FitBit Data Analysis for Bellabeat Marketing Strategy
Import Nesscesary R Libraries
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 1.3.1
## ✔ 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 errorslibrary(lubridate)
library(dplyr)
library(ggplot2)
library(tidyr)
library(janitor)##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.test
Import All Required CSV Data Files
We need four csv files:
- dailyActivity_merged
- dailyCalories_merged
- hourlyIntensities_merged
- sleepDay_merged
- weightLogInfo_merged
activity <- read_csv("/cloud/project/fitbit csv data files/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.calories <- read_csv("/cloud/project/fitbit csv data files/dailyCalories_merged.csv")## Rows: 940 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): ActivityDay
## 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.intensities <- read_csv("/cloud/project/fitbit csv data files/hourlyIntensities_merged.csv")## Rows: 22099 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): ActivityHour
## dbl (3): Id, TotalIntensity, AverageIntensity
##
## 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.sleep <- read_csv("/cloud/project/fitbit csv data files/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.weight <- read_csv("/cloud/project/fitbit csv data files/weightLogInfo_merged.csv")## Rows: 67 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Date
## dbl (6): Id, WeightKg, WeightPounds, Fat, BMI, LogId
## lgl (1): IsManualReport
##
## 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.head(activity)## # 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(weight)## # A tibble: 6 × 8
## Id Date WeightKg WeightPounds Fat BMI IsManualReport LogId
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <lgl> <dbl>
## 1 1503960366 5/2/2016 … 52.6 116. 22 22.6 TRUE 1.46e12
## 2 1503960366 5/3/2016 … 52.6 116. NA 22.6 TRUE 1.46e12
## 3 1927972279 4/13/2016… 134. 294. NA 47.5 FALSE 1.46e12
## 4 2873212765 4/21/2016… 56.7 125. NA 21.5 TRUE 1.46e12
## 5 2873212765 5/12/2016… 57.3 126. NA 21.7 TRUE 1.46e12
## 6 4319703577 4/17/2016… 72.4 160. 25 27.5 TRUE 1.46e12
Clean & Transform Data
There are some problems in the format of the timestamp data and so convert the format to resolve this problem.
Intensities Data Frame
# intensities
intensities$ActivityHour=as.POSIXct(intensities$ActivityHour, format="%m/%d/%Y %I:%M:%S %p", tz=Sys.timezone())
intensities$time <- format(intensities$ActivityHour, format = "%H:%M:%S")
intensities$date <- format(intensities$ActivityHour, format = "%m/%d/%y")Activities Data Frame
# activity
activity$ActivityDate=as.POSIXct(activity$ActivityDate, format="%m/%d/%Y", tz=Sys.timezone())
activity$date <- format(activity$ActivityDate, format = "%m/%d/%y")Sleep Data Frame
# sleep
sleep$SleepDay=as.POSIXct(sleep$SleepDay, format="%m/%d/%Y %I:%M:%S %p", tz=Sys.timezone())
sleep$date <- format(sleep$SleepDay, format = "%m/%d/%y")
Analyze Data
So first we have to check the sample size of all these data. Becasue there might be case some users not given the some type of data like sleep.
# Finding number of respondents in each data types
n_distinct(activity$Id) ## [1] 33n_distinct(calories$Id) ## [1] 33n_distinct(intensities$Id)## [1] 33n_distinct(sleep$Id)## [1] 24n_distinct(weight$Id)## [1] 8Check abnormal changes in respondents weights.
# checking for change in weight
weight%>%
group_by(Id)%>%
summarise(min(WeightKg),max(WeightKg))## # A tibble: 8 × 3
## Id `min(WeightKg)` `max(WeightKg)`
## <dbl> <dbl> <dbl>
## 1 1503960366 52.6 52.6
## 2 1927972279 134. 134.
## 3 2873212765 56.7 57.3
## 4 4319703577 72.3 72.4
## 5 4558609924 69.1 70.3
## 6 5577150313 90.7 90.7
## 7 6962181067 61 62.5
## 8 8877689391 84 85.8Check the sumarry of all data frames(Min/Max/Mean/Meadian/SD)
Activity Data Frame
activity %>%
select(TotalSteps,
TotalDistance,
SedentaryMinutes, Calories) %>%
summary()## TotalSteps TotalDistance SedentaryMinutes Calories
## Min. : 0 Min. : 0.000 Min. : 0.0 Min. : 0
## 1st Qu.: 3790 1st Qu.: 2.620 1st Qu.: 729.8 1st Qu.:1828
## Median : 7406 Median : 5.245 Median :1057.5 Median :2134
## Mean : 7638 Mean : 5.490 Mean : 991.2 Mean :2304
## 3rd Qu.:10727 3rd Qu.: 7.713 3rd Qu.:1229.5 3rd Qu.:2793
## Max. :36019 Max. :28.030 Max. :1440.0 Max. :4900Calories Data Frame
calories %>%
select(Calories) %>%
summary()## Calories
## Min. : 0
## 1st Qu.:1828
## Median :2134
## Mean :2304
## 3rd Qu.:2793
## Max. :4900Sleep Data Frame
sleep %>%
select(TotalSleepRecords, TotalMinutesAsleep, TotalTimeInBed) %>%
summary()## TotalSleepRecords TotalMinutesAsleep TotalTimeInBed
## Min. :1.000 Min. : 58.0 Min. : 61.0
## 1st Qu.:1.000 1st Qu.:361.0 1st Qu.:403.0
## Median :1.000 Median :433.0 Median :463.0
## Mean :1.119 Mean :419.5 Mean :458.6
## 3rd Qu.:1.000 3rd Qu.:490.0 3rd Qu.:526.0
## Max. :3.000 Max. :796.0 Max. :961.0Active Minuter Per Category
activity %>%
select(VeryActiveMinutes, FairlyActiveMinutes, LightlyActiveMinutes) %>%
summary()## VeryActiveMinutes FairlyActiveMinutes LightlyActiveMinutes
## Min. : 0.00 Min. : 0.00 Min. : 0.0
## 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.:127.0
## Median : 4.00 Median : 6.00 Median :199.0
## Mean : 21.16 Mean : 13.56 Mean :192.8
## 3rd Qu.: 32.00 3rd Qu.: 19.00 3rd Qu.:264.0
## Max. :210.00 Max. :143.00 Max. :518.0Weight Data Frame
weight %>%
select(WeightKg, BMI) %>%
summary()## WeightKg BMI
## Min. : 52.60 Min. :21.45
## 1st Qu.: 61.40 1st Qu.:23.96
## Median : 62.50 Median :24.39
## Mean : 72.04 Mean :25.19
## 3rd Qu.: 85.05 3rd Qu.:25.56
## Max. :133.50 Max. :47.54Insights about these summaries:
- Sedetary Min AVG: 16.5 Hours
- Average No. of Steps/Day: 7638. [CDC recommendation 10,000 Steps/Day]
- Most of of the participants activity level is light.
- Average Participant Burns: 97 Cal/Hour
- Average Participants Sleep: 7 Hours/Day
Merge Data
Before visualizing the data, two of the datasets the activity and sleep datasets on columns Id and date will be merged by inner join.
merged_data <- merge(sleep, activity, by = c('Id', 'date'))
head(merged_data) ## Id date SleepDay TotalSleepRecords TotalMinutesAsleep
## 1 1503960366 04/12/16 2016-04-12 1 327
## 2 1503960366 04/13/16 2016-04-13 2 384
## 3 1503960366 04/15/16 2016-04-15 1 412
## 4 1503960366 04/16/16 2016-04-16 2 340
## 5 1503960366 04/17/16 2016-04-17 1 700
## 6 1503960366 04/19/16 2016-04-19 1 304
## TotalTimeInBed ActivityDate TotalSteps TotalDistance TrackerDistance
## 1 346 2016-04-12 13162 8.50 8.50
## 2 407 2016-04-13 10735 6.97 6.97
## 3 442 2016-04-15 9762 6.28 6.28
## 4 367 2016-04-16 12669 8.16 8.16
## 5 712 2016-04-17 9705 6.48 6.48
## 6 320 2016-04-19 15506 9.88 9.88
## LoggedActivitiesDistance VeryActiveDistance ModeratelyActiveDistance
## 1 0 1.88 0.55
## 2 0 1.57 0.69
## 3 0 2.14 1.26
## 4 0 2.71 0.41
## 5 0 3.19 0.78
## 6 0 3.53 1.32
## LightActiveDistance SedentaryActiveDistance VeryActiveMinutes
## 1 6.06 0 25
## 2 4.71 0 21
## 3 2.83 0 29
## 4 5.04 0 36
## 5 2.51 0 38
## 6 5.03 0 50
## FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes Calories
## 1 13 328 728 1985
## 2 19 217 776 1797
## 3 34 209 726 1745
## 4 10 221 773 1863
## 5 20 164 539 1728
## 6 31 264 775 2035
Data Visualization & Share
1. Scatter Polt (Total Steps Vs Calories Burned)
To check if there is a correlation between total number of steps taken and calories burned. The more steps each participant takes, the more calories they burn.
ggplot(data = activity, aes(x = TotalSteps, y = Calories)) + geom_point(colour="purple") + geom_smooth() + labs(title = "Total Steps vs. Calories")## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
2. Scatter Polt (Total Time Asleep Vs Total Time In Bed)
We get a positive correlation between total time asleep vs total time in bed. To improve sleep quality for its users, bellabeat should consider having a section where users can customize their sleep schedule to ensure consistency.
ggplot(data = sleep, aes(x = TotalMinutesAsleep, y = TotalTimeInBed)) + geom_point(color="#E5A9A9") + labs(title = "Total time asleep vs Total time in bed")
3. Scatter Polt (Total Time Asleep Vs Total Time In Bed)
ggplot(data = merged_data, mapping = aes(x = SedentaryMinutes, y = TotalMinutesAsleep)) +
geom_point(color = "#D66BA0") + labs(title= "Sleep Duration and Sedentary Time")
cor(merged_data$TotalMinutesAsleep,merged_data$SedentaryMinutes)## [1] -0.599394Observing the graph, it’s evident that there’s a negative correlation between SedentaryMinutes and TotalMinutesAsleep. This implies that individuals who are less active tend to get less sleep.
Next, let’s explore the influence of the day of the week on our activity levels and sleep patterns.
4. Aggregate Data By Day Of The Week To Summarize Averages
merged_data <- mutate(merged_data,
day = wday(SleepDay, label = TRUE))
summarized_activity_sleep <- merged_data %>%
group_by(day) %>%
summarise(AvgDailySteps = mean(TotalSteps),
AvgAsleepMinutes = mean(TotalMinutesAsleep),
AvgAwakeTimeInBed = mean(TotalTimeInBed),
AvgSedentaryMinutes = mean(SedentaryMinutes),
AvgLightlyActiveMinutes = mean(LightlyActiveMinutes),
AvgFairlyActiveMinutes = mean(FairlyActiveMinutes),
AvgVeryActiveMinutes = mean(VeryActiveMinutes),
AvgCalories = mean(Calories))
head(summarized_activity_sleep)## # A tibble: 6 × 9
## day AvgDailySteps AvgAsleepMinutes AvgAwakeTimeInBed AvgSedentaryMinutes
## <ord> <dbl> <dbl> <dbl> <dbl>
## 1 Sun 7298. 453. 504. 688.
## 2 Mon 9340. 419. 456. 718.
## 3 Tue 9183. 405. 443. 740.
## 4 Wed 8023. 435. 470. 714.
## 5 Thu 8205. 402. 436. 701.
## 6 Fri 7901. 405. 445. 743.
## # ℹ 4 more variables: AvgLightlyActiveMinutes <dbl>,
## # AvgFairlyActiveMinutes <dbl>, AvgVeryActiveMinutes <dbl>, AvgCalories <dbl>5. Bar Graph Chart (AVG Daily StepS/Day)
According to this observation participants are most active on saturdays and least active on sundays.
ggplot(data = summarized_activity_sleep, mapping = aes(x = day, y = AvgDailySteps)) +
geom_col(fill = "purple") + labs(title = "Daily Step Count")
Share Insights
Data Analysis Recommendations:
Step Count: The average daily step count is 7,638, falling below the CDC’s recommended 8,000 steps. Encourage users to aim for at least 8,000 steps daily, emphasizing associated health benefits.
Activity Levels: Most participants are lightly active. Implement a progression system in the app to motivate users towards becoming fairly active.
Nutrition Support: Provide ideas for low-calorie breakfast, lunch, and dinner options to aid users in weight management.
Sleep Quality Improvement: Use app notifications to remind users of sufficient rest and suggest reducing sedentary time for better sleep.
Most Active Day: Participants are most active on Saturdays. Remind users to engage in physical activities like walks or jogs on Saturdays.
Least Active Day: Sundays show lower activity levels.Encourage users to maintain exercise routines on Sundays for consistent activity.