UBC STAT545 2015 cm001 Intro to course

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STAT 545A Class meeting 001 Course intro + prompts to install lots of software and sign up for lots of accounts web companion: STAT 545 web home > Syllabus > cm001 Tuesday, September 8, 2015

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Dr. Jennifer (Jenny) Bryan Department of Statistics and Michael Smith Laboratories University of British Columbia jenny@stat.ubc.ca https://github.com/jennybc http://www.stat.ubc.ca/~jenny/ @JennyBryan ← personal, professional Twitter https://github.com/STAT545-UBC http://stat545-ubc.github.io @STAT545 ← Twitter as lead instructor of this course

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statistical theory real world data STAT 545A

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The Big Data Brain Drain: Why Science is in Trouble http://jakevdp.github.io/blog/2013/10/26/big-data-brain-drain/ in a wide array of academic fields, the ability to effectively process data is superseding other more classical modes of research

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what is data science?

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The data science Venn diagram http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

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M unge Visualise M odel Communicate Tidy Question Collect Slides from Hadley Wickham's talk in the Simply Statistics Unconference http://t.co/D931Og8mq3 a horizontal data science workﬂow

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M unge Visualise M odel Communicate Tidy Question Collect W ednesday, October 30, 13 Slides from Hadley Wickham's talk in the Simply Statistics Unconference http://t.co/D931Og8mq3 We can’t focus just on this!

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http://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html?partner=rss&emc=rss&smid=tw-nytimesscience&_r=0 Data scientists spend 50 - 80% of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets. ... what data scientists call “data wrangling,” “data munging” and “data janitor work” ...

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http://mimno.infosci.cornell.edu/b/articles/carpentry/ As data science becomes all the more relevant and indeed, proﬁtable, attention has been placed on the value of cleaning a data set. David Mimno unpicks the term and the process and suggests that data carpentry may be a more suitable description. There is no such thing as pure or clean data buried in a thin layer of non-clean data. In reality, the process is more like deciding how to cut and join a piece of material. Data carpentry is not a single process but a thousand little skills and techniques. http://blogs.lse.ac.uk/impactofsocialsciences/2014/09/01/data-carpentry-skilled-craft-data-science/

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Complexity must be justified. http://www.john-foreman.com/blog/the-forgotten-job-of-a-data-scientist-editing Before you analyze your data with computers, be sure to plot it Problem first not solution backward http://simplystatistics.org/2014/05/22/10-things-statistics-taught-us-about-big-data-analysis/ http://simplystatistics.org/2014/05/22/10-things-statistics-taught-us-about-big-data-analysis/ ... the first reasonable thing you can do to a set of data often is 80% of the way to the optimal solution. Everything after that is working on getting the last 20%... (maybe could even be the 90/10 rule) http://simplystatistics.org/2014/03/20/the-8020-rule-of-statistical-methods-development/

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“All models are wrong, some models are useful.” Box, G.E.P., Robustness in the strategy of scientiﬁc model building, in Robustness in Statistics, R.L. Launer and G.N. Wilkinson, Editors. 1979, Academic Press: New York. Entia non sunt multiplicanda praeter necessitatem The principle, known as Occam’s Razor, that says: when there are two competing theories or explanations -- both compatible with observed data, known facts -- the simpler one is better. Implication for statistical analysis: if two models are equally wrong-but-compatible-with-data, the simpler one is more useful!

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small medium big size of dataset * ﬁgure is ﬁctional but I stand by this claim There are LOTS of small to medium datasets, even though it’s more trendy to talk about the big ones.

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small medium big size of dataset * ﬁgure is ﬁctional but I stand by this claim http://www.chrisstucchio.com/blog/2013/hadoop_hatred.html http://qz.com/81661/most-data-isnt-big-and-businesses-are-wasting-money-pretending-it-is/ https://www.facebook.com/dan.ariely/posts/904383595868 “Big data has become a synonym for ‘data analysis,’ which is confusing and counter-productive.” “Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.” “Too big for Excel is not ‘Big Data’.”

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big data ≠ data science big data 㱬 data science

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Two inter-related goals • Foster your development of a personal philosophy for data analysis, esp. exploratory and descriptive analysis. • Help you assemble a modern toolchain and workﬂows for data analysis. You’ll leave this course with (at least the beginnings of) a conﬁdent, deliberate attitude about how to approach data analysis and the practical skills to put your attitude into action. My hope:

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you’re going to make your own data science sampler

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“A picture is worth a thousand words”

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http://msnbcmedia1.msn.com/j/msnbc/Components/Photos/050709/050609_columbia_hmed_6p.hmedium.jpg 1986 Challenger space shuttle disaster Favorite example of Edward Tufte

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“A picture is worth a thousand words”

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“A picture is worth a thousand words” Siddhartha R. Dalal; Edward B. Fowlkes; Bruce Hoadley. Risk Analysis of the Space Shuttle: Pre-Challenger Prediction of Failure. JASA, Vol. 84, No. 408 (Dec., 1989), pp. 945-957. Access via JSTOR.

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Edward Tufte http://www.edwardtufte.com BOOK: Visual Explanations: Images and Quantities, Evidence and Narrative Ch. 5 deals with the Challenger disaster That chapter is available for $7 as a downloadable booklet: http://www.edwardtufte.com/tufte/books_textb

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“A picture is worth a thousand words” Always, always, always plot the data. Replace (or complement) ‘typical’ tables of data or statistical results with figures that are more compelling and accessible. Whenever possible, generate figures that overlay / juxtapose observed data and analytical results, e.g. the ‘fit’.

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“A picture is worth a thousand words” Why? •ﬁnd bizarre data and results when it is least embarrassing and painful •facilitate comparisons and reveal trends Recommended reference: Gelman A, Pasarica C, Dodhia R. “Let's Practice What We Preach: Turning Tables into Graphs”. The American Statistician, Volume 56, Number 2, 1 May 2002 , pp. 121-130(10). via JSTOR

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weak links in the chain: process, packaging and presentation

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we watched a video about the importance of reproducible research, data and analytical documentation, data sharing https://www.youtube.com/watch?v=N2zK3sAtr-4&feature=youtu.be

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project organization literate programming reproducible research version control / back up / archive collaboration / open science

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project organization / literate programming / reproducible research version control / back up / archive collaboration / open science Sweave knitr R markdown R packages GitHub Rforge sourceforge git subversion mercurial What the cool kids seem to be doing .... RStudio

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RStudio is an integrated development environment (IDE) for R

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R ≠ RStudio RStudio mediates your interaction with R; it would replace Emacs + ESS or Tinn-R, but not R itself Rstudio is a product of -- actually, more a driver of -- the emergence of R Markdown, knitr, R + Git(Hub)

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from Hadley Wickham’s talk in the Simply Statistics Unconference on the Future of Statistics Web PDF → HTML Latex → Markdown Static → Interactive 3 Open Open source ↑↑ Open science ↑↑ Open research ↑↑ Wednesday, October 30, 13

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Markdown HTML foo.md foo.html easy to write (and read!) easy to publish easy to read in browser

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Title (header 1, actually) ===================================== This is a Markdown document. ## Medium header (header 2, actually) It's easy to do *italics* or __make things bold__. > All models are wrong, but some are useful. An approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem. Absolute certainty is a privilege of uneducated minds-and fanatics. It is, for scientific folk, an unattainable ideal. What you do every day matters more than what you do once in a while. We cannot expect anyone to know anything we didn't teach them ourselves. Enthusiasm is a form of social courage. Code block below. Just affects formatting here but we'll get to R Markdown for the real fun soon! ``` x <- 3 * 4 ``` I can haz equations. Inline equations, such as ... the average is computed as $\frac{1}{n} \sum_{i=1} ^{n} x_{i}$. Or display equations like this: $$ \begin{equation*} |x|= \begin{cases} x & \text{if $x≥0$,} \\\\ -x &\text{if $x\le 0$.} \end{cases} \end{equation*} $$ Markdown HTML

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R markdown

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R Markdown rocks ===================================== This is an R Markdown document. ```{r} x <- rnorm(1000) head(x) ``` See how the R code gets executed and a representation thereof appears in the document? `knitr` gives you control over how to represent all conceivable types of output. In case you care, then average of the `r length(x)` random normal variates we just generated is `r round(mean(x), 3)`. Those numbers are NOT hard-wired but are computed on-the- fly. As is this figure. No more copy-paste ... copy- paste ... oops forgot to copy-paste. ```{r} plot(density(x)) ``` Note that all the previously demonstrated math typesetting still works. You don't have to choose between having math cred and being web-friendly! Inline equations, such as ... the average is computed as $\frac{1}{n} \sum_{i=1}^{n} x_{i}$. Or display equations like this: $$ \begin{equation*} |x|= \begin{cases} x & \text{if $x≥0$,} \\\\ -x &\text{if $x\le 0$.} \end{cases} \end{equation*} $$ R Markdown rocks ===================================== This is an R Markdown document. ```r x <- rnorm(1000) head(x) ``` ``` ## [1] -1.3007 0.7715 0.5585 -1.2854 1.1973 2.4157 ``` See how the R code gets executed and a representation thereof appears in the document? `knitr` gives you control over how to represent all conceivable types of output. In case you care, then average of the 1000 random normal variates we just generated is -0.081. Those numbers are NOT hard- wired but are computed on-the-fly. As is this figure. No more copy-paste ... copy-paste ... oops forgot to copy-paste. ```r plot(density(x)) ``` ![plot of chunk unnamed-chunk-2](figure/unnamed- chunk-2.png) ... R Markdown Markdown

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R Markdown rocks ===================================== This is an R Markdown document. ```r x <- rnorm(1000) head(x) ``` ``` ## [1] -1.3007 0.7715 0.5585 -1.2854 1.1973 2.4157 ``` See how the R code gets executed and a representation thereof appears in the document? `knitr` gives you control over how to represent all conceivable types of output. In case you care, then average of the 1000 random normal variates we just generated is -0.081. Those numbers are NOT hard- wired but are computed on-the-fly. As is this figure. No more copy-paste ... copy-paste ... oops forgot to copy-paste. ```r plot(density(x)) ``` ![plot of chunk unnamed-chunk-2](figure/unnamed- chunk-2.png) ... Markdown HTML

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Markdown HTML foo.md foo.html easy to write (and read!) easy to publish easy to read in browser R Markdown foo.rmd

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how do I put my source on the web? for the world or select collaborators? maybe I’ll share my data and my prose too .... how should I marshall all of that stuff? how can I collaborate with others on an analysis or package development? Advice to preserve sanity: Stop doing this via email, attachments, and tracking changes in Word. Get that stuff into plain text, put it under version control and get it out on the web.

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http://www.phdcomics.com/comics/archive.php?comicid=1531 via Ram, 2013 doi:10.1186/1751-0473-8-7

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Version control systems (VCS) were created to help groups of people develop software Git, in particular, is being “repurposed” for activities other than pure software development ... like the messy hybrid of writing, coding and data wrangling Git repository = a bunch of ﬁles you want to manage in a sane way repo = repository

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collaboration = the “killer app” of version control Learning Git has been -- and continues to be -- painful. But not nearly as crazy-making as the alternatives: - documents as email attachments - uncertainty about which version is “master” - am I working with the most recent data? - archaelogical “digs” on old email threads - uncertainty about how/if certain changes have been made or issues solved - hair-raising ZIP archives containing ﬁle salad

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Git repository = a bunch of ﬁles you want to manage in a sane way repo = repository you can set up repo ... then start your work or you can make a set of existing ﬁles and make them into a repo

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GitHub = a place to host Git repositories on the web GitHub ≠ Git

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possible, in theory more typical GitHub me you Image from https://www.atlassian.com/git/tutorial/git-basics#!clone ✗

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Many R packages are developed in the open on GitHub Nice option when someone tells you to “read the source”!

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You can see exactly how ﬁles have changed, when, and by whom. If commit message is good, you’ll see why. Commit = a formal “checkpoint” or snapshot of the state of the repository

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GitHub provides a fantastic visual “diff” view of exactly what changed. Incredibly useful.

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GitHub issues: think “bug tracker”, “to do list”.

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GitHub renders Markdown ﬁles nicely Example: links.md in workshop repo of mine

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You can see the raw Markdown too!

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GitHub renders comma (.csv) and tab (.tsv) delimited ﬁles nicely Example: Lord of the Rings data I found for STAT 545A

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http://www.wired.com/design/2013/08/how-segregated-is-your-city-this-eye-opening-map-shows-you/?viewall=true NYC

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http://www.wired.com/design/2013/08/how-segregated-is-your-city-this-eye-opening-map-shows-you/?viewall=true NYC Detroit

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http://www.coopercenter.org/demographics/Racial-Dot-Map

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Cool result is accompanied by explanation of how it was done

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https://github.com/unorthodox123/RacialDotMap

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http://blog.revolutionanalytics.com/2013/08/foodborne-chicago.html

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https://github.com/corynissen/foodborne_classiﬁer

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project organization / literate programming / reproducible research version control / back up / archive collaboration / open science knitr R markdown R packages GitHub Rforge sourceforge git subversion mercurial Now you what the fuss is about! RStudio

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≪ source is real

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Measured( Data Analy/c( Data Computa/onal( Results Ar/cle Tables Figures Numerical( Summaries Text Processing(code Analy/c(code Presenta/on(code slide(modiﬁed(from(Roger(Peng(hBp://www.biostat.jhsph.edu/~rpeng/ R((or(Python)(scripts

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http://zmjones.com/make.html Makefile Like Git, GNU Make is another old school tool that is being repurposed to meet a need in data-intensive workﬂows. Originally intended to orchestrate compiling complicated software, it’s now used to express what depends on what and keep everything “in sync”.

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course stuff

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who am I? BA in econ and german management consultant PhD biostatistics assoc prof @ UBC 50% Statistics / 50% Michael Smith Laboratories I teach and perform lots of data analysis

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Bernhard Konrad Dean Attali Luolan (Gloria) Li Jenny Bryan Julia Gustavsen Shaun Jackman http://stat545-ubc.github.io/people.html 2014 2014 2014 2015 2014 2014 2014 Andrew MacDonald 2015 team is being assembled!

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Culture of the class • Teaching you to fish (vs. giving you a fish) - It’s amazing what a determined individual can learn from documentation, small learning examples, and ... Googling. And also stackoverflow. • Rewarding engagement, intellectual generosity and curiosity - Speaking up, sharing success OR failure, showing some interest in something will earn marks. • Zero tolerance of plagiarism - Generating your own approach, writing some code, and describing the process is the whole point. Process is generally more important than product.

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Where marks will come from • Weekly homework; marked coarsely (think check, check minus, check plus), with peer evaluation • Eventually, ﬂexibility to work with a dataset you choose or to spin the problem a certain way - Think about datasets you’d like to prepare and analyze! • Adjust the difﬁculty level relative to where you are now (and where you need/want to be!) • Peer review; marked coarsely (good review vs. “needs more”) • Engagement and participation: in class, in our GitHub world

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http://stat545-ubc.github.io

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https://twitter.com/STAT545

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Homework! web companion: STAT 545 web home > Syllabus > cm001

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Twitter, GitHub ... are PUBLIC cultivate your professional / scholarly proﬁle with intention if you join, make sure @STAT545 follows you back

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how to get help Ofﬁce hours Tues/Thurs after class (or Wed this week only) Open an issue on a GitHub repository and tag one or more instructors Tweet to @STAT545 Direct twitter message to @STAT545 Email to an instructor http://stat545-ubc.github.io/help-STAT545.html

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respond to our prompt to ﬁgure out who you are! we want to match up various info with what UBC provides (e.g. Twitter handle, Github username)

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what class meetings will look like … sort of? 9:30 - 9:50 “lecture” 9:50 - 10:35 hands-on work! 10:35 - 10:55 “lecture”

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rhythm of each week submit a unit of work tues class thurs class work on your own work in class consult peers and instructors in class ofﬁce hrs online interaction via GitHub, Twitter

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the end