Introduction
fitbitr 0.1.0 is now available on CRAN! You can install it with
install.packages(“fitbitr”)or you can get the latest dev version with
## install.packages(“devtools”)
devtools::install_github(“mrkaye97/fitbitr”)
```fitbitr makes it easy to pull your Fitbit data into R and use it for whatever interests you: personal projects, visualization, medical purposes, etc.
This post shows how you might use fitbitrto pull and visualize some of your data. (Warning: It’s still a little buggy. I’d love if you could submit bug reports here: https://github.com/mrkaye97/fitbitr/issues !!).
Sleep Data
First, you should either generate a new token with generate_token() or load a cached token with load_cached_token().
library(fitbitr)
library(lubridate)
library(tidyverse)# load_cached_token(".httr-oauth")
And then you can start pulling your data!
sleep <- sleep_summary(
start_date = today() — months(3),
end_date = today()
)sleep %>%
head()
## # A tibble: 6 x 11
## log_id date start_time end_time duration efficiency
## <dbl> <date> <dttm> <dttm> <int> <int>
## 1 3.12e10 2021–03–09 2021–03–08 22:28:30 2021–03–09 07:23:30 32100000 96
## 2 3.13e10 2021–03–10 2021–03–09 22:35:30 2021–03–10 07:19:30 31440000 98
## 3 3.13e10 2021–03–11 2021–03–10 23:48:00 2021–03–11 06:51:00 25380000 95
## 4 3.13e10 2021–03–12 2021–03–11 23:08:00 2021–03–12 06:28:00 26400000 96
## 5 3.13e10 2021–03–13 2021–03–12 23:16:00 2021–03–13 07:42:30 30360000 96
## 6 3.13e10 2021–03–14 2021–03–13 22:55:00 2021–03–14 08:05:00 33000000 96
## # … with 5 more variables: minutes_to_fall_asleep <int>, minutes_asleep <int>,
## # minutes_awake <int>, minutes_after_wakeup <int>, time_in_bed <int>
Once you’ve loaded some data, you can visualize it!
library(zoo)
library(scales)
library(ggthemes)sleep <- sleep %>%
mutate(
date = as_date(date),
start_time = as_datetime(start_time),
end_time = as_datetime(end_time),
sh = ifelse(hour(start_time) < 8, hour(start_time) + 24, hour(start_time)), #create numeric times
sm = minute(start_time),
st = sh + sm/60,
eh = hour(end_time),
em = minute(end_time),
et = eh + em/60,
mst = rollmean(st, 7, fill = NA), #create moving averages
met = rollmean(et, 7, fill = NA),
year = year(start_time)
)sleep %>%
ggplot(aes(x = date))+
geom_line(aes(y = et), color = ‘coral’, alpha = .3, na.rm = T)+
geom_line(aes(y = st), color = ‘dodgerblue’, alpha = .3, na.rm = T)+
geom_line(aes(y = met), color = ‘coral’, na.rm=T)+
geom_line(aes(y = mst), color = ‘dodgerblue’, na.rm=T)+
scale_y_continuous(breaks = seq(0, 30, 2),
labels = trans_format(function(x) ifelse(x > 23, x — 24, x),
format = scales::comma_format(suffix = “:00”, accuracy = 1))
)+
labs(x = “Date”,
y = ‘Time’)+
theme_fivethirtyeight()+
scale_x_date(date_breaks = ‘1 month’, date_labels = ‘%b’, expand = c(0, 0))+
facet_grid(. ~ year, space = ‘free’, scales = ‘free_x’, switch = ‘x’) +
theme(panel.spacing.x = unit(0,”line”),
strip.placement = “outside”)
This bit of code makes a nicely formatted plot of the times you went to sleep and woke up over the past three months. You can also use `fitbitr` to expand the time window with a little help from `purrr` (the Fitbit API rate limits you, so you can’t request data for infinitely long windows in a single request).
## this will pull the past 15 months of datastart_months <- c(3, 6, 9, 12, 15)
end_months <- c(0, 3, 6, 9, 12)sleep <- map2_dfr(
start_months,
end_months,
function(x, y) sleep_summary(
today() — months(x),
today() — months(y)
)
)
After pulling the data, we can use the same code again to visualize it.
sleep <- sleep %>%
mutate(
date = as_date(date),
start_time = as_datetime(start_time),
end_time = as_datetime(end_time),
sh = ifelse(hour(start_time) < 8, hour(start_time) + 24, hour(start_time)), #create numeric times
sm = minute(start_time),
st = sh + sm/60,
eh = hour(end_time),
em = minute(end_time),
et = eh + em/60,
mst = rollmean(st, 7, fill = NA), #create moving averages
met = rollmean(et, 7, fill = NA),
year = year(start_time)
) %>%
distinct()sleep %>%
ggplot(aes(x = date))+
geom_line(aes(y = et), color = ‘coral’, alpha = .3, na.rm = T)+
geom_line(aes(y = st), color = ‘dodgerblue’, alpha = .3, na.rm = T)+
geom_line(aes(y = met), color = ‘coral’, na.rm=T)+
geom_line(aes(y = mst), color = ‘dodgerblue’, na.rm=T)+
scale_y_continuous(breaks = seq(0, 30, 2),
labels = trans_format(function(x) ifelse(x > 23, x — 24, x),
format = scales::comma_format(suffix = “:00”, accuracy = 1))
)+
labs(x = “Date”,
y = ‘Time’)+
theme_fivethirtyeight()+
scale_x_date(date_breaks = ‘1 month’, date_labels = ‘%b’, expand = c(0, 0))+
facet_grid(. ~ year, space = ‘free’, scales = ‘free_x’, switch = ‘x’) +
theme(panel.spacing.x = unit(0,”line”),
strip.placement = “outside”)
Heart Rate and Steps Data
You can also pull your heart rate data with fitbitr. Maybe we’re curious about seeing how the number of minutes spent in the “fat burn,” “cardio,” and “peak” zones correlates with the number of steps taken that day. Let’s find out!
hr <- map2_dfr(
start_months,
end_months,
function(x, y) heart_rate_zones(
today() — months(x),
today() — months(y)
)
) %>%
distinct()steps <- map2_dfr(
start_months,
end_months,
function(x, y) steps(
today() — months(x),
today() — months(y)
)
) %>%
distinct()
First, we can examine the heart rate data:
hr %>%
head()
## # A tibble: 6 x 6
## date zone min_hr max_hr minutes_in_zone calories_out
## <date> <chr> <int> <int> <int> <dbl>
## 1 2021–03–09 Out of Range 30 118 1439 2294.
## 2 2021–03–09 Fat Burn 118 144 1 9.55
## 3 2021–03–09 Cardio 144 177 0 0
## 4 2021–03–09 Peak 177 220 0 0
## 5 2021–03–10 Out of Range 30 117 1440 2495.
## 6 2021–03–10 Fat Burn 117 144 0 0
and the steps data:
steps %>%
head()
## # A tibble: 6 x 2
## date steps
## <date> <dbl>
## 1 2021–03–09 2258
## 2 2021–03–10 3368
## 3 2021–03–11 5236
## 4 2021–03–12 13964
## 5 2021–03–13 12653
## 6 2021–03–14 10547
```Now, let’s plot them against each other.
df <- hr %>%
filter(zone != “Out of Range”) %>%
group_by(date) %>%
summarize(total_minutes = sum(minutes_in_zone), .groups = “drop”) %>%
inner_join(steps, by = “date”)
df %>%
mutate(steps = as.numeric(steps)) %>%
filter(log(total_minutes) > 1) %>%
ggplot(
aes(
steps,
total_minutes
)
) +
geom_point() +
geom_smooth(method = “lm”, se = F) +
scale_x_log10() +
scale_y_log10()
Or maybe it’d be interesting to predict your zone minutes from your steps:
df %>%
mutate(steps = as.numeric(steps)) %>%
lm(total_minutes ~ steps, data = .) %>%
broom::tidy() %>%
mutate(across(where(is.numeric), round, 5))## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 3.84 3.36 1.14 0.255
## 2 steps 0.00469 0.00042 11.1 0
Wrapping Up
And that’s it! Hopefully this helped show how fitbitr makes pulling your data easy, and gets you curious about the insights you can glean from your own data. The Fitbit API gives you access to so much interesting information about yourself, your habits, your fitness, and so much more, and fitbitr is just meant to be a door into that gold mine.
