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ed193d2290
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@ -7,7 +7,6 @@ PIDS: [nokia_0000003]
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# See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files
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CREATE_PARTICIPANT_FILES:
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USERNAMES_CSV: "data/external/example_usernames.csv"
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CSV_FILE_PATH: "data/external/example_participants.csv" # see docs for required format
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PHONE_SECTION:
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ADD: True
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@ -319,10 +319,10 @@
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},
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"dbplyr": {
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"Package": "dbplyr",
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"Version": "2.1.1",
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"Version": "2.0.0",
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"Source": "Repository",
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"Repository": "CRAN",
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"Hash": "1f37fa4ab2f5f7eded42f78b9a887182"
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"Hash": "714005206038b1dda74cb1de85029a20"
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},
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"desc": {
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"Package": "desc",
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@ -4,19 +4,6 @@ rule create_example_participant_files:
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shell:
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"echo 'PHONE:\n DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524]\n PLATFORMS: [android]\n LABEL: test-01\n START_DATE: 2020-04-23 00:00:00\n END_DATE: 2020-05-04 23:59:59\nFITBIT:\n DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524]\n LABEL: test-01\n START_DATE: 2020-04-23 00:00:00\n END_DATE: 2020-05-04 23:59:59\n' >> ./data/external/participant_files/example01.yaml && echo 'PHONE:\n DEVICE_IDS: [13dbc8a3-dae3-4834-823a-4bc96a7d459d]\n PLATFORMS: [ios]\n LABEL: test-02\n START_DATE: 2020-04-23 00:00:00\n END_DATE: 2020-05-04 23:59:59\nFITBIT:\n DEVICE_IDS: [13dbc8a3-dae3-4834-823a-4bc96a7d459d]\n LABEL: test-02\n START_DATE: 2020-04-23 00:00:00\n END_DATE: 2020-05-04 23:59:59\n' >> ./data/external/participant_files/example02.yaml"
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rule prepare_participants_csv:
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input:
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username_list = config["CREATE_PARTICIPANT_FILES"]["USERNAMES_CSV"]
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params:
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data_configuration = config["PHONE_DATA_STREAMS"][config["PHONE_DATA_STREAMS"]["USE"]],
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participants_table = "participants",
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device_id_table = "light_sensor",
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start_end_date_table = "esm"
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output:
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participants_file = config["CREATE_PARTICIPANT_FILES"]["CSV_FILE_PATH"]
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script:
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"../src/data/translate_usernames_into_participants_data.R"
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rule create_participants_files:
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input:
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participants_file = config["CREATE_PARTICIPANT_FILES"]["CSV_FILE_PATH"]
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@ -29,7 +29,6 @@ library(RPostgres)
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# <stdin>:1:10: fatal error: libpq-fe.h: No such file or directory
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# compilation terminated.
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library(dbplyr)
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library(yaml)
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#' @description
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@ -106,101 +105,3 @@ pull_data <- function(stream_parameters, device, sensor, sensor_container, colum
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return(sensor_data)
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}
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#' @description
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#' Gets participants' IDs for specified usernames.
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#'
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#' @param stream_parameters The PHONE_DATA_STREAMS key in config.yaml. If you need specific parameters add them there.
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#' @param usernames A vector of usernames
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#' @param participants_container The name of the database table containing participants data, such as their username.
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#' @return A dataframe with participant IDs matching usernames
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pull_participants_ids <- function(stream_parameters, usernames, participants_container) {
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dbEngine <- get_db_engine(stream_parameters$DATABASE_GROUP)
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query_participant_id <- tbl(dbEngine, participants_container) %>%
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filter(username %in% usernames) %>%
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select(username, id)
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message(paste0("Executing the following query to get the participant's id: \n", sql_render(query_participant_id)))
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participant_data <- query_participant_id %>% collect()
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dbDisconnect(dbEngine)
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if(nrow(participant_data) == 0)
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warning(paste("We could not find requested usernames (", usernames, ") in ", participants_container))
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return(participant_data)
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}
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#' @description
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#' Gets participants' IDs for specified participant IDs
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#'
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#' @param stream_parameters The PHONE_DATA_STREAMS key in config.yaml. If you need specific parameters add them there.
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#' @param participants_ids A vector of numeric participant IDs
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#' @param device_id_container The name of the database table which will be used to determine distinct device ID. Ideally, a table that reliably contains data, but not too much.
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#' @return A dataframe with a row matching each distinct device ID with a participant ID
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pull_participants_device_ids <- function(stream_parameters, participants_ids, device_id_container) {
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dbEngine <- get_db_engine(stream_parameters$DATABASE_GROUP)
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query_device_id <- tbl(dbEngine, device_id_container) %>%
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filter(participant_id %in% !!participants_ids) %>%
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group_by(participant_id) %>%
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distinct(device_id, .keep_all = FALSE)
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message(paste0("Executing the following query to get the distinct device IDs: \n", sql_render(query_device_id)))
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device_ids <- query_device_id %>% collect()
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dbDisconnect(dbEngine)
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if(nrow(device_ids) == 0)
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warning(paste("We could not find device IDs for requested participant IDs (", participants_ids, ") in ", device_id_container))
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return(device_ids)
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}
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#' @description
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#' Gets start and end datetimes for specified participant IDs.
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#'
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#' @param stream_parameters The PHONE_DATA_STREAMS key in config.yaml. If you need specific parameters add them there.
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#' @param participants_ids A vector of numeric participant IDs
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#' @param start_end_date_container The name of the database table which will be used to determine when a participant started and ended their participation. Briefing and debriefing EMAs can be meaningfully used here.
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#' @return A dataframe relating participant IDs with their start and end datetimes.
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pull_participants_start_end_dates <- function(stream_parameters, participants_ids, start_end_date_container) {
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dbEngine <- get_db_engine(stream_parameters$DATABASE_GROUP)
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query_timestamps <- tbl(dbEngine, start_end_date_container) %>%
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filter(
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participant_id %in% !!participants_ids,
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double_esm_user_answer_timestamp > 0
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) %>%
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group_by(participant_id) %>%
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summarise(
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timestamp_min = min(double_esm_user_answer_timestamp, na.rm = TRUE),
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timestamp_max = max(double_esm_user_answer_timestamp, na.rm = TRUE)
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) %>%
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select(participant_id, timestamp_min, timestamp_max)
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message(paste0("Executing the following query to get the starting and ending datetimes: \n", sql_render(query_timestamps)))
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start_end_timestamps <- query_timestamps %>% collect()
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if(nrow(start_end_timestamps) == 0)
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warning(paste("We could not find datetimes for requested participant IDs (", participants_ids, ") in ", start_end_date_container))
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start_end_times <- start_end_timestamps %>%
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mutate(
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datetime_start = as_datetime(timestamp_min/1000, tz = "UTC"),
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datetime_end = as_datetime(timestamp_max/1000, tz = "UTC")
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) %>%
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select(-c(timestamp_min, timestamp_max))
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dbDisconnect(dbEngine)
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return(start_end_times)
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}
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@ -1,62 +0,0 @@
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source("renv/activate.R")
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source("src/data/streams/aware_postgresql/container.R")
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library(RPostgres)
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library(magrittr)
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library(tidyverse)
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library(lubridate)
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prepare_participants_file <- function() {
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username_list_csv_location <- snakemake@input[["username_list"]]
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data_configuration <- snakemake@params[["data_configuration"]]
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participants_container <- snakemake@params[["participants_table"]]
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device_id_container <- snakemake@params[["device_id_table"]]
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start_end_date_container <- snakemake@params[["start_end_date_table"]]
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output_data_file <- snakemake@output[["participants_file"]]
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platform <- "android"
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pid_format <- "p%03d"
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datetime_format <- "%Y-%m-%d %H:%M:%S"
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participant_data <- read_csv(username_list_csv_location, col_types = "c", progress = FALSE)
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usernames <- participant_data$label
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participant_ids <- pull_participants_ids(data_configuration, usernames, participants_container)
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participant_data %<>%
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left_join(participant_ids, by = c("label" = "username")) %>%
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rename(participant_id = id)
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device_ids <- pull_participants_device_ids(data_configuration, participant_data$participant_id, device_id_container)
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device_ids %<>%
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group_by(participant_id) %>%
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summarise(device_ids = list(unique(device_id)))
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participant_data %<>%
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left_join(device_ids, by = "participant_id")
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start_end_datetimes <- pull_participants_start_end_dates(data_configuration, participant_data$participant_id, start_end_date_container)
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participant_data %<>%
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left_join(start_end_datetimes, by = "participant_id")
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participant_data %<>%
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mutate(
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pid = sprintf(pid_format, participant_id),
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start_date = strftime(datetime_start, format=datetime_format, tz = "UTC", usetz = FALSE), #TODO Check what timezone is expected
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end_date = strftime(datetime_end, format=datetime_format, tz = "UTC", usetz = FALSE),
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empatica_id = "placeholder", #TODO Provide in file?
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device_id = map_chr(device_ids, str_c, collapse = ";"),
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number_of_devices = map_int(device_ids, length),
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fitbit_id = ""
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) %>%
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rowwise() %>%
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mutate(platform = str_c(replicate(number_of_devices, platform), collapse = ";")) %>%
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ungroup() %>%
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arrange(pid) %>%
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select(pid, label, start_date, end_date, empatica_id, device_id, platform, fitbit_id)
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write_csv(participant_data, output_data_file)
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}
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prepare_participants_file()
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