Facilities to work with vector and raster data in efficient repeatable and systematic work flow. Missing functionality in existing packages is included here to allow extraction from raster data with 'simple features' and 'Spatial' types and to make extraction consistent and straightforward. Extract cell numbers from raster data and return the cells, values and weights as a data frame rather than as lists of matrices or vectors. The functions here allow spatial data to be used without special handling for the format currently in use.
raster package is extremely powerful in the R ecosystem for spatial data. It can be used very efficiently to drive data extraction and summary tools using its consistent cell-index and comprehensive helper functions for converting between cell values and less abstract raster grid properties.
Tabularaster provides some more helpers for working with cells and tries to fill some of the (very few!) gaps in raster functionality. When raster returns cell values of hierarchical objects it returns a hierarchical (list) of cells to match the input query.
Tabularaster provides on a few simple functions.
as_tibble- convert to data frame with options for value column and cell, dimension and date indexing
cellnumbers- extract of cell index numbers as a simple data frame with "object ID" and "cell index"
extentFromCells- create an extent from cell index numbers
index_extent- create an index extent, essentially
extent(0, ncol(raster), 0, nrow(raster))
All functions that work with
sp Spatial also work with `sf simple features.
There is some overlap with
spex while I figure out where things belong.
Install from CRAN, or get the development version from Github.
Basic usage is to extract the cell numbers from an object, where object is a a matrix of points, a
Spatial object or a
simple features sf object.
cells <- cellnumbers(raster, object)
The value in this approach is not for getting cell numbers per se, but for using those downstream. The cell number is an index into the raster that means the geometric hard work is done, so we can apply the index for subsequent extractions, grouping aggregations, or for determining the coordinates or other structure summaries of where the cell belongs.
cells %>% mutate(value= extract(raster, cell_)) %>% group_by(object_) %>% summarize(mean(value))## summarize by cell groupingcells %>% mutate(value= extract(raster, cell_)) %>% group_by(cell_) %>% summarize(mean(value))
The utility of this is very much dependent on individual workflow, so this in its own right is not very exciting.
Tabularaster simply provides an easier way to create your tools.
See the vignettes for more.
xy argument to
fix missing vignette knitr reference
new oisst data set
bufext made defunct
added index extent functions
removed decimate function
fixed unnecessary lapply call for the points case, so that is now not slow
extra handling for as_tibble, value is now optional so that data-less rasters can be used to build cell and dimension indices
new vignette stub to list all relevant raster functions
object_ is now integer, 1-based to match the rows in query
consolidated on the central tools, removed experiments
migrated the buffer extent logic to
as_tibble for rasters
added boundary function
new data sets rastercano and polycano
new functions decimate, cellnumbers and bufext
NEWS.md file to track changes to the package.