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Doing Data Science in R: An Introduction for Social Scientists: Doing Data Science in R: An Introduction for Social Scientists
: Mark Andrews
: SAGE Publications Ltd
: 2021
: 640
:
: epub
: 27.4 MB

This approachable introduction to doing data science in R provides step-by-step advice on using the tools and statistical methods to carry out data analysis. Introducing the fundamentals of data science and R before moving into more advanced topics like Multilevel Models and Probabilistic Modelling with Stan, it builds knowledge and skills gradually.

The R programming language itself can be extended by interfacing with other programming languages like C, C++, Fortran and Python. In particular, the popular Rcpp package greatly simplifies integrating R with C++, thus allowing fast and efficient C++ code to be used seamlessly within R. Likewise, R can be easily interfaced with high-performance computing or big data tools like Hadoop, Spark, SQL, parallel computing libraries, cluster computing, and so on. Taken together, these points entail that R is an extremely powerful and extensible environment for doing any kind of statistical computing or data analysis.

This book:

Focuses on providing practical guidance for all aspects, helping readers get to grips with the tools, software, and statistical methods needed to provide the right type and level of analysis their data requires
Explores the foundations of data science and breaks down the processes involved, focusing on the link between data science and practical social science skills
Introduces R at the outset and includes extensive worked examples and R code every step of the way, ensuring students see the value of R and its connection to methods while providing hands-on practice in the software
Provides examples and datasets from different disciplines and locations demonstrate the widespread relevance, possible applications, and impact of data science across the social sciences

Doing Data Science in R: An Introduction for Social Scientists








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