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Is there any way to set the view and axes so that in my plotly animated 3D graph I can see a specific space and in this space the graph is moving? I'm trying to do an animation which shows how a detector works but right now when i play my animation the view and axes change along with the graph. I know that I can change the range of the axis but somehow it didn't change anything in my output or maybe i was doing something wrong.
Here is my code:
library(plotly)
Sx <- matrix()
Sy <- matrix()
Sz <- matrix()
N <- 360
u = seq(0, pi/2, length.out = 30)
w = seq(0, 2*pi, length.out = N)
datalist = list()
for (i in 1:N) {
Sx = cos(u) * cos(w[i])
Sy = cos(u) * sin(w[i])
Sz = sin(u)
df <- data.frame(Sx, Sy, Sz, t=i)
datalist[[i]] <- df
}
data = do.call(rbind, datalist)
plot_ly(data, x=~Sx, y =~Sy, z=~Sz, frame=~t, type = 'scatter3d', mode = 'lines')
Welcome to stackoverflow!
I'm not sure if I correctly understand your question. However, I think you are looking for a combination of range and aspectratio.
Please check the following:
library(plotly)
Sx <- matrix()
Sy <- matrix()
Sz <- matrix()
N <- 360
u = seq(0, pi/2, length.out = 30)
w = seq(0, 2*pi, length.out = N)
datalist = list()
for (i in 1:N) {
Sx = cos(u) * cos(w[i])
Sy = cos(u) * sin(w[i])
Sz = sin(u)
df <- data.frame(Sx, Sy, Sz, t=i)
datalist[[i]] <- df
}
data = do.call(rbind, datalist)
plot_ly(data, x=~Sx, y =~Sy, z=~Sz, frame=~t, type = 'scatter3d', mode = 'lines') %>%
layout(scene = list(xaxis = list(nticks = 5, range = c(-1, 1)),
yaxis = list(nticks = 5, range = c(-1, 1)),
zaxis = list(nticks = 5, range = c(-1, 1)),
aspectmode='manual',
aspectratio = list(x=1, y=1, z=1)
)) %>% animation_opts(frame = 100)
For further informarion please see this.
I see several people have answered the question for plotting with an irregular grid. I'm having trouble getting the contour lines to line up with the filled contours. Also, need to display the data point locations on the plot, and the radial spokes at 30 deg increments, and semi circles at 10, 20 30.
Ref: Plotting contours on an irregular grid
heading=seq(0,180,30)
speed=c(5,10,15,20,30)
mheading=matrix(heading,ncol=length(heading),nrow=length(speed),byrow=TRUE)
mspeed=matrix(speed,ncol=length(heading),nrow=length(speed),byrow=FALSE)
mag=mheading+mspeed
x=sin(mheading*pi/180)*mspeed
y=cos(mheading*pi/180)*mspeed
z=mag
library(akima)
df<-data.frame(x=x,y=y,z=z)
# interpolation
fld <- with(df, interp(x = x, y = y, z = z,
xo=seq(min(x),max(x),length=100),
yo=seq(min(y),max(y),length=100)))
filled.contour(x = fld$x,
y = fld$y,
z = fld$z,
color.palette =
colorRampPalette(c("white", "blue")),
xlab = "",
ylab = "",
main = "Max",
key.title = title(main = "Value", cex.main = 1),
asp=1,xlim=c(0,40),ylim=c(-30,30))
contour(x = fld$x,
y = fld$y,
z = fld$z,
color.palette =
colorRampPalette(c("white", "blue")),
xlab = "",
ylab = "",
asp=1,xlim=c(0,40),ylim=c(-30,30), add=TRUE)
Following this link, produces the following code / plot. This is "better", but there are still problems. Why is there interpolated data inside of the minimum speed radius (5), and why doesn't the contour fill/lines extend to the outer radius especially near 90 degrees?
contours=TRUE # Add contours to the plotted surface
legend=TRUE # Plot a surface data legend?
axes=TRUE # Plot axes?
points=TRUE # Plot individual data points
extrapolate=FALSE # Should we extrapolate outside data points?
single_point_overlay=0
outer.radius=30
spatial_res=1000 #Resolution of fitted surface
interp.type = 1
circle.rads <- pretty(c(0,outer.radius))
heading=seq(0,180,30)
speed=c(5,10,15,20,30)
mheading=matrix(heading,ncol=length(heading),nrow=length(speed),byrow=TRUE)
mspeed=matrix(speed,ncol=length(heading),nrow=length(speed),byrow=FALSE)
mag=mheading+mspeed
x=sin(mheading*pi/180)*mspeed
y=cos(mheading*pi/180)*mspeed
z=mag
extrapolate=FALSE # Should we extrapolate outside data points?
contour_levels = 8
col_levels=contour_levels
col_breaks_source=1
contour_breaks_source = 1
col = rev(heat.colors(col_levels))
minitics <- seq(-outer.radius, outer.radius, length.out = spatial_res)
xmini <- seq(min(x),max(x),length=spatial_res)
ymini <- seq(min(y),max(y),length=spatial_res)
# interpolate the data
if (interp.type ==1 ){
# Interp <- akima:::interp(x = x, y = y, z = z,
# extrap = extrapolate,
# xo = xmini,
# yo = ymini,
# linear = FALSE)
# Mat <- Interp[[3]]
df<-data.frame(x=x,y=y,z=z)
# interpolation
fld <- with(df, akima:::interp(x = x, y = y, z = z,
xo=xmini,
yo=ymini))
Mat_x <- fld[[1]]
Mat_y <- fld[[2]]
Mat_z <- fld[[3]]
} else if (interp.type == 2){
library(fields)
grid.list = list(x=minitics,y=minitics)
t = Tps(cbind(x,y),z,lambda=lambda)
tmp = predict.surface(t,grid.list,extrap=extrapolate)
Mat_z = tmp$z
# mark cells outside circle as NA
markNA <- matrix(minitics, ncol = spatial_res, nrow = spatial_res)
Mat_x <- markNA
Mat_y <- t(markNA)
} else {stop("interp.type value not valid")}
#
Mat_z[!(sqrt(Mat_x ^ 2 + Mat_y ^ 2) <= max(speed)*1.1)] <- NA
Mat_z[!(sqrt(Mat_x ^ 2 + Mat_y ^ 2) >= min(speed))] <- NA # <- SHOULD REMOVE INNER DATA
### Set contour_breaks based on requested source
if ((length(contour_breaks_source == 1)) & (contour_breaks_source[1] == 1)){
# contour_breaks = seq(min(z,na.rm=TRUE),max(z,na.rm=TRUE),
# by=(max(z,na.rm=TRUE)-min(z,na.rm=TRUE))/(contour_levels-1))
contour_breaks = seq(min(z,na.rm=TRUE),max(z,na.rm=TRUE),length.out = contour_levels+1)
}else if ((length(contour_breaks_source == 1)) & (contour_breaks_source[1] == 2)){
contour_breaks = seq(min(Mat_z,na.rm=TRUE),max(Mat_z,na.rm=TRUE),
by=(max(Mat_z,na.rm=TRUE)-min(Mat_z,na.rm=TRUE))/(contour_levels-1))
} else if ((length(contour_breaks_source) == 2) & (is.numeric(contour_breaks_source))){
contour_breaks = pretty(contour_breaks_source,n=contour_levels)
contour_breaks = seq(contour_breaks_source[1],contour_breaks_source[2],
by=(contour_breaks_source[2]-contour_breaks_source[1])/(contour_levels-1))
}else {stop("Invalid selection for \"contour_breaks_source\"")}
### Set color breaks based on requested source
if ((length(col_breaks_source) == 1) & (col_breaks_source[1] == 1))
{zlim=c(min(z,na.rm=TRUE),max(z,na.rm=TRUE))} else if ((length(col_breaks_source) == 1) & (col_breaks_source[1] == 2))
{zlim=c(min(Mat_z,na.rm=TRUE),max(Mat_z,na.rm=TRUE))} else if ((length(col_breaks_source) == 2) & (is.numeric(col_breaks_source)))
{zlim=col_breaks_source} else {stop("Invalid selection for \"col_breaks_source\"")}
# begin plot
Mat_plot = Mat_z
Mat_plot[which(Mat_plot<zlim[1])]=zlim[1]
Mat_plot[which(Mat_plot>zlim[2])]=zlim[2]
image(x = Mat_x, y = Mat_y, Mat_plot ,
useRaster = TRUE, asp = 1, axes = FALSE, xlab = "", ylab = "", zlim = zlim, col = col)
# add contours if desired
if (contours){
CL <- contourLines(x = Mat_x, y = Mat_y, Mat_z, levels = contour_breaks)
A <- lapply(CL, function(xy){
lines(xy$x, xy$y, col = gray(.2), lwd = .5, asp=1)
})
}
# add interpolated point if desired
if (points){
points(x,y,pch=4)
}
# add overlay point (used for trained image marking) if desired
if (single_point_overlay!=0){
points(x[single_point_overlay],y[single_point_overlay],pch=0)
}
# add radial axes if desired
if (axes){
# internals for axis markup
RMat <- function(radians){
matrix(c(cos(radians), sin(radians), -sin(radians), cos(radians)), ncol = 2)
# matrix(c(sin(radians), -cos(radians), cos(radians), sin(radians)), ncol = 2)
}
circle <- function(x, y, rad = 1, nvert = 500, angle=360){
rads <- seq(0,angle*pi/180,length.out = nvert)
# xcoords <- cos(rads) * rad + x
# ycoords <- sin(rads) * rad + y
xcoords=sin(rads)*rad + x
ycoords=cos(rads)*rad + y
cbind(xcoords, ycoords)
}
# draw circles
if (missing(circle.rads)){
circle.rads <- pretty(c(0,outer.radius))
}
endAngle = 180
for (i in circle.rads){
lines(circle(0, 0, i, angle = endAngle), col = "#66666650")
}
# put on radial spoke axes:
axis.degs <- c(0, 30, 60, 90, 120, 150)
# axis.rads <- c(0, pi / 6, pi / 3, pi / 2, 2 * pi / 3, 5 * pi / 6)
axis.rads <- axis.degs * pi/180
r.labs <- c(90, 60, 30, 0, 330, 300)
l.labs <- c(270, 240, 210, 180, 150, 120)
for (i in 1:length(axis.rads)){
if (axis.degs[i]==0) {
# endpoints <- zapsmall(c(RMat(axis.rads[i]) %*% matrix(c(1, 0, -1, 0) * outer.radius,ncol = 2)))
endpoints <- zapsmall(c(RMat(axis.rads[i]) %*% matrix(c(1, 0, 0, 0) * outer.radius,ncol = 2)))
} else if (0 < axis.degs[i] & axis.degs[i] < 90) {
endpoints <- zapsmall(c(RMat(axis.rads[i]) %*% matrix(c(1, 0, 0, 0) * outer.radius,ncol = 2)))
} else {
endpoints <- zapsmall(c(RMat(axis.rads[i]) %*% matrix(c(0, 0, -1, 0) * outer.radius,ncol = 2)))
}
segments(endpoints[1], endpoints[2], endpoints[3], endpoints[4], col = "#66666650")
endpoints <- c(RMat(axis.rads[i]) %*% matrix(c(1.1, 0, -1.1, 0) * outer.radius, ncol = 2))
lab1 <- bquote(.(r.labs[i]) * degree)
lab2 <- bquote(.(l.labs[i]) * degree)
if (0 <= r.labs[i] & r.labs[i] <= 180) text(endpoints[1], endpoints[2], lab1, xpd = TRUE)
if (0 <= l.labs[i] & l.labs[i] <= 180) text(endpoints[3], endpoints[4], lab2, xpd = TRUE)
}
# axis(2, pos = -1.25 * outer.radius, at = sort(union(circle.rads,-circle.rads)), labels = NA)
# text( -1.26 * outer.radius, sort(union(circle.rads, -circle.rads)),sort(union(circle.rads, -circle.rads)), xpd = TRUE, pos = 2)
axis(2, pos = 0 * outer.radius, at = sort(union(circle.rads,-circle.rads)), labels = NA)
text( -0.02 * outer.radius, sort(union(circle.rads, -circle.rads)),
abs(sort(union(circle.rads, -circle.rads))),
xpd = TRUE, pos = 2)
}
# add legend if desired
# this could be sloppy if there are lots of breaks, and that's why it's optional.
# another option would be to use fields:::image.plot(), using only the legend.
# There's an example for how to do so in its documentation
if (legend){
library(fields)
image.plot(legend.only=TRUE, smallplot=c(.78,.82,.1,.8), col=col, zlim=zlim)
# ylevs <- seq(-outer.radius, outer.radius, length = contour_levels+ 1)
# #ylevs <- seq(-outer.radius, outer.radius, length = length(contour_breaks))
# rect(1.2 * outer.radius, ylevs[1:(length(ylevs) - 1)], 1.3 * outer.radius, ylevs[2:length(ylevs)], col = col, border = NA, xpd = TRUE)
# rect(1.2 * outer.radius, min(ylevs), 1.3 * outer.radius, max(ylevs), border = "#66666650", xpd = TRUE)
# text(1.3 * outer.radius, ylevs[seq(1,length(ylevs),length.out=length(contour_breaks))],round(contour_breaks, 1), pos = 4, xpd = TRUE)
}
The contours and colours do not align because filled.contour produces two plots (legend and contour). After plotting, these coordinate systems are lost. (?filled.contour). This can be solved by adding the relevant commands to the plot.axes argument. Semi-circles can be drawn with draw.arc from the plotrix package, spokes with segments. The zone within a minimum radius can be covered by white segments to represent no data.
# min distance of contours lines from center
min_dist=5
# position of spokes (degrees)
spk = seq(0,180,30)
filled.contour(x = fld$x,
y = fld$y,
z = fld$z,
color.palette = colorRampPalette(c("white", "blue")),
xlab = "",
ylab = "",
main = "Max",
key.title = title(main = "Value", cex.main = 1),
asp=1, xlim=c(0,40), ylim=c(-30,30), frame.plot=F,
plot.axes = {contour(fld$x, fld$y, fld$z , add=T, levels = seq(0,max(fld$z, na.rm=T),30), drawlabels=F, col=2);
# semi circles
draw.arc(x=0,y=0,radius = (1:3)*10, deg1=90, deg2=-90, col='grey');
# cover zone within minimum radius with (draw many closely spaced white lines
segments(x0 = 0, y0 = 0, x1 = sin((0:180)*pi/180)*min_dist, y1 = cos((0:180)*pi/180)*min_dist, col='white');
# spokes with labels
segments(x0 = 0, y0 = 0, x1 = sin(spk*pi/180)*30, y1 = cos(spk*pi/180)*30, col='grey');
text(x = sin(spk*pi/180)*30, y=cos(spk*pi/180)*30, labels = spk, pos=4, cex=0.6, xpd=NA)
# data points
points(x,y, pch=16, cex=0.6);
# x axis
axis(1);
# modified y axis
axis(2, at = axisTicks(range(y), log=F), labels = abs(axisTicks(range(y), log=F)), pos = 0);
}
)
I am attempting to reproduce a corrgram (below; Fig 1) using Zuur et al (2010) reproducible R code (below) showing the frequency with which pairs of water- bird species both have zero abundance. The colour and the amount that a circle has been filled correspond to the proportion of observa- tions with double zeros. The diagonal running from bottom left to the top right represents the percentage of observations of a variable equal to zero..
I have adapted this code for my data but I am experiencing the same problem after running the code for both datasets. When I run the code, the circles inside the corrgram are not filling in, and remain empty (below; Figure 2).
I am however confused as to why I am hitting this problem. If anyone has a solution as to why this occurs, then I would be deeply appreciative for your help.
Data: By Zuur et al (2010)
The data is too large to include with this post but it can be found in the supporting materials section called ElphickBirdData.txt
R Code: Zuur et al (2010)
RiceField <- read.table(file="ElphickBirdData.txt", header = TRUE)
AllS <- c(
"TUSW", "GWFG", "WHGO", "CAGO", "MALL",
"GADW", "GWTE", "CITE", "UNTE", "AMWI", "NOPI",
"NOSH", "RIDU", "CANV", "BUFF", "WODU", "RUDU",
"EUWI", "UNDU", "PBGB", "SORA", "COOT", "COMO",
"AMBI", "BCNH", "GBHE", "SNEG", "GREG", "WFIB",
"SACR", "AMAV", "BNST", "BBPL", "KILL", "LBCU",
"GRYE", "LEYE", "LBDO", "SNIP", "DUNL", "WESA",
"LESA", "PEEP", "RUFF", "UNSH", "RBGU", "HEGU",
"CAGU", "GUSP")
#Determine species richness
Richness <- colSums(RiceField[,AllS] > 0, na.rm = TRUE)
#Remove all covariates
Birds <- RiceField[,AllS]
#To reduce the of variables in the figure, we only used the
#20 species that occured at more than 40 sites.
#As a result, N = 20. Else it becomes a mess.
Birds2 <- Birds[, Richness > 40]
N <- ncol(Birds2)
AllNames <- names(Birds2)
A <- matrix(nrow = N, ncol = N)
for (i in 1:N){
for (j in 1:N){
A[i,j] <- sum(RiceField[,AllS[i]]==0 & RiceField[,AllS[j]]==0, na.rm=TRUE)
}}
A1 <- A/2035
print(A1, digits = 2)
rownames(A1) <- AllNames
colnames(A1) <- AllNames
library(lattice)
library(RColorBrewer)
panel.corrgram.2 <- function(x, y, z, subscripts, at = pretty(z), scale = 0.8, ...)
{
require("grid", quietly = TRUE)
x <- as.numeric(x)[subscripts]
y <- as.numeric(y)[subscripts]
z <- as.numeric(z)[subscripts]
zcol <- level.colors(z, at = at, ...)
for (i in seq(along = z))
{
lims <- range(0, z[i])
tval <- 2 * base::pi *
seq(from = lims[1], to = lims[2], by = 0.01)
grid.polygon(x = x[i] + .5 * scale * c(0, sin(tval)),
y = y[i] + .5 * scale * c(0, cos(tval)),
default.units = "native",
gp = gpar(fill = zcol[i]))
grid.circle(x = x[i], y = y[i], r = .5 * scale,
default.units = "native")
}
}
levelplot(A1,xlab=NULL,ylab=NULL,
at=do.breaks(c(0.5,1.01),101),
panel=panel.corrgram.2,
scales=list(x=list(rot=90)),
colorkey=list(space="top"),
col.regions=colorRampPalette(c("red","white","blue")))
#Grey colours
levelplot(A1.bats,xlab=NULL,ylab=NULL,
at=do.breaks(c(0.5,1.01),101),
panel=panel.corrgram.2,
scales=list(x=list(rot=90)),
colorkey=list(space="top"),
col.regions=colorRampPalette(c(grey(0.8),grey(0.5),grey(0.2))))
Figure 1.
Figure 2
The cause of your problem is that grid.circles daubs grid.polygon with white. You can solved it by changing order of grid.circle and grid.polygon (or add gp = gpar(fill=NA) to grid.circle() ).
panel.corrgram.2.2 <- function(x, y, z, subscripts, at = pretty(z), scale = 0.8, ...)
{
require("grid", quietly = TRUE)
x <- as.numeric(x)[subscripts]
y <- as.numeric(y)[subscripts]
z <- as.numeric(z)[subscripts]
zcol <- level.colors(z, at = at, ...)
for (i in seq(along = z))
{
lims <- range(0, z[i])
tval <- 2 * base::pi *
seq(from = lims[1], to = lims[2], by = 0.01)
grid.circle(x = x[i], y = y[i], r = .5 * scale, # change the order
default.units = "native")
grid.polygon(x = x[i] + .5 * scale * c(0, sin(tval)),
y = y[i] + .5 * scale * c(0, cos(tval)),
default.units = "native",
gp = gpar(fill = zcol[i]))
}
}
levelplot(A1,xlab=NULL,ylab=NULL,
at=do.breaks(c(0.5,1.01),101),
panel=panel.corrgram.2.2,
scales=list(x=list(rot=90)),
colorkey=list(space="top"),
col.regions=colorRampPalette(c("red","white","blue")))
I am plotting data using a mosaic plot (with mosaicplot()) and am considering adding a numeric axis to one dimension to clarify the size of the different groups. But, I do not understand how the plot cells are aligned to the axis since it seems to range from approximately 0.2 to .98 (or something like that) on the graphics device. Here's a reproducible example:
mosaicplot(Titanic, main = "Survival on the Titanic", off = 0)
axis(1, seq(0, 1, by = 0.1))
Note how a 0-1 x-axis actually extends to the left and right of the plot. Is it possible to add a set of axis labels that is scaled correctly?
par(mfrow = c(2,1), mar = c(3,4,2,1))
mp(Titanic)
mp(Titanic, off = 0)
This one isn't difficult to fix, but there are a couple things going on:
obviously, the axis doesn't start at 0 nor does it end at something round which is what you get from pretty (used to calculate, draw, and label the ticks and labels). From these lines, we can see that the polygons are drawn from 50 to 950 along the x (depending on what is set for cex.axis):
x1 <- 30 + 20 * cex.axis/0.66
y1 <- 5
x2 <- 950
y2 <- 1000 - x1
Secondly, the plotting device is finished when the function exits which is why your attempt ranges from 0 to 1 instead of pretty(c(50, 950)), and I don't see any way to pass something through mosaicplot like new or add since
Warning message:
In mosaicplot.default(Titanic, new = TRUE) :
extra argument ‘new’ will be disregarded
So I don't think there is an easy fix without editing the source code (because seems like you would have to backtrace how far over your last plot has shifted the origin and how that translates to a new window which may not be the same for every plot and blah blah blah).
The only thing I changed was adding the final three lines.
## graphics:::mosaicplot.default
mp <- function (x, main = deparse(substitute(x)), sub = NULL, xlab = NULL,
ylab = NULL, sort = NULL, off = NULL, dir = NULL, color = NULL,
shade = FALSE, margin = NULL, cex.axis = 0.66, las = par("las"),
border = NULL, type = c("pearson", "deviance", "FT"), ...) {
mosaic.cell <- function(X, x1, y1, x2, y2, srt.x, srt.y,
adj.x, adj.y, off, dir, color, lablevx, lablevy, maxdim,
currlev, label) {
p <- ncol(X) - 2
if (dir[1L] == "v") {
xdim <- maxdim[1L]
XP <- rep.int(0, xdim)
for (i in seq_len(xdim)) XP[i] <- sum(X[X[, 1L] ==
i, p])/sum(X[, p])
if (anyNA(XP))
stop("missing values in contingency table")
white <- off[1L] * (x2 - x1)/max(1, xdim - 1)
x.l <- x1
x.r <- x1 + (1 - off[1L]) * XP[1L] * (x2 - x1)
if (xdim > 1L)
for (i in 2:xdim) {
x.l <- c(x.l, x.r[i - 1L] + white)
x.r <- c(x.r, x.r[i - 1L] + white + (1 - off[1L]) *
XP[i] * (x2 - x1))
}
if (lablevx > 0L) {
this.lab <- if (is.null(label[[1L]][1L])) {
paste(rep.int(as.character(currlev), length(currlev)),
as.character(seq_len(xdim)), sep = ".")
}
else label[[1L]]
text(x = x.l + (x.r - x.l)/2, y = 1000 - 35 *
cex.axis/0.66 + 22 * cex.axis/0.65 * (lablevx -
1), srt = srt.x, adj = adj.x, cex = cex.axis,
this.lab, xpd = NA)
}
if (p > 2L) {
for (i in seq_len(xdim)) {
if (XP[i] > 0) {
Recall(X[X[, 1L] == i, 2L:(p + 2L), drop = FALSE],
x.l[i], y1, x.r[i], y2, srt.x, srt.y, adj.x,
adj.y, off[-1L], dir[-1L], color, lablevx -
1, (i == 1L) * lablevy, maxdim[-1L],
currlev + 1, label[2:p])
}
else {
segments(rep.int(x.l[i], 3L), y1 + (y2 -
y1) * c(0, 2, 4)/5, rep.int(x.l[i], 3L),
y1 + (y2 - y1) * c(1, 3, 5)/5)
}
}
}
else {
for (i in seq_len(xdim)) {
if (XP[i] > 0) {
polygon(c(x.l[i], x.r[i], x.r[i], x.l[i]),
c(y1, y1, y2, y2), lty = if (extended)
X[i, p + 1L]
else 1L, col = color[if (extended)
X[i, p + 2L]
else i], border = border)
}
else {
segments(rep.int(x.l[i], 3L), y1 + (y2 -
y1) * c(0, 2, 4)/5, rep.int(x.l[i], 3L),
y1 + (y2 - y1) * c(1, 3, 5)/5)
}
}
}
}
else {
ydim <- maxdim[1L]
YP <- rep.int(0, ydim)
for (j in seq_len(ydim)) {
YP[j] <- sum(X[X[, 1L] == j, p])/sum(X[, p])
}
white <- off[1L] * (y2 - y1)/(max(1, ydim - 1))
y.b <- y2 - (1 - off[1L]) * YP[1L] * (y2 - y1)
y.t <- y2
if (ydim > 1L) {
for (j in 2:ydim) {
y.b <- c(y.b, y.b[j - 1] - white - (1 - off[1L]) *
YP[j] * (y2 - y1))
y.t <- c(y.t, y.b[j - 1] - white)
}
}
if (lablevy > 0L) {
this.lab <- if (is.null(label[[1L]][1L])) {
paste(rep.int(as.character(currlev), length(currlev)),
as.character(seq_len(ydim)), sep = ".")
}
else label[[1L]]
text(x = 35 * cex.axis/0.66 - 20 * cex.axis/0.66 *
(lablevy - 1), y = y.b + (y.t - y.b)/2, srt = srt.y,
adj = adj.y, cex = cex.axis, this.lab, xpd = NA)
}
if (p > 2L) {
for (j in seq_len(ydim)) {
if (YP[j] > 0) {
Recall(X[X[, 1L] == j, 2:(p + 2), drop = FALSE],
x1, y.b[j], x2, y.t[j], srt.x, srt.y, adj.x,
adj.y, off[-1L], dir[-1L], color, (j ==
1L) * lablevx, lablevy - 1, maxdim[-1L],
currlev + 1, label[2:p])
}
else {
segments(x1 + (x2 - x1) * c(0, 2, 4)/5, rep.int(y.b[j],
3L), x1 + (x2 - x1) * c(1, 3, 5)/5, rep.int(y.b[j],
3L))
}
}
}
else {
for (j in seq_len(ydim)) {
if (YP[j] > 0) {
polygon(c(x1, x2, x2, x1), c(y.b[j], y.b[j],
y.t[j], y.t[j]), lty = if (extended)
X[j, p + 1]
else 1, col = color[if (extended)
X[j, p + 2]
else j], border = border)
}
else {
segments(x1 + (x2 - x1) * c(0, 2, 4)/5, rep.int(y.b[j],
3L), x1 + (x2 - x1) * c(1, 3, 5)/5, rep.int(y.b[j],
3L))
}
}
}
}
}
srt.x <- if (las > 1)
90
else 0
srt.y <- if (las == 0 || las == 3)
90
else 0
if (is.null(dim(x)))
x <- as.array(x)
else if (is.data.frame(x))
x <- data.matrix(x)
dimd <- length(dx <- dim(x))
if (dimd == 0L || any(dx == 0L))
stop("'x' must not have 0 dimensionality")
if (!missing(...))
warning(sprintf(ngettext(length(list(...)), "extra argument %s will be disregarded",
"extra arguments %s will be disregarded"), paste(sQuote(names(list(...))),
collapse = ", ")), domain = NA)
Ind <- 1L:dx[1L]
if (dimd > 1L) {
Ind <- rep.int(Ind, prod(dx[2:dimd]))
for (i in 2:dimd) {
Ind <- cbind(Ind, c(matrix(1L:dx[i], byrow = TRUE,
nrow = prod(dx[1L:(i - 1)]), ncol = prod(dx[i:dimd]))))
}
}
Ind <- cbind(Ind, c(x))
if (is.logical(shade) && !shade) {
extended <- FALSE
Ind <- cbind(Ind, NA, NA)
}
else {
if (is.logical(shade))
shade <- c(2, 4)
else if (any(shade <= 0) || length(shade) > 5)
stop("invalid 'shade' specification")
extended <- TRUE
shade <- sort(shade)
breaks <- c(-Inf, -rev(shade), 0, shade, Inf)
color <- c(hsv(0, s = seq.int(1, to = 0, length.out = length(shade) +
1)), hsv(4/6, s = seq.int(0, to = 1, length.out = length(shade) +
1)))
if (is.null(margin))
margin <- as.list(1L:dimd)
E <- stats::loglin(x, margin, fit = TRUE, print = FALSE)$fit
type <- match.arg(type)
residuals <- switch(type, pearson = (x - E)/sqrt(E),
deviance = {
tmp <- 2 * (x * log(ifelse(x == 0, 1, x/E)) -
(x - E))
tmp <- sqrt(pmax(tmp, 0))
ifelse(x > E, tmp, -tmp)
}, FT = sqrt(x) + sqrt(x + 1) - sqrt(4 * E + 1))
Ind <- cbind(Ind, c(1 + (residuals < 0)), as.numeric(cut(residuals,
breaks)))
}
label <- dimnames(x)
if (is.null(off))
off <- if (dimd == 2)
2 * (dx - 1)
else rep.int(10, dimd)
if (length(off) != dimd)
off <- rep_len(off, dimd)
if (any(off > 50))
off <- off * 50/max(off)
if (is.null(dir) || length(dir) != dimd) {
dir <- rep_len(c("v", "h"), dimd)
}
if (!is.null(sort)) {
if (length(sort) != dimd)
stop("length of 'sort' does not conform to 'dim(x)'")
Ind[, seq_len(dimd)] <- Ind[, sort]
off <- off[sort]
dir <- dir[sort]
label <- label[sort]
}
nam.dn <- names(label)
if (is.null(xlab) && any(dir == "v"))
xlab <- nam.dn[min(which(dir == "v"))]
if (is.null(ylab) && any(dir == "h"))
ylab <- nam.dn[min(which(dir == "h"))]
ncolors <- length(tabulate(Ind[, dimd]))
if (!extended && ((is.null(color) || length(color) != ncolors))) {
color <- if (is.logical(color))
if (color[1L])
gray.colors(ncolors)
else rep.int(0, ncolors)
else if (is.null(color))
rep.int("grey", ncolors)
else rep_len(color, ncolors)
}
dev.hold()
on.exit(dev.flush())
plot.new()
if (!extended) {
opar <- par(usr = c(1, 1000, 1, 1000), mgp = c(1, 1,
0))
on.exit(par(opar), add = TRUE)
}
else {
pin <- par("pin")
rtxt <- "Standardized\nResiduals:"
rtxtCex <- min(1, pin[1L]/(strheight(rtxt, units = "inches") *
12), pin[2L]/(strwidth(rtxt, units = "inches")/4))
rtxtWidth <- 0.1
opar <- par(usr = c(1, 1000 * (1.1 + rtxtWidth), 1, 1000),
mgp = c(1, 1, 0))
on.exit(par(opar), add = TRUE)
rtxtHeight <- strwidth(rtxt, units = "i", cex = rtxtCex)/pin[2L]
text(1000 * (1.05 + 0.5 * rtxtWidth), 0, labels = rtxt,
adj = c(0, 0.25), srt = 90, cex = rtxtCex)
len <- length(shade) + 1
bh <- 0.95 * (0.95 - rtxtHeight)/(2 * len)
x.l <- 1000 * 1.05
x.r <- 1000 * (1.05 + 0.7 * rtxtWidth)
y.t <- 1000 * rev(seq.int(from = 0.95, by = -bh, length.out = 2 *
len))
y.b <- y.t - 1000 * 0.8 * bh
ltype <- c(rep.int(2, len), rep.int(1, len))
for (i in 1:(2 * len)) {
polygon(c(x.l, x.r, x.r, x.l), c(y.b[i], y.b[i],
y.t[i], y.t[i]), col = color[i], lty = ltype[i],
border = border)
}
brks <- round(breaks, 2)
y.m <- y.b + 1000 * 0.4 * bh
text(1000 * (1.05 + rtxtWidth), y.m, c(paste0("<", brks[2L]),
paste(brks[2:(2 * len - 1)], brks[3:(2 * len)], sep = ":"),
paste0(">", brks[2 * len])), srt = 90, cex = cex.axis,
xpd = NA)
}
if (!is.null(main) || !is.null(xlab) || !is.null(ylab) ||
!is.null(sub))
title(main, sub = sub, xlab = xlab, ylab = ylab)
adj.x <- adj.y <- 0.5
x1 <- 30 + 20 * cex.axis/0.66
y1 <- 5
x2 <- 950
y2 <- 1000 - x1
maxlen.xlabel <- maxlen.ylabel <- 35 * cex.axis/0.66
if (srt.x == 90) {
maxlen.xlabel <- max(strwidth(label[[dimd + 1L - match("v",
rev(dir))]], cex = cex.axis))
adj.x <- 1
y2 <- y2 - maxlen.xlabel
}
if (srt.y == 0) {
maxlen.ylabel <- max(strwidth(label[[match("h", dir)]],
cex = cex.axis))
adj.y <- 0
x1 <- x1 + maxlen.ylabel
}
mosaic.cell(Ind, x1 = x1, y1 = y1, x2 = x2, y2 = y2, srt.x = srt.x,
srt.y = srt.y, adj.x = adj.x, adj.y = adj.y, off = off/100,
dir = dir, color = color, lablevx = 2, lablevy = 2,
maxdim = apply(as.matrix(Ind[, 1L:dimd]), 2L, max),
currlev = 1, label = label)
## new stuff
at <- seq(x1, x2, length.out = 6)
axis(1, at, (at - min(at)) / diff(range(at)))
invisible()
}
I have created a wind rose using the package 'openair', for water current and direction data.
However, a default title is applied to the plot "Frequency of counts by wind direction (%)" which is not applicable to water current data. I cannot remove the title - can anyone help?
windRose(Wind, ws = "ws", wd = "wd", ws2 = NA, wd2 =NA,
ws.int = 20, angle = 10, type = "default", cols ="increment",
grid.line = NULL, width = 0.5, seg = NULL,
auto.text = TRUE, breaks = 5, offset = 10, paddle =FALSE,
key.header = "Current Speed", key.footer = "(cm/s)",
key.position = "right", key = TRUE, dig.lab = 3,
statistic = "prop.count", pollutant = NULL, annotate =
TRUE, border = NA, na.action=NULL)
Thanks!
There is another way that does not involve copying the whole function.
If you inspect the windRose code you can see that the title is set according to the value of the statistic option. In the documentation you can see that the oficial options are "prop.count", "prop.mean", "abs.count" and "frequency"; but code also checks if the argument passed to the statistic option is a list and sets the statistic options according to the list contents:
if (is.list(statistic)) {
stat.fun <- statistic$fun
stat.unit <- statistic$unit
stat.scale <- statistic$scale
stat.lab <- statistic$lab
stat.fun2 <- statistic$fun2
stat.lab2 <- statistic$lab2
stat.labcalm <- statistic$labcalm
}
the title that you want to change is defined by statistic$lab
By passing a list to the statistic option you can set among others, the title. So, an easy way to change the title is to pass a list to the statistic option with everything copied from one of the oficial options and changing the title. For example, let's say that I want to use "prop.count" with a custom title. Then I'd transform the options listed in the code:
stat.fun <- length
stat.unit <- "%"
stat.scale <- "all"
stat.lab <- "Frequency of counts by wind direction (%)"
stat.fun2 <- function(x) signif(mean(x, na.rm = TRUE),
3)
stat.lab2 <- "mean"
stat.labcalm <- function(x) round(x, 1)
into a named list with the title (lab) changed:
my.statistic <- list("fun"=length,"unit" = "%","scale" = "all", "lab" = "My title" , "fun2" = function(x) signif(mean(x, na.rm = TRUE), 3), "lab2" = "mean","labcalm" = function(x) round(x, 1))
and use it in the call to windRose:
windRose(mydata,statistic=my.statistic)
The great thing about a lot of R functions is you can type their name to see the source, in many cases. So here you could type windRose, and edit the required label as below:
windRose.2 <- function (mydata, ws = "ws", wd = "wd", ws2 = NA, wd2 = NA, ws.int = 2,
angle = 30, type = "default", cols = "default", grid.line = NULL,
width = 1, seg = NULL, auto.text = TRUE, breaks = 4, offset = 10,
paddle = TRUE, key.header = NULL, key.footer = "(m/s)", key.position = "bottom",
key = TRUE, dig.lab = 5, statistic = "prop.count", pollutant = NULL,
annotate = TRUE, border = NA, ...)
{
if (is.null(seg))
seg <- 0.9
if (length(cols) == 1 && cols == "greyscale") {
trellis.par.set(list(strip.background = list(col = "white")))
calm.col <- "black"
}
else {
calm.col <- "forestgreen"
}
current.strip <- trellis.par.get("strip.background")
on.exit(trellis.par.set("strip.background", current.strip))
if (360/angle != round(360/angle)) {
warning("In windRose(...):\n angle will produce some spoke overlap",
"\n suggest one of: 5, 6, 8, 9, 10, 12, 15, 30, 45, etc.",
call. = FALSE)
}
if (angle < 3) {
warning("In windRose(...):\n angle too small", "\n enforcing 'angle = 3'",
call. = FALSE)
angle <- 3
}
extra.args <- list(...)
extra.args$xlab <- if ("xlab" %in% names(extra.args))
quickText(extra.args$xlab, auto.text)
else quickText("", auto.text)
extra.args$ylab <- if ("ylab" %in% names(extra.args))
quickText(extra.args$ylab, auto.text)
else quickText("", auto.text)
extra.args$main <- if ("main" %in% names(extra.args))
quickText(extra.args$main, auto.text)
else quickText("", auto.text)
if (is.character(statistic)) {
ok.stat <- c("prop.count", "prop.mean", "abs.count",
"frequency")
if (!is.character(statistic) || !statistic[1] %in% ok.stat) {
warning("In windRose(...):\n statistic unrecognised",
"\n enforcing statistic = 'prop.count'", call. = FALSE)
statistic <- "prop.count"
}
if (statistic == "prop.count") {
stat.fun <- length
stat.unit <- "%"
stat.scale <- "all"
stat.lab <- ""
stat.fun2 <- function(x) signif(mean(x, na.rm = TRUE),
3)
stat.lab2 <- "mean"
stat.labcalm <- function(x) round(x, 1)
}
if (statistic == "prop.mean") {
stat.fun <- function(x) sum(x, na.rm = TRUE)
stat.unit <- "%"
stat.scale <- "panel"
stat.lab <- "Proportion contribution to the mean (%)"
stat.fun2 <- function(x) signif(mean(x, na.rm = TRUE),
3)
stat.lab2 <- "mean"
stat.labcalm <- function(x) round(x, 1)
}
if (statistic == "abs.count" | statistic == "frequency") {
stat.fun <- length
stat.unit <- ""
stat.scale <- "none"
stat.lab <- "Count by wind direction"
stat.fun2 <- function(x) round(length(x), 0)
stat.lab2 <- "count"
stat.labcalm <- function(x) round(x, 0)
}
}
if (is.list(statistic)) {
stat.fun <- statistic$fun
stat.unit <- statistic$unit
stat.scale <- statistic$scale
stat.lab <- statistic$lab
stat.fun2 <- statistic$fun2
stat.lab2 <- statistic$lab2
stat.labcalm <- statistic$labcalm
}
vars <- c(wd, ws)
diff <- FALSE
rm.neg <- TRUE
if (!is.na(ws2) & !is.na(wd2)) {
vars <- c(vars, ws2, wd2)
diff <- TRUE
rm.neg <- FALSE
mydata$ws <- mydata[, ws2] - mydata[, ws]
mydata$wd <- mydata[, wd2] - mydata[, wd]
id <- which(mydata$wd < 0)
if (length(id) > 0)
mydata$wd[id] <- mydata$wd[id] + 360
pollutant <- "ws"
key.footer <- "ws"
wd <- "wd"
ws <- "ws"
vars <- c("ws", "wd")
if (missing(angle))
angle <- 10
if (missing(offset))
offset <- 20
if (is.na(breaks[1])) {
max.br <- max(ceiling(abs(c(min(mydata$ws, na.rm = TRUE),
max(mydata$ws, na.rm = TRUE)))))
breaks <- c(-1 * max.br, 0, max.br)
}
if (missing(cols))
cols <- c("lightskyblue", "tomato")
seg <- 1
}
if (any(type %in% openair:::dateTypes))
vars <- c(vars, "date")
if (!is.null(pollutant))
vars <- c(vars, pollutant)
mydata <- openair:::checkPrep(mydata, vars, type, remove.calm = FALSE,
remove.neg = rm.neg)
mydata <- na.omit(mydata)
if (is.null(pollutant))
pollutant <- ws
mydata$x <- mydata[, pollutant]
mydata[, wd] <- angle * ceiling(mydata[, wd]/angle - 0.5)
mydata[, wd][mydata[, wd] == 0] <- 360
mydata[, wd][mydata[, ws] == 0] <- -999
if (length(breaks) == 1)
breaks <- 0:(breaks - 1) * ws.int
if (max(breaks) < max(mydata$x, na.rm = TRUE))
breaks <- c(breaks, max(mydata$x, na.rm = TRUE))
if (min(breaks) > min(mydata$x, na.rm = TRUE))
warning("Some values are below minimum break.")
breaks <- unique(breaks)
mydata$x <- cut(mydata$x, breaks = breaks, include.lowest = FALSE,
dig.lab = dig.lab)
theLabels <- gsub("[(]|[)]|[[]|[]]", "", levels(mydata$x))
theLabels <- gsub("[,]", " to ", theLabels)
prepare.grid <- function(mydata) {
if (all(is.na(mydata$x)))
return()
levels(mydata$x) <- c(paste("x", 1:length(theLabels),
sep = ""))
all <- stat.fun(mydata[, wd])
calm <- mydata[mydata[, wd] == -999, ][, pollutant]
mydata <- mydata[mydata[, wd] != -999, ]
calm <- stat.fun(calm)
weights <- tapply(mydata[, pollutant], list(mydata[,
wd], mydata$x), stat.fun)
if (stat.scale == "all") {
calm <- calm/all
weights <- weights/all
}
if (stat.scale == "panel") {
temp <- stat.fun(stat.fun(weights)) + calm
calm <- calm/temp
weights <- weights/temp
}
weights[is.na(weights)] <- 0
weights <- t(apply(weights, 1, cumsum))
if (stat.scale == "all" | stat.scale == "panel") {
weights <- weights * 100
calm <- calm * 100
}
panel.fun <- stat.fun2(mydata[, pollutant])
u <- mean(sin(2 * pi * mydata[, wd]/360))
v <- mean(cos(2 * pi * mydata[, wd]/360))
mean.wd <- atan2(u, v) * 360/2/pi
if (all(is.na(mean.wd))) {
mean.wd <- NA
}
else {
if (mean.wd < 0)
mean.wd <- mean.wd + 360
if (mean.wd > 180)
mean.wd <- mean.wd - 360
}
weights <- cbind(data.frame(weights), wd = as.numeric(row.names(weights)),
calm = calm, panel.fun = panel.fun, mean.wd = mean.wd)
weights
}
if (paddle) {
poly <- function(wd, len1, len2, width, colour, x.off = 0,
y.off = 0) {
theta <- wd * pi/180
len1 <- len1 + off.set
len2 <- len2 + off.set
x1 <- len1 * sin(theta) - width * cos(theta) + x.off
x2 <- len1 * sin(theta) + width * cos(theta) + x.off
x3 <- len2 * sin(theta) - width * cos(theta) + x.off
x4 <- len2 * sin(theta) + width * cos(theta) + x.off
y1 <- len1 * cos(theta) + width * sin(theta) + y.off
y2 <- len1 * cos(theta) - width * sin(theta) + y.off
y3 <- len2 * cos(theta) + width * sin(theta) + y.off
y4 <- len2 * cos(theta) - width * sin(theta) + y.off
lpolygon(c(x1, x2, x4, x3), c(y1, y2, y4, y3), col = colour,
border = border)
}
}
else {
poly <- function(wd, len1, len2, width, colour, x.off = 0,
y.off = 0) {
len1 <- len1 + off.set
len2 <- len2 + off.set
theta <- seq((wd - seg * angle/2), (wd + seg * angle/2),
length.out = (angle - 2) * 10)
theta <- ifelse(theta < 1, 360 - theta, theta)
theta <- theta * pi/180
x1 <- len1 * sin(theta) + x.off
x2 <- rev(len2 * sin(theta) + x.off)
y1 <- len1 * cos(theta) + x.off
y2 <- rev(len2 * cos(theta) + x.off)
lpolygon(c(x1, x2), c(y1, y2), col = colour, border = border)
}
}
mydata <- cutData(mydata, type, ...)
results.grid <- ddply(mydata, type, prepare.grid)
results.grid$calm <- stat.labcalm(results.grid$calm)
results.grid$mean.wd <- stat.labcalm(results.grid$mean.wd)
strip.dat <- openair:::strip.fun(results.grid, type, auto.text)
strip <- strip.dat[[1]]
strip.left <- strip.dat[[2]]
pol.name <- strip.dat[[3]]
if (length(theLabels) < length(cols)) {
col <- cols[1:length(theLabels)]
}
else {
col <- openColours(cols, length(theLabels))
}
max.freq <- max(results.grid[, (length(type) + 1):(length(theLabels) +
length(type))], na.rm = TRUE)
off.set <- max.freq * (offset/100)
box.widths <- seq(0.002^0.25, 0.016^0.25, length.out = length(theLabels))^4
box.widths <- box.widths * max.freq * angle/5
legend <- list(col = col, space = key.position, auto.text = auto.text,
labels = theLabels, footer = key.footer, header = key.header,
height = 0.6, width = 1.5, fit = "scale", plot.style = if (paddle) "paddle" else "other")
legend <- openair:::makeOpenKeyLegend(key, legend, "windRose")
temp <- paste(type, collapse = "+")
myform <- formula(paste("x1 ~ wd | ", temp, sep = ""))
mymax <- 2 * max.freq
myby <- if (is.null(grid.line))
pretty(c(0, mymax), 10)[2]
else grid.line
if (myby/mymax > 0.9)
myby <- mymax * 0.9
xyplot.args <- list(x = myform, xlim = 1.03 * c(-max.freq -
off.set, max.freq + off.set), ylim = 1.03 * c(-max.freq -
off.set, max.freq + off.set), data = results.grid, type = "n",
sub = stat.lab, strip = strip, strip.left = strip.left,
as.table = TRUE, aspect = 1, par.strip.text = list(cex = 0.8),
scales = list(draw = FALSE), panel = function(x, y, subscripts,
...) {
panel.xyplot(x, y, ...)
angles <- seq(0, 2 * pi, length = 360)
sapply(seq(off.set, mymax, by = myby), function(x) llines(x *
sin(angles), x * cos(angles), col = "grey85",
lwd = 1))
subdata <- results.grid[subscripts, ]
upper <- max.freq + off.set
larrows(-upper, 0, upper, 0, code = 3, length = 0.1)
larrows(0, -upper, 0, upper, code = 3, length = 0.1)
ltext(upper * -1 * 0.95, 0.07 * upper, "W", cex = 0.7)
ltext(0.07 * upper, upper * -1 * 0.95, "S", cex = 0.7)
ltext(0.07 * upper, upper * 0.95, "N", cex = 0.7)
ltext(upper * 0.95, 0.07 * upper, "E", cex = 0.7)
if (nrow(subdata) > 0) {
for (i in 1:nrow(subdata)) {
with(subdata, {
for (j in 1:length(theLabels)) {
if (j == 1) {
temp <- "poly(wd[i], 0, x1[i], width * box.widths[1], col[1])"
} else {
temp <- paste("poly(wd[i], x", j - 1,
"[i], x", j, "[i], width * box.widths[",
j, "], col[", j, "])", sep = "")
}
eval(parse(text = temp))
}
})
}
}
ltext(seq((myby + off.set), mymax, myby) * sin(pi/4),
seq((myby + off.set), mymax, myby) * cos(pi/4),
paste(seq(myby, mymax, by = myby), stat.unit,
sep = ""), cex = 0.7)
if (annotate) if (statistic != "prop.mean") {
if (!diff) {
ltext(max.freq + off.set, -max.freq - off.set,
label = paste(stat.lab2, " = ", subdata$panel.fun[1],
"\ncalm = ", subdata$calm[1], stat.unit,
sep = ""), adj = c(1, 0), cex = 0.7, col = calm.col)
}
if (diff) {
ltext(max.freq + off.set, -max.freq - off.set,
label = paste("mean ws = ", round(subdata$panel.fun[1],
1), "\nmean wd = ", round(subdata$mean.wd[1],
1), sep = ""), adj = c(1, 0), cex = 0.7,
col = calm.col)
}
} else {
ltext(max.freq + off.set, -max.freq - off.set,
label = paste(stat.lab2, " = ", subdata$panel.fun[1],
stat.unit, sep = ""), adj = c(1, 0), cex = 0.7,
col = calm.col)
}
}, legend = legend)
xyplot.args <- openair:::listUpdate(xyplot.args, extra.args)
plt <- do.call(xyplot, xyplot.args)
if (length(type) == 1)
plot(plt)
else plot(useOuterStrips(plt, strip = strip, strip.left = strip.left))
newdata <- results.grid
output <- list(plot = plt, data = newdata, call = match.call())
class(output) <- "openair"
invisible(output)
}
Here I've copied the entire source, and made a new function, windRose.2 with the only difference being stat.lab <- "Frequency of counts by wind direction (%)" is now stat.lab <- "".