Change your sleep patterns: Create healthy sleep habits based on data (feat. R)

What's the most important time of day? Recharge your mind and body with Sleep timein the morning. But if your sleep quality is poor, you're going to wake up groggy no matter how many hours you sleep. In this post, we'll analyze the data and give you practical ways to change your sleep patterns to improve them.

Analyzing sleep data

The sleep pattern image above was created using the 6 hours and 31 minutes of total sleep timeand 6 hours and 26 minutes of actual sleep timein the graph. If you analyze the patterns broken down by sleep stage, you'll notice the following characteristics

1. Deep Sleep

• The dark blue band at the bottom of the graph.
• It was concentrated between 23:30 and 01:00 at night.
• Features: It plays an important role in body recovery and immunity, but the data suggests that the duration of deep sleep is rather short.

2. Light sleep (Core Sleep)

• The middle blue colored section.
• It makes up the majority of your total sleep time and is the longest of the sleep stages.
• FeaturesThe stage before transitioning to deep sleep and REM sleep, where the body is stabilized but not deeply restored.

3. REM sleep (REM Sleep)

• Sections colored in light blue.
• It appears periodically and is known as the dreaming phase.
• Features: Important for memory and emotional resilience. Data shows a relatively stable cycle.

4. Awake time (Awake)

• The section in red.
• There was a short period of wakefulness after 06:00, which appears to be normal wakefulness.

What's wrong with sleep data and how to improve it

Problem 1: Lack of deep sleep

-. AnalyticsDeep sleep seems short compared to total sleep time, which can have a negative impact on body recovery and immunity.

-. Solution

• Minimize electronics use before bed and sleep in a dark environment.
• Stabilize your sleep rhythm by maintaining regular bedtimes and wake-up times.
• Prepare for sleep with relaxation exercises (meditation, stretching).

Problem 2: High percentage of light sleep

-. Analytics: Light sleep duration seems excessively long, and the transition to deep and REM sleep is likely to be delayed.

-. Solution

• Get plenty of sunlight during the day and get adequate physical activity to boost your sleep hormone (melatonin).
• Avoid caffeine consumption 6 hours before bedtime.

Analyzing sleep patterns - R visualization example

To better analyze your sleep data, we recommend R visualizations. Below is a code example to visualize similar sleep data.

# Install the required packages
install.packages("ggplot2")
install.packages("lubridate")
install.packages("tidyr")
install.packages("dplyr")

# Load the required libraries
library(ggplot2)
library(lubridate)
library(dplyr)
library(tidyr)

# 1. Generate time and sleep phase data
time_range <- seq(from = as.POSIXct("2023-01-01 23:00"),
                  to = as.POSIXct("2023-01-02 06:30"),
                  by = "10 min")

stages <- c(0, 1, 2, 1, 3, 0, 1, 2, 1, 3, 0, 1, 2, 1, 3, 0, 1, 2, 1, 3, 0, 1, 2, 3) # example data

Create a # data frame
sleep_data <- data.frame(Time = time_range[1:length(stages)], Stage = stages)

# 2. Clean the data: add 'Next_Time' to prepare the stage graph
sleep_data %
    mutate(Next_Time = lead(Time), Next_Stage = lead(Stage)) %>% # Add next stage and time
    filter(!is.na(Next_Time)) %>% # Remove last row with NA
    pivot_longer(cols = c(Stage, Next_Stage), names_to = "Type", values_to = "Stage")

# 3. Visualization (stage graph)
stage_colors <- c("darkblue", "blue", "cyan", "red") # colors for each sleep stage
names(stage_colors) <- c("Deep Sleep", "Light Sleep", "REM Sleep", "Awake")

stage_labels <- c("Deep Sleep", "Light Sleep", "REM Sleep", "Awake") # stage labels

Plot the # graph
ggplot(sleep_data, aes(x = Time, y = Stage, group = 1)) +
    geom_step(aes(color = factor(Stage)), size = 1.2) + graph # steps
    scale_y_continuous(
        breaks = 0:3,
        labels = stage_labels set # y-axis labels
    ) + #
    scale_color_manual(
        values = stage_colors, # assign colors step by step
        labels = stage_labels, # Assign labels for each stage
        name = "Sleep Stages" # legend title
    ) + (or
    labs(
        title = "Sleep Pattern Visualization", # Graph title
        x = "Time", # X-axis label
        y = "Sleep Stage" # Y-axis label
    ) + Β
    theme_minimal()
    theme(
        axis.text.x = element_text(angle = 45, hjust = 1) # Rotate X-axis label
    )

Code commentary

1. Installing and loading packages:
   • Install and load the ggplot2, lubridate, tidyr, and dplyr packages.
   • These packages provide the tools you need to process and visualize your data.
2. Generate data:
   • Create a time range with 10 minute intervals from 23:00 on January 1, 2023 to 06:30 on January 2, 2023.
   • Create example data (0-3) representing sleep stages.
   • Create a data frame that includes the time and sleep phase.
3. Data cleanup:
   • Use the lead() function to add the next time and step information.
   • Remove the last row with a value of NA.
   • Reorganize the data with the pivot_longer() function.
4. Prepare your visualization:
   • Define colors and labels for each sleep stage.
   • Separate the stages into 'Deep Sleep', 'Light Sleep', 'REM Sleep', and 'Awake'.
5. Generate graphs with ggplot:
   • Draw a step graph with geom_step().
   • Set the y-axis with scale_y_continuous().
   • Specify the color with scale_color_manual().
   • Set the graph title and axis labels with labs().
   • Apply a concise theme with theme_minimal().
   • Rotate the x-axis labels by 45 degrees to make them more readable.

More tips for changing your sleep patterns

1. maintain a regular sleep routine: Get into the habit of going to bed and waking up at the same time every day.

2. improve your sleep environment: Keep your bedroom dark, quiet, and at the right temperature.

3. add a relaxation activity: Calm your mind and body with meditation or stretching before bed.

4. limit caffeine and alcohol intake: Avoid caffeine within 6 hours before bedtime, and minimize alcohol.

Get organized - change your sleep patterns

Analyzing your sleep patterns and making improvements based on the data can help you live a healthier life, especially when it comes to identifying problems with your sleep patterns and making improvements that go beyond just getting more sleep. R Visualization and data analysisto take your sleep habits to the next level.

If you're wondering if it's possible to visualize sleep patterns in Python, you can use the Sleep Pattern Analysis and Python Visualization: Leveraging Data for Healthy Sleep Check out the post.