Plotting Your Fitbit Data in R

Weekly Active Zone Minutes

The first plot I will make is to display the number of “Active Zone Minutes” at a weekly level. Generally, health organizations like the CDC and WHO recommend at least 150 active minutes per week for most adults. As far as I’m aware, however, the Fitbit app always displays this metric on a daily level, which isn’t too helpful when I’m trying to figure out whether I’ve reached the guideline every week.

With the fitbitr package, you can use the functions minutes_XX_active to get your number of active minutes per day.¹ To approximate Fitbit’s Active Zone calculations, we will count 1 minute in the “fairly active” zone as 1 Active Zone Minute and 1 minute in the “very active” zone as 2 Active Zone Minutes. Below, I calculate and display my total number of Active Zone minutes every week in the last month.

# Get data
start_date = ymd("2022-02-28")
end_date = ymd("2022-03-26")
lightly_active_df = minutes_lightly_active(start_date, end_date)
fairly_active_df = minutes_fairly_active(start_date, end_date)
very_active_df = minutes_very_active(start_date, end_date)

# Clean data
## Join data frames
active_df = lightly_active_df %>%
  full_join(., fairly_active_df, by = "date") %>% 
  full_join(., very_active_df, by = "date") %>%
  mutate(week = isoweek(date))

## Aggregate over week
active_weekly_df = active_df %>%
  mutate(week = isoweek(date)) %>%
  group_by(week) %>%
  mutate(across(.cols = starts_with("minutes"), sum)) %>%
  slice(1) %>%
  ungroup()

## Calculate Active Zone Minutes
active_weekly_df = active_weekly_df %>%
  mutate(minutes_high_activity = minutes_very_active*2,
         minutes_moderate_activity = minutes_fairly_active) %>%
  select(date, week, minutes_high_activity, minutes_moderate_activity) %>%
  pivot_longer(cols = starts_with("minutes"), values_to = "minutes")

# Plot
active_weekly_df %>%
  ggplot(aes(x = week, y = minutes, fill = name)) +
  geom_col() +
  geom_hline(color = "black", linetype = "dashed", size = 1, yintercept = 150) +  
  scale_x_continuous(breaks = active_weekly_df$week,
                     labels = format(active_weekly_df$date, "%b %d")) +
  scale_fill_manual(name = "Activity Zone", 
                    values = c("#0084ff", "#44bec7"), 
                    labels = c("High", "Moderate")) +
  labs(x = "Week", y = "Active Zone Minutes") +
  theme_minimal()

Based on the plot above, it looks like there is a lot of variance in how much exercise I’m getting week to week. Generally, I have also been averaging less than the recommended 150 minutes of activity per week.

Hours of Sleep by Day of Week

Another statistic that I would like a broader overview of is sleep. Similar to exercise, I am less interested in how well I slept for any individual day than I am about how well I’m sleeping on a consistent basis.

# Get data
sleep_df = sleep_stage_summary(start_date = ymd("2022-02-28"),
                               end_date = ymd("2022-03-26"))

# Clean data
## Calculate benchmarks
mean_all_minutes = sleep_df %>%
  group_by(date) %>%
  summarize(all = sum(minutes)) %>%
  ungroup() %>%
  pull(all) %>%
  mean()

benchmark_deep = data.frame(low = 0.12*mean_all_minutes/60,
                            high = 0.23*mean_all_minutes/60,
                            stage = "deep")
benchmark_light = data.frame(low = 0.32*mean_all_minutes/60,
                             high = 0.68*mean_all_minutes/60,
                             stage = "light")
benchmark_rem = data.frame(low = 0.15*mean_all_minutes/60,
                           high = 0.25*mean_all_minutes/60,
                           stage = "rem")

sleep_df = sleep_df %>%
  filter(stage %in% c("deep", "light", "rem"))

## Calculate total time asleep
sleep_df = sleep_df %>%
  group_by(date) %>%
  group_modify(~ adorn_totals(.x, where = "row"))

## Get weekday
sleep_df = sleep_df %>%
  mutate(weekday = wday(date),
         weekday_label = wday(date, label = TRUE))

# Plot
sleep_df %>%
  ggplot(aes(x = weekday, y = minutes/60, color = stage)) +
  geom_ribbon(aes(ymin = benchmark_deep$low, ymax = benchmark_deep$high), fill = "#b7ded2", color = "#b7ded2", alpha = 0.1) +
  geom_ribbon(aes(ymin = benchmark_light$low, ymax = benchmark_light$high), fill = "#f7c297", color = "#f7c297", alpha = 0.1) +
  geom_ribbon(aes(ymin = benchmark_rem$low, ymax = benchmark_rem$high), fill = "#f6a6b2", color = "#f6a6b2", alpha = 0.1) +
  geom_hline(color = "black", linetype = "dashed", size = 1, yintercept = 8) + 
  geom_point() + 
  geom_smooth(span = 0.5, se = FALSE) +
  scale_x_continuous(breaks = sleep_df$weekday,
                     labels = sleep_df$weekday_label) +
  scale_y_continuous(breaks = c(0, 2, 4, 6, 8, 10)) +
  scale_color_manual(name = "Sleep Stage",
                     values = c("#b7ded2", "#f7c297", "#f6a6b2", "#064273"),
                     labels = c("Deep", "Light", "REM", "Total")) +
  labs(x = "Day of Week",
       y = "Hours") +
  theme_minimal()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

To visualize this, I group my sleep data by day of week (e.g. “Friday” corresponds to the night of sleep from Thursday night to Friday morning). Each point in the plot above corresponds to a separate date and I plot the time I spent in each sleep stage (“deep”, “light”, or “REM”) and my total sleep time (in dark blue). The dashed line corresponds to 8 hours of total sleep and the shaded bands correspond to benchmarks for how much time I should spend in each sleep stage according to Fitbit.

In general, I am averaging between 7 and 8 hours of sleep. There is clearly a lot of variance in how much sleep I’m getting from day to day and between different days of the week, though I would like to track my sleep longer to get a better sense of the strength of the pattern.

These are just a few examples of how you can create alternative visualizations for your Fitbit statistics. I made some plots to help me answer questions I was curious about, but there are potentially many other interesting plots you can explore with your Fitbit data. Using the fitbitr package makes the process of extracting your data in R simple. The package author has also written a blog post showing some other examples of what you can do with the package, which you may be interested in checking out.