qtime: Draw a time plot
In ggobi/cranvas: Interactive statistical graphics based on Qt
Description
Usage
Arguments
Details
See Also
Examples
Description
Draw a time-series plot.
Usage
1
2
3
Arguments
time
the variable indicating time on the horizontal axis.
y
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.
See Also
Other plots: qbar; qboxplot;
qdensity; qhist,
qspine; qmval;
qparallel
Examples
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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))
cranvas_off()
ggobi/cranvas documentation built on May 17, 2019, 3:10 a.m.
R Package Documentation
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Description Usage Arguments Details See Also Examples
Description
Draw a time-series plot.
Usage
1 2 3 |
Arguments
time |
the variable indicating time on the horizontal axis. |
y |
a vertor of all the variable names of interest. |
data |
Mutaframe data generated by |
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.
See Also
Other plots: qbar; qboxplot;
qdensity; qhist,
qspine; qmval;
qparallel
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 | 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))
cranvas_off()
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