I am trying to plot a 3x3 grid of levelplots which are all on the same color scale with one combined color key.
I have found latticeExtra and its ability to override c and combine trellis objects together, with the merge.legends flag. However, when I do this I get multiple color keys.
is a picture of what it looks like with only three of the plots.
And here is the code where I do this:
t1 <- levelplot(counts[[1]], main="", col.regions=colorRampPalette(c("white","red"))(256))
t2 <- levelplot(counts[[2]], main="", col.regions=colorRampPalette(c("white","red"))(256))
t3 <- levelplot(counts[[3]], main="", col.regions=colorRampPalette(c("white","red"))(256))
plots <- c(t1, t2, t3, merge.legends=T)
print(plots)
I also would ideally like to not have to manually make each trellis object its own variable, but rather a member of a list:
plots <- list()
for (i in 1:length(counts)){
if (i %% 3 == 0) {
plots[[i]] <- levelplot(counts[[i]], main="", col.regions=colorRampPalette(c("white","red"))(256))
}
}
plots <- c(unlist(plots), merge.legends=T)
But when I try to do this it seems that the c function is not overridden correctly. I have tried not using unlist, as well as making plots a vector, and they do not work.
Note that merge.legends = TRUE results in separate legends per plot. If you want to merge single legends using c.trellis, you have to set merge.legends = FALSE (somewhat counter-intuitive, I know...). Based on a levelplot example from ?c.trellis:
# Levelplot
levObj <- levelplot(prop.table(WorldPhones, 1) * 100)
# Combination via `c.trellis`
comb_levObj <- c(levObj, levObj, layout = c(1, 2), merge.legends = FALSE)
print(comb_levObj)
Related
I am trying to plot 18 individual plots on a 3x6 multiplot in R. To be more efficient I have created these plots as a loop, however I would like the plots in each column to have their own color (i.e. the all the plots in column 1 would be red, all the plots in column 2 would be blue etc.). Is there a way I can do this while still retaining loop format?
par(mfcol = c(3,6))
for(i in 1:6)
{
plot(sigma_trace[,i], type ='l', main = paste("Sigma Traceplot Chain", i))
plot(theta_1_trace[,i], type = 'l', main = paste("Theta[1] Traceplot Chain", i))
plot(theta_2_trace[,i], type = 'l', main = paste("Theta[2] Traceplot Chain", i))
}
So basically, I think I want each loop statement to follow the same pattern of colours. Is this possible?
Thanks.
You can make a colour palette using RColorBrewer and then call each colour in your loop. For example.
library(RColorBrewer)
# set the colour palette
cols <- brewer.pal(4,'Set2')
# variables to plot
x = (1:250)/10
y = cos(x)
# plot in the loop
op <- par(mfrow = c(2, 2))
for (i in 1:4){
plot(x, y, col=cols[i], type='l', lwd=3)
}
par(op)
Here's an overview of the package.
In Base R you can use colorRampPalette() to create gradient, or you can even just make an object with the colours that you wan to reference:
plotcolors <- colorRampPalette(c("gold","blue"))(6)
par(mfrow = c(2, 3))
for(i in 1:6){
plot(1:10,1:10,type='l',col=plotcolors[i])
}
If you want to specify all 6 of your colours its as easy as modifying the above code
plotcolors <- c("red","blue","green","black","yellow","purple")
I would like to save multiple plots (with ggplot2) to a list during a large for-loop. And then subsequently display the images in a grid (with grid.arrange)
I have tried two solutions to this:
1 storing it in a list, like so:
pltlist[["qplot"]] <- qplot
however for some reason this does save the plot correctly.
So I resorted to a second strategy which is recordPlot()
This was able to save the plot correctly, but unable to
use it in a grid.
Reproducable Example:
require(ggplot2);require(grid);require(gridExtra)
df <- data.frame(x = rnorm(100),y = rnorm(100))
histoplot <- ggplot(df, aes(x=x)) + geom_histogram(aes(y=..density..),binwidth=.1,colour="black", fill="white")
qplot <- qplot(sample = df$y, stat="qq")
pltlist <- list()
pltlist[["qplot"]] <- qplot
pltlist[["histoplot"]] <- histoplot
grid.arrange(pltlist[["qplot"]],pltlist[["histoplot"]], ncol=2)
above code works but produces the wrong graph
in my actual code
Then I tried recordPlot()
print(histoplot)
c1 <- recordPlot()
print(qplot)
c2 <- recordPlot()
I am able to display all the plots individually
but grid.arrange produces an error:
grid.arrange(replayPlot(c1),replayPlot(c2), ncol=2) # = Error
Error in gList(list(wrapvp = list(x = 0.5, y = 0.5, width = 1, height = 1, :
only 'grobs' allowed in "gList"
In this thread Saving grid.arrange() plot to file
They dicuss a solution which utilizes arrangeGrob() instead
arrangeGrob(c1, c1, ncol=2) # Error
Error in vapply(x$grobs, as.character, character(1)) :
values must be length 1,
but FUN(X[[1]]) result is length 3
I am forced to use the recordPlot() instead of saving to a list since this does not produce the same graph when saved as when it is plotted immediately, which I unfortunately cannot replicate, sorry.
In my actual code I am doing a large for-loop, looping through several variables, making a correlation with each and making scatterplots, where I name the scatterplots dependent on their significans level. I then want to re-display the plots that were significant in a grid, in a dynamic knitr report.
I am aware that I could just re-plot the plots that were significant after the for-loop instead of saving them, (I can't save as png while doing knitr either). However I would like to find a way to dynammically save the plots as R-objects and then replot them in a grid afterwards.
Thanks for Reading
"R version 3.2.1"
Windows 7 64bit - RStudio - Version 0.99.652
attached base packages:
[1] grid grDevices datasets utils graphics stats methods base
other attached packages:
[1] gridExtra_2.0.0 ggplot2_1.0.1
I can think of two solutions.
1. If your goal is to just save the list of plots as R objects, I recommend:
saveRDS(object = pltlist, file = "file_path")
This way when you wish to reload in these graphs, you can just use readRDS(). You can then put them in cowplot or gridarrange. This command works for all lists and R Objects.
One caveat to this approach is if settings/labeling for ggplot2 is dependent upon things in the environment (not the data, but stuff like settings for point size, shape, or coloring) instead of the ggplot2 function used to make the graph), your graphs won't work until you restore your dependencies. One reason to save some dependencies is to modularize your scripts to make the graphs.
Another caveat is performance: From my experience, I found it is actually faster to read in the data and remake individual graphs than load in an RDS file of all the graphs when you have a large number of graphs (100+ graphs).
2. If your goal is to save an 'image' or 'picture' of each graph (single and/or multiplot as .png, .jpeg, etc.), and later adjust things in a grid manually outside of R such as powerpoint or photoshop, I recommend:
filenames <- c("Filename_1", "Filename_2") #actual file names you want...
lapply(seq_along(pltlist), function(i) {
ggsave(filename = filenames[i], plot = pltlist[[i]], ...) #use your settings here
})
Settings I like for single plots:
lapply(seq_along(pltlist), function(i) ggsave(
plot = pltlist[[i]],
filename = paste0("plot_", i, "_", ".tiff"), #you can even paste in pltlist[[i]]$labels$title
device = "tiff", width=180, height=180, units="mm", dpi=300, compression = "lzw", #compression for tiff
path = paste0("../Blabla") #must be an existing directory.
))
You may want to do the manual approach if you're really OCD about the grid arrangement and you don't have too many of them to make for publications. Otherwise, when you do grid.arrange you'll want to do all the specifications there (adjusting font, increasing axis label size, custom colors, etc.), then adjust the width and height accordingly.
Reviving this post to add multiplot here, as it fits exactly.
require(ggplot2)
mydd <- setNames( data.frame( matrix( rep(c("x","y","z"), each=10) ),
c(rnorm(10), rnorm(10), rnorm(10)) ), c("points", "data") )
# points data
# 1 x 0.733013658
# 2 x 0.218838717
# 3 x -0.008303382
# 4 x 2.225820069
# ...
p1 <- ggplot( mydd[mydd$point == "x",] ) + geom_line( aes( 1:10, data, col=points ) )
p2 <- ggplot( mydd[mydd$point == "y",] ) + geom_line( aes( 1:10, data, col=points ) )
p3 <- ggplot( mydd[mydd$point == "z",] ) + geom_line( aes( 1:10, data, col=points ) )
multiplot(p1,p2,p3, cols=1)
multiplot:
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
library(grid)
# Make a list from the ... arguments and plotlist
plots <- c(list(...), plotlist)
numPlots = length(plots)
# If layout is NULL, then use 'cols' to determine layout
if (is.null(layout)) {
# Make the panel
# ncol: Number of columns of plots
# nrow: Number of rows needed, calculated from # of cols
layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
ncol = cols, nrow = ceiling(numPlots/cols))
}
if (numPlots==1) {
print(plots[[1]])
} else {
# Set up the page
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
# Make each plot, in the correct location
for (i in 1:numPlots) {
# Get the i,j matrix positions of the regions that contain this subplot
matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
layout.pos.col = matchidx$col))
}
}
}
Result:
I'd like to superpose a histogram and an xyplot representing the cumulative distribution function using r's lattice package.
I've tried to accomplish this with custom panel functions, but can't seem to get it right--I'm getting hung up on one plot being univariate and one being bivariate I think.
Here's an example with the two plots I want stacked vertically:
set.seed(1)
x <- rnorm(100, 0, 1)
discrete.cdf <- function(x, decreasing=FALSE){
x <- x[order(x,decreasing=FALSE)]
result <- data.frame(rank=1:length(x),x=x)
result$cdf <- result$rank/nrow(result)
return(result)
}
my.df <- discrete.cdf(x)
chart.hist <- histogram(~x, data=my.df, xlab="")
chart.cdf <- xyplot(100*cdf~x, data=my.df, type="s",
ylab="Cumulative Percent of Total")
graphics.off()
trellis.device(width = 6, height = 8)
print(chart.hist, split = c(1,1,1,2), more = TRUE)
print(chart.cdf, split = c(1,2,1,2))
I'd like these superposed in the same frame, rather than stacked.
The following code doesn't work, nor do any of the simple variations of it that I have tried:
xyplot(cdf~x,data=cdf,
panel=function(...){
panel.xyplot(...)
panel.histogram(~x)
})
You were on the right track with your custom panel function. The trick is passing the correct arguments to the panel.- functions. For panel.histogram, this means not passing a formula and supplying an appropriate value to the breaks argument:
EDIT Proper percent values on y-axis and type of plots
xyplot(100*cdf~x,data=my.df,
panel=function(...){
panel.histogram(..., breaks = do.breaks(range(x), nint = 8),
type = "percent")
panel.xyplot(..., type = "s")
})
This answer is just a placeholder until a better answer comes.
The hist() function from the graphics package has an option called add. The following does what you want in the "classical" way:
plot( my.df$x, my.df$cdf * 100, type= "l" )
hist( my.df$x, add= T )
A minor question about plotting stacked barplot in R.
The stacked bars represent the series bottom-to-top.
But the legend always shows the series top-to-bottom. I think that is also true with ggplot2::geom_bar
Is there any nicer idiom than using rev(...) twice inside either legend() or barplot() as in:
exports <- data.frame(100*rbind('Americas'=runif(6),'Asia'=runif(6),'Other'=runif(6)))
colnames(exports) <- 2004:2009
series_we_want <- c(1,2,3)
barplot( as.matrix(exports[series_we_want,]), col=mycolors, ...)
legend(x="topleft", legend=rev(rownames(exports)[series_we_want]), col=rev(mycolors) ...)
(If you omit one of the rev()'s the output is obviously meaningless. Seems like an enhance case for adding a single flag yflip=TRUE or yreverse=TRUE)
This is what I got using your code:
exports <- data.frame(100*rbind('Americas'=runif(6),'Asia'=runif(6),'Other'=runif(6)))
colnames(exports) <- 2004:2009
series_we_want <- c(1,2,3)
barplot( as.matrix(exports[series_we_want,]))
legend(x="topleft", legend=rev(rownames(exports)[series_we_want]))
try this:
exports <- data.frame(100*rbind('Americas'=runif(6),'Asia'=runif(6),'Other'=runif(6)))
colnames(exports) <- 2004:2009
series_we_want <- c(1,2,3)
test_data<-as.matrix(exports[series_we_want])
barplot( test_data,
legend.text=as.character(rev(rownames(exports)[series_we_want])),
args.legend = list(x="topleft"))
seems to produce the legend in the opposite order of what you have
I have a multiple variable time series were some of the variables have rather large ranges. I wish to make a single-page plot with multiple stacked plots of each variable were some of the variables have a log10 y-axis scaling. I am relatively new to lattice and have not been able to figure out how to effectively mix the log10 scaling with non-transformed axes and get a publication quality plot. If print.trellis is used the plots are not aligned and the padding needs some work, if c.trellis is used the layout is good, but only the y-scaling from only one plot is used. Any suggestions for an efficient solution, where I can replicate the output of c.trellis using the different y-scaling for each (original) object?
Example below:
require(lattice)
require(latticeExtra)
# make data.frame
d.date <- as.POSIXct(c("2009-12-15", "2010-01-15", "2010-02-15", "2010-03-15", "2010-04-15"))
CO2dat <- c(100,200,1000,9000,2000)
pHdat <- c(10,9,7,6,7)
tmp <- data.frame(date=d.date ,CO2dat=CO2dat ,pHdat=pHdat)
# make plots
plot1 <- xyplot(pHdat ~ date, data=tmp
, ylim=c(5,11)
, ylab="pHdat"
, xlab="Date"
, origin = 0, border = 0
, scales=list(y=list(alternating=1))
, panel = function(...){
panel.xyarea(...)
panel.xyplot(...)
}
)
# make plot with log y scale
plot2 <- xyplot(CO2dat ~ date, data=tmp
, ylim=c(10,10^4)
, ylab="CO2dat"
, xlab="Date"
, origin = 0, border = 0
, scales=list(y=list(alternating=1,log=10))
, yscale.components = yscale.components.log10ticks
, panel = function(...){
panel.xyarea(...)
panel.xyplot(...)
# plot CO2air uatm
panel.abline(h=log10(390),col="blue",type="l",...)
}
)
# plot individual figures using split
print(plot2, split=c(1,1,1,2), more=TRUE)
print(plot1, split=c(1,2,1,2), more=F)
# combine plots (more convenient)
comb <- c(plot1, plot2, x.same=F, y.same=F, layout = c(1, 2))
# plot combined figure
update(comb, ylab = c("pHdat","log10 CO2dat"))
Using #joran's idea, I can get the axes to be closer but not exact; also, reducing padding gets them closer together but changes the aspect ratio. In the picture below I've reduced the padding perhaps by too much to show the not exactness; if this close were desired, you'd clearly want to remove the x-axis labels on the top as well.
I looked into the code that sets up the layout and the margin on the left side is calculated from the width of the labels, so #joran's idea is probably the only thing that will work based on the printing using split, unless one were to rewrite the plot.trellis command. Perhaps the c method could work but I haven't found a way yet to set the scale components separately depending on the panel. That does seem more promising though.
mtheme <- standard.theme("pdf")
mtheme$layout.heights$bottom.padding <- -10
plot1b <- update(plot1, scales=list(y=list(alternating=1, at=5:10, labels=paste(" ",c(5:10)))))
plot2b <- update(plot2, par.settings=mtheme)
pdf(file="temp.pdf")
print(plot2b, split=c(1,1,1,2), more=TRUE)
print(plot1b, split=c(1,2,1,2), more=F)