R for political data science: a practical guide
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, FL ; London ; New York
CRC Press
[2021]
|
| Ausgabe: | First edition |
| Schriftenreihe: | The R Series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | xix, 439 Seiten Illustrationen, Diagramme |
| ISBN: | 9780367818890 9780367818838 |
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| adam_text | Contents Preface ix Who will find this book useful?..................................................................... About the book ............................................................................................... What to expect from thebook ....................................................................... Book structure.................................................................................................. Prerequisites ..................................................................................................... How to use the textbook in amethods course? ............................................ Contributors I Introduction to R 1 Basic R 1.1 1.2 1.3 1.4 Installation.............................................................................................. Console..................................................................................................... Script........................................................................................................ Objects (and functions) ......................................................................... 2 Data Management 2.1 2.2 2.3 2.4 2.5 Introduction to data management ....................................................... Describing a dataset............................................................................... Basic operations ..................................................................................... Chain commands..................................................................................... Recode values
......................................................................................... 3 Data Visualization 3.1 3.2 3.3 3.4 Why visualize my data?......................................................................... First steps .............................................................................................. Applied example: Localelections and data visualization .................... To continue learning............................................................................... 4 Data Loading 4.1 4.2 4.3 4.4 Introduction ........................................................................................... Different dataset formats ...................................................................... Files separated by delimiters (.csv and .tsv) ........................................ Large tabular datasets............................................................................ ix x xii xii xiv xiv xix 1 3 3 4 5 6 15 15 17 19 28 30 37 37 40 48 66 71 71 73 73 84 v
vi II Contents Models 5 Linear Models 5.1 OLSinR................................................................................................... 5.2 Bivariate model: simple linear regression............................................... 5.3 Multivariate model: multiple regression ............................................... 5.4 Model adjustment .................................................................................... 5.5 Inference in multiple linear models ........................................................ 5.6 Testing OLS assumptions ....................................................................... 87 89 90 96 103 108 109 110 6 Case Selection Based on Regressions 131 6.1 Which case study should I select for qualitative research? ................ 133 6.2 The importance of combining methods.................................................. 145 7 Panel Data 147 7.1 Introduction ............................................................................................ 147 7.2 Describing your panel dataset................................................................. 152 7.3 Modelling group-level variation.............................................................. 158 7.4 Fixed vs. random effects.......................................................................... 161 7.5 Testing for unit roots ............................................................................. 163 7.6 Robust and panel-corrected standard errors ........................................ 169 8 Logistic Models 173 8.1 Introduction
............................................................................................ 173 8.2 Use of logistic models ............................................................................. 174 8.3 How are probabilities estimated? ........................................................... 176 8.4 Model estimation...................................................................................... 182 8.5 Creating tables......................................................................................... 186 8.6 Visual representation of results .............................................................. 190 8.7 Measures to evaluate the fit of the models........................................... 200 9 Survival Models 209 9.1 Introduction ............................................................................................ 209 9.2 How do we interpret hazard rates? ....................................................... 212 9.3 Cox’s model of proportional hazards .................................................... 213 9.4 Estimating Cox Models in R ................................................................. 215 9.5 Tools to interpret and present hazard ratios........................................ 226 10 Causal inference 235 10.1 Introduction ............................................................................................ 235 10.2 Causation and causal graphs ................................................................ 237 10.3 Measuring causal effects..........................................................................
239 10.4 DAGs and statistical associations.......................................................... 241 10.5 Backdoors and do-calculus ................................................................... 243 10.6 Drawing and analyzing DAGs................................................................ 247 10.7 Making adjustments................................................................................ 256 10.8 Caveats..................................................................................................... 272
Contents III vii Applications 11 Advanced Political Data Management 11.1 11.2 11.3 11.4 11.5 13.1 Analysis of political hashtags .................................................................. 13.2 Wordfish .................................................................................................... 13.3 Structural Topic Modeling ..................................................................... 14 Networks Introduction ............................................................................................ How PCA works ..................................................................................... Basic notions in R.................................................................................. Dimensionality of the concept................................................................ Variation of the concept......................................................................... 16 Maps and Spatial Data 16.1 16.2 16.3 16.4 16.5 IV 327 328 340 346 357 Introduction ............................................................................................ 357 Basic concepts in a network..................................................................... 358 Network datasets....................................................................................... 360 Graphic presentation of a network ......................................................... 362 Measures of centrality ............................................................................ 366 15 Principal Component Analysis 15.1 15.2 15.3 15.4 15.5 307 Introduction
............................................................................................ 307 Ways to do web scraping ........................................................................ 309 Web scraping in R.................................................................................... 310 Using APIs and extracting data from Twitter..................................... 317 13 Quantitative Analysis of Political Texts 14.1 14.2 14.3 14.4 14.5 277 Introduction ............................................................................................ 277 Merging datasets....................................................................................... 279 Fuzzy or inexact join of data ................................................................ 284 Missing values’ management .................................................................. 288 Imputation of missing values .................................................................. 295 12 Web Mining 12.1 12.2 12.3 12.4 275 Introduction ............................................................................................ Spatial Data in R .................................................................................. Spatial Data Management...................................................................... Mapping in R ........................................................................................ Inference from Spatial Data................................................................... Bibliography and Index 375 376 377 378 383 389 395 395 398 401 407 413 425 Bibliography
427 Index 437
“With its tutorial approach, R for Political Data Science builds readers’ R liter acy without assuming any prior experience with the language. By the end, your practical political data science toolkit will be well-stocked, you will be more mo tivated to take the next step and study the mathematical underpinnings of the methods discussed throughout. Using R professionally will no longer feel like a pipe dream (pun intended!).” - Santiago Olivella, Assistant Professor of Political Science, University of North Carolina — Chapel Hill R for Political Data Science: A Practical Guide is a handbook for political sci entists new to R who want to learn the most useful and common ways to interpret and analyze political data. It was written by political scientists, thinking about the many real-world problems faced in their work. The book has 17 chapters and is organized in three sections. The first, on the use of R, is for those users who are learning R or are migrating from another software. The second section, on econometric models, covers OLS, binary and survival models, panel data, and causal inference. The third section is a data science toolbox of some the most useful tools in the discipline: data imputation, fuzzy merge of large datasets, web mining, quantitative text analysis, network analysis, mapping, spatial cluster analysis, and principal component analysis. Key features: • Each chapter has the most up-to-date and simple option available for each task, assuming minimal prerequisites and no previous experience in R • Makes extensive use of the Tidyverse, the group of
packages that has revolutionized the use of R • Provides a step-by-step guide that you can replicate using your own data • Includes exercises in every chapter for course use or self-study • Focuses on practical-based approaches to statistical inference rather than mathematical formulae • Supplemented by an R package, including ail data As the title suggests, this book is highly applied in nature, and is designed as a toolbox for the reader. It can be used in methods and data science courses, at both the undergraduate and graduate levels. It will be equally useful for a university student pursuing a PhD, political consultants, or a public official, all of whom need to transform their datasets into substantive and easily interpretable conclusions.
|
| adam_txt |
Contents Preface ix Who will find this book useful?. About the book . What to expect from thebook . Book structure. Prerequisites . How to use the textbook in amethods course? . Contributors I Introduction to R 1 Basic R 1.1 1.2 1.3 1.4 Installation. Console. Script. Objects (and functions) . 2 Data Management 2.1 2.2 2.3 2.4 2.5 Introduction to data management . Describing a dataset. Basic operations . Chain commands. Recode values
. 3 Data Visualization 3.1 3.2 3.3 3.4 Why visualize my data?. First steps . Applied example: Localelections and data visualization . To continue learning. 4 Data Loading 4.1 4.2 4.3 4.4 Introduction . Different dataset formats . Files separated by delimiters (.csv and .tsv) . Large tabular datasets. ix x xii xii xiv xiv xix 1 3 3 4 5 6 15 15 17 19 28 30 37 37 40 48 66 71 71 73 73 84 v
vi II Contents Models 5 Linear Models 5.1 OLSinR. 5.2 Bivariate model: simple linear regression. 5.3 Multivariate model: multiple regression . 5.4 Model adjustment . 5.5 Inference in multiple linear models . 5.6 Testing OLS assumptions . 87 89 90 96 103 108 109 110 6 Case Selection Based on Regressions 131 6.1 Which case study should I select for qualitative research? . 133 6.2 The importance of combining methods. 145 7 Panel Data 147 7.1 Introduction . 147 7.2 Describing your panel dataset. 152 7.3 Modelling group-level variation. 158 7.4 Fixed vs. random effects. 161 7.5 Testing for unit roots . 163 7.6 Robust and panel-corrected standard errors . 169 8 Logistic Models 173 8.1 Introduction
. 173 8.2 Use of logistic models . 174 8.3 How are probabilities estimated? . 176 8.4 Model estimation. 182 8.5 Creating tables. 186 8.6 Visual representation of results . 190 8.7 Measures to evaluate the fit of the models. 200 9 Survival Models 209 9.1 Introduction . 209 9.2 How do we interpret hazard rates? . 212 9.3 Cox’s model of proportional hazards . 213 9.4 Estimating Cox Models in R . 215 9.5 Tools to interpret and present hazard ratios. 226 10 Causal inference 235 10.1 Introduction . 235 10.2 Causation and causal graphs . 237 10.3 Measuring causal effects.
239 10.4 DAGs and statistical associations. 241 10.5 Backdoors and do-calculus . 243 10.6 Drawing and analyzing DAGs. 247 10.7 Making adjustments. 256 10.8 Caveats. 272
Contents III vii Applications 11 Advanced Political Data Management 11.1 11.2 11.3 11.4 11.5 13.1 Analysis of political hashtags . 13.2 Wordfish . 13.3 Structural Topic Modeling . 14 Networks Introduction . How PCA works . Basic notions in R. Dimensionality of the concept. Variation of the concept. 16 Maps and Spatial Data 16.1 16.2 16.3 16.4 16.5 IV 327 328 340 346 357 Introduction . 357 Basic concepts in a network. 358 Network datasets. 360 Graphic presentation of a network . 362 Measures of centrality . 366 15 Principal Component Analysis 15.1 15.2 15.3 15.4 15.5 307 Introduction
. 307 Ways to do web scraping . 309 Web scraping in R. 310 Using APIs and extracting data from Twitter. 317 13 Quantitative Analysis of Political Texts 14.1 14.2 14.3 14.4 14.5 277 Introduction . 277 Merging datasets. 279 Fuzzy or inexact join of data . 284 Missing values’ management . 288 Imputation of missing values . 295 12 Web Mining 12.1 12.2 12.3 12.4 275 Introduction . Spatial Data in R . Spatial Data Management. Mapping in R . Inference from Spatial Data. Bibliography and Index 375 376 377 378 383 389 395 395 398 401 407 413 425 Bibliography
427 Index 437
“With its tutorial approach, R for Political Data Science builds readers’ R liter acy without assuming any prior experience with the language. By the end, your practical political data science toolkit will be well-stocked, you will be more mo tivated to take the next step and study the mathematical underpinnings of the methods discussed throughout. Using R professionally will no longer feel like a pipe dream (pun intended!).” - Santiago Olivella, Assistant Professor of Political Science, University of North Carolina — Chapel Hill R for Political Data Science: A Practical Guide is a handbook for political sci entists new to R who want to learn the most useful and common ways to interpret and analyze political data. It was written by political scientists, thinking about the many real-world problems faced in their work. The book has 17 chapters and is organized in three sections. The first, on the use of R, is for those users who are learning R or are migrating from another software. The second section, on econometric models, covers OLS, binary and survival models, panel data, and causal inference. The third section is a data science toolbox of some the most useful tools in the discipline: data imputation, fuzzy merge of large datasets, web mining, quantitative text analysis, network analysis, mapping, spatial cluster analysis, and principal component analysis. Key features: • Each chapter has the most up-to-date and simple option available for each task, assuming minimal prerequisites and no previous experience in R • Makes extensive use of the Tidyverse, the group of
packages that has revolutionized the use of R • Provides a step-by-step guide that you can replicate using your own data • Includes exercises in every chapter for course use or self-study • Focuses on practical-based approaches to statistical inference rather than mathematical formulae • Supplemented by an R package, including ail data As the title suggests, this book is highly applied in nature, and is designed as a toolbox for the reader. It can be used in methods and data science courses, at both the undergraduate and graduate levels. It will be equally useful for a university student pursuing a PhD, political consultants, or a public official, all of whom need to transform their datasets into substantive and easily interpretable conclusions. |
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| spelling | R for political data science a practical guide edited by Francisco Urdinez, Andrés Cruz First edition Boca Raton, FL ; London ; New York CRC Press [2021] © 2021 xix, 439 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier The R Series Description based on publisher supplied metadata and other sources R Programm (DE-588)4705956-4 gnd rswk-swf Politische Wissenschaft (DE-588)4076229-4 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf (DE-588)4151278-9 Einführung gnd-content Politische Wissenschaft (DE-588)4076229-4 s Datenanalyse (DE-588)4123037-1 s R Programm (DE-588)4705956-4 s DE-604 Urdinez, Francisco (DE-588)1191293564 edt Cruz, Andrés (DE-588)1233721437 edt Erscheint auch als Online-Ausgabe, ebk 978-1-00-020447-6 Erscheint auch als Online-Ausgabe 978-1-00-301062-3 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032430217&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032430217&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
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| title | R for political data science a practical guide |
| title_auth | R for political data science a practical guide |
| title_exact_search | R for political data science a practical guide |
| title_exact_search_txtP | R for political data science a practical guide |
| title_full | R for political data science a practical guide edited by Francisco Urdinez, Andrés Cruz |
| title_fullStr | R for political data science a practical guide edited by Francisco Urdinez, Andrés Cruz |
| title_full_unstemmed | R for political data science a practical guide edited by Francisco Urdinez, Andrés Cruz |
| title_short | R for political data science |
| title_sort | r for political data science a practical guide |
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| topic_facet | R Programm Politische Wissenschaft Datenanalyse Einführung |
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| work_keys_str_mv | AT urdinezfrancisco rforpoliticaldatascienceapracticalguide AT cruzandres rforpoliticaldatascienceapracticalguide |