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R for Political Data Science: A Practical Guide 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. It was written
by political scientists, thinking about the many real-world
problems faced in their work. The book has 16 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 all 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.
R for Political Data Science: A Practical Guide 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. It was written
by political scientists, thinking about the many real-world
problems faced in their work. The book has 16 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 all 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.
No son solo palabras, estas conforman frases unidas al sentimiento,
tienen fuerza y caracter que emana del poeta Andres Cruz "Se
escaparon las palabras de algun rincon de mi alma salieron
desesperadas buscando la libertad, un verso las alcanzo justo
comenzando el alba fundiendose en una estrofa al darles la
claridad" Desde temprana edad demostro gran interes por la lectura;
las asignaturas de letras fueron sus preferidas, principalmente la
literatura clasica. En su juventud inicio sus actividades
literarias, desarrollando su gran pasion por la poesia.
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