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Humanities Data in R: Exploring Networks, Geospatial Data, Images, and Text 1st ed. 2015 [Kõva köide]

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Humanities Data in R: Exploring Networks, Geospatial Data, Images, and Text 1st ed. 2015Suurem pilt
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Püsilink: https://www.kriso.ee/db/9783319207018.html
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This pioneering book teaches readers to use R within four core analytical areas applicable to the Humanities: networks, text, geospatial data, and images. This book is also designed to be a bridge: between quantitative and qualitative methods, individual and collaborative work, and the humanities and social sciences. Humanities Data with R does not presuppose background programming experience. Early chapters take readers from R set-up to exploratory data analysis (continuous and categorical data, multivariate analysis, and advanced graphics with emphasis on aesthetics and facility). Following this, networks, geospatial data, image data, natural language processing and text analysis each have a dedicated chapter. Each chapter is grounded in examples to move readers beyond the intimidation of adding new tools to their research. Everything is hands-on: networks are explained using U.S. Supreme Court opinions, and low-level NLP methods are applied to short stories by Sir Arthur Conan

Doyle. After working through these examples with the provided data, code and book website, readers are prepared to apply new methods to their own work. The open source R programming language, with its myriad packages and popularity within the sciences and social sciences, is particularly well-suited to working with humanities data. R packages are also highlighted in an appendix. This book uses an expanded conception of the forms data may take and the information it represents. The methodology will have wide application in classrooms and self-study for the humanities, but also for use in linguistics, anthropology, and political science. Outside the classroom, this intersection of humanities and computing is particularly relevant for research and new modes of dissemination across archives, museums and libraries.

Set-up.- A Short Introduction to R.- EDA I Continuous and Categorical Data.- EDA II Multivariate Analysis.- EDA III Advanced Graphics.- Networks.- Geospatial Data.- Image Data.- Natural Language Processing.- Text Analysis.- Appendix.

Arnold and Tilton are a brilliant team, and this highly accessible book will appeal to a wide range of digital humanists. The text analysis chapters are very good, and the authors" work to develop an R package for interacting with the Stanford CoreNLP java Library fills a huge hole in the R text processing landscape.Matthew L. Jockers , University of Nebraska-Lincoln; author of Text Analysis with R for Students of Literature (Springer, 2014)This is the first book that covers analysis of all main parts of humanities data: texts, images, geospatial data, and networks. Now digital humanities finally has its perfect textbook. This is the book many of us were awaiting for years. It teaches you R (the most widely used open source data analysis platform today worldwide) using many examples. The writing is very clear, and information is organized in a logical and easy to follow manner. Whether you are just considering working with humanities data or already have experience, this is the

must read book.Lev Manovich , The Graduate Center, City University of New York; author of The Language of New Media (MIT, 2001)This book gives a concise yet broadly accessible introduction to R, through the lens of exploratory data analysis, coupled with well-planned forays into key humanities data types and their analysis -- including a nice primer on network analysis.Eric D. Kolaczyk , Boston University; author of Statistical Analysis of Network Data with R (Springer, 2014)

Arvustused

Arnold and Tilton are a brilliant team, and this highly accessible book will appeal to a wide range of digital humanists. The text analysis chapters are very good, and the authors' work to develop an R package for interacting with the Stanford CoreNLP java Library fills a huge hole in the R text processing landscape. Matthew L. Jockers, University of Nebraska-Lincoln; author of Text Analysis with R for Students of Literature (Springer, 2014)

This is the first book that covers analysis of all main parts of humanities data: texts, images, geospatial data, and networks. Now digital humanities finally has its perfect textbook. This is the book many of us were awaiting for years. It teaches you R (the most widely used open source data analysis platform today worldwide) using many examples. The writing is very clear, and information is organized in a logical and easy to follow manner. Whether you are just considering working with humanities data or already have experience, this is the must read book. Lev Manovich, The Graduate Center, City University of New York; author of The Language of New Media (MIT, 2001)









This book gives a concise yet broadly accessible introduction to R, through the lens of exploratory data analysis, coupled with well-planned forays into key humanities data types and their analysis -- including a nice primer on network analysis. Eric D. Kolaczyk, Boston University; author of Statistical Analysis of Network Data with R (Springer, 2014)

Part I Basics
1 Set-Up
3(4)
1.1 Introduction
3(1)
1.2 Structure of This Book
4(1)
1.3 Obtaining R
5(1)
1.4 Supplemental Materials
5(1)
1.5 Getting Help with R
5(1)
References
6(1)
2 A Short Introduction to R
7(18)
2.1 Introduction
7(1)
2.2 Calculator and Objects
7(2)
2.3 Numeric Vectors
9(2)
2.4 Logical Vectors
11(1)
2.5 Subsetting
12(2)
2.6 Character Vectors
14(2)
2.7 Matrices and Data Frames
16(3)
2.8 Data I/O
19(3)
2.9 Advanced Subsetting
22(2)
References
24(1)
3 EDA I: Continuous and Categorical Data
25(22)
3.1 Introduction
25(1)
3.2 Tables
26(3)
3.3 Histogram
29(2)
3.4 Quantiles
31(4)
3.5 Binning
35(2)
3.6 Control Flow
37(3)
3.7 Combining Plots
40(2)
3.8 Aggregation
42(2)
3.9 Applying Functions
44(2)
References
46(1)
4 EDA II: Multivariate Analysis
47(16)
4.1 Introduction
47(1)
4.2 Scatter Plots
47(3)
4.3 Text
50(3)
4.4 Points
53(1)
4.5 Line Plots
54(4)
4.6 Scatter Plot Matrix
58(2)
4.7 Correlation Matrix
60(3)
5 EDA III: Advanced Graphics
63(18)
5.1 Introduction
63(1)
5.2 Output Formats
63(2)
5.3 Color
65(5)
5.4 Legends
70(1)
5.5 Randomness
71(5)
5.6 Additional Parameters
76(1)
5.7 Alternative Methods
77(1)
References
78(3)
Part II Humanities Data Types
6 Networks
81(14)
6.1 Introduction
81(1)
6.2 A Basic Graph
81(3)
6.3 Citation Networks
84(3)
6.4 Graph Centrality
87(3)
6.5 Graph Communities
90(2)
6.6 Further Extensions
92(1)
References
93(2)
7 Geospatial Data
95(18)
7.1 Introduction
95(1)
7.2 From Scatter Plots to Maps
96(4)
7.3 Map Projections and Input Formats
100(5)
7.4 Enriching Tabular Data with Geospatial Data
105(2)
7.5 Enriching Geospatial Data with Tabular Data
107(3)
7.6 Further Extensions
110(1)
References
110(3)
8 Image Data
113(18)
8.1 Introduction
113(1)
8.2 Basic Image I/O
113(4)
8.3 Day/Night Photographic Corpus
117(3)
8.4 Principal Component Analysis
120(3)
8.5 K-Means
123(3)
8.6 Scatter Plot of Raster Graphics
126(1)
8.7 Extensions
127(2)
References
129(2)
9 Natural Language Processing
131(26)
9.1 Introduction
131(1)
9.2 Tokenization and Sentence Splitting
132(2)
9.3 Lemmatization and Part of Speech Tagging
134(4)
9.4 Dependencies
138(5)
9.5 Named Entity Recognition
143(2)
9.6 Coreference
145(3)
9.7 Case Study: Sherlock Holmes Main Characters
148(2)
9.8 Other Languages
150(2)
9.9 Conclusions and Extensions
152(1)
References
153(4)
10 Text Analysis
157(22)
10.1 Introduction
157(1)
10.2 Term Frequency: Inverse Document Frequency
157(5)
10.3 Topic Models
162(5)
10.4 Stylometric Analysis
167(7)
10.5 Further Methods and Extensions
174(1)
References
175(4)
Part III Appendix
11 R Packages
179(4)
11.1 Installing from Within R
179(2)
11.2 rJava
181(1)
11.3 coreNLP
181(1)
11.4 sessionInfo
182(1)
12 100 Basic Programming Exercises
183(10)
13 100 Basic Programming Solutions
193
Taylor Arnold is Senior Scientist at AT&T Labs Research and Lecturer of Statistics at Yale University. His research focuses on statistical computing, numerical linear algebra, and machine learning. He is the technical director of Photogrammar (photogrammar.yale.edu).

Lauren Tilton is a doctoral candidate in American Studies at Yale University. Her interests include documentary media, 20th century history, and visual culture. She is an active member of the digital humanities community, serving as the humanities director of Photogrammar and co-Principal Investigator of the Participatory Media project.