WellnessData

library(healthsimulation)

# Initialize a new WellnessData object with a specific seed
wd <- WellnessData$new(seed = 123)

## WellnessData object initialized with seed: 123

# Generate the dataset
wd$generate_data(n_participants = 5, n_days = 30, save_csv = TRUE)

## Data generation complete. Dataset dimensions: 145 x 9

## Data saved to: WellnessSensors.csv

# Print a summary
wd$print()

## WellnessData Dataset Summary
## ===========================
## Period     :  2023-11-02 to 2023-11-30 
## Participants:  5 
## Rows       :  145 
## Variables  :  9 
## 
##  ActivityLevel    AvgHeartRate     SleepHours      WorkHours     
##  Min.   :1.500   Min.   :62.00   Min.   :5.200   Min.   : 5.300  
##  1st Qu.:2.700   1st Qu.:69.00   1st Qu.:6.400   1st Qu.: 7.300  
##  Median :3.200   Median :72.00   Median :6.800   Median : 7.900  
##  Mean   :3.182   Mean   :71.21   Mean   :6.787   Mean   : 7.989  
##  3rd Qu.:3.700   3rd Qu.:73.00   3rd Qu.:7.200   3rd Qu.: 8.600  
##  Max.   :5.000   Max.   :80.00   Max.   :8.600   Max.   :11.300  
##  WellnessScore  
##  Min.   :46.00  
##  1st Qu.:62.00  
##  Median :68.00  
##  Mean   :70.23  
##  3rd Qu.:78.00  
##  Max.   :97.00

# Create beautiful plots
library(patchwork) # For arranging plots

# Individual participant time series
p1 <- wd$plot_participant_timeseries("P01")
print(p1)

# Correlation matrix (requires GGally)
p2 <- wd$plot_correlation_matrix()

## Warning in wd$plot_correlation_matrix(): Please install.packages('GGally') for
## correlation matrix plots.

print(p2)

## NULL

# WellnessScore trend with confidence interval
p3 <- wd$plot_metric_trend("WellnessScore", ribbon = TRUE)
print(p3)

# Temperature distribution
p4 <- wd$plot_temperature_distribution()
print(p4)

# Arrange multiple plots together (if patchwork is installed)
if(requireNamespace("patchwork", quietly = TRUE)) {
  (p3 + p4) / p1 + plot_layout(heights = c(1, 2))
}