From 6d261c5fce8f452f49cc1c955fcd825b3718b308 Mon Sep 17 00:00:00 2001 From: noerw Date: Wed, 23 Aug 2017 02:07:56 +0200 Subject: [PATCH] add vignette builds to version control --- .gitignore | 1 - inst/doc/osem-intro.R | 60 +++ inst/doc/osem-intro.Rmd | 139 +++++++ inst/doc/osem-intro.html | 822 +++++++++++++++++++++++++++++++++++++++ 4 files changed, 1021 insertions(+), 1 deletion(-) create mode 100644 inst/doc/osem-intro.R create mode 100644 inst/doc/osem-intro.Rmd create mode 100644 inst/doc/osem-intro.html diff --git a/.gitignore b/.gitignore index 1564020..c0b492c 100644 --- a/.gitignore +++ b/.gitignore @@ -4,5 +4,4 @@ .RData .Ruserdata *.log -inst/doc diff --git a/inst/doc/osem-intro.R b/inst/doc/osem-intro.R new file mode 100644 index 0000000..f47779a --- /dev/null +++ b/inst/doc/osem-intro.R @@ -0,0 +1,60 @@ +## ----setup, include=FALSE------------------------------------------------ +knitr::opts_chunk$set(echo = TRUE) + +## ------------------------------------------------------------------------ +library(magrittr) +library(opensensmapr) + +all_sensors = osem_boxes() +summary(all_sensors) + +## ----message=F, warning=F------------------------------------------------ +if (!require('maps')) install.packages('maps') +if (!require('maptools')) install.packages('maptools') +if (!require('rgeos')) install.packages('rgeos') + +plot(all_sensors) + +## ------------------------------------------------------------------------ +phenoms = osem_phenomena(all_sensors) +str(phenoms) + +## ------------------------------------------------------------------------ +phenoms[phenoms > 20] + +## ------------------------------------------------------------------------ +pm25_sensors = osem_boxes( + exposure = 'outdoor', + date = Sys.time(), # ±4 hours + phenomenon = 'PM2.5' +) + +summary(pm25_sensors) +plot(pm25_sensors) + +## ------------------------------------------------------------------------ +library(sf) +library(units) +library(lubridate) + +# 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(units::set_units(12, km)) %>% + st_transform(4326) %>% # the opensensemap expects WGS 84 + st_bbox() + +pm25 = osem_measurements( + berlin, + phenomenon = 'PM2.5', + from = now() - days(7), # defaults to 2 days + to = now() +) + +plot(pm25) + +## ------------------------------------------------------------------------ +pm25_sf = osem_as_sf(pm25) +plot(st_geometry(pm25_sf), axes = T) + diff --git a/inst/doc/osem-intro.Rmd b/inst/doc/osem-intro.Rmd new file mode 100644 index 0000000..39590c0 --- /dev/null +++ b/inst/doc/osem-intro.Rmd @@ -0,0 +1,139 @@ +--- +title: "Analyzing environmental sensor data from openSenseMap.org in R" +author: "Norwin Roosen" +date: "`r Sys.Date()`" +output: + rmarkdown::html_vignette: + fig_margin: 0 + fig_width: 6 + fig_height: 4 +vignette: > + %\VignetteIndexEntry{Analyzing environmental sensor data from openSenseMap.org in R} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +## Analyzing environmental sensor data from openSenseMap.org in R + +This package provides data ingestion functions for almost any data stored on the +open data platform for environemental sensordata . +Its main goals are to provide means for: + +- big data analysis of the measurements stored on the platform +- sensor metadata analysis (sensor counts, spatial distribution, temporal trends) + +> *Please note:* The openSenseMap API is sometimes a bit unstable when streaming +long responses, which results in `curl` complaining about `Unexpected EOF`. This +bug is being worked on upstream. Meanwhile you have to retry the request when +this occurs. + +### Exploring the dataset +Before we look at actual observations, lets get a grasp of the openSenseMap +datasets' structure. + +```{r} +library(magrittr) +library(opensensmapr) + +all_sensors = osem_boxes() +summary(all_sensors) +``` + +This gives a good overview already: As of writing this, there are more than 600 +sensor stations, of which ~50% are currently running. Most of them are placed +outdoors and have around 5 sensors each. +The oldest station is from May 2014, while the latest station was registered a +couple of minutes ago. + +Another feature of interest is the spatial distribution of the boxes. `plot()` +can help us out here. This function requires a bunch of optional dependcies though. + +```{r message=F, warning=F} +if (!require('maps')) install.packages('maps') +if (!require('maptools')) install.packages('maptools') +if (!require('rgeos')) install.packages('rgeos') + +plot(all_sensors) +``` + +It seems we have to reduce our area of interest to Germany. + +But what do these sensor stations actually measure? Lets find out. +`osem_phenomena()` gives us a named list of of the counts of each observed +phenomenon for the given set of sensor stations: + +```{r} +phenoms = osem_phenomena(all_sensors) +str(phenoms) +``` + +Thats quite some noise there, with many phenomena being measured by a single +sensor only, or many duplicated phenomena due to slightly different spellings. +We should clean that up, but for now let's just filter out the noise and find +those phenomena with high sensor numbers: + +```{r} +phenoms[phenoms > 20] +``` + +Alright, temperature it is! Fine particulate matter (PM2.5) seems to be more +interesting to analyze though. +We should check how many sensor stations provide useful data: We want only those +boxes with a PM2.5 sensor, that are placed outdoors and are currently submitting +measurements: + +```{r} +pm25_sensors = osem_boxes( + exposure = 'outdoor', + date = Sys.time(), # ±4 hours + phenomenon = 'PM2.5' +) + +summary(pm25_sensors) +plot(pm25_sensors) +``` + +Thats still more than 200 measuring stations, we can work with that. + +### Analyzing sensor data +Having analyzed the available data sources, let's finally get some measurements. +We could call `osem_measurements(pm25_sensors)` now, however we are focussing on +a restricted area of interest, the city of Berlin. +Luckily we can get the measurements filtered by a bounding box: + +```{r} +library(sf) +library(units) +library(lubridate) + +# 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(units::set_units(12, km)) %>% + st_transform(4326) %>% # the opensensemap expects WGS 84 + st_bbox() + +pm25 = osem_measurements( + berlin, + phenomenon = 'PM2.5', + from = now() - days(7), # defaults to 2 days + to = now() +) + +plot(pm25) +``` + +Now we can get started with actual spatiotemporal data analysis. First plot the +measuring locations: + +```{r} +pm25_sf = osem_as_sf(pm25) +plot(st_geometry(pm25_sf), axes = T) +``` + +`TODO` diff --git a/inst/doc/osem-intro.html b/inst/doc/osem-intro.html new file mode 100644 index 0000000..25a50a3 --- /dev/null +++ b/inst/doc/osem-intro.html @@ -0,0 +1,822 @@ + + + + + + + + + + + + + + + + +Analyzing environmental sensor data from openSenseMap.org in R + + + + + + + + + + + + + + + + + +

Analyzing environmental sensor data from openSenseMap.org in R

+

Norwin Roosen

+

2017-08-23

+ + + +
+

Analyzing environmental sensor data from openSenseMap.org in R

+

This package provides data ingestion functions for almost any data stored on the open data platform for environemental sensordata https://opensensemap.org. Its main goals are to provide means for:

+
    +
  • big data analysis of the measurements stored on the platform
    • +
  • sensor metadata analysis (sensor counts, spatial distribution, temporal trends)
    • +
+
+

Please note: The openSenseMap API is sometimes a bit unstable when streaming long responses, which results in curl complaining about Unexpected EOF. This bug is being worked on upstream. Meanwhile you have to retry the request when this occurs.

+
+
+

Exploring the dataset

+

Before we look at actual observations, lets get a grasp of the openSenseMap datasets’ structure.

+
library(magrittr)
+library(opensensmapr)
+
+all_sensors = osem_boxes()
+
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+
summary(all_sensors)
+
## box total: 701
+## 
+## boxes by exposure:
+##  indoor outdoor unknown 
+##     127     553      21 
+## 
+## boxes by model:
+##                  custom            homeEthernet   homeEthernetFeinstaub 
+##                     209                      78                       8 
+##                homeWifi       homeWifiFeinstaub        luftdaten_sds011 
+##                     106                      34                      22 
+## luftdaten_sds011_bme280 luftdaten_sds011_bmp180  luftdaten_sds011_dht11 
+##                      40                       3                      14 
+##  luftdaten_sds011_dht22 
+##                     187 
+## 
+## $last_measurement_within
+##    1h    1d   30d  365d never 
+##   313   327   418   554    63 
+## 
+## oldest box: 2014-05-28 15:36:14 (CALIMERO)
+## newest box: 2017-08-23 08:44:14 (Messstation Steinheim am Albuch)
+## 
+## sensors per box:
+##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+##   1.000   4.000   5.000   4.609   5.000  17.000
+

This gives a good overview already: As of writing this, there are more than 600 sensor stations, of which ~50% are currently running. Most of them are placed outdoors and have around 5 sensors each. The oldest station is from May 2014, while the latest station was registered a couple of minutes ago.

+

Another feature of interest is the spatial distribution of the boxes. plot() can help us out here. This function requires a bunch of optional dependcies though.

+
if (!require('maps'))     install.packages('maps')
+if (!require('maptools')) install.packages('maptools')
+if (!require('rgeos'))    install.packages('rgeos')
+
+plot(all_sensors)
+

+

It seems we have to reduce our area of interest to Germany.

+

But what do these sensor stations actually measure? Lets find out. osem_phenomena() gives us a named list of of the counts of each observed phenomenon for the given set of sensor stations:

+
phenoms = osem_phenomena(all_sensors)
+str(phenoms)
+
## List of 191
+##  $ Temperatur                              : int 644
+##  $ rel. Luftfeuchte                        : int 531
+##  $ Luftdruck                               : int 367
+##  $ PM10                                    : int 344
+##  $ PM2.5                                   : int 344
+##  $ UV-Intensität                           : int 255
+##  $ Beleuchtungsstärke                      : int 251
+##  $ Luftfeuchtigkeit                        : int 83
+##  $ Schall                                  : int 26
+##  $ Helligkeit                              : int 20
+##  $ Licht                                   : int 20
+##  $ UV                                      : int 15
+##  $ Humidity                                : int 12
+##  $ Temperature                             : int 11
+##  $ Anderer                                 : int 10
+##  $ Ilmanpaine                              : int 9
+##  $ Lämpötila                               : int 9
+##  $ Licht (digital)                         : int 9
+##  $ Valonmäärä                              : int 8
+##  $ Windgeschwindigkeit                     : int 8
+##  $ Kosteus                                 : int 7
+##  $ Luftfeuchte                             : int 7
+##  $ Lautstärke                              : int 6
+##  $ Signal                                  : int 6
+##  $ UV-säteily                              : int 6
+##  $ Wind speed                              : int 5
+##  $ Pressure                                : int 4
+##  $ temperature                             : int 4
+##  $ Windrichtung                            : int 4
+##  $ DS18B20_Probe01                         : int 3
+##  $ DS18B20_Probe02                         : int 3
+##  $ DS18B20_Probe03                         : int 3
+##  $ DS18B20_Probe04                         : int 3
+##  $ DS18B20_Probe05                         : int 3
+##  $ Light                                   : int 3
+##  $ Niederschlag                            : int 3
+##  $ UV Index                                : int 3
+##  $ UV-Säteily                              : int 3
+##  $ UV-Strahlung                            : int 3
+##  $ C2H5OH                                  : int 2
+##  $ CO                                      : int 2
+##  $ CPU-Temp                                : int 2
+##  $ Feinstaub                               : int 2
+##  $ Feinstaub PM10                          : int 2
+##  $ Feinstaub PM2,5                         : int 2
+##  $ H2                                      : int 2
+##  $ humidity                                : int 2
+##  $ Ilmankosteus                            : int 2
+##  $ NH3                                     : int 2
+##  $ NO2                                     : int 2
+##  $ Regen                                   : int 2
+##  $ rel. Luftfeuchtigkeit                   : int 2
+##  $ Relative Humidity                       : int 2
+##  $ Temperatur BMP280                       : int 2
+##  $ Temperatur DHT22                        : int 2
+##  $ Temperatur HDC1008                      : int 2
+##  $ TemperaturBME                           : int 2
+##  $ test                                    : int 2
+##  $ UV-Index                                : int 2
+##  $ Wassertemperatur                        : int 2
+##  $ Wifi-Stärke                             : int 2
+##  $ Windböen                                : int 2
+##  $ Wolkenbedeckung                         : int 2
+##  $ Air Preassure                           : int 1
+##  $ Air pressure                            : int 1
+##  $ Air Temperature                         : int 1
+##  $ Akkuspannung Terrasse                   : int 1
+##  $ Akkuspannung Unten Eingang              : int 1
+##  $ Attendance                              : int 1
+##  $ Batterie                                : int 1
+##  $ Batterieladung                          : int 1
+##  $ Battery                                 : int 1
+##  $ Beleuchtungsstaerke                     : int 1
+##  $ Beleuchtungsstärke des sichtbaren Lichts: int 1
+##  $ Bodenfeuchte                            : int 1
+##  $ Bodentemperatur                         : int 1
+##  $ C3H8                                    : int 1
+##  $ C4H10                                   : int 1
+##  $ CH4                                     : int 1
+##  $ CO2                                     : int 1
+##  $ CO2-Konzentration                       : int 1
+##  $ Dämmerung                               : int 1
+##  $ dT                                      : int 1
+##  $ Dust Sensor                             : int 1
+##  $ Dust_Concentration                      : int 1
+##  $ Eingangsspannung                        : int 1
+##  $ Feinstaub P10                           : int 1
+##  $ Feinstaub P2.5                          : int 1
+##  $ Feinstaubgehalt PM10                    : int 1
+##  $ Feinstaubgehalt PM2.5                   : int 1
+##  $ Feinstaubkonzentration                  : int 1
+##  $ Feuchte                                 : int 1
+##  $ Feuchtigkeit                            : int 1
+##  $ filedData                               : int 1
+##  $ H2, LPG, CH4, CO, Alcohol               : int 1
+##  $ Höhe                                    : int 1
+##  $ Illuminance                             : int 1
+##  $ Infrared light                          : int 1
+##  $ Intensität der ultravioletten Strahlung : int 1
+##   [list output truncated]
+

Thats quite some noise there, with many phenomena being measured by a single sensor only, or many duplicated phenomena due to slightly different spellings. We should clean that up, but for now let’s just filter out the noise and find those phenomena with high sensor numbers:

+
phenoms[phenoms > 20]
+
## $Temperatur
+## [1] 644
+## 
+## $`rel. Luftfeuchte`
+## [1] 531
+## 
+## $Luftdruck
+## [1] 367
+## 
+## $PM10
+## [1] 344
+## 
+## $PM2.5
+## [1] 344
+## 
+## $`UV-Intensität`
+## [1] 255
+## 
+## $Beleuchtungsstärke
+## [1] 251
+## 
+## $Luftfeuchtigkeit
+## [1] 83
+## 
+## $Schall
+## [1] 26
+

Alright, temperature it is! Fine particulate matter (PM2.5) seems to be more interesting to analyze though. We should check how many sensor stations provide useful data: We want only those boxes with a PM2.5 sensor, that are placed outdoors and are currently submitting measurements:

+
pm25_sensors = osem_boxes(
+  exposure = 'outdoor',
+  date = Sys.time(), # ±4 hours
+  phenomenon = 'PM2.5'
+)
+
## 
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summary(pm25_sensors)
+
## box total: 236
+## 
+## boxes by exposure:
+## outdoor 
+##     236 
+## 
+## boxes by model:
+##                  custom   homeEthernetFeinstaub                homeWifi 
+##                      18                       4                       5 
+##       homeWifiFeinstaub        luftdaten_sds011 luftdaten_sds011_bme280 
+##                      12                      15                      29 
+## luftdaten_sds011_bmp180  luftdaten_sds011_dht11  luftdaten_sds011_dht22 
+##                       1                      11                     141 
+## 
+## $last_measurement_within
+##    1h    1d   30d  365d never 
+##   230   233   234   234     2 
+## 
+## oldest box: 2016-09-11 08:17:17 (Balkon Gasselstiege)
+## newest box: 2017-08-23 08:44:14 (Messstation Steinheim am Albuch)
+## 
+## sensors per box:
+##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+##   2.000   4.000   4.000   4.271   4.000  10.000
+
plot(pm25_sensors)
+

+

Thats still more than 200 measuring stations, we can work with that.

+
+
+

Analyzing sensor data

+

Having analyzed the available data sources, let’s finally get some measurements. We could call osem_measurements(pm25_sensors) now, however we are focussing on a restricted area of interest, the city of Berlin. Luckily we can get the measurements filtered by a bounding box:

+
library(sf)
+library(units)
+library(lubridate)
+
+# 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(units::set_units(12, km)) %>%
+  st_transform(4326) %>% # the opensensemap expects WGS 84
+  st_bbox()
+
+pm25 = osem_measurements(
+  berlin,
+  phenomenon = 'PM2.5',
+  from = now() - days(7), # defaults to 2 days
+  to = now()
+)
+
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+Downloading: 300 kB     2017-08-16T13:39:35Z - 2017-08-23T13:39:35Z
+
plot(pm25)
+

+

Now we can get started with actual spatiotemporal data analysis. First plot the measuring locations:

+
pm25_sf = osem_as_sf(pm25)
+plot(st_geometry(pm25_sf), axes = T)
+

+

TODO

+
+
+ + + + + + + +