tests/testthat/_test_query_weather.R
In hubert-thieriot/crea_r_package: Query And Plot Air Quality And Weather Data from CREA Database
library(testthat)
library(lubridate)
library(DBI)
# Querying weather
test_that("Querying ISD weather works", {
con = connection()
meas_test <- measurements(city='Delhi',
poll=PM25,
date_from='2019-07-01',
date_to='2019-07-02',
average_by = 'hour',
aggregate_at_city_level = T,
add_noaa_station_ids = T,
noaa_station_radius_km = 20,
collect = F,
con = con)
# Version using NOAA data stored in CREA DB
t <- system.time({
result_db <- weather.isd.join(meas_test,
measurements_averaged_by = 'hour',
collect = T,
con = con)
})
expect_lt(t['elapsed'],30)
expect_gt(length(unique(result_db$wind_deg)), 1)
expect_gt(length(unique(result_db$rh_percent)), 1)
expect_gt(length(unique(result_db$prec_6h_mm)), 1)
expect_gt(length(unique(result_db$slp_hp)), 1)
expect_gt(length(unique(result_db$temp_c)), 1)
expect_gt(length(unique(result_db$sky_code)), 1)
# Version using NOAA data got from Worldmet package
t <- system.time({
result_worldmet <- weather.isd.join.using_worldmet(meas_test,
measurements_averaged_by = 'hour',
radius_km = 20)
})
expect_lt(t['elapsed'],300)
expect_gt(length(unique(result_db$atmos_pres)), 1)
expect_gt(length(unique(result_db$RH)), 1)
expect_gt(length(unique(result_db$air_temp)), 1)
# expect_gt(length(unique(result_db$precip)), 1)
expect_gt(length(unique(result_db$ceil_ght)), 1)
# For timezone confirmation: we check on a single day with manually fetched data
# Station 421820
# Time is UTC in this data
# 2019 06 30 13 390 230 -9999 350 21 -9999 -9999 -9999
# 2019 06 30 15 374 220 9945 320 10 5 -9999 -9999
# 2019 06 30 18 346 210 9964 20 10 5 -9999 -9999
# 2019 06 30 21 332 203 9962 250 31 4 -9999 -9999
# 2019 07 01 00 316 226 9973 270 10 2 -9999 -9999
# 2019 07 01 02 330 220 -9999 220 21 0 -9999 -9999
# 2019 07 01 03 338 228 9986 200 10 0 -9999 -9999 <-- PEAK PRESSURE AT 3AM UTC -> 8.30AM IST
# 2019 07 01 04 350 250 -9999 230 26 0 -9999 -9999
# 2019 07 01 05 370 240 -9999 50 10 0 -9999 -9999
# 2019 07 01 06 386 229 9972 270 21 0 -9999 -9999
# 2019 07 01 07 390 230 -9999 50 31 0 -9999 -9999
# 2019 07 01 08 390 220 -9999 60 15 0 -9999 -9999
# 2019 07 01 09 410 235 9948 70 21 2 -9999 -9999 <-- PEAK TEMP AT 9AM UTC -> 2.30PM IST
# 2019 07 01 10 400 220 -9999 50 31 -9999 -9999 -9999
# 2019 07 01 11 410 240 -9999 40 36 0 -9999 -9999 <-- PEAK TEMP AT 11AM UTC -> 4.30PM IST
# 2019 07 01 12 404 238 9920 50 31 0 -9999 -9999
# 2019 07 01 13 390 260 -9999 40 10 -9999 -9999 -9999
# 2019 07 01 15 368 220 9945 230 36 6 -9999 -9999
# 2019 07 01 18 342 219 9965 0 0 5 -9999 -9999
# 2019 07 01 21 320 220 9968 270 31 4 -9999 -9999
official_dates <- lubridate::force_tz(lubridate::ymd_h(c("2019-06-30 15",
"2019-06-30 18",
"2019-06-30 21",
"2019-07-01 00",
"2019-07-01 03",
"2019-07-01 06",
"2019-07-01 09",
"2019-07-01 12",
"2019-07-01 15",
"2019-07-01 18"
)),tzone="UTC")
official_temp <- c(37.4, 34.6, 33.2, 31.6, 33.8, 38.6, 41, 40.4, 36.8, 34.2)
official_pres <- c(994.5, 996.4, 996.2, 997.3, 998.6, 997.2, 994.8, 992.0, 994.5, 996.5)
result_official <- tibble(date=official_dates, air_temp=official_temp, atmos_pres=official_pres, source='official')
expect_equal(attr(result_worldmet$date[[1]],'tzone'),'Asia/Kolkata')
expect_equal(attr(result_db$date[[1]],'tzone'),'Asia/Kolkata')
date_max_temp_worldmet <- (result_worldmet %>% arrange(desc(air_temp)) %>% slice(1))$date
date_max_temp_db <- (result_db %>% arrange(desc(temp_c)) %>% slice(1))$date
expect_equal(date_max_temp_worldmet, date_max_temp_db)
expect_equal(date_max_temp_worldmet, as.POSIXct("2019-07-01 14:00:00",tz='Asia/Kolkata'))
# Compare two sources
plot_data <- bind_rows(
result_official,
result_db %>% select(date, precip=prec_6h_mm, air_temp=temp_c, atmos_pres=slp_hp) %>% mutate(source='db'),
result_worldmet %>% select(date, precip, air_temp, atmos_pres) %>% mutate(source='worldmet')
) %>% tidyr::gather("variable", "value", -c(date, source))
ggplot(plot_data %>% filter(date>='2019-07-01',date<='2019-07-02'), aes(x=date, y=value, colour=source)) + facet_wrap(~variable, scales="free_y") + geom_point(data=plot_data %>% filter(source!='official')) + geom_line(data=plot_data %>% filter(source=='official'))
})
# Querying weather
test_that("Querying GHCND precipitation works", {
city <- c('Delhi','Kolkata')
meas<- measurements(city=city,
poll=PM25,
date_from='2019-01-01',
average_by = 'hour',
aggregate_at_city_level = T,
collect = F)
t <- system.time({
result <- weather.ghcnd.join(meas)
})
expect_lt(t['elapsed'],300)
expect_gt(length(unique(result$prcp)), 1)
expect_equal(length(unique(result$city)), length(city))
# Test values are different across cities
mean <- unique((result %>% group_by(city) %>% summarize(mean=mean(prcp, na.rm=T)))$mean)
expect_equal(length(mean),length(city))
expect_false(anyNA(mean))
})
# Precipitation queries
test_that("Various precipitation data sources", {
require(worldmet)
delhi_ids <-worldmet::importNOAA(
code = "421820-99999",
year = 2017:2019,
hourly = TRUE,
precip = TRUE,
PWC = FALSE,
parallel = TRUE,
quiet = FALSE,
path = NA
)
# https://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:IN022023000/detail
delhi_ghcnd <- meteo_tidy_ghcnd(stationid='IN022023000',
keep_flags = FALSE,
var = "all",
date_min = '2017-01-01',
date_max = '2019-12-31'
)
delhi_gfs <- meteoForecast::getPointDays(c(28.7041, 77.1025), vars = "swflx", start = '2017-01-01', end='2019-12-31', service = 'gfs')
plot_data <- bind_rows(delhi_ids %>% select(date, prcp=precip_12) %>% mutate(source='ids'),
delhi_ghcnd %>% select(date, prcp) %>% mutate(source='ghcnd') %>% mutate(date=lubridate::as_datetime(date))
)
ggplot(plot_data,aes(x=date,y=prcp,colour=source)) + geom_point()
})
# AQ Weather
test_that("AQ Weather collecting measurements with weather works", {
t <- system.time({result <- aq_weather.collect(city=c('Delhi','Kolkata'),
poll=PM25,
date_from='2019-01-01',
average_by = 'hour',
aggregate_at_city_level = T,
collect=T)})
expect_lt(t['elapsed'],30)
expect_gt(length(unique(result$wind_deg)), 1)
expect_gt(length(unique(result$rh_percent)), 1)
expect_gt(length(unique(result$prec_6h_mm)), 1)
expect_gt(length(unique(result$slp_hp)), 1)
expect_gt(length(unique(result$temp_c)), 1)
expect_gt(length(unique(result$sky_code)), 1)
# Add precipitation data from
})
hubert-thieriot/crea_r_package documentation built on June 11, 2025, 11:31 p.m.
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library(testthat)
library(lubridate)
library(DBI)
# Querying weather
test_that("Querying ISD weather works", {
con = connection()
meas_test <- measurements(city='Delhi',
poll=PM25,
date_from='2019-07-01',
date_to='2019-07-02',
average_by = 'hour',
aggregate_at_city_level = T,
add_noaa_station_ids = T,
noaa_station_radius_km = 20,
collect = F,
con = con)
# Version using NOAA data stored in CREA DB
t <- system.time({
result_db <- weather.isd.join(meas_test,
measurements_averaged_by = 'hour',
collect = T,
con = con)
})
expect_lt(t['elapsed'],30)
expect_gt(length(unique(result_db$wind_deg)), 1)
expect_gt(length(unique(result_db$rh_percent)), 1)
expect_gt(length(unique(result_db$prec_6h_mm)), 1)
expect_gt(length(unique(result_db$slp_hp)), 1)
expect_gt(length(unique(result_db$temp_c)), 1)
expect_gt(length(unique(result_db$sky_code)), 1)
# Version using NOAA data got from Worldmet package
t <- system.time({
result_worldmet <- weather.isd.join.using_worldmet(meas_test,
measurements_averaged_by = 'hour',
radius_km = 20)
})
expect_lt(t['elapsed'],300)
expect_gt(length(unique(result_db$atmos_pres)), 1)
expect_gt(length(unique(result_db$RH)), 1)
expect_gt(length(unique(result_db$air_temp)), 1)
# expect_gt(length(unique(result_db$precip)), 1)
expect_gt(length(unique(result_db$ceil_ght)), 1)
# For timezone confirmation: we check on a single day with manually fetched data
# Station 421820
# Time is UTC in this data
# 2019 06 30 13 390 230 -9999 350 21 -9999 -9999 -9999
# 2019 06 30 15 374 220 9945 320 10 5 -9999 -9999
# 2019 06 30 18 346 210 9964 20 10 5 -9999 -9999
# 2019 06 30 21 332 203 9962 250 31 4 -9999 -9999
# 2019 07 01 00 316 226 9973 270 10 2 -9999 -9999
# 2019 07 01 02 330 220 -9999 220 21 0 -9999 -9999
# 2019 07 01 03 338 228 9986 200 10 0 -9999 -9999 <-- PEAK PRESSURE AT 3AM UTC -> 8.30AM IST
# 2019 07 01 04 350 250 -9999 230 26 0 -9999 -9999
# 2019 07 01 05 370 240 -9999 50 10 0 -9999 -9999
# 2019 07 01 06 386 229 9972 270 21 0 -9999 -9999
# 2019 07 01 07 390 230 -9999 50 31 0 -9999 -9999
# 2019 07 01 08 390 220 -9999 60 15 0 -9999 -9999
# 2019 07 01 09 410 235 9948 70 21 2 -9999 -9999 <-- PEAK TEMP AT 9AM UTC -> 2.30PM IST
# 2019 07 01 10 400 220 -9999 50 31 -9999 -9999 -9999
# 2019 07 01 11 410 240 -9999 40 36 0 -9999 -9999 <-- PEAK TEMP AT 11AM UTC -> 4.30PM IST
# 2019 07 01 12 404 238 9920 50 31 0 -9999 -9999
# 2019 07 01 13 390 260 -9999 40 10 -9999 -9999 -9999
# 2019 07 01 15 368 220 9945 230 36 6 -9999 -9999
# 2019 07 01 18 342 219 9965 0 0 5 -9999 -9999
# 2019 07 01 21 320 220 9968 270 31 4 -9999 -9999
official_dates <- lubridate::force_tz(lubridate::ymd_h(c("2019-06-30 15",
"2019-06-30 18",
"2019-06-30 21",
"2019-07-01 00",
"2019-07-01 03",
"2019-07-01 06",
"2019-07-01 09",
"2019-07-01 12",
"2019-07-01 15",
"2019-07-01 18"
)),tzone="UTC")
official_temp <- c(37.4, 34.6, 33.2, 31.6, 33.8, 38.6, 41, 40.4, 36.8, 34.2)
official_pres <- c(994.5, 996.4, 996.2, 997.3, 998.6, 997.2, 994.8, 992.0, 994.5, 996.5)
result_official <- tibble(date=official_dates, air_temp=official_temp, atmos_pres=official_pres, source='official')
expect_equal(attr(result_worldmet$date[[1]],'tzone'),'Asia/Kolkata')
expect_equal(attr(result_db$date[[1]],'tzone'),'Asia/Kolkata')
date_max_temp_worldmet <- (result_worldmet %>% arrange(desc(air_temp)) %>% slice(1))$date
date_max_temp_db <- (result_db %>% arrange(desc(temp_c)) %>% slice(1))$date
expect_equal(date_max_temp_worldmet, date_max_temp_db)
expect_equal(date_max_temp_worldmet, as.POSIXct("2019-07-01 14:00:00",tz='Asia/Kolkata'))
# Compare two sources
plot_data <- bind_rows(
result_official,
result_db %>% select(date, precip=prec_6h_mm, air_temp=temp_c, atmos_pres=slp_hp) %>% mutate(source='db'),
result_worldmet %>% select(date, precip, air_temp, atmos_pres) %>% mutate(source='worldmet')
) %>% tidyr::gather("variable", "value", -c(date, source))
ggplot(plot_data %>% filter(date>='2019-07-01',date<='2019-07-02'), aes(x=date, y=value, colour=source)) + facet_wrap(~variable, scales="free_y") + geom_point(data=plot_data %>% filter(source!='official')) + geom_line(data=plot_data %>% filter(source=='official'))
})
# Querying weather
test_that("Querying GHCND precipitation works", {
city <- c('Delhi','Kolkata')
meas<- measurements(city=city,
poll=PM25,
date_from='2019-01-01',
average_by = 'hour',
aggregate_at_city_level = T,
collect = F)
t <- system.time({
result <- weather.ghcnd.join(meas)
})
expect_lt(t['elapsed'],300)
expect_gt(length(unique(result$prcp)), 1)
expect_equal(length(unique(result$city)), length(city))
# Test values are different across cities
mean <- unique((result %>% group_by(city) %>% summarize(mean=mean(prcp, na.rm=T)))$mean)
expect_equal(length(mean),length(city))
expect_false(anyNA(mean))
})
# Precipitation queries
test_that("Various precipitation data sources", {
require(worldmet)
delhi_ids <-worldmet::importNOAA(
code = "421820-99999",
year = 2017:2019,
hourly = TRUE,
precip = TRUE,
PWC = FALSE,
parallel = TRUE,
quiet = FALSE,
path = NA
)
# https://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:IN022023000/detail
delhi_ghcnd <- meteo_tidy_ghcnd(stationid='IN022023000',
keep_flags = FALSE,
var = "all",
date_min = '2017-01-01',
date_max = '2019-12-31'
)
delhi_gfs <- meteoForecast::getPointDays(c(28.7041, 77.1025), vars = "swflx", start = '2017-01-01', end='2019-12-31', service = 'gfs')
plot_data <- bind_rows(delhi_ids %>% select(date, prcp=precip_12) %>% mutate(source='ids'),
delhi_ghcnd %>% select(date, prcp) %>% mutate(source='ghcnd') %>% mutate(date=lubridate::as_datetime(date))
)
ggplot(plot_data,aes(x=date,y=prcp,colour=source)) + geom_point()
})
# AQ Weather
test_that("AQ Weather collecting measurements with weather works", {
t <- system.time({result <- aq_weather.collect(city=c('Delhi','Kolkata'),
poll=PM25,
date_from='2019-01-01',
average_by = 'hour',
aggregate_at_city_level = T,
collect=T)})
expect_lt(t['elapsed'],30)
expect_gt(length(unique(result$wind_deg)), 1)
expect_gt(length(unique(result$rh_percent)), 1)
expect_gt(length(unique(result$prec_6h_mm)), 1)
expect_gt(length(unique(result$slp_hp)), 1)
expect_gt(length(unique(result$temp_c)), 1)
expect_gt(length(unique(result$sky_code)), 1)
# Add precipitation data from
})
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