A Gentle Introduction to Correspondence Analysis | Stéfan Sinclair There are some digital humanists who are competent mathematicians, but most of us experience some anxiety about the more advanced mathematics involved in the text analysis methodologies that we use. Dammit Jim, I’m a humanist, not a mathematician! The problem of course is that there are clearly some statistical and graphical techniques that can be very powerful for humanities research (if you’re unconvinced by this claim, please read on anyway). So one faces a choice: not using these techniquesusing these techniques naïvely and trusting that they’re working properly and that one is interpreting the results properlyinvesting a ton of time learning the mathematics involved, sometimes to the detriment of the original research agendacollaborating with someone who does understand the mathematics Correspondence Analysis is a good example of a technique that can appear very intimidating but that can also be a very powerful tool in the arsenal of a digital humanist. Some useful links:

Syllabus Lectures: none (directed reading course) Textbook: Class Notes (see references below) References: Online MATLAB tutorials and references: MATLAB guides Provided with the Matlab installation Getting Started with Matlab Using Matlab Using Graphs in Matlab Using GUIs in Matlab For links to these documents visit Dr. For MATLAB operation Hanselman, D. and B. For Mathematics and Numerical Methods with MATLAB Moler, C., 2004 [?] For Elementary Numerical Methods Moin, P., 2001, Fundamentals of Engineering Numerical Analysis, Cambridge University Press, New York Hoffman, J.D., 2001, Numerical Methods for Engineers and Scientists - Second Edition, Marcel Dekker, Inc., New York Chapra, S. and R. For the material to be covered in this class Abbot, M.B. and A.W. Required background We will be using Matlab as the main programming/graphics environment, therefore, students must be familiar or learn the use of Matlab during this class. Class Notes on Matlab Getting started with Matlab Class Contents

heather.cs.ucdavis.edu/~matloff/r.html Professor Norm Matloff Dept. of Computer Science University of California at Davis Davis, CA 95616 R is a wonderful programming language for statistics and data management, used widely in industry, business, government, medicine and so on. And it's free, an open source product. Downloading R: R is available for Linux, Windows and Mac systems. You can download R from its home page. For Ubuntu Linux or other Debian-related OSs, a more direct method is: % sudo apt-get install r-base Learning R: There is a perception among some that R has a steep learning curve, but I disagree. I'll list a few tutorials below (not necessarily the best, just ones I know of). "When in doubt, try it out!" Here are some resources that I might recommend for learning R: A nice centralized collection of R resrouces. Advanced R: R Programming Tools: One of the most debated topics in R online discussions is that of programming tools for R, of which there are many. People you can talk to:

Speaker Videos | Topic Modeling The first video covers introductions from the workshop’s sponsors MITH and the NEH ODH, via Neil Fraistat, Jen Guiliano, and Jen Serventi; Matthew Jockers presenting on literary topic modeling with “Thematic Change and Authorial Innovation in the 19th Century Novel”; and Robert Nelson on historical topic modeling with”Analyzing Nationalism and Other Slippery ‘Isms’”. Topic Modeling Workshop: Jockers and Nelson from MITH in MD on Vimeo. The second video covers Jo Guldi and Christopher Johnson-Roberson’s presentation “Paper Machines: A Tool for Analyzing Large-Scale Digital Corpora” Topic Modeling Workshop: Guldi and Johnson-Roberson from MITH in MD on Vimeo. The third video covers David Mimno’s presentation “The details: how we train big topic models on lots of text”. Topic Modeling Workshop: Mimno from MITH in MD on Vimeo.

Short-refcard.pdf An Introduction to R Table of Contents This is an introduction to R (“GNU S”), a language and environment for statistical computing and graphics. R is similar to the award-winning1 S system, which was developed at Bell Laboratories by John Chambers et al. It provides a wide variety of statistical and graphical techniques (linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, ...). This manual provides information on data types, programming elements, statistical modelling and graphics. This manual is for R, version 3.1.0 (2014-04-10). Copyright © 1990 W. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Preface This introduction to R is derived from an original set of notes describing the S and S-PLUS environments written in 1990–2 by Bill Venables and David M. Comments and corrections are always welcome. Suggestions to the reader 1.1 The R environment Try ?

TxDHC Webinar Series | TxDHC The TxDHC hosts a webinar 2-3 times per semester. The webinars are open to all and are announced via the TxDHC website and listserv. We simply ask that you register beforehand. All webinars are recorded and made publicly available on the TxDHC YouTube channel. The webinars available so far include: OpenRefine by Liz Grumbach and Jennifer Hecker [cc]DPLA by Charlotte Nunes [cc]Teaching Programming4Humanists by Dr. Upcoming webinars: Web Annotation: Updating an Age-Old Humanities Practice for the 21st Century by Dr. wq-package.pdf

Matrix Construction There are various ways to construct a matrix. When we construct a matrix directly with data elements, the matrix content is filled along the column orientation by default. For example, in the following code snippet, the content of B is filled along the columns consecutively. > B = matrix( + c(2, 4, 3, 1, 5, 7), + nrow=3, + ncol=2) > B # B has 3 rows and 2 columns [,1] [,2] [1,] 2 1 [2,] 4 5 [3,] 3 7 Transpose We construct the transpose of a matrix by interchanging its columns and rows with the function t . > t(B) # transpose of B [,1] [,2] [,3] [1,] 2 4 3 [2,] 1 5 7 Combining Matrices The columns of two matrices having the same number of rows can be combined into a larger matrix. > C = matrix( + c(7, 4, 2), + nrow=3, + ncol=1) > C # C has 3 rows [,1] [1,] 7 [2,] 4 [3,] 2 Then we can combine the columns of B and C with cbind. > cbind(B, C) [,1] [,2] [,3] [1,] 2 1 7 [2,] 4 5 4 [3,] 3 7 2 Deconstruction

Exploring Big Historical Data: The Historian's Macroscope Welcome to the companion site for Exploring Big Historical Data: The Historian’s Macroscope, published by Imperial College Press. If you want to buy a copy, you can purchase one for $39.00 USD. Feel free to visit our original live-written fully open draft website, which is still online – and if you like what you see, you can always buy the book! On this site you will find code, essays (things we liked from the draft that did not fit), and datafiles that go with our book. •Diversity is vital to digital history, and our readers should consider it an essential additional chapter. Illustrations in the print book are in black-and-white. If you want clickable footnotes (which you probably do!) If you’re curious who we are, you can learn more about us here. Please explore our website, and if you have questions, get in touch or check out the wonderful DH Questions & Answers Site!

Related: Tutorials and games
- R and Matlab programming