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137 lines
5.0 KiB
R
137 lines
5.0 KiB
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(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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## ----download-----------------------------------------------------------------
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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 = osem_boxes(cache = '.')
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boxes = readRDS('boxes_precomputed.rds') # read precomputed file to save resources
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## ----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(createdAt))
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exposure_colors = c(indoor = 'red', outdoor = 'lightgreen', mobile = 'blue', unknown = 'darkgrey')
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ggplot(exposure_counts, aes(x = createdAt, 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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## ----exposure_summary---------------------------------------------------------
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exposure_counts %>%
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summarise(
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oldest = min(createdAt),
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newest = max(createdAt),
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count = max(count)
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) %>%
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arrange(desc(count))
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## ----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 8 or more members
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filter(length(grouptag) >= 8 & !is.na(grouptag)) %>%
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mutate(count = row_number(createdAt))
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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 = createdAt, 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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## ----grouptag_summary---------------------------------------------------------
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grouptag_counts %>%
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summarise(
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oldest = min(createdAt),
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newest = max(createdAt),
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count = max(count)
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) %>%
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arrange(desc(count))
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## ----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(createdAt), 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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## ----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(updatedAt < now() - days(2)) %>%
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mutate(week = cut(as.Date(updatedAt), breaks = bins)) %>%
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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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## ----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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## ----exposure_duration, message=FALSE-----------------------------------------
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duration = boxes %>%
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group_by(exposure) %>%
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filter(!is.na(updatedAt)) %>%
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mutate(duration = difftime(updatedAt, createdAt, units='days'))
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ggplot(duration, 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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## ----grouptag_duration, message=FALSE-----------------------------------------
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duration = boxes %>%
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group_by(grouptag) %>%
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# only include grouptags with 8 or more members
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filter(length(grouptag) >= 8 & !is.na(grouptag) & !is.na(updatedAt)) %>%
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mutate(duration = difftime(updatedAt, createdAt, units='days'))
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ggplot(duration, 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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duration %>%
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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(), createdAt, units='days')))
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) %>%
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arrange(desc(duration_avg))
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## ----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(createdAt), breaks = 'year')) %>%
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group_by(year) %>%
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filter(!is.na(updatedAt)) %>%
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mutate(duration = difftime(updatedAt, createdAt, units='days'))
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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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