[med-svn] [r-cran-fitbitscraper] 01/02: Imported Upstream version 0.1.7
Dylan Aïssi
bob.dybian-guest at moszumanska.debian.org
Sun Jun 12 03:14:45 UTC 2016
This is an automated email from the git hooks/post-receive script.
bob.dybian-guest pushed a commit to branch master
in repository r-cran-fitbitscraper.
commit fc5389312796b3d6be79114f784c5bc3e0d46cb6
Author: Dylan Aïssi <bob.dybian at gmail.com>
Date: Sun Jun 12 05:14:25 2016 +0200
Imported Upstream version 0.1.7
---
DESCRIPTION | 8 +-
MD5 | 11 ++-
NEWS.md | 3 +
README.md | 4 +-
build/vignette.rds | Bin 0 -> 214 bytes
inst/doc/fitbitScraper-examples.R | 74 +++++++++++++++
inst/doc/fitbitScraper-examples.Rmd | 109 ++++++++++++++++++++++
inst/doc/fitbitScraper-examples.html | 173 +++++++++++++++++++++++++++++++++++
vignettes/fitbitScraper-examples.Rmd | 109 ++++++++++++++++++++++
9 files changed, 483 insertions(+), 8 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index 25a8dea..dbc5491 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
Package: fitbitScraper
Title: Scrapes Data from Fitbit
-Version: 0.1.6
+Version: 0.1.7
Author: Cory Nissen <corynissen at gmail.com> [aut, cre]
Maintainer: Cory Nissen <corynissen at gmail.com>
Description: Scrapes data from Fitbit <http://www.fitbit.com>. This does not use the official
@@ -12,7 +12,9 @@ LazyData: true
Imports: httr, stringr, jsonlite, methods, utils
URL: https://github.com/corynissen/fitbitScraper
RoxygenNote: 5.0.1
+Suggests: knitr, rmarkdown, ggplot2, ggthemes
+VignetteBuilder: knitr
NeedsCompilation: no
-Packaged: 2016-04-11 15:13:52 UTC; 60018847
+Packaged: 2016-05-19 12:20:00 UTC; 60018847
Repository: CRAN
-Date/Publication: 2016-04-11 18:38:35
+Date/Publication: 2016-05-19 20:31:18
diff --git a/MD5 b/MD5
index d27694e..ed44c3b 100644
--- a/MD5
+++ b/MD5
@@ -1,7 +1,7 @@
-0f2bb69c7fa616218574664c6aea3e36 *DESCRIPTION
+1316fb002b8d2000506141b342b5435d *DESCRIPTION
d499114c8fb7674b84d73553dd6e8d9a *LICENSE
af05ec54f552800fae2fa87191cbef9c *NAMESPACE
-88390e2e5dd3e55a0c71f936eb4f60bc *NEWS.md
+6c6b1fe6226af70ba953aec1b66d5b32 *NEWS.md
ca12202aff2a6dd5b672b54b269170fe *R/get_activity_data.R
6f22f81c632183d6bb0d26638f2741c5 *R/get_daily_data.R
bbdfa193a0b307c8f634c89681ad87d1 *R/get_intraday_data.R
@@ -9,7 +9,11 @@ c1b92b4d6f2af001a7c7227c927360f4 *R/get_premium_export.R
a363b6cd2faa8a8ccfdb2052dfb42540 *R/get_sleep_data.R
e541d7ca220d36967d1e210f94f6ce68 *R/get_weight_data.R
102dcc2840fa122eea731fa2f5b4b6c8 *R/login.R
-9f7ac5485822d04895cd5ad0a64abf34 *README.md
+a5dfa3c770b3e2fa88f19a8788a2bfeb *README.md
+2b652519e0db5c2000f87c1fb9c79f13 *build/vignette.rds
+0834d2d5146972b7fcf319fadfbb7c37 *inst/doc/fitbitScraper-examples.R
+c7eceade09e11fbd5338f2da0b26a6d6 *inst/doc/fitbitScraper-examples.Rmd
+da9afc6a287ba001a93f404c0255615c *inst/doc/fitbitScraper-examples.html
b4bd6aacbc1f19392f02d757e13ac0ee *man/get_activity_data.Rd
5780a406e827724de4d3cc2b96913fd1 *man/get_daily_data.Rd
b9312f0b0bace2795a3229bc19efeac3 *man/get_intraday_data.Rd
@@ -17,3 +21,4 @@ b9312f0b0bace2795a3229bc19efeac3 *man/get_intraday_data.Rd
cf2f8c7c4abd423007bdf5db9b0abd15 *man/get_sleep_data.Rd
dc0c871efcadc80badd75074df7629af *man/get_weight_data.Rd
88fd5d742acc1558be0f54313b60c675 *man/login.Rd
+c7eceade09e11fbd5338f2da0b26a6d6 *vignettes/fitbitScraper-examples.Rmd
diff --git a/NEWS.md b/NEWS.md
index 2a51958..3a8707d 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,4 +1,7 @@
+### fitbitScraper 0.1.7
+* added vignette
+
### fitbitScraper 0.1.6
* switch from RJSONIO to jsonlite
diff --git a/README.md b/README.md
index 5ada605..602806b 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,5 @@
-### fitbitScraper 0.1.6
+### fitbitScraper 0.1.7
This package scrapes data from fitbit.com
It only works if you use email / password to login. Not sure about facebook or google login.
@@ -12,7 +12,7 @@ library("fitbitScraper")
cookie <- login(email="corynissen at gmail.com", password="mypassword")
# 15_min_data "what" options: "steps", "distance", "floors", "active-minutes", "calories-burned"
-df <- get_activity_data(cookie, what="steps", date="2015-01-21")
+df <- get_intraday_data(cookie, what="steps", date="2015-01-21")
library("ggplot2")
ggplot(df) + geom_bar(aes(x=time, y=data, fill=data), stat="identity") +
xlab("") +ylab("steps") +
diff --git a/build/vignette.rds b/build/vignette.rds
new file mode 100644
index 0000000..ce0875b
Binary files /dev/null and b/build/vignette.rds differ
diff --git a/inst/doc/fitbitScraper-examples.R b/inst/doc/fitbitScraper-examples.R
new file mode 100644
index 0000000..833a40b
--- /dev/null
+++ b/inst/doc/fitbitScraper-examples.R
@@ -0,0 +1,74 @@
+## ------------------------------------------------------------------------
+library("fitbitScraper")
+cookie <- login(email="corynissen at gmail.com", password=Sys.getenv("FBPW"))
+
+## ---- fig.height=4, fig.width=5, message=FALSE, warning=FALSE, fig.align='center'----
+dates <- seq(as.Date("2016-04-03"), as.Date("2016-04-09"), by="day")
+df_list <- lapply(dates, function(x)
+ get_intraday_data(cookie=cookie, what="steps", as.character(x)))
+df <- do.call(rbind, df_list)
+
+library("ggplot2")
+library("ggthemes")
+ggplot(df) +
+ geom_bar(aes(x=time, y=steps), stat="identity") +
+ theme_tufte() +
+ scale_x_datetime(name="date", date_breaks="1 day", date_labels="%b-%d")
+
+## ---- fig.height=4, fig.width=5, message=FALSE, warning=FALSE, fig.align='center'----
+df <- get_daily_data(cookie=cookie, what="floors", start_date="2016-02-15",
+ end_date="2016-05-01")
+df$weekday <- format(df$time, "%A")
+avgs <- by(df$floors, df$weekday, mean)
+avgs <- data.frame(day=names(avgs), floors=as.numeric(avgs))
+avgs$day <- factor(avgs$day, levels=avgs$day[c(4, 2, 6, 7, 5, 1, 3)])
+
+ggplot(avgs) +
+ geom_bar(aes(x=day, y=floors), stat="identity") +
+ theme_tufte() +
+ xlab("") +
+ ylab("") +
+ ggtitle("Average Floors by Day 2016-02-15 to 2016-05-01") +
+ geom_text(aes(x=day,y=floors,label=round(floors, 1)),
+ vjust=1.1, colour="white") +
+ theme(axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
+ theme(plot.title=element_text(vjust=.5))
+
+## ---- fig.height=4, fig.width=5, message=FALSE, warning=FALSE, fig.align='center'----
+# don't do this...
+# mywt <- get_weight_data(cookie, start_date="2015-01-01", end_date="2015-05-01")
+start_date <- as.Date("2015-01-01")
+end_date <- as.Date("2015-05-01")
+wt_df_list <- list() # initialize a list to put the weight dataframes into
+in_range <- TRUE # indicator variable to tell when to exit while loop
+s_date <- start_date # date to start with during loop
+while(in_range){
+ e_date <- s_date + 14
+ new_df <- get_weight_data(cookie, start_date=as.character(s_date),
+ end_date=as.character(e_date))
+ wt_df_list[[as.character(s_date)]] <- new_df
+ s_date <- e_date + 1
+ if(e_date > end_date) in_range <- FALSE
+}
+wt_df <- do.call(rbind, wt_df_list)
+wt_df <- wt_df[!duplicated(wt_df$time), ]
+wt_df <- wt_df[order(wt_df$time), ]
+wt_df <- wt_df[as.Date(wt_df$time) >= start_date &
+ as.Date(wt_df$time) <= end_date, ]
+
+step_df <- get_daily_data(cookie=cookie, what="steps", start_date="2015-01-01",
+ end_date="2015-05-01")
+
+# get common date format to merge data sets...
+wt_df$date <- as.character(as.Date(wt_df$time))
+step_df$date <- as.character(as.Date(step_df$time))
+
+# merge by date
+df <- merge(wt_df, step_df, by="date")
+
+# now plot
+ggplot(df, aes(x=steps, y=weight)) +
+ geom_point() +
+ stat_smooth(se=FALSE) +
+ theme_tufte()
+
diff --git a/inst/doc/fitbitScraper-examples.Rmd b/inst/doc/fitbitScraper-examples.Rmd
new file mode 100644
index 0000000..f2b3058
--- /dev/null
+++ b/inst/doc/fitbitScraper-examples.Rmd
@@ -0,0 +1,109 @@
+---
+title: "fitbitScraper Examples"
+author: "Cory Nissen"
+date: "5/5/2016"
+output: rmarkdown::html_vignette
+vignette: >
+ %\VignetteIndexEntry{fitbitScraper Examples}
+ %\VignetteEngine{knitr::rmarkdown}
+ %\VignetteEncoding{UTF-8}
+---
+
+## Installation
+Install fitbitScraper as you would a normal library. It exists on [CRAN](https://cran.r-project.org/package=fitbitScraper), so a simple `install.packages("fitbitScraper")` should work.
+
+A development version exists on [Github](https://github.com/corynissen/fitbitScraper), and can be installed via [devtools](https://cran.r-project.org/package=devtools). `devtools::install_github("corynissen/fitbitScraper")`
+
+## Usage
+
+I've stored my password in an environment variable called "FBPW". I'll use this to login to fitbit and generate a cookie that will be used for the subsequent requests. You can type it directly in the password field, but it is generally a best practice to use an environment variable instead. Fitbit allows login via Google and Facebook. This library only works with an email / password based login.
+
+```{r}
+library("fitbitScraper")
+cookie <- login(email="corynissen at gmail.com", password=Sys.getenv("FBPW"))
+```
+
+Now you can run any of the other functions to get your data. Let's start with getting steps on a 15 minute interval for a given week...
+
+```{r, fig.height=4, fig.width=5, message=FALSE, warning=FALSE, fig.align='center'}
+dates <- seq(as.Date("2016-04-03"), as.Date("2016-04-09"), by="day")
+df_list <- lapply(dates, function(x)
+ get_intraday_data(cookie=cookie, what="steps", as.character(x)))
+df <- do.call(rbind, df_list)
+
+library("ggplot2")
+library("ggthemes")
+ggplot(df) +
+ geom_bar(aes(x=time, y=steps), stat="identity") +
+ theme_tufte() +
+ scale_x_datetime(name="date", date_breaks="1 day", date_labels="%b-%d")
+```
+
+You can get a daily summary of your data also. Here, I download data for the number of flights of stairs I climbed for the last two months. Then, compute the average number of flights of stairs by day, and graph it. Not surprisingly, I climb more stairs on the weekends when I'm home than I do at work during the week. Note that it is possible to get this data using `get_intraday_data()` for each day, but this is much more efficient using just one call to the fibit API for the entire date [...]
+
+```{r, fig.height=4, fig.width=5, message=FALSE, warning=FALSE, fig.align='center'}
+df <- get_daily_data(cookie=cookie, what="floors", start_date="2016-02-15",
+ end_date="2016-05-01")
+df$weekday <- format(df$time, "%A")
+avgs <- by(df$floors, df$weekday, mean)
+avgs <- data.frame(day=names(avgs), floors=as.numeric(avgs))
+avgs$day <- factor(avgs$day, levels=avgs$day[c(4, 2, 6, 7, 5, 1, 3)])
+
+ggplot(avgs) +
+ geom_bar(aes(x=day, y=floors), stat="identity") +
+ theme_tufte() +
+ xlab("") +
+ ylab("") +
+ ggtitle("Average Floors by Day 2016-02-15 to 2016-05-01") +
+ geom_text(aes(x=day,y=floors,label=round(floors, 1)),
+ vjust=1.1, colour="white") +
+ theme(axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
+ theme(plot.title=element_text(vjust=.5))
+```
+
+Another thing to look at, especially if you have the [Aria scale](https://www.fitbit.com/aria), is your weight. You can record your weight manually in the fitbit app, which is how I do it, or the Aria scale will sync it automatically. Any how, let's graph my steps vs. weight for a time period and see if there seems to be a correlation. Data is returned for use in a graph on the fitbit page, so if you include a date range larger than two weeks or so, it returns data for a subset of the da [...]
+
+```{r, fig.height=4, fig.width=5, message=FALSE, warning=FALSE, fig.align='center'}
+# don't do this...
+# mywt <- get_weight_data(cookie, start_date="2015-01-01", end_date="2015-05-01")
+start_date <- as.Date("2015-01-01")
+end_date <- as.Date("2015-05-01")
+wt_df_list <- list() # initialize a list to put the weight dataframes into
+in_range <- TRUE # indicator variable to tell when to exit while loop
+s_date <- start_date # date to start with during loop
+while(in_range){
+ e_date <- s_date + 14
+ new_df <- get_weight_data(cookie, start_date=as.character(s_date),
+ end_date=as.character(e_date))
+ wt_df_list[[as.character(s_date)]] <- new_df
+ s_date <- e_date + 1
+ if(e_date > end_date) in_range <- FALSE
+}
+wt_df <- do.call(rbind, wt_df_list)
+wt_df <- wt_df[!duplicated(wt_df$time), ]
+wt_df <- wt_df[order(wt_df$time), ]
+wt_df <- wt_df[as.Date(wt_df$time) >= start_date &
+ as.Date(wt_df$time) <= end_date, ]
+
+step_df <- get_daily_data(cookie=cookie, what="steps", start_date="2015-01-01",
+ end_date="2015-05-01")
+
+# get common date format to merge data sets...
+wt_df$date <- as.character(as.Date(wt_df$time))
+step_df$date <- as.character(as.Date(step_df$time))
+
+# merge by date
+df <- merge(wt_df, step_df, by="date")
+
+# now plot
+ggplot(df, aes(x=steps, y=weight)) +
+ geom_point() +
+ stat_smooth(se=FALSE) +
+ theme_tufte()
+```
+
+That last example illustrates one of the limitations of retrieving the data the way this library does. Instead of using the "official" fitbit API, this library uses the API intended for their website developer to use to build the visualizations on the web dashboard. So, there's no public documentation. This results in situations like the last one where the weight data returned by that API call is intended to be used in a chart, so they don't need more than 20 points, so that's all that i [...]
+
+
+
+
diff --git a/inst/doc/fitbitScraper-examples.html b/inst/doc/fitbitScraper-examples.html
new file mode 100644
index 0000000..abd7198
--- /dev/null
+++ b/inst/doc/fitbitScraper-examples.html
@@ -0,0 +1,173 @@
+<!DOCTYPE html>
+
+<html xmlns="http://www.w3.org/1999/xhtml">
+
+<head>
+
+<meta charset="utf-8">
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+<meta name="generator" content="pandoc" />
+<meta name="viewport" content="width=device-width, initial-scale=1">
+
+<meta name="author" content="Cory Nissen" />
+
+
+<title>fitbitScraper Examples</title>
+
+
+
+<style type="text/css">code{white-space: pre;}</style>
+<style type="text/css">
+div.sourceCode { overflow-x: auto; }
+table.sourceCode, tr.sourceCode, td.lineNumbers, td.sourceCode {
+ margin: 0; padding: 0; vertical-align: baseline; border: none; }
+table.sourceCode { width: 100%; line-height: 100%; }
+td.lineNumbers { text-align: right; padding-right: 4px; padding-left: 4px; color: #aaaaaa; border-right: 1px solid #aaaaaa; }
+td.sourceCode { padding-left: 5px; }
+code > span.kw { color: #007020; font-weight: bold; } /* Keyword */
+code > span.dt { color: #902000; } /* DataType */
+code > span.dv { color: #40a070; } /* DecVal */
+code > span.bn { color: #40a070; } /* BaseN */
+code > span.fl { color: #40a070; } /* Float */
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+code > span.st { color: #4070a0; } /* String */
+code > span.co { color: #60a0b0; font-style: italic; } /* Comment */
+code > span.ot { color: #007020; } /* Other */
+code > span.al { color: #ff0000; font-weight: bold; } /* Alert */
+code > span.fu { color: #06287e; } /* Function */
+code > span.er { color: #ff0000; font-weight: bold; } /* Error */
+code > span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warning */
+code > span.cn { color: #880000; } /* Constant */
+code > span.sc { color: #4070a0; } /* SpecialChar */
+code > span.vs { color: #4070a0; } /* VerbatimString */
+code > span.ss { color: #bb6688; } /* SpecialString */
+code > span.im { } /* Import */
+code > span.va { color: #19177c; } /* Variable */
+code > span.cf { color: #007020; font-weight: bold; } /* ControlFlow */
+code > span.op { color: #666666; } /* Operator */
+code > span.bu { } /* BuiltIn */
+code > span.ex { } /* Extension */
+code > span.pp { color: #bc7a00; } /* Preprocessor */
+code > span.at { color: #7d9029; } /* Attribute */
+code > span.do { color: #ba2121; font-style: italic; } /* Documentation */
+code > span.an { color: #60a0b0; font-weight: bold; font-style: italic; } /* Annotation */
+code > span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } /* CommentVar */
+code > span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Information */
+</style>
+
+
+<link href="data:text/css;charset=utf-8,body%20%7B%0Abackground%2Dcolor%3A%20%23fff%3B%0Amargin%3A%201em%20auto%3B%0Amax%2Dwidth%3A%20700px%3B%0Aoverflow%3A%20visible%3B%0Apadding%2Dleft%3A%202em%3B%0Apadding%2Dright%3A%202em%3B%0Afont%2Dfamily%3A%20%22Open%20Sans%22%2C%20%22Helvetica%20Neue%22%2C%20Helvetica%2C%20Arial%2C%20sans%2Dserif%3B%0Afont%2Dsize%3A%2014px%3B%0Aline%2Dheight%3A%201%2E35%3B%0A%7D%0A%23header%20%7B%0Atext%2Dalign%3A%20center%3B%0A%7D%0A%23TOC%20%7B%0Aclear%3A%20bot [...]
+
+</head>
+
+<body>
+
+
+
+<div class="fluid-row" id="header">
+
+
+<h1 class="title">fitbitScraper Examples</h1>
+<h4 class="author"><em>Cory Nissen</em></h4>
+<h4 class="date"><em>5/5/2016</em></h4>
+
+</div>
+
+
+<div id="installation" class="section level2">
+<h2>Installation</h2>
+<p>Install fitbitScraper as you would a normal library. It exists on <a href="https://cran.r-project.org/package=fitbitScraper">CRAN</a>, so a simple <code>install.packages("fitbitScraper")</code> should work.</p>
+<p>A development version exists on <a href="https://github.com/corynissen/fitbitScraper">Github</a>, and can be installed via <a href="https://cran.r-project.org/package=devtools">devtools</a>. <code>devtools::install_github("corynissen/fitbitScraper")</code></p>
+</div>
+<div id="usage" class="section level2">
+<h2>Usage</h2>
+<p>I’ve stored my password in an environment variable called “FBPW”. I’ll use this to login to fitbit and generate a cookie that will be used for the subsequent requests. You can type it directly in the password field, but it is generally a best practice to use an environment variable instead. Fitbit allows login via Google and Facebook. This library only works with an email / password based login.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"fitbitScraper"</span>)
+cookie <-<span class="st"> </span><span class="kw">login</span>(<span class="dt">email=</span><span class="st">"corynissen at gmail.com"</span>, <span class="dt">password=</span><span class="kw">Sys.getenv</span>(<span class="st">"FBPW"</span>))</code></pre></div>
+<p>Now you can run any of the other functions to get your data. Let’s start with getting steps on a 15 minute interval for a given week…</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">dates <-<span class="st"> </span><span class="kw">seq</span>(<span class="kw">as.Date</span>(<span class="st">"2016-04-03"</span>), <span class="kw">as.Date</span>(<span class="st">"2016-04-09"</span>), <span class="dt">by=</span><span class="st">"day"</span>)
+df_list <-<span class="st"> </span><span class="kw">lapply</span>(dates, function(x)
+ <span class="kw">get_intraday_data</span>(<span class="dt">cookie=</span>cookie, <span class="dt">what=</span><span class="st">"steps"</span>, <span class="kw">as.character</span>(x)))
+df <-<span class="st"> </span><span class="kw">do.call</span>(rbind, df_list)
+
+<span class="kw">library</span>(<span class="st">"ggplot2"</span>)
+<span class="kw">library</span>(<span class="st">"ggthemes"</span>)
+<span class="kw">ggplot</span>(df) +<span class="st"> </span>
+<span class="st"> </span><span class="kw">geom_bar</span>(<span class="kw">aes</span>(<span class="dt">x=</span>time, <span class="dt">y=</span>steps), <span class="dt">stat=</span><span class="st">"identity"</span>) +<span class="st"> </span>
+<span class="st"> </span><span class="kw">theme_tufte</span>() +<span class="st"> </span>
+<span class="st"> </span><span class="kw">scale_x_datetime</span>(<span class="dt">name=</span><span class="st">"date"</span>, <span class="dt">date_breaks=</span><span class="st">"1 day"</span>, <span class="dt">date_labels=</span><span class="st">"%b-%d"</span>)</code></pre></div>
+<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAeAAAAGACAMAAABC/kH9AAAAolBMVEUAAAAAADoAAGYAOjoAOpAAZmYAZrY6AAA6ADo6AGY6OpA6ZrY6kNtZWVlmAABmADpmAGZmOpBmZgBmZrZmkNtmtttmtv+QOgCQOjqQOmaQZpCQkDqQkLaQkNuQtv+Q27aQ2/+2ZgC2Zma2kJC2tma2tv+2/9u2///bkDrbkGbbkJDb29vb2//b/7bb////tmb/tpD/trb/25D//7b//9v///+8OLqoAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAMiUlEQVR4nO3cC3ubRhbGcZL1Wtusxk2a1k7SbWplL1azqZRF4vt/tR2ELiDNAGdgQBz93+cJsWUd5mh+4mZjJxlRnWTsBkjcAKw8ACsPwMoDsPIArDwAKw/AygOw8gCsPAArD8DKA7DyAKw8ACsPw [...]
+<p>You can get a daily summary of your data also. Here, I download data for the number of flights of stairs I climbed for the last two months. Then, compute the average number of flights of stairs by day, and graph it. Not surprisingly, I climb more stairs on the weekends when I’m home than I do at work during the week. Note that it is possible to get this data using <code>get_intraday_data()</code> for each day, but this is much more efficient using just one call to the fibit API for th [...]
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">df <-<span class="st"> </span><span class="kw">get_daily_data</span>(<span class="dt">cookie=</span>cookie, <span class="dt">what=</span><span class="st">"floors"</span>, <span class="dt">start_date=</span><span class="st">"2016-02-15"</span>,
+ <span class="dt">end_date=</span><span class="st">"2016-05-01"</span>)
+df$weekday <-<span class="st"> </span><span class="kw">format</span>(df$time, <span class="st">"%A"</span>)
+avgs <-<span class="st"> </span><span class="kw">by</span>(df$floors, df$weekday, mean)
+avgs <-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">day=</span><span class="kw">names</span>(avgs), <span class="dt">floors=</span><span class="kw">as.numeric</span>(avgs))
+avgs$day <-<span class="st"> </span><span class="kw">factor</span>(avgs$day, <span class="dt">levels=</span>avgs$day[<span class="kw">c</span>(<span class="dv">4</span>, <span class="dv">2</span>, <span class="dv">6</span>, <span class="dv">7</span>, <span class="dv">5</span>, <span class="dv">1</span>, <span class="dv">3</span>)])
+
+<span class="kw">ggplot</span>(avgs) +<span class="st"> </span>
+<span class="st"> </span><span class="kw">geom_bar</span>(<span class="kw">aes</span>(<span class="dt">x=</span>day, <span class="dt">y=</span>floors), <span class="dt">stat=</span><span class="st">"identity"</span>) +<span class="st"> </span>
+<span class="st"> </span><span class="kw">theme_tufte</span>() +<span class="st"> </span>
+<span class="st"> </span><span class="kw">xlab</span>(<span class="st">""</span>) +<span class="st"> </span>
+<span class="st"> </span><span class="kw">ylab</span>(<span class="st">""</span>) +<span class="st"> </span>
+<span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Average Floors by Day 2016-02-15 to 2016-05-01"</span>) +<span class="st"> </span>
+<span class="st"> </span><span class="kw">geom_text</span>(<span class="kw">aes</span>(<span class="dt">x=</span>day,<span class="dt">y=</span>floors,<span class="dt">label=</span><span class="kw">round</span>(floors, <span class="dv">1</span>)),
+ <span class="dt">vjust=</span><span class="fl">1.1</span>, <span class="dt">colour=</span><span class="st">"white"</span>) +<span class="st"> </span>
+<span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.text.y=</span><span class="kw">element_blank</span>(), <span class="dt">axis.ticks.y=</span><span class="kw">element_blank</span>()) +<span class="st"> </span>
+<span class="st"> </span><span class="kw">theme</span>(<span class="dt">plot.title=</span><span class="kw">element_text</span>(<span class="dt">vjust=</span>.<span class="dv">5</span>)) </code></pre></div>
+<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAeAAAAGACAMAAABC/kH9AAABLFBMVEUAAAAAADoAAGYAOjoAOpAAZrY6AAA6ADo6AGY6OgA6Ojo6OmY6OpA6ZmY6ZpA6ZrY6kLY6kNtZWVlZWXhZWZRZeLFZlMtmAABmADpmAGZmOpBmZgBmZrZmkLZmkNtmtrZmtttmtv94WVl4seWQOgCQOjqQOmaQZgCQZmaQZpCQkDqQkJCQkLaQkNuQtpCQtv+Q27aQ29uQ2/+UWVmUeLGUy/+xeFmxlLGx5f+2ZgC2Zjq2Zma2kDq2kJC2tma2tra2tv+225C22/+2/7a2/9u2///LlFnL/+XL///bkDrbkGbbkJDbtmbbtrbb25Db27bb29vb/7bb///lsXjl5eXl/8vl/+Xl////tmb/tpD/trb/y5T/25D/5bH/5eX//7b//8v//9v//+X///9Uo [...]
+<p>Another thing to look at, especially if you have the <a href="https://www.fitbit.com/aria">Aria scale</a>, is your weight. You can record your weight manually in the fitbit app, which is how I do it, or the Aria scale will sync it automatically. Any how, let’s graph my steps vs. weight for a time period and see if there seems to be a correlation. Data is returned for use in a graph on the fitbit page, so if you include a date range larger than two weeks or so, it returns data for a su [...]
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># don't do this...</span>
+<span class="co"># mywt <- get_weight_data(cookie, start_date="2015-01-01", end_date="2015-05-01")</span>
+start_date <-<span class="st"> </span><span class="kw">as.Date</span>(<span class="st">"2015-01-01"</span>)
+end_date <-<span class="st"> </span><span class="kw">as.Date</span>(<span class="st">"2015-05-01"</span>)
+wt_df_list <-<span class="st"> </span><span class="kw">list</span>() <span class="co"># initialize a list to put the weight dataframes into</span>
+in_range <-<span class="st"> </span><span class="ot">TRUE</span> <span class="co"># indicator variable to tell when to exit while loop</span>
+s_date <-<span class="st"> </span>start_date <span class="co"># date to start with during loop</span>
+while(in_range){
+ e_date <-<span class="st"> </span>s_date +<span class="st"> </span><span class="dv">14</span>
+ new_df <-<span class="st"> </span><span class="kw">get_weight_data</span>(cookie, <span class="dt">start_date=</span><span class="kw">as.character</span>(s_date),
+ <span class="dt">end_date=</span><span class="kw">as.character</span>(e_date))
+ wt_df_list[[<span class="kw">as.character</span>(s_date)]] <-<span class="st"> </span>new_df
+ s_date <-<span class="st"> </span>e_date +<span class="st"> </span><span class="dv">1</span>
+ if(e_date ><span class="st"> </span>end_date) in_range <-<span class="st"> </span><span class="ot">FALSE</span>
+}
+wt_df <-<span class="st"> </span><span class="kw">do.call</span>(rbind, wt_df_list)
+wt_df <-<span class="st"> </span>wt_df[!<span class="kw">duplicated</span>(wt_df$time), ]
+wt_df <-<span class="st"> </span>wt_df[<span class="kw">order</span>(wt_df$time), ]
+wt_df <-<span class="st"> </span>wt_df[<span class="kw">as.Date</span>(wt_df$time) >=<span class="st"> </span>start_date &
+<span class="st"> </span><span class="kw">as.Date</span>(wt_df$time) <=<span class="st"> </span>end_date, ]
+
+step_df <-<span class="st"> </span><span class="kw">get_daily_data</span>(<span class="dt">cookie=</span>cookie, <span class="dt">what=</span><span class="st">"steps"</span>, <span class="dt">start_date=</span><span class="st">"2015-01-01"</span>,
+ <span class="dt">end_date=</span><span class="st">"2015-05-01"</span>)
+
+<span class="co"># get common date format to merge data sets...</span>
+wt_df$date <-<span class="st"> </span><span class="kw">as.character</span>(<span class="kw">as.Date</span>(wt_df$time))
+step_df$date <-<span class="st"> </span><span class="kw">as.character</span>(<span class="kw">as.Date</span>(step_df$time))
+
+<span class="co"># merge by date</span>
+df <-<span class="st"> </span><span class="kw">merge</span>(wt_df, step_df, <span class="dt">by=</span><span class="st">"date"</span>)
+
+<span class="co"># now plot</span>
+<span class="kw">ggplot</span>(df, <span class="kw">aes</span>(<span class="dt">x=</span>steps, <span class="dt">y=</span>weight)) +<span class="st"> </span>
+<span class="st"> </span><span class="kw">geom_point</span>() +<span class="st"> </span>
+<span class="st"> </span><span class="kw">stat_smooth</span>(<span class="dt">se=</span><span class="ot">FALSE</span>) +<span class="st"> </span>
+<span class="st"> </span><span class="kw">theme_tufte</span>()</code></pre></div>
+<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAeAAAAGACAMAAABC/kH9AAAAnFBMVEUAAAAAADoAAGYAOjoAOpAAZmYAZrYzZv86AAA6AGY6OpA6ZrY6kNtmAABmADpmAGZmOpBmZgBmZrZmkNtmtrZmtv+QOgCQOjqQOmaQZpCQkDqQkNuQtv+Q27aQ2/+2ZgC2Zma2kJC2tv+225C2/9u2///bkDrbkGbbkJDbtmbb2//b/7bb////tmb/tpD/trb/25D//7b//9v///822OzOAAAACXBIWXMAAA7DAAAOwwHHb6hkAAALrElEQVR4nO2dC3vaOBpGnUw2IS00M82QtNsW0tmFzu5Ca+P//9/WMjdjG+OL5MvLOU/jgoFPHxxk2TKSvRCk8bpOANyCYHEQLA6CxUGwOAgWB8HiIFgcBIuDYHEQLA6CxUGwOAgWB8HiIFgcBIuDYHGKBc/H0 [...]
+<p>That last example illustrates one of the limitations of retrieving the data the way this library does. Instead of using the “official” fitbit API, this library uses the API intended for their website developer to use to build the visualizations on the web dashboard. So, there’s no public documentation. This results in situations like the last one where the weight data returned by that API call is intended to be used in a chart, so they don’t need more than 20 points, so that’s all tha [...]
+</div>
+
+
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+ (function () {
+ var script = document.createElement("script");
+ script.type = "text/javascript";
+ script.src = "https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+ document.getElementsByTagName("head")[0].appendChild(script);
+ })();
+</script>
+
+</body>
+</html>
diff --git a/vignettes/fitbitScraper-examples.Rmd b/vignettes/fitbitScraper-examples.Rmd
new file mode 100644
index 0000000..f2b3058
--- /dev/null
+++ b/vignettes/fitbitScraper-examples.Rmd
@@ -0,0 +1,109 @@
+---
+title: "fitbitScraper Examples"
+author: "Cory Nissen"
+date: "5/5/2016"
+output: rmarkdown::html_vignette
+vignette: >
+ %\VignetteIndexEntry{fitbitScraper Examples}
+ %\VignetteEngine{knitr::rmarkdown}
+ %\VignetteEncoding{UTF-8}
+---
+
+## Installation
+Install fitbitScraper as you would a normal library. It exists on [CRAN](https://cran.r-project.org/package=fitbitScraper), so a simple `install.packages("fitbitScraper")` should work.
+
+A development version exists on [Github](https://github.com/corynissen/fitbitScraper), and can be installed via [devtools](https://cran.r-project.org/package=devtools). `devtools::install_github("corynissen/fitbitScraper")`
+
+## Usage
+
+I've stored my password in an environment variable called "FBPW". I'll use this to login to fitbit and generate a cookie that will be used for the subsequent requests. You can type it directly in the password field, but it is generally a best practice to use an environment variable instead. Fitbit allows login via Google and Facebook. This library only works with an email / password based login.
+
+```{r}
+library("fitbitScraper")
+cookie <- login(email="corynissen at gmail.com", password=Sys.getenv("FBPW"))
+```
+
+Now you can run any of the other functions to get your data. Let's start with getting steps on a 15 minute interval for a given week...
+
+```{r, fig.height=4, fig.width=5, message=FALSE, warning=FALSE, fig.align='center'}
+dates <- seq(as.Date("2016-04-03"), as.Date("2016-04-09"), by="day")
+df_list <- lapply(dates, function(x)
+ get_intraday_data(cookie=cookie, what="steps", as.character(x)))
+df <- do.call(rbind, df_list)
+
+library("ggplot2")
+library("ggthemes")
+ggplot(df) +
+ geom_bar(aes(x=time, y=steps), stat="identity") +
+ theme_tufte() +
+ scale_x_datetime(name="date", date_breaks="1 day", date_labels="%b-%d")
+```
+
+You can get a daily summary of your data also. Here, I download data for the number of flights of stairs I climbed for the last two months. Then, compute the average number of flights of stairs by day, and graph it. Not surprisingly, I climb more stairs on the weekends when I'm home than I do at work during the week. Note that it is possible to get this data using `get_intraday_data()` for each day, but this is much more efficient using just one call to the fibit API for the entire date [...]
+
+```{r, fig.height=4, fig.width=5, message=FALSE, warning=FALSE, fig.align='center'}
+df <- get_daily_data(cookie=cookie, what="floors", start_date="2016-02-15",
+ end_date="2016-05-01")
+df$weekday <- format(df$time, "%A")
+avgs <- by(df$floors, df$weekday, mean)
+avgs <- data.frame(day=names(avgs), floors=as.numeric(avgs))
+avgs$day <- factor(avgs$day, levels=avgs$day[c(4, 2, 6, 7, 5, 1, 3)])
+
+ggplot(avgs) +
+ geom_bar(aes(x=day, y=floors), stat="identity") +
+ theme_tufte() +
+ xlab("") +
+ ylab("") +
+ ggtitle("Average Floors by Day 2016-02-15 to 2016-05-01") +
+ geom_text(aes(x=day,y=floors,label=round(floors, 1)),
+ vjust=1.1, colour="white") +
+ theme(axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
+ theme(plot.title=element_text(vjust=.5))
+```
+
+Another thing to look at, especially if you have the [Aria scale](https://www.fitbit.com/aria), is your weight. You can record your weight manually in the fitbit app, which is how I do it, or the Aria scale will sync it automatically. Any how, let's graph my steps vs. weight for a time period and see if there seems to be a correlation. Data is returned for use in a graph on the fitbit page, so if you include a date range larger than two weeks or so, it returns data for a subset of the da [...]
+
+```{r, fig.height=4, fig.width=5, message=FALSE, warning=FALSE, fig.align='center'}
+# don't do this...
+# mywt <- get_weight_data(cookie, start_date="2015-01-01", end_date="2015-05-01")
+start_date <- as.Date("2015-01-01")
+end_date <- as.Date("2015-05-01")
+wt_df_list <- list() # initialize a list to put the weight dataframes into
+in_range <- TRUE # indicator variable to tell when to exit while loop
+s_date <- start_date # date to start with during loop
+while(in_range){
+ e_date <- s_date + 14
+ new_df <- get_weight_data(cookie, start_date=as.character(s_date),
+ end_date=as.character(e_date))
+ wt_df_list[[as.character(s_date)]] <- new_df
+ s_date <- e_date + 1
+ if(e_date > end_date) in_range <- FALSE
+}
+wt_df <- do.call(rbind, wt_df_list)
+wt_df <- wt_df[!duplicated(wt_df$time), ]
+wt_df <- wt_df[order(wt_df$time), ]
+wt_df <- wt_df[as.Date(wt_df$time) >= start_date &
+ as.Date(wt_df$time) <= end_date, ]
+
+step_df <- get_daily_data(cookie=cookie, what="steps", start_date="2015-01-01",
+ end_date="2015-05-01")
+
+# get common date format to merge data sets...
+wt_df$date <- as.character(as.Date(wt_df$time))
+step_df$date <- as.character(as.Date(step_df$time))
+
+# merge by date
+df <- merge(wt_df, step_df, by="date")
+
+# now plot
+ggplot(df, aes(x=steps, y=weight)) +
+ geom_point() +
+ stat_smooth(se=FALSE) +
+ theme_tufte()
+```
+
+That last example illustrates one of the limitations of retrieving the data the way this library does. Instead of using the "official" fitbit API, this library uses the API intended for their website developer to use to build the visualizations on the web dashboard. So, there's no public documentation. This results in situations like the last one where the weight data returned by that API call is intended to be used in a chart, so they don't need more than 20 points, so that's all that i [...]
+
+
+
+
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