| Title: | 'Fitbit' Visualizations |
|---|---|
| Description: | Connection to the 'Fitbit' Web API <https://dev.fitbit.com/build/reference/web-api/> by including 'ggplot2' Visualizations, 'Leaflet' and 3-dimensional 'Rayshader' Maps. The 3-dimensional 'Rayshader' Map requires the installation of the 'CopernicusDEM' R package which includes the 30- and 90-meter elevation data. |
| Authors: | Lampros Mouselimis [aut, cre] |
| Maintainer: | Lampros Mouselimis <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.6 |
| Built: | 2025-01-03 04:45:47 UTC |
| Source: | https://github.com/mlampros/fitbitviz |
Function to crop the AOI from the downloaded DEM .tif file
crop_DEM(tif_or_vrt_dem_file, sf_buffer_obj, verbose = FALSE)crop_DEM(tif_or_vrt_dem_file, sf_buffer_obj, verbose = FALSE)
tif_or_vrt_dem_file |
a valid path to the elevation .tif or .vrt file |
sf_buffer_obj |
a simple features ('sf') object that will be used to crop the input elevation raster file ('tif_or_vrt_dem_file' parameter) |
verbose |
a boolean. If TRUE then information will be printed out in the console |
an object of class SpatRaster
## Not run: require(fitbitViz) #............................ # first extract the log-id(s) #............................ USER_ID = '99xxxx' token = 'my_long_web_api_token' log_id = extract_LOG_ID(user_id = USER_ID, token = token, after_Date = '2021-03-13', limit = 10, sort = 'asc', verbose = TRUE) str(log_id) #................................... # then return the gps-ctx data.table #................................... res_tcx = GPS_TCX_data(log_id = log_id, user_id = USER_ID, token = token, time_zone = 'Europe/Athens', verbose = TRUE) str(res_tcx) #.................................................... # then compute the sf-object buffer and raster-extend #.................................................... sf_rst_ext = extend_AOI_buffer(dat_gps_tcx = res_tcx, buffer_in_meters = 1000, CRS = 4326, verbose = TRUE) sf_rst_ext #............................................................... # Download the Copernicus DEM 30m elevation data because it has # a better resolution, it takes a bit longer to download because # the .tif file size is bigger #............................................................... dem_dir = tempdir() # dem_dir dem30 = CopernicusDEM::aoi_geom_save_tif_matches(sf_or_file = sf_rst_ext$sfc_obj, dir_save_tifs = dem_dir, resolution = 30, crs_value = 4326, threads = parallel::detectCores(), verbose = TRUE) TIF = list.files(dem_dir, pattern = '.tif', full.names = T) # TIF if (length(TIF) > 1) { #.................................................... # create a .VRT file if I have more than 1 .tif files #.................................................... file_out = file.path(dem_dir, 'VRT_mosaic_FILE.vrt') vrt_dem30 = create_VRT_from_dir(dir_tifs = dem_dir, output_path_VRT = file_out, verbose = TRUE) } if (length(TIF) == 1) { #.................................................. # if I have a single .tif file keep the first index #.................................................. file_out = TIF[1] } raysh_rst = crop_DEM(tif_or_vrt_dem_file = file_out, sf_buffer_obj = sf_rst_ext$sfc_obj, verbose = TRUE) terra::plot(raysh_rst) ## End(Not run)## Not run: require(fitbitViz) #............................ # first extract the log-id(s) #............................ USER_ID = '99xxxx' token = 'my_long_web_api_token' log_id = extract_LOG_ID(user_id = USER_ID, token = token, after_Date = '2021-03-13', limit = 10, sort = 'asc', verbose = TRUE) str(log_id) #................................... # then return the gps-ctx data.table #................................... res_tcx = GPS_TCX_data(log_id = log_id, user_id = USER_ID, token = token, time_zone = 'Europe/Athens', verbose = TRUE) str(res_tcx) #.................................................... # then compute the sf-object buffer and raster-extend #.................................................... sf_rst_ext = extend_AOI_buffer(dat_gps_tcx = res_tcx, buffer_in_meters = 1000, CRS = 4326, verbose = TRUE) sf_rst_ext #............................................................... # Download the Copernicus DEM 30m elevation data because it has # a better resolution, it takes a bit longer to download because # the .tif file size is bigger #............................................................... dem_dir = tempdir() # dem_dir dem30 = CopernicusDEM::aoi_geom_save_tif_matches(sf_or_file = sf_rst_ext$sfc_obj, dir_save_tifs = dem_dir, resolution = 30, crs_value = 4326, threads = parallel::detectCores(), verbose = TRUE) TIF = list.files(dem_dir, pattern = '.tif', full.names = T) # TIF if (length(TIF) > 1) { #.................................................... # create a .VRT file if I have more than 1 .tif files #.................................................... file_out = file.path(dem_dir, 'VRT_mosaic_FILE.vrt') vrt_dem30 = create_VRT_from_dir(dir_tifs = dem_dir, output_path_VRT = file_out, verbose = TRUE) } if (length(TIF) == 1) { #.................................................. # if I have a single .tif file keep the first index #.................................................. file_out = TIF[1] } raysh_rst = crop_DEM(tif_or_vrt_dem_file = file_out, sf_buffer_obj = sf_rst_ext$sfc_obj, verbose = TRUE) terra::plot(raysh_rst) ## End(Not run)
Extract the sf-object and raster extent based on a buffer (in meters)
extend_AOI_buffer( dat_gps_tcx, buffer_in_meters = 1000, CRS = 4326, verbose = FALSE )extend_AOI_buffer( dat_gps_tcx, buffer_in_meters = 1000, CRS = 4326, verbose = FALSE )
dat_gps_tcx |
this parameter corresponds to the output data.table of the 'GPS_TCX_data()' function |
buffer_in_meters |
an integer value specifying the buffer in meters. The bounding box of the input coordinates (longitudes, latitudes) will be extended by that many meters. The default value is 1000 meters. |
CRS |
an integer specifying the Coordinates Reference System. The recommended value for this data is 4326 (which is also the default value) |
verbose |
a boolean. If TRUE then information will be printed out in the console |
To create the buffer in meters using the 'sf' package I had to transform to another projection - by default I've used 7801 - as suggested in the following stackoverflow thread, https://stackoverflow.com/a/54754935/8302386
an object of class list
## Not run: require(fitbitViz) #............................ # first extract the log-id(s) #............................ USER_ID = '99xxxx' token = 'my_long_web_api_token' log_id = extract_LOG_ID(user_id = USER_ID, token = token, after_Date = '2021-03-13', limit = 10, sort = 'asc', verbose = TRUE) str(log_id) #................................... # then return the gps-ctx data.table #................................... res_tcx = GPS_TCX_data(log_id = log_id, user_id = USER_ID, token = token, time_zone = 'Europe/Athens', verbose = TRUE) str(res_tcx) #.................................................... # then compute the sf-object buffer and raster-extend #.................................................... sf_rst_ext = extend_AOI_buffer(dat_gps_tcx = res_tcx, buffer_in_meters = 1000, CRS = 4326, verbose = TRUE) sf_rst_ext ## End(Not run)## Not run: require(fitbitViz) #............................ # first extract the log-id(s) #............................ USER_ID = '99xxxx' token = 'my_long_web_api_token' log_id = extract_LOG_ID(user_id = USER_ID, token = token, after_Date = '2021-03-13', limit = 10, sort = 'asc', verbose = TRUE) str(log_id) #................................... # then return the gps-ctx data.table #................................... res_tcx = GPS_TCX_data(log_id = log_id, user_id = USER_ID, token = token, time_zone = 'Europe/Athens', verbose = TRUE) str(res_tcx) #.................................................... # then compute the sf-object buffer and raster-extend #.................................................... sf_rst_ext = extend_AOI_buffer(dat_gps_tcx = res_tcx, buffer_in_meters = 1000, CRS = 4326, verbose = TRUE) sf_rst_ext ## End(Not run)
Extract the log-id (it's possible that I receive more than one id)
extract_LOG_ID( user_id, token, after_Date = "2021-03-13", limit = 10, sort = "asc", verbose = FALSE )extract_LOG_ID( user_id, token, after_Date = "2021-03-13", limit = 10, sort = "asc", verbose = FALSE )
user_id |
a character string specifying the encoded ID of the user. For instance '99xxxx' of the following URL 'https://www.fitbit.com/user/99xxxx' of the user's account corresponds to the 'user_id' |
token |
a character string specifying the secret token that a user receives when registers a new application in https://dev.fitbit.com/apps |
after_Date |
a character string specifying the Date after which the log-ids will be returned. For instance, the date '2021-12-31' where the input order is 'year-month-day' |
limit |
an integer specifying the total of log-id's to return. The default value is 10 |
sort |
a character string specifying the order ('asc', 'desc') based on which the output log-id's should be sorted |
verbose |
a boolean. If TRUE then information will be printed out in the console |
an integer specifying the log ID
## Not run: require(fitbitViz) USER_ID = '99xxxx' token = 'my_long_web_api_token' log_id = extract_LOG_ID(user_id = USER_ID, token = token, after_Date = '2021-03-13', limit = 10, sort = 'asc', verbose = TRUE) log_id ## End(Not run)## Not run: require(fitbitViz) USER_ID = '99xxxx' token = 'my_long_web_api_token' log_id = extract_LOG_ID(user_id = USER_ID, token = token, after_Date = '2021-03-13', limit = 10, sort = 'asc', verbose = TRUE) log_id ## End(Not run)
Fitbit data retrieval for Blood Oxygen Saturation, Heart Rate Variability, Breathing Rate, Temperature and Cardio Fitness Score (or VO2 Max) by Date
fitbit_data_type_by_date( user_id, token, date, type = "spo2", plot = FALSE, show_nchar_case_error = 135 )fitbit_data_type_by_date( user_id, token, date, type = "spo2", plot = FALSE, show_nchar_case_error = 135 )
user_id |
a character string specifying the encoded ID of the user. For instance '99xxxx' of the following URL 'https://www.fitbit.com/user/99xxxx' of the user's account corresponds to the 'user_id' |
token |
a character string specifying the secret token that a user receives when registers a new application in https://dev.fitbit.com/apps |
date |
a character string specifying a Date. For instance, the date '2021-12-31' where the input order is 'year-month-day' |
type |
a character string specifying the fitbit data type. One of 'spo2', 'hrv', 'br', 'temp', 'cardioscore'. See the 'details' and 'references' sections for more information |
plot |
a boolean. If TRUE then the minutes data will be plotted. This parameter is applicable only to the 'spo2' and 'hrv' types because they return minute data (see the details section for more information). The remaining types ('br', 'temp', 'cardioscore') return daily data. |
show_nchar_case_error |
an integer that specifies the number of characters that will be returned in case on an error. The default value is 135 characters. |
This endpoint returns the SpO2 intraday data for a single date. SpO2 applies specifically to a user's "main sleep", which is the longest single period of time asleep on a given date. Spo2 values are calculated on a 5-minute exponentially-moving average
This endpoint returns the Heart Rate Variability (HRV) intraday data for a single date. HRV data applies specifically to a user's "main sleep", which is the longest single period of time asleep on a given date. It measures the HRV rate at various times and returns Root Mean Square of Successive Differences (rmssd), Low Frequency (LF), High Frequency (HF), and Coverage data for a given measurement. Rmssd measures short-term variability in your heart rate while asleep. LF and HF capture the power in interbeat interval fluctuations within either high frequency or low frequency bands. Finally, coverage refers to data completeness in terms of the number of interbeat intervals
This endpoint returns intraday breathing rate data for a specified date. It measures the average breathing rate throughout the day and categories your breathing rate by sleep stage. Sleep stages vary between light sleep, deep sleep, REM sleep, and full sleep
This endpoint returns the Temperature (Skin) data for a single date. It only returns a value for dates on which the Fitbit device was able to record Temperature (skin) data. Temperature (Skin) data applies specifically to a user's "main sleep", which is the longest single period of time asleep on a given date
The Cardio Fitness Score (also known as VO2 Max) endpoints are used for querying the maximum or optimum rate at which the user's heart, lungs, and muscles can effectively use oxygen during exercise
If the 'type' parameter is one of 'spo2' or 'hrv' and the 'plot' parameter is set to TRUE then the results will appear as a line plot. In case of 'hrv' a multiplot with the following variables will be displayed:
The Root Mean Square of Successive Differences (RMSSD) between heart beats. It measures short-term variability in the user's heart rate in milliseconds (ms)
Data completeness in terms of the number of interbeat intervals
The power in interbeat interval fluctuations within the high frequency band (0.15 Hz - 0.4 Hz)
The power in interbeat interval fluctuations within the low frequency band (0.04 Hz - 0.15 Hz)
a data.frame
https://dev.fitbit.com/build/reference/web-api/intraday/get-spo2-intraday-by-date/
https://dev.fitbit.com/build/reference/web-api/intraday/get-hrv-intraday-by-date/
https://dev.fitbit.com/build/reference/web-api/intraday/get-br-intraday-by-date/
https://dev.fitbit.com/build/reference/web-api/temperature/get-temperature-skin-summary-by-date
https://dev.fitbit.com/build/reference/web-api/cardio-fitness-score/get-vo2max-summary-by-date/
## Not run: require(fitbitViz) USER_ID = '99xxxx' token = 'my_long_web_api_token' res_type = fitbit_data_type_by_date(user_id = USER_ID, token = token, date = '2022-10-12', type = 'spo2', plot = TRUE, show_nchar_case_error = 135) res_type ## End(Not run)## Not run: require(fitbitViz) USER_ID = '99xxxx' token = 'my_long_web_api_token' res_type = fitbit_data_type_by_date(user_id = USER_ID, token = token, date = '2022-10-12', type = 'spo2', plot = TRUE, show_nchar_case_error = 135) res_type ## End(Not run)
Convert the GPS, TCX data to a LINESTRING
gps_lat_lon_to_LINESTRING( dat_gps_tcx, CRS = 4326, verbose = FALSE, time_split_asc_desc = NULL )gps_lat_lon_to_LINESTRING( dat_gps_tcx, CRS = 4326, verbose = FALSE, time_split_asc_desc = NULL )
dat_gps_tcx |
this parameter corresponds to the output data.table of the 'GPS_TCX_data()' function |
CRS |
an integer specifying the Coordinates Reference System. The recommended value for this data is 4326 (which is also the default value) |
verbose |
a boolean. If TRUE then information will be printed out in the console |
time_split_asc_desc |
if NULL then the maximum altitude coordinates point will be used as a split point of the route, otherwise the user can give a lubridate 'hours-minutes-seconds' object such as: lubridate::hms('17:05:00') |
Separate the Ascending and Descending coordinate points into 2 groups and give a different color to the Ascending and Descending routes
an object of class list
## Not run: require(fitbitViz) #............................ # first extract the log-id(s) #............................ USER_ID = '99xxxx' token = 'my_long_web_api_token' log_id = extract_LOG_ID(user_id = USER_ID, token = token, after_Date = '2021-03-13', limit = 10, sort = 'asc', verbose = TRUE) str(log_id) #................................... # then return the gps-ctx data.table #................................... res_tcx = GPS_TCX_data(log_id = log_id, user_id = USER_ID, token = token, time_zone = 'Europe/Athens', verbose = TRUE) str(res_tcx) #.................................................................. # By using using the maximum altitude as a split point of the route #.................................................................. linestring_dat_init = gps_lat_lon_to_LINESTRING(dat_gps_tcx = res_tcx, CRS = 4326, time_split_asc_desc = NULL, verbose = TRUE) #................................................................. # By using a customized split of the route (ascending, descending) #................................................................. linestring_dat_lubr = gps_lat_lon_to_LINESTRING(dat_gps_tcx = res_tcx, CRS = 4326, time_split_asc_desc = lubridate::hms('17:05:00'), verbose = TRUE) ## End(Not run)## Not run: require(fitbitViz) #............................ # first extract the log-id(s) #............................ USER_ID = '99xxxx' token = 'my_long_web_api_token' log_id = extract_LOG_ID(user_id = USER_ID, token = token, after_Date = '2021-03-13', limit = 10, sort = 'asc', verbose = TRUE) str(log_id) #................................... # then return the gps-ctx data.table #................................... res_tcx = GPS_TCX_data(log_id = log_id, user_id = USER_ID, token = token, time_zone = 'Europe/Athens', verbose = TRUE) str(res_tcx) #.................................................................. # By using using the maximum altitude as a split point of the route #.................................................................. linestring_dat_init = gps_lat_lon_to_LINESTRING(dat_gps_tcx = res_tcx, CRS = 4326, time_split_asc_desc = NULL, verbose = TRUE) #................................................................. # By using a customized split of the route (ascending, descending) #................................................................. linestring_dat_lubr = gps_lat_lon_to_LINESTRING(dat_gps_tcx = res_tcx, CRS = 4326, time_split_asc_desc = lubridate::hms('17:05:00'), verbose = TRUE) ## End(Not run)
The GPS-TCX data as a formated data.table
GPS_TCX_data( log_id, user_id, token, time_zone = "Europe/Athens", verbose = FALSE )GPS_TCX_data( log_id, user_id, token, time_zone = "Europe/Athens", verbose = FALSE )
log_id |
the returned log-id of the 'extract_LOG_ID()' function |
user_id |
a character string specifying the encoded ID of the user. For instance '99xxxx' of the following URL 'https://www.fitbit.com/user/99xxxx' of the user's account corresponds to the 'user_id' |
token |
a character string specifying the secret token that a user receives when registers a new application in https://dev.fitbit.com/apps |
time_zone |
a character string specifying the time zone parameter ('tz') as is defined in the 'lubridate::ymd_hms()' function |
verbose |
a boolean. If TRUE then information will be printed out in the console |
either NULL or an object of class data.table
## Not run: require(fitbitViz) #............................ # first extract the log-id(s) #............................ USER_ID = '99xxxx' token = 'my_long_web_api_token' log_id = extract_LOG_ID(user_id = USER_ID, token = token, after_Date = '2021-03-13', limit = 10, sort = 'asc', verbose = TRUE) str(log_id) #................................... # then return the gps-ctx data.table #................................... res_tcx = GPS_TCX_data(log_id = log_id, user_id = USER_ID, token = token, time_zone = 'Europe/Athens', verbose = TRUE) str(res_tcx) ## End(Not run)## Not run: require(fitbitViz) #............................ # first extract the log-id(s) #............................ USER_ID = '99xxxx' token = 'my_long_web_api_token' log_id = extract_LOG_ID(user_id = USER_ID, token = token, after_Date = '2021-03-13', limit = 10, sort = 'asc', verbose = TRUE) str(log_id) #................................... # then return the gps-ctx data.table #................................... res_tcx = GPS_TCX_data(log_id = log_id, user_id = USER_ID, token = token, time_zone = 'Europe/Athens', verbose = TRUE) str(res_tcx) ## End(Not run)
Heart Rate Intraday Heatmap (by extracting the 'min.', 'median' and 'max.' values of the day)
heart_rate_heatmap(heart_rate_intraday_data, angle_x_axis = 0)heart_rate_heatmap(heart_rate_intraday_data, angle_x_axis = 0)
heart_rate_intraday_data |
a list object specifying the intraday heart rate data (this is one of the sublists returned from the 'heart_rate_time_series' function) |
angle_x_axis |
an integer specifying the angle of the x-axis labels. The default values is 0 (it can take for instance values such as 45, 90 etc.) |
a plot object of class ggplot2
## Not run: require(fitbitViz) #........................................... # first compute the heart rate intraday data #........................................... USER_ID = '99xxxx' token = 'my_long_web_api_token' heart_dat = heart_rate_time_series(user_id = USER_ID, token = token, date_start = '2021-03-09', date_end = '2021-03-16', time_start = '00:00', time_end = '23:59', detail_level = '1min', ggplot_intraday = TRUE, verbose = TRUE, show_nchar_case_error = 135) #.......................................... # use the heart-rate-intraday data as input # to the 'heart_rate_heatmap' function #.......................................... hrt_heat = heart_rate_heatmap(heart_rate_intraday_data = heart_dat$heart_rate_intraday, angle_x_axis = 0) hrt_heat ## End(Not run)## Not run: require(fitbitViz) #........................................... # first compute the heart rate intraday data #........................................... USER_ID = '99xxxx' token = 'my_long_web_api_token' heart_dat = heart_rate_time_series(user_id = USER_ID, token = token, date_start = '2021-03-09', date_end = '2021-03-16', time_start = '00:00', time_end = '23:59', detail_level = '1min', ggplot_intraday = TRUE, verbose = TRUE, show_nchar_case_error = 135) #.......................................... # use the heart-rate-intraday data as input # to the 'heart_rate_heatmap' function #.......................................... hrt_heat = heart_rate_heatmap(heart_rate_intraday_data = heart_dat$heart_rate_intraday, angle_x_axis = 0) hrt_heat ## End(Not run)
heart rate activity time series
heart_rate_time_series( user_id, token, date_start, date_end, time_start = "00:00", time_end = "23:59", detail_level = "1min", ggplot_intraday = FALSE, ggplot_ncol = NULL, ggplot_nrow = NULL, verbose = FALSE, show_nchar_case_error = 135 )heart_rate_time_series( user_id, token, date_start, date_end, time_start = "00:00", time_end = "23:59", detail_level = "1min", ggplot_intraday = FALSE, ggplot_ncol = NULL, ggplot_nrow = NULL, verbose = FALSE, show_nchar_case_error = 135 )
user_id |
a character string specifying the encoded ID of the user. For instance '99xxxx' of the following URL 'https://www.fitbit.com/user/99xxxx' of the user's account corresponds to the 'user_id' |
token |
a character string specifying the secret token that a user receives when registers a new application in https://dev.fitbit.com/apps |
date_start |
a character string specifying a start Date. For instance, the date '2021-12-31' where the input order is 'year-month-day' |
date_end |
a character string specifying a end Date. For instance, the date '2021-12-31' where the input order is 'year-month-day' |
time_start |
a character string specifying the start time. For instance, the time '00:00' where the input order is 'hours-minutes' |
time_end |
a character string specifying the end time. For instance, the time '23:59' where the input order is 'hours-minutes' |
detail_level |
a character string specifying the detail level of the heart rate time series. It can be either '1min' or '1sec', for 1-minute and 1-second intervals |
ggplot_intraday |
a boolean. If TRUE then the ggplot of the heart rate time series will be returned too |
ggplot_ncol |
either NULL or an integer specifying the number of columns of the output ggplot |
ggplot_nrow |
either NULL or an integer specifying the number of rows of the output ggplot |
verbose |
a boolean. If TRUE then information will be printed out in the console |
show_nchar_case_error |
an integer that specifies the number of characters that will be returned in case on an error. The default value is 135 characters. |
an object of class list
## Not run: require(fitbitViz) USER_ID = '99xxxx' token = 'my_long_web_api_token' heart_dat = heart_rate_time_series(user_id = USER_ID, token = token, date_start = '2021-03-09', date_end = '2021-03-16', time_start = '00:00', time_end = '23:59', detail_level = '1min', ggplot_intraday = TRUE, verbose = TRUE, show_nchar_case_error = 135) heart_dat$plt heart_dat$heart_rate heart_dat$heart_rate_intraday ## End(Not run)## Not run: require(fitbitViz) USER_ID = '99xxxx' token = 'my_long_web_api_token' heart_dat = heart_rate_time_series(user_id = USER_ID, token = token, date_start = '2021-03-09', date_end = '2021-03-16', time_start = '00:00', time_end = '23:59', detail_level = '1min', ggplot_intraday = TRUE, verbose = TRUE, show_nchar_case_error = 135) heart_dat$plt heart_dat$heart_rate heart_dat$heart_rate_intraday ## End(Not run)
'r lifecycle::badge("deprecated")'
This function was deprecated, so please use the 'fitbit_data_type_by_date()' function instead with the 'type' parameter set to 'hrv' (Heart Rate Variability). See the documentation and the example section of the 'fitbit_data_type_by_date()' function for more details.
heart_rate_variability_sleep_time( heart_rate_data, sleep_begin = "00H 40M 0S", sleep_end = "08H 00M 0S", ggplot_hr_var = TRUE, angle_x_axis = 45 )heart_rate_variability_sleep_time( heart_rate_data, sleep_begin = "00H 40M 0S", sleep_end = "08H 00M 0S", ggplot_hr_var = TRUE, angle_x_axis = 45 )
heart_rate_data |
a list object. This is the output of the 'heart_rate_time_series()' function |
sleep_begin |
a character string specifying the begin of the sleep time. For instance, the time "00H 40M 0S" where the input order is 'hours-minutes-seconds' and the format corresponds to the 'lubridate::hms()' function |
sleep_end |
a character string specifying the end of the sleep time. For instance, the time "08H 00M 0S" where the input order is 'hours-minutes-seconds' and the format corresponds to the 'lubridate::hms()' function |
ggplot_hr_var |
a boolean. If TRUE then the ggplot of the heart rate variability will be returned |
angle_x_axis |
an integer specifying the angle of the x-axis labels. The default values is 45 (it can take for instance values such as 0, 90 etc.) |
I use the '1min' rather than the '1sec' interval because it is consistent (it shows the 1-minute differences), whereas in case of '1sec' the difference between observations varies between 1 second and less than 60 seconds
This function calculates the root mean square of successive differences (RMSSD) and a higher heart rate variability is linked with better health
Based on the Fitbit application information weblink and the Wikipedia article (https://en.wikipedia.org/wiki/Heart_rate_variability) the heart rate variability is computed normally in ms (milliseconds)
an object of class list
## Not run: require(fitbitViz) #........................................... # first compute the heart rate intraday data #........................................... USER_ID = '99xxxx' token = 'my_long_web_api_token' heart_dat = heart_rate_time_series(user_id = USER_ID, token = token, date_start = '2021-03-09', date_end = '2021-03-16', time_start = '00:00', time_end = '23:59', detail_level = '1min', ggplot_intraday = TRUE, verbose = TRUE, show_nchar_case_error = 135) #....................... # heart rate variability #....................... hrt_rt_var = heart_rate_variability_sleep_time(heart_rate_data = heart_dat, sleep_begin = "00H 40M 0S", sleep_end = "08H 00M 0S", ggplot_hr_var = TRUE, angle_x_axis = 25) hrt_rt_var ## End(Not run)## Not run: require(fitbitViz) #........................................... # first compute the heart rate intraday data #........................................... USER_ID = '99xxxx' token = 'my_long_web_api_token' heart_dat = heart_rate_time_series(user_id = USER_ID, token = token, date_start = '2021-03-09', date_end = '2021-03-16', time_start = '00:00', time_end = '23:59', detail_level = '1min', ggplot_intraday = TRUE, verbose = TRUE, show_nchar_case_error = 135) #....................... # heart rate variability #....................... hrt_rt_var = heart_rate_variability_sleep_time(heart_rate_data = heart_dat, sleep_begin = "00H 40M 0S", sleep_end = "08H 00M 0S", ggplot_hr_var = TRUE, angle_x_axis = 25) hrt_rt_var ## End(Not run)
Create a Leafet map (including information pop-ups)
leafGL_point_coords( dat_gps_tcx, color_points_column = "AltitudeMeters", provider = leaflet::providers$Esri.WorldImagery, option_viewer = rstudioapi::viewer, CRS = 4326 )leafGL_point_coords( dat_gps_tcx, color_points_column = "AltitudeMeters", provider = leaflet::providers$Esri.WorldImagery, option_viewer = rstudioapi::viewer, CRS = 4326 )
dat_gps_tcx |
this parameter corresponds to the output data.table of the 'GPS_TCX_data()' function |
color_points_column |
a character string specifying the column of the output data.table ('GPS_TCX_data()' function) that is used in the map-markers. The default value is 'AltitudeMeters' but it can be any column of type numeric |
provider |
either a character string specifying a leaflet provider (such as 'Esri.WorldImagery') or a direct call to the leaflet provider list (such as leaflet::providers$Esri.WorldImagery). The default value is leaflet::providers$Esri.WorldImagery |
option_viewer |
either NULL or rstudioapi::viewer. If NULL then the output map will be shown in the web browser |
CRS |
an integer specifying the Coordinates Reference System. The recommended value for this data is 4326 (which is also the default value) |
a leaflet map of class 'leaflet'
## Not run: require(fitbitViz) #............................ # first extract the log-id(s) #............................ USER_ID = '99xxxx' token = 'my_long_web_api_token' log_id = extract_LOG_ID(user_id = USER_ID, token = token, after_Date = '2021-03-13', limit = 10, sort = 'asc', verbose = TRUE) str(log_id) #................................... # then return the gps-ctx data.table #................................... res_tcx = GPS_TCX_data(log_id = log_id, user_id = USER_ID, token = token, time_zone = 'Europe/Athens', verbose = TRUE) str(res_tcx) #........................ # then visualize the data #........................ res_lft = leafGL_point_coords(dat_gps_tcx = res_tcx, color_points_column = 'AltitudeMeters', provider = leaflet::providers$Esri.WorldImagery, option_viewer = rstudioapi::viewer, CRS = 4326) res_lft ## End(Not run)## Not run: require(fitbitViz) #............................ # first extract the log-id(s) #............................ USER_ID = '99xxxx' token = 'my_long_web_api_token' log_id = extract_LOG_ID(user_id = USER_ID, token = token, after_Date = '2021-03-13', limit = 10, sort = 'asc', verbose = TRUE) str(log_id) #................................... # then return the gps-ctx data.table #................................... res_tcx = GPS_TCX_data(log_id = log_id, user_id = USER_ID, token = token, time_zone = 'Europe/Athens', verbose = TRUE) str(res_tcx) #........................ # then visualize the data #........................ res_lft = leafGL_point_coords(dat_gps_tcx = res_tcx, color_points_column = 'AltitudeMeters', provider = leaflet::providers$Esri.WorldImagery, option_viewer = rstudioapi::viewer, CRS = 4326) res_lft ## End(Not run)
Rayshader 3-dimensional using the Copernicus DEM elevation data
rayshader_3d_DEM( rst_buf, rst_ext, linestring_ASC_DESC = NULL, elevation_sample_points = NULL, zoom = 0.5, windowsize = c(1600, 1000), add_shadow_rescale_original = FALSE, verbose = FALSE )rayshader_3d_DEM( rst_buf, rst_ext, linestring_ASC_DESC = NULL, elevation_sample_points = NULL, zoom = 0.5, windowsize = c(1600, 1000), add_shadow_rescale_original = FALSE, verbose = FALSE )
rst_buf |
this parameter corresponds to the 'sfc_obj' object of the 'extend_AOI_buffer()' function |
rst_ext |
this parameter corresponds to the 'raster_obj_extent' object of the 'extend_AOI_buffer()' function |
linestring_ASC_DESC |
If NULL then this parameter will be ignored. Otherwise, it can be an 'sf' object or a named list of length 2 (that corresponds to the output of the 'gps_lat_lon_to_LINESTRING()' function) |
elevation_sample_points |
if NULL then this parameter will be ignored. Otherwise, it corresponds to a data.table with column names 'latitude', 'longitude' and 'AltitudeMeters'. For instance, it can consist of 3 or 4 rows that will be displayed as vertical lines in the 3-dimensionsal map to visualize sample locations of the route (the latitudes and longitudes must exist in the output data.table of the 'GPS_TCX_data()' function) |
zoom |
a float number. Lower values increase the 3-dimensional DEM output. The default value is 0.5 |
windowsize |
a numeric vector specifying the window dimensions (x,y) of the output 3-dimensional map. The default vector is c(1600, 1000) |
add_shadow_rescale_original |
a boolean. If TRUE, then 'hillshade' will be scaled to match the dimensions of 'shadowmap'. See also the 'rayshader::add_shadow()' function for more information. |
verbose |
a boolean. If TRUE then information will be printed out in the console |
it doesn't return an object but it displays a 3-dimensional 'rayshader' object
https://www.tylermw.com/a-step-by-step-guide-to-making-3d-maps-with-satellite-imagery-in-r/
## Not run: require(fitbitViz) #............................ # first extract the log-id(s) #............................ USER_ID = '99xxxx' token = 'my_long_web_api_token' log_id = extract_LOG_ID(user_id = USER_ID, token = token, after_Date = '2021-03-13', limit = 10, sort = 'asc', verbose = TRUE) str(log_id) #................................... # then return the gps-ctx data.table #................................... res_tcx = GPS_TCX_data(log_id = log_id, user_id = USER_ID, token = token, time_zone = 'Europe/Athens', verbose = TRUE) str(res_tcx) #.................................................... # then compute the sf-object buffer and raster-extend #.................................................... sf_rst_ext = extend_AOI_buffer(dat_gps_tcx = res_tcx, buffer_in_meters = 1000, CRS = 4326, verbose = TRUE) sf_rst_ext #............................................................... # Download the Copernicus DEM 30m elevation data because it has # a better resolution, it takes a bit longer to download because # the .tif file size is bigger #............................................................... dem_dir = tempdir() # dem_dir dem30 = CopernicusDEM::aoi_geom_save_tif_matches(sf_or_file = sf_rst_ext$sfc_obj, dir_save_tifs = dem_dir, resolution = 30, crs_value = 4326, threads = parallel::detectCores(), verbose = TRUE) TIF = list.files(dem_dir, pattern = '.tif', full.names = T) # TIF if (length(TIF) > 1) { #.................................................... # create a .VRT file if I have more than 1 .tif files #.................................................... file_out = file.path(dem_dir, 'VRT_mosaic_FILE.vrt') vrt_dem30 = create_VRT_from_dir(dir_tifs = dem_dir, output_path_VRT = file_out, verbose = TRUE) } if (length(TIF) == 1) { #.................................................. # if I have a single .tif file keep the first index #.................................................. file_out = TIF[1] } raysh_rst = crop_DEM(tif_or_vrt_dem_file = file_out, sf_buffer_obj = sf_rst_ext$sfc_obj, verbose = TRUE) # terra::plot(raysh_rst) #................................................................ # create the 'elevation_sample_points' data.table parameter based # on the min., middle and max. altitude of the 'res_tcx' data #................................................................ idx_3m = c(which.min(res_tcx$AltitudeMeters), as.integer(length(res_tcx$AltitudeMeters) / 2), which.max(res_tcx$AltitudeMeters)) cols_3m = c('latitude', 'longitude', 'AltitudeMeters') dat_3m = res_tcx[idx_3m, ..cols_3m] # dat_3m #............................................................... # Split the route in 2 parts based on the maximum altitude value #............................................................... linestring_dat = gps_lat_lon_to_LINESTRING(dat_gps_tcx = res_tcx, CRS = 4326, time_split_asc_desc = NULL, verbose = TRUE) #..................................................... # Conversion of the 'SpatRaster' to a raster object # because the 'rayshader' package accepts only rasters #..................................................... rst_obj = raster::raster(raysh_rst) raster::projection(rst_obj) <- terra::crs(raysh_rst, proj = TRUE) #..................................... # open the 3-dimensional rayshader map #..................................... ray_out = rayshader_3d_DEM(rst_buf = rst_obj, rst_ext = sf_rst_ext$raster_obj_extent, linestring_ASC_DESC = linestring_dat, elevation_sample_points = dat_3m, zoom = 0.5, windowsize = c(1600, 1000), add_shadow_rescale_original = FALSE, verbose = TRUE) ## End(Not run)## Not run: require(fitbitViz) #............................ # first extract the log-id(s) #............................ USER_ID = '99xxxx' token = 'my_long_web_api_token' log_id = extract_LOG_ID(user_id = USER_ID, token = token, after_Date = '2021-03-13', limit = 10, sort = 'asc', verbose = TRUE) str(log_id) #................................... # then return the gps-ctx data.table #................................... res_tcx = GPS_TCX_data(log_id = log_id, user_id = USER_ID, token = token, time_zone = 'Europe/Athens', verbose = TRUE) str(res_tcx) #.................................................... # then compute the sf-object buffer and raster-extend #.................................................... sf_rst_ext = extend_AOI_buffer(dat_gps_tcx = res_tcx, buffer_in_meters = 1000, CRS = 4326, verbose = TRUE) sf_rst_ext #............................................................... # Download the Copernicus DEM 30m elevation data because it has # a better resolution, it takes a bit longer to download because # the .tif file size is bigger #............................................................... dem_dir = tempdir() # dem_dir dem30 = CopernicusDEM::aoi_geom_save_tif_matches(sf_or_file = sf_rst_ext$sfc_obj, dir_save_tifs = dem_dir, resolution = 30, crs_value = 4326, threads = parallel::detectCores(), verbose = TRUE) TIF = list.files(dem_dir, pattern = '.tif', full.names = T) # TIF if (length(TIF) > 1) { #.................................................... # create a .VRT file if I have more than 1 .tif files #.................................................... file_out = file.path(dem_dir, 'VRT_mosaic_FILE.vrt') vrt_dem30 = create_VRT_from_dir(dir_tifs = dem_dir, output_path_VRT = file_out, verbose = TRUE) } if (length(TIF) == 1) { #.................................................. # if I have a single .tif file keep the first index #.................................................. file_out = TIF[1] } raysh_rst = crop_DEM(tif_or_vrt_dem_file = file_out, sf_buffer_obj = sf_rst_ext$sfc_obj, verbose = TRUE) # terra::plot(raysh_rst) #................................................................ # create the 'elevation_sample_points' data.table parameter based # on the min., middle and max. altitude of the 'res_tcx' data #................................................................ idx_3m = c(which.min(res_tcx$AltitudeMeters), as.integer(length(res_tcx$AltitudeMeters) / 2), which.max(res_tcx$AltitudeMeters)) cols_3m = c('latitude', 'longitude', 'AltitudeMeters') dat_3m = res_tcx[idx_3m, ..cols_3m] # dat_3m #............................................................... # Split the route in 2 parts based on the maximum altitude value #............................................................... linestring_dat = gps_lat_lon_to_LINESTRING(dat_gps_tcx = res_tcx, CRS = 4326, time_split_asc_desc = NULL, verbose = TRUE) #..................................................... # Conversion of the 'SpatRaster' to a raster object # because the 'rayshader' package accepts only rasters #..................................................... rst_obj = raster::raster(raysh_rst) raster::projection(rst_obj) <- terra::crs(raysh_rst, proj = TRUE) #..................................... # open the 3-dimensional rayshader map #..................................... ray_out = rayshader_3d_DEM(rst_buf = rst_obj, rst_ext = sf_rst_ext$raster_obj_extent, linestring_ASC_DESC = linestring_dat, elevation_sample_points = dat_3m, zoom = 0.5, windowsize = c(1600, 1000), add_shadow_rescale_original = FALSE, verbose = TRUE) ## End(Not run)
Refresh Token of an existing application
refresh_token_app(client_id, client_secret, refresh_token)refresh_token_app(client_id, client_secret, refresh_token)
client_id |
a character string specifying the 'client_id' of the registered (existing) Fitbit application |
client_secret |
a character string specifying the 'client_secret' of the registered (existing) Fitbit application |
refresh_token |
a character string specifying the 'refresh_token' of the registered (existing) Fitbit application |
A registered Fitbit application has a time limit of 8 hours. Therefore, the user has to refresh the token after the expiration using the 'client_id', 'client_secret' and 'refresh_token' that it's available for the registered application. Based on the Fitbit API Documentation "After the Access Token expiration time has passed your requests will receive a 401 HTTP error. When this happens, your app should use the Refresh Token to get a new pair of tokens"
a named list that includes access_token, expires_in, refresh_token, scope, token_type, user_id
## Not run: require(fitbitViz) # client id, client secret and refresh token of # the existing Fitbit Application Client_ID = 'xxxxxx' Client_SECRET = 'xxxxxxxxxxxxxxxxxx' Refresh_TOKEN = 'xxxxxxxxxxxxxxxxxxxxxxxx' # refresh the token res_token = refresh_token_app(client_id = Client_ID, client_secret = Client_SECRET, refresh_token = Refresh_TOKEN) res_token # use the updated token to a function USER_ID = '99xxxx' new_TOKEN = res_token$access_token, res_type = fitbit_data_type_by_date(user_id = USER_ID, token = new_TOKEN, date = '2022-10-12', type = 'spo2', show_nchar_case_error = 135) ## End(Not run)## Not run: require(fitbitViz) # client id, client secret and refresh token of # the existing Fitbit Application Client_ID = 'xxxxxx' Client_SECRET = 'xxxxxxxxxxxxxxxxxx' Refresh_TOKEN = 'xxxxxxxxxxxxxxxxxxxxxxxx' # refresh the token res_token = refresh_token_app(client_id = Client_ID, client_secret = Client_SECRET, refresh_token = Refresh_TOKEN) res_token # use the updated token to a function USER_ID = '99xxxx' new_TOKEN = res_token$access_token, res_type = fitbit_data_type_by_date(user_id = USER_ID, token = new_TOKEN, date = '2022-10-12', type = 'spo2', show_nchar_case_error = 135) ## End(Not run)
Sleep Data of single day
sleep_single_day( user_id, token, date = "2021-03-09", ggplot_color_palette = "ggsci::blue_material", show_nchar_case_error = 135, verbose = FALSE )sleep_single_day( user_id, token, date = "2021-03-09", ggplot_color_palette = "ggsci::blue_material", show_nchar_case_error = 135, verbose = FALSE )
user_id |
a character string specifying the encoded ID of the user. For instance '99xxxx' of the following URL 'https://www.fitbit.com/user/99xxxx' of the user's account corresponds to the 'user_id' |
token |
a character string specifying the secret token that a user receives when registers a new application in https://dev.fitbit.com/apps |
date |
a character string specifying the Date for which the sleep data should be returned. For instance, the date '2021-12-31' where the input order is 'year-month-day' |
ggplot_color_palette |
a character string specifying the color palette to be used. For a full list of palettes used in the ggplot see: https://pmassicotte.github.io/paletteer_gallery/ The following color-palettes were tested and work well: "rcartocolor::Purp", "rcartocolor::Teal" |
show_nchar_case_error |
an integer that specifies the number of characters that will be returned in case on an error. The default value is 135 characters. |
verbose |
a boolean. If TRUE then information will be printed out in the console |
an object of class list
## Not run: require(fitbitViz) USER_ID = '99xxxx' token = 'my_long_web_api_token' lst_out = sleep_single_day(user_id = USER_ID, token = token, date = '2021-03-09', ggplot_color_palette = 'ggsci::blue_material', show_nchar_case_error = 135, verbose = TRUE) str(lst_out) ## End(Not run)## Not run: require(fitbitViz) USER_ID = '99xxxx' token = 'my_long_web_api_token' lst_out = sleep_single_day(user_id = USER_ID, token = token, date = '2021-03-09', ggplot_color_palette = 'ggsci::blue_material', show_nchar_case_error = 135, verbose = TRUE) str(lst_out) ## End(Not run)
Sleep Data Time Series
sleep_time_series( user_id, token, date_start, date_end, ggplot_color_palette = "ggsci::blue_material", ggplot_ncol = NULL, ggplot_nrow = NULL, show_nchar_case_error = 135, verbose = FALSE )sleep_time_series( user_id, token, date_start, date_end, ggplot_color_palette = "ggsci::blue_material", ggplot_ncol = NULL, ggplot_nrow = NULL, show_nchar_case_error = 135, verbose = FALSE )
user_id |
a character string specifying the encoded ID of the user. For instance '99xxxx' of the following URL 'https://www.fitbit.com/user/99xxxx' of the user's account corresponds to the 'user_id' |
token |
a character string specifying the secret token that a user receives when registers a new application in https://dev.fitbit.com/apps |
date_start |
a character string specifying the start Date for which the sleep data should be returned. For instance, the date '2021-12-31' where the input order is 'year-month-day' |
date_end |
a character string specifying the end Date for which the sleep data should be returned. For instance, the date '2021-12-31' where the input order is 'year-month-day' |
ggplot_color_palette |
a character string specifying the color palette to be used. For a full list of palettes used in the ggplot see: https://pmassicotte.github.io/paletteer_gallery/ The following color-palettes were tested and work well: "rcartocolor::Purp", "rcartocolor::Teal" |
ggplot_ncol |
either NULL or an integer specifying the number of columns of the output ggplot |
ggplot_nrow |
either NULL or an integer specifying the number of rows of the output ggplot |
show_nchar_case_error |
an integer that specifies the number of characters that will be returned in case on an error. The default value is 135 characters. |
verbose |
a boolean. If TRUE then information will be printed out in the console |
an object of class list
## Not run: require(fitbitViz) #......................................... # first compute the sleep time time series #......................................... USER_ID = '99xxxx' token = 'my_long_web_api_token' sleep_ts = sleep_time_series(user_id = USER_ID, token = token, date_start = '2021-03-09', date_end = '2021-03-16', ggplot_color_palette = 'ggsci::blue_material', show_nchar_case_error = 135, verbose = TRUE) sleep_ts$plt_lev_segments sleep_ts$plt_lev_heatmap sleep_ts$heatmap_data #........................................... # (option to) save the ggplot to a .png file #........................................... png_file = tempfile(fileext = '.png') ggplot2::ggsave(filename = png_file, plot = sleep_ts$plt_lev_segments, device = 'png', scale = 1, width = 35, height = 25, limitsize = TRUE) ## End(Not run)## Not run: require(fitbitViz) #......................................... # first compute the sleep time time series #......................................... USER_ID = '99xxxx' token = 'my_long_web_api_token' sleep_ts = sleep_time_series(user_id = USER_ID, token = token, date_start = '2021-03-09', date_end = '2021-03-16', ggplot_color_palette = 'ggsci::blue_material', show_nchar_case_error = 135, verbose = TRUE) sleep_ts$plt_lev_segments sleep_ts$plt_lev_heatmap sleep_ts$heatmap_data #........................................... # (option to) save the ggplot to a .png file #........................................... png_file = tempfile(fileext = '.png') ggplot2::ggsave(filename = png_file, plot = sleep_ts$plt_lev_segments, device = 'png', scale = 1, width = 35, height = 25, limitsize = TRUE) ## End(Not run)