Industry-standard 2D CAD program used for laser cutters.Ī good format converter for CAD, convert between different versions of dwg files and dwg files to dxf files. Also - as Spacedman rightly pointed out this linked question was greatly informative - once you start figuring out how things are done (rather than asking why!).I'm a newbie to Wine but I was able to get 123D Design to work. It works, but from a programming perspective seems needlessly heavy on memory, particularly when your analysis grows! I'm marking this as the answer, and hopefully can inform others who are having the same stumbling blocks when starting in this sort of environment. P + geom_polygon(data=plotData2, aes(fill=wmHrs,x=long,y=lat,group=Suburb)) # populate it!Īgain - this is a solution, but perhaps not the cleanest one. p = ggplot() # Simply initialise an empty plot. Then there is some simple renaming of columns in plotData for the sake of clarity, and the following line yields (a basic) version of what I wanted, where 'wmHrs' is the variable of interest. ![]() PlotData = left_join(tidy_spdf,df_with_variables,by='id') but that's another story), it is fortified with broom::tidy as follows spdf = SpatialPolygonsDataFrame(input variables) Essentially for a polygon with 100 'corners', the same value needs to be stored 100 times.Īfter creating the SpatialPolygonDataFrame (which will soon if not already be deprecated by the sf package. The concern is largely that this creates a great deal of redundant data - one polygon or statistical area needs only one record of the variable of interest - but the way the aes() function in ggplot2 works requires that each row has this value. ![]() What needs to happen is tidy() is called on the SpatialDataFrame - after which the data is joined using left.join() from base R. Fortifying the data as suggested by yields a basic data frame type (or using broom::tidy as suggested by internal package details), with groups or ID's assigned as expected. The issue I was having is a mismatch between each element of the polygon (so for a simple rectangular poly, there'd be 4 points), and the values associated with it. So - it turns out that even though ggmap2 can manage SpatialDataFrames, assigning values to it is pretty tricky. ![]() The difference is something to do with how ggplot2 treats polygons - (my data has only 2208 unique elements, and 2208 unique polys, not 1.6 million), but I can't seem to identify how this is put together. I've tried a number of different ways of accessing these data - but am not able to actually figure out how to colour it based on anything within the element. P + an error about the number of elements within the aesthetic call: Error: Aesthetics must be either length 1 or the same as the data (1651526): colour, fill The following code p = ggplot(plotData, aes(x = long, y = lat, group = id)) I have my polygons, and in the element, I have the relevant data I'm interested in mapping/visualising, in a SpatialPolygonDataFrame called plotData. The issue is how ggplot2 deals with spatial polygons, and factors that define or are mapped to them. I'm currently on the cusp of getting done what I want to get done - but having a slight problem with referring to my data from within R's sp SpatialPolygonDataFrame object.
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