### Example with SpatRaster

Source: https://dieghernan.github.io/tidyterra/reference/glimpse.Spat.html

Demonstrates how to use glimpse() with a SpatRaster object.

```R
r <- rast(system.file("extdata/cyl_elev.tif", package = "tidyterra"))

r |> glimpse()
#> #  A SpatRaster 126 x 212 x 1 layer (26,712 cells)
#> #  Resolution (x / y): (1' 30" , 1' 30")
#> #  Geodetic CRS: lon/lat WGS 84 (EPSG:4326)
#> #  Extent (x / y) : ([7° 4' 30" W / 1° 46' 30" W] , [40° 4' 60" N / 43° 13' 60" N])
#> 
#> $ elevation_m <dbl> 700.2969, 780.3889, 706.1250, 568.9722, 584.9028, 506.7361…
```

--------------------------------

### Example with SpatVector

Source: https://dieghernan.github.io/tidyterra/reference/glimpse.Spat.html

Demonstrates how to use glimpse() with a SpatVector object, showing a reduced number of rows and then using it within a pipeline with additional options.

```R
library(terra)

# SpatVector
v <- vect(system.file("extdata/cyl.gpkg", package = "tidyterra"))

v |> glimpse(n = 2)
#> #  A SpatVector 9 x 3
#> #  Geometry type: Polygons
#> #  Projected CRS: ETRS89-extended / LAEA Europe (EPSG:3035)
#> #  CRS projection units: meter <m>
#> #  Extent (x / y) : ([2,892,687 / 3,341,372] , [2,017,622 / 2,361,600])
#> 
#> $ iso2 <chr> "ES-AV", "ES-BU", "ES-LE", "ES-P", "ES-SA", "ES-SG", "ES-SO", "ES…
#> $ cpro <chr> "05", "09", "24", "34", "37", "40", "42", "47", "49"
#> # ℹ 1 more variable : name <chr>
#> # ℹ Use `tidyterra::glimpse(n = ...)` to see more columns

# Use on a pipeline
v |
  glimpse() |
  mutate(a = 30) |
  # with options
  glimpse(geom = "WKT")
#> #  A SpatVector 9 x 3
#> #  Geometry type: Polygons
#> #  Projected CRS: ETRS89-extended / LAEA Europe (EPSG:3035)
#> #  CRS projection units: meter <m>
#> #  Extent (x / y) : ([2,892,687 / 3,341,372] , [2,017,622 / 2,361,600])
#> 
#> $ iso2 <chr> "ES-AV", "ES-BU", "ES-LE", "ES-P", "ES-SA", "ES-SG", "ES-SO", "ES…
#> $ cpro <chr> "05", "09", "24", "34", "37", "40", "42", "47", "49"
#> $ name <chr> "Avila", "Burgos", "Leon", "Palencia", "Salamanca", "Segovia"…
#> #  A SpatVector 9 x 4
#> #  Geometry type: Polygons
#> #  Projected CRS: ETRS89-extended / LAEA Europe (EPSG:3035)
#> #  CRS projection units: meter <m>
#> #  Extent (x / y) : ([2,892,687 / 3,341,372] , [2,017,622 / 2,361,600])
#> 
#> $ iso2     <chr> "ES-AV", "ES-BU", "ES-LE", "ES-P", "ES-SA", "ES-SG", "ES-SO",…
#> $ cpro     <chr> "05", "09", "24", "34", "37", "40", "42", "47", "49"
#> $ name     <chr> "Avila", "Burgos", "Leon", "Palencia", "Salamanca", "Segovia"…
#> $ a        <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30
#> $ geometry <chr> "POLYGON ((3126360.241699999 2066777.7545, 3125073.9751999993…
```

--------------------------------

### Examples

Source: https://dieghernan.github.io/tidyterra/reference/stat_spat_coordinates.html

Examples of how to use stat_spat_coordinates.

```R
# \donttest{
cyl <- terra::vect(system.file("extdata/cyl.gpkg", package = "tidyterra"))

library(ggplot2)

ggplot(cyl) +
  stat_spat_coordinates()


ggplot(cyl) +
  geom_errorbarh(
    aes(
      geometry = geometry,
      xmin = after_stat(x) - 50000,
      xmax = after_stat(x) + 50000,
      y = after_stat(y),
      height = 10000
    ),
    stat = "sf_coordinates"
  )
#> Warning: `geom_errorbarh()` was deprecated in ggplot2 4.0.0.
#> ℹ Please use the `orientation` argument of `geom_errorbar()` instead.
#> Warning: Ignoring unknown aesthetics: height

# }

```

--------------------------------

### Examples

Source: https://dieghernan.github.io/tidyterra/reference/fill.SpatVector.html

Examples of how to use the fill.SpatVector function.

```R
library(dplyr)

lux <- terra::vect(system.file("ex/lux.shp", package = "terra"))

# Leave some blanks for demo purposes

lux_blnk <- lux |>
  mutate(NAME_1 = if_else(NAME_1 != NAME_2, NA, NAME_2))

as_tibble(lux_blnk)

```

```R
# `fill()` defaults to replacing missing data from top to bottom
lux_blnk |
  fill(NAME_1) |
  as_tibble()

```

```R
# direction = "up"
lux_blnk |
  fill(NAME_1, .direction = "up") |
  as_tibble()

```

--------------------------------

### Examples

Source: https://dieghernan.github.io/tidyterra/reference/ggspatvector.html

Examples of how to use geom_spatvector.

```R
# \donttest{
# Create a SpatVector
extfile <- system.file("extdata/cyl.gpkg", package = "tidyterra")

cyl <- terra::vect(extfile)
class(cyl)
#> [1] "SpatVector"
#> attr(,"package")
#> [1] "terra"

library(ggplot2)

ggplot(cyl) +
  geom_spatvector()


# With params

ggplot(cyl) +
  geom_spatvector(aes(fill = name), color = NA) +
  scale_fill_viridis_d() +
  coord_sf(crs = 3857)


```

--------------------------------

### Examples

Source: https://dieghernan.github.io/tidyterra/reference/is_regular_grid.html

Examples of how to use the is_regular_grid function.

```R
p <- matrix(1:90, nrow = 45, ncol = 2)

is_regular_grid(p)

# Jitter location
set.seed(1234)
jitter <- runif(length(p)) / 10e4
p_jitter <- p + jitter

# Need to adjust digits
is_regular_grid(p_jitter, digits = 4)
```

--------------------------------

### Examples

Source: https://dieghernan.github.io/tidyterra/reference/rename.Spat.html

Examples demonstrating how to use `rename()` and `rename_with()` on a SpatRaster object.

```R
library(terra)
spatrast <- rast(f) |> mutate(aa = 1, bb = 2, cc = 3)

spatrast
#> class       : SpatRaster
#> size        : 212, 261, 6  (nrow, ncol, nlyr)
#> resolution  : 2445.985, 2445.985  (x, y)
#> extent      : -812067, -173664.9, 4852834, 5371383  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / Pseudo-Mercator (EPSG:3857)
#> source(s)   : memory
#> names       : cyl_tile_1, cyl_tile_2, cyl_tile_3, aa, bb, cc
#> min values  :         35,         35,         35,  1,  2,  3
#> max values  :        253,        251,        250,  1,  2,  3

spatrast |> rename(
  this_first = cyl_tile_1,
  this_second = cyl_tile_2
)
#> class       : SpatRaster
#> size        : 212, 261, 6  (nrow, ncol, nlyr)
#> resolution  : 2445.985, 2445.985  (x, y)
#> extent      : -812067, -173664.9, 4852834, 5371383  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / Pseudo-Mercator (EPSG:3857)
#> source(s)   : memory
#> names       : this_first, this_second, cyl_tile_3, aa, bb, cc
#> min values  :         35,          35,         35,  1,  2,  3
#> max values  :        253,         251,        250,  1,  2,  3

spatrast |> rename_with(
  toupper,
  .cols = starts_with("c")
)
#> class       : SpatRaster
#> size        : 212, 261, 6  (nrow, ncol, nlyr)
#> resolution  : 2445.985, 2445.985  (x, y)
#> extent      : -812067, -173664.9, 4852834, 5371383  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / Pseudo-Mercator (EPSG:3857)
#> source(s)   : memory
#> names       : CYL_TILE_1, CYL_TILE_2, CYL_TILE_3, aa, bb, CC
#> min values  :         35,         35,         35,  1,  2,  3
#> max values  :        253,        251,        250,  1,  2,  3
```

--------------------------------

### Examples

Source: https://dieghernan.github.io/tidyterra/reference/required_pkgs.Spat.html

Examples demonstrating the usage of `required_pkgs` with SpatRaster and SpatVector objects.

```R
file_path <- system.file("extdata/cyl_temp.tif", package = "tidyterra")

library(terra)

r <- rast(file_path)

# With rasters
r
#> class       : SpatRaster
#> size        : 87, 118, 3  (nrow, ncol, nlyr)
#> resolution  : 3881.255, 3881.255  (x, y)
#> extent      : -612335.4, -154347.3, 4283018, 4620687  (xmin, xmax, ymin, ymax)
#> coord. ref. : World_Robinson
#> source      : cyl_temp.tif
#> names       :   tavg_04,   tavg_05,   tavg_06
#> min values  :  1.885463,  5.817587, 10.463377
#> max values  : 13.283829, 16.740898, 21.113781
required_pkgs(r)
#> [1] "terra"

# With vectors
v <- vect(system.file("extdata/cyl.gpkg", package = "tidyterra"))
v
#> class       : SpatVector
#> geometry    : polygons
#> dimensions  : 9, 3  (geometries, attributes)
#> extent      : 2892687, 3341372, 2017622, 2361600  (xmin, xmax, ymin, ymax)
#> source      : cyl.gpkg
#> coord. ref. : ETRS89-extended / LAEA Europe (EPSG:3035)
#> names       :  iso2  cpro   name
#> type        : <chr> <chr>  <chr>
#> values      : ES-AV    05  Avila
#>               ES-BU    09 Burgos
#>               ES-LE    24   Leon
#>               ... 
required_pkgs(v)
#> [1] "terra"
```

--------------------------------

### Examples

Source: https://dieghernan.github.io/tidyterra/reference/scale_coltab.html

Example of how to use scale_fill_coltab with a SpatRaster and ggplot2.

```R
library(terra)
# Geological Eras
# Spanish Geological Survey (IGME)

r <- rast(system.file("extdata/cyl_era.tif", package = "tidyterra"))

plot(r)


# Get coltab
coltab_pal <- get_coltab_pal(r)

coltab_pal
#> Precambric-Paleozoic            Paleozoic   Paleozoic-Mesozoic 
#>            "#FFBFE9"            "#9ADDCF"            "#D79EBD" 
#>             Mesozoic    Mesozoic-Cenozoic             Cenozoic 
#>            "#A4FF74"            "#FFD480"            "#FFFFBF" 
#>          Undetermined 
#>            "#FFFFFF" 

# \donttest{
# With ggplot2 + tidyterra
library(ggplot2)

gg <- ggplot() +
  geom_spatraster(data = r)

# Default plot
gg


# With coltabs
gg +
  scale_fill_coltab(data = r)
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.

# }
```

--------------------------------

### Examples

Source: https://dieghernan.github.io/tidyterra/reference/glance.Spat.html

Examples of using glance with SpatVector and SpatRaster objects.

```R
library(terra)

# SpatVector
v <- vect(system.file("extdata/cyl.gpkg", package = "tidyterra"))

glance(v)
#> # A tibble: 1 × 10
#>   geometry  nrow  ncol     xmin     xmax     ymin    ymax source crs   crs_units
#>   <chr>    <dbl> <dbl>    <dbl>    <dbl>    <dbl>   <dbl> <chr>  <chr> <chr>    
#> 1 polygons     9     3 2892687. 3341372. 2017622.  2.36e6 cyl.g… ETRS… meter    

# SpatRaster
r <- rast(system.file("extdata/cyl_elev.tif", package = "tidyterra"))

glance(r)
#> # A tibble: 1 × 16
#>    nrow  ncol  nlyr ncell  xres  yres  xmin  xmax  ymin  ymax crs      crs_units
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>    <chr>    
#> 1   126   212     1 26712 0.025 0.025 -7.08 -1.77  40.1  43.2 lon/lat… degrees  
#> # ℹ 4 more variables: source <chr>, has_rgb <lgl>, has_colors <lgl>,
#> #   has_time <lgl>
```

--------------------------------

### Examples

Source: https://dieghernan.github.io/tidyterra/reference/summarise.SpatVector.html

Examples of using summarise.SpatVector for dissolving and not dissolving geometries.

```R
library(terra)
library(ggplot2)

v <- vect(system.file("extdata/cyl.gpkg", package = "tidyterra"))

# Grouped
gr_v <- v %>%
  mutate(start_with_s = startsWith(name, "S")) %>%
  group_by(start_with_s)

# Dissolving
diss <- gr_v %>%
  summarise(n = dplyr::n(), mean = mean(as.double(cpro)))

diss
#> class       : SpatVector
#> geometry    : polygons
#> dimensions  : 2, 3  (geometries, attributes)
#> extent      : 2892687, 3341372, 2017622, 2361600  (xmin, xmax, ymin, ymax)
#> coord. ref. : ETRS89-extended / LAEA Europe (EPSG:3035)
#> names       : start_with_s     n    mean
#> type        :        <lgl> <int>   <num>
#> values      :        FALSE     6      28
#>                       TRUE     3 39.6667

autoplot(diss, aes(fill = start_with_s)) +
  ggplot2::labs(title = "Dissolved")


# Not dissolving
no_diss <- gr_v %>%
  summarise(n = dplyr::n(), mean = mean(as.double(cpro)), .dissolve = FALSE)

# Same statistic
no_diss
#> class       : SpatVector
#> geometry    : polygons
#> dimensions  : 2, 3  (geometries, attributes)
#> extent      : 2892687, 3341372, 2017622, 2361600  (xmin, xmax, ymin, ymax)
#> coord. ref. : ETRS89-extended / LAEA Europe (EPSG:3035)
#> names       : start_with_s     n    mean
#> type        :        <lgl> <int>   <num>
#> values      :        FALSE     6      28
#>                       TRUE     3 39.6667

autoplot(no_diss, aes(fill = start_with_s)) +
  ggplot2::labs(title = "Not Dissolved")

```

--------------------------------

### Examples

Source: https://dieghernan.github.io/tidyterra/reference/relocate.Spat.html

Demonstrates how to use relocate() to reorder layers in a SpatRaster object.

```R
library(terra)

f <- system.file("extdata/cyl_tile.tif", package = "tidyterra")
spatrast <- rast(f) |> mutate(aa = 1, bb = 2, cc = 3)

names(spatrast)
#> [1] "cyl_tile_1" "cyl_tile_2" "cyl_tile_3" "aa"         "bb"        
#> [6] "cc"        

spatrast |
  relocate(bb, .before = cyl_tile_3) |
  relocate(cyl_tile_1, .after = last_col())
#> class       : SpatRaster
#> size        : 212, 261, 6  (nrow, ncol, nlyr)
#> resolution  : 2445.985, 2445.985  (x, y)
#> extent      : -812067, -173664.9, 4852834, 5371383  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / Pseudo-Mercator (EPSG:3857)
#> source(s)   : memory
#> names       :cyl_tile_2, bb, cyl_tile_3, aa, cc, cyl_tile_1
#> min values  :         35,  2,         35,  1,  3,         35
#> max values  :        251,  2,        250,  1,  3,        253
```

--------------------------------

### Discrete hypsometric tint palette example

Source: https://dieghernan.github.io/tidyterra/reference/scale_hypso.html

Demonstrates the usage of `scale_fill_hypso_d` for discrete hypsometric palettes.

```R
factor <- volcano2_rast |> mutate(cats = cut(elevation,
  breaks = c(100, 120, 130, 150, 170, 200),
  labels = c(
    "Very Low", "Low", "Average", "High",
    "Very High"
  )
))

ggplot() +
  geom_spatraster(data = factor, aes(fill = cats)) +
  scale_fill_hypso_d(na.value = "gray10", palette = "dem_poster")
```

--------------------------------

### Examples

Source: https://dieghernan.github.io/tidyterra/reference/princess_db.html

This example demonstrates how to load and use the princess_db dataset to create a custom color scale for a raster plot.

```R
# \donttest{
data("princess_db")

princess_db
#> # A tibble: 75 × 5
#>    pal       r     g     b hex    
#>    <chr> <int> <int> <int> <chr>  
#>  1 snow    227    75    98 #e34b62
#>  2 snow    138   192   229 #8ac0e5
#>  3 snow     10   100   165 #0a64a5
#>  4 snow    255   235   148 #ffeb94
#>  5 snow    255   202    53 #ffca35
#>  6 ella    215   234   246 #d7eaf6
#>  7 ella    177   222   245 #b1def5
#>  8 ella    140   197   232 #8cc5e8
#>  9 ella     59   139   189 #3b8bbd
#> 10 ella     40    98   135 #286287
#> # ℹ 65 more rows

# Select a palette
maori <- princess_db |>
  filter(pal == "maori")

f <- system.file("extdata/volcano2.tif", package = "tidyterra")
r <- terra::rast(f)

library(ggplot2)

p <- ggplot() +
  geom_spatraster(data = r) +
  labs(fill = "elevation")

p +
  scale_fill_gradientn(colors = maori$hex)

# }

```

--------------------------------

### Install tidyterra from CRAN

Source: https://dieghernan.github.io/tidyterra/index.html

Install tidyterra from CRAN.

```r
install.packages("tidyterra")
```

--------------------------------

### Examples

Source: https://dieghernan.github.io/tidyterra/reference/bind_rows.SpatVector.html

Examples of using bind_spat_rows with SpatVector and sf objects.

```R
library(terra)
v <- vect(system.file("extdata/cyl.gpkg", package = "tidyterra"))

v1 <- v[1, "cpro"]
v2 <- v[3:5, c("name", "iso2")]

# You can supply individual SpatVector as arguments:
bind_spat_rows(v1, v2)
#> class       : SpatVector
#> geometry    : polygons
#> dimensions  : 4, 3  (geometries, attributes)
#> extent      : 2892687, 3180130, 2017622, 2361600  (xmin, xmax, ymin, ymax)
#> coord. ref. : ETRS89-extended / LAEA Europe (EPSG:3035)
#> names       :  cpro     name  iso2
#> type        : <chr>    <chr> <chr>
#> values      :    05       NA    NA
#>                  NA     Leon ES-LE
#>                  NA Palencia  ES-P
#>               ...

# When you supply a column name with the `.id` argument, a new
# column is created to link each row to its original data frame
bind_spat_rows(v1, v2, .id = "id")
#> class       : SpatVector
#> geometry    : polygons
#> dimensions  : 4, 4  (geometries, attributes)
#> extent      : 2892687, 3180130, 2017622, 2361600  (xmin, xmax, ymin, ymax)
#> coord. ref. : ETRS89-extended / LAEA Europe (EPSG:3035)
#> names       :    id  cpro     name  iso2
#> type        : <chr> <chr>    <chr> <chr>
#> values      :     1    05       NA    NA
#>                   2    NA     Leon ES-LE
#>                   2    NA Palencia  ES-P
#>               ...

# \donttest{
# Use with sf
sfobj <- sf::st_as_sf(v2[1, ])

sfobj
#> Simple feature collection with 1 feature and 2 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 2926589 ymin: 2233673 xmax: 3125372 ymax: 2361600
#> Projected CRS: ETRS89-extended / LAEA Europe
#>   name  iso2                       geometry
#> 1 Leon ES-LE POLYGON ((3049427 2233673, ...

bind_spat_rows(v1, sfobj)
#> class       : SpatVector
#> geometry    : polygons
#> dimensions  : 2, 3  (geometries, attributes)
#> extent      : 2926589, 3126360, 2017622, 2361600  (xmin, xmax, ymin, ymax)
#> coord. ref. : ETRS89-extended / LAEA Europe (EPSG:3035)
#> names       :  cpro  name  iso2
#> type        : <chr> <chr> <chr>
#> values      :    05    NA    NA
#>                  NA  Leon ES-LE

```

--------------------------------

### Examples

Source: https://dieghernan.github.io/tidyterra/reference/select.Spat.html

Examples of using the select function with SpatRaster objects.

```R
library(terra)

# SpatRaster method

spatrast <- rast(
  crs = "EPSG:3857",
  nrows = 10,
  ncols = 10,
  extent = c(100, 200, 100, 200),
  nlyr = 6,
  vals = seq_len(10 * 10 * 6)
)

spatrast |> select(1)
#> class       : SpatRaster
#> size        : 10, 10,  1  (nrow, ncol, nlyr)
#> resolution  : 10, 10  (x, y)
#> extent      : 100, 200, 100, 200  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / Pseudo-Mercator (EPSG:3857)
#> source(s)   : memory
#> name        : lyr.1
#> min value   :     1
#> max value   :   100

# By name
spatrast |> select(lyr.1:lyr.4)
#> class       : SpatRaster
#> size        : 10, 10,  4  (nrow, ncol, nlyr)
#> resolution  : 10, 10  (x, y)
#> extent      : 100, 200, 100, 200  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / Pseudo-Mercator (EPSG:3857)
#> source(s)   : memory
#> names       : lyr.1, lyr.2, lyr.3, lyr.4
#> min values  :     1,   101,   201,   301
#> max values  :   100,   200,   300,   400

# Rename
spatrast |> select(a = lyr.1, c = lyr.6)
#> class       : SpatRaster
#> size        : 10, 10,  2  (nrow, ncol, nlyr)
#> resolution  : 10, 10  (x, y)
#> extent      : 100, 200, 100, 200  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / Pseudo-Mercator (EPSG:3857)
#> source(s)   : memory
#> names       :   a,   c
#> min values  :   1, 501
#> max values  : 100, 600
```

--------------------------------

### Examples

Source: https://dieghernan.github.io/tidyterra/reference/cross_blended_hypsometric_tints_db.html

This example demonstrates how to load and inspect the cross_blended_hypsometric_tints_db dataset, select a specific palette, and use it with ggplot2 to visualize raster data, including options for using elevation limits.

```r
# \donttest{

data("cross_blended_hypsometric_tints_db")

cross_blended_hypsometric_tints_db
#> # A tibble: 41 × 6
#>    pal   limit     r     g     b hex    
#>    <chr> <int> <int> <int> <int> <chr>  
#>  1 arid      0   160   152   141 #A0988D
#>  2 arid     50   170   160   150 #AAA096
#>  3 arid    200   180   170   158 #B4AA9E
#>  4 arid    600   202   190   174 #CABEAE
#>  5 arid   1000   212   201   180 #D4C9B4
#>  6 arid   2000   212   184   163 #D4B8A3
#>  7 arid   3000   212   193   179 #D4C1B3
#>  8 arid   4000   212   207   204 #D4CFCC
#>  9 arid   5000   220   220   220 #DCDCDC
#> 10 arid   6000   235   235   237 #EBEBED
#> # ℹ 31 more rows

# Select a palette
warm <- cross_blended_hypsometric_tints_db |
  filter(pal == "warm_humid")

f <- system.file("extdata/asia.tif", package = "tidyterra")
r <- terra::rast(f)

library(ggplot2)

p <- ggplot() +
  geom_spatraster(data = r) +
  labs(fill = "elevation")

p +
  scale_fill_gradientn(colors = warm$hex)


# Use with limits
p +
  scale_fill_gradientn(
    colors = warm$hex,
    values = scales::rescale(warm$limit),
    limit = range(warm$limit),
    na.value = "lightblue"
  )

# }

```

--------------------------------

### Examples

Source: https://dieghernan.github.io/tidyterra/reference/group_data.SpatVector.html

Examples demonstrating the usage of grouping functions on SpatVector objects, including ungrouped, grouped by one variable, and grouped by multiple variables.

```R
library(terra)

v <- vect(system.file("extdata/cyl.gpkg", package = "tidyterra"))
v$gr_1 <- rep_len(c("A", "A", "B"), length.out = nrow(v))
v$gr_2 <- rep_len(c("C", "D"), length.out = nrow(v))

# Ungrouped

n_groups(v)
#> [1] 1

group_vars(v)
#> character(0)

group_keys(v)
#> # A tibble: 1 × 0

group_size(v)
#> [1] 9

groups(v)
#> list()

group_rows(v)
#> <list_of<integer>[1]>
#> [[1]]
#> [1] 1 2 3 4 5 6 7 8 9
#> 
group_data(v)
#> # A tibble: 1 × 1
#>         .rows
#>   <list<int>>
#> 1         [9]

group_indices(v)
#> [1] 1 1 1 1 1 1 1 1 1

# Grouped by one var
gv <- group_by(v, gr_1)

n_groups(gv)
#> [1] 2

group_vars(gv)
#> [1] "gr_1"

group_keys(gv)
#> # A tibble: 2 × 1
#>   gr_1 
#>   <chr>
#> 1 A    
#> 2 B    

group_size(gv)
#> [1] 6 3

groups(gv)
#> [[1]]
#> gr_1
#> 

group_rows(gv)
#> <list_of<integer>[2]>
#> [[1]]
#> [1] 1 2 4 5 7 8
#> 
#> [[2]]
#> [1] 3 6 9
#> 
group_data(gv)
#> # A tibble: 2 × 2
#>   gr_1        .rows
#>   <chr> <list<int>>
#> 1 A             [6]
#> 2 B             [3]

group_indices(gv)
#> [1] 1 1 2 1 1 2 1 1 2

# Grouped by several vars

gv2 <- group_by(v, gr_1, gr_2)

n_groups(gv2)
#> [1] 4

group_vars(gv2)
#> [1] "gr_1" "gr_2"

group_keys(gv2)
#> # A tibble: 4 × 2
#>   gr_1  gr_2 
#>   <chr> <chr>
#> 1 A     C    
#> 2 A     D    
#> 3 B     C    
#> 4 B     D    

group_size(gv2)
#> [1] 3 3 2 1

groups(gv2)
#> [[1]]
#> gr_1
#> 
#> [[2]]
#> gr_2
#> 

group_rows(gv2)
#> <list_of<integer>[4]>
#> [[1]]
#> [1] 1 5 7
#> 
#> [[2]]
#> [1] 2 4 8
#> 
#> [[3]]
#> [1] 3 9
#> 
#> [[4]]
#> [1] 6
#> 
group_data(gv2)
#> # A tibble: 4 × 3
#>   gr_1  gr_2        .rows
#>   <chr> <chr> <list<int>>
#> 1 A     C             [3]
#> 2 A     D             [3]
#> 3 B     C             [2]
#> 4 B     D             [1]

group_indices(gv2)
#> [1] 1 2 3 2 1 4 1 2 3
```

--------------------------------

### Examples

Source: https://dieghernan.github.io/tidyterra/reference/arrange.SpatVector.html

Examples of using the arrange.SpatVector function with different sorting criteria.

```R
library(terra)
#> terra 1.9.27
library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:terra’:
#> 
#>     intersect, union
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union

v <- vect(system.file("extdata/cyl.gpkg", package = "tidyterra"))

# Single variable

v |>
  arrange(desc(iso2))
#> class       : SpatVector
#> geometry    : polygons
#> dimensions  : 9, 3  (geometries, attributes)
#> extent      : 2892687, 3341372, 2017622, 2361600  (xmin, xmax, ymin, ymax)
#> coord. ref. : ETRS89-extended / LAEA Europe (EPSG:3035)
#> names       :  iso2  cpro       name
#> type        : <chr> <chr>      <chr>
#> values      : ES-ZA    49     Zamora
#>               ES-VA    47 Valladolid
#>               ES-SO    42      Soria
#>               ...

# Two variables
v |>
  mutate(even = as.double(cpro) %% 2 == 0) |>
  arrange(desc(even), desc(iso2))
#> class       : SpatVector
#> geometry    : polygons
#> dimensions  : 9, 4  (geometries, attributes)
#> extent      : 2892687, 3341372, 2017622, 2361600  (xmin, xmax, ymin, ymax)
#> coord. ref. : ETRS89-extended / LAEA Europe (EPSG:3035)
#> names       :  iso2  cpro     name  even
#> type        : <chr> <chr>    <chr> <lgl>
#> values      : ES-SO    42    Soria  TRUE
#>               ES-SG    40  Segovia  TRUE
#>                ES-P    34 Palencia  TRUE
#>               ...

# With new variables
v |>
  mutate(area_geom = terra::expanse(v)) |>
  arrange(area_geom)
#> class       : SpatVector
#> geometry    : polygons
#> dimensions  : 9, 4  (geometries, attributes)
#> extent      : 2892687, 3341372, 2017622, 2361600  (xmin, xmax, ymin, ymax)
#> coord. ref. : ETRS89-extended / LAEA Europe (EPSG:3035)
#> names       :  iso2  cpro     name   area_geom
#> type        : <chr> <chr>    <chr>       <num>
#> values      : ES-SG    40  Segovia 6.92148e+09
#>                ES-P    34 Palencia 8.04248e+09
#>               ES-AV    05    Avila 8.05299e+09
#>               ...

```

--------------------------------

### Examples

Source: https://dieghernan.github.io/tidyterra/reference/pull_crs.html

Examples demonstrating how to use pull_crs with sf objects, terra objects, integers, and characters.

```R
# sf objects

sfobj <- sf::st_as_sfc("MULTIPOINT ((0 0), (1 1))", crs = 4326)

fromsf1 <- pull_crs(sfobj)
fromsf2 <- pull_crs(sf::st_crs(sfobj))

# terra

v <- terra::vect(sfobj)
r <- terra::rast(v)

fromterra1 <- pull_crs(v)
fromterra2 <- pull_crs(r)

# integers
fromint <- pull_crs(4326)

# Characters
fromchar <- pull_crs("epsg:4326")

all(
  fromsf1 == fromsf2,
  fromsf2 == fromterra1,
  fromterra1 == fromterra2,
  fromterra2 == fromint,
  fromint == fromchar
)
#> [1] TRUE

cat(fromsf1)
#> GEOGCRS["WGS 84",
#>     ENSEMBLE["World Geodetic System 1984 ensemble",
#>         MEMBER["World Geodetic System 1984 (Transit)"],
#>         MEMBER["World Geodetic System 1984 (G730)"],
#>         MEMBER["World Geodetic System 1984 (G873)"],
#>         MEMBER["World Geodetic System 1984 (G1150)"],
#>         MEMBER["World Geodetic System 1984 (G1674)"],
#>         MEMBER["World Geodetic System 1984 (G1762)"],
#>         MEMBER["World Geodetic System 1984 (G2139)"],
#>         ELLIPSOID["WGS 84",6378137,298.257223563,
#>             LENGTHUNIT["metre",1]],
#>         ENSEMBLEACCURACY[2.0]],
#>     PRIMEM["Greenwich",0,
#>         ANGLEUNIT["degree",0.0174532925199433]],
#>     CS[ellipsoidal,2],
#>         AXIS["geodetic latitude (Lat)",north,
#>             ORDER[1],
#>             ANGLEUNIT["degree",0.0174532925199433]],
#>         AXIS["geodetic longitude (Lon)",east,
#>             ORDER[2],
#>             ANGLEUNIT["degree",0.0174532925199433]],
#>     USAGE[
#>         SCOPE["Horizontal component of 3D system."],
#>         AREA["World."],
#>         BBOX[-90,-180,90,180]],
#>     ID["EPSG",4326]]
```

--------------------------------

### Install development dependencies and check package

Source: https://dieghernan.github.io/tidyterra/CONTRIBUTING.html

Install all development dependencies and ensure the package passes R CMD check.

```r
devtools::install_dev_deps()
devtools::check()
```

--------------------------------

### Basic hypsometric tint palette example

Source: https://dieghernan.github.io/tidyterra/reference/scale_hypso.html

Demonstrates the basic usage of `scale_fill_hypso_tint_c` with a specified palette.

```R
library(terra)
filepath <- system.file("extdata/volcano2.tif", package = "tidyterra")
volcano2_rast <- rast(filepath)

library(ggplot2)
ggplot() +
  geom_spatraster(data = volcano2_rast) +
  scale_fill_hypso_tint_c(palette = "colombia_hypso")
```

--------------------------------

### Example 1: Basic slicing with glimpse and autoplot

Source: https://dieghernan.github.io/tidyterra/reference/slice.Spat.html

Demonstrates how to use glimpse and autoplot with a SpatVector, showing the structure and plotting it.

```r
glimpse(v) |> autoplot(aes(fill = NAME_1))
```

--------------------------------

### With options

Source: https://dieghernan.github.io/tidyterra/reference/glimpse.Spat.html

Shows how to use glimpse() with the 'xy' option set to TRUE to include coordinate information.

```R
r |> glimpse(xy = TRUE)
```

--------------------------------

### Binned hypsometric tint palette example

Source: https://dieghernan.github.io/tidyterra/reference/scale_hypso.html

Demonstrates the usage of `scale_fill_hypso_b` for binned hypsometric palettes.

```R
ggplot() +
  geom_spatraster(data = volcano2_rast) +
  scale_fill_hypso_b(breaks = seq(70, 200, 25), palette = "wiki-2.0_hypso")
```