raster

I'm trying to place a grid over San Jose like this: Grid of San Jose You can make the grid visually using the following code: ca_cities = tigris::places(state = "CA") #using tigris package to get shape file of all CA cities sj = ca_cities[ca_cities$NAME == "San Jose",] #specifying to San Jose UTM_ZONE = "10" #the UTM zone for San Jose, will be used to convert the proj4string of sj into UTM main_sj = sj@polygons[[1]]@Polygons[[5]] #the portion of the shape file I focus on. This is the boundary of san jose #converting the main_sj polygon into a spatialpolygondataframe using the sp package tst_ps

I would like to merge polygon data and raster data into one dataframe for purposes of then using randomForests package in R. This involves first extracting the mean raster value per polygon. So far, I have the following: #load libraries library(raster) library(rgdal) library(sp) library(maptools) #import raster data r <- raster("myRasterdata.tif") #import polygon data p <- readShapePoly("myPolydata.shp") #extract mean raster value for each polygon ExtractMyData <- extract(r, p, small=TRUE, fun=mean, na.rm=TRUE, df=FALSE, nl=1, sp=TRUE) # note I have also tried this with df=TRUE and sp=FALSE

The longitude in CMIP5 future climate data is in 0 - 360 degree. How can I convert it to -180 - 180 degree using the raster package? I tried with shift(r0,-180) and shift(r0,-360) . It does not work. Any help will be appreciated. r0 here is a raster. Try rotate() . Its help page even mentions its utility with the type of data you're dealing with: Rotate a Raster* object that has x coordinates (longitude) from 0 to 360, to standard coordinates between -180 and 180 degrees. Longitude between 0 and 360 is frequently used in data from global climate models. Here's a simple reproducible example to

I would like to: Reclassify the raster ras into nine classes using reclassify Provide a colorkey with values written beside each colour (see sample plot below). The colorkey should not be split, as in the sample plot, but rather shown as a single colour ramp with 9 colours. Display the data using levelplot function (this I can do) Thanks for your help. AEZ library(rasterVis) Please use these colors: col <- colorRampPalette(c("yellow4", "yellow", "orange", "red3", "darkred")) levelplot(reclassras, col.regions = col) Below is the dput of my raster layer: ras=new("RasterLayer" , file = new("

Need solution, help will be much appreciated. In the following code I am creating three rasters. I then create a random number of point locations on this raster and I am receiving a list of three matrices with coordinates of those random locations called samples . I then take those locations and sample raster values to receive samplevalues . What I want to change is that I want to create a set of 100,150,200 and 250 random point locations ( numberv ). So after generating these locations and receiving a list of locations, each raster will be sampled length(numberv) times (in this case 4 times).

Using R, I am trying to extract data from a raster layer using a polygon layer. The polygons are much smaller than the raster cells: Now I call extract() from raster library: a <- extract(raster, polygons, weights = TRUE, small = TRUE) a # ... # [[1551]] # value weight # 209 0.03 # top left cell - more than 50% of the polygon area There are two problems - the weight is the proportion of the cell area covered by the polygon, and the weights are rounded to 1/100. In my case, only the top left cell is present in the output (value 209) - the weight of 3 other cells was rounded to zero and they

I have problems plotting a raster with factor values using ggplot2. library(ggplot2) library(raster) first, load raster data f <- system.file("external/test.grd", package="raster") r <- raster(f) extract coordinates and values val <- getValues(r) xy <- as.data.frame(xyFromCell(r,1:ncell(r))) xy <- cbind(xy,val) plot the grid using geom_raster(). Everything works fine. ggplot(xy, aes(x=x, y=y, fill=val)) + geom_raster() + coord_equal() I don not have a continuous raster, but a classified. Reclass the raster: r <- reclass(r, c(0,500,1, 500,2000,2)) val <- getValues(r) xy <- as.data.frame