Monthly Archives: May 2018

Looking for insights from Fitbit data with R

With a month’s of Fitbit data, it’s about time to harvest for some insights from this technology packed wristband.

r-fitbit4


library(lubridate)
library(dplyr)
fitbitdata = read.csv('fitbit.csv')
fitbitdata % mutate(dow = wday(Date))
fitbitdata$dowlabel <- factor(fitbitdata$dow,levels=1:7,labels=c("Mon","Tue","Wed","Thu","Fri","Sat","Sun"),ordered=TRUE)
fitbitdata$scyl <- as.factor(as.integer(fitbitdata$distance)/max(fitbitdata$distance))
head(fitbitdata)
c1 <- rainbow(7)
c2 <- rainbow(7, alpha=0.4)
c3 <- rainbow(7, v=0.8)
boxplot(fitbitdata$steps~fitbitdata$dowlabel, col=c2, medcol=c3, whiskcol=c1, staplecol=c3, boxcol=c3, outcol=c3, pch=23, cex=2, alpha=fitbitdata$scyl)

r-fitbit3

Number of steps and distance traveled data per day is collected from the Fitbit’s phone app, converted into CSV format, and then uploaded to R for data analysis. With a few lines of R code to draw a box-plot for day of week analysis, this data set with a third part statistical package will fill the gap until the Fitbit App offers something more sophisticated .

 

 

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RStudio Server and Apache

rstudio2To install R and RStudio Server on Ubuntu:

sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9
sudo add-apt-repository 'deb [arch=amd64,i386] https://cran.rstudio.com/bin/linux/ubuntu xenial/'
sudo apt-get update
sudo apt-get install r-base
sudo -i R
wget https://download2.rstudio.org/rstudio-server-1.1.453-amd64.deb
sudo gdebi rstudio-server-1.1.453-amd64.deb

Configure Apache 2.4 to proxy RStudio, install required modules.

sudo apt-get install libapache2-mod-proxy-html
sudo apt-get install libxml2-dev

Edit configuration file 000-default.conf to add the followings. Assuming RStudio runs on default port 8787 and preferred path is /rstudio:

<Proxy *>
	Allow from localhost
< /Proxy *>

RewriteEngine on
RewriteCond %{HTTP:Upgrade} =websocket
RewriteRule /rstudio/(.*) ws://localhost:8787/$1  [P,L]
RewriteCond %{HTTP:Upgrade} !=websocket
RewriteRule /rstudio/(.*) http://localhost:8787/$1 [P,L]
ProxyPass /rstudio/ http://localhost:8787/
ProxyPassReverse /rstudio/ http://localhost:8787/
ProxyRequests Off

Finally restart apache.


sudo a2enmod proxy && sudo a2enmod proxy_http && sudo a2enmod proxy_wstunnel && sudo service apache2 restart

Exploring optimization problems in Excel

Excel is able to solve optimization problems. Two commonly available tools are the build-in Solver tool and the Excel plugin for Microsoft Solver Foundation (MSF). The former is not installed by default but can be easily enabled through the Excel Options menu. The latter is a separate download available from Microsoft.

For a simple comparison of the performance of the two, the non-linear data fitting example from the MSF is used as benchmark.
msf-1

MSF provided additional menu pane within Excel for complex optimization operations.
msf-2

Optimization results and log of this benchmark run of a non-linear data fitting sample from the MSF, based on an NIST sample.
msf-10
msf-3

Goals setting screen.
msf-5

Model Display.
msf-6

On the other hand, the built-in Solver offered a simpler interface but still provide detailed reports, including answer, sensitivity, and limits reports in separate spreadsheets.
msf-7

msf-8

The built-in Excel Solver offered easy to use interface, while the Microsoft Solver Foundation is more capable for complex problems and modelling.

The NelderMead solver is selected in this benchmark by the MSF. Check out this previous installment for details of running Nelder-Mead on TI Nspire. The same data set is performed on the Nspire using Nelder-Mead to obtain the following results.
msf-9
msf-11