I have a set of data that I'm trying to create a surface plot of. I have an x,y point and a to colour by.
I can create a xy plot with the points coloured but I can't find a way to create a surface plot with my data. The data isn't on a normal grid and I would prefer to not normalize it if possible (or I could just use a very fine grid).
The data won't be outside the a radius=1 circle so this part would need to be blank.
The code and the plot is shown below.
I've tried using contour, filled.contour as well as surface3d (not what I wanted). I'm not real familiar with many packages in R so I'm not even sure where to begin looking for this info.
Any help in creating this plot would be appreciated.
thanks,
Gordon
dip<-data.frame(dip=seq(0,90,10))
ddr<-data.frame(ddr=seq(0,350,10))
a<-merge(dip,ddr)
a$colour<-hsv(h=runif(nrow(a)))
degrees.to.radians<-function(degrees){
radians=degrees*pi/180
radians
}
a$equal_angle_x<-sin(degrees.to.radians(a$ddr))*tan(degrees.to.radians((90-a$dip)/2))
a$equal_angle_y<-cos(degrees.to.radians(a$ddr))*tan(degrees.to.radians((90-a$dip)/2))
plot(a$equal_angle_x,a$equal_angle_y,col=a$colour,lwd=10)
With regards to the plot I was trying to create is below. I believe the link in the first comment should get me where I'm trying to go.
Related
I have gone through few tutorials and answers here in stackoverflow such as:
Overlap image plot on a Google Map background in R or
Plotting contours on an irregular grid or Geographical heat map of a custom property in R with ggmap or How to overlay global map on filled contour in R language or https://blog.dominodatalab.com/geographic-visualization-with-rs-ggmaps/
They either don't serve my purpose or consider the density of the data to create the image.
I am looking for a way to plot contour on a map of a certain data, and would expect the image to look something like this:
or something like this taken from https://dsparks.wordpress.com/2012/07/18/mapping-public-opinion-a-tutorial/:
I have a data here that gives a contour plot like this in plot_ly but i want this over the map given by latitudes and longitudes.
Please guide me on how this can be done. Any links to potential answers or codes would be helpful.
Ok I did some digging and figured that to plot the data -which in this case are point values randomly distributed across the Latitude and Longitude, one has to make it continuous instead of the discreetly distributed one. To do this I interpolated the data to fill in the gaps, this method is given in Plotting contours on an irregular grid and then take it from there. Now the interpolation here is done using a linear regression, one can use other methods such as IDW, Kriging, Nearest Neighbourhood etc for which R-packages are easily available. These methods are widely used in climatology and topographic analysis. To read more about interpolation methods see this paper.
I'm trying to plot the cluster obtained from fuzzy c-means clustering.
The plot should look like this.
code for the plot
plot(data$Longitude, data$Latitude, main="Fuzzy C-Means",col=data$Revised, pch=16, cex=.6,
xlab="Longitude",ylab="Latitude")
library(maps)
map("state", add=T)
However, when I tried to use clusplot the plot is displaying in opposite direction(both top and bottom and left and right) as below.
I wanna know if there's a way to reverse the plot to show in the order as the above picture.
Also, for the very dense area, it's hard to find the ellipse label. I wanna know if there's a way to show the label inside the ellipse instead of outside.
code for 2nd pic
library(cluster)
clusplot(cbind(Geocode$Longitude, Geocode$Latitude), cluster, color=TRUE,shade=TRUE,
labels=4, lines=0,col.p=cluster,
xlab="Longitude",ylab="Latitude",cex=1)
clusplot is a function that performs a lot of magic for you. In particular it projects the data set - which happens in a way you don't like, unfortunately. (Also note the scales - it centered and scaled the data, too)
clusplot.default: Creates a bivariate plot visualizing a partition (clustering) of the data. All observation are represented by points in the plot, using principal components or multidimensional scaling.
As far as I can tell, clusplot doesn't have map support, but you will want such a map I guess...
While maybe you can use the s.x.2d parameter to specify the exact projection (and this way disable automatic scaling), it probably is still difficult to add the map. Maybe look at the source of clusplot instead, and take only the parts you want?
I have a dense scatter plot on a map (produced using Python, matplotlib, and basemap). Here is a part of the image:
I'd like to solve the overlap problem. I think the way to do this is to combine this simple lat/lon coordinate mapping with the technique I often see implemented in those "spring-loaded" network (social, not computer) graphs.
Is there a simple existing algorithm to auto-magically move these points so that they are not overlapping? If so, I can easily than add a small line from each point to its the correct lat/lon coordinate where it is currently located.
Note: Hexbin and heatmap is not a solution since the discrete values are important and should not be compromised.
Here is what I need: I have an image and want to plot on specific rectangle-shaped parts of it (e.g., imagine having a picture of a chessboard and wanting to fill every square with a different color). I would like to be able to easily specify the coordinates for these parts and take these coordinates into R for plotting.
I don't have any experience with making such plots. I've thought of simply inserting an image into a plot with rasterImage (), then plotting with polygon (), but the task of setting up the coordinates for the polygon function seemed too time consuming - hence the question above.
If you have any better ideas than using a set of coordinates for the polygon function, please share. Any leads or packages suggestions would also be helpful.
thank you. Marko.
I just discovered ggmap and I've been playing around with plotting earthquake data from the USGS. I get the data in the form of Lat and Lon, depth and magnitude. I can easily plot the earthquakes as points with different colors based on depth but what I would like to do is take that depth data (just a single number) and generate contours to overlay on the map.
This seems like it should be MUCH more simple than the "Houston Crime" example I keep coming up on since I'm not doing any statistical "density" calculation or anything like that. Basically it's just a contour map on top of the google map of an area.
How do I do this (Presumably) simple, simple thing?
Thanks!
The problem of plotting a 3D surface using only a small sample of unequally spaced lat/long points and a height z (or equivalent) variable is non-trivial -- you have to estimate the values of z for all of the lat-long grid coordinates you do not have, for example using loess() or kriging to create a smooth surface.
Take a look at Methods for doing heatmaps, level / contour plots, and hexagonal binning, case #5. For a geoR example see http://www4.stat.ncsu.edu/~reich/CUSP/Ordinary_Kriging_in_R.pdf