Bellabeat Case Study

Introduction

This is the capstone project as part of my Google Data Analytics Professional Certificate course. For the analysis, I will be using R programming language and RStudio IDE.

Modus Operandi

Ask
Prepare
Process
Analyse
Share
Act

Scenario

You are a junior data analyst working on the marketing analyst team at Bellabeat, high-tech manufacturer of health-focused products for women. Bellabeat is a successful small company, but they have the potential to become a larger player in the global smart device market. Urska Srsen, cofounder and Chief Creative Officer of Bellabeat, believes that analyzing smart device fitness data could help unlock new growth opportunities for the company. You have been asked to focus on one of Bellabeat’s products and analyze smart device data to gain insight into how consumers are using their smart devices. The insights you discover will then help guide marketing strategy for the company. You will present your analysis to the Bellabeat executive team along with your high-level recommendations for Bellabeat’s marketing strategy.

Ask

What are some trends in smart device usage?
How could these trends apply to Bellabeat customers?
How could these trends help influence Bellabeat marketing strategy?

Key Tasks

Identify the business task
- The main objective is to analyze smart device usage data in order to gain insights into how customers use non-Bellabeat smart devices.
Consider key stakeholders
- Chief Creative Officer, executive team and marketing analytics team.

Deliverable

A clear statement of the business task.
- Study the people’s behaviour during a day.

Prepare

I will use publicly available data of FitBit Fitness Tracker Data. This dataset is made available by Mobius. Datasets are available here.

Key tasks

Download data and store it appropriately.
- Data has been downloaded and copies have been stored securely on my computer.
Identify how it’s organised.
- All the data is in comma-delimited (.CSV) format. There are total 18 files.
Sort and filter the data.
- For this analysis, I’m going to use Daily Activity, Hourly Calories, Hourly Intensity, Sleep, Weight Info and Hourly Steps datasets.
Determine the credibility of the data.
- For the purpose of this case study, the datasets are appropriate and it will enable me to answer the business questions.

Deliverable

A description of all data sources used
- Main source of data provided by Mobius.

Importing the packages to be used

library(dplyr)
library(readr)
library(lubridate)
library(ggplot2)
library(tidyr)

Import data in R Studio

activity <- read.csv('/home/arjit/Projects/Case Study Bellabeat/Data/dailyActivity_merged.csv')
calories <- read.csv('/home/arjit/Projects/Case Study Bellabeat/Data/hourlyCalories_merged.csv')
intensities <- read.csv('/home/arjit/Projects/Case Study Bellabeat/Data/hourlyIntensities_merged.csv')
sleep <- read.csv('/home/arjit/Projects/Case Study Bellabeat/Data/sleepDay_merged.csv')
weight <- read.csv('/home/arjit/Projects/Case Study Bellabeat/Data/weightLogInfo_merged.csv')
steps <- read.csv('/home/arjit/Projects/Case Study Bellabeat/Data/hourlySteps_merged.csv')

Process

Cleaning and processing data for analysis

Key tasks

Check the data for errors
Choose your tools
Transform the data so you can work with it effectively
Document the cleaning process

Deliverable

Documentation of any cleaning or manipulating the data

Seeing what’s there in the data

head(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

head(calories)

##           Id          ActivityHour Calories
## 1 1503960366 4/12/2016 12:00:00 AM       81
## 2 1503960366  4/12/2016 1:00:00 AM       61
## 3 1503960366  4/12/2016 2:00:00 AM       59
## 4 1503960366  4/12/2016 3:00:00 AM       47
## 5 1503960366  4/12/2016 4:00:00 AM       48
## 6 1503960366  4/12/2016 5:00:00 AM       48

head(intensities)

##           Id          ActivityHour TotalIntensity AverageIntensity
## 1 1503960366 4/12/2016 12:00:00 AM             20         0.333333
## 2 1503960366  4/12/2016 1:00:00 AM              8         0.133333
## 3 1503960366  4/12/2016 2:00:00 AM              7         0.116667
## 4 1503960366  4/12/2016 3:00:00 AM              0         0.000000
## 5 1503960366  4/12/2016 4:00:00 AM              0         0.000000
## 6 1503960366  4/12/2016 5:00:00 AM              0         0.000000

head(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

head(weight)

##           Id                  Date WeightKg WeightPounds Fat   BMI
## 1 1503960366  5/2/2016 11:59:59 PM     52.6     115.9631  22 22.65
## 2 1503960366  5/3/2016 11:59:59 PM     52.6     115.9631  NA 22.65
## 3 1927972279  4/13/2016 1:08:52 AM    133.5     294.3171  NA 47.54
## 4 2873212765 4/21/2016 11:59:59 PM     56.7     125.0021  NA 21.45
## 5 2873212765 5/12/2016 11:59:59 PM     57.3     126.3249  NA 21.69
## 6 4319703577 4/17/2016 11:59:59 PM     72.4     159.6147  25 27.45
##   IsManualReport        LogId
## 1           True 1.462234e+12
## 2           True 1.462320e+12
## 3          False 1.460510e+12
## 4           True 1.461283e+12
## 5           True 1.463098e+12
## 6           True 1.460938e+12

head(steps)

##           Id          ActivityHour StepTotal
## 1 1503960366 4/12/2016 12:00:00 AM       373
## 2 1503960366  4/12/2016 1:00:00 AM       160
## 3 1503960366  4/12/2016 2:00:00 AM       151
## 4 1503960366  4/12/2016 3:00:00 AM         0
## 5 1503960366  4/12/2016 4:00:00 AM         0
## 6 1503960366  4/12/2016 5:00:00 AM         0

Checking structure of activity

str(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 ...

Checking structure of calories

str(calories)

## 'data.frame':    22099 obs. of  3 variables:
##  $ Id          : num  1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
##  $ ActivityHour: chr  "4/12/2016 12:00:00 AM" "4/12/2016 1:00:00 AM" "4/12/2016 2:00:00 AM" "4/12/2016 3:00:00 AM" ...
##  $ Calories    : int  81 61 59 47 48 48 48 47 68 141 ...

Checking structure of intensities

str(intensities)

## 'data.frame':    22099 obs. of  4 variables:
##  $ Id              : num  1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
##  $ ActivityHour    : chr  "4/12/2016 12:00:00 AM" "4/12/2016 1:00:00 AM" "4/12/2016 2:00:00 AM" "4/12/2016 3:00:00 AM" ...
##  $ TotalIntensity  : int  20 8 7 0 0 0 0 0 13 30 ...
##  $ AverageIntensity: num  0.333 0.133 0.117 0 0 ...

Checking structure of sleep

str(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 ...

Checking structure of weight

str(weight)

## 'data.frame':    67 obs. of  8 variables:
##  $ Id            : num  1.50e+09 1.50e+09 1.93e+09 2.87e+09 2.87e+09 ...
##  $ Date          : chr  "5/2/2016 11:59:59 PM" "5/3/2016 11:59:59 PM" "4/13/2016 1:08:52 AM" "4/21/2016 11:59:59 PM" ...
##  $ WeightKg      : num  52.6 52.6 133.5 56.7 57.3 ...
##  $ WeightPounds  : num  116 116 294 125 126 ...
##  $ Fat           : int  22 NA NA NA NA 25 NA NA NA NA ...
##  $ BMI           : num  22.6 22.6 47.5 21.5 21.7 ...
##  $ IsManualReport: chr  "True" "True" "False" "True" ...
##  $ LogId         : num  1.46e+12 1.46e+12 1.46e+12 1.46e+12 1.46e+12 ...

Checking structure of steps

str(steps)

## 'data.frame':    22099 obs. of  3 variables:
##  $ Id          : num  1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
##  $ ActivityHour: chr  "4/12/2016 12:00:00 AM" "4/12/2016 1:00:00 AM" "4/12/2016 2:00:00 AM" "4/12/2016 3:00:00 AM" ...
##  $ StepTotal   : int  373 160 151 0 0 0 0 0 250 1864 ...

Correcting the format of timestamps

# intensities
intensities$ActivityHour=as.POSIXct(intensities$ActivityHour, format="%m/%d/%Y %I:%M:%S %p", tz=Sys.timezone())
intensities$time <- format(intensities$ActivityHour, format = "%H:%M:%S")
intensities$date <- format(intensities$ActivityHour, format = "%m/%d/%y")

# calories
calories$ActivityHour=as.POSIXct(calories$ActivityHour, format="%m/%d/%Y %I:%M:%S %p", tz=Sys.timezone())
calories$time <- format(calories$ActivityHour, format = "%H:%M:%S")
calories$date <- format(calories$ActivityHour, format = "%m/%d/%y")

# activity
activity$ActivityDate=as.POSIXct(activity$ActivityDate, format="%m/%d/%Y", tz=Sys.timezone())
activity$date <- format(activity$ActivityDate, format = "%m/%d/%y")

# sleep
sleep$SleepDay=as.POSIXct(sleep$SleepDay, format="%m/%d/%Y %I:%M:%S %p", tz=Sys.timezone())
sleep$date <- format(sleep$SleepDay, format = "%m/%d/%y")

# steps
steps$ActivityHour=as.POSIXct(steps$ActivityHour, format="%m/%d/%Y %I:%M:%S %p", tz=Sys.timezone())
steps$time <- format(steps$ActivityHour, format = "%H:%M:%S")
steps$date <- format(steps$ActivityHour, format = "%m/%d/%y")

Analyze

Now all the required information are in one place and ready for exploration.

Key tasks

Aggregate your data so it’s useful and accessible.
Organise and format your data.
Perform calculations.
Identify trends and relationships.

Deliverable

A summary of analysis.

Finding the number of participants

print(paste(n_distinct(activity$Id), "unique individuals in activity"))

## [1] "33 unique individuals in activity"

print(paste(n_distinct(calories$Id), "unique individuals in calories"))

## [1] "33 unique individuals in calories"

print(paste(n_distinct(intensities$Id), "unique individuals in intensities"))

## [1] "33 unique individuals in intensities"

print(paste(n_distinct(sleep$Id), "unique individuals in sleep"))

## [1] "24 unique individuals in sleep"

print(paste(n_distinct(weight$Id), "unique individuals in weight"))

## [1] "8 unique individuals in weight"

Studying the descriptive statistics of datasets

activity %>%  
  select(TotalSteps,
         TotalDistance,
         SedentaryMinutes, Calories) %>%
  summary()

##    TotalSteps    TotalDistance    SedentaryMinutes    Calories   
##  Min.   :    0   Min.   : 0.000   Min.   :   0.0   Min.   :   0  
##  1st Qu.: 3790   1st Qu.: 2.620   1st Qu.: 729.8   1st Qu.:1828  
##  Median : 7406   Median : 5.245   Median :1057.5   Median :2134  
##  Mean   : 7638   Mean   : 5.490   Mean   : 991.2   Mean   :2304  
##  3rd Qu.:10727   3rd Qu.: 7.713   3rd Qu.:1229.5   3rd Qu.:2793  
##  Max.   :36019   Max.   :28.030   Max.   :1440.0   Max.   :4900

# explore number of active minutes per category
activity %>%
  select(VeryActiveMinutes, FairlyActiveMinutes, LightlyActiveMinutes) %>%
  summary()

##  VeryActiveMinutes FairlyActiveMinutes LightlyActiveMinutes
##  Min.   :  0.00    Min.   :  0.00      Min.   :  0.0       
##  1st Qu.:  0.00    1st Qu.:  0.00      1st Qu.:127.0       
##  Median :  4.00    Median :  6.00      Median :199.0       
##  Mean   : 21.16    Mean   : 13.56      Mean   :192.8       
##  3rd Qu.: 32.00    3rd Qu.: 19.00      3rd Qu.:264.0       
##  Max.   :210.00    Max.   :143.00      Max.   :518.0

# calories
calories %>%
  select(Calories) %>%
  summary()

##     Calories     
##  Min.   : 42.00  
##  1st Qu.: 63.00  
##  Median : 83.00  
##  Mean   : 97.39  
##  3rd Qu.:108.00  
##  Max.   :948.00

# sleep
sleep %>%
  select(TotalSleepRecords, TotalMinutesAsleep, TotalTimeInBed) %>%
  summary()

##  TotalSleepRecords TotalMinutesAsleep TotalTimeInBed 
##  Min.   :1.000     Min.   : 58.0      Min.   : 61.0  
##  1st Qu.:1.000     1st Qu.:361.0      1st Qu.:403.0  
##  Median :1.000     Median :433.0      Median :463.0  
##  Mean   :1.119     Mean   :419.5      Mean   :458.6  
##  3rd Qu.:1.000     3rd Qu.:490.0      3rd Qu.:526.0  
##  Max.   :3.000     Max.   :796.0      Max.   :961.0

# weight
weight %>%
  select(WeightKg, BMI) %>%
  summary()

##     WeightKg           BMI       
##  Min.   : 52.60   Min.   :21.45  
##  1st Qu.: 61.40   1st Qu.:23.96  
##  Median : 62.50   Median :24.39  
##  Mean   : 72.04   Mean   :25.19  
##  3rd Qu.: 85.05   3rd Qu.:25.56  
##  Max.   :133.50   Max.   :47.54

Findings from the statistics:

Average sedentary time is 991 minutes or 16 hours.
The majority of the participants are lightly active.
On the average, participants sleep 1 time for 7 hours.
Average total steps per day are 7638, which is less than 8000 steps per day as suggested by CDC research.

Merging sleep data and activity

combined_sleep_activity <- merge(sleep, activity, by=c('Id', 'date'))
head(combined_sleep_activity)

##           Id     date   SleepDay TotalSleepRecords TotalMinutesAsleep
## 1 1503960366 04/12/16 2016-04-12                 1                327
## 2 1503960366 04/13/16 2016-04-13                 2                384
## 3 1503960366 04/15/16 2016-04-15                 1                412
## 4 1503960366 04/16/16 2016-04-16                 2                340
## 5 1503960366 04/17/16 2016-04-17                 1                700
## 6 1503960366 04/19/16 2016-04-19                 1                304
##   TotalTimeInBed ActivityDate TotalSteps TotalDistance TrackerDistance
## 1            346   2016-04-12      13162          8.50            8.50
## 2            407   2016-04-13      10735          6.97            6.97
## 3            442   2016-04-15       9762          6.28            6.28
## 4            367   2016-04-16      12669          8.16            8.16
## 5            712   2016-04-17       9705          6.48            6.48
## 6            320   2016-04-19      15506          9.88            9.88
##   LoggedActivitiesDistance VeryActiveDistance ModeratelyActiveDistance
## 1                        0               1.88                     0.55
## 2                        0               1.57                     0.69
## 3                        0               2.14                     1.26
## 4                        0               2.71                     0.41
## 5                        0               3.19                     0.78
## 6                        0               3.53                     1.32
##   LightActiveDistance SedentaryActiveDistance VeryActiveMinutes
## 1                6.06                       0                25
## 2                4.71                       0                21
## 3                2.83                       0                29
## 4                5.04                       0                36
## 5                2.51                       0                38
## 6                5.03                       0                50
##   FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes Calories
## 1                  13                  328              728     1985
## 2                  19                  217              776     1797
## 3                  34                  209              726     1745
## 4                  10                  221              773     1863
## 5                  20                  164              539     1728
## 6                  31                  264              775     2035

Visualize Total steps vs. Calories

ggplot(data=activity, aes(x=TotalSteps, y=Calories)) + 
  geom_point() + geom_smooth() + labs(title="Total Steps vs. Calories")

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

There is a positive correlation between Total Steps and Calories.

Visualize Total Minutes Asleep vs. Total Time in Bed

ggplot(data=sleep, aes(x=TotalMinutesAsleep, y=TotalTimeInBed)) + 
  geom_point()+ labs(title="Total Minutes Asleep vs. Total Time in Bed")

There exists a linear relationship between Total Minutes Asleep and Total Time in Bed. So, in order to improve our customers’ sleep, we can send them notification to go to sleep.

Studying the relationship between Intensity and Time.

int_new <- intensities %>%
  group_by(time) %>%
  drop_na() %>%
  summarise(mean_total_int = mean(TotalIntensity))

ggplot(data=int_new, aes(x=time, y=mean_total_int)) + geom_histogram(stat = "identity", fill='darkblue') +
  theme(axis.text.x = element_text(angle = 90)) +
  labs(title="Average Total Intensity vs. Time")

## Warning in geom_histogram(stat = "identity", fill = "darkblue"): Ignoring
## unknown parameters: `binwidth`, `bins`, and `pad`

From the graph, we can conclude that people are more active between 5 am and 10 pm.
Most activity happens between 5 pm and 7 pm. During this window, Bellabeat app can remind and motivate user to go for a run or walk.

Visualize Minutes Asleep vs. Sedentary Minutes

ggplot(data=combined_sleep_activity, aes(x=TotalMinutesAsleep, y=SedentaryMinutes)) + 
geom_point(color='darkblue') + geom_smooth() +
  labs(title="Minutes Asleep vs. Sedentary Minutes")

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

From the graph, we can conclude that there exists a negative relationship between Minutes Asleep and Sedentary Minutes.
To improve our customers’ sleep, Bellabeat app can recommend reducing sedentary time.
To further analyse the situation, we need more data.

Merging Steps data and Calories data

combined_steps_calories <- merge(steps, calories, by = c("Id", "ActivityHour"))
glimpse(combined_steps_calories)

## Rows: 22,099
## Columns: 8
## $ Id           <dbl> 1503960366, 1503960366, 1503960366, 1503960366, 150396036…
## $ ActivityHour <dttm> 2016-04-12 00:00:00, 2016-04-12 01:00:00, 2016-04-12 02:…
## $ StepTotal    <int> 373, 160, 151, 0, 0, 0, 0, 0, 250, 1864, 676, 360, 253, 2…
## $ time.x       <chr> "00:00:00", "01:00:00", "02:00:00", "03:00:00", "04:00:00…
## $ date.x       <chr> "04/12/16", "04/12/16", "04/12/16", "04/12/16", "04/12/16…
## $ Calories     <int> 81, 61, 59, 47, 48, 48, 48, 47, 68, 141, 99, 76, 73, 66, …
## $ time.y       <chr> "00:00:00", "01:00:00", "02:00:00", "03:00:00", "04:00:00…
## $ date.y       <chr> "04/12/16", "04/12/16", "04/12/16", "04/12/16", "04/12/16…

Visualizing Hourly Calorie Burn and Hourly Step Count for Each User

ggplot(data=combined_steps_calories, mapping = aes(x=Calories, y=StepTotal))+
  geom_jitter()+labs(title = "Hourly Calorie Burn and Hourly Step Count for Each User")+
facet_wrap(~Id) + geom_smooth(formula = y ~ x, method = "lm")

From the graph, we can see that there exists a positive relationship between Calorie burn and Steps.

Act

Act phase will be done by the Bellabeat’s executive team, Chief Creative Officer, Marketing Analytics team on the basis of my analysis. (Data-driven decision making)

Deliverable

Average total steps per day are 7638 which a little bit less for having health benefits for according to the CDC research. They found that taking 8,000 steps per day was associated with a 51% lower risk for all-cause mortality (or death from all causes). Taking 12,000 steps per day was associated with a 65% lower risk compared with taking 4,000 steps. Bellabeat can encourage people to take at least 8 000 explaining the benefits for their health.
If users want to lose weight, it’s probably a good idea to control daily calorie consumption. Bellabeat can suggest some ideas for low-calorie lunch and dinner.
If users want to improve their sleep, Bellabeat should consider using app notifications to go to bed.
Most activity happens between 5 pm and 7 pm - I suppose, that people go to a gym or for a walk after finishing work. Bellabeat can use this time to remind and motivate users to go for a run or walk.
As an idea: if users want to improve their sleep, the Bellabeat app can recommend reducing sedentary time.

Conclusion

Thank you for your time and interest to review my capstone project! This project helped me to walk through the data analysis process from start to finish using real-world data and business questions. To learn from the others code too, I have referred the analysis done by Anastasiia Chebotina. I’m truly excited and look forward to growing in the field of data analysis.

Bellabeat Case Study

Arjit Bhardwaj

2023-06-27

Introduction

Modus Operandi

Scenario

Ask

Key Tasks

Deliverable

Prepare

Key tasks

Deliverable

Importing the packages to be used

Import data in R Studio

Process

Key tasks

Deliverable

Seeing what’s there in the data

Checking structure of activity

Checking structure of calories

Checking structure of intensities

Checking structure of sleep

Checking structure of weight

Checking structure of steps

Correcting the format of timestamps

Analyze

Key tasks

Deliverable

Finding the number of participants

Studying the descriptive statistics of datasets

Findings from the statistics:

Merging sleep data and activity

Visualize Total steps vs. Calories

Visualize Total Minutes Asleep vs. Total Time in Bed

Studying the relationship between Intensity and Time.

Visualize Minutes Asleep vs. Sedentary Minutes

Merging Steps data and Calories data

Visualizing Hourly Calorie Burn and Hourly Step Count for Each User

Share

Key tasks

Deliverable

Main insights and conclusions

Act

Deliverable

Conclusion