improved runtime to be affordable on CRAN submission

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: opensensmapr
 Type: Package
-Title: Client for the Data API of openSenseMap.org
+Title: Client for the Data API of 'openSenseMap.org'
 Version: 0.6.0
 URL: https://github.com/sensebox/opensensmapR
 BugReports: https://github.com/sensebox/opensensmapR/issues

tests/testthat/test_box.R

@@ -2,21 +2,9 @@ source('testhelpers.R')
 context('box')
 
 try({
-  boxes = osem_boxes()
   box = osem_box('57000b8745fd40c8196ad04c')
 })
 
-test_that('a single box can be retrieved by ID', {
-  check_api()
-
-  box = osem_box(boxes$X_id[[1]])
-
-  expect_true('sensebox' %in% class(box))
-  expect_true('data.frame' %in% class(box))
-  expect_true(nrow(box) == 1)
-  expect_true(box$X_id == boxes$X_id[[1]])
-  expect_silent(osem_box(boxes$X_id[[1]]))
-})
 
 test_that('required box attributes are correctly parsed', {
   check_api()
@@ -61,11 +49,6 @@ test_that('unknown box throws', {
   expect_error(osem_box('57000b8745fd40c800000000'), 'not found')
 })
 
-test_that('[.sensebox maintains attributes', {
-  check_api()
-
-  expect_true(all(attributes(boxes[1:nrow(boxes), ]) %in% attributes(boxes)))
-})
 
 test_that("print.sensebox filters important attributes for a single box", {
   check_api()

tests/testthat/test_boxes.R

@@ -172,3 +172,44 @@ test_that('requests can be cached', {
   osem_clear_cache()
   expect_length(list.files(tempdir(), pattern = 'osemcache\\..*\\.rds'), 0)
 })
+
+context('single box from boxes')
+test_that('a single box can be retrieved by ID', {
+  check_api()
+  
+  box = osem_box(boxes$X_id[[1]])
+  
+  expect_true('sensebox' %in% class(box))
+  expect_true('data.frame' %in% class(box))
+  expect_true(nrow(box) == 1)
+  expect_true(box$X_id == boxes$X_id[[1]])
+  expect_silent(osem_box(boxes$X_id[[1]]))
+})
+
+test_that('[.sensebox maintains attributes', {
+  check_api()
+  
+  expect_true(all(attributes(boxes[1:nrow(boxes), ]) %in% attributes(boxes)))
+})
+
+context('measurements boxes')
+test_that('measurements of specific boxes can be retrieved for one phenomenon and returns a measurements data.frame', {
+  check_api()
+  
+  # fix for subsetting
+  class(boxes) = c('data.frame')
+  three_boxes = boxes[1:3, ]
+  class(boxes) = c('sensebox', 'data.frame')
+  three_boxes = osem_as_sensebox(three_boxes)
+  phens = names(osem_phenomena(three_boxes))
+  
+  measurements = osem_measurements(x = three_boxes, phenomenon = phens[[1]])
+  expect_true(is.data.frame(measurements))
+  expect_true('osem_measurements' %in% class(measurements))
+})
+
+test_that('phenomenon is required when requesting measurements, error otherwise', {
+  check_api()
+  
+  expect_error(osem_measurements(boxes), 'Parameter "phenomenon" is required')
+})

tests/testthat/test_measurements.R

@@ -1,9 +1,6 @@
 source('testhelpers.R')
 context('measurements')
 
-try({
-  boxes = osem_boxes()
-})
 
 test_that('measurements can be retrieved for a phenomenon', {
   check_api()
@@ -47,20 +44,6 @@ test_that('measurements can be retrieved for a phenomenon and exposure', {
   expect_equal(nrow(measurements), 0)
 })
 
-test_that('measurements of specific boxes can be retrieved for one phenomenon and returns a measurements data.frame', {
-  check_api()
-
-  # fix for subsetting
-  class(boxes) = c('data.frame')
-  three_boxes = boxes[1:3, ]
-  class(boxes) = c('sensebox', 'data.frame')
-  three_boxes = osem_as_sensebox(three_boxes)
-  phens = names(osem_phenomena(three_boxes))
-
-  measurements = osem_measurements(x = three_boxes, phenomenon = phens[[1]])
-  expect_true(is.data.frame(measurements))
-  expect_true('osem_measurements' %in% class(measurements))
-})
 
 test_that('measurements can be retrieved for a bounding box', {
   check_api()
@@ -105,7 +88,6 @@ test_that('phenomenon is required when requesting measurements, error otherwise'
   check_api()
 
   expect_error(osem_measurements())
-  expect_error(osem_measurements(boxes), 'Parameter "phenomenon" is required')
 
   sfc = sf::st_sfc(sf::st_linestring(x = matrix(data = c(7, 8, 50, 51), ncol = 2)), crs = 4326)
   bbox = sf::st_bbox(sfc)

vignettes/osem-intro.Rmd

@@ -115,7 +115,9 @@ berlin = st_point(c(13.4034, 52.5120)) %>%
   st_transform(4326) %>% # the opensensemap expects WGS 84
   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(
   berlin,
   phenomenon = 'PM2.5',
@@ -129,7 +131,7 @@ plot(pm25)
 Now we can get started with actual spatiotemporal data analysis.
 First, lets mask the seemingly uncalibrated sensors:
 
-```{r}
+```{r, eval=FALSE}
 outliers = filter(pm25, value > 100)$sensorId
 bad_sensors = outliers[, drop = T] %>% levels()
 
@@ -138,13 +140,13 @@ pm25 = mutate(pm25, invalid = sensorId %in% bad_sensors)
 
 Then plot the measuring locations, flagging the outliers:
 
-```{r}
+```{r, eval=FALSE}
 st_as_sf(pm25) %>% st_geometry() %>% plot(col = factor(pm25$invalid), axes = T)
 ```
 
 Removing these sensors yields a nicer time series plot:
 
-```{r}
+```{r, eval=FALSE}
 pm25 %>% filter(invalid == FALSE) %>% plot()
 ```
 

vignettes/osem-serialization.Rmd

@@ -71,7 +71,7 @@ osem_clear_cache(getwd()) # clears a custom cache
 If you want to roll your own serialization method to support custom data formats,
 here's how:
 
-```{r data, results='hide'}
+```{r data, results='hide', eval=FALSE}
 # first get our example data:
 measurements = osem_measurements('Windgeschwindigkeit')
 ```
@@ -79,7 +79,7 @@ measurements = osem_measurements('Windgeschwindigkeit')
 If you are paranoid and worry about `.rds` files not being decodable anymore
 in the (distant) future, you could serialize to a plain text format such as JSON.
 This of course comes at the cost of storage space and performance.
-```{r serialize_json}
+```{r serialize_json, eval=FALSE}
 # serializing senseBoxes to JSON, and loading from file again:
 write(jsonlite::serializeJSON(measurements), 'measurements.json')
 measurements_from_file = jsonlite::unserializeJSON(readr::read_file('measurements.json'))
@@ -90,7 +90,7 @@ 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:
 
-```{r serialize_attrs}
+```{r serialize_attrs, eval=FALSE}
 # note the toJSON call instead of serializeJSON
 write(jsonlite::toJSON(measurements), 'measurements_bad.json')
 measurements_without_attrs = jsonlite::fromJSON('measurements_bad.json')
@@ -101,6 +101,6 @@ class(measurements_with_attrs)
 ```
 The same goes for boxes via `osem_as_sensebox()`.
 
-```{r cleanup, include=FALSE}
+```{r cleanup, include=FALSE, eval=FALSE}
 file.remove('measurements.json', 'measurements_bad.json')
 ```