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https://github.com/vedantwalia/case-study---bellabeat
https://github.com/vedantwalia/case-study---bellabeat
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- Host: GitHub
- URL: https://github.com/vedantwalia/case-study---bellabeat
- Owner: vedantwalia
- Created: 2024-08-19T09:56:05.000Z (5 months ago)
- Default Branch: master
- Last Pushed: 2024-08-28T11:51:15.000Z (4 months ago)
- Last Synced: 2024-08-28T12:47:17.669Z (4 months ago)
- Size: 422 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Bellabeat Case Study
## Background Information
**Bellabeat** is a high-tech company that manufactures health-focused
smart products. Collecting data on activity, sleep, stress, and
reproductive health has allowed Bellabeat to empower women with
knowledge about their own health and habits.
### Business Task
Sršen, the company’s cofounder, would like an analysis of Bellabeat’s
available consumer data to identify opportunities for growth. She has
asked the marketing analytics team to analyze smart device usage data in
order to gain insight into how people are already using their smart
devices. Then, using this information, she would like recommendations
for how these trends can inform Bellabeat marketing strategy. Therefore,
in this case study, I will answer the following questions:
1. What are some trends in smart device usage?
2. How can these trends help influence Bellabeat marketing strategy?
### About the Data
The data for this case study comes from [Fitbit Fitness Tracker
Data](https://www.kaggle.com/datasets/arashnic/fitbit), a public domain
dataset available on Kaggle. It contains personal fitness tracker
information from 30 FitBit users. These 30 Fitbit users consented to the
submission of all personal tracker data contained in this dataset.
I will import the data set from Kaggle into RStudio where I can clean,
filter, and analyze the data.
### Limitations
- Sample size: 30 people is not a large enough sample to be
representative of all FitBit users
- Outdated: The dataset contains data from a one month period in 2016
only. For a deeper and more accurate analysis of trends, we would need
data from the current year, preferably collected for an entire year to
look at if trends vary during different times of year.
- Limited: The dataset does not contain any demographic information
about the users, including gender, age, or location, which would be
beneficial for marketing purposes to target specific customers
## Data Preparation
I can see that there are two folders of data, lets merge the files that
have the same name and
``` r
# Define the paths to the two folders
folder1 <- "/cloud/project/archive/Fitabase Data 3.12.16-4.11.16"
folder2 <- "/cloud/project/archive/Fitabase Data 4.12.16-5.12.16"
# List all files in both folders
files1 <- list.files(folder1, full.names = TRUE)
files2 <- list.files(folder2, full.names = TRUE)
# Get the file names without the path
names1 <- basename(files1)
names2 <- basename(files2)
# Find the common file names
common_files <- intersect(names1, names2)
```
Now I moved all the unique files into the work folder so that further
analysis can be done
``` r
library(dplyr)
# Loop over the common file names
for (file in common_files) {
# Construct the full paths for both files
file1_path <- file.path(folder1, file)
file2_path <- file.path(folder2, file)
# Read the files
df1 <- read.csv(file1_path)
df2 <- read.csv(file2_path)
# Merge the data frames row-wise
merged_df <- bind_rows(df1, df2)
# Save the merged file
write.csv(merged_df, file.path("/cloud/project/archive", file), row.names = FALSE)
}
# Define the paths to the two folders
folder1 <- "/cloud/project/archive/Fitabase Data 3.12.16-4.11.16"
folder2 <- "/cloud/project/archive/Fitabase Data 4.12.16-5.12.16"
new_folder <- "/cloud/project/archive"
# List all files in both folders
files1 <- list.files(folder1, full.names = TRUE)
files2 <- list.files(folder2, full.names = TRUE)
# Get the file names without the path
names1 <- basename(files1)
names2 <- basename(files2)
# Find the unique file names in folder2
unique_files <- setdiff(names2, names1)
# Loop over the unique file names
for (file in unique_files) {
# Construct the full path for the file in folder2
file2_path <- file.path(folder2, file)
# Move the file to the new folder
file.copy(file2_path, file.path(new_folder, file))
# Optionally, remove the file from folder2
file.remove(file2_path)
}
```
Now lets start the analysis
## Data Preparation
1. load packages
``` r
library(tidyverse)
library(ggplot2)
library(lubridate)
library(lm.beta)
```
2. Load CSV files containing our data
``` r
# load files
daily_activity <- read.csv("./archive/dailyActivity_merged.csv")
hourly_steps <- read.csv("./archive/hourlySteps_merged.csv")
daily_sleep <- read.csv("./archive/sleepDay_merged.csv")
weight <- read.csv("./archive/weightLogInfo_merged.csv")
```
3. Identify number of participants in each data set by counting
distinct IDs
``` r
n_distinct(daily_activity$Id)
```
## [1] 35
``` r
n_distinct(hourly_steps$Id)
```
## [1] 35
``` r
n_distinct(daily_sleep$Id)
```
## [1] 24
``` r
n_distinct(weight$Id)
```
## [1] 13
4. View and clean up the data sets
``` r
# First, the daily_sleep data
head(daily_sleep)
```
## Id SleepDay TotalSleepRecords TotalMinutesAsleep TotalTimeInBed
## 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
``` r
# The 12:00:00 AM time stamp on each observation is redundant so we should remove it to make the data easier to work with
daily_sleep$SleepDay <- (gsub('12:00:00 AM', '', daily_sleep$SleepDay))
# Renaming column
colnames(daily_sleep)[2] = "Date"
# View updated data
head(daily_sleep)
```
## Id Date TotalSleepRecords TotalMinutesAsleep TotalTimeInBed
## 1 1503960366 4/12/2016 1 327 346
## 2 1503960366 4/13/2016 2 384 407
## 3 1503960366 4/15/2016 1 412 442
## 4 1503960366 4/16/2016 2 340 367
## 5 1503960366 4/17/2016 1 700 712
## 6 1503960366 4/19/2016 1 304 320
``` r
# Next, the daily_activity data
head(daily_activity)
```
## Id ActivityDate TotalSteps TotalDistance TrackerDistance LoggedActivitiesDistance
## 1 1503960366 3/25/2016 11004 7.11 7.11 0
## 2 1503960366 3/26/2016 17609 11.55 11.55 0
## 3 1503960366 3/27/2016 12736 8.53 8.53 0
## 4 1503960366 3/28/2016 13231 8.93 8.93 0
## 5 1503960366 3/29/2016 12041 7.85 7.85 0
## 6 1503960366 3/30/2016 10970 7.16 7.16 0
## VeryActiveDistance ModeratelyActiveDistance LightActiveDistance SedentaryActiveDistance
## 1 2.57 0.46 4.07 0
## 2 6.92 0.73 3.91 0
## 3 4.66 0.16 3.71 0
## 4 3.19 0.79 4.95 0
## 5 2.16 1.09 4.61 0
## 6 2.36 0.51 4.29 0
## VeryActiveMinutes FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes Calories
## 1 33 12 205 804 1819
## 2 89 17 274 588 2154
## 3 56 5 268 605 1944
## 4 39 20 224 1080 1932
## 5 28 28 243 763 1886
## 6 30 13 223 1174 1820
``` r
# The LoggedActivitiesDistance and SedentaryActiveDistance columns don't provide much information
# so we will not use them in our analysis and can remove them
daily_activity <- daily_activity[c(-6, -10)]
# Renaming column
colnames(daily_activity)[2] = "Date"
# View updated Data
head(daily_activity)
```
## Id Date TotalSteps TotalDistance TrackerDistance VeryActiveDistance
## 1 1503960366 3/25/2016 11004 7.11 7.11 2.57
## 2 1503960366 3/26/2016 17609 11.55 11.55 6.92
## 3 1503960366 3/27/2016 12736 8.53 8.53 4.66
## 4 1503960366 3/28/2016 13231 8.93 8.93 3.19
## 5 1503960366 3/29/2016 12041 7.85 7.85 2.16
## 6 1503960366 3/30/2016 10970 7.16 7.16 2.36
## ModeratelyActiveDistance LightActiveDistance VeryActiveMinutes FairlyActiveMinutes
## 1 0.46 4.07 33 12
## 2 0.73 3.91 89 17
## 3 0.16 3.71 56 5
## 4 0.79 4.95 39 20
## 5 1.09 4.61 28 28
## 6 0.51 4.29 30 13
## LightlyActiveMinutes SedentaryMinutes Calories
## 1 205 804 1819
## 2 274 588 2154
## 3 268 605 1944
## 4 224 1080 1932
## 5 243 763 1886
## 6 223 1174 1820
``` r
# Finally, the hourly_steps data
head(hourly_steps)
```
## Id ActivityHour StepTotal
## 1 1503960366 3/12/2016 12:00:00 AM 0
## 2 1503960366 3/12/2016 1:00:00 AM 0
## 3 1503960366 3/12/2016 2:00:00 AM 0
## 4 1503960366 3/12/2016 3:00:00 AM 0
## 5 1503960366 3/12/2016 4:00:00 AM 0
## 6 1503960366 3/12/2016 5:00:00 AM 0
``` r
# In this case, the time associated with the date is relevant so we don't want to remove it,
# but the data may be easier to work with if we separate it into it's own column
hourly_steps <- hourly_steps%>% separate(ActivityHour, c("Date", "Hour"), sep = "^\\S*\\K")
# View the updated dataframe
head(hourly_steps)
```
## Id Date Hour StepTotal
## 1 1503960366 3/12/2016 12:00:00 AM 0
## 2 1503960366 3/12/2016 1:00:00 AM 0
## 3 1503960366 3/12/2016 2:00:00 AM 0
## 4 1503960366 3/12/2016 3:00:00 AM 0
## 5 1503960366 3/12/2016 4:00:00 AM 0
## 6 1503960366 3/12/2016 5:00:00 AM 0
Because the Id variable is currently numerical but should be treated as
nominal,we need to change how it is formatted in each data set.
``` r
daily_activity$Id <- as.character(daily_activity$Id)
daily_sleep$Id <- as.character(daily_sleep$Id)
hourly_steps$Id <- as.character(hourly_steps$Id)
```
## Data Exploration
1. Graph variables of interest, check for outliers in the data
``` r
summary(daily_activity$TotalSteps)
```
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 3146 6999 7281 10544 36019
``` r
ggplot(daily_activity, aes(x = TotalSteps)) +
geom_boxplot()
```
``` r
# Most of the daily total steps appear to be around 4000-11000.
# There appear to be possible outliers on the high end
steps_upper <- quantile(daily_activity$TotalSteps, .9985, na.rm = TRUE)
# This shows that 99.85% of the observations are at 28,680 or below.
# Values above this number are more than 3 standard deviations from the mean,
# indicating they are outliers.
daily_activity <- subset(daily_activity, TotalSteps <= 28680)
# 2 outliers were removed
```
2. Extract more information by running descriptive statistics
#### Sleep Data
- What is the average amount of sleep for each participant?
``` r
mean_sleep <- daily_sleep %>%
group_by(Id) %>%
summarize(mean_sleep = mean(TotalMinutesAsleep)) %>%
select(Id, mean_sleep) %>%
arrange(mean_sleep) %>%
as.data.frame()
head(mean_sleep)
```
## Id mean_sleep
## 1 2320127002 61.0000
## 2 7007744171 68.5000
## 3 4558609924 127.6000
## 4 3977333714 293.6429
## 5 1644430081 294.0000
## 6 8053475328 297.0000
- What percent of the time did participants actually spend sleeping
while laying in bed?
``` r
daily_sleep %>%
group_by(Id) %>%
mutate(percent_sleep = (TotalMinutesAsleep/TotalTimeInBed)*100) %>%
select(Id, percent_sleep) %>%
summarize(avg_persleep = mean(percent_sleep)) %>%
arrange(avg_persleep) %>%
mutate_if(is.numeric, round, 2)
```
## # A tibble: 24 × 2
## Id avg_persleep
##
## 1 3977333714 63.4
## 2 1844505072 67.8
## 3 1644430081 88.2
## 4 2320127002 88.4
## 5 4558609924 90.7
## 6 2347167796 91.0
## 7 5553957443 91.5
## 8 8378563200 91.9
## 9 4445114986 92.5
## 10 4020332650 93.0
## # ℹ 14 more rows
``` r
# Most participants slept for at least 90% of the time they spent in bed, with
# only 4 participants spending a smaller percent of time sleeping, the lowest
# being 63.37%
```
#### Activity Levels
- Summary stats of different activity levels:
``` r
library(psych)
activity_level <- daily_activity[9:12]
describe(activity_level)
```
## vars n mean sd median trimmed mad min max range skew kurtosis
## VeryActiveMinutes 1 1395 19.51 31.32 2 12.52 2.97 0 210 210 2.22 6.00
## FairlyActiveMinutes 2 1395 13.36 26.38 6 8.67 8.90 0 660 660 11.67 259.61
## LightlyActiveMinutes 3 1395 185.21 113.95 195 184.14 109.71 0 720 720 0.09 -0.10
## SedentaryMinutes 4 1395 992.60 313.51 1058 1003.61 412.16 0 1440 1440 -0.32 -0.66
## se
## VeryActiveMinutes 0.84
## FairlyActiveMinutes 0.71
## LightlyActiveMinutes 3.05
## SedentaryMinutes 8.39
- Activity levels by participant
``` r
activity_id <- daily_activity %>%
group_by(Id) %>%
summarize(sum_very = sum(VeryActiveMinutes),
sum_fairly = sum(FairlyActiveMinutes),
sum_lightly = sum(LightlyActiveMinutes),
sum_sed = sum(SedentaryMinutes)) %>%
select(Id, sum_very, sum_fairly, sum_lightly, sum_sed) %>%
as.data.frame()
head(activity_id)
```
## Id sum_very sum_fairly sum_lightly sum_sed
## 1 1503960366 1881 895 11154 41680
## 2 1624580081 97 128 6887 62258
## 3 1644430081 435 1076 7631 45198
## 4 1844505072 13 49 5481 49829
## 5 1927972279 41 44 2536 52275
## 6 2022484408 1606 870 11027 47197
#### Steps
- On average, during which hour of the day were the most steps taken?
``` r
hourly_steps %>%
group_by(Hour) %>%
summarize(mean_steps = mean(StepTotal)) %>%
select(Hour, mean_steps) %>%
arrange(desc(mean_steps)) %>%
head(1)
```
## # A tibble: 1 × 2
## Hour mean_steps
##
## 1 " 7:00:00 PM" 555.
``` r
# Answer: 6:00PM with an average of about 600 steps
# Creating a dataframe with average hourly steps for later visualization
mean_steps <- hourly_steps %>%
group_by(Hour) %>%
summarize(mean_steps = mean(StepTotal)) %>%
select(Hour, mean_steps) %>%
arrange(desc(Hour)) %>%
as.data.frame()
```
- What is the mean and standard deviation for total steps taken by
participant?
``` r
steps_byId <- hourly_steps %>%
group_by(Id) %>%
summarize(mean_steps_id = mean(StepTotal), sd_steps_id = sd(StepTotal)) %>%
mutate_if(is.numeric, round, 2) %>%
as.data.frame()
head(steps_byId)
```
## Id mean_steps_id sd_steps_id
## 1 1503960366 535.28 864.85
## 2 1624580081 215.91 688.29
## 3 1644430081 321.22 638.62
## 4 1844505072 89.22 215.53
## 5 1927972279 134.16 553.27
## 6 2022484408 504.74 931.13
## Data Visualization
1. Relationship between steps taken in a day and sedentary minutes:
``` r
ggplot(data=daily_activity, aes(x=TotalSteps, y=SedentaryMinutes)) +
geom_point() +
geom_smooth() +
labs(title="Total Steps vs. Sedentary Minutes",
x = "Steps", y = "Minutes")
```
There appears to be no correlation between total daily steps taken and
sedentary minutes. We can confirm with a simple linear regression:
``` r
sed_steps_lr <-lm(SedentaryMinutes ~ TotalSteps, data = daily_activity)
summary(sed_steps_lr)
```
##
## Call:
## lm(formula = SedentaryMinutes ~ TotalSteps, data = daily_activity)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1132.2 -237.8 110.1 255.2 539.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1132.534021 13.794633 82.10 <0.0000000000000002 ***
## TotalSteps -0.019316 0.001554 -12.43 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 297.6 on 1393 degrees of freedom
## Multiple R-squared: 0.09979, Adjusted R-squared: 0.09914
## F-statistic: 154.4 on 1 and 1393 DF, p-value: < 0.00000000000000022
Results confirm little correlation, with an r^2 value of .09
2. Average amount of time participants slept each night during the
course of the study:
``` r
# Graph the results
options(scipen = 999)
ggplot(mean_sleep, aes(x = Id, y = mean_sleep)) +
geom_col(aes(reorder(Id, +mean_sleep), y = mean_sleep)) +
labs(title = "Average Minutes of Sleep", x = "Participant Id", y = "Minutes") +
theme(axis.text.x = element_text(angle = 90)) +
geom_hline(yintercept = mean(mean_sleep$mean_sleep), color = "red")
```
The graph shows the average sleep of each participant individually, as
well as how their sleep compares to the overall average across all
participants.
3. Average steps per hour:
``` r
ggplot(mean_steps, aes(x = Hour, y = mean_steps)) +
geom_col(aes(reorder(Hour, +mean_steps), mean_steps)) +
theme(axis.text.x = element_text(angle = 90)) +
labs(title = "Average Steps Taken per Hour of Day",
x = "Hour", y = "Average Steps")
```
We can see that the most steps were taken in the evening, from 5-7pm,
and the least steps in the middle of the night, between 12-4am.
4. I’m going to combine two datasets I created previously, activity_id
and steps_byId, in order to find new relationships between
variables.
``` r
combined_data <- merge(activity_id, steps_byId, by = "Id")
# Putting just the numerical variables into a separate dataframe, then running a correlation matrix
num_data <- combined_data[-1]
cor(num_data)
```
## sum_very sum_fairly sum_lightly sum_sed mean_steps_id sd_steps_id
## sum_very 1.00000000 0.3474685 0.16981822 -0.03324059 0.73221729 0.7472171
## sum_fairly 0.34746847 1.0000000 0.15709676 -0.21954530 0.41535432 0.3791953
## sum_lightly 0.16981822 0.1570968 1.00000000 0.08589005 0.63432011 0.3985548
## sum_sed -0.03324059 -0.2195453 0.08589005 1.00000000 -0.01639851 0.1260117
## mean_steps_id 0.73221729 0.4153543 0.63432011 -0.01639851 1.00000000 0.9176734
## sd_steps_id 0.74721709 0.3791953 0.39855478 0.12601175 0.91767340 1.0000000
``` r
# Based on the correlation matrix, there is little correlation between the different activity levels, but there is a moderate (.7) correlation between mean steps taken and very active minutes.
ggplot(combined_data, aes(x = mean_steps_id, y = sum_very)) +
geom_point() +
labs(title = "Average Steps Taken in a Day Compared to Very Active Minutes",
x = "Average Steps", y = "Very Active Minutes")
```
We can see a moderate upwards trend of “very active minutes” increasing
as average steps in a day increases.
5. I completed additional visualizations in Tableau, which can be
viewed here: [Bellabeat Dashboard -
Tableau](https://public.tableau.com/views/BellabeatCaseStudy_16572546536690/Dashboard1?:language=en-US&:display_count=n&:origin=viz_share_link)
The descriptive statistical analyses and visualizations completed show
the following smart device usage trends:
- Sedentary minutes took up the majority of participants’ days and were
fairly consistent throughout the week.
- Average “very active minutes” were also consistent throughout the week
at around 20 minutes each day.
- On average, participants slept the most on Sundays, which was also the
day they took the least amount of steps
- Participants took the most steps on Tuesdays and Saturdays.
- On average, the fewest steps were taken at 3:00 and the most steps
taken at 18:00
- On average, participants slept about 390 minues, or 6.5 hours per
night
- Users who take more steps per day are more likely to engage in “very
active minutes”
## Recommendations
How can these trends help influence Bellabeat marketing strategy?
We can make marketing recommendations based on what we have learned
about how customers are currently using smart fitness devices:
1. Very few customers utilized the weight log feature, so this does not
appear to be a selling point. Focus on marketing other features such
as activity, sleep, and steps tracking, and consider further
research into how to make the weight log feature more marketable
2. Our data shows that when active, participants engaged the most in
“light” activity and did not have many “very active” minutes each
day. The company could add a “level up” feature in which
participants can earn points based on time spent being active, with
higher levels of activity earning more points. This could motivate
users to engage in active minutes more often.
3. There’s about a 1000 step decrease on Sundays compared to the other
days of the week. A notification on Sunday mornings with a goal to
hit a certain number of steps, along with a reward for hitting a
7-day streak could help close this gap and encourage customers to
use the device all days of the week.
4. Based on data showing the most usage around 6pm, it seems likely
most users have typical work hours during the day and get most of
their steps in after work. An ad targeted towards working adults
focused on easily tracking steps throughout their busy days could be
effective. A reminder notification around 12pm and 8pm can encourage
users to increase their activity levels during other break times
such as lunch and after dinner as well.
5. On average, participants got less than the CDC recommended 7 hours
of sleep per night. Continue marketing the device’s sleep tracking
feature as participants who are not getting enough sleep may want a
way to track their sleep patterns. Consider marketing along with a
meditation app or habit tracker.