vayuayan

# Install from GitHub
# install.packages("devtools")
devtools::install_github("saketlab/vayuayan", subdir = "R")

library(vayuayan)

# Find the stations in a city
stations <- cpcb_get_station_list("Mumbai")

# Daily AQI for two cities in 2022, one row per city and day
daily <- cpcb_city_daily(c("Delhi", "Mumbai"), 2022)
aggregate(value ~ city + month, daily, mean)

# Daily data for single stations
station_daily <- cpcb_station_daily(c("Anand Vihar, Delhi", "ITO, Delhi"), 2024)

# The 5 live stations nearest to central Delhi
cpcb_get_nearest_stations(c(28.6139, 77.2090), k = 5)

# Monthly PM2.5 at a point. region = "AS" downloads the smaller Asia file.
pm25_get_point(latitude = 28.61, longitude = 77.21, year = 2023, month = 11, region = "AS")

# Monthly mean PM2.5 over Delhi
delhi <- india_boundary(state = "Delhi")
pm25_monthly(delhi, 2022, region = "AS")

# Get your API key from OAQ and set it in ~/.Renviron as OAQ_API_KEY
session <- oaq_session()
oaq_list_providers(session)
sensors <- oaq_stations_table(session, "airnet")

library(dplyr)
library(ggplot2)

cpcb_city_daily("Delhi", 2022) %>%
  group_by(month) %>%
  summarise(mean_aqi = mean(value, na.rm = TRUE)) %>%
  ggplot(aes(month, mean_aqi)) +
  geom_line(colour = "#d77027") +
  scale_x_continuous(breaks = 1:12, labels = month.abb) +
  labs(title = "Delhi 2022: monthly mean AQI", x = NULL, y = "AQI")

# Mean PM2.5 for each state in a district GeoJSON
pm25_get_stats(
  geojson_file = "india_districts.geojson",
  year = 2023,
  month = 11,
  region = "AS",
  group_by = "state_name"
)