Include the Libraries

First, open up R and include these libraries

library(httr)
library(jsonlite)
library(lubridate)
library(tidyverse)

Retrieving Heartrate Data

Now that we have our auth token we can query the API for heart rate. You can find all the different calls on the web API page here. I’m interested in the highest granularity data I can get, which is an average heart rate over every 5 second interval. You can find this API call in the intraday heart rate section.

Put your Oauth 2.0 access token here

auth.code = paste("Bearer", "[Your-Code-Here]")

To call the Fitbit web API we’ll use the GET method from the httr library

#Get get intreday heartrate
query.response <- GET("https://api.fitbit.com/1/user/-/activities/heart/date/2018-01-05/1d/1sec.json",
                  add_headers(Authorization = auth.code)
                  )
#get the json response
json.heartrate <- content(query.response, "parsed")

Now to analyze the response we should convert the data into a dataframe.

heartrate.dataset <- json.heartrate$`activities-heart-intraday`$dataset
heartrate <- data.frame(
  matrix(
    unlist(heartrate.dataset),
    ncol=2, byrow=T),
  stringsAsFactors = FALSE)

#Clean response names
heartrate.formatted <- heartrate %>% rename(Time = X1, HR = X2) %>% 
  mutate(Time = ymd_hms(paste("2018-01-05", Time)),
         HR = as.numeric(HR))

Part 4: Getting the rest of your data

To get intraday heart rate by minute or second you can only query a single day at a time. Getting a single day is fairly straight forward once you have your access token. However, we are limited to 150 requests per hour, which means you can only ingest 150 days of heart rate data each hour. The following script will stop once you start receiving request limit errors. This script will wait an hour on failure and continue so you can run it over night.

getHeartrate <- function(auth.code, start.date, end.date, interval){
  start = as.Date(start.date)
  end = as.Date(end.date)
  if(!interval %in% c("min", "sec")){
    print("interval must be either 'min' or 'sec'")
    stop()
  }
  #create dataframe
  all.days = data.frame(Time = c(),HR = c())
  #loop through every day
  current.date = start
  while (current.date <= end)
  {
    query.response <- GET(paste0("https://api.fitbit.com/1/user/-/activities/heart/date/",
                                 format(current.date),
                                 "/1d/1",
                                 interval,
                                 ".json"),
                          add_headers(
                            Authorization = auth.code)
                          )
    #check for error
    if(query.response$status_code == 200){
      
      json.heartrate<- content(query.response, "parsed")
  
      new.day.dataset <- json.heartrate$`activities-heart-intraday`$dataset
      #
      if(is.null(new.day.dataset) || length(new.day.dataset) == 0){
        print(paste(
          "No data found on ",
          format(current.date),
          "skipping date and moving on"
        ))
        current.date <- current.date + 1
        next
      }
      new.day <- data.frame(matrix(unlist(new.day.dataset), ncol=2, byrow=T), stringsAsFactors = FALSE)
      
      new.day <- new.day %>% rename(Time = X1, HR = X2) %>% 
    mutate(Time = ymd_hms(paste(format(current.date), Time)),
           HR = as.numeric(HR))
      
      all.days <- rbind(all.days, new.day)
    
      current.date <- current.date + 1     
    }
    #we errored
    else{
      print(paste(
          "Error on ",
          format(current.date)
          ))
      #What was the error?
      print(query.response)
      #rate limit error?
      if(query.response$status_code == 429){
        print("Hit rate limit, waiting 1 hour")
        Sys.sleep(3600)
      }
      else{
        return(all.days)
        break
      }
      
    }
                   
  }
  return(all.days)
}

Now to get all your data into a dataframe you just need to run this function.

final.heartrate <- getHeartrate(auth.code, "2017-12-22", "2017-12-26", "sec")

Finally save all your data into an rds file so you don’t have to query the API next time.

saveRDS(final.heartrate, file = "../../RData/fitbit_heartrate_20180910.rds")

Now that you have all your heart rate data in a tidy data frame you can combine it with other data sources and do whatever analysis you want!