I have a dataset of all powerplants and I've got their locations down to the format the maps package in R likes c("arkansas,clay", "arkansas,conway", ...).
Some counties have more than one powerplant, and there are 7+ types of powerplants, so I'd like to plot them as points on a map and not just color the counties, as I can see the maps package mainly doing. Was thinking to jitter their position a bit. But I don't know how to go from state/county name to location, or plot straight up points in the maps package.
Does anyone have any suggestions?
So I couldn't figure out how to do it with the maps package, but with ggplot, it's almost trivial. The first few lines of this answer made it really easy to construct a plot I needed.
Plotting bar charts on map using ggplot2?
One trick I did use was to create R's version of a hastable from the map_data in ggplot2.
usaMap=maps_data("county")
usaMap$locCode=paste(usaMap$region,",",usaMap$subregion,sep="")
usaMap2 = usaMap[!duplicated(usaMap$locCode),]
row.names(usaMap2)=usaMap2$locCode
currentGen$long = usaMap2[currentGen$locCode,"long"]+rnorm(nrow(currentGen),0,.05)
currentGen$lat = usaMap2[currentGen$locCode,"lat"]+rnorm(nrow(currentGen),0,.05)
where currentGen is my powerplants data frame and the format of the region matches exactly the format of usaMap$locCode.
Related
I am trying to create contour plots of film thicknesses on a wafer using plotly, but would like the outputted plot to be a circle since it is a wafer instead of the default square. Do I have to somehow overlay a circle on to the plot and then exclude anything outside of it after the plot is generated? I prefer using plotly if possible since it looks nice. I've tried using ggplot as well but for some reason my data doesn't work with it since the x and y coordinates are apparently irregularly spaced. I've searched around but have not seen any results at least using R.
Thanks!
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 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.
Using R I would like to replace the points in a 2d scatter plot by a pie chart displaying additional values.
The rational behind is that I have time series data for hundreds of elements (proteins) derived from a biological experiment monitored for 4 conditions. I would like to plot the elements (categorial data) on the y axis and occurrence of a event in time on the x axis. To visualize the relative occurrence between the 4 conditions I would like to visualize this in form of a pie chart or doughnut chart overplayed onto the respective point in the scatter plot.
The overall data density is low so overlapping won't be an issue.
Is this possible in R?
I was thinking of using a manual scale in ggplot2 but could not figure out how to define a pie chart as a scale.
Also of interest would be how to best cluster this data and sort it accordingly.
Yes. pieGlyph() is one ready-to-go function from the Rgraphviz package.
Also, I would check out this Q/A for how to do things like this more generally:
How to fill a single 'pch' point on the plot with two-colours?
Especially check out ?my.symbols from the TeachingDemos package.
Lastly, in regards to ggplot2, you should check out this blog post about possible upcoming features:
http://blog.revolutionanalytics.com/2011/10/ggplot2-for-big-data.html
See also Paul Murrell. Integrating grid graphics output with base graphics output. R News, 3(2):7-12, October 2003. http://www.r-project.org/doc/Rnews/Rnews_2003-2.pdf
The code on pp 10-11 sets up the main plot axes, labels and legend, and then opens a series of smaller windows centered at each individual point on the plot and plots a small graph in each window. I've tried pie charts, mosaics and barplots, but the method is not limited to these types.
I created a network graph from data on flows between US states. For each vertex, I have the lat/long of the state.
I am hoping to recreate a network kind of graph that shows the edges, except that I set the location of each vertex to be their geographic position and have a state boundary map in the background.
I am using to igraph to create my network. There have been some cool mapping examples in ggplot2, so I am wondering if that is an option. I believe I have seen similar options using Pajek, but I am hoping to stay within R.
maps in ggplot2
Any ideas/insight would be appreciated.
Brock
You have multiple packages dealing with maps. The most easy is maps, which gives you the states map. You can plot the vertices over using the coordinates.
map("state")
points(longitute,latitude)
These plots can be manipulated and added to using the base tools, keeping in mind the x axis is the longitude and the y axis is the latitude. edges can be plotted using the segments() function.
In ggplot2 just use the map_data() function, which gives you the shape-data of the map, and the geom_polygon() to add it to the graph in whatever form you want. Again, you can add the vertices and edges using the coordinates with the appropriate ggplot2 function geom_point() and geom_segment(). The code you link at shows you how, or otherwise look at this for an example.
Next to that, you can take a look at the packages maptools, which offers more functionality and, mapproj, which allows for different projections of the same map. You can use these packages as well to calculate geographical distances in a coordinate system.
mapdata contains more databases, and covers basically the whole world. You can work with coordinates pretty nicely.