history Rmd revised
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---
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title: "Visualising the Develpment of openSenseMap.org in 2022"
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author: "Jan Stenkamp"
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date: '`r Sys.Date()`'
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output:
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html_document:
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code_folding: hide
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df_print: kable
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theme: lumen
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toc: yes
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toc_float: yes
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rmarkdown::html_vignette:
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df_print: kable
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fig_height: 5
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fig_width: 7
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toc: yes
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vignette: >
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%\VignetteIndexEntry{Visualising the History of openSenseMap.org}
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%\VignetteEncoding{UTF-8}
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%\VignetteEngine{knitr::rmarkdown}
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---
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> This vignette serves as an example on data wrangling & visualization with
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`opensensmapr`, `dplyr` and `ggplot2`.
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```{r setup, results='hide', message=FALSE, warning=FALSE}
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# required packages:
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# library(opensensmapr) # data download
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library(devtools)
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load_all(".")
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library(dplyr) # data wrangling
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library(ggplot2) # plotting
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library(lubridate) # date arithmetic
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library(zoo) # rollmean()
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```
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openSenseMap.org has grown quite a bit in the last years; it would be interesting
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to see how we got to the current `r osem_counts()$boxes` sensor stations,
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split up by various attributes of the boxes.
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While `opensensmapr` provides extensive methods of filtering boxes by attributes
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on the server, we do the filtering within R to save time and gain flexibility.
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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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# 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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boxes_all = osem_boxes()
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boxes = boxes_all
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```
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# Introduction
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In the following we just want to have a look at the boxes created in 2022, so we filter for them.
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```{r}
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boxes = filter(boxes, locationtimestamp >= "2022-01-01" & locationtimestamp <="2022-12-31")
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summary(boxes) -> summary.data.frame
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```
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<!-- This gives a good overview already: As of writing this, there are more than 11,000 -->
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<!-- sensor stations, of which ~30% are currently running. Most of them are placed -->
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<!-- outdoors and have around 5 sensors each. -->
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<!-- The oldest station is from August 2016, while the latest station was registered a -->
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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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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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if (!require('maps')) install.packages('maps')
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if (!require('maptools')) install.packages('maptools')
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if (!require('rgeos')) install.packages('rgeos')
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plot(boxes)
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```
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But what do these sensor stations actually measure? Lets find out.
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`osem_phenomena()` gives us a named list of of the counts of each observed
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phenomenon for the given set of sensor stations:
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```{r}
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phenoms = osem_phenomena(boxes)
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str(phenoms)
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```
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Thats quite some noise there, with many phenomena being measured by a single
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sensor only, or many duplicated phenomena due to slightly different spellings.
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We should clean that up, but for now let's just filter out the noise and find
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those phenomena with high sensor numbers:
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```{r}
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phenoms[phenoms > 50]
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```
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# Plot count of boxes by time {.tabset}
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By looking at the `createdAt` attribute of each box we know the exact time a box
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was registered. Because of some database migration issues the `createdAt` values are mostly wrong (~80% of boxes created 2022-03-30), so we are using the `timestamp` attribute of the `currentlocation` which should in most cases correspond to the creation date.
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With this approach we have no information about boxes that were deleted in the
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meantime, but that's okay for now.
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## ...and exposure
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```{r exposure_counts, message=FALSE}
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exposure_counts = boxes %>%
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group_by(exposure) %>%
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mutate(count = row_number(locationtimestamp))
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exposure_colors = c(indoor = 'red', outdoor = 'lightgreen', mobile = 'blue', unknown = 'darkgrey')
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ggplot(exposure_counts, aes(x = locationtimestamp, y = count, colour = exposure)) +
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geom_line() +
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scale_colour_manual(values = exposure_colors) +
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xlab('Registration Date') + ylab('senseBox count')
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```
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Outdoor boxes are growing *fast*!
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We can also see the introduction of `mobile` sensor "stations" in 2017.
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Let's have a quick summary:
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```{r exposure_summary}
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exposure_counts %>%
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summarise(
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oldest = min(locationtimestamp),
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newest = max(locationtimestamp),
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count = max(count)
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) %>%
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arrange(desc(count))
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```
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## ...and grouptag
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We can try to find out where the increases in growth came from, by analysing the
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box count by grouptag.
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Caveats: Only a small subset of boxes has a grouptag, and we should assume
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that these groups are actually bigger. Also, we can see that grouptag naming is
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inconsistent (`Luftdaten`, `luftdaten.info`, ...)
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```{r grouptag_counts, message=FALSE}
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grouptag_counts = boxes %>%
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group_by(grouptag) %>%
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# only include grouptags with 15 or more members
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filter(length(grouptag) >= 15 && !is.na(grouptag) && grouptag != '') %>%
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mutate(count = row_number(locationtimestamp))
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# helper for sorting the grouptags by boxcount
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sortLvls = function(oldFactor, ascending = TRUE) {
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lvls = table(oldFactor) %>% sort(., decreasing = !ascending) %>% names()
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factor(oldFactor, levels = lvls)
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}
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grouptag_counts$grouptag = sortLvls(grouptag_counts$grouptag, ascending = FALSE)
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ggplot(grouptag_counts, aes(x = locationtimestamp, y = count, colour = grouptag)) +
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geom_line(aes(group = grouptag)) +
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xlab('Registration Date') + ylab('senseBox count')
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```
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```{r grouptag_summary}
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grouptag_counts %>%
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summarise(
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oldest = min(locationtimestamp),
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newest = max(locationtimestamp),
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count = max(count)
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) %>%
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arrange(desc(count))
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```
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# Plot rate of growth and inactivity per week
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First we group the boxes by `locationtimestamp` into bins of one week:
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```{r growthrate_registered, warning=FALSE, message=FALSE, results='hide'}
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bins = 'week'
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mvavg_bins = 6
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growth = boxes %>%
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mutate(week = cut(as.Date(locationtimestamp), breaks = bins)) %>%
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group_by(week) %>%
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summarize(count = length(week)) %>%
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mutate(event = 'registered')
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```
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We can do the same for `updatedAt`, which informs us about the last change to
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a box, including uploaded measurements. As a lot of boxes were "updated" by the database
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migration, many of them are updated at 2022-03-30, so we try to use the `lastMeasurement`
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attribute instead of `updatedAt`. This leads to fewer boxes but also automatically excludes
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boxes which were created but never made a measurement.
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This method of determining inactive boxes is fairly inaccurate and should be
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considered an approximation, because we have no information about intermediate
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inactive phases.
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Also deleted boxes would probably have a big impact here.
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```{r growthrate_inactive, warning=FALSE, message=FALSE, results='hide'}
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inactive = boxes %>%
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# remove boxes that were updated in the last two days,
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# b/c any box becomes inactive at some point by definition of updatedAt
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filter(lastMeasurement < now() - days(2)) %>%
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mutate(week = cut(as.Date(lastMeasurement), breaks = bins)) %>%
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filter(as.Date(week) > as.Date("2021-12-31")) %>%
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group_by(week) %>%
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summarize(count = length(week)) %>%
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mutate(event = 'inactive')
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```
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Now we can combine both datasets for plotting:
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```{r growthrate, warning=FALSE, message=FALSE, results='hide'}
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boxes_by_date = bind_rows(growth, inactive) %>% group_by(event)
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ggplot(boxes_by_date, aes(x = as.Date(week), colour = event)) +
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xlab('Time') + ylab(paste('rate per ', bins)) +
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scale_x_date(date_breaks="years", date_labels="%Y") +
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scale_colour_manual(values = c(registered = 'lightgreen', inactive = 'grey')) +
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geom_point(aes(y = count), size = 0.5) +
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# moving average, make first and last value NA (to ensure identical length of vectors)
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geom_line(aes(y = rollmean(count, mvavg_bins, fill = list(NA, NULL, NA))))
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```
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And see in which weeks the most boxes become (in)active:
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```{r table_mostregistrations}
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boxes_by_date %>%
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filter(count > 50) %>%
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arrange(desc(count))
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```
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# Plot duration of boxes being active {.tabset}
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While we are looking at `locationtimestamp` and `lastMeasurement`, we can also extract the duration of activity
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of each box, and look at metrics by exposure and grouptag once more:
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## ...by exposure
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```{r exposure_duration, message=FALSE}
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durations = boxes %>%
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group_by(exposure) %>%
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filter(!is.na(lastMeasurement)) %>%
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mutate(duration = difftime(lastMeasurement, locationtimestamp, units='days')) %>%
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filter(duration >= 0)
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ggplot(durations, aes(x = exposure, y = duration)) +
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geom_boxplot() +
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coord_flip() + ylab('Duration active in Days')
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```
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The time of activity averages at only `r round(mean(durations$duration))` days,
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though there are boxes with `r round(max(durations$duration))` days of activity,
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spanning a large chunk of openSenseMap's existence.
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## ...by grouptag
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```{r grouptag_duration, message=FALSE}
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durations = boxes %>%
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filter(!is.na(lastMeasurement)) %>%
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group_by(grouptag) %>%
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# only include grouptags with 20 or more members
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filter(length(grouptag) >= 15 & !is.na(grouptag) & !is.na(lastMeasurement)) %>%
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mutate(duration = difftime(lastMeasurement, locationtimestamp, units='days')) %>%
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filter(duration >= 0)
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ggplot(durations, aes(x = grouptag, y = duration)) +
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geom_boxplot() +
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coord_flip() + ylab('Duration active in Days')
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durations %>%
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summarize(
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duration_avg = round(mean(duration)),
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duration_min = round(min(duration)),
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duration_max = round(max(duration)),
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oldest_box = round(max(difftime(now(), locationtimestamp, units='days')))
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) %>%
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arrange(desc(duration_avg))
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```
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The time of activity averages at only `r round(mean(durations$duration))` days,
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though there are boxes with `r round(max(durations$duration))` days of activity,
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spanning a large chunk of openSenseMap's existence.
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## ...by year of registration
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This is less useful, as older boxes are active for a longer time by definition.
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If you have an idea how to compensate for that, please send a [Pull Request][PR]!
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```{r year_duration, message=FALSE}
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# NOTE: boxes older than 2016 missing due to missing updatedAt in database
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duration = boxes %>%
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mutate(year = cut(as.Date(locationtimestamp), breaks = 'year')) %>%
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group_by(year) %>%
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filter(!is.na(lastMeasurement)) %>%
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mutate(duration = difftime(lastMeasurement, locationtimestamp, units='days')) %>%
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filter(duration >= 0)
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ggplot(duration, aes(x = substr(as.character(year), 0, 4), y = duration)) +
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geom_boxplot() +
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coord_flip() + ylab('Duration active in Days') + xlab('Year of Registration')
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```
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# More Visualisations
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Other visualisations come to mind, and are left as an exercise to the reader.
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If you implemented some, feel free to add them to this vignette via a [Pull Request][PR].
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* growth by phenomenon
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* growth by location -> (interactive) map
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* set inactive rate in relation to total box count
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* filter timespans with big dips in growth rate, and extrapolate the amount of
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senseBoxes that could be on the platform today, assuming there were no production issues ;)
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[PR]: https://github.com/sensebox/opensensmapr/pulls
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