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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 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 trying to make an nMDS plot of data with a nested factor. I would like the nMDS to show both factors on one plot by using symbols and colour.
In this reproducible example, if use was nested in moisture, I would like the plot to show Moisture as different symbols, and then Use as different colours.
So far I have figured out this:
library("vegan")
library("BiodiversityR")
data(dune, dune.env)
MDS <- metaMDS(dune, distance="bray", strata=dune.env$Moisture)
MDS
plot(MDS$points[,2], MDS$points[,1], type="n", main="Communities by Use",
xlab="NMDS Axis 1", ylab="NMDS Axis 2", xlim=c(-1.5,1.5), ylim=c(-1.5,1.5))
ordisymbol(MDS, dune.env, factor="Use", cex=1.25, rainbow=T, legend=T)
Which gives me the different uses as both different symbols and colours, but shows me nothing about moisture. Is it possible to make it show the different factors instead? I'm assuming it might be somewhere in the MDS$points[,] arguments but I'm not sure what exactly those are doing.
Figured it out by modifying the answer from this question: Plot points of metaMDS
data(dune, dune.env)
dune.MDS <- metaMDS(dune, distance = "bray", strata=dune.env$Moisture)
dune.MDS
pchs<- c(0:5)
gr.moi <- factor(dune.env$Moisture)
gr.use <- factor(dune.env$Use)
col.gr <- c("red", "blue", "purple")
plot(dune.MDS, type = "n", display = "sites")
orditorp(dune.MDS,display="species",col="dark grey",air=0.01)
points(dune.MDS, display = "sites", pch = pchs[gr.moi], col = col.gr[gr.use])
legend("topright", legend=levels(gr.moi), bty = "n", col= c("black"), pch = pchs)
legend("bottomright", legend = levels(gr.use), bty = "n", col = col.gr, pch=c(20),)
And it will produce a lovely plot with symbols and colours exactly how I wanted :)
I have a dataset including 100 species and therefore it's very bad to plot. So I want to pick out a subset of these species and plot them in a RDA plot. I have been following this
guideline
The code looks like this:
## load vegan
require("vegan")
## load the Dune data
data(dune, dune.env)
## PCA of the Dune data
mod <- rda(dune, scale = TRUE)
## plot the PCA
plot(mod, scaling = 3)
## build the plot up via vegan methods
scl <- 3 ## scaling == 3
colvec <- c("red2", "green4", "mediumblue")
plot(mod, type = "n", scaling = scl)
with(dune.env, points(mod, display = "sites", col = colvec[Use],
scaling = scl, pch = 21, bg = colvec[Use]))
text(mod, display = "species", scaling = scl, cex = 0.8, col = "darkcyan")
with(dune.env, legend("topright", legend = levels(Use), bty = "n",
col = colvec, pch = 21, pt.bg = colvec))
This is the plot you end up with. Now i would really like to remove some of the species from the plot, but not the analysis. So the plot only shows like Salrep, Viclat, Aloge and Poatri.
Help is appreciated.
The functions you are doing the actual plotting with have an argument select (at least text.cca() and points.cca(). select takes either a logical vector of length i indicating whether the ith thing should be plotted, or the (numeric) indices of the things to plot. The example would then become:
## Load vegan
library("vegan")
## load the Dune data
data(dune, dune.env)
## PCA of the Dune data
mod <- rda(dune, scale = TRUE)
## plot the PCA
plot(mod, scaling = 3)
## build the plot up via vegan methods
scl <- 3 ## scaling == 3
colvec <- c("red2", "green4", "mediumblue")
## Show only these spp
sppwant <- c("Salirepe", "Vicilath", "Alopgeni", "Poatriv")
sel <- names(dune) %in% sppwant
## continue plotting
plot(mod, type = "n", scaling = scl)
with(dune.env, points(mod, display = "sites", col = colvec[Use],
scaling = scl, pch = 21, bg = colvec[Use]))
text(mod, display = "species", scaling = scl, cex = 0.8, col = "darkcyan",
select = sel)
with(dune.env, legend("topright", legend = levels(Use), bty = "n",
col = colvec, pch = 21, pt.bg = colvec))
Which gives you:
You may also use the ordiselect() function from the goeveg-package:
https://CRAN.R-project.org/package=goeveg
It offers selection of species for ordination plots based on abundances and/or species fit to axes.
## Select ssp. with filter: 50% most abundant and 50% best fitting
library(goeveg)
sel <- ordiselect(dune, mod, ablim = 0.5, fitlim = 0.5)
sel # 12 species selected
The result object of the function (containing the names of selected species) can be put into the select argument (as described above).
I am relatively new to R and I am trying to get my head around how to do ordination techniques in R, so that I don't need to use other software.
I am trying to get a PCA with environmental factors in the place of species.
As I have sites which differ qualitatively (in terms of land use) I wanted to be able to show that difference in the final plot (with different colours). Therefore, I used the method a la Gavin Simpson with the package vegan. So far so good. Here is also the code that I used for that:
with(fish, status)
scl <- -1 ## scaling = -1
colvec <- c("red2", "mediumblue")
plot(pond.pca, type = "n", scaling = scl)
with(fish, points(pond.pca, display = "sites", col = colvec[status], scaling = scl, pch = 21, bg = colvec[status]))
head(with(fish, colvec[status]))
text(pond.pca, display = "species", scaling = scl, cex = 0.8, col = "darkcyan")
with(fish, legend("topright", legend = levels(status), bty = "n", col = colvec, pch = 21, pt.bg = colvec))
The problem arises when I try to put arrows for my environmental variables in the ordination plot. If I use biplot and other functions like ordiplot etc. I ll not be able to keep the different colours for my two types of sites, therefore I don't want to use those. If I use the command here:
plot(envfit(pond.pca, PondEnv38, scaling=-1), add=TRUE, col="black")
I get nice arrows, only the are not aligned (and in some cases are completely opposite) with the environmental variables that I ve given with the code before (line 5). I tried to change the scaling but they just cannot align.
Does anyone know how to deal with that problem?
Any tips would be useful.
It is not clear what you are doing wrong as you don't provide a reproducible example of the problem and I am having difficulty following your description of what is wrong. Here is a fully worked out example for you to follow that does what you seem to being trying to do.
data(varespec)
data(varechem)
ord <- rda(varespec)
set.seed(1)
(fit <- envfit(ord, varechem, perm = 999))
## make up a fake `status`
status <- factor(rep(c("Class1","Class2"), times = nrow(varespec) / 2))
> head(status)
[1] Class1 Class2 Class1 Class2 Class1 Class2
Now plot
layout(matrix(1:2, ncol = 2))
## auto version
plot(fit, add = FALSE)
## manual version with extra things
colvec <- c("red","green")
scl <- -1
plot(ord, type = "n", scaling = scl)
points(ord, display = "sites", col = colvec[status], pch = (1:2)[status])
points(ord, display = "species", pch = "+")
plot(fit, add = TRUE, col = "black")
layout(1)
Which gives
And all the arrows seem to be pointing as they would if you plotted the envfit object directly.