update inst/doc

inst/doc/osem-history.R

@@ -9,7 +9,10 @@ library(zoo)          # rollmean()
 ## ----download-----------------------------------------------------------------
 # if you want to see results for a specific subset of boxes,
 # just specify a filter such as grouptag='ifgi' here
-boxes = osem_boxes()
+
+# boxes = osem_boxes(cache = '.')
+boxes = readRDS('boxes_precomputed.rds')  # read precomputed file to save resources 
+
 
 ## ----exposure_counts, message=FALSE-------------------------------------------
 exposure_counts = boxes %>%

inst/doc/osem-history.Rmd

@@ -43,7 +43,10 @@ So the first step is to retrieve *all the boxes*:
 ```{r download}
 # if you want to see results for a specific subset of boxes,
 # just specify a filter such as grouptag='ifgi' here
-boxes = osem_boxes()
+
+# boxes = osem_boxes(cache = '.')
+boxes = readRDS('boxes_precomputed.rds')  # read precomputed file to save resources 
+
 ```
 
 # Plot count of boxes by time {.tabset}

inst/doc/osem-history_revised.R

@@ -9,18 +9,15 @@ library(zoo)          # rollmean()
 ## ----download, results='hide', message=FALSE, warning=FALSE-------------------
 # if you want to see results for a specific subset of boxes,
 # just specify a filter such as grouptag='ifgi' here
-boxes_all = osem_boxes()
-boxes = boxes_all
+
+# boxes = osem_boxes(cache = '.')
+boxes = readRDS('boxes_precomputed.rds')  # read precomputed file to save resources 
 
 ## -----------------------------------------------------------------------------
 boxes = filter(boxes, locationtimestamp >= "2022-01-01" & locationtimestamp <="2022-12-31")
 summary(boxes) -> summary.data.frame
 
-## ----message=F, warning=F-----------------------------------------------------
-if (!require('maps'))     install.packages('maps')
-if (!require('maptools')) install.packages('maptools')
-if (!require('rgeos'))    install.packages('rgeos')
-
+## ---- message=FALSE, warning=FALSE--------------------------------------------
 plot(boxes)
 
 ## -----------------------------------------------------------------------------

inst/doc/osem-history_revised.Rmd

@@ -45,8 +45,9 @@ So the first step is to retrieve *all the boxes*.
 ```{r download, results='hide', message=FALSE, warning=FALSE}
 # if you want to see results for a specific subset of boxes,
 # just specify a filter such as grouptag='ifgi' here
-boxes_all = osem_boxes()
-boxes = boxes_all
+
+# boxes = osem_boxes(cache = '.')
+boxes = readRDS('boxes_precomputed.rds')  # read precomputed file to save resources 
 ```
 # Introduction
 In the following we just want to have a look at the boxes created in 2022, so we filter for them. 
@@ -65,11 +66,7 @@ summary(boxes) -> summary.data.frame
 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(boxes)
 ```
 

inst/doc/osem-intro.R

@@ -1,20 +1,17 @@
 ## ----setup, include=FALSE-----------------------------------------------------
 knitr::opts_chunk$set(echo = TRUE)
 
-## ----results = F--------------------------------------------------------------
+## ----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 
 
 ## -----------------------------------------------------------------------------
 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')
-
+## ---- message=FALSE, warning=FALSE--------------------------------------------
 plot(all_sensors)
 
 ## -----------------------------------------------------------------------------
@@ -24,49 +21,54 @@ str(phenoms)
 ## -----------------------------------------------------------------------------
 phenoms[phenoms > 20]
 
-## ----results = F--------------------------------------------------------------
-pm25_sensors = osem_boxes(
-  exposure = 'outdoor',
-  date = Sys.time(), # ±4 hours
-  phenomenon = 'PM2.5'
-)
+## ----results = FALSE, eval=FALSE----------------------------------------------
+#  pm25_sensors = osem_boxes(
+#    exposure = 'outdoor',
+#    date = Sys.time(), # ±4 hours
+#    phenomenon = 'PM2.5'
+#  )
 
 ## -----------------------------------------------------------------------------
+pm25_sensors = readRDS('pm25_sensors.rds') # read precomputed file to save resources 
+
 summary(pm25_sensors)
 plot(pm25_sensors)
 
-## -----------------------------------------------------------------------------
+## ---- results=FALSE, message=FALSE--------------------------------------------
 library(sf)
 library(units)
 library(lubridate)
 library(dplyr)
 
-# 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(set_units(12, km)) %>%
-  st_transform(4326) %>% # the opensensemap expects WGS 84
-  st_bbox()
-
-## ----results = F--------------------------------------------------------------
-pm25 = osem_measurements(
-  berlin,
-  phenomenon = 'PM2.5',
-  from = now() - days(3), # defaults to 2 days
-  to = now()
-)
 
-plot(pm25)
+## ----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) %>%
+#    st_transform(3857) %>% # allow setting a buffer in meters
+#    st_buffer(set_units(12, km)) %>%
+#    st_transform(4326) %>% # the opensensemap expects WGS 84
+#    st_bbox()
+#  pm25 = osem_measurements(
+#    berlin,
+#    phenomenon = 'PM2.5',
+#    from = now() - days(3), # defaults to 2 days
+#    to = now()
+#  )
+#  
 
 ## -----------------------------------------------------------------------------
+pm25 = readRDS('pm25_berlin.rds') # read precomputed file to save resources 
+plot(pm25)
+
+## ---- warning=FALSE-----------------------------------------------------------
 outliers = filter(pm25, value > 100)$sensorId
-bad_sensors = outliers[, drop = T] %>% levels()
+bad_sensors = outliers[, drop = TRUE] %>% levels()
 
 pm25 = mutate(pm25, invalid = sensorId %in% bad_sensors)
 
 ## -----------------------------------------------------------------------------
-st_as_sf(pm25) %>% st_geometry() %>% plot(col = factor(pm25$invalid), axes = T)
+st_as_sf(pm25) %>% st_geometry() %>% plot(col = factor(pm25$invalid), axes = TRUE)
 
 ## -----------------------------------------------------------------------------
 pm25 %>% filter(invalid == FALSE) %>% plot()

inst/doc/osem-intro.Rmd

@@ -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()
 
 pm25 = mutate(pm25, invalid = sensorId %in% bad_sensors)
 ```
@@ -139,7 +144,7 @@ pm25 = mutate(pm25, invalid = sensorId %in% bad_sensors)
 Then plot the measuring locations, flagging the outliers:
 
 ```{r}
-st_as_sf(pm25) %>% st_geometry() %>% plot(col = factor(pm25$invalid), axes = T)
+st_as_sf(pm25) %>% st_geometry() %>% plot(col = factor(pm25$invalid), axes = TRUE)
 ```
 
 Removing these sensors yields a nicer time series plot:

inst/doc/osem-serialization.R

@@ -27,25 +27,25 @@ b = osem_boxes(grouptag = 'ifgi', cache = cacheDir)
 osem_clear_cache()        # clears default cache
 osem_clear_cache(getwd()) # clears a custom cache
 
-## ----data, results='hide'-----------------------------------------------------
-# first get our example data:
-measurements = osem_measurements('Windgeschwindigkeit')
-
-## ----serialize_json-----------------------------------------------------------
-# 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'))
-class(measurements_from_file)
-
-## ----serialize_attrs----------------------------------------------------------
-# note the toJSON call instead of serializeJSON
-write(jsonlite::toJSON(measurements), 'measurements_bad.json')
-measurements_without_attrs = jsonlite::fromJSON('measurements_bad.json')
-class(measurements_without_attrs)
-
-measurements_with_attrs = osem_as_measurements(measurements_without_attrs)
-class(measurements_with_attrs)
-
-## ----cleanup, include=FALSE---------------------------------------------------
-file.remove('measurements.json', 'measurements_bad.json')
+## ----data, results='hide', eval=FALSE-----------------------------------------
+#  # first get our example data:
+#  measurements = osem_measurements('Windgeschwindigkeit')
+
+## ----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'))
+#  class(measurements_from_file)
+
+## ----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')
+#  class(measurements_without_attrs)
+#  
+#  measurements_with_attrs = osem_as_measurements(measurements_without_attrs)
+#  class(measurements_with_attrs)
+
+## ----cleanup, include=FALSE, eval=FALSE---------------------------------------
+#  file.remove('measurements.json', 'measurements_bad.json')
 

inst/doc/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')
 ```

inst/doc/osem-serialization.html

@@ -12,7 +12,7 @@
 
 <meta name="author" content="Norwin Roosen" />
 
-<meta name="date" content="2023-02-23" />
+<meta name="date" content="2023-03-08" />
 
 <title>Caching openSenseMap Data for Reproducibility</title>
 
@@ -341,7 +341,7 @@ code > span.er { color: #a61717; background-color: #e3d2d2; }
 <h1 class="title toc-ignore">Caching openSenseMap Data for
 Reproducibility</h1>
 <h4 class="author">Norwin Roosen</h4>
-<h4 class="date">2023-02-23</h4>
+<h4 class="date">2023-03-08</h4>
 
 
 
@@ -407,21 +407,17 @@ storage space and performance.</p>
 <span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="fu">write</span>(jsonlite<span class="sc">::</span><span class="fu">serializeJSON</span>(measurements), <span class="st">&#39;measurements.json&#39;</span>)</span>
 <span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>measurements_from_file <span class="ot">=</span> jsonlite<span class="sc">::</span><span class="fu">unserializeJSON</span>(readr<span class="sc">::</span><span class="fu">read_file</span>(<span class="st">&#39;measurements.json&#39;</span>))</span>
 <span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a><span class="fu">class</span>(measurements_from_file)</span></code></pre></div>
-<pre><code>## [1] &quot;osem_measurements&quot; &quot;tbl_df&quot;            &quot;tbl&quot;              
-## [4] &quot;data.frame&quot;</code></pre>
 <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>
-<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="co"># note the toJSON call instead of serializeJSON</span></span>
-<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a><span class="fu">write</span>(jsonlite<span class="sc">::</span><span class="fu">toJSON</span>(measurements), <span class="st">&#39;measurements_bad.json&#39;</span>)</span>
-<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>measurements_without_attrs <span class="ot">=</span> jsonlite<span class="sc">::</span><span class="fu">fromJSON</span>(<span class="st">&#39;measurements_bad.json&#39;</span>)</span>
-<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a><span class="fu">class</span>(measurements_without_attrs)</span></code></pre></div>
-<pre><code>## [1] &quot;data.frame&quot;</code></pre>
-<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>measurements_with_attrs <span class="ot">=</span> <span class="fu">osem_as_measurements</span>(measurements_without_attrs)</span>
-<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a><span class="fu">class</span>(measurements_with_attrs)</span></code></pre></div>
-<pre><code>## [1] &quot;osem_measurements&quot; &quot;tbl_df&quot;            &quot;tbl&quot;              
-## [4] &quot;data.frame&quot;</code></pre>
+<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="co"># note the toJSON call instead of serializeJSON</span></span>
+<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a><span class="fu">write</span>(jsonlite<span class="sc">::</span><span class="fu">toJSON</span>(measurements), <span class="st">&#39;measurements_bad.json&#39;</span>)</span>
+<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>measurements_without_attrs <span class="ot">=</span> jsonlite<span class="sc">::</span><span class="fu">fromJSON</span>(<span class="st">&#39;measurements_bad.json&#39;</span>)</span>
+<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a><span class="fu">class</span>(measurements_without_attrs)</span>
+<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a>measurements_with_attrs <span class="ot">=</span> <span class="fu">osem_as_measurements</span>(measurements_without_attrs)</span>
+<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a><span class="fu">class</span>(measurements_with_attrs)</span></code></pre></div>
 <p>The same goes for boxes via <code>osem_as_sensebox()</code>.</p>
 </div>