build performance improvements

R/phenomena.R

@@ -18,13 +18,13 @@ osem_phenomena = function (boxes) UseMethod('osem_phenomena')
 #' # get the phenomena for a single senseBox
 #' osem_phenomena(osem_box('593bcd656ccf3b0011791f5a'))
 #'
-#' # get the phenomena for a group of senseBoxes
-#' osem_phenomena(
-#'   osem_boxes(grouptag = 'ifgi', exposure = 'outdoor', date = Sys.time())
-#' )
-#'
-#' # get phenomena with at least 30 sensors on opensensemap
 #' \donttest{
+#'   # get the phenomena for a group of senseBoxes
+#'   osem_phenomena(
+#'     osem_boxes(grouptag = 'ifgi', exposure = 'outdoor', date = Sys.time())
+#'   )
+#'
+#'   # get phenomena with at least 30 sensors on opensensemap
 #'   phenoms = osem_phenomena(osem_boxes())
 #'   names(phenoms[phenoms > 29])
 #' }

vignettes/osem-intro.Rmd

@@ -97,7 +97,7 @@ 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
+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:
 
@@ -119,7 +119,7 @@ berlin = st_point(c(13.4034, 52.5120)) %>%
 pm25 = osem_measurements(
   berlin,
   phenomenon = 'PM2.5',
-  from = now() - days(20), # defaults to 2 days
+  from = now() - days(3), # defaults to 2 days
   to = now()
 )