I was wondering if it's possible to use the locator() function to create new points with plots generated by plot.sf. I'm sorry but I don't know how to create a reproducible example, so I will do my best to explain my problem.
When I run the following code (clicking, for example, on the centroid of some non-blue regions), the locator() returns the coordinates of the selected points. They have a reasonable value. However, when I plot them, the results are not that accurate (i.e. none of the chosen point lie in the selected region). Can you explain to me what's the problem?
library(sf)
nc = st_read(system.file("gpkg/nc.gpkg", package="sf"), quiet = TRUE)
plot(nc["SID74"], reset = FALSE)
pts <- locator()
pts <- st_sfc(st_multipoint(do.call(cbind, pts)), crs = st_crs(nc))
plot(pts, add = TRUE, pch = 16)
This is an interesting problem; I don't have an answer but I suspect that the issue will be environment related.
I ran your code on Linux (with a small adjustment for target number of points / clicks) and the code behaves as expected. I was able to place three points inside the Mecklenburg (as in Charlotte of Mecklenburg-Strelitz) county.
As locator() seems to use X11 (which is in my case native, but on Windows wrapped in a kind of convoluted fashion) I speculate that your problem may be environment related.
library(sf)
nc = st_read(system.file("gpkg/nc.gpkg", package="sf"), quiet = TRUE)
plot(nc["SID74"], reset = FALSE)
pts <- locator(n = 3) # to speed up the process
pts <- st_sfc(st_multipoint(do.call(cbind, pts)), crs = st_crs(nc))
plot(pts, add = TRUE, pch = 16)
Related
I am trying to turn off the display of plot in R.
I read Disable GUI, graphics devices in R but the only solution given is to write the plot to a file.
What if I don't want to pollute the workspace and what if I don't have write permission ?
I tried options(device=NULL) but it didn't work.
The context is the package NbClust : I want what NbClust() returns but I do not want to display the plot it does.
Thanks in advance !
edit : Here is a reproducible example using data from the rattle package :)
data(wine, package="rattle")
df <- scale (wine[-1])
library(NbClust)
# This produces a graph output which I don't want
nc <- NbClust(df, min.nc=2, max.nc=15, method="kmeans")
# This is the plot I want ;)
barplot(table(nc$Best.n[1,]),
xlab="Numer of Clusters", ylab="Number of Criteria",
main="Number of Clusters Chosen by 26 Criteria")
You can wrap the call in
pdf(file = NULL)
and
dev.off()
This sends all the output to a null file which effectively hides it.
Luckily it seems that NbClust is one giant messy function with some other functions in it and lots of icky looking code. The plotting is done in one of two places.
Create a copy of NbClust:
> MyNbClust = NbClust
and then edit this function. Change the header to:
MyNbClust <-
function (data, diss = "NULL", distance = "euclidean", min.nc = 2,
max.nc = 15, method = "ward", index = "all", alphaBeale = 0.1, plotetc=FALSE)
{
and then wrap the plotting code in if blocks. Around line 1588:
if(plotetc){
par(mfrow = c(1, 2))
[etc]
cat(paste(...
}
and similarly around line 1610. Save. Now use:
nc = MyNbClust(...etc....)
and you see no plots unless you add plotetc=TRUE.
Then ask the devs to include your patch.
I have a CSV file need to draw a graph.
The graph contains nodes and edges.
Therefore, I used the following code to do it.
start.time <- Sys.time()
#Loading Packages
library(igraph)
library(readr)
library(haven)
#import data
df = read.csv('../../Pre_Draw_Graph_for_R.csv', header = TRUE, encoding = 'UTF-8')
#Creating an iGraph Style Edge List
df_Edge_List <- df
#Creating Graph
df_graph = graph.data.frame(df_Edge_List, directed = TRUE)
#df Network: First Try
#Layout Options
set.seed(3500)
layout1 <- layout.fruchterman.reingold(df_graph)
#Node or vertex Options: Color
V(df_graph)$color <- "yellow"
V(df_graph)[degree(df_graph, mode = "in") > 500]$color <- "red"
#Edge Options: Size
E(df_graph)$color <- "grey"
#Plotting
plot(df_graph, vertex.label=NA)
#plot(df_graph)
end.time <- Sys.time()
time.taken <- end.time - start.time
time.taken
I can output the following result, but the result has some problems. It shows that every node is very crowded.
I hope to increase the graph distance, but I used a lot of methods already. I cannot fix the problem. I hope to get the following result, but I cannot do it now. I want to make it clear to see the graph.
I used the following to show dput result.
dput(df_graph, file = "G.R")
Due to the data is too big, I used google link to share it
https://drive.google.com/file/d/1wdF8ZKFde8bDSrFrN9e9KfD3tm6dN_s0/view?usp=sharing
Can anyone help me? Thanks
You can get most of the way to a nice graph with a few simple changes. First, change the vertex labeling to numbers as in your second graph. Also, use layout_components so that the components are separated from each other.
library(igraph)
set.seed(1234)
LOC = layout_components(df_graph)
plot(df_graph, layout=LOC, vertex.label=1:vcount(df_graph))
This is OK, but the arrows are a mess. There are two reasons for that: the arrow heads are too big and most (all?) vertices have loops back to themselves. Let's remove the loops and reduce the size of the arrows.
This looks a lot more useable. If you want anything nicer, you will need to start moving around badly placed vertices by hand. That is a lot more work, but you get a nicer picture.
LOC[ 5,] = c(-1.5,-5)
LOC[ 9,] = c(-3,-2)
LOC[16,] = c(-3,14)
LOC[21,] = c(-3,18)
LOC[25,] = c(9,18)
LOC[29,] = c(-9,18)
LOC[35,] = c(-6,15)
LOC[30,] = c(-9,12)
LOC[31,] = c(-6,18)
LOC[37,] = c(1,14)
LOC[38,] = c(-9,15)
LOC[44,] = c(1,18)
plot(DFGS, layout=LOC, vertex.label=1:vcount(df_graph),
edge.arrow.size = 0.5)
More could be done with the hand-editing, but I will leave that to you.
I'm trying to perform Kernel density estimation in R using some GPS data that I have. My aim is to create a contoured output with each line representing 10% of the KDE. From here i want to import the output (as a shapefile or raster) into either QGIS or arcmap so I can overlay the output on top of existing environmental layers.
So far i have used AdehabitatHR to create the following output using the below code:
kud<-kernelUD(locs1[,1], h="href")
vud<-getvolumeUD(kud)
vud <- estUDm2spixdf(vud)
xyzv <- as.image.SpatialGridDataFrame(vud)
contoured<-contour(xyzv, add=TRUE)
Aside from being able to remove the colour, this is how i wish the output to appear (or near to). However i am struggling to figure out how i can export this as either a shapefile or raster? Any suggestions would be gratefully received.
With the amt package this should be relatively straightforward:
library(adehabitatHR)
library(sf)
library(amt)
data("puechabonsp")
relocs <- puechabonsp$relocs
hr <- as.data.frame(relocs) %>% make_track(X, Y, name = Name) %>%
hr_kde(trast = raster(amt::bbox(., buffer = 2000), res = 50)) %>%
hr_isopleths(level = seq(0.05, 0.95, 0.1))
# Use the sf package to write a shape file, or any other supported format
st_write(hr, "~/tmp/home_ranges.shp")
Note, it is also relatively easy to plot
library(ggplot2)
ggplot(hr) + geom_sf(fill = NA, aes(col = level))
I have a grid and I want to produce a map out of this grid with some map elements (scale, north arrow, etc). I have no problem drawing the grid and the coloring I need, but the additional map elements won't show on the map. I tried putting first=TRUE to the sp.layout argument according to the sp manual, but still no success.
I reproduced the issue with the integrated meuse dataset, so you may just copy&paste that code. I use those package versions: lattice_0.20-33 and sp_1.2-0
library(sp)
library(lattice) # required for trellis.par.set():
trellis.par.set(sp.theme()) # sets color ramp to bpy.colors()
alphaChannelSupported = function() {
!is.na(match(names(dev.cur()), c("pdf")))
}
data(meuse)
coordinates(meuse)=~x+y
data(meuse.riv)
library(gstat, pos = match(paste("package", "sp", sep=":"), search()) + 1)
data(meuse.grid)
coordinates(meuse.grid) = ~x+y
gridded(meuse.grid) = TRUE
v.uk = variogram(log(zinc)~sqrt(dist), meuse)
uk.model = fit.variogram(v.uk, vgm(1, "Exp", 300, 1))
meuse[["ff"]] = factor(meuse[["ffreq"]])
meuse.grid[["ff"]] = factor(meuse.grid[["ffreq"]])
zn.uk = krige(log(zinc)~sqrt(dist), meuse, meuse.grid, model = uk.model)
zn.uk[["se"]] = sqrt(zn.uk[["var1.var"]])
meuse.sr = SpatialPolygons(list(Polygons(list(Polygon(meuse.riv)),"meuse.riv")))
rv = list("sp.polygons", meuse.sr, fill = "lightblue")
sampling = list("sp.points", meuse.riv, color = "black")
scale = list("SpatialPolygonsRescale", layout.scale.bar(),
offset = c(180500,329800), scale = 500, fill=c("transparent","black"), which = 4)
text1 = list("sp.text", c(180500,329900), "0", cex = .5, which = 4)
text2 = list("sp.text", c(181000,329900), "500 m", cex = .5, which = 4)
arrow = list("SpatialPolygonsRescale", layout.north.arrow(),
offset = c(181300,329800),
scale = 400, which = 4)
library(RColorBrewer)
library(lattice)
trellis.par.set(sp.theme())
precip.pal <- colorRampPalette(brewer.pal(7, name="Blues"))
spplot(zn.uk, "var1.pred",
sp.layout = list(rv, sampling, scale, text1, text2),
main = "log(zinc); universal kriging standard errors",
col.regions=precip.pal,
contour=TRUE,
col='black',
pretty=TRUE,
scales=list(draw = TRUE),
labels=TRUE)
And that's how it looks...all naked:
So my questions:
Where is the scale bar, north arrow, etc hiding? Did I miss something? Every example I could find on the internet looks similar to that. On my own dataset I can see the scale bar and north arrow being drawn at first, but as soon as the grid is rendered, it superimposes the additional map elements (except for the scale text, that is shown on the map - not the bar and north arrow for some reason I don't seem to comprehend).
The error message appearing on the map just shows when I try to add the sampling locations sampling = list("sp.points", meuse.riv, color = "black"). Without this entry, the map shows without error, but also without additional map elements. How can I show the sampling points on the map (e.g. in circles whose size depends on the absolute value of this sampling point)?
This bothered me for many, many hours by now and I can't find any solution to this. In Bivand et al's textbook (2013) "Applied Spatial Data Analysis with R" I could read the following entry:
The order of items in the sp.layout argument matters; in principle objects
are drawn in the order they appear. By default, when the object of spplot has
points or lines, sp.layout items are drawn before the points to allow grids
and polygons drawn as a background. For grids and polygons, sp.layout
items are drawn afterwards (so the item will not be overdrawn by the grid
and/or polygon). For grids, adding a list element first = TRUE ensures that
the item is drawn before the grid is drawn (e.g. when filled polygons are added). Transparency may help when combining layers; it is available for the
PDF device and several other devices.
Function sp.theme returns a lattice theme that can be useful for plots
made by spplot; use trellis.par.set(sp.theme()) after a device is opened
or changed to make this effective.
However, also with this additional information I wasn't able to solve this problem. Glad for any hint!
The elements you miss are being drawn in panel four, which does not exist, so are not being drawn. Try removing the which = 4.
meuse.riv in your example is a matrix, which causes the error message, but should be a SpatialPoints object, so create sampling by:
sampling = list("sp.points", SpatialPoints(meuse.riv), color = "black")
When working from examples, my advice is to choose examples as close as possible to what you need, and only change one thing at a time.
I am trying to plot coordinate points on a map, but I get the plot.new error. Could you please help?
library(maptools)
library(ggmap)
library(mapproj)
table <- read.table("table.txt", header=TRUE, sep=",")
map <- get_map(location = 'France', zoom = 6, maptype = c("toner"))
points(table$LONG, table$LAT, pch=21, bg=color, cex=0.7, lwd=.4)
ggmap(map)
Here is an idea of what the table looks like:
CITY,LAT,LONG
Paris,48.856667,2.351944
Lyon,45.766944,4.834167
Bordeaux,44.838611,0.578334
Try geom_point:
library(maptools)
library(ggmap)
library(mapproj)
city <- c("Paris", "Lyon", "Bordeaux")
my.lat <- c(48.856667, 45.766944, 44.838611)
my.long <- c(2.351944, 4.834167, 0.578334)
points <- data.frame(lon=my.long, lat=my.lat)
map <- get_map(location = c(left = -5, bottom = 42, right=9, top = 51 ), source = 'stamen', maptype = 'toner')
france <- ggmap(map, extent = 'normal')
france + geom_point(data=points, col="red")
Try the command ?ggmap for a list of great examples. I think the manual has done a good job, because before I read your question, I didn't even know of any of these functions. Thanks! I've learned something new.
Learn to walk before you try and run.
The points function adds points to an existing graphic. You haven't got an existing graphic yet (unless you've already done something you've not showed us).
Hence if you do points before starting a plot, you'll get an error. eg:
points(1:10,1:10) # plot.new error
plot(1:10,1:10) # no error, starts a new plot
points(10:1,1:10) # adds extra points, no error.
All your stuff with ggplot is irrelevant. Also, this is not about statistics, so you should have posted to StackOverflow. I've flagged this and it might get migrated...