Example 1: Data Loader - Getting data from Fitbit API
This part explains how to connect to the Fitbit API and get your data, using DataLoader.
Step 1: You first need to make sure that you have registered an app and set it as Personal in order to retrieve intraday data. You will need the following credentials in order to connect the API: App name (or OAuth 2.0 Client ID), Client Key and Client Secret.
Step 2: We initialize a new DataLoader object, and connect to the API with OAuth2, using the credentials described above. Note that instead of providing the credentials directly as parameters, you could point to a cache file (usually named .httr-oauth) using the cache.file parameter.
mydata <- DataLoader$new()
mydata$connect(appname = "cdlr",
key = "227FWR",
secret = "3089e3d1ac5dde1aa00b54a0c8661f42"
)
Step 3: We request the data and write it to JSON files using the request method. You need to specify the type of timeseries (‘day’ or ‘intraday’), the list of activities (full list here), the start and end dates, and the folder in which the JSON files will be written.
masterPath <- system.file("extdata",
"daily-time-series",
package = "fitcoach")
mydata$request(
type = 'day',
activities = list("calories", "steps", "distance", "minutesVeryActive"),
start.date = "2016-01-01",
end.date = "2016-02-01",
path = masterPath
)
Once the JSON files have been created, they can be used for further analysis.
Example 2: Fit Analyzer - Daily File Analysis
Examples below demonstrate usage scenarios for FitAnalyzer.
Step 1: We first need to point to a folder that contains the JSON files for “daily” file analysis. These files are created by DataLoader.R.
We then create a new instance of FitAnalyzer, passing in the folder and the goal that we want to optimize on. Goals can be the following: a) calories b) steps c) distance d) floors.
The example below uses steps as the goal.
masterPath <- system.file("extdata",
"daily-time-series",
package = "fitcoach")
ana <- FitAnalyzer$new("steps")
Step 2: Next we get the data.frame ready for analysis. Note this data.frame is cleaned and augmented with additional data elements not present in the JSON file. E.g.: we augment weekday, weekend and mark rows that are valid.
timeseries.frame <-
ana$getAnalysisFrame(folder = masterPath,
analysis.type = "daily")
head(timeseries.frame)
## date calories caloriesBMR steps distance floors elevation
## 898 2015-07-31 1867 1759 195 0.14062 0 0
## 899 2015-08-01 3245 1758 12866 10.44119 13 39
## 900 2015-08-02 2867 1758 5023 3.65184 11 33
## 901 2015-08-03 2982 1758 10112 7.35157 6 18
## 902 2015-08-04 2734 1758 5725 4.16213 7 21
## 903 2015-08-05 3012 1758 9155 6.72913 5 15
## minutesSedentary minutesLightlyActive minutesFairlyActive
## 898 205 10 0
## 899 672 269 5
## 900 691 168 25
## 901 1161 143 5
## 902 836 150 18
## 903 640 267 16
## minutesVeryActive activityCalories valid weekday weekend
## 898 0 51 TRUE Friday FALSE
## 899 37 1600 TRUE Saturday TRUE
## 900 35 1196 TRUE Sunday TRUE
## 901 66 1253 TRUE Monday FALSE
## 902 23 961 TRUE Tuesday FALSE
## 903 16 1372 TRUE Wednesday FALSE
Step 3: next we find the most important variables that are enabling meeting the goals for the person. Note this call creates a glm model behind the scenes and ranks the variables based on the coefficients of the glm model. You can also get the glm fit object to do further analysis.
vars <- ana$findImportantVariables(tsDataFrame = timeseries.frame,
seed = 12345)
vars
## Overall name
## 1 83.7286565 distance
## 2 3.4383872 minutesLightlyActive
## 3 1.8757480 floors
## 4 1.8523923 elevation
## 5 1.2891208 minutesFairlyActive
## 6 1.1386025 minutesSedentary
## 7 1.1244243 minutesVeryActive
## 8 0.2122723 holiday
Getting the fit object.
fit <- ana$getFit()
summary(fit)
##
## Call:
## glm(formula = y ~ ., family = "gaussian", data = x)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1192.03 -17.96 12.12 54.98 264.72
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -83.72528 62.25542 -1.345 0.180342
## distance 1323.59328 15.80813 83.729 < 2e-16 ***
## floors -472.34421 251.81645 -1.876 0.062291 .
## elevation 153.87808 83.06992 1.852 0.065589 .
## minutesSedentary 0.05395 0.04738 1.139 0.256365
## minutesLightlyActive 1.30032 0.37818 3.438 0.000725 ***
## minutesFairlyActive 1.70467 1.32235 1.289 0.198992
## minutesVeryActive 1.66153 1.47767 1.124 0.262314
## holiday 5.05059 23.79298 0.212 0.832132
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 18358.87)
##
## Null deviance: 1752078977 on 190 degrees of freedom
## Residual deviance: 3341314 on 182 degrees of freedom
## AIC: 2428
##
## Number of Fisher Scoring iterations: 2
par(mfrow=c(2,2))
plot(fit)
Step 4: Next, we can then plot the performance of the individual, relative to the most important variables that are making a difference.
ana$showMostImportantCharts(tsDataFrame = timeseries.frame)
Step 5: We can also get the prediction on goal performance using the call below.
rows.test <- timeseries.frame[sample(1:191, 1), ]
x <- createDependentVariableFrame(master = rows.test, goal = "steps")
res <- ana$predictGoal(x)
cat(paste("Prediction for the day", ": expected steps = ", round(res)))
## Prediction for the day : expected steps = 7481
Example 3: FitAnalyzer - Intraday File Analysis
Examples below demonstrate usage scenarios for FitAnalyzer for Intraday analysis.
Step 1: We first need to point to a folder that contains the JSON files for intraday file analysis. These files are created by DataLoader.R.
We then create a new instance of FitAnalyzer passing in the folder and the goal that we want to optimize on. Goals can be the following: a) calories b) steps c) distance d) floors .
The example below uses calories as the goal
masterPath <-
system.file("extdata", "intra-daily-timeseries", package = "fitcoach")
ana <- FitAnalyzer$new("calories")
Step 2: Next we get the data.frame ready for analysis. Note that this data.frame is cleaned and augmented with additional data elements not present in the JSON file. E.g.: we augment cumulative sum during the day, weekday, weekend, etc.
intra <- ana$getAnalysisFrame(folder = masterPath, analysis.type = "intra.day")
head(intra)
## date calories intra.level intra.mets intra.calorie timeseq steps
## 1 2015-12-10 2491 0 150 18.5565 1 5319
## 2 2015-12-10 2491 0 150 18.5565 2 5319
## 3 2015-12-10 2491 0 150 18.5565 3 5319
## 4 2015-12-10 2491 0 150 18.5565 4 5319
## 5 2015-12-10 2491 0 150 18.5565 5 5319
## 6 2015-12-10 2491 0 150 18.5565 6 5319
## intra.steps floors intra.floors elevation intra.elevation distance
## 1 0 6 0 18 0 3.89512
## 2 0 6 0 18 0 3.89512
## 3 0 6 0 18 0 3.89512
## 4 0 6 0 18 0 3.89512
## 5 0 6 0 18 0 3.89512
## 6 0 6 0 18 0 3.89512
## intra.distance weekday weekend slot cumsum.calorie cumsum.steps
## 1 0 5 0 night 18.5565 0
## 2 0 5 0 night 37.1130 0
## 3 0 5 0 night 55.6695 0
## 4 0 5 0 night 74.2260 0
## 5 0 5 0 night 92.7825 0
## 6 0 5 0 night 111.3390 0
## cumsum.level cumsum.mets cumsum.distance cumsum.floors cumsum.elevation
## 1 0 150 0 0 0
## 2 0 300 0 0 0
## 3 0 450 0 0 0
## 4 0 600 0 0 0
## 5 0 750 0 0 0
## 6 0 900 0 0 0
Step 3: Next we find the most important variables that are enabling meeting the goals for the person. Note: this call creates a gbm model behind the scenes and ranks the variables based on relative.influence call to gbm model. You can also get the gbm fit object to do further analysis.
vars <- ana$findImportantVariables(intra)
vars <- sort(vars, decreasing = TRUE)
vars
## steps weekend distance floors
## 1.000000e+00 4.850034e-02 4.112410e-02 2.081519e-02
## weekday cumsum.level cumsum.floors timeseq
## 9.944454e-03 2.865114e-05 8.797090e-08 8.763791e-08
## cumsum.steps cumsum.distance cumsum.calorie cumsum.elevation
## 4.113028e-08 1.453128e-08 1.427501e-08 7.307049e-09
## intra.calorie intra.steps intra.floors intra.mets
## 6.349336e-09 9.373329e-10 3.419370e-10 2.913440e-10
## intra.distance slot intra.level elevation
## 8.783386e-11 6.107080e-11 0.000000e+00 0.000000e+00
## intra.elevation cumsum.mets
## 0.000000e+00 0.000000e+00
Plot of important variables below.
vars.frame <- data.frame(variables = names(vars), values = vars)
vars.frame$lnvalue <- log(vars.frame$values)
vars.frame <- vars.frame[1:7, ]
barplot(vars.frame$value, xlab = "variables", ylab = "relative importance",
names.arg = vars.frame$variables,
cex.names = 0.65, cex.lab = 0.65, ylim = c(0.0, 0.1))
Summary of GBM model fit below.
fit <- ana$getFit()
summary(fit)
## var rel.inf
## steps steps 8.925280e+01
## weekend weekend 4.328791e+00
## distance distance 3.670441e+00
## floors floors 1.857814e+00
## weekday weekday 8.875704e-01
## cumsum.level cumsum.level 2.557195e-03
## cumsum.floors cumsum.floors 7.851649e-06
## timeseq timeseq 7.821929e-06
## cumsum.steps cumsum.steps 3.670992e-06
## cumsum.distance cumsum.distance 1.296958e-06
## cumsum.calorie cumsum.calorie 1.274084e-06
## cumsum.elevation cumsum.elevation 6.521746e-07
## intra.calorie intra.calorie 5.666960e-07
## intra.steps intra.steps 8.365959e-08
## intra.floors intra.floors 3.051884e-08
## intra.mets intra.mets 2.600326e-08
## intra.distance intra.distance 7.839418e-09
## slot slot 5.450740e-09
## intra.level intra.level 0.000000e+00
## elevation elevation 0.000000e+00
## intra.elevation intra.elevation 0.000000e+00
## cumsum.mets cumsum.mets 0.000000e+00
Step 4: Next we can then plot the performance of the individual relative to the most important variables that are making a difference. For the 4 most important variables, the average value for every 15 min of a day is plotted, along with the moving average (using geom_smooth from ggplot2).
ana$showMostImportantCharts(tsDataFrame = intra)
Step 5: We can also get the prediction on goal performance using the call below.
rows.test <- intra[sample(1:191, 1), ] # Take any random input for test
res <- ana$predictGoal(rows.test)
cat(paste("Prediction for the day", ": expected calories =", round(res)))
## Prediction for the day : expected calories = 2517
Example 4: FitUtil - Illustration for usage of FitUtil functions
Approach to get a clean data.frame from JSON files.
# masterPath is the folder containing JSON files
masterPath <- system.file("extdata", "daily-time-series", package = "fitcoach")
# Create the data.frame. This is not cleaned
master <- createTsMasterFrame(masterPath)
# Identify and Mark rows that are valid. i.e distance for the day >0
master <- markValidRows(master)
# Filter Valid rows only
master <- master[master$valid == TRUE, ]
# Augment data with additional information. Eg: weekday information
master <- augmentData(master)
head(master)
## date calories caloriesBMR steps distance floors elevation
## 898 2015-07-31 1867 1759 195 0.14062 0 0
## 899 2015-08-01 3245 1758 12866 10.44119 13 39
## 900 2015-08-02 2867 1758 5023 3.65184 11 33
## 901 2015-08-03 2982 1758 10112 7.35157 6 18
## 902 2015-08-04 2734 1758 5725 4.16213 7 21
## 903 2015-08-05 3012 1758 9155 6.72913 5 15
## minutesSedentary minutesLightlyActive minutesFairlyActive
## 898 205 10 0
## 899 672 269 5
## 900 691 168 25
## 901 1161 143 5
## 902 836 150 18
## 903 640 267 16
## minutesVeryActive activityCalories valid weekday weekend
## 898 0 51 TRUE Friday FALSE
## 899 37 1600 TRUE Saturday TRUE
## 900 35 1196 TRUE Sunday TRUE
## 901 66 1253 TRUE Monday FALSE
## 902 23 961 TRUE Tuesday FALSE
## 903 16 1372 TRUE Wednesday FALSE
fitcoach/vignettes/summary.Rmd 0000644 0001751 0000144 00000006146 12705264065 016237 0 ustar hornik users ---
title: "Project Summary"
author: "Niraj Juneja, Charles de Lassence"
output: rmarkdown::html_document
vignette: >
%\VignetteIndexEntry{Vignette Title}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Fitcoach project summary
We are glad to submit the fitcoach package. As part of the project we have been able to accomplish the following:
1. Connect R to Fitbit API and expose an individual’s Fitbit data as a Dataframe for further analysis.
+ DataLoader.R does this. __[Example 1 shows how to do this](examples\fitcoach-usage.html)__
+ DataLoader enables getting both the *Daily time-series* data and *Intra-day time-series* data at 15 min breaks for the individual.
+ Note : intra-day data will require the user to create a new app on Fitbit website and is only available for app owner. i.e User A cannot access Intra-day data from User B. This is a Fitbit restriction
2. Created [FitAnalyzer R6](..\R\FitAnalyzer.R) class that provides an __opinionated__ but focussed implementation for analyzing Fitbit Data. It is likely that this workflow might not work for all. In this situation, the user can directly use Fitbit functions provided in [FitUtil.R](..\R\FitUtil.R) and create a customized analysis flow.
3. In line with the project proposal, we were able to build the following.
+ Ability to set goals and find the most significant variables that are enabling meeting the goals. The [Example 2 and Example 3 here](examples\fitcoach-usage.html) demonstrates this flow.
+ Ability to call a function and provide recommendations for the rest of the day. The [Example 2 and 3](examples\fitcoach-usage.html) demonstrate this flow. This is implemented for both daily and intra-day scenarios.
+ Provide advanced charts in ggplot2. The [Example 2 and Example 3 here](examples\fitcoach-usage.html) demonstrate the graphs.
4. Package Design Philosophy
+ DataLoader.R is an R6 class because it encapsulates a unique OAuth2.0 based flow for accessing Fitbit API. The goal of this class is to orchestrate the Fitbit connection flow and download __daily__ or __intraday__ json files into a folder so that the json files can be analyzed further.
+ FitAnalyzer.R is an R6 class with a opinioned workflow for analysis. The class maintains state related to goal, analysis type among other things. It does not store the data.frame used for analysis to avoid memory issues. The user is expected to hold on to the data.frame
+ FitUtil.R is a utility file that has functions for Fitbit analysis.
+ We are using [GLM](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/glm.html) for daily file and [GBM](https://cran.r-project.org/web/packages/gbm/gbm.pdf) for intraday file analysis. intra-day file analysis has a lot more data points and hence GBM works well here.For daily file, we have 1 datapoint per day. Hence, we decided to use GLM because we do not expect a lot of data in this file.
__Please refer to the detailed [Example ](examples\fitcoach-usage.html) to understand the usage of the package__
fitcoach/vignettes/summary.html 0000644 0001751 0000144 00002363220 12705264065 016462 0 ustar hornik users
Fitcoach project summary
We are glad to submit the fitcoach package. As part of the project we have been able to accompalish the following
- Connect R to fitbit API and expose an individual’s fitbit data as a Dataframe for further analysis.
- DataLoader.R does this. Example 1 shows how to do this
- DataLoader enables getting both the Daily time-series data and Intra-day time-series data at 15 min breaks for the individual.
- Note : intra-day data will require the user to create a new app on fitbit website and is only available for app owner. i.e User A cannot access Intra-day data from User B. This is a fitbit restriction
Created FitAnalyzer R6 class that provides an opinionated but focussed implementation for analyzing Fitbit Data. It is likely that this workflow might not work for all. In this situation, the user can directly use fitbit functions provided in FitUtil.R and create a customized analysis flow.
- In line with the project proposal we were able to build the following.
- Ability to set goals and find the most significant variables that are enabling meeting the goals. The Example 2 and Example 3 here demonstrates this flow.
- Ability to call a function and provide recommendations for the rest of the day. The Example 2 and 3 demonstrates this flow. This is implemented for both daily and intra-day scenarios.
- Provide advanced charts in ggplot2. The Example 2 and Example 3 here demonstrates the graphs.
- Package Design Philosophy
- DataLoader.R is an R6 class because it encaptulates a unique oauth2.0 based flow for accessing fitbit api. The goal of this class is to orchestrate the fitbit connection flow and download daily or intraday json files into a folder so that the json files can be analyzed further.
- FitAnalyzer.R is an R6 class with a opinioned workflow for analysis. The class maintains state related to goal, analysis type among other things. It does not store the data.frame used for analysis to avoid memory issues. The user is expected to hold on to the data.frame
- FitUtil.R is a utility file that has functions for fitbit analysis.
- We are using GLM for daily file and GBM for intraday file analysis. intra-day file analysis has a lot more data points and hence GBM works well here.For daily file, we have 1 datapoint per day. Hence, we decided to use GLM because we do not expect a lot of data in this file.
Please refer to the detailed Example to understand the usage of the package
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fitcoach/DESCRIPTION 0000644 0001751 0000144 00000001372 12705346061 013565 0 ustar hornik users Package: fitcoach
Type: Package
Title: Personalized Coach for Fitbit and R API
Version: 1.0
Author: Niraj Juneja [aut, cre], Charles de Lassence [aut, cre]
Maintainer: Niraj Juneja