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Update config docs and create participant files script

pull/128/head
JulioV 2021-03-11 14:30:42 -05:00
parent 1e66dad838
commit 2ee45995f2
6 changed files with 81 additions and 96 deletions
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1
Snakefile
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@ -253,7 +253,6 @@ for provider in config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"].keys():
for provider in config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"].keys():
if config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_intraday_raw.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_intraday_parsed.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled_with_datetime.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/fitbit_sleep_intraday_features/fitbit_sleep_intraday_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))

14
config.yaml
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@ -11,21 +11,19 @@ PIDS: [test01]
# See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files
CREATE_PARTICIPANT_FILES:
SOURCE:
TYPE: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE
DATABASE_GROUP: *database_group
CSV_FILE_PATH: "data/external/example_participants.csv" # see docs for required format
TIMEZONE: *timezone
PHONE_SECTION:
ADD: TRUE
DEVICE_ID_COLUMN: device_id # column name
IGNORED_DEVICE_IDS: []
FITBIT_SECTION:
ADD: FALSE
DEVICE_ID_COLUMN: device_id # column name
DEVICE_ID_COLUMN: fitbit_id # column name
IGNORED_DEVICE_IDS: []
EMPATICA_SECTION:
ADD: FALSE
ADD: True
DEVICE_ID_COLUMN: empatica_id # column name
IGNORED_DEVICE_IDS: []
# See https://www.rapids.science/latest/setup/configuration/#time-segments
TIME_SEGMENTS: &time_segments
@ -466,10 +464,10 @@ FITBIT_STEPS_INTRADAY:
########################################################################################################################
EMPATICA_DATA_STREAMS:
USE: empatica_zipfiles
USE: empatica_zip
# AVAILABLE:
empatica_zipfiles:
empatica_zip:
FOLDER: data/external/empatica
# Sensors ------

81
docs/setup/configuration.md
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@ -62,7 +62,8 @@ Participant files link together multiple devices (smartphones and wearables) to
LABEL: test01
START_DATE: 2020-04-23
END_DATE: 2020-10-28
EMPATICA: # Empatica doesn't have a device_id because the devices produce zip files per participant
EMPATICA:
DEVICE_IDS: [empatica1]
LABEL: test01
START_DATE: 2020-04-23
END_DATE: 2020-10-28
@ -91,6 +92,7 @@ Participant files link together multiple devices (smartphones and wearables) to
| Key                      | Description |
|------------------|-----------------------------------------------------------------------------------------------------------|
| `[DEVICE_IDS]` | An array of the strings that uniquely identify each Empatica device used by this participant. Since the most common use case involves having multiple zip files from a single device for each person, set this device id to an arbitrary string (we usually use their `pid`) |
| `[LABEL]` | A string that is used in reports and visualizations. |
| `[START_DATE]` | A string with format `YYYY-MM-DD` or `YYYY-MM-DD HH:MM:SS`. Only data collected *after* this date time will be included in the analysis. By default, `YYYY-MM-DD` is interpreted as `YYYY-MM-DD 00:00:00`. |
| `[END_DATE]` | A string with format `YYYY-MM-DD` or `YYYY-MM-DD HH:MM:SS`. Only data collected *before* this date time will be included in the analysis. By default, `YYYY-MM-DD` is interpreted as `YYYY-MM-DD 00:00:00`. |
@ -102,14 +104,14 @@ You can use a CSV file with a row per participant to automatically create partic
In previous versions of RAPIDS, you could create participant files automatically using the `aware_device` table. We deprecated this option but you can still achieve the same results if you export the output of the following SQL query as a CSV file and follow the instructions below:
```sql
SELECT device_id, device_id as fitbit_id, CONCAT("p", _id) as pid, if(brand = "iPhone", "ios", "android") as platform, CONCAT("p", _id) as label, DATE_FORMAT(FROM_UNIXTIME((timestamp/1000)- 86400), "%Y-%m-%d") as start_date, CURRENT_DATE as end_date from aware_device order by _id;
SELECT device_id, device_id as fitbit_id, CONCAT("p", _id) as empatica_id, CONCAT("p", _id) as pid, if(brand = "iPhone", "ios", "android") as platform, CONCAT("p", _id) as label, DATE_FORMAT(FROM_UNIXTIME((timestamp/1000)- 86400), "%Y-%m-%d") as start_date, CURRENT_DATE as end_date from aware_device order by _id;
```
In your `config.yaml`:
1. Set `CSV_FILE_PATH` to a CSV file path that complies with the specs described below
2. Set the devices (`PHONE`, `FITBIT`, `EMPATICA`) `[ADD]` flag to `TRUE` depending on what devices you used in your study.
3. Set `[DEVICE_ID_COLUMN]` to the name of the column in your CSV file that uniquely identifies each device (only for `PHONE` and `FITBIT`).
3. Set `[DEVICE_ID_COLUMN]` to the column's name in your CSV file that uniquely identifies each device.
```yaml
CREATE_PARTICIPANT_FILES:
@ -122,8 +124,10 @@ CREATE_PARTICIPANT_FILES:
ADD: FALSE # or FALSE
DEVICE_ID_COLUMN: fitbit_id # column name
IGNORED_DEVICE_IDS: []
EMPATICA_SECTION: # Empatica doesn't have a device_id column because the devices produce zip files per participant
ADD: FALSE # or FALSE
EMPATICA_SECTION:
ADD: FALSE
DEVICE_ID_COLUMN: empatica_id # column name
IGNORED_DEVICE_IDS: []
```
Your CSV file (`[CSV_FILE_PATH]`) should have the following columns (headers) but the values within each column can be empty:
@ -132,6 +136,7 @@ Your CSV file (`[CSV_FILE_PATH]`) should have the following columns (headers) bu
|------------------|-----------------------------------------------------------------------------------------------------------|
| phone device id | The name of this column has to match `[PHONE_SECTION][DEVICE_ID_COLUMN]`. Separate multiple ids with `;` |
| fitbit device id | The name of this column has to match `[FITBIT_SECTION][DEVICE_ID_COLUMN]`. Separate multiple ids with `;` |
| empatica device id | The name of this column has to match `[EMPATICA_SECTION][DEVICE_ID_COLUMN]`. Since the most common use case involves having multiple zip files from a single device for each person, set this device id to an arbitrary string (we usually use their `pid`) |
| pid | Unique identifiers with the format pXXX (your participant files will be named with this string) |
| platform | Use `android`, `ios` or `infer` as explained above, separate values with `;` |
| label | A human readable string that is used in reports and visualizations. |
@ -142,9 +147,9 @@ Your CSV file (`[CSV_FILE_PATH]`) should have the following columns (headers) bu
We added white spaces to this example to make it easy to read but you don't have to.
```csv
device_id ,fitbit_id ,pid ,label ,platform ,start_date ,end_date
a748ee1a-1d0b-4ae9-9074-279a2b6ba524;dsadas-2324-fgsf-sdwr-gdfgs4rfsdf43 ,fitbit1 ,p01 ,julio ,android;ios ,2020-01-01 ,2021-01-01
4c4cf7a1-0340-44bc-be0f-d5053bf7390c ,fitbit2 ,p02 ,meng ,ios ,2021-01-01 ,2022-01-01
device_id ,fitbit_id, empatica_id ,pid ,label ,platform ,start_date ,end_date
a748ee1a-1d0b-4ae9-9074-279a2b6ba524;dsadas-2324-fgsf-sdwr-gdfgs4rfsdf43 ,fitbit1 , p01 ,p01 ,julio ,android;ios ,2020-01-01 ,2021-01-01
4c4cf7a1-0340-44bc-be0f-d5053bf7390c ,fitbit2 , p02 ,p02 ,meng ,ios ,2021-01-01 ,2022-01-01
```
Then run
@ -364,8 +369,8 @@ Parameters for `[TIMEZONE]`
|`[SINGLE][TZCODE]`| The time zone code from this [list](https://en.wikipedia.org/wiki/List_of_tz_database_time_zones) to be used across all devices |
|`[MULTIPLE][TZCODES_FILE]`| A CSV file containing the time and code from this [list](https://en.wikipedia.org/wiki/List_of_tz_database_time_zones) visited by each device in the study. Multiple devices can be linked to the same person, read more in [Participants Files](#participant-files) |
|`[MULTIPLE][IF_MISSING_TZCODE]`| When a device is missing from `[TZCODES_FILE]` Set this flag to `STOP` to stop RAPIDS execution and show an error, or to `USE_DEFAULT` to assign the time zone specified in `[DEFAULT_TZCODE]` to any such devices |
|`[MULTIPLE][FITBIT][ALLOW_MULTIPLE_TZ_PER_DEVICE]`| You only need to care about this flag if one or more Fitbit devices sensed data in one or more time zone, and you want RAPIDS to take into account this in its feature computation. Read more in "How does RAPIDS handle Fitbit devices?" below. |
|`[MULTIPLE][FITBIT][INFER_FROM_SMARTPHONE_TZ]`| You only need to care about this flag if one or more Fitbit devices sensed data in one or more time zone, and you want RAPIDS to take into account this in its feature computation. Read more in "How does RAPIDS handle Fitbit devices?" below. |
|`[MULTIPLE][FITBIT][ALLOW_MULTIPLE_TZ_PER_DEVICE]`| You only need to care about this flag if one or more Fitbit devices sensed data in one or more time zones, and you want RAPIDS to take into account this in its feature computation. Read more in "How does RAPIDS handle Fitbit devices?" below. |
|`[MULTIPLE][FITBIT][INFER_FROM_SMARTPHONE_TZ]`| You only need to care about this flag if one or more Fitbit devices sensed data in one or more time zones, and you want RAPIDS to take into account this in its feature computation. Read more in "How does RAPIDS handle Fitbit devices?" below. |
??? info "Format of `TZCODES_FILE`"
`TZCODES_FILE` has three columns and a row for each time zone a device visited (a device can be a smartphone or wearable (Fitbit/Empatica)):
@ -415,7 +420,7 @@ Parameters for `[TIMEZONE]`
??? note "How does RAPIDS handle Fitbit devices?"
Fitbit devices are not time zone aware and they always log data with a local date-time string.
- When none of the Fitbit devices in your study changed time zones (e.g., `p01` was always in New York and `p02` as always in Amsterdam), you can set a single time zone per Fitbit device id along with a timestamp 0 (you can still assign multiple time zones to smartphone device ids)
- When none of the Fitbit devices in your study changed time zones (e.g., `p01` was always in New York and `p02` was always in Amsterdam), you can set a single time zone per Fitbit device id along with a timestamp 0 (you can still assign multiple time zones to smartphone device ids)
```csv
device_id, tzcode, timestamp
fitbit123, America/New_York, 0
@ -426,10 +431,6 @@ Parameters for `[TIMEZONE]`
If you want to `ALLOW_MULTIPLE_TZ_PER_DEVICE` you will need to add any time zone changes per device in the `TZCODES_FILE` as explained above. You could obtain this data by hand but if your participants also used a smartphone during your study, you can use their time zone logs. Recall that in RAPIDS every participant is represented with a participant file `pXX.yaml`, this file links together multiple devices and we will use it to know what smartphone time zone data should be applied to Fitbit devices. Thus set `INFER_FROM_SMARTPHONE_TZ` to `TRUE`, if you have included smartphone time zone data in your `TZCODE_FILE` and you want to make a participant's Fitbit data time zone aware with their respective smartphone data.
??? note "How does RAPIDS handle Empatica devices?"
Empatica devices do not have a device id, since the raw data can only be exported in zip files per device that are saved in a folder per participant (e.g. `data/external/empatica/{pid}`).
Therefore, in your `TZCODES_FILE`, use the participant's ids (PIDs) instead of the device's ids. Remember a person could have used one or more devices with different device ids, but every person only gets a single PID (e.g. `p01`, a.k.a the name of their participant file `p01.yaml`).
---
## Data Stream Configuration
@ -441,9 +442,12 @@ Modify the following keys in your `config.yaml` depending on the [data stream](.
```yaml
PHONE_DATA_STREAMS:
TYPE: aware_mysql
USE: aware_mysql
# AVAILABLE:
aware_mysql:
DATABASE_GROUP: MY_GROUP
aware_csv:
FOLDER: data/external/aware_csv
```
@ -467,98 +471,101 @@ Modify the following keys in your `config.yaml` depending on the [data stream](.
=== "Fitbit"
Set `[FITBIT_DATA_STREAMS][TYPE]` to the Fitbit data stream you want to process (e.g. `fitbitjson_mysql`) and configure its parameters (e.g. `[DATABASE_GROUP]`).
Set `[FITBIT_DATA_STREAMS][TYPE]` to the Fitbit data stream you want to process (e.g. `fitbitjson_mysql`) and configure its parameters (e.g. `[DATABASE_GROUP]`). Ignore the parameters of the other streams you are not using (e.g. `[FOLDER]` of `aware_csv`).
Ignore the parameters of streams you are not using (e.g. `[FOLDER]` of `aware_csv`).
!!! warning
You will probably have to tell RAPIDS the name of the columns where you stored your Fitbit data. To do this, modify your chosen stream's `format.yaml` column mappings to match your raw data column names.
```yaml
FITBIT_DATA_STREAMS:
TYPE: fitbitjson_mysql
USE: fitbitjson_mysql
# AVAILABLE:
fitbitjson_mysql:
DATABASE_GROUP: MY_GROUP
COLUMN_MAPPINGS_READY: False
SLEEP_SUMMARY_EPISODE_DAY_ANCHOR: False
fitbitjson_csv:
FOLDER: data/external/fitbit_csv
COLUMN_MAPPINGS_READY: False
SLEEP_SUMMARY_EPISODE_DAY_ANCHOR: False
fitbitparsed_mysql:
DATABASE_GROUP: MY_GROUP
COLUMN_MAPPINGS_READY: False
SLEEP_SUMMARY_EPISODE_DAY_ANCHOR: False
fitbitparsed_csv:
FOLDER: data/external/fitbit_csv
COLUMN_MAPPINGS_READY: False
SLEEP_SUMMARY_EPISODE_DAY_ANCHOR: False
```
=== "fitbitjson_mysql"
This data stream process Fitbit data inside a JSON column as obtained from the Fitbit API and stored in a MySQL database.
This data stream process Fitbit data inside a JSON column as obtained from the Fitbit API and stored in a MySQL database. Read more about its column mappings and mutations in [`fitbitjson_mysql`](../../datastreams/fitbitjson-mysql#format).
| Key | Description |
|---------------------|----------------------------------------------------------------------------------------------------------------------------|
| `[DATABASE_GROUP]` | A database credentials group. Read the instructions below to set it up |
| `[COLUMN_MAPPINGS_READY]` | Set this to `True` after you have modified this stream's `format.yaml` column mappings to match your raw data column names: [`fitbitjson_mysql`](../../datastreams/fitbitjson-mysql#format) |
| `[SLEEP_SUMMARY_EPISODE_DAY_ANCHOR]` | One of `start` or `end`. Summary sleep episodes are considered as events based on either the start timestamp or end timestamp (they will belong to the day where they start or end). |
--8<---- "docs/snippets/database.md"
=== "fitbitjson_csv"
This data stream process Fitbit data inside a JSON column as obtained from the Fitbit API and stored in a CSV file.
This data stream process Fitbit data inside a JSON column as obtained from the Fitbit API and stored in a CSV file. Read more about its column mappings and mutations in [`fitbitjson_csv`](../../datastreams/fitbitjson-csv#format).
| Key | Description |
|---------------------|----------------------------------------------------------------------------------------------------------------------------|
| `[FOLDER]` | Folder where you have to place a CSV file **per** Fitbit sensor. Each file has to contain all the data from every participant you want to process. |
| `[COLUMN_MAPPINGS_READY]` | Set this to `True` after you have modified this stream's `format.yaml` column mappings to match your raw data column names: [`fitbitjson_csv`](../../datastreams/fitbitjson-csv#format) |
| `[SLEEP_SUMMARY_EPISODE_DAY_ANCHOR]` | One of `start` or `end`. Summary sleep episodes are considered as events based on either the start timestamp or end timestamp (they will belong to the day where they start or end). |
=== "fitbitparsed_mysql"
This data stream process Fitbit data stored in multiple columns after being parsed from the JSON column returned by Fitbit API and stored in a MySQL database.
This data stream process Fitbit data stored in multiple columns after being parsed from the JSON column returned by Fitbit API and stored in a MySQL database. Read more about its column mappings and mutations in [`fitbitparsed_mysql`](../../datastreams/fitbitparsed-mysql#format).
| Key | Description |
|---------------------|----------------------------------------------------------------------------------------------------------------------------|
| `[DATABASE_GROUP]` | A database credentials group. Read the instructions below to set it up |
| `[COLUMN_MAPPINGS_READY]` | Set this to `True` after you have modified this stream's `format.yaml` column mappings to match your raw data column names: [`fitbitparsed_mysql`](../../datastreams/fitbitparsed-mysql#format) |
| `[SLEEP_SUMMARY_EPISODE_DAY_ANCHOR]` | One of `start` or `end`. Summary sleep episodes are considered as events based on either the start timestamp or end timestamp (they will belong to the day where they start or end). |
--8<---- "docs/snippets/database.md"
=== "fitbitparsed_csv"
This data stream process Fitbit data stored in multiple columns (plain text) after being parsed from the JSON column returned by Fitbit API and stored in a CSV file.
This data stream process Fitbit data stored in multiple columns (plain text) after being parsed from the JSON column returned by Fitbit API and stored in a CSV file. Read more about its column mappings and mutations in [`fitbitparsed_csv`](../../datastreams/fitbitparsed-csv#format).
| Key | Description |
|---------------------|----------------------------------------------------------------------------------------------------------------------------|
| `[FOLDER]` | Folder where you have to place a CSV file **per** Fitbit sensor. Each file has to contain all the data from every participant you want to process. |
| `[COLUMN_MAPPINGS_READY]` | Set this to `True` after you have modified this stream's `format.yaml` column mappings to match your raw data column names: [`fitbitparsed_csv`](../../datastreams/fitbitparsed-csv#format) |
| `[SLEEP_SUMMARY_EPISODE_DAY_ANCHOR]` | One of `start` or `end`. Summary sleep episodes are considered as events based on either the start timestamp or end timestamp (they will belong to the day where they start or end). |
=== "Empatica"
Set `[USE]` to the Empatica data stream you want to use, see the table in [introduction to data streams](../../datastreams/data-streams-introduction). Configure any parameters as inidicated below.
Set `[USE]` to the Empatica data stream you want to use; see the table in [introduction to data streams](../../datastreams/data-streams-introduction). Configure any parameters as indicated below.
```yaml
EMPATICA_DATA_STREAMS:
USE: empatica_zipfiles
USE: empatica_zip
# AVAILABLE:
empatica_zipfiles:
empatica_zip:
FOLDER: data/external/empatica
```
=== "empatica_zipfiles"
=== "empatica_zip"
| Key | Description |
|---------------------|----------------------------------------------------------------------------------------------------------------------------|
| `[FOLDER]` | The relative path to a folder containing one subfolder per participant. The name of a participant folder should match their pid in `config[PIDS]`, for example `p01`. Each participant folder can have one or more zip files with any name; in other words, the sensor data contained in those zip files belongs to a single participant. The zip files are [automatically](https://support.empatica.com/hc/en-us/articles/201608896-Data-export-and-formatting-from-E4-connect-) generated by Empatica and have a CSV file per sensor (`ACC`, `HR`, `TEMP`, `EDA`, `BVP`, `TAGS`). All CSV files of the same type contained in one or more zip files are uncompressed, parsed, sorted by timestamp, and joinned together.|
| `[FOLDER]` | The relative path to a folder containing one subfolder per participant. The name of a participant folder should match their device_id assigned in their participant file. Each participant folder can have one or more zip files with any name; in other words, the sensor data in those zip files belong to a single participant. The zip files are [automatically](https://support.empatica.com/hc/en-us/articles/201608896-Data-export-and-formatting-from-E4-connect-) generated by Empatica and have a CSV file per sensor (`ACC`, `HR`, `TEMP`, `EDA`, `BVP`, `TAGS`). All CSV files of the same type contained in one or more zip files are uncompressed, parsed, sorted by timestamp, and joined together.|
??? example "Example of an EMPATICA FOLDER"
In the file tree below, we want to process the data of three participants: `p01`, `p02`, and `p03`. `p01` has two zip files, `p02` has only one zip file, and `p03` has three zip files. Each zip will have a CSV file per sensor that are joinned together and process by RAPIDS. These zip files are generated by Empatica.
In the file tree below, we want to process three participants' data: `p01`, `p02`, and `p03`. `p01` has two zip files, `p02` has only one zip file, and `p03` has three zip files. Each zip has a CSV file per sensor that are joined together and processed by RAPIDS.
```bash
data/ # this folder exists in the root RAPIDS folder
external/

2
rules/preprocessing.smk
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@ -17,7 +17,7 @@ rule create_example_participant_files:
rule create_participants_files:
input:
participants_file = [] if config["CREATE_PARTICIPANT_FILES"]["SOURCE"]["TYPE"] == "AWARE_DEVICE_TABLE" else config["CREATE_PARTICIPANT_FILES"]["SOURCE"]["CSV_FILE_PATH"]
participants_file = config["CREATE_PARTICIPANT_FILES"]["CSV_FILE_PATH"]
params:
config = config["CREATE_PARTICIPANT_FILES"]
script:

38
src/data/create_participants_files.R
View File

@ -11,43 +11,25 @@ group <- config$SOURCE$DATABASE_GROUP
timezone <- config$SOURCE$TIMEZONE
phone_device_id_column = config$PHONE_SECTION$DEVICE_ID_COLUMN
fitbit_device_id_column = config$FITBIT_SECTION$DEVICE_ID_COLUMN
empatica_device_id_column = config$EMPATICA_SECTION$DEVICE_ID_COLUMN
add_phone_section = config$PHONE_SECTION$ADD
add_fitbit_section = config$FITBIT_SECTION$ADD
add_empatica_section = config$EMPATICA_SECTION$ADD
phone_ignored = config$PHONE_SECTION$IGNORED_DEVICE_IDS
fitbit_ignored = config$FITBIT_SECTION$IGNORED_DEVICE_IDS
empatica_ignored = config$EMPATICA_SECTION$IGNORED_DEVICE_IDS
rmysql.settingsfile <- "./.env"
if(config$SOURCE$TYPE == "AWARE_DEVICE_TABLE"){
database <- dbConnect(MariaDB(), default.file = rmysql.settingsfile, group = group)
if(config$FITBIT_SECTION$ADD == TRUE){
query <- paste("SELECT",phone_device_id_column, ",",fitbit_device_id_column," as _temp_fitbit_id, brand, label, timestamp FROM aware_device order by timestamp asc")
fitbit_device_id_column <- "_temp_fitbit_id"
}
else
query <- paste("SELECT ",phone_device_id_column,", brand, label, timestamp FROM aware_device order by timestamp asc")
participants <- dbGetQuery(database, query)
dbDisconnect(database)
participants <- participants %>%
mutate(pid = if_else(row_number()<10, paste0("p","0",row_number()), paste0("p", row_number())),
platform = if_else(brand == "iPhone", "ios", "android"), brand = NULL,
label = iconv(if_else(label == "", "EMPTY_LABEL", label), from = "UTF-8", to = "UTF-8", sub=''),
start_date = format(as.POSIXct(timestamp / 1000, origin = "1970-01-01", tz = timezone), "%Y-%m-%d"),
end_date = format(Sys.Date(), "%Y-%m-%d"),
!!phone_device_id_column := if_else(!!rlang::sym(phone_device_id_column) %in% phone_ignored, NA_character_, !!rlang::sym(phone_device_id_column)),
!!fitbit_device_id_column := if_else(!!rlang::sym(fitbit_device_id_column) %in% fitbit_ignored, NA_character_, !!rlang::sym(fitbit_device_id_column)))
} else if(config$SOURCE$TYPE == "CSV_FILE"){
participants <- read_csv(config$SOURCE$CSV_FILE_PATH, col_types=cols_only(device_id="c",pid="c",label="c",platform="c",
start_date=col_date(format = "%Y-%m-%d"),end_date=col_date(format = "%Y-%m-%d"),fitbit_id="c")) %>%
participants <- read_csv(config$CSV_FILE_PATH, col_types=cols_only(device_id="c",pid="c",label="c",platform="c",
start_date=col_date(format = "%Y-%m-%d"),end_date=col_date(format = "%Y-%m-%d"),fitbit_id="c",empatica_id="c")) %>%
mutate(start_date = as.character(start_date), end_date = as.character(end_date)) # we read as date to validate format
participants <- participants %>%
mutate(!!phone_device_id_column := str_replace(!!rlang::sym(phone_device_id_column), ";",","),
participants <- participants %>%
mutate(!!phone_device_id_column := str_replace(!!rlang::sym(phone_device_id_column), ";",","),
platform = str_replace(platform, ";",","),
!!phone_device_id_column := if_else(!!rlang::sym(phone_device_id_column) %in% phone_ignored, NA_character_, !!rlang::sym(phone_device_id_column)),
!!empatica_device_id_column := if_else(!!rlang::sym(empatica_device_id_column) %in% empatica_ignored, NA_character_, !!rlang::sym(empatica_device_id_column)),
!!fitbit_device_id_column := if_else(!!rlang::sym(fitbit_device_id_column) %in% fitbit_ignored, NA_character_, !!rlang::sym(fitbit_device_id_column)))
}
dir.create(file.path("./data/external/participant_files/"))
@ -73,8 +55,8 @@ participants %>%
} else
lines <- append(lines, empty_fitbit)
if(add_empatica_section == TRUE){
lines <- append(lines, c("EMPATICA:",
if(add_empatica_section == TRUE && !is.na(row[empatica_device_id_column])){
lines <- append(lines, c("EMPATICA:", paste0(" DEVICE_IDS: [",row[empatica_device_id_column],"]"),
paste(" LABEL:",row$label), paste(" START_DATE:", start_date), paste(" END_DATE:", end_date)))
} else
lines <- append(lines, empty_empatica)
@ -83,7 +65,7 @@ participants %>%
writeLines(lines, file_connection)
close(file_connection)
}, add_phone_section, add_fitbit_section, phone_device_id_column, fitbit_device_id_column)
}, add_phone_section, add_fitbit_section, phone_device_id_column, fitbit_device_id_column, empatica_device_id_column)
file_lines <-readLines("./config.yaml")
for (i in 1:length(file_lines)){

23
tools/config.schema.yaml
View File

@ -158,22 +158,11 @@ properties:
CREATE_PARTICIPANT_FILES:
type: object
required: [SOURCE, PHONE_SECTION, FITBIT_SECTION]
required: [CSV_FILE_PATH, PHONE_SECTION, FITBIT_SECTION,EMPATICA_SECTION]
properties:
SOURCE:
type: object
required: [TYPE]
properties:
TYPE:
type: string
enum: [AWARE_DEVICE_TABLE, CSV_FILE]
DATABASE_GROUP:
type: string
CSV_FILE_PATH:
type: string
pattern: "^.*\\.csv$"
TIMEZONE:
type: string
PHONE_SECTION:
type: object
properties:
@ -195,6 +184,16 @@ properties:
type: array
items:
type: string
EMPATICA_SECTION:
properties:
ADD:
type: boolean
DEVICE_ID_COLUMN:
type: string
IGNORED_DEVICE_IDS:
type: array
items:
type: string
TIME_SEGMENTS:
type: object