I am trying to create the plot like following (many times I end up drawing a plot like this by hand, but this time I want to plot it myself).
Here is my data and my trial:
myd <- data.frame (period = c("Triassic", "Jurasic",
"Cretaceous", "Cenzoic"), myears = c(245, 208, 145, 65),
label = c(226, 176,105, 32 ))
myd2 <- data.frame (event = c("Diansaurs_strt", "Birds",
"Diansaurs_ext", "Human"), myears = c(235, 200, 60, 0.5))
myd2$x <- -0.25
with (myd2, plot(x,myears,ylim=c(0,250),
xlim = c(0, 10), axes=F,xlab="",ylab="",type="p",pch=17))
with (myd2,text(x,myears,event,pos=4,xpd=T))
axis(side=2,at = myd$label, labels = myd$period)
I have issues particularly matching of axis with plot and orientation of text and points. Any other idea or improvement help appreciated.
For constructing novel plots "from the ground up", and for maximal control over individual graphical elements, the grid graphical system is hard to beat:
library(grid)
## Set up plotting area with reasonable x-y limits
## and a "native" scale related to the scale of the data.
x <- -1:1
y <- extendrange(c(myd$myears, myd2$myears))
dvp <- dataViewport(x, y, name = "figure")
grid.newpage()
pushViewport(dvp)
## Plot the central timeline
grid.lines(unit(0, "native"), unit(c(0,245), "native"),
gp = gpar(col="dodgerblue"))
## Annotate LHS
grid.segments(x0=0.5, x1=0.47,
y0=unit(c(0, myd$myears), "native"),
y1=unit(c(0, myd$myears), "native"),
gp=gpar(col="dodgerblue"))
grid.text(label=c(0, myd$myears), x=0.44, y=unit(c(0, myd$myears), "native"))
grid.text(label=myd$period, x=0.3, y=unit(myd$label, "native"),
just=0, gp=gpar(col="dodgerblue", fontface="italic"))
## Annotate RHS
## Create a function that plots a pointer to the specified coordinate
pointer <- function(x, y, width=1) {
grid.polygon(x = x + unit(width*(c(0, .1, .1)), "npc"),
y = y + unit(width*(c(0, .03, -.03)), "npc"),
gp = gpar(fill="dodgerblue", col="blue", lwd=2))
}
## Call it once for each milestone
for(y in myd2$myears) {
pointer(unit(.5, "npc"), y=unit(y, "native"), width=0.3)
}
## Or, if you just want blue line segments instead of those gaudy pointers:
## grid.segments(x0=0.5, x1=0.53,
## y0=unit(c(myd2$myears), "native"),
## y1=unit(c(myd2$myears), "native"), gp=gpar(col="dodgerblue"))
grid.text(label=myd2$event, x=0.55, y=unit(myd2$myears, "native"),
just=0)
You can try something like this to get you started:
myd <- data.frame(period = c("", "Triassic", "Jurasic",
"Cretaceous", "Cenzoic", ""),
myears = c(260, 245, 208, 145, 65, -5),
label = c(260, 226, 176,105, 32, -5))
myd2 <- data.frame(event = c("Dinosaurs_strt", "Birds",
"Dinosaurs_ext", "Human"),
myears = c(235, 200, 60, 0.5))
myd2$x <- 1
with(myd2, plot(x, myears, ylim = c(-5, 250), xlim = c(0, 10),
axes = FALSE, xlab = "", ylab = "", type = "n"))
with(myd2, text(x, myears, event, pos = 4, xpd = TRUE))
axis(side = 2, at = myd$label, labels = myd$period, las = 2)
X0 <- rep(myd2$x, 4)
Y0 <- myd2$myears
X1 <- rep(-.25, 4)
Y1 <- Y0
arrows(X0, Y0, X1, Y1)
I've added an extra empty element at the start and end of your data in "myd" to help with the axis. Then, instead of using pch, I've used arrows to match the right hand labels with the axis.
Some tweaking could probably make it look a lot nicer.
Here are some enhancements ( I suggest to add 0 for now just to make scale well):
myd <- data.frame (period = c("Triassic", "Jurasic",
"Cretaceous", "Cenzoic", "now"), myears = c(245, 208, 145, 65, 0),
label = c(226, 176,105, 32, NA ))
myd2 <- data.frame (event = c("Diansaurs_strt", "Birds", "Diansaurs_ext", "Human"),
myears = c(235, 200, 60, 0.5))
myd2$x <- -0.25
with (myd2, plot(x,myears,ylim=c(0,250), xlim = c(0, 10),
axes=F,xlab="",ylab="",type="p",pch=17, col = "green"))
with (myd2, plot(x,myears,ylim=c(0,250),
xlim = c(0, 10), axes=F,xlab="",ylab="",type="p",pch="-", col = "green"))
with (myd2,text(x,myears,event,pos=4,xpd=T), col = "green")
axis(side=2,at = myd$label, labels = myd$period, tick = FALSE,
las = 2, col = "green", )
axis(side=2,at = myd$myears, labels = myd$myears, las = 2, col = "green")
There are few issues remaining you might want to change oriantation of the arrow (I belief that you can someway find <- symbol, but I do not know how to).
For drawing the triangles look at the my.symbols and ms.polygon functions in the TeachingDemos package.
In your right graph above the Dinosaurs are moved up, if you want this in general (moving labels that would otherwise be too close or overlap) then look at the spread.labs function in the TeachingDemos package.
Some other possible functions that could help with the plot are text, mtext, grconvertX, grconvertY, segments, and axis.
Related
My example data is as follows:
df <- data.frame(study = c("Hodaie","Kerrigan","Lee","Andrade","Lim"), SR = c(0.5460, 0.2270, 0.7540, 0.6420, 0.5000), SE = c(12.30, 15.70, 12.80, 13.80, 9.00), Patients = c(5, 5, 3, 6, 4))
I want to conduct the meta-analysis with SR(single group percentage), SE (standard error that I can compute based on sample size and percentage), and patients(sample size for each study), and I hope I could get the following forest plot (I found this example in an article, and they also have one group percentage data, but I can't find which R statement or argument they used):
Could anyone tell me which R statement or argument that I could use to conduct the meta-analysis and generate the forest plot above? Thank you!
I am sure there are plenty of ways to do this using packages but it can be accomplished in base R (and there are likely more elegant solutions using base R). The way I do it is to first build a blank plot much larger than the needed graphing portion, then overlay the relevant elements on it. I find one has more control over it this way. A basic example that could get you started is below. If you are new to R (based on your name NewRUser), I suggest running it line-by-line to see how it all works. Again, this is only one way and there are likely better approaches. Good luck!
Sample Data
#### Sample Data (modified from OP)
df <- data.frame(Study = c("Hodaie","Kerrigan","Lee","Andrade","Lim"),
SR = c(0.5460, 0.2270, 0.7540, 0.6420, 0.5000),
SE = c(12.30, 15.70, 12.80, 13.80, 9.00),
Patients = c(5, 5, 3, 6, 4),
ci_lo = c(30, -8.0, 50, 37, 32),
ci_hi = c(78, 53, 100, 91, 67))
### Set up plotting elements
n.studies <- nrow(df)
yy <- n.studies:1
seqx <- seq(-100, 100, 50)
## blank plot much larger than needed
plot(range(-550, 200), range(0, n.studies), type = 'n', axes = F, xlab = '', ylab = '') #blank plot, much bigger than plotting portion needed
# Set up axes
axis(side = 1, at = seqx, labels = seqx, cex.axis = 1, mgp = c(2, 1.5, 1)) # add axis and label (bottom)
mtext(side = 1, at = 0, 'Seizure Reduction', line = 2.5, cex = 0.85, padj = 1)
axis(side = 3, at = seqx, labels = seqx, cex.axis = 1, mgp = c(2, 1.5, 1)) # add axis and label (top)
mtext(side = 3, at = 0, 'Seizure Reduction', line = 2.5, cex = 0.85, padj = -1)
## add lines and dots
segments(df[, "ci_lo"], yy, df[,"ci_hi"], yy) # add lines
points(df[,"SR"]*100, yy, pch = 19) # add points
segments(x0 = 0, y0 = max(yy), y1 = 0, lty = 3, lwd = 0.75) #vertical line # 0
### Add text information
par(xpd = TRUE)
text(x = -550, y = yy, df[,"Study"], pos = 4)
text(x = -450, y = yy, df[,"SR"]*100, pos = 4)
text(x = -350, y = yy, df[,"SE"], pos = 4)
text(x = -250, y = yy, df[,"Patients"], pos = 4)
text(x = 150, y = yy, paste0(df[,"ci_lo"], "-", df[,"ci_hi"]), pos = 4)
text(x = c(seq(-550, -250, 100), 150), y = max(yy)+0.75,
c(colnames(df)[1:4], "CI"), pos = 4, font = 2)
# Add legend
legend(x = 50, y = 0.5, c("Point estimate", "95% Confidence interval"),
pch = c(19, NA), lty = c(NA, 19), bty = "n", cex = 0.65)
I was wondering if it is possible to seperate two plots from eachother (both should be on the same plot, using double Y axis). So the double plot should be split into two but without actually plotting them seperate - par(mfrow(1,2)).
I was trying to imitate it with layout plot, or with latticeExtra, ggplot but no success.
I have two different dataset one for the exchange rate one for the logaritmic returns.
par(mar=c(4,4,3,4))
plot(rates$EURHUF~rates$Date, type="l", ylab="Rate", main="EUR/HUF", xlab="Time")
par(new=TRUE)
plot(reteslog$EURHUF~rateslog$Date, type="l", xaxt="n", yaxt="n", ylab="", xlab="", col="red")
axis(side=4)
mtext("Log return", side=4, line=3)
legend("topleft", c("EUR/HUF Rates","EUR/HUF Logreturns"), col=c("black", "red"), lty=c(1,1))
So far I am here, I just don't know how to seperate them or scale them (maybe using margin, or layout?)
Thank you very much guys for helping
I have a solution to this that isn't too outlandish, and is entirely in base, which is nice. For it to work, you just need to be able to force all of your data onto the same scale, which usually isn't a hassle.
The idea is that once your data is on the same scale, you can plot it all normally, and then add in custom axes that show the respective scales of the different data.
set.seed(1986)
d01 <- sample(x = 1:20,
size = 200,
replace = TRUE)
d02 <- sample(x = 31:45,
size = 200,
replace = TRUE)
# pdf(file = "<some/path/to/image.pdf>",
# width = 4L,
# height = 4L) # plot to a pdf
jpeg(file = "<some/path/to/image.jpeg>") # plot to a jpeg
par(mar=c(3.5, 3.5, 2, 3.5)) # parameters to make things prettier
par(mgp=c(2.2, 1, 0)) # parameters to make things prettier
plot(x = 0,
y = 0,
type = "n",
xlim = c(1, 200),
ylim = c(1, 50),
xlab = "Label 01!",
ylab = "Label 02!",
axes = FALSE,
frame.plot = TRUE)
points(d01,
pch = 1,
col = "blue") # data 01
points(d02,
pch = 2,
col = "red") # data 02
mtext("Label 03!",
side = 4,
line = 2) # your extra y axis label
xticks <- seq(from = 0,
to = 200,
by = 50) # tick mark labels
xtickpositions <- seq(from = 0,
to = 200,
by = 50) # tick mark positions on the x axis
axis(side = 1,
at = xtickpositions,
labels = xticks,
col.axis="black",
las = 2,
lwd = 0,
lwd.ticks = 1,
tck = -0.025) # add your tick marks
y01ticks <- seq(from = 0,
to = 1,
by = 0.1) # tick mark labels
y01tickpositions <- seq(from = 0,
to = 50,
by = 5) # tick mark positions on the y01 axis
axis(side = 2,
at = y01tickpositions,
labels = y01ticks,
las = 2,
lwd = 0,
lwd.ticks = 1,
tck = -0.025) # add your tick marks
y02ticks <- seq(from = 0,
to = 50,
by = 5L) # tick mark labels
y02tickpositions <- seq(from = 0,
to = 50,
by = 5) # tick mark positions on the y02 axis
axis(side = 4,
at = y02tickpositions,
labels = y02ticks,
las = 2,
lwd = 0,
lwd.ticks = 1,
tck = -0.025) # add your tick marks
dev.off() # close plotting device
A few notes:
Sizing for this plot was originally set for a pdf, which unfortunately cannot be uploaded here, however that device call is included as commented out code above. You can always play with parameters to find out what works best for you.
It can be advantageous to plot all of your axis labels with mtext().
Including simple example data in your original post is often much more helpful than the exact data you're working with. As of me writing this, I don't really know what your data looks like because I don't have access to those objects.
I am having a 3D plot in which the points are colored acording to some extra vector. My problem is to add a color gradient legend. This is my code:
x = matrix(NA,100,6)
#x value
x[,1] = runif(100, 0, 10)
#y value
x[,2] = runif(100, 0, 10)
#z value
x[,3] = x[,1]+x[,2]
#additional value
x[,4] = runif(100, 0, 1)
#find out in which interval each additional value is
intervals = seq(0,1,1/10)
x[,5] = findInterval(x[,4], intervals)
colours = topo.colors(length(intervals))
x[,6] = colours[x[,5]]
library(rgl)
plot3d(as.numeric(x[,1]),as.numeric(x.stab.in[,2]), as.numeric(x[,3]),
type="p", col=x[,6], size=2, xlab = "x(t)", ylab = "y(t)",
zlab = "z(t)")
decorate3d(xlab = "x", ylab = "y", zlab = "z")
legend3d("topright", legend = intervals, pch = 16, col = colours, cex=1, inset=c(0.02))
grid3d(c("x", "y+", "z"),col = "gray")
The plot looks like this
but I want the legend in a gradient form. That means I don't want separate points for each color but one box in which the colors fade into each other.
Here is a possible solution if you are okay with using scatterplot3d package instead of rgl. It is basically same but non-interactive. Here is your code modified to produce your expected result.
x = matrix(NA,100,6)
#x value
x[,1] = runif(100, 0, 10)
#y value
x[,2] = runif(100, 0, 10)
#z value
x[,3] = x[,1]+x[,2]
#additional value
x[,4] = runif(100, 0, 1)
#find out in which interval each additional value is
intervals = seq(0,1,1/10)
x[,5] = findInterval(x[,4], intervals)
#produce gradient of colors
#you can define different colors (two or more)
gradient <- colorRampPalette(colors = c("yellow", "green", "blue"))
colours <- gradient(length(intervals))
x[,6] = colours[x[,5]]
library(scatterplot3d)
png('3d.png', width = 600, height = 400)
layout(matrix(1:2, ncol=2), width = c(3, 1), height = c(1, 1))
scatterplot3d(as.numeric(x[,1]),as.numeric(x[,2]), as.numeric(x[,3]), type = 'p',
cex.symbols = 1.25, color=x[,6], pch = 16, xlab = "x(t)", ylab = "y(t)", zlab = "z(t)")
plot(x = rep(1, 100), y = seq_along(x[,6]),
pch = 15, cex = 2.5,
col = gradient(length(x[,6])),
ann = F, axes = F, xlim = c(1, 2))
axis(side = 2, at = seq(1, nrow(x), length.out = 11),
labels = 1:11,
line = 0.15)
dev.off()
This will plot the following graph
Here is another solution if you want to plot a gradient on an interactive 3d plot, such as if you needed to animate the plot into a movie.
require(car)
require(rgl)
require(RColorBrewer)
require(mgcv)
require(magick) #Only for creating the animation of the plot as a gif
#Creating mock dataset
Example_Data <- data.frame(Axis1 = rnorm(100),
Axis2 = rnorm(100),
Axis3 = rnorm(100))
Example_Data$Value <- Example_Data$Axis1+Example_Data$Axis2
#Defining function that takes a vector of numeric values and converts them to
#a spectrum of rgb colors to help color my scatter3d plot
get_colors <- function(values){
v <- (values - min(values))/diff(range(values))
x <- colorRamp(rev(brewer.pal(11, "Spectral")))(v)
rgb(x[,1], x[,2], x[,3], maxColorValue = 255)
}
#Writing function that takes a vector of numeric values and a title and creates
#a gradient legend based on those values and the title and suitable for addition
#to a scatter3d plot via a call to bgplot3d()
#Note, I didn't have time to make this automatically adjust text position/size for different size
#plot windows, so values may need to be adjusted manually depending on the size of the plot window.
gradient_legend_3d <- function(values, title){
min_val <- min(values)
max_val <- max(values)
x <- colorRamp(brewer.pal(11, "Spectral"))((0:20)/20)
colors <- rgb(x[,1], x[,2], x[,3], maxColorValue = 255)
legend_image <- as.raster(matrix(colors, ncol=1))
plot(c(0,1),c(0,1),type = 'n', axes = F,xlab = '', ylab = '', main = '') #Generates a blank plot
text(x=0.92, y = seq(0.5, 1,l=5), labels = signif(seq(min_val, max_val,l=5), 2), cex = 1.5) #Creates the numeric labels on the scale
text(x = 0.85, y = 1, labels = title, adj = 1, srt = 90, cex = 1.5) #Determines where the title is placed
rasterImage(legend_image, 0.87, 0.5, 0.9,1) #Values can be modified here to alter where and how wide/tall the gradient is drawn in the plotting area
}
#Creating scatter3d plot
scatter3d(x = Example_Data$Axis1, y = Example_Data$Axis2, z = Example_Data$Axis3, xlab = "Axis1", ylab = "Axis2", zlab = "Axis3", surface = F, grid = F, ellipsoid = F, fogtype = "none", point.col = get_colors(Example_Data$Value))
#Changing size of plotting window and orientation to optimize for addition of static legend
#This may not work on another machine, so the window may need to be adjusted manually
par3d(windowRect = c(0,23,1536,824))
par3d(userMatrix = matrix(c(-0.98181450, -0.02413967, 0.18830180, 0, -0.03652956, 0.99736959, -0.06260729, 0, -0.18629514, -0.06834736, -0.98011345, 0, 0, 0, 0, 1), nrow = 4, ncol = 4, byrow = T))
#Adding legend
bgplot3d(gradient_legend_3d(Example_Data$Value, "Point Value"))
#Animating plot and saving as gif
movie3d(spin3d(axis = c(0,1,0), rpm = 5), duration = 12, dir = getwd(), fps = 5, convert = FALSE, clean = FALSE)
frames <- NULL
for(j in 0:60){
if(j == 1){
frames <- image_read(sprintf("%s%03d.png", "movie", j))
} else {
frames <- c(frames, image_read(sprintf("%s%03d.png", "movie", j)))
}
}
animation <- image_animate(frames, fps = 10, optimize = TRUE)
image_write(animation, path = "Example.gif")
for(j in 0:60){
unlink(sprintf("%s%03d.png", "movie", j))
}
See link to view 3d plot generated by this code:
gif of 3d plot with gradient color scale
I want to add labels to each single line in the plot below:
a <- 1:2000
b <- a - a[1]
plot(1, type = "n", xlab = "Scale parameter", ylab = "No. of days", xlim = c(0, 90), ylim = c(0, 150))
shape.range <- seq(from = 2, to = 10, by = 1)
scale.range <- seq(from = 10, to = 70, by = 1)
for(sh in seq_along(shape.range)){
sh.ref <- shape.range[sh]
for(sc in seq_along(scale.range)){
sc.ref <- scale.range[sc]
p <- 1 - exp(-(b/sc.ref)^sh.ref)
p.l <- which.max(p >= 0.97)
points(sc.ref, p.l, cex = 0.5, pch = 19)
# text(80, # how to insert the value of y here such that the label ends up at the end of the each line, labels = paste0(sh.ref))
}
}
Not everything needs to be ggplots. Base graphics is much easier to tweak sometimes.
Just add these lines after your code.
text(20,100,"Text left")
text(60,20,"Text right")
I'd like to do a vertical histogram. Ideally I should be able to put multiple on a single plot per day.
If this could be combined with quantmod experimental chart_Series or some other library capable of drawing bars for a time series that would be great. Please see the attached screenshot. Ideally I could plot something like this.
Is there anything built in or existing libraries that can help with this?
I wrote something a year or so ago to do vertical histograms in base graphics. Here it is, with a usage example.
VerticalHist <- function(x, xscale = NULL, xwidth, hist,
fillCol = "gray80", lineCol = "gray40") {
## x (required) is the x position to draw the histogram
## xscale (optional) is the "height" of the tallest bar (horizontally),
## it has sensible default behavior
## xwidth (required) is the horizontal spacing between histograms
## hist (required) is an object of type "histogram"
## (or a list / df with $breaks and $density)
## fillCol and lineCol... exactly what you think.
binWidth <- hist$breaks[2] - hist$breaks[1]
if (is.null(xscale)) xscale <- xwidth * 0.90 / max(hist$density)
n <- length(hist$density)
x.l <- rep(x, n)
x.r <- x.l + hist$density * xscale
y.b <- hist$breaks[1:n]
y.t <- hist$breaks[2:(n + 1)]
rect(xleft = x.l, ybottom = y.b, xright = x.r, ytop = y.t,
col = fillCol, border = lineCol)
}
## Usage example
require(plyr) ## Just needed for the round_any() in this example
n <- 1000
numberOfHists <- 4
data <- data.frame(ReleaseDOY = rnorm(n, 110, 20),
bin = as.factor(rep(c(1, 2, 3, 4), n / 4)))
binWidth <- 1
binStarts <- c(1, 2, 3, 4)
binMids <- binStarts + binWidth / 2
axisCol <- "gray80"
## Data handling
DOYrange <- range(data$ReleaseDOY)
DOYrange <- c(round_any(DOYrange[1], 15, floor),
round_any(DOYrange[2], 15, ceiling))
## Get the histogram obects
histList <- with(data, tapply(ReleaseDOY, bin, hist, plot = FALSE,
breaks = seq(DOYrange[1], DOYrange[2], by = 5)))
DOYmean <- with(data, tapply(ReleaseDOY, bin, mean))
## Plotting
par(mar = c(5, 5, 1, 1) + .1)
plot(c(0, 5), DOYrange, type = "n",
ann = FALSE, axes = FALSE, xaxs = "i", yaxs = "i")
axis(1, cex.axis = 1.2, col = axisCol)
mtext(side = 1, outer = F, line = 3, "Length at tagging (mm)",
cex = 1.2)
axis(2, cex.axis = 1.2, las = 1, line = -.7, col = "white",
at = c(75, 107, 138, 169),
labels = c("March", "April", "May", "June"), tck = 0)
mtext(side = 2, outer = F, line = 3.5, "Date tagged", cex = 1.2)
box(bty = "L", col = axisCol)
## Gridlines
abline(h = c(60, 92, 123, 154, 184), col = "gray80")
biggestDensity <- max(unlist(lapply(histList, function(h){max(h[[4]])})))
xscale <- binWidth * .9 / biggestDensity
## Plot the histograms
for (lengthBin in 1:numberOfHists) {
VerticalHist(binStarts[lengthBin], xscale = xscale,
xwidth = binWidth, histList[[lengthBin]])
}
Violin plots might be close enough to what you want. They are density plots that have been mirrored through one axis, like a hybrid of a boxplot and a density plot. (Much easier to understanding by example than description. :-) )
Here is a simple (somewhat ugly) example of the ggplot2 implementation of them:
library(ggplot2)
library(lubridate)
data(economics) #sample dataset
# calculate year to group by using lubridate's year function
economics$year<-year(economics$date)
# get a subset
subset<-economics[economics$year>2003&economics$year<2007,]
ggplot(subset,aes(x=date,y=unemploy))+
geom_line()+geom_violin(aes(group=year),alpha=0.5)
A prettier example would be:
ggplot(subset,aes(x=date,y=unemploy))+
geom_violin(aes(group=year,colour=year,fill=year),alpha=0.5,
kernel="rectangular")+ # passes to stat_density, makes violin rectangular
geom_line(size=1.5)+ # make the line (wider than normal)
xlab("Year")+ # label one axis
ylab("Unemployment")+ # label the other
theme_bw()+ # make white background on plot
theme(legend.position = "none") # suppress legend
To include ranges instead of or in addition to the line, you would use geom_linerange or geom_pointrange.
If you use grid graphics then you can create rotated viewports whereever you want them and plot to the rotated viewport. You just need a function that will plot using grid graphics into a specified viewport, I would suggest ggplot2 or possibly lattice for this.
In base graphics you could write your own function to plot the rotated histogram (modify the plot.histogram function or just write your own from scratch using rect or other tools). Then you can use the subplot function from the TeachingDemos package to place the plot wherever you want on a larger plot.