# 3. stars tidyverse methods

#### Edzer Pebesma

For a better version of the stars vignettes see https://r-spatial.github.io/stars/articles/

This vignette shows how some of the tidyverse verbs can be used on `stars` objects.

The `stars` and `tidyverse` packages are loaded by

    library(stars)
    library(dplyr)

Methods now available for class `stars` are

    methods(class = "stars")
    ##  [1] $<-               %in%              Math              Ops
    ##  [5] [                 [<-               [[<-              adrop
    ##  [9] aggregate         aperm             as.Date           as.POSIXct
    ## [13] as.data.frame     as.tbl_cube       as_tibble         c
    ## [17] coerce            contour           cut               dim
    ## [21] dimnames          dimnames<-        drop_units        droplevels
    ## [25] expand_dimensions filter            hist              image
    ## [29] initialize        is.na             merge             mutate
    ## [33] plot              prcomp            predict           print
    ## [37] pull              rename            select            show
    ## [41] slice             slotsFromS3       split             st_apply
    ## [45] st_area           st_as_sf          st_as_sfc         st_as_stars
    ## [49] st_bbox           st_coordinates    st_crop           st_crs
    ## [53] st_crs<-          st_dimensions     st_dimensions<-   st_downsample
    ## [57] st_extract        st_geometry       st_geotransform   st_geotransform<-
    ## [61] st_interpolate_aw st_intersects     st_join           st_mosaic
    ## [65] st_normalize      st_redimension    st_rotate         st_sample
    ## [69] st_set_bbox       st_transform      st_write          time
    ## [73] transmute         write_stars
    ## see '?methods' for accessing help and source code

We will work with a three-band section of a landsat image:

    system.file("tif/L7_ETMs.tif", package = "stars") %>%
        read_stars -> x
    x
    ## stars object with 3 dimensions and 1 attribute
    ## attribute(s):
    ##              Min. 1st Qu. Median     Mean 3rd Qu. Max.
    ## L7_ETMs.tif     1      54     69 68.91242      86  255
    ## dimension(s):
    ##      from  to  offset delta                     refsys point x/y
    ## x       1 349  288776  28.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
    ## y       1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y]
    ## band    1   6      NA    NA                         NA    NA

## `slice`

`slice` slices a sub-array out of the cube; this is done by specifying the dimension on which to act, and the slice number.

    x %>% slice(band, 6) -> x6
    x6
    ## stars object with 2 dimensions and 1 attribute
    ## attribute(s):
    ##              Min. 1st Qu. Median     Mean 3rd Qu. Max.
    ## L7_ETMs.tif     1      32     60 59.97521      88  255
    ## dimension(s):
    ##   from  to  offset delta                     refsys point x/y
    ## x    1 349  288776  28.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
    ## y    1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y]

It returns a lower-dimensional array if a single element is selected along the slice dimension.

## `filter`

Similar to `slice`, `filter` selects on dimensions but evaluates their values rather than their index: in

    x %>% filter(x > 289000, x < 291000, band > 3) -> x7
    x7
    ## stars object with 3 dimensions and 1 attribute
    ## attribute(s):
    ##              Min. 1st Qu. Median     Mean 3rd Qu. Max.
    ## L7_ETMs.tif     5      54     70 71.79194      88  252
    ## dimension(s):
    ##      from  to  offset delta                     refsys point x/y
    ## x       1  70  289004  28.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
    ## y       1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y]
    ## band    1   3       4     1                         NA    NA

the subarray is created based on the x coordinate values.

Note that `filter` converts the object to a `tbl_cube`, and uses the `dplyr` `filter` method for `tbl_cube` objects. This has the limitation that `stars` objects with rectilinear, curvilinear or simple feature geometries cannot be handled. For such objects, using regular `[` selection or using `st_crop` may be an alternative.

## `pull`

`pull` pulls out an array from a stars object:

    x %>% pull(1) -> x8
    class(x8)
    ## [1] "array"
    dim(x8)
    ##    x    y band
    ##  349  352    6

## `mutate`

    x %>% mutate(band2 = 2 * L7_ETMs.tif) -> x2
    x2
    ## stars object with 3 dimensions and 2 attributes
    ## attribute(s):
    ##              Min. 1st Qu. Median      Mean 3rd Qu. Max.
    ## L7_ETMs.tif     1      54     69  68.91242      86  255
    ## band2           2     108    138 137.82484     172  510
    ## dimension(s):
    ##      from  to  offset delta                     refsys point x/y
    ## x       1 349  288776  28.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
    ## y       1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y]
    ## band    1   6      NA    NA                         NA    NA

## `select`

`select` selects an attribute, or a set of attributes:

    x2 %>% select(band2) -> x9
    x9
    ## stars object with 3 dimensions and 1 attribute
    ## attribute(s):
    ##        Min. 1st Qu. Median     Mean 3rd Qu. Max.
    ## band2     2     108    138 137.8248     172  510
    ## dimension(s):
    ##      from  to  offset delta                     refsys point x/y
    ## x       1 349  288776  28.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
    ## y       1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y]
    ## band    1   6      NA    NA                         NA    NA

## `geom_stars`

`geom_raster` is a `ggplot2` geom function that accepts `stars` objects as its `data` argument and

- sets up the raster or vector spatial coordinates as plot dimensions, and the first attribute as the fill variable
- allows for downsampling (without choosing a suitable downsampling level)
- chooses between using `geom_raster`, `geom_rect` and `geom_sf` depending on whether the geometry is regular, rectilinear or has vector geometries

An example use is

    library(ggplot2)
    library(viridis)
    ggplot() +
      geom_stars(data = x) +
      coord_equal() +
      facet_wrap(~band) +
      theme_void() +
      scale_fill_viridis() +
      scale_x_discrete(expand = c(0, 0)) +
      scale_y_discrete(expand = c(0, 0))