Google Data Analytics Capstone: Bellabeat Fitness Tracker Analytics
Nilosree Sengupta
28/10/2021
Google Data Analytics: Bellabeat Case Study
This is one of the case studies of the : Google Data Analytics Capstone: Complete a Case Study
1. Ask Phase :
To identify Business Problem:This Business case study research Analytics is done To get insights regarding the sleep and activity intensity of the people, being a fitness tracker.
2. Prepare Phase :
The dataset is collected from Kaggle : https://www.kaggle.com/arashnic/fitbit/metadata . It contains 18 csv files.
3. Process Phase:
We will explore the data. The 2 main parameters to observwe will be sleep and activity, to track the fitness of the people.
So, Let’s get started!
Packages required for Exploring the FitBit Data
We’ll be using the tidyverse package as well as the skimr, here, and janitor packages for help with this project.
Loading the CSV files
Here we’ll create our data frames for review. The data frames I’ll be working with in this review will be creating objects for:
daily_activity, daily_calories, daily_sleep, weight_log_info, daily_intensities
Load Libraries
library('tidyverse')
library('janitor')
library('skimr')
library('here')
library('dplyr')
library('lubridate')
library('ggplot2')
Loading dataframes
daily_activity <- read.csv("dailyActivity_merged.csv")
daily_calories <- read.csv("dailyCalories_merged.csv")
sleep_day <- read.csv("sleepDay_merged.csv")
daily_intensities <- read.csv("dailyIntensities_merged.csv")
weight_log <- read.csv("weightLogInfo_merged.csv")
Exploring the dataframes
1. Exploring Daily_activity
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 colnames(daily_activity)
[1] "Id" "ActivityDate"
[3] "TotalSteps" "TotalDistance"
[5] "TrackerDistance" "LoggedActivitiesDistance"
[7] "VeryActiveDistance" "ModeratelyActiveDistance"
[9] "LightActiveDistance" "SedentaryActiveDistance"
[11] "VeryActiveMinutes" "FairlyActiveMinutes"
[13] "LightlyActiveMinutes" "SedentaryMinutes"
[15] "Calories"
glimpse(daily_activity)Rows: 940
Columns: 15
$ Id <dbl> 1503960366, 1503960366, 1503960366, 1503960~
$ ActivityDate <chr> "4/12/2016", "4/13/2016", "4/14/2016", "4/1~
$ TotalSteps <int> 13162, 10735, 10460, 9762, 12669, 9705, 130~
$ TotalDistance <dbl> 8.50, 6.97, 6.74, 6.28, 8.16, 6.48, 8.59, 9~
$ TrackerDistance <dbl> 8.50, 6.97, 6.74, 6.28, 8.16, 6.48, 8.59, 9~
$ 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~
$ ModeratelyActiveDistance <dbl> 0.55, 0.69, 0.40, 1.26, 0.41, 0.78, 0.64, 1~
$ LightActiveDistance <dbl> 6.06, 4.71, 3.91, 2.83, 5.04, 2.51, 4.71, 5~
$ 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,~
$ FairlyActiveMinutes <int> 13, 19, 11, 34, 10, 20, 16, 31, 12, 8, 27, ~
$ LightlyActiveMinutes <int> 328, 217, 181, 209, 221, 164, 233, 264, 205~
$ SedentaryMinutes <int> 728, 776, 1218, 726, 773, 539, 1149, 775, 8~
$ Calories <int> 1985, 1797, 1776, 1745, 1863, 1728, 1921, 2~
> ```
2. Exploring daily_calories
head(daily_calories)
Id ActivityDay Calories
1 1503960366 4/12/2016 1985
2 1503960366 4/13/2016 1797
3 1503960366 4/14/2016 1776
4 1503960366 4/15/2016 1745
5 1503960366 4/16/2016 1863
6 1503960366 4/17/2016 1728
[1] "Id" "ActivityDay" "Calories"
glimpse(daily_calories)Rows: 940
Columns: 3
$ Id <dbl> 1503960366, 1503960366, 1503960366, 1503960366, 15039603~
$ ActivityDay <chr> "4/12/2016", "4/13/2016", "4/14/2016", "4/15/2016", "4/1~
$ Calories <int> 1985, 1797, 1776, 1745, 1863, 1728, 1921, 2035, 1786, 17~3.Exploring sleep_day
head(sleep_day)
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
colnames(sleep_day)
[1] "Id" "SleepDay" "TotalSleepRecords"
[4] "TotalMinutesAsleep" "TotalTimeInBed"
glimpse(sleep_day)
Rows: 413
Columns: 5
$ Id <dbl> 1503960366, 1503960366, 1503960366, 1503960366, 1~
$ SleepDay <chr> "4/12/2016 12:00:00 AM", "4/13/2016 12:00:00 AM",~
$ 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,~
$ TotalTimeInBed <int> 346, 407, 442, 367, 712, 320, 377, 364, 384, 449,~4.Exploring daily_intensities
head(daily_intensities)
Id ActivityDay SedentaryMinutes LightlyActiveMinutes
1 1503960366 4/12/2016 728 328
2 1503960366 4/13/2016 776 217
3 1503960366 4/14/2016 1218 181
4 1503960366 4/15/2016 726 209
5 1503960366 4/16/2016 773 221
6 1503960366 4/17/2016 539 164
FairlyActiveMinutes VeryActiveMinutes SedentaryActiveDistance
1 13 25 0
2 19 21 0
3 11 30 0
4 34 29 0
5 10 36 0
6 20 38 0
LightActiveDistance ModeratelyActiveDistance VeryActiveDistance
1 6.06 0.55 1.88
2 4.71 0.69 1.57
3 3.91 0.40 2.44
4 2.83 1.26 2.14
5 5.04 0.41 2.71
6 2.51 0.78 3.19
colnames(daily_intensities)
[1] "Id" "ActivityDay"
[3] "SedentaryMinutes" "LightlyActiveMinutes"
[5] "FairlyActiveMinutes" "VeryActiveMinutes"
[7] "SedentaryActiveDistance" "LightActiveDistance"
[9] "ModeratelyActiveDistance" "VeryActiveDistance"
glimpse(daily_intensities)Rows: 940
Columns: 10
$ Id <dbl> 1503960366, 1503960366, 1503960366, 1503960~
$ ActivityDay <chr> "4/12/2016", "4/13/2016", "4/14/2016", "4/1~
$ SedentaryMinutes <int> 728, 776, 1218, 726, 773, 539, 1149, 775, 8~
$ LightlyActiveMinutes <int> 328, 217, 181, 209, 221, 164, 233, 264, 205~
$ FairlyActiveMinutes <int> 13, 19, 11, 34, 10, 20, 16, 31, 12, 8, 27, ~
$ VeryActiveMinutes <int> 25, 21, 30, 29, 36, 38, 42, 50, 28, 19, 66,~
$ SedentaryActiveDistance <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ LightActiveDistance <dbl> 6.06, 4.71, 3.91, 2.83, 5.04, 2.51, 4.71, 5~
$ ModeratelyActiveDistance <dbl> 0.55, 0.69, 0.40, 1.26, 0.41, 0.78, 0.64, 1~
$ VeryActiveDistance <dbl> 1.88, 1.57, 2.44, 2.14, 2.71, 3.19, 3.25, 3~Checking the number of unique participants
>n_distinct(daily_activity$Id)
[1] 33
>n_distinct(sleep_day$Id)
[1] 24
Cleaning daily_activity dataframe
new_daily_activity <- daily_activity %>%
distinct() %>%
mutate(ActivityDate=mdy(ActivityDate)) %>%
mutate(TotalActiveTime = VeryActiveMinutes+FairlyActiveMinutes+LightlyActiveMinutes) %>% ### To calculate Total Active Time
group_by(Id,ActivityDate)
head(new_daily_activity)
### A tibble: 6 x 16
### Groups: Id, ActivityDate [6]
Id ActivityDate TotalSteps TotalDistance TrackerDistance
<dbl> <date> <int> <dbl> <dbl>
1 1503960366 2016-04-12 13162 8.5 8.5
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
### ... with 11 more variables: LoggedActivitiesDistance <dbl>,
### VeryActiveDistance <dbl>, ModeratelyActiveDistance <dbl>,
### LightActiveDistance <dbl>, SedentaryActiveDistance <dbl>,
### VeryActiveMinutes <int>, FairlyActiveMinutes <int>,
### LightlyActiveMinutes <int>, SedentaryMinutes <int>, Calories <int>,
### TotalActiveTime <int>Cleaning & transforming sleep_day datatset
new_sleep_day <- sleep_day %>%
distinct() %>%
mutate(SleepDay=mdy_hms(SleepDay)) %>%
group_by(Id)
head(new_sleep_day)
### A tibble: 6 x 5
### Groups: Id [1]
Id SleepDay TotalSleepRecor~ TotalMinutesAsl~ TotalTimeInBed
<dbl> <dttm> <int> <int> <int>
1 1.50e9 2016-04-12 00:00:00 1 327 346
2 1.50e9 2016-04-13 00:00:00 2 384 407
3 1.50e9 2016-04-15 00:00:00 1 412 442
4 1.50e9 2016-04-16 00:00:00 2 340 367
5 1.50e9 2016-04-17 00:00:00 1 700 712
6 1.50e9 2016-04-19 00:00:00 1 304 320
4. Analyze and Share Phase
Exploring relationships in the new_daily_activty dataframe
Calculating the Averages
avg_very_active_min <- mean(new_daily_activity$VeryActiveMinutes)
avg_fairly_active_min <- mean(new_daily_activity$FairlyActiveMinutes)
avg_ligthly_active_min <- mean(new_daily_activity$LightlyActiveMinutes)
avg_sedentary_min <- mean(new_daily_activity$SedentaryMinutes)Creating a new dataframe to make a plot
ave_min_df <- data.frame(activity= c("Very Active", "Fairly Active", "Lightly Active", "Sedentary"), minutes= c(avg_very_active_min,avg_fairly_active_min,avg_ligthly_active_min,avg_sedentary_min))Rounding the minutes to integers
new_ave_min_df <- ave_min_df %>%
mutate_if(is.numeric, round, digits = 0)Plotting a bar graph
ggplot(data=new_ave_min_df,aes(x=reorder(activity, minutes), y=minutes, fill=activity) ) +
geom_bar(stat = 'identity')+
labs(title= "Activity Intensity", x="Activity",y="Average Minutes")+
geom_text(aes(label = minutes), vjust = 0)
PLOT 1.
Finding the correlation between TotalActive Time and TotalSteps
cor.test(new_daily_activity$TotalActiveTime,new_daily_activity$TotalSteps)
Pearson's product-moment correlation
data: new_daily_activity$TotalActiveTime and new_daily_activity$TotalSteps
t = 37.249, df = 938, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.7452981 0.7970065
sample estimates:
cor
0.7724293 Plotting the relationship between TotalActive Time vs TotalSteps
ggplot(data=new_daily_activity, aes(x=TotalSteps, y=TotalActiveTime)) +
labs(title = "Total Active Time over Total Steps ")+
geom_point()+
geom_smooth(method='lm')+
annotate ("text", x=20000, y=700, label="R = 0.772")
PLOT 2.
Exploring relationships in the new_sleep_day dataframe
Exploring sleep records
Categorising the hours of sleep
new_sleep_day <- mutate(new_sleep_day, sleep_adequacy = case_when(
TotalMinutesAsleep < 300 ~ "Less than 5hr",
TotalMinutesAsleep >=300 & TotalMinutesAsleep < 420 ~ "5 to 7hr",
TotalMinutesAsleep >= 420 & TotalMinutesAsleep < 540 ~ "7 to 9hr",
TotalMinutesAsleep > 540 ~ "More than 9hr"))
head(new_sleep_day)
### A tibble: 6 x 6
### Groups: Id [1]
Id SleepDay TotalSleepRecor~ TotalMinutesAsl~ TotalTimeInBed
<dbl> <dttm> <int> <int> <int>
1 1.50e9 2016-04-12 00:00:00 1 327 346
2 1.50e9 2016-04-13 00:00:00 2 384 407
3 1.50e9 2016-04-15 00:00:00 1 412 442
4 1.50e9 2016-04-16 00:00:00 2 340 367
5 1.50e9 2016-04-17 00:00:00 1 700 712
6 1.50e9 2016-04-19 00:00:00 1 304 320
### ... with 1 more variable: sleep_adequacy <chr>
Plotting Sleep Adequacy
new_sleep_day$sleep_adequacy = factor(new_sleep_day$sleep_adequacy, levels = c('Less than 5hr', '5 to 7hr', '7 to 9hr','More than 9hr'))
ggplot(data=new_sleep_day,aes(x=sleep_adequacy),fill=sleep_adequacy)+
geom_bar()+
labs(title= "Sleep Adequacy", x="Sleep adequacy",y="Count")
PLOT 3.
Finding the correlation between TotalMinutesAsleep and TotalTimeInBed
cor.test(new_sleep_day$TotalTimeInBed,new_sleep_day$TotalMinutesAsleep)
Pearson's product-moment correlation
data: new_sleep_day$TotalTimeInBed and new_sleep_day$TotalMinutesAsleep
t = 51.28, df = 408, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.9161262 0.9423551
sample estimates:
cor
0.9304224
Plotting TotalMinutesAsleep vs TotalTimeInBed
ggplot(data=new_sleep_day, aes(x=TotalTimeInBed, y=TotalMinutesAsleep)) +
labs(title = "Total Minutes Asleep vs Total Time in Bed")+
geom_point()+
geom_smooth(method='lm')+
annotate ("text", x=200, y=750, label="R = 0.930")
PLOT 4.
Exploring relationships between new_daily_activity and new_sleep_record dataframe
Combining the dataframes
combined_data <-
left_join(new_daily_activity,new_sleep_day, by= c("Id"="Id", "ActivityDate"="SleepDay"))
head(combined_data)
### A tibble: 6 x 20
### Groups: Id, ActivityDate [6]
Id ActivityDate TotalSteps TotalDistance TrackerDistance
<dbl> <dttm> <int> <dbl> <dbl>
1 1503960366 2016-04-12 00:00:00 13162 8.5 8.5
2 1503960366 2016-04-13 00:00:00 10735 6.97 6.97
3 1503960366 2016-04-14 00:00:00 10460 6.74 6.74
4 1503960366 2016-04-15 00:00:00 9762 6.28 6.28
5 1503960366 2016-04-16 00:00:00 12669 8.16 8.16
6 1503960366 2016-04-17 00:00:00 9705 6.48 6.48
### ... with 15 more variables: LoggedActivitiesDistance <dbl>,
### VeryActiveDistance <dbl>, ModeratelyActiveDistance <dbl>,
### LightActiveDistance <dbl>, SedentaryActiveDistance <dbl>,
### VeryActiveMinutes <int>, FairlyActiveMinutes <int>,
### LightlyActiveMinutes <int>, SedentaryMinutes <int>, Calories <int>,
### TotalActiveTime <int>, TotalSleepRecords <int>, TotalMinutesAsleep <int>,
### TotalTimeInBed <int>, sleep_adequacy <fct>Exploring relationships between SedentaryMinutes and sleep_adequacy
Plotting boxplot between Sleep Adequacy and Sedentary Minutes
combined_data %>%
filter(!is.na(sleep_adequacy)) %>%
ggplot(aes(x=sleep_adequacy,y=SedentaryMinutes,fill=sleep_adequacy)) +
geom_boxplot()+
scale_x_discrete(limits=c("Less than 5hr", "5 to 7hr", "7 to 9hr","More than 9hr"))+
stat_summary(geom="text", fun=mean,
aes(label=sprintf("%1.1f", ..y..)),
position=position_nudge(x=0.5), size=2)
PLOT 5.
Creating a dataframe of averages
avg_combined_data <- combined_data %>%
group_by(Id) %>%
drop_na() %>%
summarise(avg_total_active=mean(TotalActiveTime),avg_sedentary= mean(SedentaryMinutes),avg_total_minutes_asleep=mean(TotalMinutesAsleep),avg_time_in_bed=mean(TotalTimeInBed) )
avg_combined_data
### A tibble: 24 x 5
Id avg_total_active avg_sedentary avg_total_minute~ avg_time_in_bed
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1503960366 291. 759. 360. 383.
2 1644430081 263. 920. 294 346
3 1844505072 147. 443. 652 961
4 1927972279 85 977. 417 438.
5 2026352035 257. 654. 506. 538.
6 2320127002 242 1129 61 69
7 2347167796 271. 628. 447. 491.
8 3977333714 263. 716. 294. 461.
9 4020332650 249 842. 349. 380.
10 4319703577 259. 643. 477. 502.
### ... with 14 more rowsFinding Correlation
cor.test(avg_combined_data$avg_sedentary,avg_combined_data$avg_total_minutes_asleep) Pearson's product-moment correlation
data: avg_combined_data$avg_sedentary and avg_combined_data$avg_total_minutes_asleep
t = -8.8868, df = 22, p-value = 9.875e-09
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.9491575 -0.7477410
sample estimates:
cor
-0.8843774 5. Act Phase :
Observations :
From the given plot 1 we can conclude that the number of fairly active people is the least at 14 whereas the number of people sedentary is the highest at 991. the people who are very active and lightly active are in the between. The correlation stands out at 0.77.
From the plot 2 we can conclude that the maximum number of people can take a total number of steps of around 10000 as the number of steps increase and the number of activity times decrease as hence there are hardly a very few people who are around 30,000 steps. R=0.72
From plot three we can conclude that the maximum number of people sleep from 729 hours and the least number of people sleep for more than 9 hours whereas there is a considerable amount of people who sleep less than 5 hours and 5 to 7 hours.
The correlation stands out at 0.93.
From plot 4 we can conclude that the graph moves in a directly proportional manner. People tend to be asleep during the time when they are in bed. R=0.93
From plot five we can conclude that the level of sleep adequacy is maximum for 5 hours and least after around 9 hours. we can see that the sleep adequacy tends to decrease after 5 hours of sleep. The correlation stands out at -0.88
Recommendations :
In the era of technology,it’s generally seen that whenever a product becomes versatile which can be used for multiple purposes it tends to attract more buyers as it also looks attractive then its existing competitors.
To make the product more versatile several things can be added to it:
1. Heart rate monitor to monitor the heart rate of all the users.
2. Calorie tracker to track the calories burnt while walking
3. Alarm for the user to set goals and routines accordingly to maintain fitness.
4. Some Targets can be placed for users with Levels of challenges like Level 1,Level2,Level3 and so on, then giving scores will motivate users to be in activities more.