Humanities data in R: exploring networks, geospatial data, images, and text
This book teaches readers to integrate data analysis techniques into humanities research practices using the R programming language. Methods for general-purpose visualization and analysis are introduced first, followed by domain-specific techniques for working with networks, text, geospatial data, t...
Gespeichert in:
| Vorheriger Titel: | Humanities data in R |
|---|---|
| Hauptverfasser: | , |
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer
[2024]
|
| Ausgabe: | Second edition |
| Schriftenreihe: | Quantitative methods in the humanities and social sciences
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | This book teaches readers to integrate data analysis techniques into humanities research practices using the R programming language. Methods for general-purpose visualization and analysis are introduced first, followed by domain-specific techniques for working with networks, text, geospatial data, temporal data, and images. The book is designed to be a bridge between quantitative and qualitative methods, individual and collaborative work, and the humanities and social sciences. The second edition of the text is a significant revision, with almost every aspect of the text rewritten in some way. The most notable difference is the incorporation of new R packages such as ggplot2 and dplyr that center broad data-science concepts.This 2nd edition of Humanities Data with R does not presuppose background programming experience. Early chapters take readers from R set-up to exploratory data analysis, with one chapter dedicated to each stage of the data-science pipeline (data collection, visualization, manipulation, and relational joins). Following this, text analysis, networks, temporal data, geospatial data, and image analysis each have a dedicated chapter. These are grounded in examples to move readers beyond the intimidation of adding new tools to their research. The final section of the book extends the core material with additional computer science techniques for processing large datasets.Everything is hands-on: image analysis is explained using digitized photographs from the 1930s, and networks are applied to page links on Wikipedia. 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.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 |
| Beschreibung: | xiv, 284 Seiten Illustrationen, Diagramme, Karten 235 mm |
| ISBN: | 9783031625657 |
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| 520 | |a This book teaches readers to integrate data analysis techniques into humanities research practices using the R programming language. Methods for general-purpose visualization and analysis are introduced first, followed by domain-specific techniques for working with networks, text, geospatial data, temporal data, and images. The book is designed to be a bridge between quantitative and qualitative methods, individual and collaborative work, and the humanities and social sciences. The second edition of the text is a significant revision, with almost every aspect of the text rewritten in some way. The most notable difference is the incorporation of new R packages such as ggplot2 and dplyr that center broad data-science concepts.This 2nd edition of Humanities Data with R does not presuppose background programming experience. | ||
| 520 | |a Early chapters take readers from R set-up to exploratory data analysis, with one chapter dedicated to each stage of the data-science pipeline (data collection, visualization, manipulation, and relational joins). Following this, text analysis, networks, temporal data, geospatial data, and image analysis each have a dedicated chapter. These are grounded in examples to move readers beyond the intimidation of adding new tools to their research. The final section of the book extends the core material with additional computer science techniques for processing large datasets.Everything is hands-on: image analysis is explained using digitized photographs from the 1930s, and networks are applied to page links on Wikipedia. After working through these examples with the provided data, code and book website, readers are prepared to apply new methods to their own work. | ||
| 520 | |a 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.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 | ||
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Datensatz im Suchindex
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Contents Part I 1 Working with Data in R. 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 Introduction. Setup. Working with R and R Markdown. Running R Code. Functions in R. Loading Data in R. Datasets. Formatting R Code. Extensions. EDA I: Grammar of Graphics. 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 3 Core Introduction. Text Geometry. Lines and Bars. Optional Aesthetics.
Scales. Labels and Themes. Conventions for Graphics Code. Extensions. EDA II: Organizing Data. 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 Introduction. Choosing Rows. Data and Layers. Selecting Columns. Arranging Rows. Summarize and Group By. Geometries for Summaries. Mutate. Extensions. 3 3 4 5 7 8 10 12 15 15 17 17 21 24 27 30 34 35 36 39 39 40 43 45 47 48 51 33 37 xi
xii 4 Contents EDA III: Restructuring Data. 4.1 Introduction. 4.2 Joining by Relation. 4.3 Mutating and Filtering Joins. 4.4 4.5 4.6 4.7 5 Collecting Data. 5.1 Introduction. 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 Part II 6 77 77 78 79 80 81 82 83 83 84 Data Types Introduction. Working with a Textual Corpus. Natural Language Processing Pipeline. Term Frequency-Inverse Document Frequency (TF-IDF). Document Distance. Dimensionality Reduction. Word Relationships . Texts in Other Languages. Extensions. Network
Data. 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 8 Rectangular Data. Naming Variables. What Goes in a Cell. Dates. Output Format. Data Dictionary. Summary of Data Collection Guidelines. Extensions. Textual Data. 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7 Pivot Longer. Pivot Wider. Patterns for Table Pivots. Extensions. 59 59 59 64 67 71 73 75 Introduction. Creating a Network
Object. Centrality. Clusters. Co-citation Networks . Directed Networks. Distance Networks. Nearest Neighbor Networks. Extensions. 89 89 90 91 97 99 104 109 Ill 114 117 117 118 122 127 129 131 134 135 140 Temporal Data. 143 8.1 Introduction. 143 8.2 Temporal Data and Ordering . 144
xiii Contents Date Objects. Datetime Objects. Language and Time Zones. Manipulating Dates and Datetimes. Window Functions and Range Joins. Extensions. 147 151 154 160 162 167 Spatial Data. 169 169 169 174 179 183 189 198 8.3 8.4 8.5 8.6 8.7 8.8 9 9.1 9.2 9.3 9.4 9.5 9.6 9.7 10 Image Data. 10.1 10.2 10.3 10.4 10.5 10.6 10.7 10.8 10.9 Part III 11 Introduction. Loading Images . Pixels and Color. Computer Vision . Object Detection. Face Detection. Pose
Detection. Embeddings . Extensions. 201 201 201 205 212 213 218 223 226 232 Additional Methods Programming in R. 235 11.1 11.2 11.3 11.4 11.5 11.6 11.7 11.8 12 Introduction. Spatial Points. Polygons. Spatial Metrics. Spatial Joins. Raster Maps. Extensions. Introduction. Vectors. Data Types and Lists. Selecting and Modifying Vectors.
Matrices. Control Flow. Functional Programming. Extensions. 235 236 237 239 241 243 246 248 Data Formats. 249 249 250 254 260 263 268 12.1 12.2 12.3 12.4 12.5 12.6 Introduction. Strings . Regular Expressions. JSONData. XML and HTML Formats. XML Path Language (XPath).
xlv Contents 12.7Building Datasets Through an API. 270 12.8 Extensions. 275 References. 277 Index. 281 |
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| spelling | Arnold, Taylor Verfasser (DE-588)1081454458 aut Humanities data in R exploring networks, geospatial data, images, and text Taylor Arnold, Lauren Tilton Second edition Cham Springer [2024] xiv, 284 Seiten Illustrationen, Diagramme, Karten 235 mm txt rdacontent n rdamedia nc rdacarrier Quantitative methods in the humanities and social sciences This book teaches readers to integrate data analysis techniques into humanities research practices using the R programming language. Methods for general-purpose visualization and analysis are introduced first, followed by domain-specific techniques for working with networks, text, geospatial data, temporal data, and images. The book is designed to be a bridge between quantitative and qualitative methods, individual and collaborative work, and the humanities and social sciences. The second edition of the text is a significant revision, with almost every aspect of the text rewritten in some way. The most notable difference is the incorporation of new R packages such as ggplot2 and dplyr that center broad data-science concepts.This 2nd edition of Humanities Data with R does not presuppose background programming experience. Early chapters take readers from R set-up to exploratory data analysis, with one chapter dedicated to each stage of the data-science pipeline (data collection, visualization, manipulation, and relational joins). Following this, text analysis, networks, temporal data, geospatial data, and image analysis each have a dedicated chapter. These are grounded in examples to move readers beyond the intimidation of adding new tools to their research. The final section of the book extends the core material with additional computer science techniques for processing large datasets.Everything is hands-on: image analysis is explained using digitized photographs from the 1930s, and networks are applied to page links on Wikipedia. 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.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 bicssc bisacsh Digital humanities Sociology—Methodology Computational linguistics Anthropology Mathematical statistics—Data processing Quantitative Bildanalyse (DE-588)4176604-0 gnd rswk-swf Netzwerkanalyse Soziologie (DE-588)4205975-6 gnd rswk-swf Digital Humanities (DE-588)1038714850 gnd rswk-swf Sprachanalyse (DE-588)4129916-4 gnd rswk-swf Hardcover, Softcover / Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik Digital Humanities (DE-588)1038714850 s Netzwerkanalyse Soziologie (DE-588)4205975-6 s Quantitative Bildanalyse (DE-588)4176604-0 s Sprachanalyse (DE-588)4129916-4 s DE-604 Tilton, Lauren Verfasser (DE-588)1081455101 aut Erscheint auch als Online-Ausgabe 978-3-031-62566-4 (DE-604)BV049818301 Vorangegangen ist Humanities data in R 2015 (DE-604)BV042978701 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035152565&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Arnold, Taylor Tilton, Lauren Humanities data in R exploring networks, geospatial data, images, and text bicssc bisacsh Digital humanities Sociology—Methodology Computational linguistics Anthropology Mathematical statistics—Data processing Quantitative Bildanalyse (DE-588)4176604-0 gnd Netzwerkanalyse Soziologie (DE-588)4205975-6 gnd Digital Humanities (DE-588)1038714850 gnd Sprachanalyse (DE-588)4129916-4 gnd |
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| title | Humanities data in R exploring networks, geospatial data, images, and text |
| title_auth | Humanities data in R exploring networks, geospatial data, images, and text |
| title_exact_search | Humanities data in R exploring networks, geospatial data, images, and text |
| title_full | Humanities data in R exploring networks, geospatial data, images, and text Taylor Arnold, Lauren Tilton |
| title_fullStr | Humanities data in R exploring networks, geospatial data, images, and text Taylor Arnold, Lauren Tilton |
| title_full_unstemmed | Humanities data in R exploring networks, geospatial data, images, and text Taylor Arnold, Lauren Tilton |
| title_old | Humanities data in R |
| title_short | Humanities data in R |
| title_sort | humanities data in r exploring networks geospatial data images and text |
| title_sub | exploring networks, geospatial data, images, and text |
| topic | bicssc bisacsh Digital humanities Sociology—Methodology Computational linguistics Anthropology Mathematical statistics—Data processing Quantitative Bildanalyse (DE-588)4176604-0 gnd Netzwerkanalyse Soziologie (DE-588)4205975-6 gnd Digital Humanities (DE-588)1038714850 gnd Sprachanalyse (DE-588)4129916-4 gnd |
| topic_facet | bicssc bisacsh Digital humanities Sociology—Methodology Computational linguistics Anthropology Mathematical statistics—Data processing Quantitative Bildanalyse Netzwerkanalyse Soziologie Digital Humanities Sprachanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035152565&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT arnoldtaylor humanitiesdatainrexploringnetworksgeospatialdataimagesandtext AT tiltonlauren humanitiesdatainrexploringnetworksgeospatialdataimagesandtext |