@ -28,11 +28,12 @@ Its main goals are to provide means for:
Before we look at actual observations, lets get a grasp of the openSenseMap
datasets' structure.
```{r results = F}
```{r results = FALSE}
library(magrittr)
library(opensensmapr)
all_sensors = osem_boxes()
# all_sensors = osem_boxes(cache = '.')
all_sensors = readRDS('boxes_precomputed.rds') # read precomputed file to save resources
```
```{r}
summary(all_sensors)
@ -47,11 +48,7 @@ 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 dependencies though.
```{r message=F, warning=F}
if (!require('maps')) install.packages('maps')
if (!require('maptools')) install.packages('maptools')
if (!require('rgeos')) install.packages('rgeos')
```{r, message=FALSE, warning=FALSE}
plot(all_sensors)
```
@ -81,7 +78,7 @@ 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 results = F}
```{r results = FALSE, eval=FALSE}
pm25_sensors = osem_boxes(
exposure = 'outdoor',
date = Sys.time(), # ±4 hours
@ -89,6 +86,8 @@ pm25_sensors = osem_boxes(
)
```
```{r}
pm25_sensors = readRDS('pm25_sensors.rds') # read precomputed file to save resources
summary(pm25_sensors)
plot(pm25_sensors)
```
@ -101,12 +100,16 @@ We could call `osem_measurements(pm25_sensors)` now, however we are focusing on
a restricted area of interest, the city of Berlin.
Luckily we can get the measurements filtered by a bounding box:
```{r}
```{r, results=FALSE, message=FALSE}
library(sf)
library(units)
library(lubridate)
library(dplyr)
```
Since the API takes quite long to response measurements, especially filtered on space and time, we do not run the following chunks for publication of the package on CRAN.
```{r 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) %>%
@ -114,8 +117,6 @@ berlin = st_point(c(13.4034, 52.5120)) %>%
st_buffer(set_units(12, km)) %>%
st_transform(4326) %>% # the opensensemap expects WGS 84
st_bbox()
```
```{r results = F}
pm25 = osem_measurements(
berlin,
phenomenon = 'PM2.5',
@ -123,15 +124,19 @@ pm25 = osem_measurements(
to = now()
)
```
```{r}
pm25 = readRDS('pm25_berlin.rds') # read precomputed file to save resources
plot(pm25)
```
Now we can get started with actual spatiotemporal data analysis.
First, lets mask the seemingly uncalibrated sensors:
```{r}
```{r, warning=FALSE}
outliers = filter(pm25, value > 100)$sensorId
bad_sensors = outliers[, drop = T] %>% levels()
bad_sensors = outliers[, drop = TRUE] %>% levels()
<p>This method also persists the R object metadata (classes,
attributes). If you were to use a serialization method that can’t
persist object metadata, you could re-apply it with the following
functions:</p>
<divclass="sourceCode"id="cb10"><preclass="sourceCode r"><codeclass="sourceCode r"><spanid="cb10-1"><ahref="#cb10-1"aria-hidden="true"tabindex="-1"></a><spanclass="co"># note the toJSON call instead of serializeJSON</span></span>
<divclass="sourceCode"id="cb9"><preclass="sourceCode r"><codeclass="sourceCode r"><spanid="cb9-1"><ahref="#cb9-1"aria-hidden="true"tabindex="-1"></a><spanclass="co"># note the toJSON call instead of serializeJSON</span></span>
jan
2 years ago