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...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Arnold, Taylor [verfasserIn] Tilton, Lauren [verfasserIn] |
|---|
| Format: |
Buch |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
Cham, Switzerland: Springer ; 2024 |
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| Ausgabe: |
Second edition |
| Systematik: |
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| Umfang: |
xiv, 284 Seiten ; Illustrationen, Karten |
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| Weitere Ausgabe: |
Erscheint auch als Online-Ausgabe Arnold, Taylor: Humanities Data in R - Cham : Springer International Publishing, 2024 |
|---|
| Reihe: |
Quantitative Methods in the Humanities and Social Sciences |
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| Links: | |
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| ISBN: |
978-3-031-62565-7 |
| Katalog-ID: |
1896854036 |
|---|
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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. 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 | ||
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9783031625657 Gebunden : EUR 118,50 978-3-031-62565-7 (DE-627)1896854036 (DE-599)KXP1896854036 (OCoLC)1450305868 DE-627 ger DE-627 rda eng 519.5 SEPA 23 MR 2000 rvk (DE-625)rvk/123487: Arnold, Taylor verfasserin (DE-588)1081454458 (DE-627)846121425 (DE-576)45442129X aut Humanities data in R exploring networks , geospatial data, images, and text Taylor Arnold, Lauren Tilton Second edition Cham, Switzerland Springer [2024] xiv, 284 Seiten Illustrationen, Karten Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Quantitative Methods in the Humanities and Social Sciences - Part I Core.- Working with Data in R.- EDA I: Grammar of Graphics.- EDA II: Organizing Data.- EDA III: Restructuring Data.- Collecting Data.- Part II Data Types.- Textual Data.- Network Data.- Temporal Data.- Spatial Data.- Image Data.- Part III Additional Methods.- Programming in R.- Data Formats. 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 Anthropologie Anthropology COMPUTERS / Computer Science COMPUTERS / Mathematical & Statistical Software COMPUTERS / Natural Language Processing Computational linguistics Computer-Anwendungen in Kunst und Geisteswissenschaften Computer-Anwendungen in den Sozial- und Verhaltenswissenschaften Computerlinguistik und Korpuslinguistik Information technology: general issues MATHEMATICS / Probability & Statistics / General Mathematical & statistical software Mathematische und statistische Software Probability & statistics SOCIAL SCIENCE / Anthropology / General SOCIAL SCIENCE / Sociology / General Social research & statistics Sociology Soziologie Wahrscheinlichkeitsrechnung und Statistik Tilton, Lauren verfasserin (DE-588)1081455101 (DE-627)846121905 (DE-576)454421966 aut 9783031625664 Erscheint auch als Online-Ausgabe Arnold, Taylor Humanities Data in R 2nd ed. 2024. Cham : Springer International Publishing, 2024 1 Online-Ressource(XIV, 284 p. 80 illus., 50 illus. in color.) (DE-627)1896935060 9783031625664 https://www.dietmardreier.de/annot/564C42696D677C7C393738333033313632353635377C7C434F50.jpg?sq=2 Verlag Cover GBV_ILN_370 ISIL_DE-1373 SYSFLAG_1 GBV_KXP GBV_ILN_754 ISIL_DE-B1532 GBV_ILN_2010 ISIL_DE-15 MR 2000 Allgemeine Werke und Lehrbücher Soziologie Sozialwissenschaftliche Theorien und Methoden Methoden der Sozialforschung Allgemeine Werke und Lehrbücher (DE-627)1271138913 (DE-625)rvk/123487: (DE-576)201138913 BO 045F 519.5 370 01 4370 4570796117 00 EG MR 2000 051 u --%%-- a 26-08-24 754 01 3754 4560008752 Dreier_printPDA_2024 pda a 29-07-24 2010 01 DE-15 4565373548 00 --%%-- --%%-- --%%-- --%%-- l01 12-08-24 370 01 4370 HC058691 370 01 4370 2024-0498 754 01 3754 Dreier_printPDA_2024 370 01 4370 2024009 |
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9783031625657 Gebunden : EUR 118,50 978-3-031-62565-7 (DE-627)1896854036 (DE-599)KXP1896854036 (OCoLC)1450305868 DE-627 ger DE-627 rda eng 519.5 SEPA 23 MR 2000 rvk (DE-625)rvk/123487: Arnold, Taylor verfasserin (DE-588)1081454458 (DE-627)846121425 (DE-576)45442129X aut Humanities data in R exploring networks , geospatial data, images, and text Taylor Arnold, Lauren Tilton Second edition Cham, Switzerland Springer [2024] xiv, 284 Seiten Illustrationen, Karten Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Quantitative Methods in the Humanities and Social Sciences - Part I Core.- Working with Data in R.- EDA I: Grammar of Graphics.- EDA II: Organizing Data.- EDA III: Restructuring Data.- Collecting Data.- Part II Data Types.- Textual Data.- Network Data.- Temporal Data.- Spatial Data.- Image Data.- Part III Additional Methods.- Programming in R.- Data Formats. 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 Anthropologie Anthropology COMPUTERS / Computer Science COMPUTERS / Mathematical & Statistical Software COMPUTERS / Natural Language Processing Computational linguistics Computer-Anwendungen in Kunst und Geisteswissenschaften Computer-Anwendungen in den Sozial- und Verhaltenswissenschaften Computerlinguistik und Korpuslinguistik Information technology: general issues MATHEMATICS / Probability & Statistics / General Mathematical & statistical software Mathematische und statistische Software Probability & statistics SOCIAL SCIENCE / Anthropology / General SOCIAL SCIENCE / Sociology / General Social research & statistics Sociology Soziologie Wahrscheinlichkeitsrechnung und Statistik Tilton, Lauren verfasserin (DE-588)1081455101 (DE-627)846121905 (DE-576)454421966 aut 9783031625664 Erscheint auch als Online-Ausgabe Arnold, Taylor Humanities Data in R 2nd ed. 2024. Cham : Springer International Publishing, 2024 1 Online-Ressource(XIV, 284 p. 80 illus., 50 illus. in color.) (DE-627)1896935060 9783031625664 https://www.dietmardreier.de/annot/564C42696D677C7C393738333033313632353635377C7C434F50.jpg?sq=2 Verlag Cover GBV_ILN_370 ISIL_DE-1373 SYSFLAG_1 GBV_KXP GBV_ILN_754 ISIL_DE-B1532 GBV_ILN_2010 ISIL_DE-15 MR 2000 Allgemeine Werke und Lehrbücher Soziologie Sozialwissenschaftliche Theorien und Methoden Methoden der Sozialforschung Allgemeine Werke und Lehrbücher (DE-627)1271138913 (DE-625)rvk/123487: (DE-576)201138913 BO 045F 519.5 370 01 4370 4570796117 00 EG MR 2000 051 u --%%-- a 26-08-24 754 01 3754 4560008752 Dreier_printPDA_2024 pda a 29-07-24 2010 01 DE-15 4565373548 00 --%%-- --%%-- --%%-- --%%-- l01 12-08-24 370 01 4370 HC058691 370 01 4370 2024-0498 754 01 3754 Dreier_printPDA_2024 370 01 4370 2024009 |
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9783031625657 Gebunden : EUR 118,50 978-3-031-62565-7 (DE-627)1896854036 (DE-599)KXP1896854036 (OCoLC)1450305868 DE-627 ger DE-627 rda eng 519.5 SEPA 23 MR 2000 rvk (DE-625)rvk/123487: Arnold, Taylor verfasserin (DE-588)1081454458 (DE-627)846121425 (DE-576)45442129X aut Humanities data in R exploring networks , geospatial data, images, and text Taylor Arnold, Lauren Tilton Second edition Cham, Switzerland Springer [2024] xiv, 284 Seiten Illustrationen, Karten Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Quantitative Methods in the Humanities and Social Sciences - Part I Core.- Working with Data in R.- EDA I: Grammar of Graphics.- EDA II: Organizing Data.- EDA III: Restructuring Data.- Collecting Data.- Part II Data Types.- Textual Data.- Network Data.- Temporal Data.- Spatial Data.- Image Data.- Part III Additional Methods.- Programming in R.- Data Formats. 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 Anthropologie Anthropology COMPUTERS / Computer Science COMPUTERS / Mathematical & Statistical Software COMPUTERS / Natural Language Processing Computational linguistics Computer-Anwendungen in Kunst und Geisteswissenschaften Computer-Anwendungen in den Sozial- und Verhaltenswissenschaften Computerlinguistik und Korpuslinguistik Information technology: general issues MATHEMATICS / Probability & Statistics / General Mathematical & statistical software Mathematische und statistische Software Probability & statistics SOCIAL SCIENCE / Anthropology / General SOCIAL SCIENCE / Sociology / General Social research & statistics Sociology Soziologie Wahrscheinlichkeitsrechnung und Statistik Tilton, Lauren verfasserin (DE-588)1081455101 (DE-627)846121905 (DE-576)454421966 aut 9783031625664 Erscheint auch als Online-Ausgabe Arnold, Taylor Humanities Data in R 2nd ed. 2024. Cham : Springer International Publishing, 2024 1 Online-Ressource(XIV, 284 p. 80 illus., 50 illus. in color.) (DE-627)1896935060 9783031625664 https://www.dietmardreier.de/annot/564C42696D677C7C393738333033313632353635377C7C434F50.jpg?sq=2 Verlag Cover GBV_ILN_370 ISIL_DE-1373 SYSFLAG_1 GBV_KXP GBV_ILN_754 ISIL_DE-B1532 GBV_ILN_2010 ISIL_DE-15 MR 2000 Allgemeine Werke und Lehrbücher Soziologie Sozialwissenschaftliche Theorien und Methoden Methoden der Sozialforschung Allgemeine Werke und Lehrbücher (DE-627)1271138913 (DE-625)rvk/123487: (DE-576)201138913 BO 045F 519.5 370 01 4370 4570796117 00 EG MR 2000 051 u --%%-- a 26-08-24 754 01 3754 4560008752 Dreier_printPDA_2024 pda a 29-07-24 2010 01 DE-15 4565373548 00 --%%-- --%%-- --%%-- --%%-- l01 12-08-24 370 01 4370 HC058691 370 01 4370 2024-0498 754 01 3754 Dreier_printPDA_2024 370 01 4370 2024009 |
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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 |
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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 |
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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 |
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