119 lines
5.4 KiB
R
119 lines
5.4 KiB
R
source("renv/activate.R")
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library("tidyverse")
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library("readr")
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library("tidyr")
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library("lubridate")
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library("yaml")
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source("src/data/datetime/assign_to_time_segment.R")
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source("src/data/datetime/assign_to_multiple_timezones.R")
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split_local_date_time <- function(data){
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data <- data %>%
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separate(local_date_time, c("local_date","local_time"), "\\s", remove = FALSE) %>%
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separate(local_time, c("local_hour", "local_minute"), ":", remove = FALSE, extra = "drop") %>%
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mutate(local_hour = as.numeric(local_hour),
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local_minute = as.numeric(local_minute))
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return(data)
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}
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is_valid_timezone <- function(timezone) {
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return(timezone %in% (OlsonNames()))
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}
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validate_user_timezones <- function(timezone_parameters){
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if(!timezone_parameters$TYPE %in% c("SINGLE", "MULTIPLE"))
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stop("Invalid [TIMEZONE][TYPE], only valid options are SINGLE or MULTIPLE")
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if(timezone_parameters$TYPE == "SINGLE"){
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if(!is_valid_timezone(timezone_parameters$SINGLE$TZCODE))
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stop(paste("[TIMEZONE][SINGLE][TZCODE] is not a valid timezone: ", timezone_parameters$SINGLE$TZCODE))
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} else if(timezone_parameters$TYPE == "MULTIPLE"){
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tz_codes <- read.csv(timezone_parameters$MULTIPLE$TZCODES_FILE)
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valid_file_columns <- c("device_id", "timestamp", "tzcode")
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if(length(colnames(tz_codes)) != length(valid_file_columns) || !setequal(colnames(tz_codes), valid_file_columns))
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stop(paste("[TIMEZONE][MULTIPLE][TZCODES_FILE] has does not have the required columns. You provided",paste(colnames(tz_codes), collapse=","),"but we need",paste(valid_file_columns, collapse=",")))
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invalid_tz_codes <- tz_codes %>%
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mutate(row = (1:n()) + 1,
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tzcode = trimws(tzcode, which="both"),
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is_valid = is_valid_timezone(tzcode)) %>%
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filter(is_valid == FALSE)
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if(nrow(invalid_tz_codes) > 0)
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stop(paste("[TIMEZONE][MULTIPLE][TZCODES_FILE] has invalid time zone codes. In file ", timezone_parameters$MULTIPLE$TZCODES_FILE, ".\nAffected rows=[", paste(invalid_tz_codes %>% pull(row),collapse=","), "], with invalid codes=[", paste(invalid_tz_codes %>% pull(tzcode),collapse=",") ,"]"))
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}
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}
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create_mising_temporal_column <- function(data, device_type){
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if(device_type == "fitbit"){
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# For fibit we infere timestamp from Fitbit's local date time
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return(data %>%
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group_by(local_timezone) %>%
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nest() %>%
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mutate(data = map2(data, local_timezone, function(nested_data, tz){
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return(nested_data %>% mutate(timestamp = as.numeric(ymd_hms(local_date_time, tz=tz)) * 1000) %>% drop_na(timestamp))
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})) %>%
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unnest(cols = everything()))
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} else {
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# For the rest of devices we infere local date time from timestamp
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return(data %>%
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group_by(local_timezone) %>%
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nest() %>%
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mutate(data = map2(data, local_timezone, function(nested_data, tz){
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return(nested_data %>% mutate(local_date_time = format(as_datetime(timestamp / 1000, tz=tz), format="%Y-%m-%d %H:%M:%S")) %>% drop_na(local_date_time) )
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})) %>%
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unnest(cols = everything()))
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}
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}
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filter_wanted_dates <- function(output, participant_file, device_type){
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participant_data <- read_yaml(participant_file)
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device_type <- toupper(device_type)
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start_date <- participant_data[[device_type]]$START_DATE
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end_date <- participant_data[[device_type]]$END_DATE
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if(!is.null(start_date)){
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start_date <- parse_date_time(start_date, orders = c("ymd", "ymdhMs", "ymdhM", "ymdh"))
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if(is.na(start_date))
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stop(paste0("[",device_type, "][START_DATE] does not have one of these valid formats: [ymd, ymd hms, ymd hm, ymd h], you typed: '", participant_data[[device_type]]$START_DATE, "' in ", participant_file))
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output <- output %>% filter(ymd_hms(local_date_time) >= start_date)
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}
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if(!is.null(end_date)){
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end_date <- parse_date_time(end_date, orders = c("ymd", "ymdhMs", "ymdhM", "ymdh"))
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if(is.na(end_date))
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stop(paste0("[",device_type, "][END_DATE] does not have one of these valid formats: [ymd, ymd hms, ymd hm, ymd h], you typed: '", participant_data[[device_type]]$END_DATE, "' in ", participant_file))
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output <- output %>% filter(ymd_hms(local_date_time) <= end_date)
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}
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return(output)
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}
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readable_datetime <- function(){
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input <- read.csv(snakemake@input[["sensor_input"]]) %>% arrange(timestamp)
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time_segments <- read.csv(snakemake@input[["time_segments"]])
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participant_file <- snakemake@input[["pid_file"]]
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device_type <- snakemake@params[["device_type"]]
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timezone_parameters <- snakemake@params[["timezone_parameters"]]
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pid <- snakemake@params[["pid"]]
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time_segments_type <- snakemake@params[["time_segments_type"]]
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include_past_periodic_segments <- snakemake@params[["include_past_periodic_segments"]]
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validate_user_timezones(timezone_parameters)
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if(timezone_parameters$TYPE == "SINGLE")
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output <- input %>% mutate(local_timezone = timezone_parameters$SINGLE$TZCODE)
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else if(timezone_parameters$TYPE == "MULTIPLE")
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output <- multiple_time_zone_assignment(input, timezone_parameters, device_type, pid, participant_file)
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output <- create_mising_temporal_column(output, device_type)
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output <- split_local_date_time(output)
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output <- assign_to_time_segment(output, time_segments, time_segments_type, include_past_periodic_segments)
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output <- filter_wanted_dates(output, participant_file, device_type)
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write_csv(output, snakemake@output[[1]])
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}
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readable_datetime() |