APIs & Interactive Maps with Leaflet in R

Today I made my first API requests in R! Using the Open Notify API, I pulled the information of when the International Space Station (ISS) is scheduled to pass over United States state capitals then mapped them using {leaflet}. This exercise is part of lab 3 in the curriculum of Cal Poly’s Stat 431.

This Open-Notify API provides predictions of pass times for a given location when given the corresponding latitude and longitude.

U.S. State Captials Information

To get the latitudes and longitudes of US state capitals, I used this resource.

library(tidyverse)
library(httr)     ## for working with the API
library(jsonlite) ## to work with the JSON data

# Get the long & lats of all the US state capitals
capitals <- read.table("https://people.sc.fsu.edu/~jburkardt/datasets/states/state_capitals_ll.txt", col.names = c("state","latitude","longitude"))

# Get the state capital names
capital_names <- read.table("https://people.sc.fsu.edu/~jburkardt/datasets/states/state_capitals_name.txt", col.names = c("state","capital"))

capitals <- bind_cols(capitals, capital_names)
state latitude longitude state1 capital
AL 32.36154 -86.27912 AL Montgomery
AK 58.30194 -134.41974 AK Juneau
AZ 33.44846 -112.07384 AZ Phoenix
AR 34.73601 -92.33112 AR Little Rock
CA 38.55560 -121.46893 CA Sacramento

Pass Times for U.S. State Captials

After getting the capitals information, I requested the ISS data from the Open Notify API. To see the structure of the response and how to get the information I needed, I looked at the information for one capital first.

# Getting the data for the first state
response <- GET("http://api.open-notify.org/iss-pass.json", query = list(lat = capitals$latitude[1], lon = capitals$longitude[1]))

# Extract the data from the response
data = fromJSON(rawToChar(response$content))

# Looking at the first pass time
data$response[1,]
##   duration   risetime
## 1      640 1591235377
# Convert unix time to datetime
as.POSIXct(as.numeric(data$response[1,][2]), origin="1970-01-01")
## [1] "2020-06-03 18:49:37 PDT"

Now that I knew the structure of the data, I iterated the process of requesting the next three pass times from the API for each state capital.

# Initialize dataframe
capitals_pass_times <- tibble(state = character(),
       capital = character(),
       lat = numeric(),
       lon = numeric(),
       duration = numeric(),
       risetime_num = character(),
       risetime = numeric())

# Loop for all states
for (s in 1:nrow(capitals)) {
  # Getting the data for the first state
  response <- GET("http://api.open-notify.org/iss-pass.json", query = list(lat = capitals$latitude[s], lon = capitals$longitude[s]))
  # Extract the data from the response
  data = fromJSON(rawToChar(response$content))
  
  # Add the next 3 predicted pass times to dataframe
  for (i in 1:3) {
  capitals_pass_times <- capitals_pass_times %>% add_row(state = capitals$state[s],
       capital = capitals$capital[s],
       lat = capitals$latitude[s], 
       lon = capitals$longitude[s],
       duration = as.numeric(data$response[i,]["duration"]),
       risetime_num = paste0("risetime_",i),
       risetime = as.numeric(data$response[i,]["risetime"]))
  }
}
state capital lat lon duration risetime_num risetime
AL Montgomery 32.36154 -86.27912 640 risetime_1 1591235377
AL Montgomery 32.36154 -86.27912 563 risetime_2 1591241207
AL Montgomery 32.36154 -86.27912 537 risetime_3 1591289620
AK Juneau 58.30194 -134.41974 619 risetime_1 1591234747
AK Juneau 58.30194 -134.41974 587 risetime_2 1591240530
AK Juneau 58.30194 -134.41974 437 risetime_3 1591246349

Mapping the Capitals & Displaying the Pass Times

Using the {leaflet} package, I made a map with the US state capitals showing the next three predicted pass times for each capital. When hovering over a capital, the next predicted pass time will show. When clicking a capital, you’ll be able to see the next three predicted pass times.

library(leaflet)

# Pivot table
capitals_pass_times <- pivot_wider(capitals_pass_times, id_cols = c(state,capital,lat,lon), names_from = risetime_num, values_from = risetime)

# Convert unix time to datetime
capitals_pass_times <- capitals_pass_times %>% 
  mutate_at(c("risetime_1", "risetime_2", "risetime_3"), ~as.POSIXct(., origin="1970-01-01")) %>% 
  arrange(risetime_1)

# Get ISS icon
ISSicon <- makeIcon(iconUrl = "http://open-notify.org/Open-Notify-API/map/ISSIcon.png",
    iconWidth = 15, iconHeight = 15)

# Map with leaflet
m <- leaflet(data = capitals_pass_times) %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(lng = ~lon, lat = ~lat,
             label = paste0(capitals_pass_times$capital, ", ",  capitals_pass_times$state, " - Next predicted passtime: ", capitals_pass_times$risetime_1),
             popup = paste0(capitals_pass_times$capital, ", ",  capitals_pass_times$state,  " - Next predicted passtimes: ", capitals_pass_times$risetime_1, ", ", capitals_pass_times$risetime_2, ", ", capitals_pass_times$risetime_3), 
             icon = ISSicon)

m

Drawing the Route of the ISS

To see the expected pass order of the ISS, I added polylines in order of pass times.

route <- leaflet(data = capitals_pass_times) %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addPolylines(lat = ~lat, lng = ~lon, color = "red") %>% 
  addMarkers(lng = ~lon, lat = ~lat, 
             label = paste0(capitals_pass_times$capital, ", ",  capitals_pass_times$state, " - Next predicted passtime: ", capitals_pass_times$risetime_1),
             popup = paste0(capitals_pass_times$capital, ", ",  capitals_pass_times$state,  " - Next predicted passtimes: ", capitals_pass_times$risetime_1, ", ", capitals_pass_times$risetime_2, ", ", capitals_pass_times$risetime_3), 
             icon = ISSicon)

route
Isabella Benabaye
Isabella Benabaye
Data and Reporting Analyst in Los Angeles, CA
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