Smart Device Usage Analysis

Introduction

This is an analysis of smart device usage data of about thirty (30) Fitbit users who consented to the submission of personal tracker data. The aim is to gain insight into how people are already using their smart devices and then use this insight to make high-level recommendations for how these trends can inform Bellabeat marketing strategy.

Data Cleaning and Manipulation

Loading 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 1.9.3     ✔ 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 (lubridate)
library (dplyr)

Importing the csv files

Daily_Activity <- read.csv ("dailyActivity_merged.csv")
Daily_Sleep <- read.csv ("sleepDay_merged.csv")

Checking the structure of the data frames

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 ...

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 ...

Correcting the inconsistencies on the date columns

Changing the data type of the date columns

Daily_Activity$ActivityDate <- as.Date(Daily_Activity$ActivityDate, "%m/%d/%Y")
Daily_Sleep$SleepDay <- as.Date(Daily_Sleep$SleepDay, "%m/%d/%Y")

Renaming the date columns

Daily_Activity <- Daily_Activity %>%
  rename (Date = ActivityDate)

Daily_Sleep <- Daily_Sleep%>%
  rename (Date = SleepDay)

Inspecting correction

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 ...
##  $ Date                    : Date, format: "2016-04-12" "2016-04-13" ...
##  $ 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 ...

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 ...
##  $ Date              : Date, format: "2016-04-12" "2016-04-13" ...
##  $ 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 ...

Further investigation and cleaning

Checking for null values

sum (is.na(Daily_Activity))

## [1] 0

sum (is.na(Daily_Sleep))

## [1] 0

Checking for duplicates

sum (duplicated(Daily_Activity))

## [1] 0

sum (duplicated(Daily_Sleep))

## [1] 3

Removing duplicates from Daily_Sleep

Daily_sleep <- unique (Daily_Sleep)
sum (duplicated(Daily_sleep))

## [1] 0

Checking the number of Participants

n_distinct (Daily_Activity$Id)

## [1] 33

n_distinct (Daily_sleep$Id)

## [1] 24

Merging data

Daily_Activity_Sleep <- merge (Daily_Activity, Daily_sleep, by = c ("Id", "Date"))

Adding more columns

Daily_Activity_Sleep$Weekday <- format (as.Date(Daily_Activity_Sleep$Date), "%A") #Adds Weekday column

Daily_Activity_Sleep$TotalActiveMinutes = rowSums (Daily_Activity_Sleep [ c ("VeryActiveMinutes", #Adds Total active minutes column
                                                                          "FairlyActiveMinutes",
                                                                          "LightlyActiveMinutes")])

colnames (Daily_Activity_Sleep)

##  [1] "Id"                       "Date"                    
##  [3] "TotalSteps"               "TotalDistance"           
##  [5] "TrackerDistance"          "LoggedActivitiesDistance"
##  [7] "VeryActiveDistance"       "ModeratelyActiveDistance"
##  [9] "LightActiveDistance"      "SedentaryActiveDistance" 
## [11] "VeryActiveMinutes"        "FairlyActiveMinutes"     
## [13] "LightlyActiveMinutes"     "SedentaryMinutes"        
## [15] "Calories"                 "TotalSleepRecords"       
## [17] "TotalMinutesAsleep"       "TotalTimeInBed"          
## [19] "Weekday"                  "TotalActiveMinutes"

str (Daily_Activity_Sleep)

## 'data.frame':    410 obs. of  20 variables:
##  $ Id                      : num  1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
##  $ Date                    : Date, format: "2016-04-12" "2016-04-13" ...
##  $ TotalSteps              : int  13162 10735 9762 12669 9705 15506 10544 9819 14371 10039 ...
##  $ TotalDistance           : num  8.5 6.97 6.28 8.16 6.48 ...
##  $ TrackerDistance         : num  8.5 6.97 6.28 8.16 6.48 ...
##  $ LoggedActivitiesDistance: num  0 0 0 0 0 0 0 0 0 0 ...
##  $ VeryActiveDistance      : num  1.88 1.57 2.14 2.71 3.19 ...
##  $ ModeratelyActiveDistance: num  0.55 0.69 1.26 0.41 0.78 ...
##  $ LightActiveDistance     : num  6.06 4.71 2.83 5.04 2.51 ...
##  $ SedentaryActiveDistance : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ VeryActiveMinutes       : int  25 21 29 36 38 50 28 19 41 39 ...
##  $ FairlyActiveMinutes     : int  13 19 34 10 20 31 12 8 21 5 ...
##  $ LightlyActiveMinutes    : int  328 217 209 221 164 264 205 211 262 238 ...
##  $ SedentaryMinutes        : int  728 776 726 773 539 775 818 838 732 709 ...
##  $ Calories                : int  1985 1797 1745 1863 1728 2035 1786 1775 1949 1788 ...
##  $ 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 ...
##  $ Weekday                 : chr  "Tuesday" "Wednesday" "Friday" "Saturday" ...
##  $ TotalActiveMinutes      : num  366 257 272 267 222 345 245 238 324 282 ...

n_distinct (Daily_Activity_Sleep$Id)

## [1] 24

Analysis

Quick summary statistics

Daily_Activity_Sleep %>%
  select (TotalSteps,
          TotalDistance,
          SedentaryMinutes,
          TotalActiveMinutes,
          Calories,
          TotalSleepRecords,
          TotalMinutesAsleep,
          TotalTimeInBed)%>%
  summary ()

##    TotalSteps    TotalDistance    SedentaryMinutes TotalActiveMinutes
##  Min.   :   17   Min.   : 0.010   Min.   :   0.0   Min.   :  2.0     
##  1st Qu.: 5189   1st Qu.: 3.592   1st Qu.: 631.2   1st Qu.:206.5     
##  Median : 8913   Median : 6.270   Median : 717.0   Median :263.5     
##  Mean   : 8515   Mean   : 6.012   Mean   : 712.1   Mean   :259.5     
##  3rd Qu.:11370   3rd Qu.: 8.005   3rd Qu.: 782.8   3rd Qu.:315.5     
##  Max.   :22770   Max.   :17.540   Max.   :1265.0   Max.   :540.0     
##     Calories    TotalSleepRecords TotalMinutesAsleep TotalTimeInBed 
##  Min.   : 257   Min.   :1.00      Min.   : 58.0      Min.   : 61.0  
##  1st Qu.:1841   1st Qu.:1.00      1st Qu.:361.0      1st Qu.:403.8  
##  Median :2207   Median :1.00      Median :432.5      Median :463.0  
##  Mean   :2389   Mean   :1.12      Mean   :419.2      Mean   :458.5  
##  3rd Qu.:2920   3rd Qu.:1.00      3rd Qu.:490.0      3rd Qu.:526.0  
##  Max.   :4900   Max.   :3.00      Max.   :796.0      Max.   :961.0

Comparing average distance (Very active distance, Moderately active distance and Light active distance)

Daily_Activity_Sleep %>%
  summarise (Very_active_distance = mean (VeryActiveDistance), 
             Moderately_active_distance = mean (ModeratelyActiveDistance),
             Light_active_distance = mean (LightActiveDistance)) %>%
  round(3)

##   Very_active_distance Moderately_active_distance Light_active_distance
## 1                1.446                      0.744                 3.791

Average steps by weekday

Daily_Activity_Sleep$Weekday <- factor (Daily_Activity_Sleep$Weekday, levels = c ("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday",
                                          "Friday", "Saturday")) 
Daily_Activity_Sleep %>%  
  group_by (Weekday) %>%
  summarise (Total_Steps = mean (TotalSteps))

## # A tibble: 7 × 2
##   Weekday   Total_Steps
##   <fct>           <dbl>
## 1 Sunday          7298.
## 2 Monday          9273.
## 3 Tuesday         9183.
## 4 Wednesday       8023.
## 5 Thursday        8184.
## 6 Friday          7901.
## 7 Saturday        9871.

Average minutes asleep by weekday

Daily_Activity_Sleep %>%  
  group_by (Weekday) %>%
  summarise (Sleep = mean (TotalMinutesAsleep))

## # A tibble: 7 × 2
##   Weekday   Sleep
##   <fct>     <dbl>
## 1 Sunday     453.
## 2 Monday     420.
## 3 Tuesday    405.
## 4 Wednesday  435.
## 5 Thursday   401.
## 6 Friday     405.
## 7 Saturday   419.

Number of days per Id

Daily_Activity_Sleep %>% 
  group_by (Id) %>%
  summarise (Number_of_days = n ()) %>%
  print (n = 24)

## # A tibble: 24 × 2
##            Id Number_of_days
##         <dbl>          <int>
##  1 1503960366             25
##  2 1644430081              4
##  3 1844505072              3
##  4 1927972279              5
##  5 2026352035             28
##  6 2320127002              1
##  7 2347167796             15
##  8 3977333714             28
##  9 4020332650              8
## 10 4319703577             26
## 11 4388161847             23
## 12 4445114986             28
## 13 4558609924              5
## 14 4702921684             27
## 15 5553957443             31
## 16 5577150313             26
## 17 6117666160             18
## 18 6775888955              3
## 19 6962181067             31
## 20 7007744171              2
## 21 7086361926             24
## 22 8053475328              3
## 23 8378563200             31
## 24 8792009665             15

Consistency classification

Daily_Activity_Sleep %>% 
  group_by(Id) %>%
  summarise (Number_of_days = n ()) %>%
  mutate (Consistency = case_when(
    Number_of_days >= 1 & Number_of_days <= 10 ~ "Inconsistent",
    Number_of_days >= 11 & Number_of_days <= 20 ~ "Moderate", 
    Number_of_days >= 21 & Number_of_days <= 31 ~ "Consistent" 
  )) %>%
  count(Consistency)

## # A tibble: 3 × 2
##   Consistency      n
##   <chr>        <int>
## 1 Consistent      12
## 2 Inconsistent     9
## 3 Moderate         3

Exporting data

Activity_Sleep <- Daily_Activity_Sleep
write.csv(Activity_Sleep, file = 'Activity_Sleep.csv')

Smart Device Usage Analysis

Precious C. Ogbusu

2025-03-12

Introduction

Data Cleaning and Manipulation

Loading packages

Importing the csv files

Checking the structure of the data frames

Correcting the inconsistencies on the date columns

Changing the data type of the date columns

Renaming the date columns

Inspecting correction

Further investigation and cleaning

Checking for null values

Checking for duplicates

Removing duplicates from Daily_Sleep

Checking the number of Participants

Merging data

Adding more columns

Analysis

Quick summary statistics

Comparing average distance (Very active distance, Moderately active distance and Light active distance)

Average steps by weekday

Average minutes asleep by weekday

Number of days per Id

Consistency classification

Exporting data