plotTseries.fn {CGManalyzer}

function to plot time series data

Description

function to plot time series data

Usage

plotTseries.fn(x, y, xAt = NA, xLab = NA, yRange = NA, Frame = TRUE, xlab = "",
ylab = "", pch = 1, lty = 1, col.point = 1, col.line = 1, cex.point = 1, lwd = 1)

Arguments

x

time in continuous value such as in seconds or minutes, (e.g. the return from timeSeqConversion.fn)

y

measured response value

xAt

a vector to indicate where the labels in the x-axis are

xLab

a vector to indicate what the labels in the x-axis are

yRange

range for y in the plot

Frame

whether the plot frame should be drawn

xlab

as in plot()

ylab

as in plot()

pch

as in plot()

lty

as in plot()

col.point

the color for the points

col.line

the color for the line

cex.point

cex for the points

lwd

as in plot()

Details

function to plot time series data

Author(s)

Xiaohua Douglas Zhang

References

Zhang XD, Zhang Z, Wang D. 2018. CGManalyzer: an R package for analyzing continuous glucose monitoring studies. Bioinformatics 34(9): 1609-1611 (DOI: 10.1093/bioinformatics/btx826).

Examples

library(CGManalyzer)
package.name <- "CGManalyzer"
source( system.file("SPEC", "SPECexample.R", package = package.name) )
data.df0 <- read.table(paste(dataFolder, dataFiles[1], sep="/"),
            skip=Skip, header=Header, comment.char=Comment.char, sep=Sep)
if( !Header ) {
	data.df0 <- data.df0[, 1:length(columnNames)]
    dimnames(data.df0)[[2]] <-  columnNames
}
if( !is.na(idxNA) ) data.df0[ data.df0[, responseName] == idxNA, responseName] <- NA
for( i in 1:length(timeStamp.column) ) {
	if(i==1) { timeStamp.vec <- data.df0[, timeStamp.column[i] ] } else {
		 timeStamp.vec <- paste0(timeStamp.vec, " ", data.df0[, timeStamp.column[i] ])
	}
}
Time.mat <- timeSeqConversion.fn(time.stamp=timeStamp.vec, time.format=time.format,
            timeUnit=timeUnit)
data.df <- data.frame( timeStamp.vec, Time.mat[,1], data.df0[,responseName] )
dimnames(data.df)[[2]] <- c("timeStamp", "timeSeries", responseName)
data.df <- data.df[ order(data.df[, "timeSeries"]), ]
plotTseries.fn(	x=data.df[, "timeSeries"], y=data.df[, responseName],
				xAt=0:14*720, xLab=0:14/2, yRange=NA, Frame=TRUE,
				xlab="Time in Days", ylab=responseName, pch=1, lty=1,
				col.point=1, col.line=1, cex.point=0.5, lwd=1 )