DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

Numbers

Numbers in R can be integer, double, or complex. The actual type is generally determined by R based on context and defaults. Numbers do tend to be double unless they are in some way restricted to being integers, or you wish to store http://en.wikipedia.org/wiki/Complex_numbercomplex numbers.

> typeof(1) 	    # "double"
> typeof(1.3)       # "double"
> typeof(1:2)       # "integer" - this is a vector of two integers 1 and 2
> typeof((1:2)[1])  # "integer" - this is the first element of the vector
> typeof(2+5i)	    # "complex" - complex numbers have real and imaginary parts

The basic numeric operators include:

  \verb|+| Addition        \verb|-| Subtraction   \verb|*| Multiplication      \verb|/| Division
  \verb|^| Exponentiation  \verb|:| Sequence      \verb|%/%| Integer division  \verb|%%| Remainder

See the documentation for Arithmetic for details:

> ?Arithmetic

Typical numeric functions include:

> 1:5                   # 1 2 3 4 5
> round(1234.56)	# 1235
> trunc(1234.56)	# 1234

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