## Data Analysis and Graphics Using R: An Example-Based ApproachDiscover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is done and why. The companion website has code and datasets, allowing readers to reproduce all analyses, along with solutions to selected exercises and updates. Assuming basic statistical knowledge and some experience with data analysis (but not R), the book is ideal for research scientists, final-year undergraduate or graduate-level students of applied statistics, and practising statisticians. It is both for learning and for reference. This third edition expands upon topics such as Bayesian inference for regression, errors in variables, generalized linear mixed models, and random forests. |

### What people are saying - Write a review

User Review - Flag as inappropriate

Seems this is the one book that comes to the top of the list when you start leaning R. So I started here. It's a review of the language and not data modelling. But obviously you need to learn the first one before you can use it to do the second one.

My only complaint is where can you find the datasets? For example:

package ‘MAAS’ is not available (for R version 3.2.1)

Not sure why someone would mess up R so that you can't use the Animals and other data sets mentioned in the book. Same with (austpop) but you can find that one the internet.

User Review - Flag as inappropriate

This book is very interesting, easily understood and provide good guidance

for statistics, especially in the exploration of data using graphs.

### Contents

1 | |

2 Styles of data analysis | 43 |

3 Statistical models | 77 |

4 A review of inference concepts | 102 |

5 Regression with a single predictor | 142 |

6 Multiple linear regression | 170 |

7 Exploiting the linear model framework | 217 |

8 Generalized linear models and survival analysis | 244 |

12 Multivariate data exploration and discrimination | 377 |

13 Regression on principal component or discriminant scores | 410 |

14 The R system additional topics | 427 |

15 Graphs in R | 472 |

Epilogue | 493 |

495 | |

507 | |

514 | |

9 Time series models | 283 |

10 Multilevel models and repeated measures | 303 |

11 Treebased classification and regression | 351 |

### Other editions - View all

Data Analysis and Graphics Using R: An Example-based Approach John Maindonald,John Braun Limited preview - 2003 |

Data Analysis and Graphics Using R: An Example-based Approach John Maindonald,John Braun Limited preview - 2006 |

Data Analysis and Graphics Using R: An Example-based Approach John Maindonald,John Braun No preview available - 2006 |

### Common terms and phrases

alternative analysis of variance argument assumptions autocorrelation bootstrap boxplots calculations Chapter classiﬁcation coefﬁcients columns comparison conﬁdence interval correlation cross-validation curve DAAG DAAG package data frame data set default degrees of freedom deviance discriminant elements Error t value Estimate Std example explanatory variables F-statistic factor females Figure ﬁle ﬁnd ﬁrst ﬁt ﬁtted values ﬁtting ﬁve ﬁxed function gives graph graphics groups Intercept labels lattice levels logarithmic logarithmic scale logdist logistic regression males mean square measures methodology methods multiple normal distribution Note objects observations obtained outliers output p-value panel parameter partial autocorrelation points polynomial population predictive accuracy principal components propensity score random effects randomForest regression model residuals rows sample scale scatterplot scores shows signiﬁcant simulation slope speciﬁed spline split standard deviation standard error statistical Subsection sum of squares Table transformation treatment tree value Pr(>|t variation vector weight workspace