library(tidyverse) # For data manipulation
library(skimr) # For data summary
library(janitor) # For cleaning column names
library(lubridate) # For date manipulation
library(readr) # For reading CSV files
library(dplyr) # For data manipulation
  

The selected datasets were imported using R. Below, you can find the codes used to import the dataset.

dailyActivity     <- read_csv("data/dailyActivity_merged.csv")

hourlyCalories    <- read_csv("data/hourlyCalories_merged.csv")
hourlyIntensities <- read_csv("data/hourlyIntensities_merged.csv")
hourlySteps       <- read_csv("data/hourlySteps_merged.csv")

minuteCalories    <- read_csv("data/minuteCaloriesNarrow_merged.csv")
minuteIntensities <- read_csv("data/minuteIntensitiesNarrow_merged.csv")
minuteMETs        <- read_csv("data/minuteMETsNarrow_merged.csv")
minuteSteps       <- read_csv("data/minuteStepsNarrow_merged.csv")

sleepInfo         <- read_csv("data/sleepDay_merged.csv")
weightInfo        <- read_csv("data/weightLogInfo_merged.csv")
  

3.3. Process: Data cleaning and verification

Now that I learned about the datasets, it is time to focus on the data cleaning and verification processes. They ensure that the data is clean and structured into a suitable format that makes it easy to analyze; the following steps are taken to make it happen.

Data Cleaning Checklist

• Check preliminary requirements by reviewing the business objectives, backing up data beforehand, and fixing any errors at their source when possible.
• Remove unwanted data, such as duplicates, inaccuracies, or incorrect entries.
• Check data accuracy and correctness by validating value ranges (e.g., minimums, maximums, percentages), binary and numeric types, and correcting spelling, variable names, capitalization, punctuation, and extra spaces.
• Manage formatting by standardizing font, size, color, bolding, and italicizing where needed for clarity in reporting or analysis.
• Handle missing values through context-appropriate methods like imputation, removal, or retention.
• Check for merging or splitting opportunities to address inconsistent or misaligned data columns.
• Ensure data is correctly fielded, making sure, for example, that a country name isn’t placed in a city column.
• Verify data length for specific fields (e.g., confirming that year entries are exactly four digits).

# Check for duplicates
sum(duplicated(sleepInfo))
  

To remove the duplicates, the unique() function was used, and to verify the removal of the duplicates, the above code was used again; this time, there was no duplicate.

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# verify the removal of the duplicates
sleepInfo <- unique(sleepInfo)
  

To identify NA values, the following code was used for each selected dataset(here, sleepDay_merged dataset); however, no NA value was found.

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# To identify NA values
sum(is.na(sleepInfo))
  

The following describes the details of each steps:

Data accuracy and correctness:

• Min and Max of each variable were checked (e.g., active minute, active distance, and weight columns should not have any negative value, and the values should be in a reasonable range)
• Dates and times were also examined to be in the proper format. The data types were “character”; therefore, some modifications would be required (discussed in next slides).
• The data spelling and naming columns (e.g., using a single name for all date (ActivityDate), time (ActivityTime), and the total steps taken (TotalSteps) columns in different datasets), capitalization, and any potential extra space were checked and removed.

Data formatting: the datasets were checked to have a consistent format (e.g., font type, font color, font size, etc.)

Missing and misfielded values: there were missing values under some of the columns; in some cases, the variables were removed (above table); in other cases, I had to just ignore them, as I did not have access to the respondents to complete the missing values. No misfielded value was found

Length of data: The Id columns were checked in all datasets to have exactly ten digits and no error was found

Check for merging/splitting possibilities: Merging

According to the selected datasets characteristics table (slides 9 and 10), 3 datasets (hourlyCalories_merged , hourlyIntensities_merged and hourlySteps_merged) have two common variables (Id, ActivityHour) and an equal number of rows. The 3 datasets merged into a single one.

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library(dplyr)

hourlyParameters <- left_join(hourlyCalories, hourlyIntensities, by = c("id", "activity_hour"))
hourlyParameters <- left_join(hourlyParameters, hourlySteps, by = c("id", "activity_hour"))
  

Also, 3 datasets (dailyActivity_merged, sleepDay_merged, and weightLogInfo_merged) contain costumers daily data. Therefore, it would be possible to merge them into a single one ( dailyParameters_merged ) using R merge() function .

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dailyParameters <- merge(dailyActivity, sleepInfo, by = c("id", "date"), all.x = TRUE, all.y = FALSE)
dailyParameters <- merge(dailyParameters, weightInfo, by = c("id", "date"), all.x = TRUE, all.y = FALSE)
  

Similarly, 4 datasets (minuteCaloriesNarrow_merged, minuteIntensitiesNarrow_merged, minuteMETsNarrow_merged, and minuteStepsNarrow_merged) have two common variables (Id, ActivityMinute) and an equal number of rows. Therefore, to merge the 4 datasets into a single one (minuteParameters_merged), the following R codes were used:

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minuteParameters <- left_join(minuteCalories, minuteIntensities, by = c("id", "activity_minute"))
minuteParameters <- left_join(minuteParameters, minuteMETs, by = c("id", "activity_minute"))
minuteParameters <- left_join(minuteParameters, minuteSteps, by = c("id", "activity_minute"))
  

By merging the above datasets, the total number of selected datasets decreased from 10 to 3 following datasets:

• dailyParameters_merged.csv.
• hourlyParameters_merged.csv.
• minuteParameters_merged.csv.

3.5 Data merging/splitting possibilities

Check for merging/splitting possibilities: Splitting.

• All datasets had a column with date and/or time data. These columns were split into two date and time columns . For example, the following R codes were used for the minuteParameters_merged.csv dataset (which was created in the merging process in the previous slide) to split the activity_minute column (containing date and time data) into two Date and Time columns. An extra column (Weekday) is also inserted to convert the dates to weekdays:
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minuteParameters <- minuteParameters %>%
  mutate(activity_minute = parse_date_time(activity_minute, "%m/%d/%Y %I:%M:%S %p"),
         Date = as.Date(activity_minute),
         Time = format(activity_minute, "%H:%M:%S"),
         Weekday = weekdays(Date))
  

In the sleepDay_merged dataset, the time for all records was 12:00:00 AM. Therefore, before merging it with the dailyActivity_merged dataset (which was discussed in previous slide), the date-time format was changed to the date only using the following R codes:

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sleepInfo <- sleepInfo %>%
 rename(Date=SleepDay) %>% 
 mutate(Date=as.Date(Date,format="%m/%d/%Y")) sleepInfo$Weekday <- weekdays(sleepInfo$Date)
  

Step 4. Analyze:

4.1 Analyze: General statistics

Putting data to work in the “Analyze” step. It would be necessary to perform calculations, identify trends and relationships, uncover new insights and discover potential solutions to the business tasks.

Initial analysis of the datasets: Codes

First, general statistics were extracted, including the average, standard deviation, minimum, maximum, and percentiles of some of the selected variables in each dataset.

I started with dailyParameters_merged dataset. similar process was repeated for the hourlyParameters_merged_splitted and the minuteParameters_merged_splitted datasets.

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dailyParameters %>% 
select(steps,total_distance, very_active_minutes, fairly_active_minutes, lightly_active_minutes, sedentary_minutes, calories, total_minutes_asleep, total_time_in_bed, weight_kg, bmi) %>% 
summary
  

Initial analysis of the datasets: Daily parameters

The initial analysis of the selected variables of the dailyParameters_merged dataset are as follows:

The analysis highlights:

The daily average of the total steps is 7,638; it is less than 10,000 steps that most fitness tracking devices recommend I take daily. However, according to US Department of Health and Human Services guidelines, a range between 7,000 to 13,000 steps a day will help to get the full benefits that exercise, including protecting against diseases like cancer and diabetes and helping with weight loss

Users travel approximately an average distance of 5.5 kilometers (5.49 kms) a day

The maximum daily steps taken is 36,019 which is nearly equal to 28 kilometers daily total distance

The population spend nearly 16.5 hours in sedentary time, which is surprisingly high; according to British Heart Foundation, adults of working age in England average about 9.5 hours per day of sedentary time. Users also had an average of 3.2 hours of light activity, and only half an hour of active time (fairly and very active time)

The average calories burned (2,304) seems reasonable. According to the Dietary Guidelines for Americans 2020–2025, the average adult woman expends roughly 1,600 to 2,400 calories per day, while the average adult man expends 2,000 to 3,000

Data shows that while people spend an average of more than 7.5 hours in bed, they sleep about 7 hours a day. That means they are awake in bed for half an hour on average. It also seems normal, as sleep foundation suggests 7-9 hours of daily sleep for adults with 26-64 years old

The average BMI (a person’s weight divided by the square of height) is 25.19. According to Center for Disease Control and prevention, BMI between 25.0 to 29.9 falls within the overweight range

Initial analysis of the datasets: Hourly and minute parameters

The initial analysis of the selected variables of the hourlyParameters_merged_splitted dataset are as follows (RStudio output):

The data for Total intensity and Average intensity is not clear, mainly because the unit for the mentioned variables is not stated. However, these variables will be used in our next analysis.

Users took about 320 steps and burned an average of 97 calories per hour, which seems to be in line with the average daily total steps (7,638) and average daily calories burned (2,304).

The initial analysis of the selected variables of the minuteParameters_merged_splitted dataset are as follows (RStudio output)

The metabolic equivalent of task (MET) is the ratio of the work metabolic rate to the resting metabolic rate.For example, one MET is the rate of energy expenditure while at rest. A four MET activity expends four times the energy used by the body at rest. If a person does a 4 MET activity for 30 minutes, he or she has done 4 x 30 = 120 MET-minutes (or 2.0 MET-hours) of physical activity.

The data shows that the average MET-minutes is 1.469. Considering the definition of MET, this could correspond to a light activity with a MET value of 2 (such as walking slowly at ~3 km/h) performed for about 0.73 minutes (since 2 × 0.73 ≈ 1.469), or other combinations of very brief light activities. This aligns with generally low activity levels in the dataset..

The average total steps taken by users per hour is close to 5.3 steps, and the number of calories burned is 1.6

4.2 Analyze: Identifying trends and relationships

1. Activity time vs. Calories

Relationships between the Activity level (very active, fairly active, lightly active, and sedentary), and the Activity duration was examined. For Sedentary activity level, the negative slope of the regression line shows that as I spend more sedentary time, the number of calories consumed decreases

Sedentary time (minutes) vs. number of calories consumed

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dailyParameters %>%
  ggplot() +
  geom_point(aes(x = sedentary_minutes, y = calories), color = "darksalmon") +
  geom_smooth(aes(x = sedentary_minutes, y = calories), method = lm, se = FALSE, color = "cornsilk4") +
  labs(
    title = "Calorie vs. Sedentary Time",
    subtitle = "The relationship between the sedentary time of the users and the number of calories burned",
    caption = "Data collected from 33 users",
    x = "X: Sedentary time (minutes)",
    y = "Y: Number of calories burned"
  ) +
  annotate("text", x = 50, y = 2750, label = "Regression line", color = "cornsilk4", size = 3, fontface = "bold") +
  theme_minimal()
  

The p-value is equal to 0.001; a typical threshold is 0.05, and anything smaller is considered statistically significant.

2.Activity time vs. Calories

Very, fairly, and lightly active time (minutes) vs. number of calories consumed Sample codes for “Very active” level

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#veryactiveminutes
dailyParameters %>%
  ggplot() +
  geom_point(aes(x = very_active_minutes, y = calories), color = "darkolivegreen3") +
  geom_smooth(aes(x = very_active_minutes, y = calories), method = lm, se = FALSE, color = "cornsilk4", size = 0.5) +
  labs(
    title = "Calorie vs. very_active_minutes",
    subtitle = "Relationship btw the very_active_minutes of the users and the number of calories burned",
    caption = "Data collected from 33 users",
    x = "X: very_active_minutes (minutes)",
    y = "Y: Number of calories burned"
  ) +
  annotate("text", x = 50, y = 2750, label = "Regression line", color = "cornsilk4", size = 3, fontface = "bold") +
  theme_minimal()
  

If you are more active (“very” or “fairly”), you may burn more calories for less activity time.

As mentioned earlier, the average adult woman expends roughly 1,600 to 2,400 calories and the average adult man expends 2,000 to 3,000. The diagrams confirm that the majority of data points are concentrated between the above range (1600-3000 calories/day).

3.Sedentary time vs. Sleep

Data shows that sedentary time has an inverse effect on sleep time. Therefore, it seems that more physical activity can improve users' sleep time

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dailyParameters %>%
  drop_na() %>%
  ggplot(aes(x = sedentary_minutes, y = total_minutes_asleep)) +
  geom_point(color = "darksalmon") +
  geom_smooth(method = "lm", se = FALSE, color = "cornsilk4",size=0.5) +
  labs(
    title = "Sedentary time vs. Total sleep time",
    subtitle = "The relationship between users' sedentary time and their total sleep time",
    caption = "Data collected from 33 users",
    x = "X: Users' sedentary time (minutes)",
    y = "Y: Users' total sleep time (minutes)"
  ) +
  theme_minimal()
  

The diagram shows that as users’ sedentary time increases, total sleep time decreases (negative slope).

In other word, lack of physical activity seems to have a negative effect on users’ sleep time.

4. Activity time vs. BMI

Activity duration (minutes) of the users at different levels vs. BMI

A linear regression model describes the relationship between a dependent variable (y), and one or more independent variables (x).

To examine the relationship between users' different activity levels and their weight, BMI was used as a better indicator mainly because it considers the users' height.

Here, p-value and R-squared (R²) were considered in each activity level to determine any potential relationship.

P-value is used in hypothesis testing to help decide whether to reject the null hypothesis (primarily, the one that indicates there is no relationship between the variables). The smaller the p-value (a typical threshold is 0.05; anything smaller is considered statistically significant), the more likely to reject the null hypothesis.

R-squared is a goodness-of-fit measure for linear regression, and always is between 0–1. Usually, the larger the R², the better the regression model fits your observations.

Sample codes for “Very active” level

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#veryActiveBMI_correlation
veryActive_BMI_correlation <- lm(very_active_minutes ~ bmi, data = dailyParameters)

summary(veryActive_BMI_correlation)
  

The relationship can be considered statistically significant at the ‘Fairly active’ and “Lightly active” levels; at other activity levels, no correlation is seen.

5.Share of daily activity levels

While users spend most of their time in sedentary state, the duration of moderate-to-vigorous physical activity is largely meets World Health Organization guidelines

Moderate-intensity activity: WHO recommends 150–300 minutes per week, or 21–42 minutes per day. Among the users, 82% (27 out of 33) met this guideline.

Vigorous-intensity activity: WHO recommends 75–100 minutes per week, or 11–21 minutes per day. Compliance was slightly higher, with 88% (29 out of 33 users) meeting the recommendation.

Over 80% of users had enough daily moderate, and vigorous-intensity physical activity.

The users spent the majority of their time in sedentary mode (81% of the time), while they were active (very or fairly) only 3% of the time

6.Total steps vs. Calories

There is a positive relationship between the total steps taken and the number of calories burned (p-value < 0.05). In other words, as the number of steps taken increases, the calorie consumption increases.

Total steps taken by the users vs. number of calories consumed.

The bar chart shows that there is a positive significant correlation between the user's total steps and the number of calories burned (regression line). The histogram on the right shows the distribution of total daily steps taken. The more daily steps you take, the more calories you burn

7.Activity time vs. Total steps

As the plots suggest, the longer sedentary time, the fewer steps users take; all other relationships are positive. In addition, it is observed that as the activity level increases, less time is required to take a certain number of steps..

Activity duration (minutes) at different levels vs. total number of steps taken by the users.

8.Analyze: Identifying trends and relationships – Activity time by the weekdays

Monday has the highest sedentary time, and Thursday has the least. On the other hand, Saturday has the highest active time, and Sunday has the least

The average sedentary time and the average active time (the sum of `Very active’, ‘Fairly active, and ‘Lightly active’ time averages) by the weekdays were calculated and plotted using Microsoft Excel pivot table and charts

The above charts show that sedentary time is among the lowest on Saturday (left chart), while active time is at the peak (right chart)

#Total number of steps and total intensity by hours
hourlyParameters <- hourlyParameters %>%
  mutate(hour = hour(mdy_hms(activity_hour)))

# Then group by hour instead of raw timestamp
hourlyStepsSummary <- hourlyParameters %>%
  group_by(hour) %>%
  summarise(avgTotalSteps = mean(step_total, na.rm = TRUE))

hourlyIntensitySummary <- hourlyParameters %>%
  group_by(hour) %>%
  summarise(avgTotalIntensity = mean(total_intensity, na.rm = TRUE))

# Scale factor for secondary axis
scale_factor <- 0.05 * max(hourlyStepsSummary$avgTotalSteps, na.rm = TRUE)

# Plot
ggplot() +
  geom_col(data = hourlyStepsSummary, aes(x = factor(hour), y = avgTotalSteps), fill = "darksalmon") +
  geom_line(data = hourlyIntensitySummary,
            aes(x = factor(hour), y = avgTotalIntensity * scale_factor, group = 1),
            color = "dimgrey") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, face = "bold")) +
  labs(
    title = "Total Steps and Intensity per Hour",
    subtitle = "The total number of steps taken and the degree of intensity per hour",
    caption = "Data collected from 33 users",
    x = "Hour of Day",
    y = "Total Steps (bars) / Degree of Intensity (line)"
  ) +
  scale_y_continuous(
    sec.axis = sec_axis(~ . / scale_factor, name = "Intensity")
  )
  

As it may seem, the total steps taken and the degree of intensity follow a relatively similar pattern. Also, data shows that most steps are taken by users in the afternoon and evening.

TotalSteps~TotalIntensity: p-value < 0.05, R2: 0.8028.

The histogram shows that as the day begins, the number of steps taken by users increases gradually until 5 PM, and then decreases until the end of the day(bell-shaped distribution); interestingly, the degree of intensity follows the same pattern.

Two significant intervals are 12-2 PM and 5-7 PM (probably after working hours), where the number of steps (and of course, the degree of intensity) is higher than the other hours of the day.

10.Relationship between total steps taken and sedentary minutes

Monday has the highest sedentary time, and Thursday has the least. On the other hand, Saturday has the highest active time, and Sunday has the least

Inverse Relationship:

More active users (those with higher step counts) are less sedentary — a logically expected pattern.

This supports the idea that increasing daily steps can help reduce sedentary behavior.

Two Activity Clusters:

There appear to be two main bands of sedentary behavior (one higher, one lower) even for the same range of steps, suggesting:

Some users may be getting in steps via short bursts (e.g., workouts) but still sitting for long durations and Others may be spreading out their activity better through the day.

Outliers:

A few users have very high steps (20k–30k+) and still high sedentary minutes, which may reflect unusual routines (e.g., workers with a long commute but active jobs).

11.Relationship between time in bed and minutes asleep

observations:

Strong linear correlation: Most data points lie close to the line, indicating that as minutes asleep increase, time in bed increases almost proportionally.This suggests a consistent pattern of sleep behavior across most users.

Efficiency observed: The slope near the main cluster implies high sleep efficiency (users are asleep for most of their time in bed).The closeness of many points to the diagonal suggests users spend only a small portion of time in bed awake.

Step 5 Share

During the “share” phase, it is necessary to visualize the findings and determine the best way to communicate them to stakeholders.

Step 6 Act

6.1 Act: Insights

After data analysis, creating meaningful visualizations, and sharing them with the stakeholders, I have to act on the findings. The deliverables should be prepared, and the business tasks should be answered, including high-level recommendations based on insights gained.

• Number of steps:
• The daily average of total steps taken by users is 7638. That is less than the 10,000 steps most references recommend.
• Activity duration and total steps taken by users have a negative relationship at the sedentary level. A positive correlation can be seen in the other three activity levels ("very active", “fairly active" and “lightly active").
• The total steps taken and the degree of intensity are highly correlated (p-value < 0.05, R2: 0.802).
• Users take more steps in the afternoons (12-2 PM) and evenings (5-7 PM) than at other times of the day.

• Calories:
• The average calories burned (2,304) seems reasonable, compared to the official guidelines (1,600 to 3,000). Most data points are also concentrated between the above range
• There is a positive significant correlation between the activity duration and the number of calories burned in “very active”, “fairly active”, and “lightly active” levels. This means that if you are more active, you can probably burn more calories for less activity duration.
• At the “sedentary” level, the relationship is negative.
• The calorie consumption increases as the number of steps taken increases.

• Activity levels:
• Users spent 81% of their time in sedentary mode (lack of physical activity), which is surprisingly high. They were active (very or fairly) only 3% of the time.
• Sedentary time seems to have a negative effect on sleep time. Therefore, more physical activity can probably improve users' sleep quality and time.
• Users spend a daily average of more than 7.5 hours in bed (7 hours of sleep and half an hour in bed).
• Monday has the highest sedentary time, and Thursday has the least. Saturday has the highest active time, and Sunday has the least.

• Weight:
• The average BMI is 25.19 which falls into the "overweight” category" (25.0 to 29.9).
• The average of metabolic equivalent of task (MET) is 14.69, which represents light activity (e.g., walking at a slow pace) for a short duration (less than 10 minutes).
• It appears to be little or no correlation between the user’s “activity time” and “weight”; only at the ‘Fairly active’ and “Lightly active” levels, the correlation seems to be tatistically significant (p-value < 0.05).

6.2 Act: Recommendations

Based on insights gained from data analysis, it would be possible to apply the insights to Bellabeat products and propose high-level recommendations to guide the company’s marketing strategy (the business task).

6.2.1 Recommendations

• The following steps are a general process, the Bellabeat is recommended to follow for each insight discussed earlierin 6.1:
• Share each insight with current and potential customers using smart marketing methods (digital marketing, marketing campaigns, classic ads, etc.)
• Highlight the potential risks, including exposure to various diseases (cancer, diabetes, etc.), weight gain, etc., that an unhealthy lifestyle could cause, preferably with statistics.
• Improve the Bellabeat app and include a user-friendly notification system to provide users with customized alerts. The objective is to inform users about their health habits, and encourage them to improve them.
• Promote the benefits of using Bellabeat products and the app to avoid negative health habits.

6.2.2 Recommendations

• Bellabeat can focus on the “activity level”, “activity duration”, and “total steps” of the users, and improve related products and functions of the app. It should be highlighted that users spent more than 80% of their time in sedentary mode, while they were active (very or fairly) only 3% of the time. They took less than the 10,000 steps (7638 steps) that most guidelines recommend, and therefore, had less than average activity intensity (high positive correlation between “total steps” and “intensity”)
• The company can organize social challenges and special events, targeting days when sedentary time may be at its peak (Mondays), active time seems to be at its lowest (Sundays), or hours of a day when the number of steps taken is less, using the Bellabeat app. It can also offer incentives such as some company products, special discounts, free membership, etc. to the winners
• Bellabeat should explain the importance of physical activity on sleep quality and sleep duration. Bellabeat needs to improve sleep tracking function in both its products (e.g., required hardware) and the app. It should emphasize the fact that the usage of Bellabeat’s smart devices could help customers increase their activity levels and monitor their sleep habits
• The average calorie intake of users appears to be in the acceptable range (2,304 calories per day, which is within the recommended range of 1,600 to 3,000). However, the average BMI (25.19) is in the "overweight" category, and the average MET (14.69) indicates very light activity for short periods of time. Therefore, it may be a good idea to encourage users to actively manage their weight/BMI (weight management practices)
• The direct positive correlation between “activity duration” and “calorie consumption” means that if you are more active, you can burn more calories for less activity duration. Bellabeat smart devices and the app can help customers monitor their activity level and try to improve it
• Further analysis needs to be done to get more accurate and reliable results.It is mainly because there were some data limitations (i.e. the small sample size for some ‘FitBit’ datasets, outdated datasets and potential changes in consumer habits, missing or manual data input in some cases, etc.), and I were unable to modify it properly due to the data collection approach (survey in 2016)

Skills Used

Data Analysis Skills in R.

• Data Cleaning: Preparing raw data for analysis by handling missing values, outliers, and inconsistencies.
• Data Visualization:Creating informative and aesthetically pleasing visualizations using ggplot2
• Statistical Analysis:Performing statistical tests and modeling to draw insights from data.
• ggplot2:Utilizing the ggplot2 package for advanced data visualization
• dplyr:Using dplyr for efficient data manipulation and transformation.
• Tidying Data:Structuring data in a tidy format for easier analysis and visualization.
• R Markdown:Documenting and presenting data analysis using R Markdown for reproducible research.
• Exploratory Data Analysis (EDA):Analyzing data to summarize its main characteristics, often with visual methods.
• Data Importing and Exporting:Reading data from various sources and writing results to files.
• Date Functions:Handling and manipulating date and time data.

Limitations and future scope

To begin with, it must be highlighted that the public domain dataset i.e., the meta- data used for this case study has its limitations.

There is no demographic information on the users, there is no clarity of the Fitbit devices the users wore and if at all they were same devices, there is a variance in the metadata as a result.

Also, the way the data was originally captured is not qualitative enough to have analysed and there- fore required serious cleaning and manipulation prior to the actual analysis.

For the purpose of future work, the metadata can be combined with other relevant available data to uncover deeper insights into trends, and usage patterns of the users which definitely will lead to better recommendation for Bellabeat’s product portfolio as well as their marketing team’s strategy

References

1. Wickham et al., (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686.
2. Garrett Grolemund, Hadley Wickham (2011). Dates and Times Made Easy with lubridate. Journal of Statistical Software, 40(3), 1-25, https://www.jstatsoft.org/v40/i03/.
3. Kirill Müller (2021). hms: Pretty Time of Day. R package version 1.1.1, https://CRAN.R-project.org/package=hms.
4. Hadley Wickham and Dana Seidel (2020). scales: Scale Functions for Visualization. R package version 1.1.1, https://CRAN.R-project.org/package=scales.
5. Sam Firke (2021). janitor: Simple Tools for Examining and Cleaning Dirty Data. R package version 2.1.0, https://CRAN.R-project.org/package=janitor.
6. Elin Waring, Michael Quinn, Amelia McNamara, Eduardo Arino de la Rubia, Hao Zhu and Shannon Ellis (2021). skimr: Compact and Flexible Summaries of Data. R package version 2.1.3, https://CRAN.R-project.org/package=skimr.
7. Joshua Kunst (2022). highcharter: A Wrapper for the ‘Highcharts’ Library. R package version 0.9.4, https://CRAN.R-project.org/package=highcharter.
8. Brinton, Julia E, et al. “Establishing Linkages between Distributed Survey Responses and Consumer Wearable Device Datasets: A Pilot Protocol.” JMIR Research Protocols, vol. 6, no. 4, 2017, https://doi.org/10.2196/resprot.6513.
9. CDC. "Assessing Your Weight" CDC, September 17, 2020, https://www.cdc.gov/healthyweight/assessing/index.html.
10. CDC. "How much physical activity do adults need?" CDC, October 7, 2020, https://www.cdc.gov/physicalactivity/basics/adults/index.htm.
11. CDC. "How Much Sleep Do I Need?" CDC, March 2, 2017, https://www.cdc.gov/sleep/about/?CDC_AAref_Val=https://www.cdc.gov/sleep/about_sleep/how_much_sleep.html
12. Fitbit. "What should I know about Fitbit sleep stages?" Fitbit, 2022, https://help.fitbit.com/articles/enUS/Helparticle/2163.htm.
13. Möbius. “Fitbit Fitness Tracker Data.” Kaggle, 16 Dec. 2020, https://www.kaggle.com/arashnic/fitbit.
14. Asimakopoulos, Stavros, Grigorios Asimakopoulos, and Frank Spillers. "Motivation and user engagement in fitness tracking: Heuristics for mobile healthcare wearables." In Informatics, vol. 4, no. 1, p. 5. MDPI, 2017, https://www.mdpi.com/2227-9709/4/1/5.

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