DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Introduction Data Mining with Rattle Data Exploring Data Transforming Data Cluster Analysis Association Analysis Classification and Regression Models Evaluation and Deployment Issues Moving into R Troubleshooting Beyond Rattle R Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models Reporting Cluster Analysis Text Mining Text Mining Algorithms Bagging Bayes Classifier Cluster Analysis Conditional Trees Hierarchical Clustering K-Nearest Neighbours Linear Models Neural Networks Support Vector Machines 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 Installing GTK, R, and Rattle Glossary Bibliography Index

Dates and Times

To calculate the differences between times use XnullXR functionsR functions (R function)R functionsR libraries (R library)R functionsR option (R option)R functionsR packages (R package)R functionsDatasets (Dataset)R functionsR functionsdifftime.

When importing data from a CSV file, for example, dates are simply read as factors. These can easily be converted to date objects using XnullXR functionsR functions (R function)R functionsR libraries (R library)R functionsR option (R option)R functionsR packages (R package)R functionsDatasets (Dataset)R functionsR functionsas.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"

read.csvXnullXR functionsR functions (R function)R functionsR libraries (R library)R functionsR option (R option)R functionsR packages (R package)R functionsDatasets (Dataset)read.csvR functionsR functions

The default format is "%Y-%m-%d". See the help for XnullXR functionsR functions (R function)R functionsR libraries (R library)R functionsR option (R option)R functionsR packages (R package)R functionsDatasets (Dataset)R functionsR functionsstrftime 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.

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

To compare date values use XnullXR functionsR functions (R function)R functionsR libraries (R library)R functionsR option (R option)R functionsR packages (R package)R functionsDatasets (Dataset)R functionsR functionsas.Date:

> ds > as.Date("2005-05-22")
[1] FALSE  TRUE    NA

To view the methods associated with the Date class:

> methods(class = "Date")
 [1] as.character.Date  as.data.frame.Date as.POSIXct.Date    Axis.Date*        
 [5] c.Date             cut.Date           -.Date             [<-.Date          
 [9] [.Date             [[.Date            +.Date             diff.Date         
[13] format.Date        hist.Date*         is.numeric.Date    julian.Date       
[17] Math.Date          mean.Date          months.Date        Ops.Date          
[21] plot.Date*         print.Date         quarters.Date      rep.Date          
[25] round.Date         seq.Date           summary.Date       Summary.Date      
[29] trunc.Date         weekdays.Date     

   Non-visible functions are asterisked

To aggregate by month, some alternatives:

> library(chron)
> dts=seq.dates("1/1/01","12/31/03")
> rnum=rnorm(1:length(dts))
> df=data.frame(date=dts,obs=rnum)
> aggregate(df[,2],list(year=years(df[,1]),month=months(df[,1])),sum)
> library(zoo)
> aggregate(zoo(rnum, dts), as.yearmon, sum)
> aggregate(rnum, list(dts = as.yearmon(dts)), sum)

aggregateXnullXR functionsR functions (R function)R functionsR libraries (R library)R functionsR option (R option)R functionsR packages (R package)R functionsDatasets (Dataset)aggregateR functionsR functionsas.yearmonXnullXR functionsR functions (R function)R functionsR libraries (R library)R functionsR option (R option)R functionsR packages (R package)R functionsDatasets (Dataset)as.yearmonR functionsR functionsyearsXnullXR functionsR functions (R function)R functionsR libraries (R library)R functionsR option (R option)R functionsR packages (R package)R functionsDatasets (Dataset)yearsR functionsR functionsmonthsXnullXR functionsR functions (R function)R functionsR libraries (R library)R functionsR option (R option)R functionsR packages (R package)R functionsDatasets (Dataset)monthsR functionsR functionsseq.datesXnullXR functionsR functions (R function)R functionsR libraries (R library)R functionsR option (R option)R functionsR packages (R package)R functionsDatasets (Dataset)seq.datesR functionsR functionszooXnullXR functionsR functions (R function)R functionsR libraries (R library)R functionsR option (R option)R functionsR packages (R package)R functionsDatasets (Dataset)zooR functionsR functions

Extract the year from a vector of dates:

> dates <- c("26 Jan 1974", "April 3, 2002", "23 June, 1999", "2007")
>  gsub(".*([1-9][0-9]{3}).*", "\\1", dates)
[1] "1974" "2002" "1999" "2007"

> 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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