R for political data science: a practical guide
Saved in:
| Other Authors: | , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Boca Raton ; London ; New York
CRC Press
2021
|
| Edition: | First edition |
| Series: | The R series
|
| Subjects: | |
| Links: | https://ebookcentral.proquest.com/lib/th-deggendorf/detail.action?docID=6340974 https://ebookcentral.proquest.com/lib/munchentech/detail.action?docID=6340974 |
| Item Description: | Description based on publisher supplied metadata and other sources |
| Physical Description: | 1 Online-Ressource (xix, 439 Seiten) Illustrationen, Diagramme |
| ISBN: | 9781000204476 9781003010623 |
Staff View
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| 505 | 8 | |a Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Who will find this book useful? -- About the book -- What to expect from the book -- Book structure -- Prerequisites -- How to use the textbook in a methods course? -- Contributors -- Part I: Introduction to R -- 1. Basic R -- 1.1 Installation -- 1.2 Console -- 1.3 Script -- 1.4 Objects (and functions) -- 2. Data Management -- 2.1 Introduction to data management -- 2.2 Describing a dataset -- 2.3 Basic operations -- 2.4 Chain commands -- 2.5 Recode values -- 3. Data Visualization -- 3.1 Why visualize my data? -- 3.2 First steps -- 3.3 Applied example: Local elections and data visualization -- 3.4 To continue learning -- 4. Data Loading -- 4.1 Introduction -- 4.2 Different dataset formats -- 4.3 Files separated by delimiters (.csv and .tsv) -- 4.4 Large tabular datasets -- Part II: Models -- 5. Linear Models -- 5.1 OLS in R -- 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 -- 6. Case Selection Based on Regressions -- 6.1 Which case study should I select for qualitative research? -- 6.2 The importance of combining methods -- 7. Panel Data -- 7.1 Introduction -- 7.2 Describing your panel dataset -- 7.3 Modelling group-level variation -- 7.4 Fixed vs. random effects -- 7.5 Testing for unit roots -- 7.6 Robust and panel-corrected standard errors -- 8. Logistic Models -- 8.1 Introduction -- 8.2 Use of logistic models -- 8.3 How are probabilities estimated? -- 8.4 Model estimation -- 8.5 Creating tables -- 8.6 Visual representation of results -- 8.7 Measures to evaluate the fit of the models -- 9. Survival Models -- 9.1 Introduction -- 9.2 How do we interpret hazard rates? -- 9.3 Cox's model of proportional hazards | |
| 505 | 8 | |a 9.4 Estimating Cox Models in R -- 9.5 Tools to interpret and present hazard ratios -- 10. Causal inference -- 10.1 Introduction -- 10.2 Causation and causal graphs -- 10.3 Measuring causal effects -- 10.4 DAGs and statistical associations -- 10.5 Backdoors and -- 10.6 Drawing and analyzing DAGs -- 10.7 Making adjustments -- 10.8 Caveats -- Part III: Applications -- 11. Advanced Political Data Management -- 11.1 Introduction -- 11.2 Merging datasets -- 11.3 Fuzzy or inexact join of data -- 11.4 Missing values' management -- 11.5 Imputation of missing values -- 12. Web Mining -- 12.1 Introduction -- 12.2 Ways to do web scraping -- 12.3 Web scraping in R -- 12.4 Using APIs and extracting data from Twitter -- 13. Quantitative Analysis of Political Texts -- 13.1 Analysis of political hashtags -- 13.2 Wordfish -- 13.3 Structural Topic Modeling -- 14. Networks -- 14.1 Introduction -- 14.2 Basic concepts in a network -- 14.3 Network datasets -- 14.4 Graphic presentation of a network -- 14.5 Measures of centrality -- 15. Principal Component Analysis -- 15.1 Introduction -- 15.2 How PCA works -- 15.3 Basic notions in R -- 15.4 Dimensionality of the concept -- 15.5 Variation of the concept -- 16. Maps and Spatial Data -- 16.1 Introduction -- 16.2 Spatial Data in R -- 16.3 Spatial Data Management -- 16.4 Mapping in R -- 16.5 Inference from Spatial Data -- IV Bibliography and Index -- Bibliography -- Index | |
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| contents | Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Who will find this book useful? -- About the book -- What to expect from the book -- Book structure -- Prerequisites -- How to use the textbook in a methods course? -- Contributors -- Part I: Introduction to R -- 1. Basic R -- 1.1 Installation -- 1.2 Console -- 1.3 Script -- 1.4 Objects (and functions) -- 2. Data Management -- 2.1 Introduction to data management -- 2.2 Describing a dataset -- 2.3 Basic operations -- 2.4 Chain commands -- 2.5 Recode values -- 3. Data Visualization -- 3.1 Why visualize my data? -- 3.2 First steps -- 3.3 Applied example: Local elections and data visualization -- 3.4 To continue learning -- 4. Data Loading -- 4.1 Introduction -- 4.2 Different dataset formats -- 4.3 Files separated by delimiters (.csv and .tsv) -- 4.4 Large tabular datasets -- Part II: Models -- 5. Linear Models -- 5.1 OLS in R -- 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 -- 6. Case Selection Based on Regressions -- 6.1 Which case study should I select for qualitative research? -- 6.2 The importance of combining methods -- 7. Panel Data -- 7.1 Introduction -- 7.2 Describing your panel dataset -- 7.3 Modelling group-level variation -- 7.4 Fixed vs. random effects -- 7.5 Testing for unit roots -- 7.6 Robust and panel-corrected standard errors -- 8. Logistic Models -- 8.1 Introduction -- 8.2 Use of logistic models -- 8.3 How are probabilities estimated? -- 8.4 Model estimation -- 8.5 Creating tables -- 8.6 Visual representation of results -- 8.7 Measures to evaluate the fit of the models -- 9. Survival Models -- 9.1 Introduction -- 9.2 How do we interpret hazard rates? -- 9.3 Cox's model of proportional hazards 9.4 Estimating Cox Models in R -- 9.5 Tools to interpret and present hazard ratios -- 10. Causal inference -- 10.1 Introduction -- 10.2 Causation and causal graphs -- 10.3 Measuring causal effects -- 10.4 DAGs and statistical associations -- 10.5 Backdoors and -- 10.6 Drawing and analyzing DAGs -- 10.7 Making adjustments -- 10.8 Caveats -- Part III: Applications -- 11. Advanced Political Data Management -- 11.1 Introduction -- 11.2 Merging datasets -- 11.3 Fuzzy or inexact join of data -- 11.4 Missing values' management -- 11.5 Imputation of missing values -- 12. Web Mining -- 12.1 Introduction -- 12.2 Ways to do web scraping -- 12.3 Web scraping in R -- 12.4 Using APIs and extracting data from Twitter -- 13. Quantitative Analysis of Political Texts -- 13.1 Analysis of political hashtags -- 13.2 Wordfish -- 13.3 Structural Topic Modeling -- 14. Networks -- 14.1 Introduction -- 14.2 Basic concepts in a network -- 14.3 Network datasets -- 14.4 Graphic presentation of a network -- 14.5 Measures of centrality -- 15. Principal Component Analysis -- 15.1 Introduction -- 15.2 How PCA works -- 15.3 Basic notions in R -- 15.4 Dimensionality of the concept -- 15.5 Variation of the concept -- 16. Maps and Spatial Data -- 16.1 Introduction -- 16.2 Spatial Data in R -- 16.3 Spatial Data Management -- 16.4 Mapping in R -- 16.5 Inference from Spatial Data -- IV Bibliography and Index -- Bibliography -- Index |
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| dewey-full | 519.502855133 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.502855133 |
| dewey-search | 519.502855133 |
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| dewey-tens | 510 - Mathematics |
| discipline | Informatik Politologie Mathematik |
| edition | First edition |
| format | Electronic eBook |
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| id | DE-604.BV047441956 |
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| indexdate | 2024-12-20T19:19:40Z |
| institution | BVB |
| isbn | 9781000204476 9781003010623 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032844108 |
| oclc_num | 1203584482 |
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| owner_facet | DE-91 DE-BY-TUM DE-1050 |
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| spellingShingle | R for political data science a practical guide Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Who will find this book useful? -- About the book -- What to expect from the book -- Book structure -- Prerequisites -- How to use the textbook in a methods course? -- Contributors -- Part I: Introduction to R -- 1. Basic R -- 1.1 Installation -- 1.2 Console -- 1.3 Script -- 1.4 Objects (and functions) -- 2. Data Management -- 2.1 Introduction to data management -- 2.2 Describing a dataset -- 2.3 Basic operations -- 2.4 Chain commands -- 2.5 Recode values -- 3. Data Visualization -- 3.1 Why visualize my data? -- 3.2 First steps -- 3.3 Applied example: Local elections and data visualization -- 3.4 To continue learning -- 4. Data Loading -- 4.1 Introduction -- 4.2 Different dataset formats -- 4.3 Files separated by delimiters (.csv and .tsv) -- 4.4 Large tabular datasets -- Part II: Models -- 5. Linear Models -- 5.1 OLS in R -- 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 -- 6. Case Selection Based on Regressions -- 6.1 Which case study should I select for qualitative research? -- 6.2 The importance of combining methods -- 7. Panel Data -- 7.1 Introduction -- 7.2 Describing your panel dataset -- 7.3 Modelling group-level variation -- 7.4 Fixed vs. random effects -- 7.5 Testing for unit roots -- 7.6 Robust and panel-corrected standard errors -- 8. Logistic Models -- 8.1 Introduction -- 8.2 Use of logistic models -- 8.3 How are probabilities estimated? -- 8.4 Model estimation -- 8.5 Creating tables -- 8.6 Visual representation of results -- 8.7 Measures to evaluate the fit of the models -- 9. Survival Models -- 9.1 Introduction -- 9.2 How do we interpret hazard rates? -- 9.3 Cox's model of proportional hazards 9.4 Estimating Cox Models in R -- 9.5 Tools to interpret and present hazard ratios -- 10. Causal inference -- 10.1 Introduction -- 10.2 Causation and causal graphs -- 10.3 Measuring causal effects -- 10.4 DAGs and statistical associations -- 10.5 Backdoors and -- 10.6 Drawing and analyzing DAGs -- 10.7 Making adjustments -- 10.8 Caveats -- Part III: Applications -- 11. Advanced Political Data Management -- 11.1 Introduction -- 11.2 Merging datasets -- 11.3 Fuzzy or inexact join of data -- 11.4 Missing values' management -- 11.5 Imputation of missing values -- 12. Web Mining -- 12.1 Introduction -- 12.2 Ways to do web scraping -- 12.3 Web scraping in R -- 12.4 Using APIs and extracting data from Twitter -- 13. Quantitative Analysis of Political Texts -- 13.1 Analysis of political hashtags -- 13.2 Wordfish -- 13.3 Structural Topic Modeling -- 14. Networks -- 14.1 Introduction -- 14.2 Basic concepts in a network -- 14.3 Network datasets -- 14.4 Graphic presentation of a network -- 14.5 Measures of centrality -- 15. Principal Component Analysis -- 15.1 Introduction -- 15.2 How PCA works -- 15.3 Basic notions in R -- 15.4 Dimensionality of the concept -- 15.5 Variation of the concept -- 16. Maps and Spatial Data -- 16.1 Introduction -- 16.2 Spatial Data in R -- 16.3 Spatial Data Management -- 16.4 Mapping in R -- 16.5 Inference from Spatial Data -- IV Bibliography and Index -- Bibliography -- Index R (Computer program language) R Programm (DE-588)4705956-4 gnd Datenanalyse (DE-588)4123037-1 gnd Politische Wissenschaft (DE-588)4076229-4 gnd |
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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_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 |
| title_sub | a practical guide |
| topic | R (Computer program language) R Programm (DE-588)4705956-4 gnd Datenanalyse (DE-588)4123037-1 gnd Politische Wissenschaft (DE-588)4076229-4 gnd |
| topic_facet | R (Computer program language) R Programm Datenanalyse Politische Wissenschaft Einführung |
| work_keys_str_mv | AT urdinezfrancisco rforpoliticaldatascienceapracticalguide AT cruzandres rforpoliticaldatascienceapracticalguide |