Basically, I have a spLines Object with a bunch of lines, and after I am rasterizing it - rast2 <- rasterize(spLines, rast, fun='count') - in order to get a grid frequency map. However, I'd like to convert and save that rast2 Object into a Shapefile. Is that possible?
Thanks in advance!
this raster package will do the trick but there's a much faster way to do it if you have python installed by using gdal_polygonise.py
Related
I have a RGB tiff files (from cellProfiler) which I want to import to R, label and arrange - as part of a high throughput analysis. The closest I get is using:
library(tiff)
library(raster)
imageTiff <- tiff::readTIFF(imagePath[i])
rasterTiff <- raster::as.raster(imageTiff)
raster::plot(rasterTiff)
raster::plot plots the image nicely but I can't catch the output and use it with gridExtra or add labels.
In addition I tried rasterVis with levelPlot and multiple other ways importing the tiff and then converting them to grob or ggplots.
However, I can't get anything to work and would like to ask if R is even suited at all for that task?
Thank you very much for your help!
Okay, I think that is the most straight forward way and possible also the most obvious one.
I import JPEG or TIFF files with jpeg::readJPEG or tiff::readTIFF respectively. Both transform the images to a raster format which is compatible with rasterGrid() and following grid.arrange etc.
library(jpeg)
library(tiff)
library(grid)
imageJPEG <- grid::rasterGrob(jpeg::readJPEG("test.jpeg"))
imageTIFF <- grid::rasterGrob(tiff::readTIFF("test.tiff"))
grid.arrange(imageJPEG , imageJPEG , imageJPEG)
grid.arrange(imageTIFF , imageTIFF, imageTIFF)
For my purpose that is perfect since tasterGrob does not alter the raster matrix values. Labeling might be a bit tricky but overall it is a grid/grob problem from here on.
I am running into a problem with the "writeRaster" function in the raster package in R. I am importing a raster (TIF) that I made in ArcGIS (a distance to feature raster).
My goal was to resample the distance raster to the correct resolution and extent, then "mask" it with the appropriate raster to crop it to the shape I require. When I check the results of the mask with the basic plot function, everything looks great and I can see that each pixel in the new masked raster has a distance value.
However, when I write this raster to a file using the writeRaster function, the resulting raster looks like "swiss cheese" and has missing values for any distance over 35km. After much reading, I cannot find any documentation to suggest that there is a way to modify the maximum value set by writeRaster---or that it should even be setting a max value. I have included my code and the basic plots below. A big thank you to anyone who attempts to help me with this!
#Read in distance to fresh water raster
distFW <- raster("D:/Academia/Arc Data/Grackle/NicaCR_90mlayers/dist_FW.tif")
[plot(distFW)][1]
#Resample this layer to the desired resolution and template
NiCR_DistFW<-as.integer(resample(distFW,NiCRrast.tmpl,method="ngb"))
#essentially the same as the first plot
[plot(NiCR_DistFW)][2]
#Mask the resampled raster to the desired shape
NiCR.DistFW.mask.utm <- mask(NiCR_DistFW,NiCR_Mask) #with CA countries cut out.
[plot(NiCR.DistFW.mask.utm)][3]
#write raster to file (this is where things get weird)
writeRaster(x=NiCR.DistFW.mask.utm, filename='DistFWmask2.tif', format='GTiff', datatype='INT2S') #a way to ensure INT2S
#read the newly written raster file in to R so we can review it
dFW <-raster("DistFWMask2.tif")
[plot(dFW)_writeRaster_result][4]
[1]: https://i.stack.imgur.com/v9RkK.jpg
[2]: https://i.stack.imgur.com/v2DG3.jpg
[3]: https://i.stack.imgur.com/cCwJe.jpg
[4]: https://i.stack.imgur.com/MjWj7.jpg
As you can see from plot 4, an undesirable max value has been set. I was the raster I write to file to look like the one in plot 3, not plot 4.
Thanks in advance for any advice.
Well friends, after taking an hour to detail my question I managed to figure out the answer myself. It had to do with setting the datatype.
INT2S has a maximum value of 32,767
by switching it to INT4S, I capture the full range of values in my raster.
Problem solved!
Is there an easy way to convert a Spatial Lines into a Spatial Polygon object within R?
Reproducible Example
I have put together a reusable dataset here, which is downloaded from OpenStreetMaps through the overpass package. This extracts the locations of a few airports in South England:
devtools::install_github("hrbrmstr/overpass")
library(overpass)
library(raster)
library(sp)
# Write Query
query_airport <- '
(node["aeroway"="aerodrome"](50.8, -1.6,51.1, -1.1);
way["aeroway"="aerodrome"](50.8, -1.6,51.1, -1.1);
relation["aeroway"="aerodrome"](50.8, -1.6,51.1, -1.1);
);
out body;
>;
out skel qt;
'
# Run query
shp_airports <- overpass::overpass_query(query_airport, quiet = TRUE)
crs(shp_airports) <- CRS("+init=epsg:4326") # Add coordinates
shp_airports <- shp_airports[,1]
# Plot Results
plot(shp_airports, axes = T)
However, the data is of the class "SpatialLinesDataFrame". This really messes things up if you want to do any form of spatial joins or intersections, as it only acknowledges the edge of the region.
Potential Leads
I was exploring the use of SpatialLines2PolySet within the maptools package, but in my time exploring I produced nothing but error codes, so I didn't think there would be any worth including these within the question. There is some guidance about these functions here: https://rdrr.io/rforge/maptools/man/SpatialLines2PolySet.html
Notes
I have searched the web and SO to see find similar questions and struggled to find any questions directly referring to this. A lot seem to reference converting SpatialPoints -> SpatialLineDataFrames , but not SpatialLineDataFrames -> SpatialPolygonDataFrames. This question is similar but lacks any answers (or a reproducible dataset): Close a spatial line into a polygon using a shapefile
In addition, it seems strange that this would be difficult as it is something which can be done so easily in ArcGIS using the "Feature to Polygon" tool. This function requires no additional arguments specified and it works perfectly.
A way to solve the problem would be to use the library sf. After your query
library(sp)
library(raster)
library(sf)
sf_airports <- st_as_sf(shp_airports)
sf_airports_polygons <- st_polygonize(sf_airports)
shp_airports <- as(sf_airports_polygons, "Spatial") # If you want sp
class(shp_airports)
How to convert from shape into polygon in R? There was shape2poly(shapefiles) but this function have been removed, are shapefiles, maptools, spdep still packages for handling maps in R?
I tend to use the OGR stuff, as it lets me work with data from a range of sources (geodatabases, kml, etc).
library(rgdal)
mylayer <- readOGR(dsn="/path/to/folder/containing/shapefile",
layer="shapefilename-minus-dot-shp")
I'm trying to use a kriging function to create vertical maps of chemical parameters in an ocean transect, and I'm having a hard time getting started.
My data look like this:
horiz=rep(1:5, 5)
depth=runif(25)
value = horiz+runif(25)/5
df <- data.frame(horiz, depth, value)
The autoKrige function in the automap package looks like it should do the job for me but it takes an object of class SpatialPointsDataFrame. As far as I can tell, the function spTransform in package rgdal creates SpatialPointsDataFrame objects, but there are two problems:
OSX binaries of this aren't available from CRAN, and my copy of RStudio running on OXS 10.7 doesn't seem to be able to install it, and
This function seems to work on lat/long data and correct distance values for the curvature of the Earth. Since I'm dealing with a vertical plane (and short distances, scale of hundreds of meters) I don't want to correct my distances.
There's an excellent discussion of kriging in R here, but due to the issues listed above I don't quite understand how to apply it to my specific problem.
I want a matrix or dataframe describing a grid of points with interpolated values for my chemical parameters, which I can then plot (ideally using ggplot2). I suspect that the solution to my problem is considerably easier than I'm making it out to be.
So there a a few question you want answered:
The spTransform function does not create SPDF's, but transforms between projections. To create a SPDF you can use a simple data.frame as a start. To transform df to a SPDF:
coordinates(df) = c("horiz", "depth")
OS X binaries of rgdal can be found at http://www.kyngchaos.com. But I doubt if you need rgdal.
spTransform can operate on latlong data, but also on projected data. But I do not think you need rgdal, or spTransform, see also point 1.
After you create the SPDF using point 1, you can use the info at the post you mentioned to go on.