The Book of R, 2nd Edition : A First Course in Programming and Statistics

Even if you have no programming experience and little more than a grounding in basic math, The Book of R, 2nd Edition will teach you everything you need to know for using R effectively in statistical analysis. You'll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data, performing statistical tests, and modelling. You'll also learn how to create impressive data visualizations with R's graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package. Over 30 of hands-on exercises (with downloadable solutions) take you from theory to practice, as you explore: the fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops; statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R; how to access R's thousands of functions, libraries, and data sets, and how to draw valid and useful conclusions from your data How to create publication-quality graphics of your results.

Even if you have no programming experience and little more than a grounding in basic math, The Book of R, 2nd Edition will teach you everything you need to know for using R effectively in statistical analysis. You''ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data, performing statistical tests, and modelling. You''ll also learn how to create impressive data visualizations with R''s graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package. Over 30 of hands-on exercises (with downloadable solutions) take you from theory to practice, as you explore: the fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops; statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R; how to access R''s thousands of functions, libraries, and data sets, and how to draw valid and useful conclusions from your data How to create publication-quality graphics of your results.