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

Time and Dates

Dates are read as factors into R from a CSV file. These can be easily converted to date objects using as.Date:

> ds <- read.csv("authors.csv")
> ds$Notified
   [1]            2005/06/05 2005/06/05 
> as.Date(ds$Notified, format="%Y/%m/%d")
   [1] NA           "2005-06-05" "2005-06-05"

The default format is "%Y-%m-%d". See the help for strftime for an explanation of the format. Any extra text found in the string after the text has been consumed by the format string will simply be ignored. But if the format is not found at the beginning of the string then a NA is returned.

> as.Date(c("2005-05-22 12:35:00", "2005-05-23 abc","abc 2005-05-24"))
[1] "2005-05-22" "2005-05-23" NA

To compare date values simply use as.Date:

if (ds$dt > as.Date("2005-12-15")) ...

> as.POSIXlt('2005-7-1')
[1] "2005-07-01"
> unlist(as.POSIXlt('2005-7-1'))
  sec   min  hour  mday   mon  year  wday  yday isdst 
    0     0     0     1     6   105     5   181     0

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