This vignette serves as an example on data wrangling & visualization with
opensensmapr
,dplyr
andggplot2
.
# required packages: library(opensensmapr) # data download library(dplyr) # data wrangling library(ggplot2) # plotting library(lubridate) # date arithmetic library(zoo) # rollmean()
openSenseMap.org has grown quite a bit in the last years; it would be interesting
to see how we got to the current r osem_counts()$boxes
sensor stations,
split up by various attributes of the boxes.
While opensensmapr
provides extensive methods of filtering boxes by attributes
on the server, we do the filtering within R to save time and gain flexibility.
So the first step is to retrieve all the boxes:
# if you want to see results for a specific subset of boxes, # just specify a filter such as grouptag='ifgi' here # boxes = osem_boxes(cache = '.') boxes = readRDS('boxes_precomputed.rds') # read precomputed file to save resources
By looking at the createdAt
attribute of each box we know the exact time a box
was registered.
With this approach we have no information about boxes that were deleted in the
meantime, but that's okay for now.
exposure_counts = boxes %>% group_by(exposure) %>% mutate(count = row_number(createdAt)) exposure_colors = c(indoor = 'red', outdoor = 'lightgreen', mobile = 'blue', unknown = 'darkgrey') ggplot(exposure_counts, aes(x = createdAt, y = count, colour = exposure)) + geom_line() + scale_colour_manual(values = exposure_colors) + xlab('Registration Date') + ylab('senseBox count')
Outdoor boxes are growing fast!
We can also see the introduction of mobile
sensor "stations" in 2017. While
mobile boxes are still few, we can expect a quick rise in 2018 once the new
senseBox MCU with GPS support is released.
Let's have a quick summary:
exposure_counts %>% summarise( oldest = min(createdAt), newest = max(createdAt), count = max(count) ) %>% arrange(desc(count))
We can try to find out where the increases in growth came from, by analysing the box count by grouptag.
Caveats: Only a small subset of boxes has a grouptag, and we should assume
that these groups are actually bigger. Also, we can see that grouptag naming is
inconsistent (Luftdaten
, luftdaten.info
, ...)
grouptag_counts = boxes %>% group_by(grouptag) %>% # only include grouptags with 8 or more members filter(length(grouptag) >= 8 & !is.na(grouptag)) %>% mutate(count = row_number(createdAt)) # helper for sorting the grouptags by boxcount sortLvls = function(oldFactor, ascending = TRUE) { lvls = table(oldFactor) %>% sort(., decreasing = !ascending) %>% names() factor(oldFactor, levels = lvls) } grouptag_counts$grouptag = sortLvls(grouptag_counts$grouptag, ascending = FALSE) ggplot(grouptag_counts, aes(x = createdAt, y = count, colour = grouptag)) + geom_line(aes(group = grouptag)) + xlab('Registration Date') + ylab('senseBox count')
grouptag_counts %>% summarise( oldest = min(createdAt), newest = max(createdAt), count = max(count) ) %>% arrange(desc(count))
First we group the boxes by createdAt
into bins of one week:
bins = 'week' mvavg_bins = 6 growth = boxes %>% mutate(week = cut(as.Date(createdAt), breaks = bins)) %>% group_by(week) %>% summarize(count = length(week)) %>% mutate(event = 'registered')
We can do the same for updatedAt
, which informs us about the last change to
a box, including uploaded measurements.
This method of determining inactive boxes is fairly inaccurate and should be
considered an approximation, because we have no information about intermediate
inactive phases.
Also deleted boxes would probably have a big impact here.
inactive = boxes %>% # remove boxes that were updated in the last two days, # b/c any box becomes inactive at some point by definition of updatedAt filter(updatedAt < now() - days(2)) %>% mutate(week = cut(as.Date(updatedAt), breaks = bins)) %>% group_by(week) %>% summarize(count = length(week)) %>% mutate(event = 'inactive')
Now we can combine both datasets for plotting:
boxes_by_date = bind_rows(growth, inactive) %>% group_by(event) ggplot(boxes_by_date, aes(x = as.Date(week), colour = event)) + xlab('Time') + ylab(paste('rate per ', bins)) + scale_x_date(date_breaks="years", date_labels="%Y") + scale_colour_manual(values = c(registered = 'lightgreen', inactive = 'grey')) + geom_point(aes(y = count), size = 0.5) + # moving average, make first and last value NA (to ensure identical length of vectors) geom_line(aes(y = rollmean(count, mvavg_bins, fill = list(NA, NULL, NA))))
We see a sudden rise in early 2017, which lines up with the fast growing grouptag Luftdaten
.
This was enabled by an integration of openSenseMap.org into the firmware of the
air quality monitoring project luftdaten.info.
The dips in mid 2017 and early 2018 could possibly be explained by production/delivery issues
of the senseBox hardware, but I have no data on the exact time frames to verify.
While we are looking at createdAt
and updatedAt
, we can also extract the duration of activity
of each box, and look at metrics by exposure and grouptag once more:
duration = boxes %>% group_by(exposure) %>% filter(!is.na(updatedAt)) %>% mutate(duration = difftime(updatedAt, createdAt, units='days')) ggplot(duration, aes(x = exposure, y = duration)) + geom_boxplot() + coord_flip() + ylab('Duration active in Days')
The time of activity averages at only r round(mean(duration$duration))
days,
though there are boxes with r round(max(duration$duration))
days of activity,
spanning a large chunk of openSenseMap's existence.
duration = boxes %>% group_by(grouptag) %>% # only include grouptags with 8 or more members filter(length(grouptag) >= 8 & !is.na(grouptag) & !is.na(updatedAt)) %>% mutate(duration = difftime(updatedAt, createdAt, units='days')) ggplot(duration, aes(x = grouptag, y = duration)) + geom_boxplot() + coord_flip() + ylab('Duration active in Days') duration %>% summarize( duration_avg = round(mean(duration)), duration_min = round(min(duration)), duration_max = round(max(duration)), oldest_box = round(max(difftime(now(), createdAt, units='days'))) ) %>% arrange(desc(duration_avg))
The time of activity averages at only r round(mean(duration$duration))
days,
though there are boxes with r round(max(duration$duration))
days of activity,
spanning a large chunk of openSenseMap's existence.
This is less useful, as older boxes are active for a longer time by definition. If you have an idea how to compensate for that, please send a Pull Request!
# NOTE: boxes older than 2016 missing due to missing updatedAt in database duration = boxes %>% mutate(year = cut(as.Date(createdAt), breaks = 'year')) %>% group_by(year) %>% filter(!is.na(updatedAt)) %>% mutate(duration = difftime(updatedAt, createdAt, units='days')) ggplot(duration, aes(x = substr(as.character(year), 0, 4), y = duration)) + geom_boxplot() + coord_flip() + ylab('Duration active in Days') + xlab('Year of Registration')
Other visualisations come to mind, and are left as an exercise to the reader. If you implemented some, feel free to add them to this vignette via a Pull Request.
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