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opensensmapR/inst/doc/osem-history_revised.R

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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()
## ----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
## -----------------------------------------------------------------------------
boxes = filter(boxes, locationtimestamp >= "2022-01-01" & locationtimestamp <="2022-12-31")
summary(boxes) -> summary.data.frame
## ---- message=FALSE, warning=FALSE--------------------------------------------
plot(boxes)
## -----------------------------------------------------------------------------
phenoms = osem_phenomena(boxes)
str(phenoms)
## -----------------------------------------------------------------------------
phenoms[phenoms > 50]
## ----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')
## ----exposure_summary---------------------------------------------------------
exposure_counts %>%
summarise(
oldest = min(locationtimestamp),
newest = max(locationtimestamp),
count = max(count)
) %>%
arrange(desc(count))
## ----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')
## ----grouptag_summary---------------------------------------------------------
grouptag_counts %>%
summarise(
oldest = min(locationtimestamp),
newest = max(locationtimestamp),
count = max(count)
) %>%
arrange(desc(count))
## ----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')
## ----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')
## ----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))))
## ----table_mostregistrations--------------------------------------------------
boxes_by_date %>%
filter(count > 50) %>%
arrange(desc(count))
## ----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')
## ----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))
## ----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')