@ -115,7 +115,9 @@ berlin = st_point(c(13.4034, 52.5120)) %>%
st_transform(4326) %>% # the opensensemap expects WGS 84
st_transform(4326) %>% # the opensensemap expects WGS 84
st_bbox()
st_bbox()
```
```
```{r results = F}
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 results = F, eval=FALSE}
pm25 = osem_measurements(
pm25 = osem_measurements(
berlin,
berlin,
phenomenon = 'PM2.5',
phenomenon = 'PM2.5',
@ -129,7 +131,7 @@ plot(pm25)
Now we can get started with actual spatiotemporal data analysis.
Now we can get started with actual spatiotemporal data analysis.
First, lets mask the seemingly uncalibrated sensors:
First, lets mask the seemingly uncalibrated sensors: