Getting started with the R-package senseBox

Johannes Friedrich

2021-11-02

Source: vignettes/Getting_started_with_the_R_package_senseBox.Rmd

Motivation

The senseBox is a do-it-yourself kit for stationary and mobile sensor stations. With senseBox you can make measurements, helping to answer a variety of scientific questions and to support Citizen Science Projects from the local to global scale. Data collected by senseBox can increase the measured density of various enviromental factors and can contribute to more precise statements regarding climate, pollution or traffic.

openSenseMap is the platform for the publishing of senseBox and other open sensor data. Each senseBox transmits its measurements directly to the map, where anyone can observe, analyze and download the data.

This is the point where this R-pacakge comes into play: The R-package senseBox provides access to the senseBox API and enables the user to download, analyse and visualise the data provided by registered senseBoxes.

With pacakges like ggplot2 or plotly R is the perfect tool to visualise data. In the next chapter you find a short introduction to the basic usage of this R-package and some examples how to visualise the data.

Usage

Get some information about the senseBox project and list all senseBoxIds

## Loading required package: httr
variable value
Number of senseBoxes 8831
Number of Measurements 10059504770
Number of measurements in last minute 6333

With the function get_senseBox_Ids() you obtain all available senseBox Ids and the name of the staion.

Id_list <- get_senseBox_Ids()
senseBoxId name
5386026e5f08822009b8b60d CALIMERO
538ee6a4a83415541576b777 SenGIS Arbeitsgruppe - Uni Hohenheim
5391be52a8341554157792e6 LeKa Berlin
539c00c2a83415541578eaf5 IV Gummersbach
539fec94a8341554157931d7 The PaderWarrior Reloded
53a0017aa834155415793281 Alt-Lietzow

You are just interested in senseBoxes, which collect data from “indoor”? So use the function search_senseBox().

indoor <- search_senseBox(exposure =  "indoor")
senseBoxId exposure name createdAt model phenomena unit sensorIds sensorType long lat grouptag updatedAt weblink lastMeasurementAt description height
54d79ea81b93e970075153f4 indoor Aasee-Station 2015-02-08 17:36:40 custom UV , Beleuchtungsstärke, Luftdruck , rel. Luftfeuchte , Temperatur UV-Index, lx , Pa , % , °C 54d79ea81b93e970075153f6, 54d79ea81b93e970075153f7, 54d79ea81b93e970075153f8, 54d79ea81b93e970075153f9, 54d79ea81b93e970075153fa GUVA-S12D, TSL2561 , BMP085 , DHT11 , DHT11 7.604985 51.94647 NA NA NA NA NA NA
54e73244a807ade00f85f5bd indoor PhotonicNet 2015-02-20 13:10:28 custom UV , Beleuchtungsstärke, Luftdruck , rel. Luftfeuchte , Temperatur UV-Index, lx , Pa , % , °C 54e73244a807ade00f85f5bf, 54e73244a807ade00f85f5c0, 54e73244a807ade00f85f5c1, 54e73244a807ade00f85f5c2, 54e73244a807ade00f85f5c3 GUVA-S12D, TSL2561 , BMP085 , DHT11 , DHT11 9.635454 52.41255 NA NA NA NA NA NA
54e7a5faa807ade00f868aab indoor fablab potsdam 2016-08-28 16:54:36 custom UV , Beleuchtungsstärke, Luftdruck , rel. Luftfeuchte , Temperatur UV-Index, lx , Pa , % , °C 54e7a5faa807ade00f868aad, 54e7a5faa807ade00f868aae, 54e7a5faa807ade00f868aaf, 54e7a5faa807ade00f868ab0, 54e7a5faa807ade00f868ab1 GUVA-S12D, TSL2561 , BMP085 , DHT11 , DHT11 13.080662 52.39649 MakeLight 2016-12-17 14:55:05 NA NA NA NA

We chose one ID for the following examples

senseBoxId <- "592ca4b851d3460011ea2635"

Show location of senseBox

location <- get_senseBox_location(senseBoxId)

library(leaflet)
library(htmltools)

leaflet(location) %>%
  addProviderTiles(providers$OpenStreetMap) %>% 
  addTiles() %>%  
  addMarkers(~long, ~lat, popup = ~htmltools::htmlEscape(name))

Sensor Id information

Get some information about the senseBox sensors

sensor_info <- get_senseBox_sensor_info(senseBoxId)
name phenomena unit sensorIds sensorType
Leipziger65-Süd PM10 , PM2.5 , Temperatur , rel. Luftfeuchte µg/m³, µg/m³, °C , % 592ca4b851d3460011ea2636, 592ca4b851d3460011ea2637, 592ca4b851d3460011ea2638, 592ca4b851d3460011ea2639 SDS 011, SDS 011, DHT22 , DHT22

With the argument tidy = TRUE a tidy data.frame will be produced:

sensor_info <- get_senseBox_sensor_info(senseBoxId, tidy = TRUE)
name phenomena unit sensorIds sensorType
Leipziger65-Süd PM10 µg/m³ 592ca4b851d3460011ea2636 SDS 011
Leipziger65-Süd PM2.5 µg/m³ 592ca4b851d3460011ea2637 SDS 011
Leipziger65-Süd Temperatur °C 592ca4b851d3460011ea2638 DHT22
Leipziger65-Süd rel. Luftfeuchte % 592ca4b851d3460011ea2639 DHT22

Download senseBox data

We can now download data from the senseBox, either from a specific sensorId or from all sensors within the sensBox. In the following we are downloading all available sensors.

data_all <- get_senseBox_data(senseBoxId)

When you are interested in just a selection of sensors, just submit the sensorIds to the function get_senseBox_data().

sensor_ids <- get_senseBox_sensor_Ids(senseBoxId)

data_sel <- get_senseBox_data(senseBoxId, 
                              sensorId = sensor_ids$sensorIds[1:2])

When using the above code, by default, the data from the last 48 h will be downloaded. You can donwload up to 10,000 records and sepcify the date of the record. The maximum time frame for downloading data is back to one month from now. Use the argument fromDate and toDate to specify the desired time frame. Note that in contrast to the code above, all sensors from the senseBox are requested (by default) because argument sensorId is not set.

data_timeframe <- get_senseBox_data(senseBoxId, 
                                    fromDate = "2017-11-11 11:11:11", 
                                    toDate = "2017-11-12 11:11:11")

Visualising the results from all sensors is one of the main aims and we recommend using the R-package ggplot2. We provide a sample code next and you just have to change the data executed in the function melt().

library(ggplot2)
library(reshape2)
library(scales)


data_melt <- melt(data_timeframe, id.vars = c("createdAt", "value"))

ggplot(data_melt, aes(x = createdAt, y = value, colour = L2)) +
  geom_line() +
  scale_x_datetime(labels = date_format("%H:%M", tz = Sys.timezone())) +
  facet_wrap(~L2, scales = "free") +
  theme(legend.position = "bottom",
        legend.title = element_blank())

Download archived data

Receiving data with get_senseBox_data() is limited to 10.000 data points. But every day all measured sensor data are stored in the senseBox archive. With the function get_senseBox_archive() the zip file from a specific senseBox for a date can be downloaded. You have to unzip the folder by yourself and every sensor data is stored in a csv file.

get_senseBox_archive(senseBoxId,
                    date = "2018-07-31")

Some senseBox statistics

infos <- get_senseBox_info(Id_list$senseBoxId, parallel = TRUE)
infos %>% 
  select(name, phenomena) %>%  
  unnest() %>% 
  count(phenomena) %>% 
  arrange(desc(n)) %>% 
  top_n(10, n) %>% 
  ggplot() +
  geom_histogram(aes(x = phenomena, y = n), stat = "identity") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
## Warning: `cols` is now required when using unnest().
## Please use `cols = c(phenomena)`
## Warning: Ignoring unknown parameters: binwidth, bins, pad

infos %>% 
  group_by(exposure, grouptag) %>% 
  summarise(n = n()) %>% 
  arrange(desc(n)) %>% 
  na.omit() %>% 
  top_n(5, n) %>% 
  
  ggplot() +
  geom_bar(aes(x = grouptag, y = n, fill = exposure), stat = "identity") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
## `summarise()` has grouped output by 'exposure'. You can override using the `.groups` argument.

infos %>% 
 mutate(year = year(createdAt),
        month = month(createdAt),
        day = day(createdAt)) %>% 
  group_by(year, exposure) %>% 
  summarise(n = n()) %>% 
  arrange(desc(year)) %>% 
  
  ggplot() +
  geom_bar(aes(x = year, y = n, fill = exposure), stat = "identity")
## `summarise()` has grouped output by 'year'. You can override using the `.groups` argument.

Available senseBoxes

This site offers a list of all available senseBoxes (27-01-2020). You can search for any keyword you are interested in.