diff --git a/inst/doc/osem-intro.R b/inst/doc/osem-intro.R
index f47779a..e51d194 100644
--- a/inst/doc/osem-intro.R
+++ b/inst/doc/osem-intro.R
@@ -1,11 +1,13 @@
 ## ----setup, include=FALSE------------------------------------------------
 knitr::opts_chunk$set(echo = TRUE)
 
-## ------------------------------------------------------------------------
+## ----results = F---------------------------------------------------------
 library(magrittr)
 library(opensensmapr)
 
 all_sensors = osem_boxes()
+
+## ------------------------------------------------------------------------
 summary(all_sensors)
 
 ## ----message=F, warning=F------------------------------------------------
@@ -22,13 +24,14 @@ str(phenoms)
 ## ------------------------------------------------------------------------
 phenoms[phenoms > 20]
 
-## ------------------------------------------------------------------------
+## ----results = F---------------------------------------------------------
 pm25_sensors = osem_boxes(
   exposure = 'outdoor',
   date = Sys.time(), # ±4 hours
   phenomenon = 'PM2.5'
 )
 
+## ------------------------------------------------------------------------
 summary(pm25_sensors)
 plot(pm25_sensors)
 
@@ -45,6 +48,7 @@ berlin = st_point(c(13.4034, 52.5120)) %>%
   st_transform(4326) %>% # the opensensemap expects WGS 84
   st_bbox()
 
+## ----results = F---------------------------------------------------------
 pm25 = osem_measurements(
   berlin,
   phenomenon = 'PM2.5',
diff --git a/inst/doc/osem-intro.Rmd b/inst/doc/osem-intro.Rmd
index 39590c0..5d6126a 100644
--- a/inst/doc/osem-intro.Rmd
+++ b/inst/doc/osem-intro.Rmd
@@ -35,11 +35,13 @@ this occurs.
 Before we look at actual observations, lets get a grasp of the openSenseMap
 datasets' structure.
 
-```{r}
+```{r results = F}
 library(magrittr)
 library(opensensmapr)
 
 all_sensors = osem_boxes()
+```
+```{r}
 summary(all_sensors)
 ```
 
@@ -86,13 +88,14 @@ 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}
+```{r results = F}
 pm25_sensors = osem_boxes(
   exposure = 'outdoor',
   date = Sys.time(), # ±4 hours
   phenomenon = 'PM2.5'
 )
-
+```
+```{r}
 summary(pm25_sensors)
 plot(pm25_sensors)
 ```
@@ -117,7 +120,8 @@ berlin = st_point(c(13.4034, 52.5120)) %>%
   st_buffer(units::set_units(12, km)) %>%
   st_transform(4326) %>% # the opensensemap expects WGS 84
   st_bbox()
-
+```
+```{r results = F}
 pm25 = osem_measurements(
   berlin,
   phenomenon = 'PM2.5',
@@ -136,4 +140,4 @@ pm25_sf = osem_as_sf(pm25)
 plot(st_geometry(pm25_sf), axes = T)
 ```
 
-`TODO`
+further analysis: `TODO`
diff --git a/inst/doc/osem-intro.html b/inst/doc/osem-intro.html
index 25a50a3..f0f4286 100644
--- a/inst/doc/osem-intro.html
+++ b/inst/doc/osem-intro.html
@@ -91,141 +91,6 @@ code > span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Inf
 <span class="kw">library</span>(opensensmapr)
 
 all_sensors =<span class="st"> </span><span class="kw">osem_boxes</span>()</code></pre></div>
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 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(all_sensors)</code></pre></div>
 <pre><code>## box total: 701
 ## 
@@ -245,7 +110,7 @@ Downloading: 120 kB</code></pre>
 ## 
 ## $last_measurement_within
 ##    1h    1d   30d  365d never 
-##   313   327   418   554    63 
+##   312   327   418   554    63 
 ## 
 ## oldest box: 2014-05-28 15:36:14 (CALIMERO)
 ## newest box: 2017-08-23 08:44:14 (Messstation Steinheim am Albuch)
@@ -400,35 +265,6 @@ if (!<span class="kw">require</span>(<span class="st">'rgeos'</span>))    <span
   <span class="dt">date =</span> <span class="kw">Sys.time</span>(), <span class="co"># ±4 hours</span>
   <span class="dt">phenomenon =</span> <span class="st">'PM2.5'</span>
 )</code></pre></div>
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 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(pm25_sensors)</code></pre></div>
 <pre><code>## box total: 236
 ## 
@@ -471,338 +307,21 @@ berlin =<span class="st"> </span><span class="kw">st_point</span>(<span class="k
 <span class="st">  </span><span class="kw">st_transform</span>(<span class="dv">3857</span>) %&gt;%<span class="st"> </span><span class="co"># allow setting a buffer in meters</span>
 <span class="st">  </span><span class="kw">st_buffer</span>(units::<span class="kw">set_units</span>(<span class="dv">12</span>, km)) %&gt;%
 <span class="st">  </span><span class="kw">st_transform</span>(<span class="dv">4326</span>) %&gt;%<span class="st"> </span><span class="co"># the opensensemap expects WGS 84</span>
-<span class="st">  </span><span class="kw">st_bbox</span>()
-
-pm25 =<span class="st"> </span><span class="kw">osem_measurements</span>(
+<span class="st">  </span><span class="kw">st_bbox</span>()</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">pm25 =<span class="st"> </span><span class="kw">osem_measurements</span>(
   berlin,
   <span class="dt">phenomenon =</span> <span class="st">'PM2.5'</span>,
   <span class="dt">from =</span> <span class="kw">now</span>() -<span class="st"> </span><span class="kw">days</span>(<span class="dv">7</span>), <span class="co"># defaults to 2 days</span>
   <span class="dt">to =</span> <span class="kw">now</span>()
-)</code></pre></div>
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-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plot</span>(pm25)</code></pre></div>
-<p><img 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" /><!-- --></p>
+)
+
+<span class="kw">plot</span>(pm25)</code></pre></div>
+<p><img src="data:image/png;base64,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" /><!-- --></p>
 <p>Now we can get started with actual spatiotemporal data analysis. First plot the measuring locations:</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">pm25_sf =<span class="st"> </span><span class="kw">osem_as_sf</span>(pm25)
 <span class="kw">plot</span>(<span class="kw">st_geometry</span>(pm25_sf), <span class="dt">axes =</span> T)</code></pre></div>
 <p><img src="data:image/png;base64,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" /><!-- --></p>
-<p><code>TODO</code></p>
+<p>further analysis: <code>TODO</code></p>
 </div>
 </div>
 
diff --git a/vignettes/osem-intro.Rmd b/vignettes/osem-intro.Rmd
index 39590c0..5d6126a 100644
--- a/vignettes/osem-intro.Rmd
+++ b/vignettes/osem-intro.Rmd
@@ -35,11 +35,13 @@ this occurs.
 Before we look at actual observations, lets get a grasp of the openSenseMap
 datasets' structure.
 
-```{r}
+```{r results = F}
 library(magrittr)
 library(opensensmapr)
 
 all_sensors = osem_boxes()
+```
+```{r}
 summary(all_sensors)
 ```
 
@@ -86,13 +88,14 @@ 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}
+```{r results = F}
 pm25_sensors = osem_boxes(
   exposure = 'outdoor',
   date = Sys.time(), # ±4 hours
   phenomenon = 'PM2.5'
 )
-
+```
+```{r}
 summary(pm25_sensors)
 plot(pm25_sensors)
 ```
@@ -117,7 +120,8 @@ berlin = st_point(c(13.4034, 52.5120)) %>%
   st_buffer(units::set_units(12, km)) %>%
   st_transform(4326) %>% # the opensensemap expects WGS 84
   st_bbox()
-
+```
+```{r results = F}
 pm25 = osem_measurements(
   berlin,
   phenomenon = 'PM2.5',
@@ -136,4 +140,4 @@ pm25_sf = osem_as_sf(pm25)
 plot(st_geometry(pm25_sf), axes = T)
 ```
 
-`TODO`
+further analysis: `TODO`