raster <-> stars migration table

Here's a first attempt at the `raster <-> stars` migration table we talked about in #112. Many things are still to define, and I have added my comments on what could or should be implemented (not necessarily in `stars`) in my humble opinion.
This migration table is currently very rough and many things are for sure imprecise or plain wrong. Any comment or correction is hugely appreciated. In a future I might add some info on `xarray` and `GDAL` too, but I'll have to do some more research since I do not use them much.

I post this here so we can get some useful discussion before it goes on the wiki.

## LEGEND

? = I am not sure / I do not know how to do that in `stars`

\* = Not present, _could_ be implemented

\# = Not present, _should_ be implemented (imho)


## Creating objects
raster | stars
------------ | -------------
{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
------------ | -------------
merge | c (only for adjacent objects) #
mosaic | ?
crop | filter
extend | #
trim | #
aggregate | # (aggregate.stars, WIP?)
disaggregate | #
resample | {st_transform, st_warp}
projectRaster | {st_transform, st_warp}
shift | #
flip | #
rotate | *
t | ?

## Cell based computation
raster | stars
------------ | -------------
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
------------ | -------------
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
------------ | -------------
Predict | ?
Interpolate | ?

## Data type conversion
raster | stars
------------ | -------------
rasterize | st_as_stars
rasterToPoints | st_as_sf(as_points=TRUE)
rasterToPolygons | st_as_sf(as_points=FALSE, ...)
rasterToContour | # (st_contour)
rasterFromXYZ |
rasterFromCells |

## Summarizing
raster | stars
------------ | -------------
cellStats | st_apply
summary | summary(as.vector(. %>% pull))
freq | *
crosstab |
unique | unique(as.vector(. %>% pull))
zonal | *

## Accessing values of objects
raster | stars
------------ | -------------
getValues | {pull, [[ ]]}
getValuesBlock | {[ ] , slice, filter} %>% pull
getValuesFocal | {[ ] , slice, filter} %>% pull
as.matrix | is present, but does weird stuff *
as.array | is present, but does weird stuff *
extract (by cell) | {[ ] , slice, filter}
extract (by poligon) | [sf_object]
extract (by point) | #
sampleRandom | *
sampleRegular | *
minValue |
maxValue |
setMinMax |

## Plotting
raster | stars
------------ | -------------
plot | plot, geom_stars
plotRGB | ?
spplot |
image |
persp |
contour |
filledContour |
text |
hist |
barplot |
density |
pairs |
boxplot |

## Getting and setting dimensions
raster | stars
------------ | -------------
ncol | st_dimensions(.)$x %>% with(to - from + 1)    (I am not sure how to generally get the x dimension name - here it is x)
nrow | st_dimensions(.)$y %>% with(to - from + 1)    (I am not sure how to generally get the y dimension name - here it is y)
ncell | multiply the above
res | st_dimensions - can also not be a constant in case of irregular grids
nlayers | st_dimensions(.)$time %>% with(to - from + 1)    (there is no way to generally get the z dimension name - here it is time)
names | names
xres | st_dimensions - can also not be a constant in case of irregular grids
yres | st_dimensions - can also not be a constant in case of irregular grids
xmin | st_bbox[1]
xmax | st_bbox[3]
ymin | st_bbox[2]
ymax | st_bbox[4]
extent | st_bbox
origin |
projection | st_crs
isLonLat | st_crs(.) == st_crs(4326)
filename | not available by design
bandnr |
nbands |
compareRaster | *
NAvalue |

## Computing row, column, cell numbers and coordinates
raster | stars
------------ | -------------
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 | not available by design
yFromCell | not available by design
xyFromCell | not available by design
colFromX | *
rowFromY | *
rowColFromCell | not available by design
cellFromXY | not available by design
cellFromRowCol | not available by design
cellsFromExtent | not available by design
coordinates | st_coordinates
validCell | not available by design
validCol | col %>% between(st_dimensions(.)$x$from, st_dimensions(.)$x$to)    (I am not sure how to generally get the x dimension name - here it is x)
validRow | row %>% between(st_dimensions(.)$y$from, st_dimensions(.)$y$to)    (I am not sure how to generally get the y dimension name - here it is 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](https://cran.r-project.org/web/packages/raster/raster.pdf).

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raster <-> stars migration table #122

LEGEND

Creating objects

Changing spatial extent and/or resolution of objects

Cell based computation

Spatial contextual computation

Model predictions

Data type conversion

Summarizing

Accessing values of objects

Plotting

Getting and setting dimensions

Computing row, column, cell numbers and coordinates

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raster	stars
{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}

raster	stars
merge	c (only for adjacent objects) #
mosaic	?
crop	filter
extend	#
trim	#
aggregate	# (aggregate.stars, WIP?)
disaggregate	#
resample	{st_transform, st_warp}
projectRaster	{st_transform, st_warp}
shift	#
flip	#
rotate	*
t	?

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

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

raster	stars
rasterize	st_as_stars
rasterToPoints	st_as_sf(as_points=TRUE)
rasterToPolygons	st_as_sf(as_points=FALSE, ...)
rasterToContour	# (st_contour)
rasterFromXYZ
rasterFromCells

raster	stars
cellStats	st_apply
summary	summary(as.vector(. %>% pull))
freq	*
crosstab
unique	unique(as.vector(. %>% pull))
zonal	*

raster	stars
getValues	{pull, [[ ]]}
getValuesBlock	{[ ] , slice, filter} %>% pull
getValuesFocal	{[ ] , slice, filter} %>% pull
as.matrix	is present, but does weird stuff *
as.array	is present, but does weird stuff *
extract (by cell)	{[ ] , slice, filter}
extract (by poligon)	[sf_object]
extract (by point)	#
sampleRandom	*
sampleRegular	*
minValue
maxValue
setMinMax

raster	stars
plot	plot, geom_stars
plotRGB	?
spplot
image
persp
contour
filledContour
text
hist
barplot
density
pairs
boxplot

raster	stars
ncol	st_dimensions(.)$x %>% with(to - from + 1) (I am not sure how to generally get the x dimension name - here it is x)
nrow	st_dimensions(.)$y %>% with(to - from + 1) (I am not sure how to generally get the y dimension name - here it is y)
ncell	multiply the above
res	st_dimensions - can also not be a constant in case of irregular grids
nlayers	st_dimensions(.)$time %>% with(to - from + 1) (there is no way to generally get the z dimension name - here it is time)
names	names
xres	st_dimensions - can also not be a constant in case of irregular grids
yres	st_dimensions - can also not be a constant in case of irregular grids
xmin	st_bbox[1]
xmax	st_bbox[3]
ymin	st_bbox[2]
ymax	st_bbox[4]
extent	st_bbox
origin
projection	st_crs
isLonLat	st_crs(.) == st_crs(4326)
filename	not available by design
bandnr
nbands
compareRaster	*
NAvalue

raster	stars
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	not available by design
yFromCell	not available by design
xyFromCell	not available by design
colFromX	*
rowFromY	*
rowColFromCell	not available by design
cellFromXY	not available by design
cellFromRowCol	not available by design
cellsFromExtent	not available by design
coordinates	st_coordinates
validCell	not available by design
validCol	col %>% between(st_dimensions(.)$x$from, st_dimensions(.)$x$to) (I am not sure how to generally get the x dimension name - here it is x)
validRow	row %>% between(st_dimensions(.)$y$from, st_dimensions(.)$y$to) (I am not sure how to generally get the y dimension name - here it is y)
setValues	[ ] <-
writeRaster	write_stars (currently uses GDAL, somewhat limited)
KML

raster <-> stars migration table #122

Description

LEGEND

Creating objects

Changing spatial extent and/or resolution of objects

Cell based computation

Spatial contextual computation

Model predictions

Data type conversion

Summarizing

Accessing values of objects

Plotting

Getting and setting dimensions

Computing row, column, cell numbers and coordinates

Metadata

Metadata

Assignees

Labels

Type

Projects

Milestone

Relationships

Development

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