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287 lines
8.3 KiB
287 lines
8.3 KiB
---
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title: "Health stats"
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output:
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html_document:
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toc: yes
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pdf_document: default
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html_notebook:
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code_folding: hide
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toc: yes
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---
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```{r setup, include=FALSE}
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knitr::opts_chunk$set(echo = FALSE, warning = FALSE)
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```
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# Summary
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This documents provides an overview of health data recorded by *Gadgetbridge* using a *Pebble* smartwatch.
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# Preparation
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Put the *GadgetBridge* database in the current folder, then knit the current `.Rmd` file.
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You should also set you **timezone** in the corresponding variable.
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* `PEBBLE_HEALTH_ACTIVITY_SAMPLE` is a simple table containing steps and timestamps.
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* `PEBBLE_HEALTH_ACTIVITY_OVERLAY` is more complex. It tracks activities (sleep, deep sleep, nap…) with a start and an end date. Note that date may overlap: you can be both in sleep and deep sleep.
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I have decided that any event happening after 8pm is registered for the next day. For instance:
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* if you go to bed at 9pm on Tuesday and wake up at 7am on Wednesday, the data will return 10 hours of sleep on Wednesday
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* if you go to bed at 7pm on Tuesday, a part of your sleep will be added to Tuesday. The cutoff won't exactly be 8pm, it will depend on the duration of the first sleep session recorded by you watch.
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```{r include=FALSE}
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# Load data ------------------------------------------------------------------
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library(DBI)
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library(RSQLite)
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library(tidyverse)
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library(lubridate)
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library(scales)
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con <- DBI::dbConnect(RSQLite::SQLite(), dbname = "gadgetbridge")
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steps_data <- dbReadTable(con, "PEBBLE_HEALTH_ACTIVITY_SAMPLE")
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sleep_data <- dbReadTable(con, "PEBBLE_HEALTH_ACTIVITY_OVERLAY")
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dbDisconnect(con)
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#str(steps_data)
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# Transform data -------------------------------------------------------------
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## Interprate timestamp
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timezone = "Europe/Paris"
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steps_data$datetime <- as_datetime(steps_data$TIMESTAMP)
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sleep_data$datetime_from <- with_tz(as_datetime(sleep_data$TIMESTAMP_FROM), timezone)
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sleep_data$datetime_to <- with_tz(as_datetime(sleep_data$TIMESTAMP_TO), timezone)
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## Calculate activity duration
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## Data recorded after 8pm is attached to the next day
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sleep_data$day <- date(sleep_data$datetime_from + hours(4))
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sleep_data$duration <- int_length(
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sleep_data$datetime_from %--% sleep_data$datetime_to
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) / period_to_seconds(minutes(1))
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sleep_data$bed_time <- case_when(
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sleep_data$RAW_KIND == 1 ~ sleep_data$datetime_from
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)
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sleep_data$wakeup_time <- case_when(
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sleep_data$RAW_KIND == 1 ~ sleep_data$datetime_to
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)
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## Convert RAW_KIND to the corresponding activity and summarize values
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sleep_data <- sleep_data %>%
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spread(RAW_KIND,duration,sep="_") %>%
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group_by(day) %>%
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summarise(
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sleep = sum(RAW_KIND_1, na.rm = TRUE),
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deep_sleep = sum(RAW_KIND_2, na.rm = TRUE),
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nap = sum(RAW_KIND_3, na.rm = TRUE),
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deep_nap = sum(RAW_KIND_4, na.rm = TRUE),
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walk = sum(RAW_KIND_5, na.rm = TRUE),
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run = sum(RAW_KIND_6, na.rm = TRUE), # Really not sure about this one
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bed_time = min(bed_time, na.rm = TRUE),
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wakeup_time = max(wakeup_time, na.rm = TRUE)
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)
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```
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# Visualisation
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## Steps
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### Distribution of steps
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```{r}
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data <- steps_data %>%
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mutate(date = date(datetime)) %>%
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select(date,STEPS) %>%
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group_by(date) %>%
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summarise(steps = sum(STEPS))
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ggplot(data, aes(steps)) +
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geom_histogram(binwidth = 500) +
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theme_minimal() +
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labs(x="Steps", y="Number of occurences")
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```
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### Distribution of steps per day of week
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```{r}
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data <- steps_data %>%
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mutate(
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date = date(datetime),
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wday = wday(
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datetime,
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label = TRUE,
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week_start = getOption("lubridate.week.start", 1)
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)
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) %>%
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select(date, wday, STEPS) %>%
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group_by(date, wday) %>%
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summarise(steps = sum(STEPS))
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ggplot(data, aes(x=wday,y=steps)) +
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geom_boxplot() +
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theme_minimal() +
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labs(x="Day of week", y="Number of steps")
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```
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### Number of steps per month
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```{r}
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data <- steps_data %>%
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mutate(month = floor_date(datetime, unit = "month")) %>%
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select(month,STEPS) %>%
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group_by(month) %>%
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summarise(steps = sum(STEPS))
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ggplot(data, aes(x=month, y=steps)) +
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geom_col() +
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scale_x_datetime(labels = date_format("%Y-%m")) +
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scale_y_continuous(
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breaks = seq(0,500000,50000),
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labels=function(x) format(x, big.mark = " ")
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) +
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geom_smooth(method = lm) +
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theme_minimal() +
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labs(x="Month", y="Number of steps")
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```
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### Average number of steps per hour of the day, year after year
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```{r}
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data <- steps_data %>%
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mutate(
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date = date(datetime),
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time = datetime-floor_date(datetime, unit="day"),
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year = year(datetime)
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) %>%
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group_by(date) %>%
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mutate(cumsteps = cumsum(STEPS)) %>%
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select(date, time, year, cumsteps) %>%
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ungroup() %>%
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group_by(time, year) %>%
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summarise(min = min(cumsteps), max = max(cumsteps), average = mean(cumsteps))
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ggplot(data) +
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geom_step(aes(x=time, y=average)) +
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theme_minimal() +
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labs(title = "Average number of steps per hour of the day", x="Hour", y="Number of steps") +
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scale_x_continuous(
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breaks = seq(
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0,
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period_to_seconds(hours(24)),
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period_to_seconds(hours(1))
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),
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labels = seq(0,24,1)
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) +
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facet_wrap(vars(year))
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```
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### Active time per week
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```{r}
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data <- sleep_data %>%
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mutate(
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wday = wday(
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day,
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label = TRUE,
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week_start = getOption("lubridate.week.start", 1)
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)
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) %>%
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select(day, wday, walk) %>%
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group_by(day, wday) %>%
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summarise(walk_time = sum(walk)/60)
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ggplot(data, aes(x=wday,y=walk_time)) +
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geom_boxplot() +
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theme_minimal() +
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labs(x="Day of week", y="Hours active")
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```
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## Sleep
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### Distribution of sleep duration
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```{r}
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data <- sleep_data %>%
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group_by(day) %>%
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summarise(sleep_duration = sum(sleep)/60)
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ggplot(data) +
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geom_histogram(aes(sleep_duration), bins = 50) +
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scale_x_continuous(breaks = seq(0,12,1)) +
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theme_minimal() +
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labs(x="Sleep duration (hours)", y="Number of occurences")
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```
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### Distribution of deep sleep duration
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```{r}
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data <- sleep_data %>%
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group_by(day) %>%
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summarise(deep_sleep_duration = sum(deep_sleep)/60)
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ggplot(data) +
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geom_histogram(aes(deep_sleep_duration),bins = 50) +
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scale_x_continuous(breaks = seq(0,12,1)) +
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theme_minimal() +
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labs(x="Deep sleep duration (hours)", y="Number of occurences")
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```
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### Distribution of sleep duration per day of week, year after year
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```{r}
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data <- sleep_data %>%
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mutate(
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wday = wday(
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day,
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label = TRUE,
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week_start = getOption("lubridate.week.start", 1)
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),
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year = year(day)
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) %>%
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select(year, day, wday, sleep) %>%
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group_by(year, day, wday) %>%
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summarise(sleep_duration = sum(sleep)/60)
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ggplot(data, aes(x=wday,y=sleep_duration)) +
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geom_boxplot() +
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theme_minimal() +
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labs(x="Day of week", y="Sleep duration") +
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facet_grid(rows = vars(year), )
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```
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### Distribution of nap duration
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```{r}
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data <- sleep_data %>%
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filter(nap > 0) %>%
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group_by(day) %>%
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summarise(nap_time = sum(nap))
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ggplot(data) +
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geom_histogram(aes(nap_time), bins = 10) +
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scale_x_continuous(breaks = seq(0,240,15)) +
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theme_minimal() +
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labs(x="Nap duration (minutes)", y="Number of occurences")
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```
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### Time of bed and waking up by year
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```{r}
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data <- sleep_data %>%
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mutate(
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year = year(day),
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month = floor_date(day, unit = "month"),
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bed_time_hms = hms::as.hms(
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period_to_seconds(
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hours(hour(bed_time)) + minutes(minute(bed_time))
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)
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),
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wakeup_time_hms = hms::as.hms(
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period_to_seconds(
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hours(hour(wakeup_time))+minutes(minute(wakeup_time))
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)
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)
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) %>%
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drop_na(bed_time_hms) %>%
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drop_na(wakeup_time_hms)
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ggplot(data) +
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geom_histogram(aes(bed_time_hms), fill="orange", alpha=0.5, bins=30) +
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geom_histogram(aes(wakeup_time_hms), fill="blue", alpha=0.5, bins=30) +
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scale_x_continuous(
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breaks = seq(
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0,
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period_to_seconds(hours(24)),
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period_to_seconds(hours(1))
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),
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labels = seq(0,24,1)
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) +
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theme_minimal() +
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labs(x="Bed time and wakeup time", y="Number of occurences") +
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facet_grid(rows = vars(year),scales="free_y")
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```
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