Installing and loading required packages

options(repos = c(CRAN = "https://cloud.r-project.org/"))

install.packages("tidyverse")

## Installing package into 'C:/Users/mayaa/AppData/Local/R/win-library/4.3'
## (as 'lib' is unspecified)

## package 'tidyverse' successfully unpacked and MD5 sums checked
## 
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##  C:\Users\mayaa\AppData\Local\Temp\RtmpCEZ21p\downloaded_packages

install.packages("lubridate")

## Installing package into 'C:/Users/mayaa/AppData/Local/R/win-library/4.3'
## (as 'lib' is unspecified)

## package 'lubridate' successfully unpacked and MD5 sums checked
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##  C:\Users\mayaa\AppData\Local\Temp\RtmpCEZ21p\downloaded_packages

install.packages("ggplot2")

## Installing package into 'C:/Users/mayaa/AppData/Local/R/win-library/4.3'
## (as 'lib' is unspecified)

## package 'ggplot2' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\mayaa\AppData\Local\Temp\RtmpCEZ21p\downloaded_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(ggplot2)
library(readr)
library(dplyr)

Processing Data

In the Processing Phase, I cleaned, transformed, and prepared the data to ensure accuracy and consistency before analysis using R studio as the main tool.

Importing and Combining March–April and April–May Data

# Combine files for heart rate seconds
heartrateSeconds_combined <- rbind(
  read_csv("20160312_20160411/heartrate_seconds_merged.csv"),
  read_csv("20160412_20160512/heartrate_seconds_merged.csv")
)

## Rows: 1154681 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Time
## dbl (2): Id, Value
## 
## 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.
## Rows: 2483658 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Time
## dbl (2): Id, Value
## 
## 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.

# Combine files for minute calories
minuteCalories_combined <- rbind(
  read_csv("20160312_20160411/minuteCaloriesNarrow_merged.csv"),
  read_csv("20160412_20160512/minuteCaloriesNarrow_merged.csv")
)

## Rows: 1445040 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): ActivityMinute
## 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.
## Rows: 1325580 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): ActivityMinute
## 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.

# Combine files for minute intensity
minuteIntensity_combined <- rbind(
  read_csv("20160312_20160411/minuteIntensitiesNarrow_merged.csv"),
  read_csv("20160412_20160512/minuteIntensitiesNarrow_merged.csv")
)

## Rows: 1445040 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): ActivityMinute
## dbl (2): Id, Intensity
## 
## 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.
## Rows: 1325580 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): ActivityMinute
## dbl (2): Id, Intensity
## 
## 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.

# Combine files for minute sleep
minuteSleep_combined <- rbind(
  read_csv("20160312_20160411/minuteSleep_merged.csv"),
  read_csv("20160412_20160512/minuteSleep_merged.csv")
)

## Rows: 198559 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): date
## dbl (3): Id, value, logId
## 
## 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.
## Rows: 188521 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): date
## dbl (3): Id, value, logId
## 
## 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.

# Combine files for minute steps
minuteSteps_combined <- rbind(
  read_csv("20160312_20160411/minuteStepsNarrow_merged.csv"),
  read_csv("20160412_20160512/minuteStepsNarrow_merged.csv")
)

## Rows: 1445040 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): ActivityMinute
## dbl (2): Id, Steps
## 
## 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.
## Rows: 1325580 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): ActivityMinute
## dbl (2): Id, Steps
## 
## 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.

Checking for Duplicates

sum(duplicated(heartrateSeconds_combined))

## [1] 23424

sum(duplicated(minuteSteps_combined))

## [1] 10500

sum(duplicated(minuteCalories_combined))

## [1] 10500

sum(duplicated(minuteIntensity_combined))

## [1] 10500

sum(duplicated(minuteSleep_combined))

## [1] 4300

Remove Duplicates

remove_duplicates <- function(df) {
  df[!duplicated(df), ]
}

heartrateSeconds_combined <- remove_duplicates(heartrateSeconds_combined)
minuteSteps_combined <- remove_duplicates(minuteSteps_combined)
minuteCalories_combined <- remove_duplicates(minuteCalories_combined)
minuteIntensity_combined <- remove_duplicates(minuteIntensity_combined)
minuteSleep_combined <- remove_duplicates(minuteSleep_combined)

Convert Date and Time Fields to Proper Formats

heartrateSeconds_combined$Time <- as.POSIXct(heartrateSeconds_combined$Time, format = "%m/%d/%Y %I:%M:%S %p")
minuteSteps_combined$ActivityMinute <- as.POSIXct(minuteSteps_combined$ActivityMinute, format = "%m/%d/%Y %I:%M:%S %p")
minuteCalories_combined$ActivityMinute <- as.POSIXct(minuteCalories_combined$ActivityMinute, format = "%m/%d/%Y %I:%M:%S %p")
minuteIntensity_combined$ActivityMinute <- as.POSIXct(minuteIntensity_combined$ActivityMinute, format = "%m/%d/%Y %I:%M:%S %p")
minuteSleep_combined$date <- as.POSIXct(minuteSleep_combined$date, format = "%m/%d/%Y %I:%M:%S %p")

Check Structure of Data Frames

str(heartrateSeconds_combined)

## tibble [3,614,915 × 3] (S3: tbl_df/tbl/data.frame)
##  $ Id   : num [1:3614915] 2.02e+09 2.02e+09 2.02e+09 2.02e+09 2.02e+09 ...
##  $ Time : POSIXct[1:3614915], format: "2016-04-01 07:54:00" "2016-04-01 07:54:05" ...
##  $ Value: num [1:3614915] 93 91 96 98 100 101 104 105 102 106 ...

str(minuteSteps_combined)

## tibble [2,760,120 × 3] (S3: tbl_df/tbl/data.frame)
##  $ Id            : num [1:2760120] 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
##  $ ActivityMinute: POSIXct[1:2760120], format: "2016-03-12 00:00:00" "2016-03-12 00:01:00" ...
##  $ Steps         : num [1:2760120] 0 0 0 0 0 0 0 0 0 0 ...

str(minuteCalories_combined)

## tibble [2,760,120 × 3] (S3: tbl_df/tbl/data.frame)
##  $ Id            : num [1:2760120] 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
##  $ ActivityMinute: POSIXct[1:2760120], format: "2016-03-12 00:00:00" "2016-03-12 00:01:00" ...
##  $ Calories      : num [1:2760120] 0.797 0.797 0.797 0.797 0.797 ...

str(minuteIntensity_combined)

## tibble [2,760,120 × 3] (S3: tbl_df/tbl/data.frame)
##  $ Id            : num [1:2760120] 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
##  $ ActivityMinute: POSIXct[1:2760120], format: "2016-03-12 00:00:00" "2016-03-12 00:01:00" ...
##  $ Intensity     : num [1:2760120] 0 0 0 0 0 0 0 0 0 0 ...

str(minuteSleep_combined)

## tibble [382,780 × 4] (S3: tbl_df/tbl/data.frame)
##  $ Id   : num [1:382780] 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
##  $ date : POSIXct[1:382780], format: "2016-03-13 02:39:30" "2016-03-13 02:40:30" ...
##  $ value: num [1:382780] 1 1 1 1 1 1 2 2 1 1 ...
##  $ logId: num [1:382780] 1.11e+10 1.11e+10 1.11e+10 1.11e+10 1.11e+10 ...

Check for Missing Values

sum(is.na(heartrateSeconds_combined))

## [1] 0

sum(is.na(minuteSteps_combined))

## [1] 0

sum(is.na(minuteCalories_combined))

## [1] 0

sum(is.na(minuteIntensity_combined))

## [1] 0

sum(is.na(minuteSleep_combined))

## [1] 0

Analyze Data

Hourly Analysis of Heart Rate, Calories, and Steps

heartrateSeconds_combined$Hour <- format(heartrateSeconds_combined$Time, "%H")
minuteCalories_combined$Hour <- format(minuteCalories_combined$ActivityMinute, "%H")
minuteSteps_combined$Hour <- format(minuteSteps_combined$ActivityMinute, "%H")

avg_heartrate_per_hour <- heartrateSeconds_combined %>%
  group_by(Hour) %>%
  summarize(AverageHeartRate = mean(Value, na.rm = TRUE), 
            MinHeartRate = min(Value, na.rm = TRUE),
            MaxHeartRate = max(Value, na.rm = TRUE))

calories_per_hour <- minuteCalories_combined %>%
  group_by(Hour) %>%
  summarize(AverageCalories = mean(Calories, na.rm = TRUE), 
            MinCalories = min(Calories, na.rm = TRUE),
            MaxCalories = max(Calories, na.rm = TRUE))

steps_per_hour <- minuteSteps_combined %>%
  group_by(Hour) %>%
  summarize(AverageSteps = mean(Steps, na.rm = TRUE), 
            MinSteps = min(Steps, na.rm = TRUE),
            MaxSteps = max(Steps, na.rm = TRUE))

combined_hourly_summary <- avg_heartrate_per_hour %>%
  left_join(calories_per_hour, by = "Hour") %>%
  left_join(steps_per_hour, by = "Hour")

options(max.print = 10000)
print(combined_hourly_summary, n = Inf)

## # A tibble: 24 × 10
##    Hour  AverageHeartRate MinHeartRate MaxHeartRate AverageCalories MinCalories
##    <chr>            <dbl>        <dbl>        <dbl>           <dbl>       <dbl>
##  1 00                67.9           42          177            1.20      0.703 
##  2 01                66.8           40          142            1.17      0.703 
##  3 02                64.5           36          139            1.14      0.703 
##  4 03                62.0           37          121            1.13      0.703 
##  5 04                61.1           39          135            1.13      0.703 
##  6 05                63.5           40          157            1.33      0.703 
##  7 06                73.2           36          169            1.40      0.0485
##  8 07                75.6           38          184            1.54      0.703 
##  9 08                77.7           42          172            1.68      0.703 
## 10 09                80.8           42          189            1.77      0.703 
## 11 10                80.7           43          185            1.80      0.703 
## 12 11                81.2           44          176            1.82      0.703 
## 13 12                83.8           44          179            1.92      0.703 
## 14 13                81.8           38          199            1.86      0.703 
## 15 14                82.3           43          182            1.89      0.703 
## 16 15                81.3           44          181            1.77      0.703 
## 17 16                86.2           45          203            1.86      0.703 
## 18 17                84.7           44          203            1.95      0     
## 19 18                85.6           39          195            1.99      0     
## 20 19                84.4           44          171            1.98      0.703 
## 21 20                80.0           40          182            1.72      0.703 
## 22 21                76.2           40          191            1.57      0.703 
## 23 22                73.2           40          191            1.44      0.562 
## 24 23                70.1           41          142            1.29      0     
## # ℹ 4 more variables: MaxCalories <dbl>, AverageSteps <dbl>, MinSteps <dbl>,
## #   MaxSteps <dbl>

Daily Analysis of Heart Rate, Calories, and Steps

heartrateSeconds_combined$DayOfWeek <- weekdays(heartrateSeconds_combined$Time)
minuteCalories_combined$DayOfWeek <- weekdays(minuteCalories_combined$ActivityMinute)
minuteSteps_combined$DayOfWeek <- weekdays(minuteSteps_combined$ActivityMinute)

avg_heartrate_per_weekday <- heartrateSeconds_combined %>%
  group_by(DayOfWeek) %>%
  summarize(AverageHeartRate = mean(Value, na.rm = TRUE), 
            MinHeartRate = min(Value, na.rm = TRUE),
            MaxHeartRate = max(Value, na.rm = TRUE))

calories_per_weekday <- minuteCalories_combined %>%
  group_by(DayOfWeek) %>%
  summarize(AverageCalories = mean(Calories, na.rm = TRUE), 
            MinCalories = min(Calories, na.rm = TRUE),
            MaxCalories = max(Calories, na.rm = TRUE))

steps_per_weekday <- minuteSteps_combined %>%
  group_by(DayOfWeek) %>%
  summarize(AverageSteps = mean(Steps, na.rm = TRUE), 
            MinSteps = min(Steps, na.rm = TRUE),
            MaxSteps = max(Steps, na.rm = TRUE))

combined_weekly_summary <- avg_heartrate_per_weekday %>%
  left_join(calories_per_weekday, by = "DayOfWeek") %>%
  left_join(steps_per_weekday, by = "DayOfWeek")

options(max.print = 10000)
print(combined_weekly_summary, n = Inf)

## # A tibble: 7 × 10
##   DayOfWeek AverageHeartRate MinHeartRate MaxHeartRate AverageCalories
##   <chr>                <dbl>        <dbl>        <dbl>           <dbl>
## 1 Friday                78.8           37          191            1.60
## 2 Monday                77.4           41          181            1.58
## 3 Saturday              80.1           40          189            1.62
## 4 Sunday                77.6           36          199            1.56
## 5 Thursday              78.1           40          203            1.60
## 6 Tuesday               77.5           40          195            1.61
## 7 Wednesday             77.4           36          181            1.61
## # ℹ 5 more variables: MinCalories <dbl>, MaxCalories <dbl>, AverageSteps <dbl>,
## #   MinSteps <dbl>, MaxSteps <dbl>

Analyzing Activity Intensity Data

minuteIntensity_combined <- minuteIntensity_combined %>%
  mutate(
    Hour = hour(ActivityMinute),
    DayOfWeek = wday(ActivityMinute, label = TRUE, abbr = FALSE)
  )

intensity_summary <- minuteIntensity_combined %>%
  group_by(Intensity) %>%
  summarize(
    Count = n(),  # Total counts for each intensity
    .groups = 'drop'
  ) %>%
  mutate(Intensity = paste0(Intensity, " (", case_when(
    Intensity == 0 ~ "Sedentary",
    Intensity == 1 ~ "Light",
    Intensity == 2 ~ "Moderate",
    Intensity == 3 ~ "Very Active"
  ), ")")) 

most_intensity_by_hour <- minuteIntensity_combined %>%
  group_by(Hour, Intensity) %>%
  summarize(Count = n(), .groups = 'drop') %>%
  group_by(Intensity) %>%
  slice_max(Count, n = 1) %>%
  ungroup() %>% 
  mutate(Intensity = paste0(Intensity, " (", case_when(
    Intensity == 0 ~ "Sedentary",
    Intensity == 1 ~ "Light",
    Intensity == 2 ~ "Moderate",
    Intensity == 3 ~ "Very Active"
  ), ")")) 

most_intensity_by_weekday <- minuteIntensity_combined %>%
  group_by(DayOfWeek, Intensity) %>%
  summarize(Count = n(), .groups = 'drop') %>%
  group_by(Intensity) %>%
  slice_max(Count, n = 1) %>%
  ungroup() %>% 
  mutate(Intensity = paste0(Intensity, " (", case_when(
    Intensity == 0 ~ "Sedentary",
    Intensity == 1 ~ "Light",
    Intensity == 2 ~ "Moderate",
    Intensity == 3 ~ "Very Active"
  ), ")")) 

options(max.print = 10000)
print(intensity_summary, n = Inf)

## # A tibble: 4 × 2
##   Intensity         Count
##   <chr>             <int>
## 1 0 (Sedentary)   2337269
## 2 1 (Light)        359021
## 3 2 (Moderate)      25052
## 4 3 (Very Active)   38778

print(most_intensity_by_hour, n = Inf)

## # A tibble: 4 × 3
##    Hour Intensity        Count
##   <int> <chr>            <int>
## 1     3 0 (Sedentary)   115042
## 2    18 1 (Light)        25170
## 3    19 2 (Moderate)      2437
## 4    19 3 (Very Active)   3486

print(most_intensity_by_weekday, n = Inf)

## # A tibble: 4 × 3
##   DayOfWeek Intensity        Count
##   <ord>     <chr>            <int>
## 1 Tuesday   0 (Sedentary)   342184
## 2 Saturday  1 (Light)        54787
## 3 Saturday  2 (Moderate)      4017
## 4 Tuesday   3 (Very Active)   5889

Analyzing Sleep Patterns

minuteSleep_combined <- minuteSleep_combined %>%
  mutate(
    Hour = hour(date),
    Weekday = wday(date, label = TRUE, abbr = FALSE),
    SleepStateLabel = case_when(
      value == 1 ~ "1 (Asleep)",
      value == 2 ~ "2 (Restless)",
      value == 3 ~ "3 (Awake)"
    )
  )

sleep_summary <- minuteSleep_combined %>%
  group_by(value) %>%
  summarize(
    Count = n(),  
    .groups = 'drop'
  ) %>%
  mutate(value = paste0(value, " (", case_when(
    value == 1 ~ "Asleep",
    value == 2 ~ "Restless",
    value == 3 ~ "Awake"
  ), ")")) 

most_sleep_by_hour <- minuteSleep_combined %>%
  group_by(Hour, SleepStateLabel) %>%
  summarize(Count = n(), .groups = 'drop') %>%
  group_by(SleepStateLabel) %>%
  slice_max(Count, n = 1) %>%
  ungroup()

most_sleep_by_weekday <- minuteSleep_combined %>%
  group_by(Weekday, SleepStateLabel) %>%
  summarize(Count = n(), .groups = 'drop') %>%
  group_by(SleepStateLabel) %>%
  slice_max(Count, n = 1) %>%
  ungroup()

options(max.print = 10000)
print(sleep_summary, n = Inf)

## # A tibble: 3 × 2
##   value         Count
##   <chr>         <int>
## 1 1 (Asleep)   351922
## 2 2 (Restless)  26846
## 3 3 (Awake)      4012

print(most_sleep_by_hour, n = Inf)

## # A tibble: 3 × 3
##    Hour SleepStateLabel Count
##   <int> <chr>           <int>
## 1     3 1 (Asleep)      42715
## 2     6 2 (Restless)     2684
## 3    22 3 (Awake)         437

print(most_sleep_by_weekday, n = Inf)

## # A tibble: 3 × 3
##   Weekday  SleepStateLabel Count
##   <ord>    <chr>           <int>
## 1 Sunday   1 (Asleep)      56648
## 2 Saturday 2 (Restless)     4622
## 3 Sunday   3 (Awake)         804

Combining Data into Comprehensive Datasets

# Create 'combination' folder if it doesn't exist
if (!dir.exists("combination")) {
    dir.create("combination")
  }

# 1. Combo 1: Heart Rate, Steps, and Calories with Hour and Weekday ------------------------
heart_steps_calories_combined <- minuteSteps_combined %>%
  left_join(heartrateSeconds_combined, by = c("Id", "ActivityMinute" = "Time")) %>%
  left_join(minuteCalories_combined, by = c("Id", "ActivityMinute")) %>%
  mutate(
    hour = hour(ActivityMinute),
    weekday = wday(ActivityMinute, label = TRUE)
  )

write.csv(heart_steps_calories_combined, "combination/heart_steps_calories.csv", row.names = FALSE)

# 2. Combo 2: Steps, Intensity, and Calories with Hour and Weekday ------------------------
steps_intensity_calories_combined <- minuteSteps_combined %>%
  left_join(minuteIntensity_combined, by = c("Id", "ActivityMinute")) %>%
  left_join(minuteCalories_combined, by = c("Id", "ActivityMinute")) %>%
  mutate(
    hour = hour(ActivityMinute),              
    weekday = wday(ActivityMinute, label = TRUE)  
  )

write.csv(steps_intensity_calories_combined, "combination/steps_intensity_calories.csv", row.names = FALSE)

# 3. Combo 3: Heart Rate, Intensity, and Sleep with Hour and Weekday ------------------------
heart_intensity_sleep_combined <- heartrateSeconds_combined %>%
  left_join(minuteIntensity_combined, by = c("Id", "Time" = "ActivityMinute")) %>%
  left_join(minuteSleep_combined, by = c("Id", "Time" = "date")) %>%
  mutate(
    hour = hour(Time),                        
    weekday = wday(Time, label = TRUE)  
  )

write.csv(heart_intensity_sleep_combined, "combination/heart_intensity_sleep.csv", row.names = FALSE)


# 4. Combo 4: Calories, Steps, Sleep with Hour and Weekday ------------------------
calories_steps_sleep_combined <- minuteCalories_combined %>%
  left_join(minuteSteps_combined, by = c("Id", "ActivityMinute")) %>%
  left_join(minuteSleep_combined, by = c("Id", "ActivityMinute" = "date")) %>%
  mutate(
    Weekday = wday(ActivityMinute, label = TRUE),  # Extract weekday (abbreviated)
    Hour = hour(ActivityMinute)                    # Extract hour
  )

# Write the combined data to CSV
write.csv(calories_steps_sleep_combined, "combination/calories_steps_sleep_combined.csv", row.names = FALSE)

Visualizations

In the Visualization Phase, I created a range of visual representations to effectively communicate the insights gained from the analysis. Building upon the previously combined datasets, I utilized Tableau as my primary tool to create various charts and graphs that illustrate key trends and patterns to provide actionable insights to enhance marketing strategies.

knitr::include_graphics("Dashboard 1.png")

knitr::include_graphics("Dashboard 2.png")

knitr::include_graphics("Dashboard 3.png")

knitr::include_graphics("Dashboard 4.png")

knitr::include_graphics("Dashboard 4.1.png")

knitr::include_graphics("Dashboard 5.png")

Bellabeat Data Analysis Case Study

Maya Abapo

2024-10-27

Installing and loading required packages

Processing Data

In the Processing Phase, I cleaned, transformed, and prepared the data to ensure accuracy and consistency before analysis using R studio as the main tool.

Importing and Combining March–April and April–May Data

Checking for Duplicates

Remove Duplicates

Convert Date and Time Fields to Proper Formats

Check Structure of Data Frames

Check for Missing Values

Analyze Data

Hourly Analysis of Heart Rate, Calories, and Steps

Daily Analysis of Heart Rate, Calories, and Steps

Analyzing Activity Intensity Data

Analyzing Sleep Patterns

Combining Data into Comprehensive Datasets

Visualizations