How `raster` functions map to `stars` functions

Here's an attempt at the table describing how raster functions map to stars functions, discussed in issue #122. This table uses the functionality of the raster package as a template; it may be incomplete, imprecise or plain wrong, so take it with a pinch of salt. Any comment or correction is hugely appreciated, please contribute!

Some of the functions (filter, slice, mutate, select, pull, ...) are provided via dplyr, which must be loaded. See ?stars::dplyr.

COMMENT LEGEND

? = Not sure / unknown

* = Not present, low priority

# = Not present, high priority

NA = Not available by design

Creating objects

raster	stars	Comment
{raster, stack, brick} (read)	read_stars or read_stars(along = ...)
{stack, brick} (concatenate layers)	c or c(along = ...)
subset	{[ ] , slice, filter}
addLayer	c() or c(along = ...)
dropLayer	{[ ] , slice, filter}
unstack	combine lapply and {[ ] , slice, filter}

Changing spatial extent and/or resolution of objects

raster	stars	Note/comment
merge	c	#, currently only works for adjacent objects
mosaic
crop	filter, st_crop
setExtent		# maybe use st_warp?
trim		#
aggregate	aggregate	WIP; raster's aggregate with fact=2 will not work, use st_warp in that case?
disaggregate		# use st_warp(use_gdal = TRUE)?
resample	{st_transform, st_warp}
projectRaster	{st_transform, st_warp}
shift		#, now use st_set_dimensions
flip	[] with reversed index	#
rotate		*
t		NA

Cell based computation

raster	stars	Note/comment
calc	st_apply
overlay	c(along = , ...) %>% st_apply(...)
cover	[ ] <-
mask	[ ] <-
cut	cut
subs
reclassify		* forcats::fct_recode ?
init	[ ] <-
stackApply	{[ ] , slice, filter} %>% st_apply
stackSelect

Spatial contextual computation

raster	stars	Note/comment
distance		#
gridDistance		*
distanceFromPoints		#
direction		*
focal		*
localFun		*
boundaries	st_as_sf(as_points=FALSE, merge=TRUE, connect8=TRUE)
clump
adjacent		*
area	st_area
terrain		#
Moran

Model predictions

raster	stars	Note/comment
predict	predict
interpolate	gstat::idw, gstat::krige	st_warp has raster-raster interpolations of gdalwarp

Data type conversion

raster	stars	Note/comment
rasterize	st_as_stars
rasterToPoints	st_as_sf(as_points=TRUE)
rasterToPolygons	st_as_sf(as_points=FALSE, ...), st_polygonize
rasterToContour		st_contour
rasterFromXYZ
rasterFromCells

Summarizing

raster	stars	Note/comment
cellStats	st_apply
summary	print, summary(as.vector(. %>% pull))
freq	table	*
crosstab
unique	unique(as.vector(. %>% pull))
zonal		*

Accessing values of objects

raster	stars	Note/comment
getValues	{pull, }
getValuesBlock	{[ ] , slice, filter} %>% pull
getValuesFocal	{[ ] , slice, filter} %>% pull
as.matrix		currently behaves somewhat unexpectedly *
as.array		currently behaves somewhat unexpectedly *
extract (by cell)	{[ ] , slice, filter}
extract (by polygon)	x[sf_object]
extract (by point)	aggregate(stars_object, sf_object, function(x) x[1], as_points = FALSE)
sampleRandom		*
sampleRegular		*
minValue	purrr::map(x, min)
maxValue	purrr::map(x, max)
setMinMax	NA

Plotting

raster	stars	Note/comment
plot	plot, geom_stars
plotRGB	plot(x, rgb =...)
spplot	-
image	image
persp	-
contour	(st_contour, then sf::plot)
filledContour	(same)
text	text
hist	hist(x[[1]])
barplot
density
pairs
boxplot

Getting and setting dimensions

raster	stars	Note/comment
ncol	dim(x)[1]	or use name instead of 1; cols may be the second dimension!
nrow	dim(x)[2]	or use name instead of 2; rows may be the first dimension!
ncell	prod(dim(x))
res	st_dimensions	can also not be a constant in case of rectilinear or curvilinear grids
nlayers	-	there is no concept of layers in stars
names	names
xres	st_dimensions, look for delta	may not be a constant in case of rectilinear or curvilinear grids
yres	st_dimensions, look for delta	may not be a constant in case of rectilinear or curvilinear grids
xmin	st_bbox(x)[1]
xmax	st_bbox(x)[3]
ymin	st_bbox(x)[2]
ymax	st_bbox(x)[4]
extent	st_bbox(x)	different ordering of numbers
origin	-
projection	st_crs(x)
isLonLat	st_is_longlat(st_crs(x))
filename		stars_proxy objects carry file names where otherwise the array data is
bandnr		stars has no general concept of bands
nbands	dim(x)[3]	may also be time; bands may also be in another dimension, or have another name
compareRaster	all.equal(st_dimensions(x), st_dimensions(y))	*
NAvalue	-

Computing row, column, cell numbers and coordinates

raster	stars	Note/comment
xFromCol	st_get_dimension_values(., 'x')[col]	I am not sure how to generally get the x dimension name - here it is x
yFromRow	st_get_dimension_values(., 'y')[row]	I am not sure how to generally get the y dimension name - here it is y
xFromCell
yFromCell
xyFromCell
colFromX		*
rowFromY		*
rowColFromCell		NA
cellFromXY		NA
cellFromRowCol		NA
cellsFromExtent		NA
coordinates	st_coordinates
validCell		NA
validCol	col %>% between(st_dimensions(.)$x$from, st_dimensions(.)$x$to)	raster columns are not always named "x"
validRow	row %>% between(st_dimensions(.)$y$from, st_dimensions(.)$y$to)	raster columns are not always named "y"
setValues	[ ] <-
writeRaster	write_stars	currently uses GDAL, somewhat limited
KML

The format of this table follows the raster-package entry in the raster manual.