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use cached data to speed up building of vignettes
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4 changed files with 4 additions and 4 deletions
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@ -43,7 +43,7 @@ So the first step is to retrieve *all the boxes*:
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```{r download}
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```{r download}
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# if you want to see results for a specific subset of boxes,
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# if you want to see results for a specific subset of boxes,
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# just specify a filter such as grouptag='ifgi' here
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# just specify a filter such as grouptag='ifgi' here
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boxes = osem_boxes()
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boxes = osem_boxes(cache = '.')
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```
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```
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# Plot count of boxes by time {.tabset}
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# Plot count of boxes by time {.tabset}
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@ -45,7 +45,7 @@ So the first step is to retrieve *all the boxes*.
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```{r download, results='hide', message=FALSE, warning=FALSE}
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```{r download, results='hide', message=FALSE, warning=FALSE}
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# if you want to see results for a specific subset of boxes,
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# if you want to see results for a specific subset of boxes,
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# just specify a filter such as grouptag='ifgi' here
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# just specify a filter such as grouptag='ifgi' here
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boxes_all = osem_boxes()
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boxes_all = osem_boxes(cache = '.')
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boxes = boxes_all
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boxes = boxes_all
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```
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```
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# Introduction
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# Introduction
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@ -32,7 +32,7 @@ datasets' structure.
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library(magrittr)
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library(magrittr)
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library(opensensmapr)
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library(opensensmapr)
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all_sensors = osem_boxes()
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all_sensors = osem_boxes(cache = '.')
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```
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```
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```{r}
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```{r}
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summary(all_sensors)
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summary(all_sensors)
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@ -101,7 +101,7 @@ We could call `osem_measurements(pm25_sensors)` now, however we are focusing on
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a restricted area of interest, the city of Berlin.
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a restricted area of interest, the city of Berlin.
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Luckily we can get the measurements filtered by a bounding box:
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Luckily we can get the measurements filtered by a bounding box:
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```{r}
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```{r. eval = FALSE}
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library(sf)
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library(sf)
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library(units)
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library(units)
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library(lubridate)
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library(lubridate)
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BIN
vignettes/osemcache.c54710f66b662e29dd86b089962b0f598e47eddb.rds
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BIN
vignettes/osemcache.c54710f66b662e29dd86b089962b0f598e47eddb.rds
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