", syria_fatalities)),
tags$div(style = "font-size: 18px; color: #333;", paste("Lebanon: ", lebanon_fatalities))
)
})
}
shinyApp(ui = ui, server = server)
rsconnect::deployApp()
rsconnect::deployApp()
rsconnect::deployApp()
rsconnect::deployApp()
rsconnect::deployApp()
rsconnect::deployApp()
rsconnect::deployApp()
rsconnect::deployApp()