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An invaluable, step-by-step guide to data management in R for social science researchers. This book will show you how to recode data, combine data from different sources, document data, and import data from statistical packages other than R. It explores both qualitative and quantitative data and is packed with a range of supportive learning features such as code examples, overview boxes, images, tables, and diagrams.
An invaluable, step-by-step guide to data management in R for social science researchers.
This book will show you how to recode data, combine data from different sources, document data, and import data from statistical packages other than R. It explores both qualitative and quantitative data and is packed with a range of supportive learning features such as code examples, overview boxes, images, tables, and diagrams.
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Autorenporträt
I am a political scientist with research interests in political sociology, political behaviour, comparative politics, and political methodology. In the first two areas I focus on electoral behaviour, political attitudes and behaviour in Germany, the relation between social structure and politics, and on political knowledge and political sophistication. My methodological research interests focus on the spatial modelling of party manifestos, survey research methodology in general, computational statistics and ecological inference. On these topics I have published a monograph, a variety of book chapters as well as research articles in Acta Politica, Electoral Studies, the European Journal of Political Research, German Politics, Perspectives on Politics, and Political Analysis.
Inhaltsangabe
1. Introduction 2. Building Blocks of Data 3. Data Frames 4. Data Tables and Tidyverse 5. Handling Data from Social Science Surveys 6. Managing Data from Complex Samples 7. Dates, Times, and Time Series 8. Spatial/Geographical data 9. Text as Data
1. Introduction 2. Building Blocks of Data 3. Data Frames 4. Data Tables and Tidyverse 5. Handling Data from Social Science Surveys 6. Managing Data from Complex Samples 7. Dates, Times, and Time Series 8. Spatial/Geographical data 9. Text as Data
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