Description Usage Arguments Details Value Note Author(s) See Also Examples
Describes plot method for class "rose"
1 2 3 4 5 6 7 8 9 10  ## S4 method for signature 'rose,missing'
plot(
x,
transf = function(x) sqrt(x),
subset.col = NULL,
warn = TRUE,
general = general.control(),
grid = grid.control(),
title = title.control(),
key = key.control())

x 
Object 
transf 
Transformation function for 
subset.col 
Display only a subset of the columns of

warn 
Logical. If 
general 
Settings for general appearance of plot, defined in

grid 
Settings for appearance of guiding elements of rose such
as circles, rays and labels, defined in 
title 
Settings for title, defined in

key 
Settings for appearance of the legend, defined in

This function appeals especially to environmental specialists who
often have response variables, which depend from cyclic variables like
the direction of wind, the hour of the day, the month etc. All these
variables are displayed usually clockwise,
starting with 0 in the north (12 o' clock). We call this
kind of coordinates 'clock coordinates', to distinct them from the
polar coordinates as used in mathematical context. The
rose
object is displayed as the time on a clock,
measuring the angle defined in slot cyclVar
in the clockwise
direction from the north.
The eye takes the area of a graphical object as a measure of its
size. This is why the default transformation of [email protected]
is
chosen to be the square root. For equal distance roses use the
transformation function function(x) x
.
All labels, titles and line sizes are defined in multiples of cex.
This graphic function is based on package grid:
Viewport vp.rose
which was used to draw the rose and viewport
vp.key
which was used to draw the key may be addressed by
pushviewport()
after having drawn the figure.
No value returned
The function is designed to use the area on the active viewport in an
optimal way, but the plot is not scalable after having been drawn.
Furthermore this function is still experimental so that some features
may change in future versions.
You will find another nice example for this plot method in AirQual
Rene Locher
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  hour < rep(0:23,100)
WD < c(rnorm(24*90, mean=sample(c(190,220,50),24*90,
replace = TRUE),sd=10),
rnorm(24*10, mean=360, sd=180))%%360
dat < data.frame(A = (2*cos((hour+6)/6*pi)+
2*cos((WD+60)/180*pi)+rnorm(24*100,4))^2,
B = (2*cos((hour+4)/6*pi)+rnorm(24*100,1,8))^2)
dat$B[dat$B>1000] < 1000
## two different response variables, scalar summary function
mean.dayrose < rose(dat[,c("A","B")],
cyclVar = hour,
n.cyclVar = 24,
circle = 24,
FUN=mean, na.rm=TRUE)
## one response variable, vector summary function
quant.windrose < rose(dat$A,
cyclVar = WD,
n.cyclVar = 16, circle = 360,
FUN=quantile, na.rm=TRUE)
## one response variable, second (non cyclic) explanatory variable,
## scalar summary function
windrose < rose(dat[,c("A")],
cyclVar = WD,
n.cyclVar=8,
circle = 360,
cut = dat$B,
breaks = c(0,10,100,1000),
include.lowest = TRUE, dig.lab = 4,
FUN = function(x) sum(!is.na(x)))
grid.newpage()
plot(mean.dayrose,
general = general.control(
mar = rep(1,4),
stacked = FALSE,
lwd = 3,
lty = c(1:2)),
grid = grid.control(
circ.n = 2,
circ.sub.n = 2,
circ.lwd = 2,
circ.sub.col = "black",
ray.n = 12,
cyclVar.lab = seq(0,by=2,to=22)),
title = title.control(text = "unstacked dayrose"),
key = key.control(title = "Mean",
between = 0))
grid.newpage()
plot(quant.windrose)
grid.newpage()
plot(windrose,
general = general.control(
stacked = TRUE,
lwd = 3),
grid = grid.control(
circ.n = 2,
circ.sub.n = 2),
title = title.control(
text = "Stacked windrose:\nCounts of AValues"),
key = key.control(title = "Value of B"))
if (require(SwissAir)){
data(AirQual)
dat <
data.frame(month =as.numeric(substr(AirQual$start,4,5)),
hour = as.numeric(substr(AirQual$start,12,13)),
WD = AirQual$ad.WD,
NOx = AirQual$ad.NOx,
## NO2 = AirQual$ad.NOxAirQual$ad.NO,
## NO = AirQual$ad.NO,
O3 = AirQual$ad.O3,
Ox = AirQual$ad.O3+AirQual$ad.NOxAirQual$ad.NO)
## Windrose
windrose < rose(dat$WD,
cyclVar = dat$WD, n.cyclVar = 32, circle = 360,
FUN = function(x) sum(!is.na(x)))
grid.newpage()
plot(windrose,
general =
general.control(lwd = 2),
grid =
grid.control(circ.n = 2,
circ.sub.n = 2))
## median of concentrations as a function of daytime
## from May to September
med.dayrose < rose(dat[,c("NOx","O3","Ox")],
subset= dat$month>4 & dat$month<10,
cyclVar=dat$hour, n.cyclVar=24, circle=24,
FUN=median, na.rm=TRUE)
## line type version of rose
grid.newpage()
plot(med.dayrose,
general = general.control(lwd=2, type="l"),
grid =
grid.control(ray.n = 12,
circ.n =2,
circ.sub.n = 2,
cyclVar.lab = seq(0,by=2,to=22)),
title = title.control(text =
"Day Rose of Medians\nduring summer"))
## quantiles of concentrations as a function of daytime
## from May to September
quant.dayrose < rose(dat$NOx,
subset= dat$month>4 & dat$month<10,
cyclVar=dat$hour, n.cyclVar=24, circle=24,
FUN=quantile, na.rm=TRUE)
grid.newpage()
plot(quant.dayrose,
general =
general.control(mar = c(0.3, 0.3, 0.3, 2),
lwd = 2),
grid =
grid.control(ray.n = 12,
cyclVar.lab = seq(0,by=2,to=22)),
title = title.control(text = "Concentration of NOx [ppb]\nduring summer"),
key = key.control(title = "Quantiles"))
} else print("Package SwissAir is not available")

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