Statistics in R
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Joost van de Weijer
Abstract
The R Project for Statistical Computing is one of the most comprehensive and widely used software options for statistical analysis. Moreover, it is open source, freely available and entirely cross-platform. It is for these reasons that the following chapters all employ R to demonstrate the application and interpretation of statistics. Like the commercially available software SAS, but unlike three other widely used suites (SPSS, Stata, and Statistica), R is principally used in command line. The need to work with commands rather than a graphical user interface can be a challenge for novice users, especially when combined with the task of learning statistics. However, commands given in a step-by-step fashion is arguably simpler than a graphic interface, which can overwhelm the novice user with options. This chapter is an introduction to R focusing on how to import data and make sure those data are in the correct format for analysis. Knowledge of each of these steps is assumed in the following chapters.
Abstract
The R Project for Statistical Computing is one of the most comprehensive and widely used software options for statistical analysis. Moreover, it is open source, freely available and entirely cross-platform. It is for these reasons that the following chapters all employ R to demonstrate the application and interpretation of statistics. Like the commercially available software SAS, but unlike three other widely used suites (SPSS, Stata, and Statistica), R is principally used in command line. The need to work with commands rather than a graphical user interface can be a challenge for novice users, especially when combined with the task of learning statistics. However, commands given in a step-by-step fashion is arguably simpler than a graphic interface, which can overwhelm the novice user with options. This chapter is an introduction to R focusing on how to import data and make sure those data are in the correct format for analysis. Knowledge of each of these steps is assumed in the following chapters.
Chapters in this book
- Prelim pages i
- Table of contents v
- Contributors vii
- Outline 1
-
Section 1. Polysemy and synonymy
- Polysemy and synonymy 7
- Competing ‘transfer’ constructions in Dutch 39
- Rethinking constructional polysemy 61
- Quantifying polysemy in Cognitive Sociolinguistics 87
- The many uses of run 117
- Visualizing distances in a set of near-synonyms 145
- A case for the multifactorial assessment of learner language 179
- Dutch causative constructions 205
- The semasiological structure of Polish myśleć ‘to think’ 223
- A multifactorial corpus analysis of grammatical synonymy 253
- A diachronic corpus-based multivariate analysis of “I think that” vs. “I think zero” 279
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Section 2. Statistical techniques
- Techniques and tools 307
- Statistics in R 343
- Frequency tables 365
- Collostructional analysis 391
- Cluster analysis 405
- Correspondence analysis 443
- Logistic regression 487
- Name index 535
- Subject index 541
Chapters in this book
- Prelim pages i
- Table of contents v
- Contributors vii
- Outline 1
-
Section 1. Polysemy and synonymy
- Polysemy and synonymy 7
- Competing ‘transfer’ constructions in Dutch 39
- Rethinking constructional polysemy 61
- Quantifying polysemy in Cognitive Sociolinguistics 87
- The many uses of run 117
- Visualizing distances in a set of near-synonyms 145
- A case for the multifactorial assessment of learner language 179
- Dutch causative constructions 205
- The semasiological structure of Polish myśleć ‘to think’ 223
- A multifactorial corpus analysis of grammatical synonymy 253
- A diachronic corpus-based multivariate analysis of “I think that” vs. “I think zero” 279
-
Section 2. Statistical techniques
- Techniques and tools 307
- Statistics in R 343
- Frequency tables 365
- Collostructional analysis 391
- Cluster analysis 405
- Correspondence analysis 443
- Logistic regression 487
- Name index 535
- Subject index 541
