## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)

## ----results = FALSE----------------------------------------------------------
library(magrittr)
library(opensensmapr)

# all_sensors = osem_boxes(cache = '.')
all_sensors = readRDS('boxes_precomputed.rds')  # read precomputed file to save resources 

## -----------------------------------------------------------------------------
summary(all_sensors)

## ---- message=FALSE, warning=FALSE--------------------------------------------
plot(all_sensors)

## -----------------------------------------------------------------------------
phenoms = osem_phenomena(all_sensors)
str(phenoms)

## -----------------------------------------------------------------------------
phenoms[phenoms > 20]

## ----results = FALSE, eval=FALSE----------------------------------------------
#  pm25_sensors = osem_boxes(
#    exposure = 'outdoor',
#    date = Sys.time(), # ±4 hours
#    phenomenon = 'PM2.5'
#  )

## -----------------------------------------------------------------------------
pm25_sensors = readRDS('pm25_sensors.rds') # read precomputed file to save resources 

summary(pm25_sensors)
plot(pm25_sensors)

## ---- results=FALSE, message=FALSE--------------------------------------------
library(sf)
library(units)
library(lubridate)
library(dplyr)


## ----bbox, results = FALSE, eval=FALSE----------------------------------------
#  # construct a bounding box: 12 kilometers around Berlin
#  berlin = st_point(c(13.4034, 52.5120)) %>%
#    st_sfc(crs = 4326) %>%
#    st_transform(3857) %>% # allow setting a buffer in meters
#    st_buffer(set_units(12, km)) %>%
#    st_transform(4326) %>% # the opensensemap expects WGS 84
#    st_bbox()
#  pm25 = osem_measurements(
#    berlin,
#    phenomenon = 'PM2.5',
#    from = now() - days(3), # defaults to 2 days
#    to = now()
#  )
#  

## -----------------------------------------------------------------------------
pm25 = readRDS('pm25_berlin.rds') # read precomputed file to save resources 
plot(pm25)

## ---- warning=FALSE-----------------------------------------------------------
outliers = filter(pm25, value > 100)$sensorId
bad_sensors = outliers[, drop = TRUE] %>% levels()

pm25 = mutate(pm25, invalid = sensorId %in% bad_sensors)

## -----------------------------------------------------------------------------
st_as_sf(pm25) %>% st_geometry() %>% plot(col = factor(pm25$invalid), axes = TRUE)

## -----------------------------------------------------------------------------
pm25 %>% filter(invalid == FALSE) %>% plot()