#Introduction
###This is a capstone project for google data analytics course,
###Adeeba Amreen
###Date 16th January 2024
#Objective
###Introducing marketing strategies for bellabeat fitbit devices for the customers
###who use the smart devices.
###I'm going to analyse the data based on correlation activity level, sleep and calories burnt.
#Description of the data used
#I would like to express my gratitude to Möbius for supplying the pertinent dataset needed to study the use of smart wellness devices and their trends.
#License: CC0: Public Domain
#Source: https://zenodo.org/record/53894#.X9oeh3Uzaao
#Privacy: These datasets were generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring.
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 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ 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('janitor')
##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.test
library('skimr')
library('here')
## here() starts at /cloud/project
library('dplyr')
library(lubridate)
library(ggplot2)
##Uploading the required datasets
library(readr)
dailyActivity_merged <- read_csv("dailyActivity_merged.csv")
## Rows: 940 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): ActivityDate
## dbl (14): Id, TotalSteps, TotalDistance, TrackerDistance, LoggedActivitiesDi...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
sleepDay_merged <- read_csv("sleepDay_merged.csv")
## Rows: 413 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): SleepDay
## dbl (4): Id, TotalSleepRecords, TotalMinutesAsleep, TotalTimeInBed
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##Summary of the dataset
head(dailyActivity_merged)
## # A tibble: 6 × 15
## Id ActivityDate TotalSteps TotalDistance TrackerDistance
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 1503960366 4/12/2016 13162 8.5 8.5
## 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
## # ℹ 10 more variables: LoggedActivitiesDistance <dbl>,
## # VeryActiveDistance <dbl>, ModeratelyActiveDistance <dbl>,
## # LightActiveDistance <dbl>, SedentaryActiveDistance <dbl>,
## # VeryActiveMinutes <dbl>, FairlyActiveMinutes <dbl>,
## # LightlyActiveMinutes <dbl>, SedentaryMinutes <dbl>, Calories <dbl>
head(sleepDay_merged)
## # A tibble: 6 × 5
## Id SleepDay TotalSleepRecords TotalMinutesAsleep TotalTimeInBed
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 1503960366 4/12/2016 12:0… 1 327 346
## 2 1503960366 4/13/2016 12:0… 2 384 407
## 3 1503960366 4/15/2016 12:0… 1 412 442
## 4 1503960366 4/16/2016 12:0… 2 340 367
## 5 1503960366 4/17/2016 12:0… 1 700 712
## 6 1503960366 4/19/2016 12:0… 1 304 320
str(dailyActivity_merged)
## spc_tbl_ [940 × 15] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Id : num [1:940] 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ ActivityDate : chr [1:940] "4/12/2016" "4/13/2016" "4/14/2016" "4/15/2016" ...
## $ TotalSteps : num [1:940] 13162 10735 10460 9762 12669 ...
## $ TotalDistance : num [1:940] 8.5 6.97 6.74 6.28 8.16 ...
## $ TrackerDistance : num [1:940] 8.5 6.97 6.74 6.28 8.16 ...
## $ LoggedActivitiesDistance: num [1:940] 0 0 0 0 0 0 0 0 0 0 ...
## $ VeryActiveDistance : num [1:940] 1.88 1.57 2.44 2.14 2.71 ...
## $ ModeratelyActiveDistance: num [1:940] 0.55 0.69 0.4 1.26 0.41 ...
## $ LightActiveDistance : num [1:940] 6.06 4.71 3.91 2.83 5.04 ...
## $ SedentaryActiveDistance : num [1:940] 0 0 0 0 0 0 0 0 0 0 ...
## $ VeryActiveMinutes : num [1:940] 25 21 30 29 36 38 42 50 28 19 ...
## $ FairlyActiveMinutes : num [1:940] 13 19 11 34 10 20 16 31 12 8 ...
## $ LightlyActiveMinutes : num [1:940] 328 217 181 209 221 164 233 264 205 211 ...
## $ SedentaryMinutes : num [1:940] 728 776 1218 726 773 ...
## $ Calories : num [1:940] 1985 1797 1776 1745 1863 ...
## - attr(*, "spec")=
## .. cols(
## .. Id = col_double(),
## .. ActivityDate = col_character(),
## .. TotalSteps = col_double(),
## .. TotalDistance = col_double(),
## .. TrackerDistance = col_double(),
## .. LoggedActivitiesDistance = col_double(),
## .. VeryActiveDistance = col_double(),
## .. ModeratelyActiveDistance = col_double(),
## .. LightActiveDistance = col_double(),
## .. SedentaryActiveDistance = col_double(),
## .. VeryActiveMinutes = col_double(),
## .. FairlyActiveMinutes = col_double(),
## .. LightlyActiveMinutes = col_double(),
## .. SedentaryMinutes = col_double(),
## .. Calories = col_double()
## .. )
## - attr(*, "problems")=<externalptr>
str(sleepDay_merged)
## spc_tbl_ [413 × 5] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Id : num [1:413] 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ SleepDay : chr [1:413] "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 : num [1:413] 1 2 1 2 1 1 1 1 1 1 ...
## $ TotalMinutesAsleep: num [1:413] 327 384 412 340 700 304 360 325 361 430 ...
## $ TotalTimeInBed : num [1:413] 346 407 442 367 712 320 377 364 384 449 ...
## - attr(*, "spec")=
## .. cols(
## .. Id = col_double(),
## .. SleepDay = col_character(),
## .. TotalSleepRecords = col_double(),
## .. TotalMinutesAsleep = col_double(),
## .. TotalTimeInBed = col_double()
## .. )
## - attr(*, "problems")=<externalptr>
#Cleaning dates, converting the activity date and Sleep day
dailyActivity_merged$ActivityDate <- as.Date(dailyActivity_merged$ActivityDate, "%m/%d/%Y")
sleepDay_merged$SleepDay <- parse_date_time(sleepDay_merged$SleepDay, orders = 'mdy HMS')
sleepDay_merged$SleepDay <- as.Date(sleepDay_merged$SleepDay, "%m/%d/%y %h:%m:%s")
#Merging the datasets of activity and sleep timing using left join and replacing NA with zero
daily_activity_sleep <- merge(x= dailyActivity_merged, y= sleepDay_merged,
by.x = c("Id", "ActivityDate"), by.y = c("Id", "SleepDay"), all.x = TRUE)
daily_activity_sleep [is.na(daily_activity_sleep)] <- 0
head(daily_activity_sleep)
## 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
## TotalSleepRecords TotalMinutesAsleep TotalTimeInBed
## 1 1 327 346
## 2 2 384 407
## 3 0 0 0
## 4 1 412 442
## 5 2 340 367
## 6 1 700 712
#Summary of merged data set
summary(daily_activity_sleep)
## Id ActivityDate TotalSteps TotalDistance
## Min. :1.504e+09 Min. :2016-04-12 Min. : 0 Min. : 0.000
## 1st Qu.:2.320e+09 1st Qu.:2016-04-19 1st Qu.: 3795 1st Qu.: 2.620
## Median :4.445e+09 Median :2016-04-26 Median : 7439 Median : 5.260
## Mean :4.858e+09 Mean :2016-04-26 Mean : 7652 Mean : 5.503
## 3rd Qu.:6.962e+09 3rd Qu.:2016-05-04 3rd Qu.:10734 3rd Qu.: 7.720
## Max. :8.878e+09 Max. :2016-05-12 Max. :36019 Max. :28.030
## TrackerDistance LoggedActivitiesDistance VeryActiveDistance
## Min. : 0.000 Min. :0.000 Min. : 0.000
## 1st Qu.: 2.620 1st Qu.:0.000 1st Qu.: 0.000
## Median : 5.260 Median :0.000 Median : 0.220
## Mean : 5.489 Mean :0.110 Mean : 1.504
## 3rd Qu.: 7.715 3rd Qu.:0.000 3rd Qu.: 2.065
## Max. :28.030 Max. :4.942 Max. :21.920
## ModeratelyActiveDistance LightActiveDistance SedentaryActiveDistance
## Min. :0.0000 Min. : 0.000 Min. :0.000000
## 1st Qu.:0.0000 1st Qu.: 1.950 1st Qu.:0.000000
## Median :0.2400 Median : 3.380 Median :0.000000
## Mean :0.5709 Mean : 3.349 Mean :0.001601
## 3rd Qu.:0.8050 3rd Qu.: 4.790 3rd Qu.:0.000000
## Max. :6.4800 Max. :10.710 Max. :0.110000
## VeryActiveMinutes FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes
## Min. : 0.00 Min. : 0.00 Min. : 0 Min. : 0.0
## 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.:127 1st Qu.: 729.0
## Median : 4.00 Median : 7.00 Median :199 Median :1057.0
## Mean : 21.24 Mean : 13.63 Mean :193 Mean : 990.4
## 3rd Qu.: 32.00 3rd Qu.: 19.00 3rd Qu.:264 3rd Qu.:1229.0
## Max. :210.00 Max. :143.00 Max. :518 Max. :1440.0
## Calories TotalSleepRecords TotalMinutesAsleep TotalTimeInBed
## Min. : 0 Min. :0.0000 Min. : 0.0 Min. : 0.0
## 1st Qu.:1830 1st Qu.:0.0000 1st Qu.: 0.0 1st Qu.: 0.0
## Median :2140 Median :0.0000 Median : 0.0 Median : 0.0
## Mean :2308 Mean :0.4899 Mean :183.7 Mean :200.9
## 3rd Qu.:2796 3rd Qu.:1.0000 3rd Qu.:417.5 3rd Qu.:450.5
## Max. :4900 Max. :3.0000 Max. :796.0 Max. :961.0
#Data Analysis
#Merge Data Frames
#We are utilizing a left join to combine the data while combining two data frames because of a discrepancy in the number of observations. Every observation that does not match will display NA, which is substituted with a zero in this case.
daily_activity_sleep <- merge(x= dailyActivity_merged, y= sleepDay_merged,
by.x = c("Id", "ActivityDate"), by.y = c("Id", "SleepDay"), all.x = TRUE)
daily_activity_sleep [is.na(daily_activity_sleep)] <- 0
head(daily_activity_sleep)
## 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
## TotalSleepRecords TotalMinutesAsleep TotalTimeInBed
## 1 1 327 346
## 2 2 384 407
## 3 0 0 0
## 4 1 412 442
## 5 2 340 367
## 6 1 700 712
# Create Categories
#Based on sleep, distance and calories burnt
daily_activity_sleep <- daily_activity_sleep %>%
mutate(sleep_categories = case_when(
TotalMinutesAsleep >360 & TotalMinutesAsleep <= 480 ~ "6h-8h",
TotalMinutesAsleep > 480 ~ "> 8h",
TRUE ~ "< 6h"
)) %>%
mutate(calorie_categories = case_when(
Calories > 1500 & Calories <= 2500 ~ "1.5k-2.5k",
Calories > 2500 ~ "> 2.5k",
TRUE ~ "< 1.5k"
))
head(daily_activity_sleep)
## 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
## TotalSleepRecords TotalMinutesAsleep TotalTimeInBed sleep_categories
## 1 1 327 346 < 6h
## 2 2 384 407 6h-8h
## 3 0 0 0 < 6h
## 4 1 412 442 6h-8h
## 5 2 340 367 < 6h
## 6 1 700 712 > 8h
## calorie_categories
## 1 1.5k-2.5k
## 2 1.5k-2.5k
## 3 1.5k-2.5k
## 4 1.5k-2.5k
## 5 1.5k-2.5k
## 6 1.5k-2.5k
#Creating visualization
ggplot(data= daily_activity_sleep) +
geom_boxplot(mapping= aes(x=sleep_categories, y= Calories, fill= sleep_categories))+ggtitle("Sleep VS Calories Burnt")
ggplot(data= daily_activity_sleep) +
geom_boxplot(mapping= aes(x=calorie_categories, y= TotalSteps, fill= calorie_categories))+ggtitle("Total Steps VS Calories Burnt")
#Summary of Data Analysis
### 1. There is a correlation between number of sleep hours and the calories burnt.
### 2. There is a correlation between number of steps and the calories burnt.
### 3. It is evident from the box plot that people who sleep 6-8 hours burn relatively more calories than people who sleep less than 6 hours and more than 8 hours
### 4. It is also evident that number of step taken effects the calories burnt, people who have average of 15000 steps have burnt more than 2.5k calories. The key finding steps are also significant other than distance.
#Business Recommendations
### There is a strong correlation between proper number of hours of sleep and calories, so it is important for the people to maintain the 6-8hrs of sleep, not more or less.
### Number of steps are significant in calories reduction, so it is essential for people to track the number of steps for better fitness tracking.
### The marketing strategies, can be made to inform customers about how efficiently the bellabeat products can track and help the cutomers to follow the healthy lifestyle, with convenience, with just wearing a bracelet with no hassle.