132 lines
6.5 KiB
R
132 lines
6.5 KiB
R
source("renv/activate.R")
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library(dplyr)
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library(stringr)
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unify_ios_battery <- function(ios_battery){
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# We only need to unify battery data for iOS client V1. V2 does it out-of-the-box
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# V1 will not have rows where battery_status is equal to 4
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if(nrow(ios_battery %>% filter(battery_status == 4)) == 0)
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ios_battery <- ios_battery %>%
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mutate(battery_status = replace(battery_status, battery_status == 3, 5),
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battery_status = replace(battery_status, battery_status == 1, 3))
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return(ios_battery)
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}
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unify_ios_calls <- function(ios_calls){
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# Android’s call types 1=incoming, 2=outgoing, 3=missed
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# iOS' call status 1=incoming, 2=connected, 3=dialing, 4=disconnected
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# iOS' call types based on call status: (1,2,4)=incoming=1, (3,2,4)=outgoing=2, (1,4) or (3,4)=missed=3
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# Sometimes (due to a possible bug in Aware) sequences get logged on the exact same timestamp, thus 3-item sequences can be 2,3,4 or 3,2,4
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# Even tho iOS stores the duration of ringing/dialing for missed calls, we set it to 0 to match Android
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ios_calls <- ios_calls %>%
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arrange(trace, timestamp, call_type) %>%
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group_by(trace) %>%
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# search for the disconnect event, as it is common to outgoing, received and missed calls
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mutate(completed_call = ifelse(call_type == 4, 2, 0),
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# assign the same ID to all events before a 4
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completed_call = cumsum(c(1, head(completed_call, -1) != tail(completed_call, -1))),
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# hack to match ID of last event (4) to that of the previous rows
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completed_call = ifelse(call_type == 4, completed_call - 1, completed_call)) %>%
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summarise(call_type_sequence = paste(call_type, collapse = ","), # collapse all events before a 4
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# use this if Android measures calls' duration from pick up to hang up
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# duration = last(call_duration),
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# sanity check, timestamp_diff should be equal or close to duration sum
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# timestamp_diff = trunc((last(timestamp) - first(timestamp)) / 1000)
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# use this if Android measures calls' duration from dialing/ringing to hang up
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call_duration = sum(call_duration),
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timestamp = first(timestamp),
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utc_date_time = first(utc_date_time),
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local_date_time = first(local_date_time),
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local_date = first(local_date),
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local_time = first(local_time),
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local_hour = first(local_hour),
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local_minute = first(local_minute),
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local_day_segment = first(local_day_segment)
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) %>%
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mutate(call_type = case_when(
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call_type_sequence == "1,2,4" | call_type_sequence == "2,1,4" ~ 1, # incoming
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call_type_sequence == "1,4" ~ 3, # missed
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call_type_sequence == "3,2,4" | call_type_sequence == "2,3,4" ~ 2, # outgoing
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call_type_sequence == "3,4" ~ 4, # outgoing missed, we create this temp missed state to assign a duration of 0 below
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TRUE ~ -1), # other, call sequences without a disconnect (4) event are discarded
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# assign a duration of 0 to incoming and outgoing missed calls
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call_duration = ifelse(call_type == 3 | call_type == 4, 0, call_duration),
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# get rid of the temp missed call type, set to 3 to match Android
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call_type = ifelse(call_type == 4, 3, call_type)
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) %>%
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# discard sequences without an event 4 (disconnect)
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filter(call_type > 0) %>%
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ungroup() %>%
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arrange(timestamp)
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return(ios_calls)
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}
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clean_ios_activity_column <- function(ios_gar){
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ios_gar <- ios_gar %>%
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mutate(activities = str_replace_all(activities, pattern = '("|\\[|\\])', replacement = ""))
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existent_multiple_activities <- ios_gar %>%
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filter(str_detect(activities, ",")) %>%
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group_by(activities) %>%
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summarise(mutiple_activities = unique(activities)) %>%
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pull(mutiple_activities)
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known_multiple_activities <- c("stationary,automotive")
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unkown_multiple_actvities <- setdiff(existent_multiple_activities, known_multiple_activities)
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if(length(unkown_multiple_actvities) > 0){
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stop(paste0("There are unkwown combinations of ios activities, you need to implement the decision of the ones to keep: ", unkown_multiple_actvities))
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}
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ios_gar <- ios_gar %>%
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mutate(activities = str_replace_all(activities, pattern = "stationary,automotive", replacement = "automotive"))
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return(ios_gar)
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}
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unify_ios_gar <- function(ios_gar){
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# We only need to unify Google Activity Recognition data for iOS
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# discard rows where activities column is blank
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ios_gar <- ios_gar[-which(ios_gar$activities == ""), ]
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# clean "activities" column of ios_gar
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ios_gar <- clean_ios_activity_column(ios_gar)
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# make it compatible with android version: generate "activity_name" and "activity_type" columns
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ios_gar <- ios_gar %>%
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mutate(activity_name = case_when(activities == "automotive" ~ "in_vehicle",
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activities == "cycling" ~ "on_bicycle",
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activities == "walking" | activities == "running" ~ "on_foot",
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activities == "stationary" ~ "still"),
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activity_type = case_when(activities == "automotive" ~ 0,
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activities == "cycling" ~ 1,
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activities == "walking" | activities == "running" ~ 2,
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activities == "stationary" ~ 3,
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activities == "unknown" ~ 4))
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return(ios_gar)
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}
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sensor_data <- read.csv(snakemake@input[["sensor_data"]], stringsAsFactors = FALSE)
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participant_info <- snakemake@input[["participant_info"]]
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sensor <- snakemake@params[["sensor"]]
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platform <- readLines(participant_info, n=2)[[2]]
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if(sensor == "calls"){
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if(platform == "ios"){
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sensor_data = unify_ios_calls(sensor_data)
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}
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# android calls remain unchanged
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} else if(sensor == "battery"){
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if(platform == "ios"){
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sensor_data = unify_ios_battery(sensor_data)
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}
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# android battery remains unchanged
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} else if(sensor == "plugin_ios_activity_recognition"){
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sensor_data = unify_ios_gar(sensor_data)
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}
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write.csv(sensor_data, snakemake@output[[1]], row.names = FALSE)
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