ASK

Analyze the smart device data to identify trends of how those fitness trackers are used and then apply the insight to a specific product to help build a marketing strategy.
Key stakeholders: Marketing team, Manager, Co-Founder

PREPARE

Summary of the dataset: The data from Fitbit look quite good and can be used to explore users’ habits of using smart device to track their health measurment, it includes data about steps, activity, sleep, heart rate, intensity, calories burned, MET, and weight. With time recorded by daily, hour and minute.
Storage: With this amount of data, I will use R the most for better analyze, visualize and share the report with stakeholders.
Source and privacy: The data is from kaggle and it is open-source, so that I had downloaded from kaggle to use. And there is no way to check for the privacy of the dataset.
Limitations of the dataset: The main problem with this dataset is that it only includes 30 observations, which is quite small and may include to inaccurate results. Moreover, according to Bellabeat, their products will focus on women and designed specifically for them, but the dataset of Fitbit indentifies users by their ID, and there is no “sex” column. Also, the date in many tables are not set as date type.

PROCESS

Install and run packages

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(ggplot2)
library(skimr)
library(janitor)

## 
## Attaching package: 'janitor'
## 
## The following objects are masked from 'package:stats':
## 
##     chisq.test, fisher.test

library(corrplot)

## corrplot 0.95 loaded

Import the dataset

With the dataset, after checking by excel, I see that I only need 2 tables to work with: * dailyActivity * dailySleep Because in the dailyActivity, most of other data like calories, intensities,… have been included, so that I do not need to include other tables.

daily_activity <- read.csv("~/Downloads/archive/mturkfitbit_export_4.12.16-5.12.16/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv")
daily_sleep <- read.csv("~/Downloads/archive/mturkfitbit_export_4.12.16-5.12.16/Fitabase Data 4.12.16-5.12.16/sleepDay_merged.csv")

Other tabels like heartrate_second, weight_log may seem usefull but the heartrate does not tell a lot about how people use the device cause everyone’s heartrate is totally different, and the weight_log only contains 8 observations which are not enough

Preview the dataset

head(daily_activity)

##           Id ActivityDate TotalSteps TotalDistance TrackerDistance
## 1 1503960366    4/12/2016      13162          8.50            8.50
## 2 1503960366    4/13/2016      10735          6.97            6.97
## 3 1503960366    4/14/2016      10460          6.74            6.74
## 4 1503960366    4/15/2016       9762          6.28            6.28
## 5 1503960366    4/16/2016      12669          8.16            8.16
## 6 1503960366    4/17/2016       9705          6.48            6.48
##   LoggedActivitiesDistance VeryActiveDistance ModeratelyActiveDistance
## 1                        0               1.88                     0.55
## 2                        0               1.57                     0.69
## 3                        0               2.44                     0.40
## 4                        0               2.14                     1.26
## 5                        0               2.71                     0.41
## 6                        0               3.19                     0.78
##   LightActiveDistance SedentaryActiveDistance VeryActiveMinutes
## 1                6.06                       0                25
## 2                4.71                       0                21
## 3                3.91                       0                30
## 4                2.83                       0                29
## 5                5.04                       0                36
## 6                2.51                       0                38
##   FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes Calories
## 1                  13                  328              728     1985
## 2                  19                  217              776     1797
## 3                  11                  181             1218     1776
## 4                  34                  209              726     1745
## 5                  10                  221              773     1863
## 6                  20                  164              539     1728

str(daily_activity)

## 'data.frame':    940 obs. of  15 variables:
##  $ Id                      : num  1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
##  $ ActivityDate            : chr  "4/12/2016" "4/13/2016" "4/14/2016" "4/15/2016" ...
##  $ TotalSteps              : int  13162 10735 10460 9762 12669 9705 13019 15506 10544 9819 ...
##  $ TotalDistance           : num  8.5 6.97 6.74 6.28 8.16 ...
##  $ TrackerDistance         : num  8.5 6.97 6.74 6.28 8.16 ...
##  $ LoggedActivitiesDistance: num  0 0 0 0 0 0 0 0 0 0 ...
##  $ VeryActiveDistance      : num  1.88 1.57 2.44 2.14 2.71 ...
##  $ ModeratelyActiveDistance: num  0.55 0.69 0.4 1.26 0.41 ...
##  $ LightActiveDistance     : num  6.06 4.71 3.91 2.83 5.04 ...
##  $ SedentaryActiveDistance : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ VeryActiveMinutes       : int  25 21 30 29 36 38 42 50 28 19 ...
##  $ FairlyActiveMinutes     : int  13 19 11 34 10 20 16 31 12 8 ...
##  $ LightlyActiveMinutes    : int  328 217 181 209 221 164 233 264 205 211 ...
##  $ SedentaryMinutes        : int  728 776 1218 726 773 539 1149 775 818 838 ...
##  $ Calories                : int  1985 1797 1776 1745 1863 1728 1921 2035 1786 1775 ...

head(daily_sleep)

##           Id              SleepDay TotalSleepRecords TotalMinutesAsleep
## 1 1503960366 4/12/2016 12:00:00 AM                 1                327
## 2 1503960366 4/13/2016 12:00:00 AM                 2                384
## 3 1503960366 4/15/2016 12:00:00 AM                 1                412
## 4 1503960366 4/16/2016 12:00:00 AM                 2                340
## 5 1503960366 4/17/2016 12:00:00 AM                 1                700
## 6 1503960366 4/19/2016 12:00:00 AM                 1                304
##   TotalTimeInBed
## 1            346
## 2            407
## 3            442
## 4            367
## 5            712
## 6            320

str(daily_sleep)

## 'data.frame':    413 obs. of  5 variables:
##  $ Id                : num  1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
##  $ SleepDay          : chr  "4/12/2016 12:00:00 AM" "4/13/2016 12:00:00 AM" "4/15/2016 12:00:00 AM" "4/16/2016 12:00:00 AM" ...
##  $ TotalSleepRecords : int  1 2 1 2 1 1 1 1 1 1 ...
##  $ TotalMinutesAsleep: int  327 384 412 340 700 304 360 325 361 430 ...
##  $ TotalTimeInBed    : int  346 407 442 367 712 320 377 364 384 449 ...

Cleaning data

I would like to merge 2 tables: dailyActivity and dailySleep together by ID and Date.

daily_activity$ActivityDate <- as.Date.character(daily_activity$ActivityDate, format="%m/%d/%Y")
daily_sleep$SleepDay <- as.Date.character(daily_sleep$SleepDay, format="%m/%d/%Y")

Check the tables again after being transformed:

head(daily_activity)

##           Id ActivityDate TotalSteps TotalDistance TrackerDistance
## 1 1503960366   2016-04-12      13162          8.50            8.50
## 2 1503960366   2016-04-13      10735          6.97            6.97
## 3 1503960366   2016-04-14      10460          6.74            6.74
## 4 1503960366   2016-04-15       9762          6.28            6.28
## 5 1503960366   2016-04-16      12669          8.16            8.16
## 6 1503960366   2016-04-17       9705          6.48            6.48
##   LoggedActivitiesDistance VeryActiveDistance ModeratelyActiveDistance
## 1                        0               1.88                     0.55
## 2                        0               1.57                     0.69
## 3                        0               2.44                     0.40
## 4                        0               2.14                     1.26
## 5                        0               2.71                     0.41
## 6                        0               3.19                     0.78
##   LightActiveDistance SedentaryActiveDistance VeryActiveMinutes
## 1                6.06                       0                25
## 2                4.71                       0                21
## 3                3.91                       0                30
## 4                2.83                       0                29
## 5                5.04                       0                36
## 6                2.51                       0                38
##   FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes Calories
## 1                  13                  328              728     1985
## 2                  19                  217              776     1797
## 3                  11                  181             1218     1776
## 4                  34                  209              726     1745
## 5                  10                  221              773     1863
## 6                  20                  164              539     1728

glimpse(daily_activity)

## Rows: 940
## Columns: 15
## $ Id                       <dbl> 1503960366, 1503960366, 1503960366, 150396036…
## $ ActivityDate             <date> 2016-04-12, 2016-04-13, 2016-04-14, 2016-04-…
## $ TotalSteps               <int> 13162, 10735, 10460, 9762, 12669, 9705, 13019…
## $ TotalDistance            <dbl> 8.50, 6.97, 6.74, 6.28, 8.16, 6.48, 8.59, 9.8…
## $ TrackerDistance          <dbl> 8.50, 6.97, 6.74, 6.28, 8.16, 6.48, 8.59, 9.8…
## $ LoggedActivitiesDistance <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ VeryActiveDistance       <dbl> 1.88, 1.57, 2.44, 2.14, 2.71, 3.19, 3.25, 3.5…
## $ ModeratelyActiveDistance <dbl> 0.55, 0.69, 0.40, 1.26, 0.41, 0.78, 0.64, 1.3…
## $ LightActiveDistance      <dbl> 6.06, 4.71, 3.91, 2.83, 5.04, 2.51, 4.71, 5.0…
## $ SedentaryActiveDistance  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ VeryActiveMinutes        <int> 25, 21, 30, 29, 36, 38, 42, 50, 28, 19, 66, 4…
## $ FairlyActiveMinutes      <int> 13, 19, 11, 34, 10, 20, 16, 31, 12, 8, 27, 21…
## $ LightlyActiveMinutes     <int> 328, 217, 181, 209, 221, 164, 233, 264, 205, …
## $ SedentaryMinutes         <int> 728, 776, 1218, 726, 773, 539, 1149, 775, 818…
## $ Calories                 <int> 1985, 1797, 1776, 1745, 1863, 1728, 1921, 203…

head(daily_sleep)

##           Id   SleepDay TotalSleepRecords TotalMinutesAsleep TotalTimeInBed
## 1 1503960366 2016-04-12                 1                327            346
## 2 1503960366 2016-04-13                 2                384            407
## 3 1503960366 2016-04-15                 1                412            442
## 4 1503960366 2016-04-16                 2                340            367
## 5 1503960366 2016-04-17                 1                700            712
## 6 1503960366 2016-04-19                 1                304            320

glimpse(daily_sleep)

## Rows: 413
## Columns: 5
## $ Id                 <dbl> 1503960366, 1503960366, 1503960366, 1503960366, 150…
## $ SleepDay           <date> 2016-04-12, 2016-04-13, 2016-04-15, 2016-04-16, 20…
## $ TotalSleepRecords  <int> 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ TotalMinutesAsleep <int> 327, 384, 412, 340, 700, 304, 360, 325, 361, 430, 2…
## $ TotalTimeInBed     <int> 346, 407, 442, 367, 712, 320, 377, 364, 384, 449, 3…

Change column names:

daily_sleep <- rename(daily_sleep, date = SleepDay)
daily_activity <- rename(daily_activity, date = ActivityDate)

Merge:

merged_daily_activity <- merge(x = daily_activity, y = daily_sleep, by=c("Id","date"), all.x = TRUE)

Check the new table:

head(merged_daily_activity)

##           Id       date TotalSteps TotalDistance TrackerDistance
## 1 1503960366 2016-04-12      13162          8.50            8.50
## 2 1503960366 2016-04-13      10735          6.97            6.97
## 3 1503960366 2016-04-14      10460          6.74            6.74
## 4 1503960366 2016-04-15       9762          6.28            6.28
## 5 1503960366 2016-04-16      12669          8.16            8.16
## 6 1503960366 2016-04-17       9705          6.48            6.48
##   LoggedActivitiesDistance VeryActiveDistance ModeratelyActiveDistance
## 1                        0               1.88                     0.55
## 2                        0               1.57                     0.69
## 3                        0               2.44                     0.40
## 4                        0               2.14                     1.26
## 5                        0               2.71                     0.41
## 6                        0               3.19                     0.78
##   LightActiveDistance SedentaryActiveDistance VeryActiveMinutes
## 1                6.06                       0                25
## 2                4.71                       0                21
## 3                3.91                       0                30
## 4                2.83                       0                29
## 5                5.04                       0                36
## 6                2.51                       0                38
##   FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes Calories
## 1                  13                  328              728     1985
## 2                  19                  217              776     1797
## 3                  11                  181             1218     1776
## 4                  34                  209              726     1745
## 5                  10                  221              773     1863
## 6                  20                  164              539     1728
##   TotalSleepRecords TotalMinutesAsleep TotalTimeInBed
## 1                 1                327            346
## 2                 2                384            407
## 3                NA                 NA             NA
## 4                 1                412            442
## 5                 2                340            367
## 6                 1                700            712

glimpse(merged_daily_activity)

## Rows: 943
## Columns: 18
## $ Id                       <dbl> 1503960366, 1503960366, 1503960366, 150396036…
## $ date                     <date> 2016-04-12, 2016-04-13, 2016-04-14, 2016-04-…
## $ TotalSteps               <int> 13162, 10735, 10460, 9762, 12669, 9705, 13019…
## $ TotalDistance            <dbl> 8.50, 6.97, 6.74, 6.28, 8.16, 6.48, 8.59, 9.8…
## $ TrackerDistance          <dbl> 8.50, 6.97, 6.74, 6.28, 8.16, 6.48, 8.59, 9.8…
## $ LoggedActivitiesDistance <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ VeryActiveDistance       <dbl> 1.88, 1.57, 2.44, 2.14, 2.71, 3.19, 3.25, 3.5…
## $ ModeratelyActiveDistance <dbl> 0.55, 0.69, 0.40, 1.26, 0.41, 0.78, 0.64, 1.3…
## $ LightActiveDistance      <dbl> 6.06, 4.71, 3.91, 2.83, 5.04, 2.51, 4.71, 5.0…
## $ SedentaryActiveDistance  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ VeryActiveMinutes        <int> 25, 21, 30, 29, 36, 38, 42, 50, 28, 19, 66, 4…
## $ FairlyActiveMinutes      <int> 13, 19, 11, 34, 10, 20, 16, 31, 12, 8, 27, 21…
## $ LightlyActiveMinutes     <int> 328, 217, 181, 209, 221, 164, 233, 264, 205, …
## $ SedentaryMinutes         <int> 728, 776, 1218, 726, 773, 539, 1149, 775, 818…
## $ Calories                 <int> 1985, 1797, 1776, 1745, 1863, 1728, 1921, 203…
## $ TotalSleepRecords        <int> 1, 2, NA, 1, 2, 1, NA, 1, 1, 1, NA, 1, 1, 1, …
## $ TotalMinutesAsleep       <int> 327, 384, NA, 412, 340, 700, NA, 304, 360, 32…
## $ TotalTimeInBed           <int> 346, 407, NA, 442, 367, 712, NA, 320, 377, 36…

Now, I already have a new table which has the data I need to analyze. For easy analysis, I will do 1 more step which is turn all columns to lowercase

colnames(merged_daily_activity) <- tolower(colnames(merged_daily_activity))

ANALYZE & SHARE

Now, I will first try to see the correlations between the variables. And I will put the Calories burned first and highlight it as the main variable. And I do not choose the trackerdistance cause it is the same with totaldistance.

data_correlation <- select(merged_daily_activity,calories,totalsteps:sedentaryminutes,-trackerdistance)

Because when merging the data from daily_activity with data from daily_sleep, there are some N/A value, so that I will create 2 different dataframes: data_correlation and data_correlation_sleep. Additionally, I transform the daily_sleep with a new variable to calculate how much time people take to actually sleep.

merged_daily_activity <- transform(merged_daily_activity,totaltimetosleep = totaltimeinbed - totalminutesasleep)
data_correlation_sleep <- select(merged_daily_activity,calories,totalsteps:totaltimetosleep,-trackerdistance) %>% 
  filter(!is.na(totaltimeinbed))

I will do the correlation function now

corrplot(cor(data_correlation))

corrplot(cor(data_correlation_sleep))

When look at the the first plot, we can easily see that totalsteps, totaldistance, veryactivedistance, lightactivedistance, veryactiveminutes, fairlyactiveminutes and lightlyactiveminuts have a positive affect with calories burned.
Although the moderatelyactivedistance does not affect the calories much, the lightactives does. So we can conclude that the intensity does not affect much to calories burned, and what really affect is how long, how many steps people take.
With the plot having sleep data, we can see that all sleep-related variables had negative effects on the calories burned.
Now, let’s make a graph that shows more details about the correlation between distance, steps and calories.

ggplot(data=merged_daily_activity, aes(y=totaldistance,x=calories,color=totalsteps))+
  geom_point()+
  geom_smooth(method = "loess", se = TRUE)+
  labs(title = "Distance and Steps vs Calories")

## `geom_smooth()` using formula = 'y ~ x'

## Warning: The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?

From the graph, we can confirm that the more steps and distance people do, the more calories will be burned. So that this is a point where the marketing team can utilize to develop the strategy, which is about tracking the distance or steps per day of user, and have some functionabilities to give user the information about how they’re doing and how many steps do they need to burn a specific amount of calories.
Additionally, there is one data that I’m pretty curious about, which is the hear rate. Let’s check it out see the number of distinct Id

heart_rate <- read.csv("~/Downloads/archive/mturkfitbit_export_4.12.16-5.12.16/Fitabase Data 4.12.16-5.12.16/heartrate_seconds_merged.csv")

There is 14 people in the data frame, it is not enough but still usable. First, let’s do some formatting to make it easier to analyze

heart_rate <- rename_with(heart_rate,tolower)
heart_rate$time <- as.Date.character(heart_rate$time, format="%m/%d/%Y")

And then, according to uihc, even when doing intense activity, the heart rate still does not cross 200 bpm, so that I will check who have the bpm value over 200

heart_rate %>% 
  filter(value >= 200)

##            id       time value
## 1  2022484408 2016-04-21   200
## 2  2022484408 2016-04-21   202
## 3  2022484408 2016-04-21   203
## 4  2022484408 2016-04-21   202
## 5  2022484408 2016-04-21   203
## 6  2022484408 2016-04-21   203
## 7  2022484408 2016-04-21   203
## 8  2022484408 2016-04-21   203
## 9  2022484408 2016-04-21   201
## 10 2022484408 2016-04-21   200
## 11 2022484408 2016-04-21   200
## 12 2022484408 2016-04-21   202
## 13 2022484408 2016-04-21   203
## 14 2022484408 2016-04-21   203
## 15 2022484408 2016-04-21   203
## 16 2022484408 2016-04-21   202
## 17 2022484408 2016-04-21   200

We can see that only the person with id 2022484408 has been recorded with the value over 200 and on 2016-04-21. Based on that, we will go back to the merged_daily_activity to see what he did on that day.

merged_daily_activity %>% 
  filter(id==2022484408, date == "2016-04-21") %>% 
  select(totalsteps,totaldistance,calories)

##   totalsteps totaldistance calories
## 1      12453          8.74     3158

Totalsteps are 12453, totaldistance is 8.74, calories are 3158. Let’s compare these numbers to the average.

merged_daily_activity %>% 
  summarise(mean1 = mean(totalsteps), mean2=mean(totaldistance),mean3=mean(calories))

##      mean1    mean2    mean3
## 1 7652.189 5.502853 2307.508

Clearly, all measurements that he had are much higher than the average.

ACT

From the insights above, I will give out 2 recommendations:
Because the distance/steps are much more effective than the intensity when talking about amount of calories burned, I suggest we should add a feature that user can measure their steps, and give them a goal so that they should know that their activity in a day is enough or not.
And 1 more feature is the alert when the bpm of user is too high, and improve the sensor that if user is not doing any intense activity, but their heart rate is still high (>200 bpm), it will alert user and even their emergency number if they do not turn of the alert.

Bellabeat capstone

Quang Hưng

2025-01-05

ASK