Damani Hylton 2023-01-20
Identify opportunities for growth by analyzing smart device usage in order to gain insight into how consumers use non-Bellabeat smart devices.
Bellabeat is a high-tech company that manufactures health-focused smart products. Since it was founded in 2013, Bellabeat has grown rapidly and quickly positioned itself as a tech-driven wellness company for women. Collecting data on activity, sleep, stress, and reproductive health has allowed Bellabeat to empower women with knowledge about their own health and habits.
- Fitbit Fitness Tracker Data from Kaggle. https://www.kaggle.com/datasets/arashnic/fitbit
- Thirty Fitbit users consented to the submission of their personal tracker data.
- Organized in long format and I grouped the daily, hourly, and minutes files in a folder.
- Reliable? Yes
- Orginial? Yes
- Comprehensive? No (Only 30 participants)
- Current? Yes
- Cited? Yes
Notes: setting up my R environment by loading the ‘tidyverse’, ‘ggplot2’ , ‘lubridate’ , ‘dplyr’, ‘janitor’ and ‘readr’ packages:
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0 ✔ purrr 1.0.0
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.5.0
## ✔ readr 2.1.3 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(ggplot2)
library(lubridate)## Loading required package: timechange
##
## Attaching package: 'lubridate'
##
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
library(dplyr)
library(janitor)##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.test
library(readr)Importing dailyActivity_merged csv
daily_fitness_data <- read_csv("~/Data_Analyst_Portfolio/BellaBeat_Project/20160412-20160512_Fitbit_Data/daily_metrics_merged/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.
Importing sleepDay_merged csv
sleep_Total <- read_csv("~/Data_Analyst_Portfolio/BellaBeat_Project/20160412-20160512_Fitbit_Data/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.
Importing hourlySteps_merged csv
hourSteps <- read_csv("~/Data_Analyst_Portfolio/BellaBeat_Project/20160412-20160512_Fitbit_Data/hourlySteps_merged.csv")## Rows: 22099 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): ActivityHour
## dbl (2): Id, StepTotal
##
## 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.
Importing hourlyCalories_merged csv
hourlyCalories <- read_csv("~/Data_Analyst_Portfolio/BellaBeat_Project/20160412-20160512_Fitbit_Data/hourlyCalories_merged.csv")## Rows: 22099 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): ActivityHour
## dbl (2): Id, Calories
##
## 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.
1- Looking at daily_fitness_data.
head(daily_fitness_data)## # A tibble: 6 × 15
## Id Activ…¹ Total…² Total…³ Track…⁴ Logge…⁵ VeryA…⁶ Moder…⁷ Light…⁸ Seden…⁹
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1.50e9 4/12/2… 13162 8.5 8.5 0 1.88 0.550 6.06 0
## 2 1.50e9 4/13/2… 10735 6.97 6.97 0 1.57 0.690 4.71 0
## 3 1.50e9 4/14/2… 10460 6.74 6.74 0 2.44 0.400 3.91 0
## 4 1.50e9 4/15/2… 9762 6.28 6.28 0 2.14 1.26 2.83 0
## 5 1.50e9 4/16/2… 12669 8.16 8.16 0 2.71 0.410 5.04 0
## 6 1.50e9 4/17/2… 9705 6.48 6.48 0 3.19 0.780 2.51 0
## # … with 5 more variables: VeryActiveMinutes <dbl>, FairlyActiveMinutes <dbl>,
## # LightlyActiveMinutes <dbl>, SedentaryMinutes <dbl>, Calories <dbl>, and
## # abbreviated variable names ¹ActivityDate, ²TotalSteps, ³TotalDistance,
## # ⁴TrackerDistance, ⁵LoggedActivitiesDistance, ⁶VeryActiveDistance,
## # ⁷ModeratelyActiveDistance, ⁸LightActiveDistance, ⁹SedentaryActiveDistance
2- Identifying column names for daily_fitness_data
colnames(daily_fitness_data)## [1] "Id" "ActivityDate"
## [3] "TotalSteps" "TotalDistance"
## [5] "TrackerDistance" "LoggedActivitiesDistance"
## [7] "VeryActiveDistance" "ModeratelyActiveDistance"
## [9] "LightActiveDistance" "SedentaryActiveDistance"
## [11] "VeryActiveMinutes" "FairlyActiveMinutes"
## [13] "LightlyActiveMinutes" "SedentaryMinutes"
## [15] "Calories"
3- Looking at sleep_Total data
head(sleep_Total)## # A tibble: 6 × 5
## Id SleepDay TotalSleepRecords TotalMinutesAsleep TotalT…¹
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 1503960366 4/12/2016 12:00:00 AM 1 327 346
## 2 1503960366 4/13/2016 12:00:00 AM 2 384 407
## 3 1503960366 4/15/2016 12:00:00 AM 1 412 442
## 4 1503960366 4/16/2016 12:00:00 AM 2 340 367
## 5 1503960366 4/17/2016 12:00:00 AM 1 700 712
## 6 1503960366 4/19/2016 12:00:00 AM 1 304 320
## # … with abbreviated variable name ¹TotalTimeInBed
4- Looking at hourlySteps_merged data
head(hourSteps)## # A tibble: 6 × 3
## Id ActivityHour StepTotal
## <dbl> <chr> <dbl>
## 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
5- Looking at hourlyCalories_merged data
head(hourlyCalories)## # A tibble: 6 × 3
## Id ActivityHour Calories
## <dbl> <chr> <dbl>
## 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
1- Notes: Cleaned the column names for better readability, renamed the ‘Activity Date’ column to ‘date’, assigned YYYY-MM-DD format and changed column datatype from character to ‘Date’
cleaned_daily_fitness <- clean_names(daily_fitness_data)2- Renamed the ‘activity_date’ column to ‘date’.
cleaned_daily_fitness <- cleaned_daily_fitness %>%
rename(date = activity_date)3- Changed date format to YYYY-MM-DD
cleaned_daily_fitness$date <- mdy(cleaned_daily_fitness$date)4- Added Weekday column to specify Days of the week
cleaned_daily_fitness <- cleaned_daily_fitness %>%
mutate(week_day = weekdays(as.Date.factor(date)))See the changes made to cleaned_daily_fitness
str(cleaned_daily_fitness)## tibble [940 × 16] (S3: tbl_df/tbl/data.frame)
## $ id : num [1:940] 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ date : Date[1:940], format: "2016-04-12" "2016-04-13" ...
## $ total_steps : num [1:940] 13162 10735 10460 9762 12669 ...
## $ total_distance : num [1:940] 8.5 6.97 6.74 6.28 8.16 ...
## $ tracker_distance : num [1:940] 8.5 6.97 6.74 6.28 8.16 ...
## $ logged_activities_distance: num [1:940] 0 0 0 0 0 0 0 0 0 0 ...
## $ very_active_distance : num [1:940] 1.88 1.57 2.44 2.14 2.71 ...
## $ moderately_active_distance: num [1:940] 0.55 0.69 0.4 1.26 0.41 ...
## $ light_active_distance : num [1:940] 6.06 4.71 3.91 2.83 5.04 ...
## $ sedentary_active_distance : num [1:940] 0 0 0 0 0 0 0 0 0 0 ...
## $ very_active_minutes : num [1:940] 25 21 30 29 36 38 42 50 28 19 ...
## $ fairly_active_minutes : num [1:940] 13 19 11 34 10 20 16 31 12 8 ...
## $ lightly_active_minutes : num [1:940] 328 217 181 209 221 164 233 264 205 211 ...
## $ sedentary_minutes : num [1:940] 728 776 1218 726 773 ...
## $ calories : num [1:940] 1985 1797 1776 1745 1863 ...
## $ week_day : chr [1:940] "Tuesday" "Wednesday" "Thursday" "Friday" ...
5- Cleaned hourlySteps_merged data Notes: Cleaning up the name format of the columns and changing the date format to YYYY-MM-DD-HH-MM
cleaned_hourlySteps <- clean_names(hourSteps)
cleaned_hourlySteps$activity_hour <- mdy_hms(cleaned_hourlySteps$activity_hour)6- Cleaned sleep_Total data and renamed ‘sleep_day’ to ‘date’
cleaned_sleep_Total <- clean_names(sleep_Total) %>%
rename(date = sleep_day)
cleaned_sleep_Total$date <- mdy_hms(cleaned_sleep_Total$date)7- Cleaned hourlyCalories data and changed the date format to YYYY-MM-DD-HH-MM
cleaned_hourly_Calories <- clean_names(hourlyCalories)
cleaned_hourly_Calories$activity_hour <- mdy_hms(cleaned_hourly_Calories$activity_hour)Since all the data frames have ‘id’ in common, let’s check how many unique participants and rows are in each dataframe:
n_distinct(cleaned_daily_fitness$id)## [1] 33
n_distinct(cleaned_sleep_Total$id)## [1] 24
nrow(cleaned_sleep_Total)## [1] 413
nrow(unique(cleaned_sleep_Total))## [1] 410
8- Removed duplicate rows found in ‘cleaned_sleep_total’
cleaned_sleep_Total <- unique(cleaned_sleep_Total)
nrow(cleaned_sleep_Total)## [1] 410
9- Note: Not all participants logged sleep data, as such, merge data frames by ‘id’ and leave out the participants that did not record sleep data for this merge.
combined_merge <- merge(cleaned_daily_fitness, cleaned_sleep_Total)
n_distinct(combined_merge$id)## [1] 24
10- Merging the hourly steps and calories data frames by ‘id’ and ‘activity hour’.
combined_hourly <- cleaned_hourlySteps %>%
inner_join(cleaned_hourly_Calories, by = c("id", "activity_hour"))11- Merging average sleep and average steps taken by participants and creating a new data frame ‘sleep_vs_steps’.
avg_sleeping_mins <- combined_merge %>%
group_by(id) %>%
summarize(avg_sleeping_mins = mean(total_minutes_asleep))
avg_total_steps <- combined_merge %>%
group_by(id) %>%
summarize(avg_total_steps = mean(total_steps))
sleep_vs_steps <- merge(avg_sleeping_mins, avg_total_steps)Looking at a summary of the combined_merge data
combined_merge %>%
select(total_steps,
calories,
total_minutes_asleep,
total_time_in_bed) %>%
summary()## total_steps calories total_minutes_asleep total_time_in_bed
## Min. : 17 Min. : 257 Min. : 58.0 Min. : 61.0
## 1st Qu.: 5189 1st Qu.:1841 1st Qu.:361.0 1st Qu.:403.8
## Median : 8913 Median :2207 Median :432.5 Median :463.0
## Mean : 8515 Mean :2389 Mean :419.2 Mean :458.5
## 3rd Qu.:11370 3rd Qu.:2920 3rd Qu.:490.0 3rd Qu.:526.0
## Max. :22770 Max. :4900 Max. :796.0 Max. :961.0
Summary of combined_hourly data
combined_hourly %>%
select(activity_hour,
step_total,
calories) %>%
summary()## activity_hour step_total calories
## Min. :2016-04-12 00:00:00.00 Min. : 0.0 Min. : 42.00
## 1st Qu.:2016-04-19 01:00:00.00 1st Qu.: 0.0 1st Qu.: 63.00
## Median :2016-04-26 06:00:00.00 Median : 40.0 Median : 83.00
## Mean :2016-04-26 11:46:42.58 Mean : 320.2 Mean : 97.39
## 3rd Qu.:2016-05-03 19:00:00.00 3rd Qu.: 357.0 3rd Qu.:108.00
## Max. :2016-05-12 15:00:00.00 Max. :10554.0 Max. :948.00
Summary of sleep_vs_steps data
sleep_vs_steps %>%
select(avg_sleeping_mins,
avg_total_steps) %>%
summary()## avg_sleeping_mins avg_total_steps
## Min. : 61.0 Min. : 1490
## 1st Qu.:336.3 1st Qu.: 5106
## Median :417.2 Median : 8336
## Mean :377.4 Mean : 7880
## 3rd Qu.:449.3 3rd Qu.: 9394
## Max. :652.0 Max. :19079
According to the U.S. Department of Health and Human Services, most adults need 7 or more hours of good-quality sleep each night. As such, I am filtering participants that get less than 7 hours sleeping and less than the group average in steps taken daily.
sleep_vs_steps %>%
filter(avg_sleeping_mins <= 420,
avg_total_steps <= 7890)## id avg_sleeping_mins avg_total_steps
## 1 1927972279 417.0000 1490.000
## 2 2320127002 61.0000 5079.000
## 3 4020332650 349.3750 6596.750
## 4 4445114986 385.1786 4756.179
## 5 6775888955 349.6667 3499.000
## 6 7007744171 68.5000 5115.500
Checking out if more sleep on average, leads to more average total steps per day
ggplot(data = sleep_vs_steps, mapping = aes(x = avg_sleeping_mins, y = avg_total_steps)) +
geom_jitter(color = "brown") +
geom_smooth() +
labs(title = "Overall Average Mins Asleep vs Average Total Steps", subtitle = "Per participant", x = "Average Mins Asleep", y = "Average Total Steps")## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
As shown, it does NOT seem, at least for these 30 women, that more sleep on average, leads to more average total steps. Running a correlation test seems to confirm this.
cor(sleep_vs_steps$avg_sleeping_mins, sleep_vs_steps$avg_total_steps)## [1] -0.06633235
What days of the week are participants most likely to burn the most calories?
week_dayz <- factor(combined_merge$week_day, levels = c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))
ggplot(data = combined_merge, mapping = aes(x = week_dayz, y = calories, color = week_dayz)) +
geom_point(position = "jitter", stat = "identity") +
labs(title = "Calories burnt throughout the week") +
ylab("Calories") +
scale_x_discrete("Week Day", guide = guide_axis(angle = 90)) +
scale_color_discrete("Legend")
Thursdays and Saturdays seem like the days participants are burning the most calories, with Fridays and Sundays being the least.
Does more time asleep lead to a more active day? Comparing the total minutes asleep and total steps taken to see how they vary during the week.
ggplot(data = combined_merge, mapping = aes(x = total_minutes_asleep, y = total_steps, color = week_dayz, size = total_time_in_bed)) +
geom_point(alpha = 0.5) +
labs(title = "Total Minutes Asleep vs Total Steps", subtitle = "Per Weekday", x = "Total Minutes Asleep", y = "Total Steps") +
theme(legend.position = "right") +
scale_color_discrete("Week Day")
Hourly Activity: what hours are the participants most active?
twenty_four_hrs <- format(as.POSIXct(combined_hourly$activity_hour), format = "%H")
ggplot(data = combined_hourly, aes(x = twenty_four_hrs, y = step_total, fill = twenty_four_hrs)) +
geom_bar(stat = "identity") +
labs(title = "Hourly Steps", x = "Activity Hour", y = "Step Total")
combined_merge %>%
group_by(id) %>%
summarize(total_steps_per_id = sum(total_steps))## # A tibble: 24 × 2
## id total_steps_per_id
## <dbl> <dbl>
## 1 1503960366 310142
## 2 1644430081 31871
## 3 1844505072 10431
## 4 1927972279 7450
## 5 2026352035 157323
## 6 2320127002 5079
## 7 2347167796 127998
## 8 3977333714 314104
## 9 4020332650 52774
## 10 4319703577 185261
## # … with 14 more rows
This graph shows that the participants are most active at 6-7pm in the weekdays. Graph also shows there’s room for improvement at the 3pm time slot as there is considerable decline at that time.
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The Bellabeat Membership (app) should notify it’s users when they ‘fall’ out of the average inactivity period during certain times of the day. One way to monitor activity is by steps taken per hour and if the app calculates that the user would not make their recommended steps per day, give them notifications and reminders.
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Bellabeat should incorporate the Bellabeat membership into everyday living and activity monitoring. If for some reason a user has been in bed but spending less time sleeping for a couple days, someone from Bellabeat could do a quick check in on the user, either via text or video.
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Wellness sessions can also take place right from the Bellabeat membership app* that allows users to unwind on a stressful day, while also incorporating light exercises, for example yoga. To capitalize on this, looking into exercise equipment, like an stationary bike, could be a new revenue stream.
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Users of the Bellabeat membership app* should have access to chefs’ personalized menu, that can cater meals that heal, nurture and rejuvenate users for another day.