Fitbit 03 – Getting and wrangling all data

Previous post in this series: Fitbit 02 – Getting and wrangling sleep data.

This post will wrap-up the getting and wrangling of Fitbit data using fitbitscraper. This is the list of data that was gathered [1]:

• Steps
• Distance
• Floors
• Very active minutes (“MinutesVery”)
• Calories burned
• Resting heart rate (“RestingHeart”)
• Sleep
• Weight.

For each dataset, the data was gathered then wrangled as separate tidy data frames. Each data contained a unique date per row. Most datasets required minimal wrangling. A previous post outlined the extra effort required to wrangle sleep data due to split sleep sessions and some extra looping to gather all weight data.

Each data frame contains a Date column. The data frames are joined by the unique dates to create one big happy data frame of Fitbitness. Each row is a date containing columns of fitness factors.

Now what? I feel like a falafel. I’m going to eat a falafel [2].

With this tidy dataset I will continue the analytics journey in future posts. For now, I wish to quickly visualise the data. Writing lines of code for plots in R is not-so-quick. Thankfully there’s a point-and-click visualisation package available called ggraptR. Installing and launching the package is achieved as follows. 

devtools::install_github('cargomoose/raptR', force = TRUE) # install
library("ggraptR") # load
ggraptR() # launch

My main hypothesis was that steps/distance may correlate with weight. There was no relationship observed on a scatter plot. This is preliminary, future post will focus on exploratory data analysis. Prior to data analysis I need to ask some driving questions.

I plotted Date vs Weight. My weight fell gradually from October 2015 through to December. I was on a week-long Sydney to Adelaide road trip during the end of December, got a parking ticket in Adelaide and did not have recorded weights whilst on the road. My weight steadily increased since. Not a lot of exercise, quite a lot of banana Tim Tams.

After sequential pointing-and-clicking, I overlayed this time plot with another factor - the “AwakeBetweenDuration”. In the previous post I noted I wake-up in the middle of the night. It may take hours before I fall asleep again. The tidy dataset holds the number of minutes awake between such sessions. The bigger the bubble, the longer I was awake between sleep sessions.

Here’s a driving question: what accounts for the nights when I am awake for long durations? I was awake some nights in October, December (some of my road trip nights – I couldn’t drive for one of those days as I was exhausted), January and then April. February and March appeared almost blissful. Why? Tell me data, why?  

Here is the Fitbit data wrangling code published on GitHub, FitbitWrangling.R: https://github.com/muhsinkarim/fitbit Replace “your_email” and “your_password” with your email and your password used to log into your Fitbit account and dashboard.

References and notes

1. The fitbitscraper function get_activity_data() will return rows of activities per day including walking and running. I only have activity data from 15^th February 2016. Since I’m analysing data since October 2015 (where I have weight data from my Fitbit scales) I chose not in include activity data in the tidy dataset.

2. I ate two.

Where's my repo? Using the GitHub app

Sharing code with the internets is made possible with GitHub. In a previous post I outlined how to add, commit and push code on GitHub via Git Bash, a scary looking terminal that sane people avoid. I’ve since played with the GitHub app which makes sharing even easier. Here I will create a repo and share my Fitbit R data wrangling code.

I’ve assumed one has a GitHub account and the GitHub app installed. I first need to create a repo. On the GitHub site I clicked on the “+” symbol and selected “New repository”. There’s also a helpful green “New repository” button which would have saved me one click. 

I entered “fitbit” as my repository name then selected “Create repository” at the bottom.

On the next screen (above) I selected “Set up in Desktop”. An “External Protocol Request” popup appeared and I selected “Launch Application”. Then magic happened. Magic. I was prompted to select where I wished to place my repo on my local machine (below). My repos exist under Documents > GitHub. Hitting OK, the repo cloned. My fitbit repo appeared on the left hand side of the GitHub app. 

Browsing to the fitbit repo directory on my local, I pasted the code I wish to share.

Returning to the GitHub app, it’s detected the addition of my script. I enter something in the “Summary” text field then select “Commit to master” below the “Publish” top right.

Then returning to GitHub, I hit refresh on my fitbit repo, and boom! My script appears on the internets. As I make changes to the script on my local, the GitHub app will allow me to commit changes and publish online.

Fitbit 02 – Getting and wrangling sleep data

Previous post in this series: Fitbit 01 – Getting and wrangling weight, steps and calories burned data.

Let’s continue to get and wrangle Fitbit data. This post will tackle sleep data and address my unattractive trait of not easily letting things go.

I applied the following R package fitbitScraper function wrapped in a data frame:

dfSleep <- as.data.frame(get_sleep_data(cookie, start_date = startDate, end_date = endDate))

There’s a lot of data. Below is the list of columns I want to keep:

• df.startDateTime – The start datetime of sleep.
• df.endDateTime – The end datetime of sleep.
• df.sleepDuration – The minutes between the start and end time. df.sleepDuration = df.awakeDuration + df.restlessDuration + df.minAsleep".
• df.awakeDuration – The minutes of wakefulness.
• df.restlessDuration – The minutes of restlessness.
• df.minAsleep – The minutes of actual sleep. I want to maximise this data field. More sleep less grumpiness.

If you’re a normalish human being, you would look at the sleep data and examine it for any anomalies. Your keen eye will note that some sleep durations are split across two or more sessions. That is, you went to bed, woke up in the middle of the night, then fell asleep again. Depending on how long you were awake for, Fitbit will record separate sessions.

I am working towards a tidy dataset with each row representing a unique date with the day’s Fitbit data. Separate sleep sessions cause duplicate dates. Being a normalish human being, you write code that will group the split sleep sessions back together. Unique dates per sleep lends to tidy data.

If you’re not a normal human being, you examine the intricate nature of these split sleep sessions and spend way too much time writing code that groups the data back together.

I am not a normal human being.

Split sessions occur for two reasons. The first occurs when I wake up in the early hours of the morning. An example is below.

On the night of the 22^nd March I fell asleep at 23:37. I woke up at 3:49 on the 23^rd. After hating my life and eating morning chocolate, I finally fell asleep again at 4:51. Technically the second session date should be displayed as the 23^rd of March, not the 22^nd. These display dates are akin to the “date I tried to fall asleep”. The datetimes record the true date and times.

When I combine these separate sessions, the new sleep start time will be 23:37 and the new sleep end time will be 07:46. The SleepDuration, SleepAwakeDuration, SleepRestlessDuration, SleepMinAsleep, SleepAwakeCount and SleepRestlessCount values will be summed together. I would also like to note the number of minutes I spent awake between sessions and the number of separate sessions (two in this example).

The second split sleep session type appears to be a glitch with sessions being separated by a difference of one minute. An example is below. 

On the 23^rd of February I have sleep sessions ending at 03:47 then resuming at 03:38. There are multiple instances where this occurs in my datasets. As before, the sleep variables from both sessions will be summed. I don’t need to note the minutes between the sessions as the one minute difference is meaningless. Further, the number of sleep sessions should be recorded as one, not two.

There’s a final consideration with split sleep sessions. Consider the below.

According this this display, on the 8th April I slept from 22:23 to 5:27, then on the 21:06 to the 23:57 on the same day. No I didn’t! The sleep session datetimes are overlapping. The session displayed on the 8th April from 21:06 to 23:57 should read the 9th April, not the 8th. This session needs to be combined with the sessions displayed on the 9th April from the 23:58 to 6:03. I may not be a normal human being, but I have my limits. For this last case, I did not write a patch of code that could group the data appropriately. Since there were few instances when such overlapping sessions occurred, I let it go – such sessions were removed from the dataset resulting in missing sleep data for that particular date.

Here is a quick plot of the average number of minutes asleep per weekday. Nothing out of the ordinary. I sleep an average 7.6 hours on Sunday nights and an average 6.4 hours on Wednesday nights. I can’t think of a reason why I get fewer hours on a Wednesday night. 

I spend most of my time wrangling data. I come across problems in datasets as described above often, and write code to return the numbers back to reality as much as possible. When the costs (time and effort) outweigh the benefits (more clean data) I have to let some data go and remove it.

I have outlined the code at the end of this post for the avid reader.

#### Sleep        
        
    ### Get data
    
        dfSleep <- as.data.frame(get_sleep_data(cookie, start_date = startDate, end_date = endDate))


    ### Keep key columns
    
        dfSleep <- dfSleep[ , c("df.date", "df.startDateTime", "df.endDateTime", "df.sleepDuration", "df.awakeDuration", 
                                "df.restlessDuration", "df.minAsleep")]

        ## Rename colnames
        # Date is sleep date attempt
        colnames(dfSleep) <- c("Date","SleepStartDatetime", "SleepEndDatetime", "SleepDuration", 
                               "SleepAwakeDuration", "SleepRestlessDuration", "SleepMinAsleep")


    ### Combine the split sleep sessions
    
        ## Index the Dates that are duplicated along with their original
        duplicatedDates <- unique(dfSleep$Date[which(duplicated(dfSleep$Date))])
        dfSleep$Combine <- ""
        dfSleep$Combine[which(dfSleep$Date %in% duplicatedDates)] <- 
            dfSleep$Date[which(dfSleep$Date %in% duplicatedDates)]
        
        ## Subset the combine indexed rows
        dfSubset <- dfSleep[which(dfSleep$Combine != ""), ]

        ## Aggregate rows marked to combine
        dfSubset <- 
            dfSubset %>%
            group_by(Combine) %>%
            summarise(Date = unique(Date),
                      SleepStartDatetime = min(SleepStartDatetime), # Earliest datetime
                      SleepEndDatetime = max(SleepEndDatetime), # Latest datetime
                      SleepDuration = sum(SleepDuration),
                      SleepAwakeDuration = sum(SleepAwakeDuration),
                      SleepRestlessDuration = sum(SleepRestlessDuration),
                      SleepMinAsleep = sum(SleepMinAsleep),
                      SleepSessions = n() # Number of split sleep sessions
            )


    ### Get the minutes awake between split sessions    
    
        ## Calculate sleep duration using start and end time using floor (round down)
        dfSubset$AwakeBetweenDuration <- floor((as.POSIXct(dfSubset$SleepEndDatetime) - as.POSIXct(dfSubset$SleepStartDatetime)) * 60)
        dfSubset$AwakeBetweenDuration <- dfSubset$AwakeBetweenDuration - dfSubset$SleepDuration
        
        ## Set any duration less than five minutes to zero
        dfSubset$AwakeBetweenDuration[which(dfSubset$AwakeBetweenDuration < 5)] <- 0


    ### Remove rows of combined sleep sessions
    
        ## Remove any row with AwakenBetweenDuration greater than five hours
        dfSubset <- dfSubset[-which(dfSubset$AwakeBetweenDuration > 60*5), ]


    ### Replace split sessions in dfSleep with dfSubset
    
        ## Prepare dfSubset for merging
        
        ## Remove duplicate dates from dfSleep
        dfSleep <- dfSleep[-which(nchar(dfSleep$Combine) > 0), ]
        
        ## Add new columns
        dfSleep$SleepSessions <- 1
        dfSleep$AwakeBetweenDuration <- 0
        
        ## Remove Combine column
        dfSubset <- dfSubset[ , -which(colnames(dfSubset) == "Combine")]
        dfSleep <- dfSleep[ , -which(colnames(dfSleep) == "Combine")]
        
        ## Bind dfSubset
        dfSleep <- rbind.data.frame(dfSleep, dfSubset)

        
    ### Coerce as date
        
        dfSleep$Date <- as.Date(dfSleep$Date)