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Does anyone have an idea how is this kind of chart plotted? It seems like heat map. However, instead of using color, size of each cell is used to indicate the magnitude. I want to plot a figure like this but I don't know how to realize it. Can this be done in R or Matlab?
Try scatter:
scatter(x,y,sz,c,'s','filled');
where x and y are the positions of each square, sz is the size (must be a vector of the same length as x and y), and c is a 3xlength(x) matrix with the color value for each entry. The labels for the plot can be input with set(gcf,properties) or xticklabels:
X=30;
Y=10;
[x,y]=meshgrid(1:X,1:Y);
x=reshape(x,[size(x,1)*size(x,2) 1]);
y=reshape(y,[size(y,1)*size(y,2) 1]);
sz=50;
sz=sz*(1+rand(size(x)));
c=[1*ones(length(x),1) repmat(rand(size(x)),[1 2])];
scatter(x,y,sz,c,'s','filled');
xlab={'ACC';'BLCA';etc}
xticks(1:X)
xticklabels(xlab)
set(get(gca,'XLabel'),'Rotation',90);
ylab={'RAPGEB6';etc}
yticks(1:Y)
yticklabels(ylab)
EDIT: yticks & co are only available for >R2016b, if you don't have a newer version you should use set instead:
set(gca,'XTick',1:X,'XTickLabel',xlab,'XTickLabelRotation',90) %rotation only available for >R2014b
set(gca,'YTick',1:Y,'YTickLabel',ylab)
in R, you should use ggplot2 that allows you to map your values (gene expression in your case?) onto the size variable. Here, I did a simulation that resembles your data structure:
my_data <- matrix(rnorm(8*26,mean=0,sd=1), nrow=8, ncol=26,
dimnames = list(paste0("gene",1:8), LETTERS))
Then, you can process the data frame to be ready for ggplot2 data visualization:
library(reshape)
dat_m <- melt(my_data, varnames = c("gene", "cancer"))
Now, use ggplot2::geom_tile() to map the values onto the size variable. You may update additional features of the plot.
library(ggplot2)
ggplot(data=dat_m, aes(cancer, gene)) +
geom_tile(aes(size=value, fill="red"), color="white") +
scale_fill_discrete(guide=FALSE) + ##hide scale
scale_size_continuous(guide=FALSE) ##hide another scale
In R, corrplotpackage can be used. Specifically, you have to use method = 'square' when creating the plot.
Try this as an example:
library(corrplot)
corrplot(cor(mtcars), method = 'square', col = 'red')
I am trying to represent niche of species by drawing inertia ellipses. The function to do this in ade4 is niche. Here is an example:
data(trichometeo)
pca1 <- dudi.pca(trichometeo$meteo, scan = FALSE)
nic1 <- niche(pca1, log(trichometeo$fau + 1), scan = FALSE)
s.distri(dfxy = nic1$ls, dfdistri = eval.parent(as.list(nic1$call)[[3]]))
This graph is not really clear.
PCA is done on environmental variables.
Each point of the PCA is a study site. In each study site, several species have been observed. The ellipses are the niches of each species.
When building the ellipse of one species, a weight is given to each of the study sites (the points) according to the relative abundance of the species. The center of gravity of these weighed points is the center of the ellipsoid. The width of the ellipse is linked to the variance of the weighed points.
so there is no scatterplot with a factor i could use to use stat_ellipse.
Any suggestions on how to do that in ggplot graphics ?
thank you
So, finally i found how to plot ellipses in ggplot.It is explained in the first part of the answer. The second part describes how to extract ellipsoid coordinates from niche analysis in ade4.
Draw a simple ellipsoid in ggplot In order to do that, you have to build a data frame with to columns x and y for coordinates of some of the points that compose the ellipse, and use geom_polygon as follow:
> dput(test)
structure(list(x = c(-0.74970124137657, -0.776450364352299, -0.804256933708176,
-0.833011209618567, -0.862599712093033, -0.892905668830007, -0.923809476063724,
-0.955189170585639, -0.986920911077492, -1.01887946685642, -1.05093871210323,
-1.08297212362341, -1.11485328017637, -1.14645636140231, -1.1776566443777,
-1.20833099583969, -1.23835835813684, -1.26762022698836, -1.29600111916637,
-1.32338902825544, -1.34967586669074, -1.37475789233028, -1.39853611787775,
-1.42091670154015, -1.44181131737848, -1.46113750388986, -1.47881898944545,
-1.49478599329964, -1.50897550098285, -1.52133151299083, -1.53180526578912,
-1.5403554242604, -1.54694824483541, -1.55155770866329, -1.55416562429619,
-1.55476169948255, -1.55334358178592, -1.54991686786897, -1.54449508140603,
-1.53709961971128, -1.52775966929336, -1.51651209066957, -1.50340127289419,
-1.48847895837522, -1.47180403867071, -1.45344232207069, -1.43346627388192,
-1.41195473044039, -1.38899258798039, -1.3646704675878, -1.3390843575601,
-1.31233523458437, -1.28452866522849, -1.2557743893181, -1.22618588684363,
-1.19587993010666, -1.16497612287294, -1.13359642835103, -1.10186468785917,
-1.06990613208025, -1.03784688683344, -1.00581347531326, -0.973932318760297,
-0.94232923753436, -0.911128954558971, -0.880454603096979, -0.850427240799828,
-0.821165371948307, -0.792784479770296, -0.765396570681226, -0.739109732245926,
-0.714027706606386, -0.690249481058919, -0.66786889739652, -0.646974281558191,
-0.627648095046803, -0.609966609491222, -0.593999605637033, -0.579810097953819,
-0.567454085945835, -0.556980333147553, -0.548430174676264, -0.541837354101259,
-0.537227890273375, -0.534619974640476, -0.534023899454122, -0.535442017150752,
-0.538868731067695, -0.544290517530637, -0.551685979225388, -0.561025929643302,
-0.572273508267099, -0.585384326042479, -0.600306640561448, -0.616981560265954,
-0.63534327686597, -0.655319325054748, -0.676830868496273, -0.699793010956271,
-0.724115131348859), y = c(0.325013216091984, 0.336960163623126,
0.346538198705152, 0.353709521209382, 0.358445829202159, 0.360728430639646,
0.360548317136793, 0.357906199519309, 0.352812505018361, 0.345287336119057,
0.335360391225119, 0.323070847452856, 0.308467206016976, 0.291607100818459,
0.272557070989873, 0.251392298295821, 0.228196310424862, 0.203060651343884,
0.176084520015889, 0.147374378907005, 0.117043533827776, 0.085211686766882,
0.0520044634820859, 0.0175529177127555, -0.0180069860293719,
-0.0545349090500008, -0.0918866923249894, -0.129914925430158,
-0.168469528302887, -0.207398343539562, -0.246547736891326, -0.285763203588276,
-0.324889978099203, -0.363773644920435, -0.402260747983286, -0.440199396275052,
-0.477439863283444, -0.513835177898732, -0.549241704441568, -0.583519709527388,
-0.616533913530252, -0.648154024469714, -0.678255252213751, -0.7067188009684,
-0.733432338110486, -0.758290437513162, -0.781194995614671, -0.802055618588285,
-0.820789979085438, -0.837324141144163, -0.851592851980555, -0.863539799511696,
-0.873117834593722, -0.880289157097953, -0.885025465090729, -0.887308066528217,
-0.887127953025363, -0.884485835407879, -0.879392140906931, -0.871866972007626,
-0.861940027113689, -0.849650483341425, -0.835046841905545, -0.818186736707027,
-0.799136706878442, -0.77797193418439, -0.754775946313431, -0.729640287232453,
-0.702664155904458, -0.673954014795575, -0.643623169716345, -0.611791322655452,
-0.578584099370656, -0.544132553601326, -0.508572649859198, -0.47204472683857,
-0.434692943563581, -0.396664710458413, -0.358110107585684, -0.31918129234901,
-0.280031898997246, -0.240816432300296, -0.201689657789369, -0.162805990968137,
-0.124318887905287, -0.0863802396135213, -0.0491397726051291,
-0.012744457989841, 0.0226620685529939, 0.0569400736388145, 0.0899542776416779,
0.12157438858114, 0.151675616325177, 0.180139165079826, 0.206852702221912,
0.231710801624588, 0.254615359726098, 0.275475982699712, 0.294210343196865,
0.31074450525559)), .Names = c("x", "y"), row.names = c(NA, -100L
), class = "data.frame")
then just plot the polygon:
ggplot()+geom_polygon(data=test, aes(x=x, y=y))
For this specific issue: how to extract ellipses coordinates from a niche analysis with ade4:
plots from ade4 can be put in an oject:
data(trichometeo)
pca1 <- dudi.pca(trichometeo$meteo, scan = FALSE)
nic1 <- niche(pca1, log(trichometeo$fau + 1), scan = FALSE)
p1<-s.distri(dfxy = nic1$ls, dfdistri = eval.parent(as.list(nic1$call)[[3]]))
p1 is an object of class S4, and it is possible to access slots with data using # as follow:
p1#s.misc$ellipse
this command display a list containing, for each species:
one vector of x coordinates of the ellipse
one vector of y coordinates
one vector with coordinates of the axes of the ellipse
To exctract these coordinates, you use sapply
listx=sapply(p1#s.misc$ellipse, "[", "x")
listy=sapply(p1#s.misc$ellipse, "[", "y")
then transform them into a data frame:
tabx=do.call(data.frame, listx)
taby=do.call(data.frame, listy)
and combine them in one data frame (i use melt from reshape package to have a long data frame for ggplot)
tabx.long=melt(tabx)
taby.long=melt(taby)
tab.fin=cbind.data.frame(tabx.long,taby.long)
you can then use this dataframe with the method explained above
I'm trying to add this plot of a function defined on Veneto (italian region)
obtained by an image and contour:
image(X,Y,evalmati,col=heat.colors(100), xlab="", ylab="", asp=1,zlim=zlimits,main=title)
contour(X,Y,evalmati,add=T)
(here you can find objects: https://dl.dropboxusercontent.com/u/47720440/bounty.RData)
on a Google Map background.
I tried two ways:
PACKAGE RGoogleMaps
I downloaded the map mbackground
MapVeneto<-GetMap.bbox(lonR=c(10.53,13.18),latR=c(44.7,46.76),size = c(640,640),MINIMUMSIZE=TRUE)
PlotOnStaticMap(MapVeneto)
but i don't know the commands useful to add the plot defined by image and contour to the map
PACKAGE loa
I tried this way:
lat.loa<-NULL
lon.loa<-NULL
z.loa<-NULL
nx=dim(evalmati)[1]
ny=dim(evalmati)[2]
for (i in 1:nx)
{
for (j in 1:ny)
{
if(!is.na(evalmati[i,j]))
{
lon.loa<-c(lon.loa,X[i])
lat.loa<-c(lat.loa,Y[j])
z.loa<-c(z.loa,evalmati[i,j])
}
}
}
GoogleMap(z.loa ~ lat.loa*lon.loa,col.regions=c("red","yellow"),labels=TRUE,contour=TRUE,alpha.regions=list(alpha=.5, alpha=.5),panel=panel.contourplot)
but the plot wasn't like the first one:
in the legend of this plot I have 7 colors, and the plot use only these values. image plot is more accurate.
How can I add image plot to GoogleMaps background?
If the use of a GoogleMap map is not mandatory (e.g. if you only need to visualize the coastline + some depth/altitude information on the map), you could use the package marmap to do what you want. Please note that you will need to install the latest development version of marmap available on github to use readGEBCO.bathy() since the format of the files generated when downloading GEBCO files has been altered recently. The data from the NOAA servers is fine but not very accurate in your region of interest (only one minute resolution vs half a minute for GEBCO). Here is the data from GEBCO I used to produce the map : GEBCO file
library(marmap)
# Get hypsometric and bathymetric data from either NOAA or GEBCO servers
# bath <- getNOAA.bathy(lon1=10, lon2=14, lat1=44, lat2=47, res=1, keep=TRUE)
bath <- readGEBCO.bathy("GEBCO_2014_2D_10.0_44.0_14.0_47.0.nc")
# Create color palettes for sea and land
blues <- c("lightsteelblue4", "lightsteelblue3", "lightsteelblue2", "lightsteelblue1")
greys <- c(grey(0.6), grey(0.93), grey(0.99))
# Plot the hypsometric/bathymetric map
plot(bath, land=T, im=T, lwd=.03, bpal = list(c(0, max(bath), greys), c(min(bath), 0, blues)))
plot(bath, n=1, add=T, lwd=.5) # Add coastline
# Transform your data into a bathy object
rownames(evalmati) <- X
colnames(evalmati) <- Y
class(evalmati) <- "bathy"
# Overlay evalmati on the map
plot(evalmati, land=T, im=T, lwd=.1, bpal=col2alpha(heat.colors(100),.7), add=T, drawlabels=TRUE) # use deep= shallow= step= to adjust contour lines
plot(outline.buffer(evalmati),add=TRUE, n=1) # Outline of the data
# Add cities locations and names
library(maps)
map.cities(country="Italy", label=T, minpop=50000)
Since your evalmati data is now a bathy object, you can adjust its appearance on the map like you would for the map background (adjust the number and width of contour lines, adjust the color gradient, etc). plot.bath() uses both image() and contour() so you should be able to get the same results as when you plot with image(). Please take a look at the help for plot.bathy() and the package vignettes for more examples.
I am not realy inside the subject, but Lovelace, R. "Introduction to visualising spatial data in R" might help you
https://github.com/Robinlovelace/Creating-maps-in-R/raw/master/intro-spatial-rl.pdf From section "Adding base maps to ggplot2 with ggmap" with small changes and data from https://github.com/Robinlovelace/Creating-maps-in-R/archive/master.zip
library(dplyr)
library(ggmap)
library(rgdal)
lnd_sport_wgs84 <- readOGR(dsn = "./Creating-maps-in-R-master/data",
layer = "london_sport") %>%
spTransform(CRS("+init=epsg:4326"))
lnd_wgs84_f <- lnd_sport_wgs84 %>%
fortify(region = "ons_label") %>%
left_join(lnd_sport_wgs84#data,
by = c("id" = "ons_label"))
ggmap(get_map(location = bbox(lnd_sport_wgs84) )) +
geom_polygon(data = lnd_wgs84_f,
aes(x = long, y = lat, group = group, fill = Partic_Per),
alpha = 0.5)
my goal is to create a 3D-Visualization in R. I have a shapefile of urban districts (Ortsteile) in Berlin and want to highlight the value (inhabitants/km²) as a z-value. I have implemented the shapefile into R and coloured the value for desnity ("Einwohnerd") as followed:
library(rgdal)
library(sp)
berlin=readOGR(dsn="C...etc.", layer="Ortsteile")
berlin#data
col <- rainbow(length(levels(berlin#data$Name)))
spplot(berlin, "Einwohnerd", col.regions=col, main="Ortsteil Berlins", sub="Datensatz der Stadt Berlin", lwd=.8, col="black")
How it is posible to refer a certain polygon (urban district) to a z-value (inhabitant/km²) and how can I highlight this z-value?
Hope that someone will have an answer!
Best regars
SB
Thanks for the answer, but I am still on my wy to find out the best to use the density as z-value so that I can create a 3D Model. I found out that it is not possible to use the polygons of the shape but that it is possible to rasterize the polygon and to use a matrix for a different perspective and rotation.
Here is the code but the final 3D visualization looks not sharp and good enough. Maybe it would be better to calculate the the z-value in anther way so that the first values did not start so high or to use the center of the polygon and than to draw a column in z-direction:
library(rgdal)
library(sp)
setwd("C:\\...")
berlin=readOGR(dsn="C:\\...\\Ortsteile", layer="Ortsteile")
col <- rainbow(length(levels(berlin#data$Name)))
spplot(berlin, "Einwohnerd", col.regions=col, main="Ortsteil Berlins",
sub="Datensatz der Stadt Berlin", lwd=.8, col="black")
library(raster)
raster <- raster(nrows=100, ncols=200, extent(berlin))
test <- rasterize(berlin, raster, field="Einwohnerd")
persp(test, theta = 40, phi = 40, col = "gold", border = NA, shade = 0.5)
for(i in seq(0,90,10)){
persp(test, theta = 40, phi = i, col = "gold", border = NA, shade = 0.5)
}
library(rgl)
library(colorRamps)
mat <- matrix(test[], nrow=test#nrows, byrow=TRUE)
image(mat)
persp3d(z = mat, clab = "m")
persp3d(z = mat, col = rainbow(10),border = "black")
persp3d(z = mat, facets = FALSE, curtain = TRUE)
Is this what you had in mind?
library(ggplot2)
library(rgdal) # for readOGR(...) and spTransform(...)
library(RColorBrewer) # for brewer.pal(...)
setwd("<directory with shapefile>")
map <- readOGR(dsn=".",layer="Ortsteile")
map <- spTransform(map,CRS=CRS("+init=epsg:4839"))
map.data <- data.frame(id=rownames(map#data), map#data)
map.df <- fortify(map)
map.df <- merge(map.df,map.data,by="id")
ggplot(map.df, aes(x=long, y=lat, group=group))+
geom_polygon(aes(fill=Einwohnerd))+
geom_path(colour="grey")+
scale_fill_gradientn(colours=rev(brewer.pal(10,"Spectral")))+
theme(axis.text=element_blank())+
labs(title="Berlin Ortsteile", x="", y="")+
coord_fixed()
Explanation
This is a great question, in that it provides an example of a very basic choropleth map using ggplot in R.
Shapefiles can be read into R using readOGR(...), producing SpatialDataFrame objects. The latter have basically two sections: a polygons section containing the coordinates of the polygon boundaries, and a data section containing information from the attributes table in the shapefile. These can be referenced, respectively, as map#polygons and map#data.
The code above reads the shapefile and transforms the coordinates to epsg:4839. Then we prepend the polygon ids (stored in the rownames) to the other information in map#data, creating map.data. Then we use the fortify(...) function in ggplot to convert the polygons to a dataframe suitable for plotting (map.df). This dataframe has a column id which corresponds to the id column in map.data. Then we merge the attribute information (map.data) into map.df based on the id column.
The ggplot calls create the map layers and render the map, as follows:
ggplot: set the default dataset to map.df; identify x- and y-axis columns
geom_polygon: identify column for fill (color of polygon)
geom_path: polygon boundaries
theme: turn off axis text
labs: title, turn off x- and y-axis labels
coord_fixed: ensures that the map is not distorted
A note on scale_fill_gradientn(...): this function assigns colors to the fill values by interpolating a color palette provided in the colours= parameter. Here we use the Spectral palette from www.colorbrewer.org. Unfotrunately, this palette has the colors revered (blue - red), so we use rev(...) to reverse the color order (high=red, low=blue). If you prefer the more highly saturated colors common in matlab, use library(colorRamps) and replace the call to scale_fill_gradientn(...) with:
scale_fill_gradientn(colours=matlab.like(10))+
See this example
This was created in matlab by making two scatter plots independently, creating images of each, then using the imagesc to draw them into the same figure and then finally setting the alpha of the top image to 0.5.
I would like to do this in R or matlab without using images, since creating an image does not preserve the axis scale information, nor can I overlay a grid (e.g. using 'grid on' in matlab). Ideally I wold like to do this properly in matlab, but would also be happy with a solution in R. It seems like it should be possible but I can't for the life of me figure it out.
So generally, I would like to be able to set the alpha of an entire plotted object (i.e. of a matlab plot handle in matlab parlance...)
Thanks,
Ben.
EDIT: The data in the above example is actually 2D. The plotted points are from a computer simulation. Each point represents 'amplitude' (y-axis) (an emergent property specific to the simulation I'm running), plotted against 'performance' (x-axis).
EDIT 2: There are 1796400 points in each data set.
Using ggplot2 you can add together two geom_point's and make them transparent using the alpha parameter. ggplot2 als adds up transparency, and I think this is what you want. This should work, although I haven't run this.
dat = data.frame(x = runif(1000), y = runif(1000), cat = rep(c("A","B"), each = 500))
ggplot(aes(x = x, y = y, color = cat), data = dat) + geom_point(alpha = 0.3)
ggplot2 is awesome!
This is an example of calculating and drawing a convex hull:
library(automap)
library(ggplot2)
library(plyr)
loadMeuse()
theme_set(theme_bw())
meuse = as.data.frame(meuse)
chull_per_soil = ddply(meuse, .(soil),
function(sub) sub[chull(sub$x, sub$y),c("x","y")])
ggplot(aes(x = x, y = y), data = meuse) +
geom_point(aes(size = log(zinc), color = ffreq)) +
geom_polygon(aes(color = soil), data = chull_per_soil, fill = NA) +
coord_equal()
which leads to the following illustration:
You could first export the two data sets as bitmap images, re-import them, add transparency:
library(grid)
N <- 1e7 # Warning: slow
d <- data.frame(x1=rnorm(N),
x2=rnorm(N, 0.8, 0.9),
y=rnorm(N, 0.8, 0.2),
z=rnorm(N, 0.2, 0.4))
v <- with(d, dataViewport(c(x1,x2),c(y, z)))
png("layer1.png", bg="transparent")
with(d, grid.points(x1,y, vp=v,default="native",pch=".",gp=gpar(col="blue")))
dev.off()
png("layer2.png", bg="transparent")
with(d, grid.points(x2,z, vp=v,default="native",pch=".",gp=gpar(col="red")))
dev.off()
library(png)
i1 <- readPNG("layer1.png", native=FALSE)
i2 <- readPNG("layer2.png", native=FALSE)
ghostize <- function(r, alpha=0.5)
matrix(adjustcolor(rgb(r[,,1],r[,,2],r[,,3],r[,,4]), alpha.f=alpha), nrow=dim(r)[1])
grid.newpage()
grid.rect(gp=gpar(fill="white"))
grid.raster(ghostize(i1))
grid.raster(ghostize(i2))
you can add these as layers in, say, ggplot2.
Use the transparency capability of color descriptions. You can define a color as a sequence of four 2-byte words: muddy <- "#888888FF" . The first three pairs set the RGB colors (00 to FF); the final pair sets the transparency level.
AFAIK, your best option with Matlab is to just make your own plot function. The scatter plot points unfortunately do not yet have a transparency attribute so you cannot affect it. However, if you create, say, most crudely, a bunch of loops which draw many tiny circles, you can then easily give them an alpha value and obtain a transparent set of data points.