qtime: Draw a time plot

Description

Draw a time-series plot.

Usage

qtime(time, y, data, group = NULL, shift = c(1, 4, 7, 12, 24), alpha = 1, size = 2,  asp = NULL, series.stats = ifelse(nrow(data) < 1000, TRUE, FALSE), fun.base = min,  main = NULL, xlab = NULL, ylab = NULL, ...)

Arguments

time

the variable indicating time on the horizontal axis.

a vertor of all the variable names of interest.

data

Mutaframe data generated by qdata.

group

Similar to period, but is used for longitudinal data grouping.

shift

Wrapping speed selector. The default possible speeds are 1,7(for days a week),12(for months),24(for hours).

size

Point size, default to be 2.

alpha

Transparency level, 1=completely opaque, default to be 1.

asp

Ratio between width and height of the plot.

series.stats

Whether to show the statistics which measure the similarity between series when wrapping. It gives the ACF, corr, and R square for one, two, and more series respectively.

fun.base

a function to compute the baseline of the area plot

main

main title for the plot.

xlab

label on horizontal axis, default is name of x variable

ylab

label on vertical axis, default is name of y variable

Details

Arrow up/down: in-/de-crease size of points.

Arrow left/right: wrap the time series in the initial mode, and drag the series in the series selection mode.

Shift + right: Time series will be folded directly to the width of maximal value in argument shift.

Shift + left: Time series will be backed to the original xaxis position.

Key '+'/'-': de-/in-crease alpha level (starts at alpha=1 by default).

Key 'u'/'d': separate/mix the series groups or the period by shifting them up and down.

Shift + 'u'/'d': for multivariate y's, separate/mix them by shifting up and down.

Key 'g': change the wrapping period circularly in the values of parameter 'shift'.

Key 'm': Switch the mode for series selection. Default to be off. When there are more than one series in the plot, users can turn it on to hold a series and shift the series horizontally by dragging with the mouse.

Key 'y': y-wrapping

Shift + 'y': y-wrapping backward

Key 'r': switch to the area plot

Key 'f': fold the series to a horizon plot

Wheel: Zoom in/out. Then users can drag the series horizontally to see the details.

Examples

Run this code

library(cranvas)

## example 1: NASA temperature data
nasa2221 <- subset(nasa, Gridx == 22 & Gridy == 21)
nasa2221$Year <- factor(nasa2221$Year)
qnasa <- qdata(nasa2221)

qtime(TimeIndx,ts,qnasa,shift=c(1,12))
qscatter(ts,ps_tovs,data=qnasa)

qtime("TimeIndx","ts",qnasa,Year,shift=c(1,12))
qtime(TimeIndx,c("ts","ca_med","ps_tovs"),qnasa,shift=c(1,12))
qtime("TimeIndx",c(ts,ca_med,ps_tovs),qnasa,Year)


##
nasa2221 <- subset(nasa, Gridx %in% c(14,17,20) & Gridy == 21)
#nasa2221 <- subset(nasa, Gridy == 21)
nasa2221$Year <- factor(nasa2221$Year)
nasa2221$Gridx <- factor(nasa2221$Gridx)
qnasa <- qdata(nasa2221)
qtime("TimeIndx",c(ts,ca_med,o3_tovs),qnasa,Gridx,shift=c(1,12))
qscatter(o3_tovs,ts,data=qnasa)
library(ggplot2)
nasa.locs <- subset(cranvas::nasa, TimeIndx == 1)
nasa.locs$loc <- paste(nasa.locs$Gridx, nasa.locs$Gridy, sep=",")
qplot(Long, Lat, data=nasa.locs, geom="text", label = loc)

## example 2: Remifentanil in the nlme package
library(nlme)
qRem <- qdata(Remifentanil[complete.cases(Remifentanil) & Remifentanil$ID==1,])
qtime(Time, conc, qRem)

Remi <- Remifentanil[complete.cases(Remifentanil),]
Remi$ID <- factor(Remi$ID)
qRemi <- qdata(Remi)
qtime(Time, conc, qRemi, group=ID)
qscatter(Amt, conc, data=qRemi)
# for categorical brushing self-link dataset by ID:
# id <- link_cat(qRemi, "ID")
# remove_link(qRemi, id)


## example 3: Wages
library(dplyr)
wages.num <- summarise(group_by(wages, id), n=length(lnw))
indx <- wages.num$id[wages.num$n > 11]
wages.sub <- subset(wages, id %in% indx)
nindiv <- length(unique(wages.sub$id))
wages.sub$idno <- factor(wages.sub$id, labels=1:nindiv)
wages.sub.demog <- summarise(group_by(wages.sub, idno), n=length(lnw),
                       avlnw = mean(lnw, na.rm=T),
                       #trendlnw = lsfit(exper, lnw)$coef[2],
                       trendlnw = max(lnw, na.rm=T) - min(lnw, na.rm=T),
                       black = black[1],
                       hispanic = hispanic[1],
                       ged = ged[1], hgc = hgc[1],
                       avunemp = mean(uerate, na.rm=T))
#qwage <- qdata(wages[as.integer(as.character(wages$id))<2000,1:3])
qwages <- qdata(wages.sub[,c(11,2:3)])
qtime(exper, lnw, qwages, group=idno)
# id <- link_cat(wage, "id")
# remove_link(wage, id)

indx <- wages.num$id[wages.num$n > 3]
wages.sub2 <- subset(wages, id %in% indx)
wages.sub2.demog <- summarise(group_by(wages.sub2, id), n=length(lnw),
                             avlnw = mean(lnw, na.rm=T),
                             #trendlnw = coefficients(lm(lnw ~ exper))[2],
                             rangelnw = max(lnw, na.rm=T) - min(lnw, na.rm=T),
                             sdlnw = sd(lnw, na.rm=T),
                             startlnw = lnw[1],
                             endlnw = lnw[length(lnw)],
                             inclnw = (lnw[length(lnw)]-lnw[1])/lnw[1]*100.0,
                             black = black[1],
                             hispanic = hispanic[1],
                             ged = ged[1], hgc = hgc[1],
                             avunemp = mean(uerate, na.rm=T))
qwages <- qdata(wages.sub2[,1:3])
qwages.demog <- qdata(wages.sub2.demog)
id = link_cat(qwages.demog, "id", qwages, "id")
qtime(exper, lnw, qwages, group=id)
qscatter(startlnw, inclnw, qwages.demog)
qbar(hgc, qwages.demog)
qhist(avunemp, qwages.demog)
remove_link(qwages.demog, id[1])
remove_link(qwages, id[2])
# Now need to link scatterplots of demographics with time series

## example 4: Lynx - for posterity
# Good to show off wrapping to investigate irregular series
qlynx <- qdata(data.frame(Time=1:114, lynx))
qtime(Time, lynx, qlynx, shift=1:13)


## example 5: Sunspots - for posterity
# Good to show off wrapping to investigate irregular series
qsun <- qdata(data.frame(Time=1:2820, sunspots))
qtime(Time, sunspots, qsun, shift=c(1,c(1,6,7,13,26)*10))


## example 6: Pigs
qpig <- qdata(pigs)
qtime(TIME, c("GILTS","PROFIT","PRODUCTION","HERDSZ"), qpig, shift=c(1,4))


## example 7: flu trends
flu.data <- read.table("http://www.google.org/flutrends/us/data.txt", skip=11, sep=",", header=TRUE)
# Get only states
flu.data <- flu.data[, c(1, 3:53)]
# Melt data, and rename variables
library(reshape)
flu.melt <- melt(flu.data, id.vars="Date")
flu.melt$Date <- as.Date(flu.melt$Date)
colnames(flu.melt)[2] <- "State"
colnames(flu.melt)[3] <- "FluSearches"
flu.melt$days <- as.vector(difftime(flu.melt$Date,as.Date('2002-12-31')))
summary(flu.melt$Date)
flu.melt$Date[flu.melt$days>2500&flu.melt$days<2520]
qflu <- qdata(flu.melt)
qtime(days, FluSearches, data=qflu, group="State",shift=c(1,7,28,364))
# winter of 2014
flu2014 <- subset(flu.melt, days>3960)
ord <- names(sort(tapply(flu2014$FluSearches,flu2014$State,function(x)which(x>(max(x)/5*3))[1])))
flu2014$State <- factor(flu2014$State,levels=ord)
qflu <- qdata(flu2014)
qtime(days, FluSearches, data=qflu, group="State",shift=c(1,7,28,35,91))

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Description

Usage

Arguments

Details

See Also

Examples