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R for political data science : a practical guide / Francisco Urdinez, Andres Cruz.

By: Urdinez, FranciscoContributor(s): Cruz, AndresMaterial type: TextTextSeries: 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"--
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Holdings
Item type Current library Call number Status Date due Barcode
Books Books Gabriel Afolabi Ojo Central Library (Headquarters).
JA71.7 .R46 2021 (Browse shelf(Opens below)) Available 0187076
Books Books Gabriel Afolabi Ojo Central Library (Headquarters).
JA71.7 .R46 2021 (Browse shelf(Opens below)) Available 0187077
Books 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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