library("dplyr", warn.conflicts = F) library(stringr) unify_ios_screen <- function(ios_screen){ # In Android we only process UNLOCK to OFF episodes. In iOS we only process UNLOCK to LOCKED episodes, # thus, we replace LOCKED with OFF episodes (2 to 0) so we can use Android's code for iOS ios_screen <- ios_screen %>% # only keep consecutive pairs of 3,2 events filter( (screen_status == 3 & lead(screen_status) == 2) | (screen_status == 2 & lag(screen_status) == 3) ) %>% mutate(screen_status = replace(screen_status, screen_status == 2, 0)) return(ios_screen) } unify_ios_battery <- function(ios_battery){ # We only need to unify battery data for iOS client V1. V2 does it out-of-the-box # V1 will not have rows where battery_status is equal to 4 if(nrow(ios_battery %>% filter(battery_status == 4)) == 0) ios_battery <- ios_battery %>% mutate(battery_status = replace(battery_status, battery_status == 3, 5), battery_status = replace(battery_status, battery_status == 1, 3)) return(ios_battery) } unify_ios_calls <- function(ios_calls){ # Android’s call types 1=incoming, 2=outgoing, 3=missed # iOS' call status 1=incoming, 2=connected, 3=dialing, 4=disconnected # iOS' call types based on call status: (1,2,4)=incoming=1, (3,2,4)=outgoing=2, (1,4) or (3,4)=missed=3 # 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 # Even tho iOS stores the duration of ringing/dialing for missed calls, we set it to 0 to match Android ios_calls <- ios_calls %>% arrange(trace, timestamp, call_type) %>% group_by(trace) %>% # search for the disconnect event, as it is common to outgoing, received and missed calls mutate(completed_call = ifelse(call_type == 4, 2, 0), # assign the same ID to all events before a 4 completed_call = cumsum(c(1, head(completed_call, -1) != tail(completed_call, -1))), # hack to match ID of last event (4) to that of the previous rows completed_call = ifelse(call_type == 4, completed_call - 1, completed_call)) # We check utc_date_time and local_date_time exist because sometimes we call this function from # download_dataset to unify multi-platform participants. At that point such time columns are missing if("utc_date_time" %in% colnames(ios_calls) && "local_date_time" %in% colnames(ios_calls)){ ios_calls <- ios_calls %>% summarise(call_type_sequence = paste(call_type, collapse = ","), # collapse all events before a 4 # sanity check, timestamp_diff should be equal or close to duration sum # timestamp_diff = trunc((last(timestamp) - first(timestamp)) / 1000) # use call_duration = last(call_duration) if you want duration from pick up to hang up # use call_duration = sum(call_duration) if you want duration from dialing/ringing to hang up call_duration = last(call_duration), timestamp = first(timestamp), utc_date_time = first(utc_date_time), local_date_time = first(local_date_time), local_date = first(local_date), local_time = first(local_time), local_hour = first(local_hour), local_minute = first(local_minute), local_timezone = first(local_timezone), assigned_segments = first(assigned_segments)) } else { ios_calls <- ios_calls %>% summarise(call_type_sequence = paste(call_type, collapse = ","), call_duration = sum(call_duration), timestamp = first(timestamp)) } ios_calls <- ios_calls %>% mutate(call_type = case_when( call_type_sequence == "1,2,4" | call_type_sequence == "2,1,4" ~ 1, # incoming call_type_sequence == "1,4" ~ 3, # missed call_type_sequence == "3,2,4" | call_type_sequence == "2,3,4" ~ 2, # outgoing call_type_sequence == "3,4" ~ 4, # outgoing missed, we create this temp missed state to assign a duration of 0 below TRUE ~ -1), # other, call sequences without a disconnect (4) event are discarded # assign a duration of 0 to incoming and outgoing missed calls call_duration = ifelse(call_type == 3 | call_type == 4, 0, call_duration), # get rid of the temp missed call type, set to 2 to match Android. See https://github.com/carissalow/rapids/issues/79 call_type = ifelse(call_type == 4, 2, call_type) ) %>% # discard sequences without an event 4 (disconnect) filter(call_type > 0) %>% ungroup() %>% arrange(timestamp) return(ios_calls) } clean_ios_activity_column <- function(ios_gar){ ios_gar <- ios_gar %>% mutate(activities = str_replace_all(activities, pattern = '("|\\[|\\])', replacement = "")) existent_multiple_activities <- ios_gar %>% filter(str_detect(activities, ",")) %>% group_by(activities) %>% summarise(mutiple_activities = unique(activities)) %>% pull(mutiple_activities) known_multiple_activities <- c("stationary,automotive") unkown_multiple_actvities <- setdiff(existent_multiple_activities, known_multiple_activities) if(length(unkown_multiple_actvities) > 0){ stop(paste0("There are unkwown combinations of ios activities, you need to implement the decision of the ones to keep: ", unkown_multiple_actvities)) } ios_gar <- ios_gar %>% mutate(activities = str_replace_all(activities, pattern = "stationary,automotive", replacement = "automotive")) return(ios_gar) } unify_ios_activity_recognition <- function(ios_gar){ # We only need to unify Google Activity Recognition data for iOS # discard rows where activities column is blank ios_gar <- ios_gar[-which(ios_gar$activities == ""), ] # clean "activities" column of ios_gar ios_gar <- clean_ios_activity_column(ios_gar) # make it compatible with android version: generate "activity_name" and "activity_type" columns ios_gar <- ios_gar %>% mutate(activity_name = case_when(activities == "automotive" ~ "in_vehicle", activities == "cycling" ~ "on_bicycle", activities == "walking" ~ "walking", activities == "running" ~ "running", activities == "stationary" ~ "still"), activity_type = case_when(activities == "automotive" ~ 0, activities == "cycling" ~ 1, activities == "walking" ~ 7, activities == "running" ~ 8, activities == "stationary" ~ 3, activities == "unknown" ~ 4)) return(ios_gar) } unify_ios_conversation <- function(conversation){ if(nrow(conversation) > 0){ duration_check <- conversation %>% select(double_convo_start, double_convo_end) %>% mutate(start_is_seconds = double_convo_start <= 9999999999, end_is_seconds = double_convo_end <= 9999999999) # Values smaller than 9999999999 are in seconds instead of milliseconds start_end_in_seconds = sum(duration_check$start_is_seconds) + sum(duration_check$end_is_seconds) if(start_end_in_seconds > 0) # convert seconds to milliseconds conversation <- conversation %>% mutate(double_convo_start = double_convo_start * 1000, double_convo_end = double_convo_end * 1000) } return(conversation) } # This function is used in download_dataset.R unify_raw_data <- function(dbEngine, sensor_table, sensor, timestamp_filter, aware_multiplatform_tables, device_ids, platforms){ # If platforms is 'multiple', fetch each device_id's platform from aware_device, otherwise, use those given by the user if(length(platforms) == 1 && platforms == "multiple") devices_platforms <- dbGetQuery(dbEngine, paste0("SELECT device_id,brand FROM aware_device WHERE device_id IN ('", paste0(device_ids, collapse = "','"), "')")) %>% mutate(platform = ifelse(brand == "iPhone", "ios", "android")) else devices_platforms <- data.frame(device_id = device_ids, platform = platforms) # Get existent tables in database available_tables_in_db <- dbGetQuery(dbEngine, paste0("SELECT table_name FROM information_schema.tables WHERE table_schema='", dbGetInfo(dbEngine)$dbname,"'"))[[1]] if(!any(sensor_table %in% available_tables_in_db)) stop(paste0("You requested data from these table(s) ", paste0(sensor_table, collapse=", "), " but they don't exist in your database ", dbGetInfo(dbEngine)$dbname)) # Parse the table names for activity recognition and conversation plugins because they are different between android and ios ar_tables <- setNames(aware_multiplatform_tables[1:2], c("android", "ios")) conversation_tables <- setNames(aware_multiplatform_tables[3:4], c("android", "ios")) participants_sensordata <- list() for(i in 1:nrow(devices_platforms)) { row <- devices_platforms[i,] device_id <- row$device_id platform <- row$platform # Handle special cases when tables for the same sensor have different names for Android and iOS (AR and conversation) if(length(sensor_table) == 1) table <- sensor_table else if(all(sensor_table == ar_tables)) table <- ar_tables[[platform]] else if(all(sensor_table == conversation_tables)) table <- conversation_tables[[platform]] if(table %in% available_tables_in_db){ query <- paste0("SELECT * FROM ", table, " WHERE device_id IN ('", device_id, "')", timestamp_filter) sensor_data <- unify_data(dbGetQuery(dbEngine, query), sensor, platform) participants_sensordata <- append(participants_sensordata, list(sensor_data)) }else{ warning(paste0("Missing ", table, " table. We unified the data from ", paste0(devices_platforms$device_id, collapse = " and "), " but without records from this missing table for ", device_id)) } } unified_data <- bind_rows(participants_sensordata) return(unified_data) } # This function is used in unify_ios_android.R and unify_raw_data function unify_data <- function(sensor_data, sensor, platform){ if(sensor == "phone_calls" & platform == "ios"){ sensor_data = unify_ios_calls(sensor_data) } else if(sensor == "phone_battery" & platform == "ios"){ sensor_data = unify_ios_battery(sensor_data) } else if(sensor == "phone_activity_recognition" & platform == "ios"){ sensor_data = unify_ios_activity_recognition(sensor_data) } else if(sensor == "phone_screen" & platform == "ios"){ sensor_data = unify_ios_screen(sensor_data) } else if(sensor == "phone_conversation" & platform == "ios"){ sensor_data = unify_ios_conversation(sensor_data) } return(sensor_data) }