R Multipoint Conversion – Converting Points to Multipoint with Z Values

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I have a dataset of POINTS with Z values and I would like to convert it to a MULTIPOINT dataset grouped by ID

I used the code from Converting points to polygons by group and it successfully grouped my points to multipoint but it dropped the z value.

Here is the code from the top answer of the other question which I based my own on:

set.seed(999)
xy = data.frame(x=runif(24), y=runif(24))
xy$ID = rep(1:6, each = 4)
head(xy)

xys = st_as_sf(xy, coords=c("x","y"))

polys = xys %>% 
  dplyr::group_by(ID) %>% 
  dplyr::summarise() %>%
  st_cast("POLYGON")

plot(polys)

Here is what I actually used on my data

lsp_mp2 <- lsp %>%
  dplyr::group_by(lsp$ID) %>%
  dply::summarise() %>%
  st_cast('MULTIPOINT')

How do I retain the Z value?

There is a column in my data with a Z value. The output of this function gave me an attribute table with only one column. All of the other columns were lost with this function.

Best Answer

Make a spatial sample data set with xyz geometry and an ID column:

d = data.frame(x=runif(20),y=runif(20),z=runif(20),id=sample(1:3,20,TRUE))
sd = st_as_sf(d, coords=1:3)

split it by the ID:

sdsplit = split(sd, sd$id)

run st_combine on each element:

sdcombine = Map(st_combine, sdsplit)

and join them all together:

sdset = do.call(c, sdcombine)

giving MULTIPOINT geometries for the ID groupings with XYZ preserved

> sdset
Geometry set for 3 features 
Geometry type: MULTIPOINT
Dimension:     XYZ
Bounding box:  xmin: 0.0440282 ymin: 0.03704024 xmax: 0.9970125 ymax: 0.9578563
z_range:       zmin: 0.3112239 zmax: 0.7384504
CRS:           NA
MULTIPOINT Z ((0.2610551 0.9356371 0.3112239), ...
MULTIPOINT Z ((0.7179385 0.3060261 0.0004098632...
MULTIPOINT Z ((0.8213335 0.3986064 0.8561131), ...

And if you want that as a one-liner (and I'll use Reduce instead of do.call for no good reason):

sdset = Reduce(c, Map(st_combine,split(sd,sd$id)))

Only uses base packages and sf.

Related Solutions

[GIS] Using sampleRandom() from large raster without NA values in R

Just pad your desired number of random samples and then sample back down to the correct n. This should account for the occasional NA that are produced and subsequently removed with the na.rm=TRUE argument.

    require(raster)
    # Create example data
    r1 <- raster(ncols=500, nrows=500, xmn=0)
      r1[] <- runif(ncell(r1))
    r2 <- raster(ncols=500, nrows=500, xmn=0)
      r2[] <- runif(ncell(r2))  
    r <- stack(r1,r2)

    # Sample size
    n=50

    # Random sample of raster  
    r.samp <- sampleRandom(r, size=(n+20), na.rm=TRUE, sp=FALSE, asRaster=FALSE) 
      dim( r.samp )[1]

   # Create a random sample of n size to subset r.samp
   #   (works with dataframe, matrix and sp objects)
   r.samp <- r.samp[sample( 1:dim(r.samp)[1], n),]
    dim ( r.samp )[1]

If you can read the raster into memory an approach in sp would be to use rgdal to create a SpatialGridDataFrame the coerce it into a SpatialPointsDataFrame so you can easily remove NA's and end up with a point object of your subsample. You can then sample subsequent rasters using this sp point object. The @data dataframe can be extract and coerced into a matrix for your purposes.

require(sp)
require(rgdal)
require(raster)

n=50 # Number of random samples

# Read raster data using rgdal, results in SpatialGridDataFrame 
r <- readGDAL(system.file("external/test.ag", package="sp")[1])
  class(r)
    spplot(r, "band1")

# Coerce into SpatialPointsDataFrame    
r <- as(r, "SpatialPointsDataFrame")      

# remove NA's   
r@data <- na.omit(r.pts@data)
  plot(r, pch=20)

# Create random sample. Object is a SpatialPointsDataFrame     
r.samp <- r[sample(1:dim(r)[1], n),]
  plot(r.samp, pch=20, col="red", add=TRUE)   
    class(r.samp)

#  Use r.samp sp object for additional sampling 
#    Add extra column and coerce to raster stack
r2 <- readGDAL(system.file("external/test.ag", package="sp")[1])
  r2@data <- data.frame(r2@data, band2=runif(dim(r2)[1]) ) 
    r2 <- stack(r2)

# Extract raster values using r.samp object
r.samp@data <- data.frame(r.samp@data, band2=extract(r2[[2]], r.samp))
  str(r.samp@data)

Creating Lines Between Point Pairs in R – Spatial Analysis

Here is a tidyverse method of doing it, starting with your table from before converting to sf. The approach is to create a long-form table where each row is a start or end point, but include a lineid so that you can group_by on it and summarise to union the right points together, and then st_cast to LINESTRING.

library(tidyverse)
library(sf)
#> Linking to GEOS 3.6.1, GDAL 2.2.3, proj.4 4.9.3
table <- structure(list(NOMBRE = c("AL011900", "AL011900", "AL011900", "AL011900", "AL021900", "AL021900", "AL021900", "AL041905", "AL041905", "AL041905", "AL041905"), LAT = c(15, 15.2, 15.3, 15.4, 19, 19.5, 20, 36.3, 37.9, 39.6, 41), LONG = c(-42.1, -43.4, -44.7, -45.6, -59.3, -60, -60.6, -48.6, -47.9, -47.1, -46), INT = c(18.0054, 18.0054, 18.0054, 18.0054, 33.4386, 36.0108, 38.583, 46.2996, 43.7274, 41.1552, 41.1552)), row.names = c(NA, -11L), class = c("tbl_df", "tbl", "data.frame"), spec = structure(list(cols = list(NOMBRE = structure(list(), class = c("collector_character", "collector")), LAT = structure(list(), class = c("collector_double", "collector")), LONG = structure(list(), class = c("collector_double", "collector")), FECHA = structure(list(format = ""), class = c("collector_datetime", "collector")), INT = structure(list(), class = c("collector_double", "collector"))), default = structure(list(), class = c("collector_guess", "collector"))), class = "col_spec"))

table_sf <- table %>%
  group_by(NOMBRE) %>%
  mutate(
    lineid = row_number(), # create a lineid
    LONG_end = lead(LONG), # create the end point coords for each start point
    LAT_end = lead(LAT)
  ) %>% 
  unite(start, LONG, LAT) %>% # collect coords into one column for reshaping
  unite(end, LONG_end, LAT_end) %>%
  filter(end != "NA_NA") %>% # remove nas (last points in a NOMBRE group don't start lines)
  gather(start_end, coords, start, end) %>% # reshape to long
  separate(coords, c("LONG", "LAT"), sep = "_") %>% # convert our text coordinates back to individual numeric columns
  mutate_at(vars(LONG, LAT), as.numeric) %>%
  st_as_sf(coords = c("LONG", "LAT")) %>% # create points
  group_by(NOMBRE, INT, lineid) %>%
  summarise() %>% # union points into lines using our created lineid
  st_cast("LINESTRING")

plot(table_sf[, 1:2])

You can see in the plot that each line between two points has its own INT as requested.

Example

Best Answer

Related Solutions

[GIS] Using sampleRandom() from large raster without NA values in R

Creating Lines Between Point Pairs in R – Spatial Analysis

Related Question