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

Plot Time Series

This example creates a time series dataset recording two observations at each time step. The date and times are converted to chron objects, and then a zoo series is created, which is then plotted.

[width=0.8]rplot-time-basic

year <- c(rep(2005,10))
doy  <- c(rep(173,5), rep(174,5))
time <- c(15,30,45,100,115,15,30,45,100,115)
obs1 <- c(0.022128,0.035036,0.051632,0.071916,0.081136,
          0.07837,0.083902,0.126314,0.080214,0.117094)
obs2 <- c(0.533074667,0.887982667,1.284938,1.845450333,2.145839333,
          2.145126667,2.392422,3.60253,2.330776333,3.5277)
obs  <- cbind(year, doy, time, obs1, obs2)

library(chron)
library(zoo)
datetimes <- chron(paste(1, 1, obs[,1], sep="/"), obs[,3]/24) + obs[,2] - 1
zobs <- zoo(obs[,4:5], datetimes)
plot(zobs, plot.type = "single")

http://rattle.togaware.com/code/rplot-time-basic.R

Support further development through the purchase of the PDF version of the book.
Brought to you by Togaware.