I'm still relatively inexperienced manipulating plots in R, and am in need of assistance. I ran a redundancy analysis in R using the rda() function, but now I need to simplify the figure to exclude unnecessary information. The code I'm currently using is:
abio1516<-read.csv("1516 descriptors.csv")
attach(abio1516)
bio1516<-read.csv("1516habund.csv")
attach(bio1516)
rda1516<-rda(bio1516[,2:18],abio1516[,2:6])
anova(rda1516)
RsquareAdj(rda1516)
summary(rda1516)
varpart(bio1516[,2:18],~Distance_to_source,~Depth, ~Veg._cover, ~Surface_area,data=abio1516)
plot(rda1516,bty="n",xaxt="n",yaxt="n",main="1516; P=, R^2=",
ylab="Driven by , Var explained=",xlab="Driven by , Var explained=")
The produced plot looks like this:
Please help me modify my code to: exclude the sites (sit#), all axes, and the internal dashed lines.
I'd also like to either expand the size of the field, or move the vector labels to all fit in the plotting field.
updated as per responses, working code below this point
plot(rda,bty="n",xaxt="n",yaxt="n",type="n",main="xxx",ylab="xxx",xlab="xxx
Overall best:xxx")
abline(h=0,v=0,col="white",lwd=3)
points(rda,display="species",col="blue")
points(rda,display="cn",col="black")
text(rda,display="cn",col="black")
Start by plotting the rda with type = "n" which generates an empty plot to which you can add the things you want. The dotted lines are hard coded into the plot.cca function, so you need either make your own version, or use abline to hide them (then use box to cover up the holes in the axes).
require(vegan)
data(dune, dune.env)
rda1516 <- rda(dune~., data = dune.env)
plot(rda1516, type = "n")
abline(h = 0, v = 0, col = "white", lwd = 3)
box()
points(rda1516, display = "species")
points(rda1516, display = "cn", col = "blue")
text(rda1516, display = "cn", col = "blue")
If the text labels are not in the correct position, you can use the argument pos to move them (make a vector as long as the number of arrows you have with the integers 1 - 4 to move the label down, left, up, or right. (there might be better solutions to this)
Related
I have a list of plots that I have assigned names to, and then converted to plot titles as suggested by https://stackoverflow.com/a/14790376/9335733. The titles happen to appear over the top x-axis title and so I attempt to move them as suggested here: https://stackoverflow.com/a/44618277/9335733. The overall code looks as follows:
lapply(names(Cast.files), function (x) plot(Cast.files[[x]],
main = x,
adj = 0, #adjust title to the farthest left
line =2.5 #adjust title up 2.5
)
)
It should be noted that plot is now converted from base R to the oce package for analyzing oceanographic data, but calls the same arguments from base R plot.
The problem becomes that in trying to move the title, the axis labels move as well and overlap. Any suggestions?
Edit: Here is what the image looks like before:
And after:
You might also want to look into the oma= argument in par(), which provides an "outer" margin which can be used to put a nice title. Something like:
library(oce)
data(ctd)
par(oma=c(0, 0, 1, 0))
plot(ctd)
title('Title', outer=TRUE)
This was solved by adding a title argument outside of the plot function as follows:
lapply(names(Cast.files), function (x) plot(Cast.files[[x]],
which = c("temperature", "salinity", "sigmaT","conductivity"),
Tlim = c(11,12),
Slim = c(29,32),
col = "red")
+ title(main = x, adj = 0.48, line = 3.5)#adding the titles at a specific location
)
This allowed for plots that looked like:
If you use the title function, rather than setting main within plot, it would allow you to change the line without affecting anything else in the plot.
I'm new to R and want to plot specific points over an existing plot. I'm using the swiss data frame, which I visualize through the plot(swiss) function.
After this, want to add outliers given by the Mahalanobis distance:
mu_hat <- apply(swiss, 2, mean); sigma_hat <- cov(swiss)
mahalanobis_distance <- mahalanobis(swiss, mu_hat, sigma_hat)
outliers <- swiss[names(mahalanobis_distance[mahalanobis_distance > 10]),]
points(outliers, pch = 'x', col = 'red')
but this last line has no effect, as the outlier points aren't added to the previous plot. I see that if repeat this procedure on a pair of variables, say
plot(swiss[2:3])
points(outliers[2:3], pch = 'x', col = 'red')
the red points are added to the plot.
Ask: is there any restriction to how the points() function can be used for a multivariate data frame?
Here's a solution using GGally::ggpairs. It's a little ugly as we need to modify the ggally_points function to specify the desired color scheme.
I've assumed that mu_hat = colMeans(swiss) and sigma_hat = cov(swiss).
library(dplyr)
library(GGally)
swiss %>%
bind_cols(distance = mahalanobis(swiss, colMeans(swiss), cov(swiss))) %>%
mutate(is_outlier = ifelse(distance > 10, "yes", "no")) %>%
ggpairs(columns = 1:6,
mapping = aes(color = is_outlier),
upper = list(continuous = function(data, mapping, ...) {
ggally_points(data = data, mapping = mapping) +
scale_colour_manual(values = c("black", "red"))
}),
lower = list(continuous = function(data, mapping, ...) {
ggally_points(data = data, mapping = mapping) +
scale_colour_manual(values = c("black", "red"))
}),
axisLabels = "internal")
Unfortunately this isn't possible the way you're currently doing things. When plotting a data frame R produces many plots and aligns them. What you're actually seeing there is 6 by 6 = 36 individual plots which have all been aligned to look nice.
When you use the dots command, it tells it to place the dots on the current plot. Which doesn't really make sense when you have 36 plots, at least not the way you want it to.
ggplot is a really powerful tool in R, it provides far greater combustibility. For example you could set up the dataframe to include your outliers, but have them labelled as "outlier" and place it in each plot that you have set up as facets. The more you explore it you might find there are better plots which suit your needs as well.
Plotting a dataframe in base R is a good exploratory tool. You could set up those outliers as a separate dataframe and plot it, so you can see each of the 6 by 6 plots side by side and compare. It all depends on your goal. If you're goal is to produce exactly as you've described, the ggplot2 package will help you create something more professional. As #Gregor suggested in the comments, looking up the function ggpairs from the GGally package would be a good place to start.
A quick google image search shows some funky plots akin to what you're after and then some!
Find it here
I am plotting a SpatialPoint dataframe in R using spplot, and I would like to use a colorbar rather than a legend, to portray color values. (It's more efficient, and I want the map to "match" previous, raster data maps.) I'm sure this is possible, but can find no examples of it online. Could anyone give me a hint?
My current code is:
my.palette <- brewer.pal(n = 9, name = "Spectral")
my.palette<- rev(my.palette)
pols1 <- list("sp.lines", as(ugborder, 'SpatialLines'), col = gray(0.4), lwd = 1)
pols2 <- list("sp.polygons", as(water_ug, 'SpatialPolygons'), fill = 'skyblue1',col="transparent", first = FALSE)
spplot(ughouseszn,zcol="lzn_sg_clng",cex = .75,
key.space="right", digits=1,
par.settings = list(axis.line = list(col = 'transparent')),
xlim = bbox(ugborder)[1, ],ylim = bbox(ugborder)[2, ],
col.regions = my.palette, cuts=8,
sp.layout=list(pols1, pols2))
Where ugborder and water_ug give Uganda's borders and water, ughouseszn is a SpatialPointsDataframe, and the resulting map is here:
(As a side note, I'm hoping that adding a colorbar will lead to a more efficient use of space -- right now there's a lot of extra space at the top and bottom of Uganda's border, which is useless, and also does NOT appear when I map raster data using spplot, with the same pols1 and pols2.)
If "lzn_sg_clng" is converted to a factor, does this give you the map you desire? (rendering by the category rather than a graduated scale).
I´m recently trying to analyse my data and want to make the graphs a little nicer but I´m failing at this.
So I have a data set with 144 sites and 5 environmental variables. It´s basically about the substrate composition around an island and the fish abundance. On this island there is supposed to be a difference in the substrate composition between the north and the southside. Right now I am doing a pca and with the biplot function it works quite fine, but I would like to change the plot a bit.
I need one where the sites are just points and not numbered, arrows point to the different variable and the sites are colored according to their location (north or southside). So I tried everything i could find.
Most examples where with the dune data and suggested something like this:
library(vegan)
library(biplot)
data(dune)
mod <- rda(dune, scale = TRUE)
biplot(mod, scaling = 3, type = c("text", "points"))
So according to this I would just need to say text and points and R would label the variables and just make points for the sites. When i do this, however I get the Error:
Error in plot.default(x, type = "n", xlim = xlim, ylim = ylim, col = col[1L], :
formal argument "type" matched by multiple actual arguments
No idea how to get around this.
So next strategy I found, is to make a plot manually like this:
require("vegan")
data(dune, dune.env)
mod <- rda(dune, scale = TRUE)
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("bottomright", legend = levels(Use), bty = "n",
col = colvec, pch = 21, pt.bg = colvec))
This works fine so far as well, I get different colors and points, but now the arrows are missing. So I found that this should be corrected easy, if i just put "display="bp"" in the text line. But this doesn´t work either. Everytime I put "bp" R says:
Error in match.arg(display) :
argument "display" is missing, with no default
So I´m kind of desperate now. I looked through all the answers here and I don´t understand why display="bp" and type=c("text","points") is not working for me.
If anyone has an idea i would be super grateful.
https://www.dropbox.com/sh/y8xzq0bs6mus727/AADmasrXxUp6JTTHN5Gr9eufa?dl=0
This is the link to my dropbox folder. It contains my R-script and the csv files. The one named environmentalvariables_Kon1 also contains the data about north and southside.
So yeah...if anyone could help me. That would be awesome. I really don´t know what to do anymore.
Best regards,
Nancy
You can add arrows with arrows(). See the code for vegan:::biplot.rda to see how it works in the original function.
With your plot, add
g <- scores(mod, display = "species")
len <- 1
arrows(0, 0, len * g[, 1], len * g[, 2], length = 0.05, col = "darkcyan")
You might want to adjust the value of len to make the arrows longer
When I use barchart() and I get something like this (I know the image is not a bar chart but my auto.key produces the same legend):
I would like to fill the points and make them larger or set them to rectangles with the corresponding color.
When I use densityplot() and I get something like this:
I would like to make the lines "thicker" if possible.
See ?xyplot. Some details:
For your first question about changing colors use col argument, e.g.
barplot(table(mtcars$am, mtcars$gear), col = c("green", "yellow"))
But if you want to deal with a scatterplot instead of barplot (confused here) with modified symbols, then auto.key is not on option unfortunately, but something like this would work without problems:
xyplot(mtcars$hp ~ mtcars$wt, groups = mtcars$gear,
key = list(text = list(as.character(unique(mtcars$gear))),
points = list(pch = 10:12, col = 12:14)), pch = 10:12, col = 12:14)
For your second question use lwd:
densityplot(mtcars$hp, lwd = 3)
I just spent a good chunk of time on essentially this same problem. For some reason, the #daroczig style approach wasn't working for changing line types (including for the key) in a densityplot.
In any case, I think the "right" approach is to use trellis.par.set along with auto.key like so:
# Maybe we'll want this later
old.pars <- trellis.par.get()
trellis.par.set(superpose.symbol=list(pch = 10:12, col = 12:14))
xyplot(hp ~ wt, data=mtcars, groups = gear, auto.key=TRUE)
# Optionally put things back how they were
trellis.par.set(old.pars)
There's actually less typing this way (especially if you don't count my saving and restoring the original trellis pars), and less redundancy (allowing for DRY coding). Also, for the life of me, I can't figure out how to easily make multiple columns using key, but you can add columns as one of the elements of the auto.key list.
Also, make sure you're changing the right element! For example, if you changed plot.symbol (which sure sounds like the right thing), it would not do anything. Generally, for things based on xyplot, I believe superpose.* are the right elements to actually modify the symbols, lines, etc.
daroczig's answer is what I typically do when I face this kind of situation. In general, however, I prefer to use lattice default colors instead of specifying my own colors.
You can do that by doing this:
lattice.theme <- trellis.par.get()
col <- lattice.theme$superpose.symbol$col
pl <- xyplot(X ~ Y, groups=Z, data=dframe, pch=1:nlevels(dframe$Z),
type='o', key=list(text=list(levels(dframe$Z)), space='top',
points=list(pch=1:nlevels(dframe$Z), col=col),
lines=list(col=col),
columns=nlevels(dframe$Z)))