R for political data science : a practical guide / Francisco Urdinez, Andres Cruz.
Material type: TextSeries: Chapman & Hall/CRC the R SeriesPublisher: Boca Raton : Taylor and Francis, 2020Description: pages cmISBN: 9780367818890; 9780367818838Subject(s): POLITICAL SCIENCESummary: "This is a handbook for political scientists new to R who want to learn the most useful and common ways to interpret and analyze political data. This book is divided into 3 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"--Item type | Current library | Call number | Status | Date due | Barcode |
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Books | Gabriel Afolabi Ojo Central Library (Headquarters). | JA71.7 .R46 2021 (Browse shelf(Opens below)) | Available | 0187076 | |
Books | Gabriel Afolabi Ojo Central Library (Headquarters). | JA71.7 .R46 2021 (Browse shelf(Opens below)) | Available | 0187077 | |
Books | Gabriel Afolabi Ojo Central Library (Headquarters). | JA71.7 .R46 2021 (Browse shelf(Opens below)) | Available | 0187078 |
"This is a handbook for political scientists new to R who want to learn the most useful and common ways to interpret and analyze political data. This book is divided into 3 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"--
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