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I made a Temperature-Salinity plot and need to scale the size of the points to a different variable. A temperature-salinity plot is a plot that takes the temperature and salinity and finds the density and plots it based on three values. In the example picture, the curved lines represent density.
Here is what a row of my data looks like:
Temperature
Salinity
pCO2
23.253
36.929
352.7
Heres my code of my plot (this code basically makes an empty graph and you have to manually make each point an object and apply it to the graph):
#FORMING GRAPH FRAME:
library(shape)
library(marelac)
library(plot3D)
S.seq <- seq(from = 20, to = 40, length.out = 100)
t.seq <- seq(from = 20, to = 40, length.out = 100)
sig.mat <- outer(S.seq, t.seq, FUN = function(S, t) sw_dens(S = S, t = t) - 1000)
#GRAPH FRAME:
contour2D(x = S.seq, y = t.seq, z = sig.mat, lwd = 3,
xlab = 'Salinity', ylab = 'Temperature (°C)',main = 'Surface T-S')
Here is an example of making the objects for the graph:
t2s = 23.253
S2s = 36.929
And then apply those objects to the graph:
scatter2D(S2s, t2s, pch=20, col ='darkgreen', cex= 1, add= TRUE,
clim = range(sig.mat), colkey = FALSE)
Here is an example of a final product with all the points on it:
plot
But I need the points to be scaled to the pCO2 value.
Or if you know an easier way to do this type of plot, any feedback is helpful.
I am trying to visualize the trajectory of multiple participants in a virtual room using R. I have a participant entering from the right (black square) and moving toward the left, where there is an exit door (red square). Sometimes there is an obstacle right in the middle of the room (circle), and the participant goes around it.
To visualize multiple participants’ trajectories on the same graph (i.e., multiple lines), I have used the function plot to set up the plot itself (and the first line) and then I have used the function lines to add other trajectories after that.
Below you can see an example with two lines; in the experiment, I have many more (as now I have collected data from about 20 participants.)
library(shape)
# black line
pos_x <- c(5.04,4.68,4.39,4.09,3.73,3.37,3.07,2.77,2.47,2.11)
pos_z <- c(0.74,0.69,0.64,0.60,0.56,0.52,0.50,0.50,0.50,0.51)
df1 <- cbind.data.frame(pos_x,pos_z)
x.2 <- df1$pos_x
z.2 <- df1$pos_z
plot(x.2,z.2,type="l", xlim=range(x.2), ylim=c(-1,3.5), xlab="x", ylab="z", main = "Two trajectories")
filledrectangle(wx = 0.2, wy = 0.2,col = "black", mid = c(5.16, 1), angle = 0)
filledrectangle(wx = 0.2, wy = 0.2,col = "red", mid = c(2, 1), angle = 0)
plotcircle(mid = c(3.4, 1), r = 0.05)
# red line
pos_x <- c(5.14,4.84,4.24,3.64,3.34,2.74,2.15)
pos_z <- c(0.17,0.13,0.01,-0.2,0.01,0.10,0.17)
df2 <- cbind.data.frame(pos_x,pos_z)
x.3 <- df2$pos_x
z.3 <- df2$pos_z
lines(x.3, z.3, xlim=range(x.3), ylim=c(-1,3.5), pch=16, col="red")
What I would like to do now is to find the average between these two lines. Ideally, I would like to be able to average multiple lines and add an interval for the standard deviation.
The first thing I have tried is to build an interpolation; the problem is that the start and end point are different, so I cannot average the points:
plot(x.2, z.2, xlim=range(x.2), ylim=c(-1,3.5), xlab="x", ylab="z", main = "Interpolation")
points(approx(x.2, z.2), col = 2, pch = "*")
points(x.3, z.3)
points(approx(x.3, z.3), col = 2, pch = "*")
I have then found a suggestion here: use the R library dtw.
I have looked up the library and the companion paper.
This is a typical example from the paper, in which "two non-overlapping windows" are extracted from a reference electrocardiogram. The dataset "aami3a" is a time series object.
library("dtw")
data("aami3a")
ref <- window(aami3a, start = 0, end = 2)
test <- window(aami3a, start = 2.7, end = 5)
alignment <- dtw(test, ref)
alignment$distance
The problem is that in all these examples the data is either structured as a time series object or the two lines are functions of a common matrix (see also the R quickstart example in the documentation and this other tutorial.)
How can I reorganize my data to make the function work? Or do you know of any other way to create an average?
You could map equivalent points from the start to the end of each path (i.e. find the midpoint between the two lines at the start of each path, the midpoint between the two lines after a quarter of each path is complete, after a half, at the end, etc.
The way to do that is to use interpolation (via approx):
pos_x_a <- c(5.04,4.68,4.39,4.09,3.73,3.37,3.07,2.77,2.47,2.11)
pos_z_a <- c(0.74,0.69,0.64,0.60,0.56,0.52,0.50,0.50,0.50,0.51)
pos_x_b <- c(5.14,4.84,4.24,3.64,3.34,2.74,2.15)
pos_z_b <- c(0.17,0.13,0.01,-0.2,0.01,0.10,0.17)
pos_t_a <- seq(0, 1, length.out = length(pos_x_a))
pos_t_b <- seq(0, 1, length.out = length(pos_x_b))
a_x <- approx(pos_t_a, pos_x_a, seq(0, 1, 0.01))$y
a_y <- approx(pos_t_a, pos_z_a, seq(0, 1, 0.01))$y
b_x <- approx(pos_t_b, pos_x_b, seq(0, 1, 0.01))$y
b_y <- approx(pos_t_b, pos_z_b, seq(0, 1, 0.01))$y
plot(a_x, a_y, type = "l", ylim = c(-1, 3))
lines(b_x, b_y, col = "red")
lines((a_x + b_x)/2, (a_y + b_y)/2, col = "blue", lty = 2)
We get a better idea of how this averaging has occurred by joining the points on each line that were used to get the average:
for(i in seq_along(a_x)) segments(a_x[i], a_y[i], b_x[i], b_y[i], col = "gray")
I am using R and I have two data frames: carrots and cucumbers. Each data frame has a single numeric column that lists the length of all measured carrots (total: 100k carrots) and cucumbers (total: 50k cucumbers).
I wish to plot two histograms - carrot length and cucumbers lengths - on the same plot. They overlap, so I guess I also need some transparency. I also need to use relative frequencies not absolute numbers since the number of instances in each group is different.
Something like this would be nice but I don't understand how to create it from my two tables:
Here is an even simpler solution using base graphics and alpha-blending (which does not work on all graphics devices):
set.seed(42)
p1 <- hist(rnorm(500,4)) # centered at 4
p2 <- hist(rnorm(500,6)) # centered at 6
plot( p1, col=rgb(0,0,1,1/4), xlim=c(0,10)) # first histogram
plot( p2, col=rgb(1,0,0,1/4), xlim=c(0,10), add=T) # second
The key is that the colours are semi-transparent.
Edit, more than two years later: As this just got an upvote, I figure I may as well add a visual of what the code produces as alpha-blending is so darn useful:
That image you linked to was for density curves, not histograms.
If you've been reading on ggplot then maybe the only thing you're missing is combining your two data frames into one long one.
So, let's start with something like what you have, two separate sets of data and combine them.
carrots <- data.frame(length = rnorm(100000, 6, 2))
cukes <- data.frame(length = rnorm(50000, 7, 2.5))
# Now, combine your two dataframes into one.
# First make a new column in each that will be
# a variable to identify where they came from later.
carrots$veg <- 'carrot'
cukes$veg <- 'cuke'
# and combine into your new data frame vegLengths
vegLengths <- rbind(carrots, cukes)
After that, which is unnecessary if your data is in long format already, you only need one line to make your plot.
ggplot(vegLengths, aes(length, fill = veg)) + geom_density(alpha = 0.2)
Now, if you really did want histograms the following will work. Note that you must change position from the default "stack" argument. You might miss that if you don't really have an idea of what your data should look like. A higher alpha looks better there. Also note that I made it density histograms. It's easy to remove the y = ..density.. to get it back to counts.
ggplot(vegLengths, aes(length, fill = veg)) +
geom_histogram(alpha = 0.5, aes(y = ..density..), position = 'identity')
On additional thing, I commented on Dirk's question that all of the arguments could simply be in the hist command. I was asked how that could be done. What follows produces exactly Dirk's figure.
set.seed(42)
hist(rnorm(500,4), col=rgb(0,0,1,1/4), xlim=c(0,10))
hist(rnorm(500,6), col=rgb(1,0,0,1/4), xlim=c(0,10), add = TRUE)
Here's a function I wrote that uses pseudo-transparency to represent overlapping histograms
plotOverlappingHist <- function(a, b, colors=c("white","gray20","gray50"),
breaks=NULL, xlim=NULL, ylim=NULL){
ahist=NULL
bhist=NULL
if(!(is.null(breaks))){
ahist=hist(a,breaks=breaks,plot=F)
bhist=hist(b,breaks=breaks,plot=F)
} else {
ahist=hist(a,plot=F)
bhist=hist(b,plot=F)
dist = ahist$breaks[2]-ahist$breaks[1]
breaks = seq(min(ahist$breaks,bhist$breaks),max(ahist$breaks,bhist$breaks),dist)
ahist=hist(a,breaks=breaks,plot=F)
bhist=hist(b,breaks=breaks,plot=F)
}
if(is.null(xlim)){
xlim = c(min(ahist$breaks,bhist$breaks),max(ahist$breaks,bhist$breaks))
}
if(is.null(ylim)){
ylim = c(0,max(ahist$counts,bhist$counts))
}
overlap = ahist
for(i in 1:length(overlap$counts)){
if(ahist$counts[i] > 0 & bhist$counts[i] > 0){
overlap$counts[i] = min(ahist$counts[i],bhist$counts[i])
} else {
overlap$counts[i] = 0
}
}
plot(ahist, xlim=xlim, ylim=ylim, col=colors[1])
plot(bhist, xlim=xlim, ylim=ylim, col=colors[2], add=T)
plot(overlap, xlim=xlim, ylim=ylim, col=colors[3], add=T)
}
Here's another way to do it using R's support for transparent colors
a=rnorm(1000, 3, 1)
b=rnorm(1000, 6, 1)
hist(a, xlim=c(0,10), col="red")
hist(b, add=T, col=rgb(0, 1, 0, 0.5) )
The results end up looking something like this:
Already beautiful answers are there, but I thought of adding this. Looks good to me.
(Copied random numbers from #Dirk). library(scales) is needed`
set.seed(42)
hist(rnorm(500,4),xlim=c(0,10),col='skyblue',border=F)
hist(rnorm(500,6),add=T,col=scales::alpha('red',.5),border=F)
The result is...
Update: This overlapping function may also be useful to some.
hist0 <- function(...,col='skyblue',border=T) hist(...,col=col,border=border)
I feel result from hist0 is prettier to look than hist
hist2 <- function(var1, var2,name1='',name2='',
breaks = min(max(length(var1), length(var2)),20),
main0 = "", alpha0 = 0.5,grey=0,border=F,...) {
library(scales)
colh <- c(rgb(0, 1, 0, alpha0), rgb(1, 0, 0, alpha0))
if(grey) colh <- c(alpha(grey(0.1,alpha0)), alpha(grey(0.9,alpha0)))
max0 = max(var1, var2)
min0 = min(var1, var2)
den1_max <- hist(var1, breaks = breaks, plot = F)$density %>% max
den2_max <- hist(var2, breaks = breaks, plot = F)$density %>% max
den_max <- max(den2_max, den1_max)*1.2
var1 %>% hist0(xlim = c(min0 , max0) , breaks = breaks,
freq = F, col = colh[1], ylim = c(0, den_max), main = main0,border=border,...)
var2 %>% hist0(xlim = c(min0 , max0), breaks = breaks,
freq = F, col = colh[2], ylim = c(0, den_max), add = T,border=border,...)
legend(min0,den_max, legend = c(
ifelse(nchar(name1)==0,substitute(var1) %>% deparse,name1),
ifelse(nchar(name2)==0,substitute(var2) %>% deparse,name2),
"Overlap"), fill = c('white','white', colh[1]), bty = "n", cex=1,ncol=3)
legend(min0,den_max, legend = c(
ifelse(nchar(name1)==0,substitute(var1) %>% deparse,name1),
ifelse(nchar(name2)==0,substitute(var2) %>% deparse,name2),
"Overlap"), fill = c(colh, colh[2]), bty = "n", cex=1,ncol=3) }
The result of
par(mar=c(3, 4, 3, 2) + 0.1)
set.seed(100)
hist2(rnorm(10000,2),rnorm(10000,3),breaks = 50)
is
Here is an example of how you can do it in "classic" R graphics:
## generate some random data
carrotLengths <- rnorm(1000,15,5)
cucumberLengths <- rnorm(200,20,7)
## calculate the histograms - don't plot yet
histCarrot <- hist(carrotLengths,plot = FALSE)
histCucumber <- hist(cucumberLengths,plot = FALSE)
## calculate the range of the graph
xlim <- range(histCucumber$breaks,histCarrot$breaks)
ylim <- range(0,histCucumber$density,
histCarrot$density)
## plot the first graph
plot(histCarrot,xlim = xlim, ylim = ylim,
col = rgb(1,0,0,0.4),xlab = 'Lengths',
freq = FALSE, ## relative, not absolute frequency
main = 'Distribution of carrots and cucumbers')
## plot the second graph on top of this
opar <- par(new = FALSE)
plot(histCucumber,xlim = xlim, ylim = ylim,
xaxt = 'n', yaxt = 'n', ## don't add axes
col = rgb(0,0,1,0.4), add = TRUE,
freq = FALSE) ## relative, not absolute frequency
## add a legend in the corner
legend('topleft',c('Carrots','Cucumbers'),
fill = rgb(1:0,0,0:1,0.4), bty = 'n',
border = NA)
par(opar)
The only issue with this is that it looks much better if the histogram breaks are aligned, which may have to be done manually (in the arguments passed to hist).
Here's the version like the ggplot2 one I gave only in base R. I copied some from #nullglob.
generate the data
carrots <- rnorm(100000,5,2)
cukes <- rnorm(50000,7,2.5)
You don't need to put it into a data frame like with ggplot2. The drawback of this method is that you have to write out a lot more of the details of the plot. The advantage is that you have control over more details of the plot.
## calculate the density - don't plot yet
densCarrot <- density(carrots)
densCuke <- density(cukes)
## calculate the range of the graph
xlim <- range(densCuke$x,densCarrot$x)
ylim <- range(0,densCuke$y, densCarrot$y)
#pick the colours
carrotCol <- rgb(1,0,0,0.2)
cukeCol <- rgb(0,0,1,0.2)
## plot the carrots and set up most of the plot parameters
plot(densCarrot, xlim = xlim, ylim = ylim, xlab = 'Lengths',
main = 'Distribution of carrots and cucumbers',
panel.first = grid())
#put our density plots in
polygon(densCarrot, density = -1, col = carrotCol)
polygon(densCuke, density = -1, col = cukeCol)
## add a legend in the corner
legend('topleft',c('Carrots','Cucumbers'),
fill = c(carrotCol, cukeCol), bty = 'n',
border = NA)
#Dirk Eddelbuettel: The basic idea is excellent but the code as shown can be improved. [Takes long to explain, hence a separate answer and not a comment.]
The hist() function by default draws plots, so you need to add the plot=FALSE option. Moreover, it is clearer to establish the plot area by a plot(0,0,type="n",...) call in which you can add the axis labels, plot title etc. Finally, I would like to mention that one could also use shading to distinguish between the two histograms. Here is the code:
set.seed(42)
p1 <- hist(rnorm(500,4),plot=FALSE)
p2 <- hist(rnorm(500,6),plot=FALSE)
plot(0,0,type="n",xlim=c(0,10),ylim=c(0,100),xlab="x",ylab="freq",main="Two histograms")
plot(p1,col="green",density=10,angle=135,add=TRUE)
plot(p2,col="blue",density=10,angle=45,add=TRUE)
And here is the result (a bit too wide because of RStudio :-) ):
Plotly's R API might be useful for you. The graph below is here.
library(plotly)
#add username and key
p <- plotly(username="Username", key="API_KEY")
#generate data
x0 = rnorm(500)
x1 = rnorm(500)+1
#arrange your graph
data0 = list(x=x0,
name = "Carrots",
type='histogramx',
opacity = 0.8)
data1 = list(x=x1,
name = "Cukes",
type='histogramx',
opacity = 0.8)
#specify type as 'overlay'
layout <- list(barmode='overlay',
plot_bgcolor = 'rgba(249,249,251,.85)')
#format response, and use 'browseURL' to open graph tab in your browser.
response = p$plotly(data0, data1, kwargs=list(layout=layout))
url = response$url
filename = response$filename
browseURL(response$url)
Full disclosure: I'm on the team.
So many great answers but since I've just written a function (plotMultipleHistograms() in 'basicPlotteR' package) function to do this, I thought I would add another answer.
The advantage of this function is that it automatically sets appropriate X and Y axis limits and defines a common set of bins that it uses across all the distributions.
Here's how to use it:
# Install the plotteR package
install.packages("devtools")
devtools::install_github("JosephCrispell/basicPlotteR")
library(basicPlotteR)
# Set the seed
set.seed(254534)
# Create random samples from a normal distribution
distributions <- list(rnorm(500, mean=5, sd=0.5),
rnorm(500, mean=8, sd=5),
rnorm(500, mean=20, sd=2))
# Plot overlapping histograms
plotMultipleHistograms(distributions, nBins=20,
colours=c(rgb(1,0,0, 0.5), rgb(0,0,1, 0.5), rgb(0,1,0, 0.5)),
las=1, main="Samples from normal distribution", xlab="Value")
The plotMultipleHistograms() function can take any number of distributions, and all the general plotting parameters should work with it (for example: las, main, etc.).
I am working on a bit of code that creates a plot consisting of multiple individual graphs, one per ID, showing longitudinal data. For visual purposes, I am limiting the number of graphs to 20 per plot using par, but there are more than 20 IDs in the dataset, and therefore I need multiple plots.
Current problem: how to avoid overwriting an earlier plot with a new plot once the code moves beyond the 20th (or N*20th) ID. I think I need to use plot.new(), but not clear how to work this in, and could not find previous post that exactly addressed this.
My code:
# Create sample data by sampling
Start <- as.Date("2012-01-01")
End <- as.Date("2013-01-01")
data1 <- data.frame(ID = sort(rep(seq(64),3)), VisitDate = sort((Start + sample.int(End-Start, 192))), Count = rnorm(192, mean = 300, sd = 12), Treat = sample(0:1, 192, replace = TRUE))
# calculate days elapsed since start date, by ID
data1$VisitDate <- with(data1,as.Date(VisitDate,format="%y-%b-%d"))
data1$Days <- unlist(with(data1,tapply(VisitDate,ID,function(x){x-x[1]})))
#Define plot function
plot_one <- function(d){
with(d, plot(Days, Count, t="n", tck=1, main=unique(d$ID), cex.main = 0.8, ylab = "", yaxt = 'n', xlab = "", xaxt="n", xlim=c(0,8), ylim=c(0,500))) # set limits
grid(lwd = 0.3, lty = 7)
with(d[d$Treat == 0,], points(Days, Count, col = 1))
with(d[d$Treat == 1,], points(Days, Count, col = 2))
}
#Create multiple plot figure
par(mfrow=c(4,5), oma = c(0.5,0.5,0.5,0.5), mar = c(0.5,0.5,0.5,0.5))
plyr::d_ply(data1, "ID", plot_one)
If you are using windows, you call windows(). If you are using a Mac, you call quartz(). These will open a new device so that your next call to (e.g.) plot() will not overwrite your existing plots.
I would like to overlay 2 density plots on the same device with R. How can I do that? I searched the web but I didn't find any obvious solution.
My idea would be to read data from a text file (columns) and then use
plot(density(MyData$Column1))
plot(density(MyData$Column2), add=T)
Or something in this spirit.
use lines for the second one:
plot(density(MyData$Column1))
lines(density(MyData$Column2))
make sure the limits of the first plot are suitable, though.
ggplot2 is another graphics package that handles things like the range issue Gavin mentions in a pretty slick way. It also handles auto generating appropriate legends and just generally has a more polished feel in my opinion out of the box with less manual manipulation.
library(ggplot2)
#Sample data
dat <- data.frame(dens = c(rnorm(100), rnorm(100, 10, 5))
, lines = rep(c("a", "b"), each = 100))
#Plot.
ggplot(dat, aes(x = dens, fill = lines)) + geom_density(alpha = 0.5)
Adding base graphics version that takes care of y-axis limits, add colors and works for any number of columns:
If we have a data set:
myData <- data.frame(std.nromal=rnorm(1000, m=0, sd=1),
wide.normal=rnorm(1000, m=0, sd=2),
exponent=rexp(1000, rate=1),
uniform=runif(1000, min=-3, max=3)
)
Then to plot the densities:
dens <- apply(myData, 2, density)
plot(NA, xlim=range(sapply(dens, "[", "x")), ylim=range(sapply(dens, "[", "y")))
mapply(lines, dens, col=1:length(dens))
legend("topright", legend=names(dens), fill=1:length(dens))
Which gives:
Just to provide a complete set, here's a version of Chase's answer using lattice:
dat <- data.frame(dens = c(rnorm(100), rnorm(100, 10, 5))
, lines = rep(c("a", "b"), each = 100))
densityplot(~dens,data=dat,groups = lines,
plot.points = FALSE, ref = TRUE,
auto.key = list(space = "right"))
which produces a plot like this:
That's how I do it in base (it's actually mentionned in the first answer comments but I'll show the full code here, including legend as I can not comment yet...)
First you need to get the info on the max values for the y axis from the density plots. So you need to actually compute the densities separately first
dta_A <- density(VarA, na.rm = TRUE)
dta_B <- density(VarB, na.rm = TRUE)
Then plot them according to the first answer and define min and max values for the y axis that you just got. (I set the min value to 0)
plot(dta_A, col = "blue", main = "2 densities on one plot"),
ylim = c(0, max(dta_A$y,dta_B$y)))
lines(dta_B, col = "red")
Then add a legend to the top right corner
legend("topright", c("VarA","VarB"), lty = c(1,1), col = c("blue","red"))
I took the above lattice example and made a nifty function. There is probably a better way to do this with reshape via melt/cast. (Comment or edit if you see an improvement.)
multi.density.plot=function(data,main=paste(names(data),collapse = ' vs '),...){
##combines multiple density plots together when given a list
df=data.frame();
for(n in names(data)){
idf=data.frame(x=data[[n]],label=rep(n,length(data[[n]])))
df=rbind(df,idf)
}
densityplot(~x,data=df,groups = label,plot.points = F, ref = T, auto.key = list(space = "right"),main=main,...)
}
Example usage:
multi.density.plot(list(BN1=bn1$V1,BN2=bn2$V1),main='BN1 vs BN2')
multi.density.plot(list(BN1=bn1$V1,BN2=bn2$V1))
You can use the ggjoy package. Let's say that we have three different beta distributions such as:
set.seed(5)
b1<-data.frame(Variant= "Variant 1", Values = rbeta(1000, 101, 1001))
b2<-data.frame(Variant= "Variant 2", Values = rbeta(1000, 111, 1011))
b3<-data.frame(Variant= "Variant 3", Values = rbeta(1000, 11, 101))
df<-rbind(b1,b2,b3)
You can get the three different distributions as follows:
library(tidyverse)
library(ggjoy)
ggplot(df, aes(x=Values, y=Variant))+
geom_joy(scale = 2, alpha=0.5) +
scale_y_discrete(expand=c(0.01, 0)) +
scale_x_continuous(expand=c(0.01, 0)) +
theme_joy()
Whenever there are issues of mismatched axis limits, the right tool in base graphics is to use matplot. The key is to leverage the from and to arguments to density.default. It's a bit hackish, but fairly straightforward to roll yourself:
set.seed(102349)
x1 = rnorm(1000, mean = 5, sd = 3)
x2 = rnorm(5000, mean = 2, sd = 8)
xrng = range(x1, x2)
#force the x values at which density is
# evaluated to be the same between 'density'
# calls by specifying 'from' and 'to'
# (and possibly 'n', if you'd like)
kde1 = density(x1, from = xrng[1L], to = xrng[2L])
kde2 = density(x2, from = xrng[1L], to = xrng[2L])
matplot(kde1$x, cbind(kde1$y, kde2$y))
Add bells and whistles as desired (matplot accepts all the standard plot/par arguments, e.g. lty, type, col, lwd, ...).