opensensmapR/inst/doc/osem-history_revised.Rmd

---
title: "Visualising the Development 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 Development 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 = osem_boxes(cache = '.')
boxes = readRDS('boxes_precomputed.rds')  # read precomputed file to save resources 
```
# 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=FALSE, warning=FALSE}
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