Showing posts with label code. Show all posts
Showing posts with label code. Show all posts

Thursday, 12 June 2014

Ozone Weather

An open source weather app that calculates UV light intensity for the user's current time and location and gives a suggestion for how long it will take to make vitamin D. It also forecasts UV light intensities for the coming week. The code is up on Github.


Background:

This project is based on a tutorial from Ray Wenderlich's great website. (sample app: SimpleWeather). It adds Ozone data from the TEMIS database, using libxml2 and Hpple to parse the .html based on another tutorial how to parse HTML on iOS. It also includes my astronomy code from previous projects for calculating solar angles and estimating UV intensities.


Technologies

The iOS best practices tutorial included a number of interesting technologies. I've ignored the image blur filter they use in the main view, but I keep these:

  • cocoapods -- package manager and dependency tracker software. Basic usage:
    • > edit podfile cocoapods searches for a file named 'podfile'. Include the dependencies to be included with the project there (see example in the directory)
    • > pod install obtains the dependencies, creates a directory pods/ to hold the dependencies. It also creates a .xcworkspace file to hold the new project with dependencies. Use this workspace in xcode.
    • > pod update will update a dependency to the latest versions. I had to do this in this project because the TSMessage project had suffered from a problem at GitHub and they needed to put in a bug fix which broke my older version. Cocoapods to the rescue!
  • Mantle -- a project from GitHub for creating data models: aids conversions between JSON <--> NSObject (very handy with a json data feed, as we'll see!). I only wish there were more useful tools for directly converting a dictionary into an object. I didn't come up with a good solution for the ozone data, but at least it works, and it's pretty easy to see how it could be improved.
  • ReactiveCocoa -- allows you to use functional programming constructions in iOS apps. Ever run into a spaghetti of callbacks with KVO? Functional Reactive programming may not be the ultimate solution, but it certainly provides a different paradigm that applies to many common situations. Wow. If you haven't used it you, you gotta try this stuff.
  • hpple I needed an HTML parser for iOS. There are a lot of choices, but this one had a decent tutorial at Ray Wenderlich.com, and seemed easy to use (and it was). The parsing problem is pretty small as I only want to do a single page that hasn't changed in years. Regex would have worked, but I wanted to learn how to do it better.
  • TSMessages -- a ticker-style alert message system.
  • git for version control -- the technology driving collaborative development on github. Can't say I've mastered the learning curve yet. This is one of those situations where a video explanation really helps. Jessica Kerr (@jessitron) does a great one, git happens -- with sticky notes! (this particular presentation is for coders coming from a background in subversion, but I've also seen Jessica do a great intro for novices who haven't heard of version control at all. Just search for git happens.

As with any newly hatched project, there's the question of what to add to .gitignore. For a project using cocoapods, see the pros and cons at guides.cocoapods.org. For this project, I want to keep it lightweight, but also keep track of the dependencies. To do this, I'll add the pods/ directory to .gitignore, but keep the podfile, podfile.lock and other files under version control. I used a stackoverflow post to get an appropriate .gitignore file.


TEMIS

The ozone data I'm using is basically scraping a website. It is old website, by the Tropospheric Emission Monitoring Internet Service (TEMIS) and lacks a friendly api. Raw science. To get the column ozone for a location, I need to query the website with a url string of the type http://www.temis.nl/uvradiation/nrt/uvindex.php?lon=5.18&lat=52.1, where the lon and lat values are provided by my code. The response is only available as .html, so I need to get this response and parse it to extract the desired column ozone values. I'll use hpple to parse the html response and it's xpath query system to walk the DOM and extract values from the relevant table.

The entire page is formated as a series of nested tables. The page header is one table; a second table holds the body of the page with one column holding the frame to the left, a blank column, and a column holding the data table I'm interested in.

html -> body -> 2nd table -> tbody -> tr -> 3rd td -> dl -> 
dd -> table ->tbody ->
    tr -> td -> <h2> location </h2>
    tr -> 3x td -> (headers tagged <i>) Date, UV index, ozone
    tr -> 3x td -> (data values) day Month year, .1f, .1f DU

There are a lot of XML parsers and JSON parsers available. In my perfect world, .html files could be parsed as xml, but it doesn't work out that way. Many normal tags in .html are not xml compliant, so most xml parsers break down right away, inclduing the NSXMLParser included in iOS. Parsing .html is not an uncommon problem, so there are a number of librarires on GitHub that people have used. I was able to combine the Hpple parser library with reactive cocoa to create a pipeline straight from the .html response to my model objects.

There are still a few wrinkles that could use ironing. When dealing with locatioins time zones and solar data, times and dates become difficult to handle. The native date-handling in iOS doesn't make it easier. In particular, the UV Index published by TEMIS is for solar noon at the lat,lon of the location. There is no accurate way to use only iOS internals to capture this date correctly, particularly when daylight savings time is taken into account in different jurisdictions. Astronomy calculations are needed to assign these values to correct times.


Lessons

DateFormats:

'HH' parses 24hr time, hh parses am, pm time. 'hh' won't parse 17:20.
'YYYY' doesn't mean 2014. For normal years you need 'yyyy'.
The full spec for iOS 7 is at unicode.org

ReactiveCocoa:

NSLog is useful for getting info on intermediate stages.
There are some mysteries about what filter: and ignore: do that I should get a grip on.
There is a lot of potential in this library, and many functions to explore. map: is your friend. Use it flexibly.

There are many useful discussions in the issues on GitHub and on SO. One word of warning: this library is changing rapidly -- 2.0 was recently released and 3.0 is being crafted. The terminology is shifting, even for core ideas. Older posts may contain outdated code, and that's likely to change even faster with Apple's introduction of Swift!

  • prefer RACSignal over RACSequence.
  • Prefer 1-way binding with RAC over 2-way binding. (see discussion of issue proposign to drop RACChannel)
  • avoid subscribeNext:, doError:, and doNext:. These break the functional paradigm. See this StackOverflow question on fetching a collection Note: RACAble was replaced with RACObserve in 2.0.

I'm finding it somewhat difficlt to chain my signals together in the correct way, probably because I have some processing to do with different parts of the ozone signal.

Here's a helpful bit of code from techsfo.com/blog/2013/08 for managing nested asynchronous network calls in which one call depends on the results of the previous. Note: this is from August, so before RAC 2.0. I believe weakself is now created with the decorator pattern @weakify and destroyed with the decorator @strongify.


__weak id weakSelf = self;
[[[[[self signalGetNetworkStep1] flattenMap:^RACStream*(id *x) {
   // perform your custom business logic
   return [weakSelf signalGetNetworkStep2:x];
}] flattenMap:^RACStream*(id *y) {
   // perform additional business logic
   return [weakSelf signalGetNetworkStep3:y];
}] flattenMap:^RACStream*(id *z) {
   // more business logic
   return [weakSelf signalGetNetworkStep4:z];
}] subscribeNext:^(id*w) {
   // last business logic
}];

Unit Testing

The code coverage isn't good, but I did create some logic tests for my astronomy code using XCTest. My test class wouldn't let me call any private methods in the class I was testing, and I didn't want to move those function definitions into the public interface. Luckily I found another way: create a category in your test class (stackoverlfow).

// category used to test private methods
@interface OWSolarWrapper (Test)

// private functions from OWSolarWrapper.m
// date functions
-(NSNumber *) julianDateFor:(NSDate *)date;
-(NSNumber *) julianCenturyForJulianDate:(NSNumber *)julianDate;
-(NSNumber *) julianDateRelative2003For:(NSDate *)date;

// basic astronomy calcs
-(NSNumber *) equationOfTimeFor:(NSDate *)date;
-(NSDictionary *) solarParametersForDate:(NSDate *)date;

@end
Beware of floatValue:

[[NSNumber numberWithDouble:3.14159283] floatValue]
Write an extension so this raises an error! floatValue is only 24 bit, so it truncates after 6 decimal places. This introduced a bizarre rounding error in my astronomy code.

Location Testing:

Nice comment under the original tutorial:

If you want a specific location not included in xcode, you can create a gpx file for any location. You then import it into xcode to include it in your xcode location toggles.
        - marciokoko on March 4, 2014, 9:18 AM

but to calculate solar positions, I really need TimeZone information along with my testing locations, which .gpx doesn't include.

TODO

  • use different sky images for the background to reflect the weather prediction.
  • The original background of the table view had a blur filter attached. This was accomplished with a library, but I think similar is possible with CALayer. Might be good to explore a CALayer filter on the table-view cells or the underlying scrollview. The filter would respond to scrollview position.
  • UV information is only currently encoded as uvIndex ranges giving a color on the icon background. That's a start, but I'd like to do some calculation and determine two kinds of risk:
    1. . oveall intensity -- risk of acute sunburn, and
    2. . relatively high ratios of UVA at high intensity -- risk of deep damage that is harder for the body to repair.

    To do this, I need to work more at combining the weather signals, possibly changing the model significantly. Thank goodness for git branches!

Friday, 28 February 2014

Intro to GitHub (for scientists)

The problem

I talked to a young woman yesterday who is a bio-engineering postdoc at Stanford. She has some code that she'd like to 'upload' to GitHub. She admitted that, well, actually, she hadn't managed to get any of her code onto her GitHub account yet, and she looked so overwhelmed and dejected that I felt bad. I know that frustration.
So here is yet another blog post to try and help with the learning curve that is git. I'm writing this for the scientist who has written some code and wants to share it on GitHub. Most scientists write code in order to accomplish a particular task, and thus are not familiar with professional programming practices, including writing documentation, unit testing, SCM (source control management) and version control. There are great benefits to learning these techniques, and their usefulness is becoming more and more apparent to academics as research relies more and more on computer programs. In fact, the table of contents for the journal Nature Methods just arrived in my inbox with a lead editorial on reproducible research. I quote:

Nature Methods strongly encourages researchers to take advantage of the opportunity that code repositories, such as GitHub, provide to improve a software tool before submission. Even if others do not examine and test the code, the act of preparing the code and necessary documentation for deposit and use by others will help avoid delays in publication.
In short, if you are coding and publishing work derived from your code, the process of uploading your code for collaboration will help bring it up to a publishable quality. And yes, it's even more important than making the figures pretty.

GitHub == collaboration

Firstly, if you just want to upload some code, you need to take step back, a deep breath and 'think different'. There is no 'upload' button on GitHub for a good reason. It is not built for uploading code and leaving it to rot on a server, but for fostering collaboration between programmers. Thus, in order to share your code on GitHub, you first need to get it ready for collaboration. To do that, you need to set up version control. This is somewhat more complicated than finding the 'track changes' setting in Microsoft Word, but it is also far more useful.
As you try to think different, keep in mind that GitHub was built with a particular set of workflows in mind. Those workflows have to do with managing a code-base that is constantly being updated by multiple contributors. GitHub tries to ease the difficulties of this type of collaboration by bringing several things together.
  • version control -- similar to 'track changes' in a word processor, but for whole projects
  • cloud-based storage -- simultaneously backup and share your code
  • user accounts -- keep it private, share with a team or make it public as your needs change
  • social -- there's messaging, so you can talk, argue, document, discuss.... collaborate!
But version control is the main thing that sets GitHub apart from any number of social sharing platforms, and makes it so powerful for people who code. To use GitHub effectively you therefore need to understand the basics of version control with git. It's hairy and scary at first, but no worse than... well, ok, it is worse than a lot of things, but sometimes a learning curve is the price you pay for really capable tools. So, put a set of bookmarks in your browser, make a cheatsheet and keep it handy. A local cheatsheet with a good searchable title does wonders. If you're thinking 'Oh, I'll just use it once' or 'I'll remember', well, git is for professionals. Are you a professional?

Git == version control

Version control is to 'track changes' for a text document, as Superman is to Tarzan; as Microsoft Word is to TextEdit, as New York City is to Detroit. Firstly, with version control, you are tracking changes to an entire project, not a single document. Changes are tracked line by line with comments and attribution through time as the project grows, changes, and splits; as subroutines develop into full projects of their own; as new owners take control of the code base. It is flexible and thorough and reliable. You should learn to use it.
Use case: You've got some code you developed for your thesis that you want to upload to GitHub. Maybe someone else will find it useful. There are GUI front ends to git, which may help with many tasks, but git was designed to be run from the command line, and this simple use case it not too difficult to master at that level, so let's just go for it.

Download and install git

Walk through the steps at github set-up. Today, they suggest that you download their native app, but note that this only manages part of the workflow. The steps you have to do to get git working still involve the terminal. Be brave. The steps are:
  1. get a GitHub account
  2. download and install git
  3. link your local system to your GitHub account
    • tell git your name, email, GitHub login information
    • set up security keys so that GitHub knows you are you

Prepare your codebase

There are a couple of adjustments you probably want to make before releasing your code in the wild. GitHub recommends that all code comes with a License, a Readme and a .gitignore file.
  • License If your code comes with a license, it's easier for collaborators to re-use the code and build on it. Specifically, it makes clear what they are allowed to do. It may not be important to you, but your code will be easier to share if you make it clear what the rules are.
  • Readme
    You have to explain your work at some point. If you do it in a file named README, GitHub will automatically put it on the front page of the repository. This is very helpful for anyone trying to understand what you did and why. The README can be just a text file. If it is in markdown, perhaps with additional flavoring GitHub will render it with headings and styles, which is much nicer for the reader and not difficult for the writer.
  • .gitignore
    Some files don't need to be tracked. For instance, some old Mac directories contain .DS_Store files with directory display information for the Finder app. That doesn't need to be part of the repository. So here's my .gitignore for an old MatLab project:
      $cat .gitignore
      .DS_Store 
    
    Pretty simple. You might also add *.log or tmp/ to the .gitignore file, depending on your context. Basically, any files that are automatically generated or updated on compile should not be tracked.

Make a local repository

The project you want to get onto GitHub probably consists of a directory or directory tree containing a series of text files, and possibly some image files or data files. In order for git to track changes to this project, you have to put these files into a repository. This is simple to do once you've prepared it for sharing.
Find your command line. On a Mac, you can use the Terminal app. Navigate to the directory holding your project. If you have never ever used the command line before, this might be challenging. If you want to dive in, you can certainly do so with three little commands: ls, cd, pwd. You can look at the man pages for these commands by typing, for example $man ls, or you could try a crash course in using the command line.
Once you get to your project directory, type:
$git init   
You should see a message from git, something like:
Initialized empty git repository in /Users/suz/programming/octave/OrX/.git/
This initialises an empty repository, which looks like a file named .git. You can check that it's there by typing
$ls -a
Git gives some feedback about what it has done, but I often find it useful to check with
$git status
after each command to see what has happened.
Now we can get the project into the repository. To do this, first type
$git add .   
This prepares git to add your files to the repository, a process known as 'staging'.
Note: The . tells git to stage all the files in the directory tree to the repository. This is very handy when we add files because they don't all have to be specified by name. On the other hand, it isn't ideal, because there are often binary files or .log or even image files that update automatically. You won't want to keep track of those changes. Fortunately, git will automatically look for the .gitignore file we already prepared to get the list of exceptions.

Ready for some commitment? Type:
$git commit -m 'initial commit' 
You should get a full list of the files that git is committing to the repository. Check the status when it's done and you should give a reassuring message:
# On branch master
nothing to commit (working directory clean)

Success! now your project is actually in the repository and git can track any changes to the files. The repository will also keep all the messages that you put with each commit. Always use the -m flag and take the time to add a meaningful message.

Congratulations! Your code is now in a git repository, under version control. You are ready to collaborate.

Share your work


Make a repository on GitHub

  1. Log into your GitHub account
  2. Create a new repository on GitHub
    On your profile page, in the 'Repositories' tab is a list of repositories that you've contributed to. At the upper right should be a bright green 'New' button.
  3. Follow the directions in the form, adding an informative description so others know what treasure they have found.
Congratulations! You have a GitHub repository to share your code from!

Link the repositories

In git terminology, the current state of your code is the head of a branch. By default, the initial commit is called the 'master' branch. You can make other local branches, and probably should to try out new features. You can also make remote branches. At this point, your new GitHub repository is essentially an empty remote branch. By custom, this branch will be referred to as 'origin'. To point git to it, type (on one line), with your appropriate username and project title:

$git remote add origin https://github.com/username/project.git

This command translates roughly as "Dear git; Please be so kind as to add a connection to a remote repository. I will be referring to the remote repository as 'origin' in our future correspondence. It can be found at https://...... Thank you for your kind assistance in this matter. Sincerely, yours truly, esq."

Upload your code

Ok, ok, there is no upload on GitHub, but it is payoff time. Once you have a local repository linked to a remote repository, you can just push the code from one to the other.

$ git push -u origin master

Translation: "Dear git; Please push the recent changes I committed to my local repository, known as master, into the remote repository known as origin. Also, please be aware that I'd like this to be the default remote repository, sometimes referred to as 'upstream'. Thank you again for your kind assistance. I am forever in your debt. Sincerely, Yours truly, esq. and, etc."

Success!!! You have now successfully pushed your code to GitHub.

Or at least I hope you were successful. If not, if you've tried to follow this post and the directions at GitHub and you still feel lost, there is more help out there. Many universities are running Software Carpentry bootcamps to help students and faculty develop more professional programming skills. The skills taught aim to improve software collaboration and impart the skills needed to carry out reproducible research. Two key tools they teach are version control with git and collaboration via GitHub.
Live long and collaborate!

Wednesday, 29 January 2014

UV reactive bead spectra

UV reactive bead spectra

Background

I was interested in designing some educational experiments using UV-reactive beads to teach about the presence/absence and intensity of UV light under different atmospheric conditions. The beads turn from clear or colorless to various bright colors when exposed to UV light. Unfortunately, these beads are just too sensitive – they even react strongly to the stray 400 nm light coming through glass windows. And reach full color intensity within a minute, even at 51° N in February! It's fun to watch, but not very useful for detecting physiological UV conditions for vitamin D synthesis.

I don't have a lot of scientific information abou these, so the range of UV light needed for the color change is not well characterized, and the resultant absorbance spectra of the beads (related to their aparent colors) is not readily available. I decided to study them with a reflectance spectrophotometer to see what I could learn.

It may not be useful for the educational project I had in mind, but I'll write it up here anyway, along with the R-code used to analyze the data….

Experimental

I ordered UV Reactive beads from UV gear, UK.

I used a table in the garden on a sunny day for the experiments. The sky was open directly above and direct sunlight came from the south. There were a few leafless tree branches in the way, and some buildings nearby, so it was not full sky exposure, but at least there was full direct sunlight. The table was covered with a small white blanket to provide a consistent background. The beads equibilbrated 10-15 min in direct sunlight before spectral measurements were made.

I used an Ocean Optics model USB2000+ spectrometer connected to a 1 meter UV-Vis fiber optic cable (part number QP400-1-UV-VIS from Ocean Optics) with a cosine correction filter at the end (Ocean Optics part CC-3-UV-S) to minimize stray light. The spectrometer was connected by USB to a MacBook Pro (OS X mountain lion 10.8.1) running SpectraSuite software for data collection. Spectra were measured with 30 averages in reflectance mode and saved as tab-delimited text files. The reflectance measurements required a 'reference spectrum' (light spectrum) of the white blanket and a second, 'dark' reference, which was obtained by blocking the end of the cosine correction filter. To collect the reflectance spectra, I pointed the fiber optic light pipe at individual beads from a distance of < 0.5 cm.

Spectral analysis can be done in the SpectraSuite software, however, many basic functions had not (yet?) been implemented in the OS X version of the software. Also, I wanted to practice my R skills, so I decided to load it into R and see what I could learn. R has multiple spectroscopy packages, and in fact a new one has come out since I started this project. I looked into using hyperspec, but the data structure seemed overly complicated for the simple analysis I had in mind. So what follows is my simple R analysis of reflectance data on sunlight-exposed UV beads.

Load in the Data

# change working directory
try(setwd("/Users/suz/Documents/vitD Schools/UV bead exp 19022013/spectrometer data/"))

# data is tab-separated, with a header. The end of file wasn't recognized
# by R automatically, but a read function can specify the number of rows
# to read.
spec.read <- function(spec.name) {
    read.table(spec.name, sep = "\t", skip = 17, nrows = 2048)
}

# the function can then read in all the files ending in '.txt', put them
# in a list.
spec.files <- list.files(pattern = ".txt")
df.list <- lapply(spec.files, spec.read)

# convert the list to a matrix: the first column is the wavelengths and
# the other columns are experimental data -- one reflectance measurement
# at each wavelength.
spec.mat <- matrix(df.list[[1]][, 1], nrow = 2048, ncol = (1 + length(spec.files)))
spec.mat[, 2:11] <- sapply(df.list, "[[", 2)

matplot(spec.mat[, 1], spec.mat[, 2:11], type = "l", lty = 1, lwd = 3, xlab = "wavelength (nm)", 
    ylab = "reflectance", main = "UV color changing beads, after UV exposure")
text(500, 25000, "Raw Data")

Looks like we need to do some clean-up!

Clean up the data

Baselines and edges

At the edges of the spectral range, the reflectance data is dominated by noise, so it isn't useful. The baselines for the different spectra also need aligning, and we'll scale them to the same intensity for comparison. The intensity range observed in the data depends somewhat on the angle of the probe, even with a cos filter in place.

# define terms for the processing:
nm.min <- 400
nm.max <- 800  # edges of the displayed spectrum
base.min <- 720
base.max <- 900  # define baseline correction range
peak.range.min <- 420
peak.range.max <- 680  # where to find peaks for scaling.

# remove ends of the data range that consist of noise
spec.mat <- spec.mat[(spec.mat[, 1] > nm.min) & (spec.mat[, 1] < nm.max), ]

# normalize baselines, set baseline range = 0.
spec.base <- colMeans(spec.mat[(spec.mat[, 1] > base.min) & (spec.mat[, 1] < 
    base.max), ])
spec.base[1] <- 0  # don't shift the wavelengths
spec.mat <- scale(spec.mat, center = spec.base, scale = FALSE)

Choose colors for the plot by relating the file names to R's built-in color names.

bead.col <- sapply(strsplit(spec.files, " bead"), "[[", 1)
# replace un-recognized colors with r-recognized colors (see 'colors()')
bead.col <- gsub("darker pink", "magenta", bead.col)
bead.col <- gsub("dk ", "dark", bead.col)
bead.col <- gsub("lt ", "light", bead.col)
bead.col <- gsub("lighter ", "light", bead.col)

# plot corrected data
matplot(spec.mat[, 1], spec.mat[, 2:11], type = "l", lty = 1, lwd = 3, col = bead.col, 
    xlab = "wavelength (nm)", ylab = "reflectance", main = "UV color changing beads, after UV exposure")
text(500, 10, "Baseline Corrected")

From this plot, we can see that the lighter colored beads have smaller peaks than the darker beads. The lighter color probably represents less dye in the beads. There seems to be a lot of variation in the peak intensity of some beads, particularly the yellow beads and the dark blue beads. Based on the width of the peaks, the purple and magenta beads appear to have mixtures of dyes for both pink and blue colors. The dark blue beads appear to be either a mixture of all the dye colors or a mixture of pink and blue, but much more dye is used than for the paler pink or blue beads. The yellow bead spectra is oddly shaped on the short wavelength side, probably due to instrument cutoffs around 400 nm.

Scaling and smoothing

Now I'll scale the data to the same range. It turns out that the R command 'scale' is perfect for this.

# scale the peaks based on the min reflected intensity
peak.range <- which((spec.mat[, 1] > peak.range.min) & (spec.mat[, 1] < peak.range.max))
spec.min <- apply(spec.mat[peak.range, ], 2, min)
spec.min[1] <- 1  # don't scale the wavelengths
spec.mat <- scale(spec.mat, center = FALSE, scale = abs(spec.min))

The spectra are also jittery due to noise. This can be removed by filtering. This filters over a range of 10 points.

data.mat <- spec.mat[, 2:11]
dataf.mat <- apply(data.mat, 2, filter, rep(1, 10))
dataf.mat <- dataf.mat/10
specf.mat <- matrix(c(spec.mat[, 1], dataf.mat), nrow = dim(spec.mat)[1], ncol = dim(spec.mat)[2], 
    byrow = FALSE)
matplot(specf.mat[, 1], specf.mat[, 2:11], type = "l", lty = 1, lwd = 3, col = bead.col, 
    xlab = "wavelength (nm)", ylab = "reflectance", main = "UV color changing beads, after UV exposure")
text(500, 0.2, "Scaled and Smoothed")

I could attempt to prettify the spectra further by using actual colors from pictures of the beads. It looks like the Bioconductor project has a package 'EBImage' that should be just what I want, but it looks like I need to update to R version 3.0.1 in order to run it. So I guess I get some spot colors from JImage.

bead.yellow <- rgb(185, 155, 85, maxColorValue = 255)
bead.orange <- rgb(208, 155, 82, maxColorValue = 255)
bead.purple <- rgb(110, 25, 125, maxColorValue = 255)
bead.pink <- rgb(190, 100, 120, maxColorValue = 255)
bead.dkblu <- rgb(22, 18, 120, maxColorValue = 255)
bead.ltblu <- rgb(130, 135, 157, maxColorValue = 255)
bead.dkpink <- rgb(200, 25, 140, maxColorValue = 255)

bead.col2 <- c(bead.dkpink, bead.dkblu, bead.dkblu, bead.pink, bead.ltblu, bead.orange, 
    bead.pink, bead.purple, bead.yellow, bead.yellow)

matplot(specf.mat[, 1], specf.mat[, 2:11], type = "l", lty = 1, lwd = 3, col = bead.col2, 
    xlab = "wavelength (nm)", ylab = "reflectance", main = "UV color changing beads, after UV exposure")
text(500, 0.2, "Colors from Photo")

Analysis

I can carry this anlaysis further by quantifying the wavelength of the peak absorbance and the peak width (usually Full Width at Half Maximum – FWHM) for each spectrum. This could be useful in further analyses, reports, or as a feature in a machine learning approach.

To extract this information, I could try to fit a series of gaussians to the peak, representing the fraction of pink, blue or yellow dye present, but the quality of the data doesn't really justify this, particularly as I don't have a good shape for the yellow absorbance peak or adequate reference data for each of the dyes. As a quick and dirty method, I'll take the median position of the data values that are at 95% of peak. That should give something in the center of the peak. Since the peaks have all been scaled, that corresponds to the center of the data values < -0.95.

Likewise, the usual peak width (FWHM) woud be the range of values < -0.5, however, the poor baseline at shorter wavelengths makes this range more or less unusable. For this reason, we can take a more well-behaved approaximate peak width measurement as the range of reflectance values < -0.8.

# approximate peak wavelength
indcs <- apply(specf.mat[, 2:11], 2, function(x) median(which(x <= (-0.95))))
pks <- specf.mat[indcs, 1]

# approximate peak width
lowidx <- apply(specf.mat[, 2:11], 2, function(x) min(which(x <= (-0.8))))
highidx <- apply(specf.mat[, 2:11], 2, function(x) max(which(x <= (-0.8))))

pkwidth80 <- (specf.mat[240, 1] - specf.mat[140, 1])/100 * (highidx - lowidx)

features <- data.frame(pks, pkwidth80, bead.col, bead.col2)
features <- features[order(pks), ]
features
##      pks pkwidth80  bead.col bead.col2
## 10 436.4     75.21    yellow   #B99B55
## 6  449.8     88.86    orange   #D09B52
## 9  451.7     98.07    yellow   #B99B55
## 7  529.3    111.35      pink   #BE6478
## 4  533.6     99.18 lightpink   #BE6478
## 1  549.8    110.61   magenta   #C8198C
## 8  568.7    133.10    purple   #6E197D
## 3  581.1    179.93  darkblue   #161278
## 2  585.4    136.79  darkblue   #161278
## 5  603.0     76.32 lightblue   #82879D

# save your work!
write.csv(features, "UVbead data features.csv", quote = TRUE)

One side effect of this is that we now have a small table of metrics that describe the relatively large original data set reasonably accurately and could be used to classify new data. In current data analytics parlance, this is known as 'feature extraction'. In traditional science, these characterizing features could be combined with others from different studies (crystallography, electrochemistry, UV-vis, IR, Raman, NMR, …) to help predict the effects of a chemical change or a different chemical environment on the behaviour of the molecule. Such studies are traditionally used to help direct synthetic chemists toward better products.

Conclusions

From this analysis, we can see that the blue beads are absorbing at longer wavelengths than the yellow, and the pink and purple beads have absorbances in between. The darker colors have broader absorbance peaks than the lighter colors, with the darkest blue having a range that appears to cover the purple, yellow and blue regions. These darker beads probably contain combinations of the dyes used for the different colors, and not just more of the dye used for the light blue beads.

The reflectance spectra are not as observant as our eyes. For instance, the yellow and orange beads are readily distinguished by eye, but not so clearly in the observed spectra or the extracted features. This may be due to the 400nm cutoff of the light pipe, which distorts the peak shapes for the yellow and orange beads. Ideally, we could observe the changes in the absorption spectra over the whole range 280-800 nm as a function of time. The current reflectance spectrometer setup, however, is only capable of capturing the 400-750 nm range. This means that we do not have access to the interesting behavior of these dyes at shorter wavelengths.

Chemically, I expect that absorption of the UV light in the 300-360 nm range causes a reversible conformation change in the dye molecules, as has long been known for the azo-benzenes and their derivatives. After the conformation change, the absorption maximum of the dye is shifted to a much longer wavelength. If the UV dyes in these beads are very closely related to each other, is likely that the yellow beads absorb at relatively short UV wavelengths and the blue beads at longer wavelengths before the transition, however, without measuring the UV absorption spectra we cannot know this. It is quite possible that their UV absorption spectra are nearly indistinguishable and the effects of their chemical differences are only apparent in the visible spectra. Without access to better hardware or more information about the molecules involved there is no way to know.

Next steps

We do have access to one thing that varies in the appropriate way. The experiments shown here were taken at relatively low UV light levels (February in England). During the summer, the angle of variaton of the sun is much larger. At dawn, it is at the horizon, while at noon, it is about 60° higher. Since light scattering in the atmosphere is a strong function of wavelength, the relative intensities of light at 300, 360, and 400 nm will vary with the angle of the sun. If the beads have different absorption peaks in the UV range, the time dependence of their color changes should vary by the time of day.

The simplest way to measure this is not with a spectrometer, but probably by following color changes in video images.

Tuesday, 10 December 2013

NHS hack day thoughts

What a glorious weekend! The sun finally came out, and while my family was out enjoying the countryside and biking to the beach, I spent it in central London at the NHS Hackday.

Not that I'm complaining. I had a great time. There were some really talented people there, and some very committed parents, doctors, programmers and just plain technically minded people. It was a really interesting weekend.

The hackers present were a pleasant mix of odd-ball grad student types, young doctors, programmers, developers and random 'IT' people, all with an interest in trying to contribute something to the efficiency, ability and smooth running of the NHS. The elephant in the room is whether any of these projects will ever become useful. Several applications had very useful ideas. The winning app 'Waitless' aimed to provide an SMS service in which people could send an SMS and get information about the distance to local NHS services and the likely waiting time once they get there. This way, someone with an earache could make an informed choice to go to their local walk-in clinic instead of the A&E department depending on wait times, opening hours, and distance.

The progress made on these apps over the course of the weekend was astounding, with several nearly becoming usable services, and certainly good proof of principle demos by the time of presentation at 3pm on Sunday. It is truly amazing what a team of 8 can get done in a weekend with modern developer tools, available APIs, open source software, and online services. Wow.

And me? well, not so much...
I ended up working on the aptly named FAIL project: 'Fatal Accident Inquiry Learning', which attempted to apply machine learning techniques to Scottish Fatal Accident Inquiry (FAI) Reports. Unfortunately, I spent most of the first day struggling to get nltk and various supporting technologies installed on my system and most of the second day learning the very basics of working with these technologies.  Carl had somewhat more success in getting some snippets of the reports into Carrot2, but the results were less than impressive.

Challenges:
1) I need more experience with Python
Everything I know about Python, I learned from Code Academy and from my homework for 'Intro to Data Science' on Coursera. The homework was a pretty good introduction for this project, as it involved sentiment analysis of a tweet stream. I was able to do some basic filtering and use the structure of the base homework code. There were some difficulties in translating the learning from the homework analyses of short tweets to these much larger, richer records, and I struggled to create a workflow between parts of the analysis.

2) I need more time with natural language processing
The FAIs are long legal text documents. It is possible to extract text from them, but it isn't easy. The text has some consistent elements, but is not in a consistent form. Some dates are 'day month year' format, while others are given as 'month day, year' and still others are 'day of the month of year'. This makes it somewhat difficult to even extract basic information such as the age of the victim. It should be possible to get this by using a grammar with NLTK, but well, I didn't manage to come to grips with it in 45 minutes I gave to it. Perhaps not surprising. Similarly with bi-grams, tri-grams, collocations and point mutation importance (PMI) to collect information on phrases and unusual words. I learned a lot, but wasn't able to put much of it into use. Yet.

...hopefully I'll find some time to try out topic modelling on this data at some point.

3) Relevance of the project
Ideally, we'd like to analyse these reports and make some inferences that are relevant for the NHS and that could lead to improvements in quality of care. Unfortunately, the data set we are looking at is not like hospital episode statistics -- it is not a statistic. Although there were some 1652 fatal accidents in Scotland in 2011, only 28 FAI reports were published that year. Our dataset consists of the 82 such published inquiry results from the last few years. Some inquiries are published long after the incident, but this indicates that inquiries are held for less than 2% of fatal accidents.

Inquiries can be called whenever there are unusual circumstances. They are required in some circumstances, such as when a death occurs in custody. By definition, then, these accidents are the outliers. Some of them are candidates for the 'Darwin Awards': tragedies begot by stupidity. Others are simply tragedies.

The Scottish authorities hold these inquiries with an eye toward preventing further accidents, and such investigations do have impact on our daily lives. Protocols for how often the highway lines are repainted, police guidelines for how people in custody are transported, and yes, even those ubiquitous labels: this is not a toy; not for children under 3 yrs of age; do not play on or around. FAIs are the fault-checking analyses that lead to health and safety advice.

So we tried various approaches to extracting information and comparing text in the reports, but ultimately we did not come up with a truly compelling use case for the data or inferences from it.

Observations on the data:
  • Accident statistics are interesting reading.
  • Each of these accidents is a story of its own. 
  • Men are in more fatal accidents than women. For all ages, nearly 65% of the accident victims are men.  For men < 65 years of age, the ratio climbs to nearly 75%. 
  • Fatal accidents are more common in older people. In 2011, 57% of the male accident victims were over 65 yo. At the same time, among female victims, 76% were over 65 yo. 
  • In younger age groups, poisoning is the most common cause of accidental fatalities. In 2011, there were no poisonings in children < 15 yo. The statistics include alcohol poisoning.
  • Falls are the most common fatal accident type for people over 65 years, and over 60% of the victims are women, showing a sharp contrast with all other accident types and ages. 
Observations on the hack day:
  • don't forget your coffee cup
  • wander around and see what different groups are doing  -- don't wait until the pub afterwards to find the person with a degree in computational linguistics!
  • what's your goal? if it's social, be social. If it's coding, join a group
  • you will probably learn more from a larger team with more varied skills
  • how competitive do you want to be? 
  • a video of a good use case is impressive
  • it's often more efficient to ask for help


 

Monday, 30 September 2013

Coursera: Introduction to Data Science (Course Review)

I finished this a few months ago, but it will probably be offered again, so here's a review. This course was taught by Bill Howe at University of Washington and offered as a MOOC on Coursera.

Course Description: 
The Coursera description promises newbie to data ninja in 8 weeks. Workload 8-10 hours/ week. Those of us who have finished our statistical mechanics homework at 2 AM know that such promises are not only empty, but rather a guarantee of a course that is over-ambitious. As the description implies, this is an overview course that tries to do too much. Every student should realise this at the outset: an introductory course that claims to cover everything is certain to be a rough ride. (I know. I've taught some.)

Lectures: 
The lectures covered some really interesting content, and the lecturer appears to know the industry very well, particularly the Microsoft perspective and tools. I assume that many of his continuing education students are Microsoft employees who want to update particular skills. Such students are not the run-of-the mill for UW.

When I was a TA at UW, the undergraduates needed slow feeding with very small spoons. Lecture halls were filled with slumped bodies under baseball caps. The evening courses, in contrast, were filled with lively, interested, adults who learned independently and came to class with lists of questions. This course is aimed at those active, interested adult learners. That said, the number of hours listed for this class is a gross underestimate for the material covered and the assignments given.

Professor Howe's lecture style is not always engaging, and a lot of material is covered. There were often over 3 hours worth of lecture material to review during the week. Along with following links and reading supporting papers, this left very little time for the assignments themselves. Prof. Howe did a good job of introducing and comparing a range of current technology choices (particularly the comparison of different database technologies). As a data science newbie, I would have liked a bit more information and emphasis on use cases for different types of databases.

The database parts of the course were well presented, and this covered subjects that I hadn't seen in my other work. The data analysis elements were not so clearly taught, though, and there are better (slower) ways to learn this material on Coursera. If you have time, Jeff Leek's course 'Data Analysis' covers this much more thoroughly. Andrew Ng's now legendary Machine Learning course is also good, although more mathematically oriented, with less emphasis on organisation, data munging techniques, and communicating results.

Later lectures in this Intro. to Data Science course appeared to have incorrect answers in the in-lecture questions. I got bored of trying to keep track of the errors and inconsistencies in the course. The material needs a thorough editing before the next showing.

Assignments:  
The lectures were not particularly good preparation for the homework assignments. A lot of independent learning was required to make progress in the course. The assignments were also relatively difficult compared to what I expected from the course description. The first assignment was a sentiment analysis of a Tweet stream written in Python. I have a pretty good programming background, having started with Basic back in 1983, visiting Fortran, MatLab, Igor, Unix utilities, C, Ruby and continuing to objective-C, R and Functional programming in Scala. I pick things up quickly. The course description did not require a programming background, yet I had to spend several hours learning the ins and outs of Python from Code Academy before I could get a handle on the assignments.

The level of the first assignments was not commensurate with expectations from the course description. I learned a lot, more than I expected, in fact, and I can now implement a matrix multiplication in Python, SQL, or MapReduce based on the homework assignments. The auto-grader for the 1st assignment never did accept my answer for the final part. It also didn't give sufficient feedback for me to solve the problem, which probably had something to do with text encoding, but was very frustrating none-the-less. This sort of issue doesn't teach much. Save your perseverance for things that matter.

Overall, the assignments were challenging. I learned a lot, but not always what the point of the assignment was. I think there were a lot of complaints (more than normal) about the difficulty of the assignments, and later assignments were quite a bit easier than earlier ones. Assignments covered:

  • Python:   Tweet stream sentiment analysis
  • SQL:   Queries, tables and matrix multiplication
  • Tableau Visualization:    FAA Bird Strike Data 
    • write-up and peer assessment
    • note: I had to use Tableau via Amazon web services as it only runs on Windows.
  • MapReduce:   data joins, basic network analysis, matrix multiplication
  • Kaggle: Take part in a competition (I did facial keypoints detection)
    • write-up and peer assessment: ranking on the leaderboard did not matter.
A couple of the assignment deadlines were changed after the deadline had passed. This is very unfair to people who have worked hard to make the deadline, although it was reasonable in the case of the MapReduce homework where we were using a new web system that was supposed to be able to handle the volume of students. This is a continuing problem with MOOCs that have > 100k students enrolled. Any time the professor makes an assignment that will run on new technology, be prepared for a very frustrating experience. In my opinion, new web-based technology should not be used for graded assignments in MOOCs. They should be tested first as an optional assignment or a staged assignment so that 100k students are not accessing it in the same week.

Overall Recommendation:
Students:  I hope that the professor will offer this course in a pared-down form. As it is, if you're already awesome at Python and SQL, go ahead and dive in. Everyone else should consider this a taster course and audit only, at least with the current assignments. Be selective about which parts you choose to look at. If you experience slowdowns or poor behaviour with particular technologies in the assignments, put it aside and try again when the course is over or the deadline is passed. It seems like a class, but it's a free platform and you get what you pay for. A lot of professors are using this to try out new technologies, so don't expect it to all work as advertised.

Saturday, 6 July 2013

Idiom in R: results you can C

Computing for Data Analysis was a pretty good introduction to R, but did not really talk about R idiom, which can make the difference between code that runs and code that runs quickly. Here is a basic example.

Using sprintf statements for formatting filenames: Consider a series of files. The goal is to read them all into R, but the filenames include a constant width variable: we're looking to load filenames such as ./data/001.csv and ./data/011.csv up to ./data/999.csv. How do we construct the name strings in R?

The numbers in the file names need to be padded and converted to the appropriate strings. Here are three ways of doing the padding.

The R way:

# setup
directory <- "data"
id = 1:999

# method 1
pad.R <- function(id) {
    num <- sprintf("%03d", as.integer(id))
    path <- paste("./", directory, "/", num, ".csv", sep = "")
    return(path)
}

A brute-force method:

# method 2
pad.brute <- function(id) {
    num <- rep("", length(id))
    for (n in 1:length(id)) {
        if (id[n] < 10)  num[n] <- paste("00", id[n], sep = "") 
  else if (id[n] < 100)  num[n] <- paste("0", id[n], sep = "") 
  else num[n] <- as.character(id[n])
    }

    path <- paste("./", directory, "/", num, ".csv", sep = "")
    return(path)
}

… but we know that for-loops are notoriously slow in R, so we could take a hybrid approach and define a function to take a single number as input and convert it. Then that function could be used with one of R's apply methods to convert the vector in one go.

A hybrid method:

# method 3
padder <- function(num) {
    if (num < 10) return(paste("00", num, sep = "")) 
 else if (num < 100) return(paste("0", num, sep = "")) 
 else return(as.character(num))
}

pad.hybrid <- function(id) {
    num <- sapply(id, padder)
    path <- paste("./", directory, "/", num, ".csv", sep = "")
    return(path)
}

Comparison

These approaches all give the same results, but they are noticeably different.

system.time(path <- pad.R(id))
##    user  system elapsed 
##   0.001   0.000   0.001
system.time(path <- pad.brute(id))
##    user  system elapsed 
##   0.007   0.000   0.008
system.time(path <- pad.hybrid(id))
##    user  system elapsed 
##   0.004   0.000   0.004
path[c(3, 13, 103)]
## [1] "./data/003.csv" "./data/013.csv" "./data/103.csv"

For speed, they are equivalent when run on 1 or two elements at a time. However, when run on the full 999 element vector as shown here, both the 'brute force' and 'hybrid' methods are significantly slower than sprintf.

The discussions on the course forums did give a different perspective. Several self-identified 'professional programmers' preferred the if, if-else, else approach I've used in both methods 2 and 3. They considered it more readable and thus more maintainable.

I don't think this is the best approach. If you are a professional programmer, you are familiar with idiom, in whatever language you work in. You know that there are readable, maintainable, ways of doing what needs to be done efficiently. At it's root, deep down underneath, R is in the C family of languages. The basic in/out is based on the C standard library <stdio.h>. The professional way to use R is to use that R idiom efficiently and in a way that other R programmers will understand.

So learn your sprintf formatting codes. They may look like magic numbers the first time you meet them, but they are systematic and ubiquitous. They will be useful in many other contexts, including modern languages like Python and Java and therefore even Scala and Clojure. They will also speed up your code, and don't worry, most other professionals will understand them.

Tuesday, 16 April 2013

Big Data Hackathon London: A few lessons learned

I spent most of my weekend at the Big Data Hackathon London. I'm not hard-core, and I didn't pull an all nighter, but then, even the winning visualisation team said the code written between 4-7 am was rubbish. Better to get some sleep. This was my first ever 'hackathon', and part of the fun was just observing the phenomenon.

The basics: 

The hackathon was organised by Data Science London, and I found out about it through the Data Science Meetup group. I highly recommend this group if you are interested in learning about current methods and tools in Data Science. Their meetings are very interesting and educational, but you have to be very quick with the RSVP -- there's a lot of competition for the limited spaces. As always, the organisers did a great job. I didn't manage to take home any of the swag or awards, but I certainly drank my share of the coffee. And the whole weekend was totally free. Well done!

The hackathon took place at The Hub Westminster, which was a very nice, light, open space. The talk space holds about 100 or so people, and there is desk space and stand-up area where the food is served for milling around and meeting people. The space is well organized with good systems for internet and power. A pleasure to work in.

  • Lesson learned: Bring your own mug to cut down on waste 

The hackathon had three different categories of challenges that teams could submit.
  1. data science challenge 
  2. data visualization challenge 
  3. free-style data challenge 
Most people who came did not have a team lined up. The winning team in the data visualisation challenge got together when two of them carried signs around saying 'Node.js' and 'd3'. The other two thought this was a good idea, and a winning team was formed. One of the team members later said that their goal had just been to improve their javascript skills. The visualisation was quite lovely, and should be showing up in a 'major UK publication' someday soon.
  • Lesson learned: MongoDB + Node.js + d3 = powerful stuff 
  • Lesson learned: Connect a team through the technology you want to learn 
The hackathon started out with a presentation on Microsoft Azure and the suggestion that we use a free trial account (good for 3 months) to do our analysis.

After the talk, someone asked about setting up R on the system. I approached them after the talk, and that was the beginnings of a team. Our team, 'State of the A[R]t' set up an Azure account, and we were able to get R working on a Ubuntu virtual machine without much difficulty. Wenming Ye's blog was helpful for this. It's probably even more helpful if you want to use Python. The Kaggle assessment of the data science submissions relied on the ROCR package, and this relied on gplot, which required us to build R 3.0 from code. Fortunately, one of our team was ace at this and we had it running quite quickly. Meanwhile, the rest of us were looking at the data.

  • Lesson learned: Technology is broad and deep. Someone will like doing the parts you hate. Let them do it. (I have to re-learn this continuously. I try to do too much on my own.) 

The hackathon has a tight schedule. There were talks all afternoon, and if I had gone to all the talks, I would not have made much progress with the data. However, missing all the talks probably wasn't the best strategy either. Next time, I'll try to keep my head up and look around for which talks are truly interesting. Talks were presented by the hackathon sponsors, so highlighted their newest technologies. I can only hope that the talks will be posted so I can catch up with the parts I missed.

  • Lesson learned: It's about learning. Think about what are the learning opportunities today? Will the talks be available tomorrow? 

On Sunday, the data analysis winners each gave a brief indication of what they did. We ended up 89th overall, and we only did that well because one of my team-mates took a careful look at the original benchmark code. I don't think we were alone in this, as 15 teams finished within 0.00001 of us. Re-assuringly, though, we were working along similar lines to the winning team.

  • Lesson learned: Find a good starting place. 

The benchmark was not quite the simple logistic regression we expected from the description. We would have done much better if we had taken the time to look at the code for the benchmark as a first step.

  • Lesson learned: Work efficiently -- write functions or scripts for each step. 

At 12:32 on Sunday, I had a model that resembled the winning model. Maybe it would have done better than 89th, but I didn't get a chance to find out. It took me too long to make the model into a submittable prediction! I should have anticipated this, because the 1st submission also took ages. If I had written some of the steps into functions, it could have been much faster, and the team would have done better.

Overall, it was great fun. I met some lovely people and I learned a lot. Coursera's offerings, including Jeff Leek's 'Data Analysis' and Roger Peng's 'Computing for Data Analysis' gave me a good background for taking part in this event. Hopefully, learning some Network Analysis and a bit of Scala will prove useful for the next one.

Saturday, 2 February 2013

Programming with Mommy

Sometimes the things you do turn around and bite you, and sometimes they make you smile.

So this afternoon I was watching this video in which Greg Wilson talks about programming techniques, programming fashions and the importance of evidence in deciding what to do and how to go about it. There's a section in the middle about the "why-women-can't-be-good-programmers" debate, and he mentions this book, which discusses it at length, with evidence.

So I got to thinking about coding and myself and my daughter. And it just so happens that we were chatting about Angry Bird this morning:
P:  Mommy, did you have Angry Birds when you were little?
S:  No... no, we didn't have anything like Angry Birds. We could listen to music on tapes or records; we could watch television. There weren't many computers. There weren't any videos or CD's. I remember the 1st video game. It came out when I was about 15. Actually, I can show you what it looked like... 

So we went and looked at 'Paddle Ball' at Khan Academy.

It's not the original Pong (nor is it the version that I remember seeing at a friend's house -- that was probably on an Atari VCS). It is close enough to that game that she could get the idea: not Angry Birds. And she could get another idea -- there was the code on the left side of the screen, and we could change it. We could make the ball pink, the background red, the paddle purple. We could change the sizes of the objects, and their speeds. We could interact with the game in a different way, and we did.

So my daughter got her introduction to programming at age 4.


Tuesday, 23 October 2012

Coursera: Data Programming in R, post II

I've finished my course work for Computing for Data Analysis, on Coursera, so I thought I'd take the time do do a quick review.

Overall:
I'm glad I took the course. A structured learning timeline with specific targets and an active discussion board is very valuable. It's far better than learning in isolation through random web tutorials and linked resources. 

The course is not for everyone:
 If you haven't done any programming, R is not a good first language. This course will not be a friendly introduction to programming. In particular, R is quirky and the command syntax is difficult to read. Many commands have similar names, but subtly different behaviors.  The help files are opaque and the examples frequently esoteric. The R programming environment lacks some very basic coding tools such as code completion, although these are probably available in other environments such as R-Studio or ESS.  If you want to learn basic programming, take a course in Python.

R will be a lot easier to digest if you are comfortable with statistics or matrix algebra, can read mathematical notation without difficulty, and have done a bit of programming. If you want flexible data analysis and publication quality output from free software, you'll be very happy. Most of what you want to do can be done with the core functions of R. It's probably best to learn what they can do before reaching for a package. This may save a lot of time later on when the package gets superseded by another one. The core of R will still be there, unchanged. That said, R appears to have a pretty good package management system, so incorporating packages that rely on packages seems to work very well.

If you are coming from an object oriented language such as Java, Ruby or C++, the scoping rules are a leetle different. This seems to be very powerful when used well, but it's mind bending. I haven't really managed to bend my mind around this one enough yet.

Level of the course: 
It would definitely help to have some programming and problem solving background going in. Students needed good problem solving to do the exercises. The basics of the language were taught in Powerpoint slides. The information in the videos was enough to get through the quizzes, but the exercises required more: R-help, R-bloggers, stackoverflow were very useful. 
There was very little discussion of speed optimisation or the tradeoffs in using different programming approaches in R. The final exercise could be solved with for loops, and judging by the forums, many students resorted to them. There was no penalty for this in the grading. The exercises, however were thoughtfully put together, and did provide a good platform for learning to leverage the language, with a little creativity and perseverance. 

There was an introduction to the differences between S3 and S4 classes, but no discussion of more advanced technologies such as refactoring, unit testing, version control, or documentation. I saw one reference to software carpentry on the forums, but there was no reference to how to incorporate these methods in R specifically. Function prototypes were provided for the exercises, and these included useful comments, setting a good standard. However, there was no mention of Runit (testing, TDD), Git (version control), or .Rd files or Roxygen (creating documentation). So if you want to learn how to incorporate these into your work with R, don't look here.  

Time Commitment: 
The course website suggests 2-4 hours / week. I was able to fit the video viewing and exercises into that time frame, on the outside. I spent an extra couple of hours reading and commenting on the forums and looking further / honing more satisfying solutions to the exercises.

What next?
R is quirky. I won't remember much of what I learned beyond a month or two at most. There is clearly a steep learning curve here, and thus a big difference between introduction, competence, and mastery.  At this point, I've had an introduction. In order to progress, I'll need some projects to work on.  

Resources for the future:
... which is just a tiny tip of the iceberg. Let me know about more in the comments.


Sunday, 30 September 2012

Learning Some R

I'm following the free course: Programming for Data analysis, taught by Roger D. Peng of Johns Hopkins Bloomberg School of Public Health, and the Simply Statistics blog. It's offered through Coursera.org.

I should be over-prepared for this course. It's supposed to be completely introductory, with only limited programming or statistics background required, but I'm interested in learning R. So this provides an interesting introduction where I can find out about MOOC's and how they work. My latest app update is 'waiting for review' on iTunes, so I should be able to find a couple hours a week to put into it. 

The first week's lectures covered downloading and installing R, how to get help, some basic data types and selecting data from vectors, lists, etc. Data input and output. Although this is a programming class, the lectures were presented as slide-decks with voice-over. The slides could be downloaded as pdf, and translations were available as subtitles. Each slide had 2-5 sentences with some information about an R command, possibly including an example. I found this easy to follow, but low on information density. I listened to the lectures while making cupcakes for a bakesale. Then I increased the speed to 1.5x. I'll get more from referring to the pdf slides while doing the exercises.

There were no suggestions for outside materials, although some resources were referred to in the 'getting help' lecture video. 

Some 30,000+ students have apparently signed up for the course. The discussion board has several pages worth of questions. The introduce - yourself thread has the most views with nearly 5000. The other most popular threads have 1100 views or so at this point, so there is quite a bit of activity. Several of the "students" are already quite accomplished with R, and they are posting visualizations and code. This is a great learning resource. The rest of us are trying to share resources we find on the web:

Links to free R resources


It's not clear how many students will make it through the 1st quiz, much less the 1st assignment, but complaints on the discussion forum are significant, and are mostly coming from people without much programming background. Since the 1st assignment is not due until the end of week 2, the 1st week's lectures did not cover all the relevant material, many students feeling lost. The 'Not sure where to start…" thread has 2100 views. It would be nice if the 1st lectures were designed to allow you to get started writing code. That's not really necessary to get started using R, but it is necessary to do the course. Fortunately, the boards are monitored, and the lectures for the 2nd week were released a bit early to help with this. 

I do wish the lectures had a more theoretical founding. This is one of my pet peeves about unix world in general, although I don't think I'm alone. Nothing seems to ever be related to anything else. Although there is a full lecture on the history of R, it goes through where R was developed, who developed it, but not what it's theoretical bases were or why it was made the way it was. With every language there are underlying assumptions about what forms of data are important and how it should be saved and treated. Understanding these can make learning and using the language much easier. No such luck here. Is read.table fundamental? How does it relate to read in unix or C or other common languages of the early R days? This seems like arcane knowledge, but it is the kind of thing that forms an actual education. If programming languages aren't taught as isolated functions and control characters, but as a historical web of intellectual developments, students have a framework for learning. I'm not sure Professor Peng, as a statistician, has this framework himself (it isn't encouraged outside of liberal arts schools), so I'm probably just shouting into the wind. 

If you are used to learning in a classroom, the MOOC may seem very unusual. It is somewhere between independent learning and actually going to class. So far, the materials presented in the lecture have not been enough to even get full marks on the 1st quiz. (There's a gotcha about vector recycling in R which will not be apparent unless you either know it already, get lucky, or try it at the R command line.) The best way to learn is probably to have the R command line open and try things out during lecture. Even using the command line is not made easy, however, because the lectures are not structured as problem, discussion, resolution, but rather as a list of things you might find useful. You have to come up with the examples yourself, and most people will need outside help. 

So, many students are pooling expertise on the discussion forum to figure it out. This includes posts that benchmark possible solutions, and a lot of hints on how to get started. The great majority of answers are helpful and supportive. Perusing the forum will make the assignments doable. 

So overall, I'm not particularly impressed with the lecture style or structure of the teaching and information. On the other hand, I'm impressed with the breadth of the emerging student community. The assignments are challenging, and the outcome should be a reasonably good understanding. This learning won't come from the lectures, though. In other words, follow the excellent suggestions on how to succeed in a MOOC. 

And if you think that my complaints must be unique to this course, try looking at this blog post.