Fitbitr
Interact with Fitbit data in R using Fitbit API
Install / Use
/learn @teramonagi/FitbitrREADME
<!-- README.md is generated from README.Rmd. Please edit that file -->
fitbitr
fitbitr package allows users to interact with Fitbit data in R using Fitbit API.
This package allows for most of the read and write methods that you might want to use.
Installation
fitbitr isn't available from CRAN yet, but you can get it from github with:
# install.packages("devtools")
devtools::install_github("teramonagi/fitbitr")
Preparation
API key
To get your own token (API key), you have to register your own application in here. For your reference, we share our setting:
After registration, you can get your own FITBIT_KEY and FITBIT_SECRET (referred to as OAuth 2.0 Client ID and Client Secret in the next figure).
If you set the following variables as a global variable, this package will use these values for API key.
# As a global variable
FITBIT_KEY <- "<your-fitbit-key>"
FITBIT_SECRET <- "<your-firbit-secret>"
# If you want, Default: "http://localhost:1410/"
# FITBIT_CALLBACK <- "<your-fitbit-callback>"
Or, you can set these values as a environment variable
Sys.setenv(FITBIT_KEY = "<your-fitbit-key>", FITBIT_SECRET = "<your-firbit-secret>")
Load libraries
library("fitbitr")
library("ggplot2") # for visualization in this document
Get Fitbit API token
You can get your Fitbit toekn using fitbitr::oauth_token():
# Get token
token <- fitbitr::oauth_token()
This function open a web browser autmatically and return Fitbit token.
How to use
Activity
# Example date
date <- "2017-12-29"
# Get daily activity summary
str(get_activity_summary(token, date))
#> List of 3
#> $ activities: list()
#> $ goals :List of 5
#> ..$ activeMinutes: int 30
#> ..$ caloriesOut : int 2790
#> ..$ distance : num 8.05
#> ..$ floors : int 10
#> ..$ steps : int 10000
#> $ summary :List of 15
#> ..$ activeScore : int -1
#> ..$ activityCalories : int 2563
#> ..$ caloriesBMR : int 1732
#> ..$ caloriesOut : int 4031
#> ..$ distances :'data.frame': 7 obs. of 2 variables:
#> .. ..$ activity: chr [1:7] "total" "tracker" "loggedActivities" "veryActive" ...
#> .. ..$ distance: num [1:7] 14.33 14.33 0 9.72 1.11 ...
#> ..$ elevation : int 0
#> ..$ fairlyActiveMinutes : int 67
#> ..$ floors : int 0
#> ..$ heartRateZones :'data.frame': 4 obs. of 5 variables:
#> .. ..$ caloriesOut: num [1:4] 1905 1320 132 651
#> .. ..$ max : int [1:4] 92 128 156 220
#> .. ..$ min : int [1:4] 30 92 128 156
#> .. ..$ minutes : int [1:4] 1163 217 12 42
#> .. ..$ name : chr [1:4] "範囲外" "脂肪燃焼" "有酸素運動" "ピーク"
#> ..$ lightlyActiveMinutes: int 186
#> ..$ marginalCalories : int 1825
#> ..$ restingHeartRate : int 58
#> ..$ sedentaryMinutes : int 724
#> ..$ steps : int 17394
#> ..$ veryActiveMinutes : int 114
# Get Activity Time Series
get_activity_time_series(token, "steps", date=date, period="7d")
#> dateTime value
#> 1 2017-12-23 14310
#> 2 2017-12-24 16136
#> 3 2017-12-25 18147
#> 4 2017-12-26 10815
#> 5 2017-12-27 11003
#> 6 2017-12-28 21025
#> 7 2017-12-29 17394
# Get activity intraday time series
# You have to use a **personal** key and secret.
df <- get_activity_intraday_time_series(token, "steps", date, detail_level="15min")
df$time <- as.POSIXct(strptime(paste0(df$dateTime, " ", df$dataset_time), "%Y-%m-%d %H:%M:%S"))
ggplot2::ggplot(df, aes(x=time, y=dataset_value)) + geom_line()
# Get Activity Types (complicated nested list)
length(get_activity_types(token))
#> [1] 1
# Get Activity Type (Yoga=52001)
get_activity_type(token, 52001)
#> accessLevel hasSpeed id mets name
#> 1 PUBLIC FALSE 52001 4.3 Yoga
# Get Frequent Activities
get_frequent_activities(token)
#> activityId calories description
#> 1 90013 0 Walking less than 2 mph, strolling very slowly
#> 2 90009 0 Running - 5 mph (12 min/mile)
#> 3 1071 0
#> 4 15000 0
#> 5 20047 0
#> 6 3001 0
#> distance duration name
#> 1 0 1485000 Walk
#> 2 0 3789000 Run
#> 3 0 1126000 Outdoor Bike
#> 4 0 2560000 Sport
#> 5 0 2560000 Elliptical
#> 6 0 2253000 Aerobic Workout
# Get Recent Activities
get_recent_activity_types(token)
#> activityId calories description
#> 1 90009 0 Running - 5 mph (12 min/mile)
#> 2 1071 0
#> 3 90013 0 Walking less than 2 mph, strolling very slowly
#> 4 15000 0
#> 5 20047 0
#> distance duration name
#> 1 0 3789000 Run
#> 2 0 1126000 Outdoor Bike
#> 3 0 1485000 Walk
#> 4 0 2560000 Sport
#> 5 0 2560000 Elliptical
# Add, get and delete favorite activities
add_favorite_activity(token, 52001)
get_favorite_activities(token)
#> activityId description mets name
#> 1 52001 4.3 Yoga
delete_favorite_activity(token, 52001)
get_favorite_activities(token)
#> list()
get_activity_goals(token, period="daily")
#> activeMinutes caloriesOut distance floors steps
#> 1 30 2790 10.3 10 100000
update_activity_goals(token, period="daily", distance=10.3)
#> activeMinutes caloriesOut distance floors steps
#> 1 30 2790 10.3 10 100000
get_activity_goals(token, period="daily")
#> activeMinutes caloriesOut distance floors steps
#> 1 30 2790 10.3 10 100000
get_activity_goals(token, period="weekly")
#> distance floors steps
#> 1 56.33 70 100000
update_activity_goals(token, period="weekly", steps="100000")
#> distance floors steps
#> 1 56.33 70 100000
get_activity_goals(token, period="weekly")
#> distance floors steps
#> 1 56.33 70 100000
# Get Lifetime Stats
get_lifetime_stats(token)
#> total_distance_date total_distance_value total_floors_date
#> 1 2017-12-17 199.3834 2016-04-17
#> total_floors_value tracker_distance_date tracker_distance_value
#> 1 36 2017-12-17 199.3834
#> tracker_floors_date tracker_floors_value total_activeScore
#> 1 2016-04-17 36 -1
#> total_caloriesOut total_distance total_floors total_steps
#> 1 -1 6869.16 4737 8876257
#> tracker_activeScore tracker_caloriesOut tracker_distance tracker_floors
#> 1 -1 -1 6869.16 4737
#> tracker_steps
#> 1 8876257
You can find more details in here
Heart Rate
# Set a date for example
date <- "2016-04-01"
# Get heart rate time series
df <- get_heart_rate_time_series(token, date=date, period="7d")
#> Warning in bind_rows_(x, .id): binding factor and character vector,
#> coercing into character vector
#> Warning in bind_rows_(x, .id): binding character and factor vector,
#> coercing into character vector
ggplot(df, aes(x=date, y=peak_max)) + geom_line()
# Get intraday heart rate time series
df <- get_heart_rate_intraday_time_series(token, date=date, detail_level="15min")
ggplot(df, aes(x=time, y=value)) + geom_line()
You can find more details here.
Sleep
# Get Sleep Logs(date is character or Date)
x <- get_sleep_logs(token, "2016-03-30")
print(head(x$sleep))
#> awakeCount awakeDuration awakeningsCount dateOfSleep duration efficiency
#> 1 0 0 14 2016-03-30 21420000 55
#> 2 0 0 14 2016-03-30 21420000 55
#> 3 0 0 14 2016-03-30 21420000 55
#> 4 0 0 14 2016-03-30 21420000 55
#> 5 0 0 14 2016-03-30 21420000 55
#> 6 0 0 14 2016-03-30 21420000 55
#> endTime isMainSleep logId minutesAfterWakeup
#> 1 2016-03-30T04:06:30.000 TRUE 11255066551 0
#> 2 2016-03-30T04:06:30.000 TRUE 11255066551 0
#> 3 2016-03-30T04:06:30.000 TRUE 11255066551 0
#> 4 2016-03-30T04:06:30.000 TRUE 11255066551 0
#> 5 2016-03-30T04:06:30.000 TRUE 11255066551 0
#> 6 2016-03-30T04:06:30.000 TRUE 11255066551 0
#> minutesAsleep minutesAwake minutesToFallAsleep restlessCount
#> 1 197 160
