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@ -1,6 +1,17 @@
|
||||
# opensensmapr changelog
|
||||
This project does its best to adhere to semantic versioning.
|
||||
|
||||
### 2023-02-20: v0.6.0
|
||||
- fix package bugs to pass CRAN tests after 4 years of maintenance break
|
||||
- updated hyperlinks
|
||||
- dont throw error for empty sensors
|
||||
- updated tests
|
||||
- updated maintainer
|
||||
- updated vignettes
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||||
- new features:
|
||||
- added param bbox for osem_boxes function
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||||
- support of multiple grouptags
|
||||
|
||||
### 2019-02-09: v0.5.1
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||||
- fix package to work with API v6
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||||
- box$lastMeasurement may be missing now for long inactive boxes
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||||
@ -0,0 +1,302 @@
|
||||
---
|
||||
title: "Visualising the Develpment of openSenseMap.org in 2022"
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||||
author: "Jan Stenkamp"
|
||||
date: '`r Sys.Date()`'
|
||||
output:
|
||||
html_document:
|
||||
code_folding: hide
|
||||
df_print: kable
|
||||
theme: lumen
|
||||
toc: yes
|
||||
toc_float: yes
|
||||
rmarkdown::html_vignette:
|
||||
df_print: kable
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||||
fig_height: 5
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||||
fig_width: 7
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||||
toc: yes
|
||||
vignette: >
|
||||
%\VignetteIndexEntry{Visualising the History of openSenseMap.org}
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||||
%\VignetteEncoding{UTF-8}
|
||||
%\VignetteEngine{knitr::rmarkdown}
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||||
---
|
||||
|
||||
> This vignette serves as an example on data wrangling & visualization with
|
||||
`opensensmapr`, `dplyr` and `ggplot2`.
|
||||
|
||||
```{r setup, results='hide', message=FALSE, warning=FALSE}
|
||||
# required packages:
|
||||
# library(opensensmapr) # data download
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||||
library(devtools)
|
||||
load_all(".")
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||||
library(dplyr) # data wrangling
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||||
library(ggplot2) # plotting
|
||||
library(lubridate) # date arithmetic
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||||
library(zoo) # rollmean()
|
||||
```
|
||||
|
||||
openSenseMap.org has grown quite a bit in the last years; it would be interesting
|
||||
to see how we got to the current `r osem_counts()$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*.
|
||||
|
||||
```{r download, results='hide', message=FALSE, warning=FALSE}
|
||||
# if you want to see results for a specific subset of boxes,
|
||||
# just specify a filter such as grouptag='ifgi' here
|
||||
boxes_all = osem_boxes()
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||||
boxes = boxes_all
|
||||
```
|
||||
# Introduction
|
||||
In the following we just want to have a look at the boxes created in 2022, so we filter for them.
|
||||
|
||||
```{r}
|
||||
boxes = filter(boxes, locationtimestamp >= "2022-01-01" & locationtimestamp <="2022-12-31")
|
||||
summary(boxes) -> summary.data.frame
|
||||
```
|
||||
|
||||
<!-- This gives a good overview already: As of writing this, there are more than 11,000 -->
|
||||
<!-- sensor stations, of which ~30% are currently running. Most of them are placed -->
|
||||
<!-- outdoors and have around 5 sensors each. -->
|
||||
<!-- The oldest station is from August 2016, while the latest station was registered a -->
|
||||
<!-- couple of minutes ago. -->
|
||||
|
||||
Another feature of interest is the spatial distribution of the boxes: `plot()`
|
||||
can help us out here. This function requires a bunch of optional dependencies though.
|
||||
|
||||
```{r message=F, warning=F}
|
||||
if (!require('maps')) install.packages('maps')
|
||||
if (!require('maptools')) install.packages('maptools')
|
||||
if (!require('rgeos')) install.packages('rgeos')
|
||||
|
||||
plot(boxes)
|
||||
```
|
||||
|
||||
But what do these sensor stations actually measure? Lets find out.
|
||||
`osem_phenomena()` gives us a named list of of the counts of each observed
|
||||
phenomenon for the given set of sensor stations:
|
||||
|
||||
```{r}
|
||||
phenoms = osem_phenomena(boxes)
|
||||
str(phenoms)
|
||||
```
|
||||
|
||||
Thats quite some noise there, with many phenomena being measured by a single
|
||||
sensor only, or many duplicated phenomena due to slightly different spellings.
|
||||
We should clean that up, but for now let's just filter out the noise and find
|
||||
those phenomena with high sensor numbers:
|
||||
|
||||
```{r}
|
||||
phenoms[phenoms > 50]
|
||||
```
|
||||
|
||||
|
||||
# Plot count of boxes by time {.tabset}
|
||||
By looking at the `createdAt` attribute of each box we know the exact time a box
|
||||
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.
|
||||
|
||||
With this approach we have no information about boxes that were deleted in the
|
||||
meantime, but that's okay for now.
|
||||
|
||||
## ...and exposure
|
||||
```{r exposure_counts, message=FALSE}
|
||||
exposure_counts = boxes %>%
|
||||
group_by(exposure) %>%
|
||||
mutate(count = row_number(locationtimestamp))
|
||||
|
||||
exposure_colors = c(indoor = 'red', outdoor = 'lightgreen', mobile = 'blue', unknown = 'darkgrey')
|
||||
ggplot(exposure_counts, aes(x = locationtimestamp, 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.
|
||||
|
||||
Let's have a quick summary:
|
||||
```{r exposure_summary}
|
||||
exposure_counts %>%
|
||||
summarise(
|
||||
oldest = min(locationtimestamp),
|
||||
newest = max(locationtimestamp),
|
||||
count = max(count)
|
||||
) %>%
|
||||
arrange(desc(count))
|
||||
```
|
||||
|
||||
## ...and grouptag
|
||||
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`, ...)
|
||||
|
||||
```{r grouptag_counts, message=FALSE}
|
||||
grouptag_counts = boxes %>%
|
||||
group_by(grouptag) %>%
|
||||
# only include grouptags with 15 or more members
|
||||
filter(length(grouptag) >= 15 && !is.na(grouptag) && grouptag != '') %>%
|
||||
mutate(count = row_number(locationtimestamp))
|
||||
|
||||
# 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 = locationtimestamp, y = count, colour = grouptag)) +
|
||||
geom_line(aes(group = grouptag)) +
|
||||
xlab('Registration Date') + ylab('senseBox count')
|
||||
```
|
||||
|
||||
```{r grouptag_summary}
|
||||
grouptag_counts %>%
|
||||
summarise(
|
||||
oldest = min(locationtimestamp),
|
||||
newest = max(locationtimestamp),
|
||||
count = max(count)
|
||||
) %>%
|
||||
arrange(desc(count))
|
||||
```
|
||||
|
||||
# Plot rate of growth and inactivity per week
|
||||
First we group the boxes by `locationtimestamp` into bins of one week:
|
||||
```{r growthrate_registered, warning=FALSE, message=FALSE, results='hide'}
|
||||
bins = 'week'
|
||||
mvavg_bins = 6
|
||||
|
||||
growth = boxes %>%
|
||||
mutate(week = cut(as.Date(locationtimestamp), 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. As a lot of boxes were "updated" by the database
|
||||
migration, many of them are updated at 2022-03-30, so we try to use the `lastMeasurement`
|
||||
attribute instead of `updatedAt`. This leads to fewer boxes but also automatically excludes
|
||||
boxes which were created but never made a measurement.
|
||||
|
||||
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.
|
||||
```{r growthrate_inactive, warning=FALSE, message=FALSE, results='hide'}
|
||||
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(lastMeasurement < now() - days(2)) %>%
|
||||
mutate(week = cut(as.Date(lastMeasurement), breaks = bins)) %>%
|
||||
filter(as.Date(week) > as.Date("2021-12-31")) %>%
|
||||
group_by(week) %>%
|
||||
summarize(count = length(week)) %>%
|
||||
mutate(event = 'inactive')
|
||||
```
|
||||
|
||||
Now we can combine both datasets for plotting:
|
||||
```{r growthrate, warning=FALSE, message=FALSE, results='hide'}
|
||||
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))))
|
||||
```
|
||||
|
||||
And see in which weeks the most boxes become (in)active:
|
||||
```{r table_mostregistrations}
|
||||
boxes_by_date %>%
|
||||
filter(count > 50) %>%
|
||||
arrange(desc(count))
|
||||
```
|
||||
|
||||
# Plot duration of boxes being active {.tabset}
|
||||
While we are looking at `locationtimestamp` and `lastMeasurement`, we can also extract the duration of activity
|
||||
of each box, and look at metrics by exposure and grouptag once more:
|
||||
|
||||
## ...by exposure
|
||||
```{r exposure_duration, message=FALSE}
|
||||
durations = boxes %>%
|
||||
group_by(exposure) %>%
|
||||
filter(!is.na(lastMeasurement)) %>%
|
||||
mutate(duration = difftime(lastMeasurement, locationtimestamp, units='days')) %>%
|
||||
filter(duration >= 0)
|
||||
|
||||
ggplot(durations, aes(x = exposure, y = duration)) +
|
||||
geom_boxplot() +
|
||||
coord_flip() + ylab('Duration active in Days')
|
||||
```
|
||||
|
||||
The time of activity averages at only `r round(mean(durations$duration))` days,
|
||||
though there are boxes with `r round(max(durations$duration))` days of activity,
|
||||
spanning a large chunk of openSenseMap's existence.
|
||||
|
||||
## ...by grouptag
|
||||
```{r grouptag_duration, message=FALSE}
|
||||
durations = boxes %>%
|
||||
filter(!is.na(lastMeasurement)) %>%
|
||||
group_by(grouptag) %>%
|
||||
# only include grouptags with 20 or more members
|
||||
filter(length(grouptag) >= 15 & !is.na(grouptag) & !is.na(lastMeasurement)) %>%
|
||||
mutate(duration = difftime(lastMeasurement, locationtimestamp, units='days')) %>%
|
||||
filter(duration >= 0)
|
||||
|
||||
ggplot(durations, aes(x = grouptag, y = duration)) +
|
||||
geom_boxplot() +
|
||||
coord_flip() + ylab('Duration active in Days')
|
||||
|
||||
durations %>%
|
||||
summarize(
|
||||
duration_avg = round(mean(duration)),
|
||||
duration_min = round(min(duration)),
|
||||
duration_max = round(max(duration)),
|
||||
oldest_box = round(max(difftime(now(), locationtimestamp, units='days')))
|
||||
) %>%
|
||||
arrange(desc(duration_avg))
|
||||
```
|
||||
|
||||
The time of activity averages at only `r round(mean(durations$duration))` days,
|
||||
though there are boxes with `r round(max(durations$duration))` days of activity,
|
||||
spanning a large chunk of openSenseMap's existence.
|
||||
|
||||
## ...by year of registration
|
||||
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][PR]!
|
||||
|
||||
```{r year_duration, message=FALSE}
|
||||
# NOTE: boxes older than 2016 missing due to missing updatedAt in database
|
||||
duration = boxes %>%
|
||||
mutate(year = cut(as.Date(locationtimestamp), breaks = 'year')) %>%
|
||||
group_by(year) %>%
|
||||
filter(!is.na(lastMeasurement)) %>%
|
||||
mutate(duration = difftime(lastMeasurement, locationtimestamp, units='days')) %>%
|
||||
filter(duration >= 0)
|
||||
|
||||
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')
|
||||
```
|
||||
|
||||
# More Visualisations
|
||||
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][PR].
|
||||
|
||||
* growth by phenomenon
|
||||
* growth by location -> (interactive) map
|
||||
* set inactive rate in relation to total box count
|
||||
* filter timespans with big dips in growth rate, and extrapolate the amount of
|
||||
senseBoxes that could be on the platform today, assuming there were no production issues ;)
|
||||
|
||||
[PR]: https://github.com/sensebox/opensensmapr/pulls
|
||||
|
||||
|
||||
File diff suppressed because one or more lines are too long
@ -0,0 +1,17 @@
|
||||
% Generated by roxygen2: do not edit by hand
|
||||
% Please edit documentation in R/api.R
|
||||
\name{osem_ensure_api_available}
|
||||
\alias{osem_ensure_api_available}
|
||||
\title{Check if the given openSenseMap API endpoint is available}
|
||||
\usage{
|
||||
osem_ensure_api_available(endpoint = osem_endpoint())
|
||||
}
|
||||
\arguments{
|
||||
\item{endpoint}{The API base URL to check, defaulting to \code{\link{osem_endpoint}}}
|
||||
}
|
||||
\value{
|
||||
\code{TRUE} if the API is available, otherwise \code{stop()} is called.
|
||||
}
|
||||
\description{
|
||||
Check if the given openSenseMap API endpoint is available
|
||||
}
|
||||
@ -0,0 +1,300 @@
|
||||
---
|
||||
title: "Visualising the Develpment of openSenseMap.org in 2022"
|
||||
author: "Jan Stenkamp"
|
||||
date: '`r Sys.Date()`'
|
||||
output:
|
||||
html_document:
|
||||
code_folding: hide
|
||||
df_print: kable
|
||||
theme: lumen
|
||||
toc: yes
|
||||
toc_float: yes
|
||||
rmarkdown::html_vignette:
|
||||
df_print: kable
|
||||
fig_height: 5
|
||||
fig_width: 7
|
||||
toc: yes
|
||||
vignette: >
|
||||
%\VignetteIndexEntry{Visualising the Develpment of openSenseMap.org in 2022}
|
||||
%\VignetteEncoding{UTF-8}
|
||||
%\VignetteEngine{knitr::rmarkdown}
|
||||
---
|
||||
|
||||
> This vignette serves as an example on data wrangling & visualization with
|
||||
`opensensmapr`, `dplyr` and `ggplot2`.
|
||||
|
||||
```{r setup, results='hide', message=FALSE, warning=FALSE}
|
||||
# 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*.
|
||||
|
||||
```{r download, results='hide', message=FALSE, warning=FALSE}
|
||||
# if you want to see results for a specific subset of boxes,
|
||||
# just specify a filter such as grouptag='ifgi' here
|
||||
boxes_all = osem_boxes()
|
||||
boxes = boxes_all
|
||||
```
|
||||
# Introduction
|
||||
In the following we just want to have a look at the boxes created in 2022, so we filter for them.
|
||||
|
||||
```{r}
|
||||
boxes = filter(boxes, locationtimestamp >= "2022-01-01" & locationtimestamp <="2022-12-31")
|
||||
summary(boxes) -> summary.data.frame
|
||||
```
|
||||
|
||||
<!-- This gives a good overview already: As of writing this, there are more than 11,000 -->
|
||||
<!-- sensor stations, of which ~30% are currently running. Most of them are placed -->
|
||||
<!-- outdoors and have around 5 sensors each. -->
|
||||
<!-- The oldest station is from August 2016, while the latest station was registered a -->
|
||||
<!-- couple of minutes ago. -->
|
||||
|
||||
Another feature of interest is the spatial distribution of the boxes: `plot()`
|
||||
can help us out here. This function requires a bunch of optional dependencies though.
|
||||
|
||||
```{r message=F, warning=F}
|
||||
if (!require('maps')) install.packages('maps')
|
||||
if (!require('maptools')) install.packages('maptools')
|
||||
if (!require('rgeos')) install.packages('rgeos')
|
||||
|
||||
plot(boxes)
|
||||
```
|
||||
|
||||
But what do these sensor stations actually measure? Lets find out.
|
||||
`osem_phenomena()` gives us a named list of of the counts of each observed
|
||||
phenomenon for the given set of sensor stations:
|
||||
|
||||
```{r}
|
||||
phenoms = osem_phenomena(boxes)
|
||||
str(phenoms)
|
||||
```
|
||||
|
||||
Thats quite some noise there, with many phenomena being measured by a single
|
||||
sensor only, or many duplicated phenomena due to slightly different spellings.
|
||||
We should clean that up, but for now let's just filter out the noise and find
|
||||
those phenomena with high sensor numbers:
|
||||
|
||||
```{r}
|
||||
phenoms[phenoms > 50]
|
||||
```
|
||||
|
||||
|
||||
# Plot count of boxes by time {.tabset}
|
||||
By looking at the `createdAt` attribute of each box we know the exact time a box
|
||||
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.
|
||||
|
||||
With this approach we have no information about boxes that were deleted in the
|
||||
meantime, but that's okay for now.
|
||||
|
||||
## ...and exposure
|
||||
```{r exposure_counts, message=FALSE}
|
||||
exposure_counts = boxes %>%
|
||||
group_by(exposure) %>%
|
||||
mutate(count = row_number(locationtimestamp))
|
||||
|
||||
exposure_colors = c(indoor = 'red', outdoor = 'lightgreen', mobile = 'blue', unknown = 'darkgrey')
|
||||
ggplot(exposure_counts, aes(x = locationtimestamp, 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.
|
||||
|
||||
Let's have a quick summary:
|
||||
```{r exposure_summary}
|
||||
exposure_counts %>%
|
||||
summarise(
|
||||
oldest = min(locationtimestamp),
|
||||
newest = max(locationtimestamp),
|
||||
count = max(count)
|
||||
) %>%
|
||||
arrange(desc(count))
|
||||
```
|
||||
|
||||
## ...and grouptag
|
||||
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`, ...)
|
||||
|
||||
```{r grouptag_counts, message=FALSE}
|
||||
grouptag_counts = boxes %>%
|
||||
group_by(grouptag) %>%
|
||||
# only include grouptags with 15 or more members
|
||||
filter(length(grouptag) >= 15 & !is.na(grouptag) & grouptag != '') %>%
|
||||
mutate(count = row_number(locationtimestamp))
|
||||
|
||||
# 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 = locationtimestamp, y = count, colour = grouptag)) +
|
||||
geom_line(aes(group = grouptag)) +
|
||||
xlab('Registration Date') + ylab('senseBox count')
|
||||
```
|
||||
|
||||
```{r grouptag_summary}
|
||||
grouptag_counts %>%
|
||||
summarise(
|
||||
oldest = min(locationtimestamp),
|
||||
newest = max(locationtimestamp),
|
||||
count = max(count)
|
||||
) %>%
|
||||
arrange(desc(count))
|
||||
```
|
||||
|
||||
# Plot rate of growth and inactivity per week
|
||||
First we group the boxes by `locationtimestamp` into bins of one week:
|
||||
```{r growthrate_registered, warning=FALSE, message=FALSE, results='hide'}
|
||||
bins = 'week'
|
||||
mvavg_bins = 6
|
||||
|
||||
growth = boxes %>%
|
||||
mutate(week = cut(as.Date(locationtimestamp), 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. As a lot of boxes were "updated" by the database
|
||||
migration, many of them are updated at 2022-03-30, so we try to use the `lastMeasurement`
|
||||
attribute instead of `updatedAt`. This leads to fewer boxes but also automatically excludes
|
||||
boxes which were created but never made a measurement.
|
||||
|
||||
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.
|
||||
```{r growthrate_inactive, warning=FALSE, message=FALSE, results='hide'}
|
||||
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(lastMeasurement < now() - days(2)) %>%
|
||||
mutate(week = cut(as.Date(lastMeasurement), breaks = bins)) %>%
|
||||
filter(as.Date(week) > as.Date("2021-12-31")) %>%
|
||||
group_by(week) %>%
|
||||
summarize(count = length(week)) %>%
|
||||
mutate(event = 'inactive')
|
||||
```
|
||||
|
||||
Now we can combine both datasets for plotting:
|
||||
```{r growthrate, warning=FALSE, message=FALSE, results='hide'}
|
||||
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))))
|
||||
```
|
||||
|
||||
And see in which weeks the most boxes become (in)active:
|
||||
```{r table_mostregistrations}
|
||||
boxes_by_date %>%
|
||||
filter(count > 50) %>%
|
||||
arrange(desc(count))
|
||||
```
|
||||
|
||||
# Plot duration of boxes being active {.tabset}
|
||||
While we are looking at `locationtimestamp` and `lastMeasurement`, we can also extract the duration of activity
|
||||
of each box, and look at metrics by exposure and grouptag once more:
|
||||
|
||||
## ...by exposure
|
||||
```{r exposure_duration, message=FALSE}
|
||||
durations = boxes %>%
|
||||
group_by(exposure) %>%
|
||||
filter(!is.na(lastMeasurement)) %>%
|
||||
mutate(duration = difftime(lastMeasurement, locationtimestamp, units='days')) %>%
|
||||
filter(duration >= 0)
|
||||
|
||||
ggplot(durations, aes(x = exposure, y = duration)) +
|
||||
geom_boxplot() +
|
||||
coord_flip() + ylab('Duration active in Days')
|
||||
```
|
||||
|
||||
The time of activity averages at only `r round(mean(durations$duration))` days,
|
||||
though there are boxes with `r round(max(durations$duration))` days of activity,
|
||||
spanning a large chunk of openSenseMap's existence.
|
||||
|
||||
## ...by grouptag
|
||||
```{r grouptag_duration, message=FALSE}
|
||||
durations = boxes %>%
|
||||
filter(!is.na(lastMeasurement)) %>%
|
||||
group_by(grouptag) %>%
|
||||
# only include grouptags with 20 or more members
|
||||
filter(length(grouptag) >= 15 & !is.na(grouptag) & !is.na(lastMeasurement)) %>%
|
||||
mutate(duration = difftime(lastMeasurement, locationtimestamp, units='days')) %>%
|
||||
filter(duration >= 0)
|
||||
|
||||
ggplot(durations, aes(x = grouptag, y = duration)) +
|
||||
geom_boxplot() +
|
||||
coord_flip() + ylab('Duration active in Days')
|
||||
|
||||
durations %>%
|
||||
summarize(
|
||||
duration_avg = round(mean(duration)),
|
||||
duration_min = round(min(duration)),
|
||||
duration_max = round(max(duration)),
|
||||
oldest_box = round(max(difftime(now(), locationtimestamp, units='days')))
|
||||
) %>%
|
||||
arrange(desc(duration_avg))
|
||||
```
|
||||
|
||||
The time of activity averages at only `r round(mean(durations$duration))` days,
|
||||
though there are boxes with `r round(max(durations$duration))` days of activity,
|
||||
spanning a large chunk of openSenseMap's existence.
|
||||
|
||||
## ...by year of registration
|
||||
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][PR]!
|
||||
|
||||
```{r year_duration, message=FALSE}
|
||||
# NOTE: boxes older than 2016 missing due to missing updatedAt in database
|
||||
duration = boxes %>%
|
||||
mutate(year = cut(as.Date(locationtimestamp), breaks = 'year')) %>%
|
||||
group_by(year) %>%
|
||||
filter(!is.na(lastMeasurement)) %>%
|
||||
mutate(duration = difftime(lastMeasurement, locationtimestamp, units='days')) %>%
|
||||
filter(duration >= 0)
|
||||
|
||||
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')
|
||||
```
|
||||
|
||||
# More Visualisations
|
||||
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][PR].
|
||||
|
||||
* growth by phenomenon
|
||||
* growth by location -> (interactive) map
|
||||
* set inactive rate in relation to total box count
|
||||
* filter timespans with big dips in growth rate, and extrapolate the amount of
|
||||
senseBoxes that could be on the platform today, assuming there were no production issues ;)
|
||||
|
||||
[PR]: https://github.com/sensebox/opensensmapr/pulls
|
||||
|
||||
|
||||
Loading…
Reference in New Issue