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remove note about #3, fix typos
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2 changed files with 5 additions and 10 deletions
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@ -41,7 +41,7 @@ print.sensebox = function(x, ...) {
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#' @export
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#' @export
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summary.sensebox = function(object, ...) {
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summary.sensebox = function(object, ...) {
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cat('box total:', nrow(object), fill = T)
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cat('boxes total:', nrow(object), fill = T)
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cat('\nboxes by exposure:')
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cat('\nboxes by exposure:')
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table(object$exposure) %>% print()
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table(object$exposure) %>% print()
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cat('\nboxes by model:')
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cat('\nboxes by model:')
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@ -26,11 +26,6 @@ Its main goals are to provide means for:
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- big data analysis of the measurements stored on the platform
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- big data analysis of the measurements stored on the platform
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- sensor metadata analysis (sensor counts, spatial distribution, temporal trends)
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- sensor metadata analysis (sensor counts, spatial distribution, temporal trends)
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> *Please note:* The openSenseMap API is sometimes a bit unstable when streaming
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long responses, which results in `curl` complaining about `Unexpected EOF`. This
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bug is being worked on upstream. Meanwhile you have to retry the request when
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this occurs.
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### Exploring the dataset
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### Exploring the dataset
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Before we look at actual observations, lets get a grasp of the openSenseMap
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Before we look at actual observations, lets get a grasp of the openSenseMap
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datasets' structure.
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datasets' structure.
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@ -45,14 +40,14 @@ all_sensors = osem_boxes()
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summary(all_sensors)
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summary(all_sensors)
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```
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```
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This gives a good overview already: As of writing this, there are more than 600
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This gives a good overview already: As of writing this, there are more than 700
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sensor stations, of which ~50% are currently running. Most of them are placed
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sensor stations, of which ~50% are currently running. Most of them are placed
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outdoors and have around 5 sensors each.
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outdoors and have around 5 sensors each.
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The oldest station is from May 2014, while the latest station was registered a
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The oldest station is from May 2014, while the latest station was registered a
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couple of minutes ago.
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couple of minutes ago.
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Another feature of interest is the spatial distribution of the boxes. `plot()`
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Another feature of interest is the spatial distribution of the boxes: `plot()`
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can help us out here. This function requires a bunch of optional dependcies though.
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can help us out here. This function requires a bunch of optional dependencies though.
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```{r message=F, warning=F}
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```{r message=F, warning=F}
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if (!require('maps')) install.packages('maps')
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if (!require('maps')) install.packages('maps')
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@ -117,7 +112,7 @@ library(lubridate)
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berlin = st_point(c(13.4034, 52.5120)) %>%
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berlin = st_point(c(13.4034, 52.5120)) %>%
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st_sfc(crs = 4326) %>%
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st_sfc(crs = 4326) %>%
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st_transform(3857) %>% # allow setting a buffer in meters
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st_transform(3857) %>% # allow setting a buffer in meters
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st_buffer(units::set_units(12, km)) %>%
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st_buffer(set_units(12, km)) %>%
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st_transform(4326) %>% # the opensensemap expects WGS 84
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st_transform(4326) %>% # the opensensemap expects WGS 84
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st_bbox()
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st_bbox()
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```
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```
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