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This is a compendium of questions arisen on the use of the tidyterra package and the potential solutions to it (mostly related with the use of terra and ggplot2 at this stage). You can ask for help or search previous questions in the following links.

You can also ask in Stack Overflow using the tag tidyterra.

  • Report a Bug [link].
  • Ask a question [link].

Example data

Source

This article uses a sample of LiDAR for Scotland Phase 5 - DSM provided by The Scottish Remote Sensing Portal. This data is made available under the Open Government Licence v3.

About the file

The file holyroodpark.tif represents the DEM1 of Holyrood Park, Edinburgh (Scotland), including Arthur’s Seat, an extinct volcano, pretty much as the famous Maungawhau / Mount Eden volcano represented in datasets::volcano.

The original file has been cropped and down-sampled for demo purposes, holyroodpark.tif is available online in https://github.com/dieghernan/tidyterra/tree/main/data-raw folder.

NA values are shown in gray color

This is the default behavior produced by the ggplot2 package. tidyterra color scales (i.e., scale_fill_whitebox_c(), etc.), has by default the parameter na.value set to "transparent", that prevents NA values to be filled2.

library(terra)
library(tidyterra)
library(ggplot2)

# Get a raster data from Holyrood Park, Edinburgh
holyrood <- "holyroodpark.tif"

r <- holyrood %>%
  rast() %>%
  filter(elevation > 80 & elevation < 180)

# Default
def <- ggplot() +
  geom_spatraster(data = r)

def +
  labs(
    title = "Default on ggplot2",
    subtitle = "NA values in grey"
  )


# Modify with scales
def +
  scale_fill_continuous(na.value = "transparent") +
  labs(
    title = "Default colors on ggplot2",
    subtitle = "But NAs are not plotted"
  )


# Use a different scale provided by ggplot2
def +
  scale_fill_viridis_c(na.value = "orange") +
  labs(
    title = "Use any fill_* scale of ggplot2",
    subtitle = "Note that na.value = 'orange'"
  )

Labeling contours

Use geom_spatraster_contour_text() Experimental:

library(terra)
library(tidyterra)
library(ggplot2)

holyrood <- "holyroodpark.tif"

r <- rast(holyrood)

ggplot() +
  geom_spatraster_contour_text(data = r) +
  labs(title = "Labelling contours")



# With options and aes

# Use a labeller function so only selected breaks are labelled
labeller <- function(labs) {
  # Must return a function
  function(x) {
    x[!x %in% labs] <- NA
    scales::label_comma(suffix = " m.")(x)
  }
}

# Common labels across ggplot

labs <- c(100, 140, 180, 220)

ggplot(r) +
  geom_spatraster_contour_text(
    data = r, aes(
      linewidth = after_stat(level),
      size = after_stat(level),
      color = after_stat(level)
    ),
    breaks = seq(100, 250, 10),
    # Just label some isolines
    label_format = labeller(labs = labs),
    family = "mono",
    fontface = "bold"
  ) +
  scale_linewidth_continuous(range = c(0.1, 0.5), breaks = labs) +
  scale_color_gradient(low = "grey50", high = "grey10", breaks = labs) +
  scale_size_continuous(range = c(2, 3), breaks = labs) +
  # Integrate scales
  guides(
    linewidth = guide_legend("meters"),
    size = guide_legend("meters"),
    color = guide_legend("meters")
  ) +
  # Theme and titles
  theme_bw() +
  theme(text = element_text(family = "mono")) +
  labs(
    title = "Labelling contours",
    subtitle = "With options: b/w plot"
  )

Other alternatives

Thanks to fortify.SpatRaster() you can use your SpatRaster straight away with the metR package (see Hexagonal grids and other geoms). Use the parameter(s) bins/binwidth/breaks to align both labels and lines:

library(metR)
br <- seq(100, 250, 10)
labs <- c(100, 140, 180, 220)

# Replicate previous map with tidyterra + metR strategy
ggplot(r, aes(x, y)) +
  geom_spatraster_contour(
    data = r,
    aes(
      linewidth = after_stat(level),
      color = after_stat(level)
    ),
    breaks = br,
    # Don't inherit fortified aes
    inherit.aes = FALSE
  ) +
  geom_text_contour(
    aes(
      z = elevation, color = after_stat(level),
      size = after_stat(level)
    ),
    breaks = br,
    # Text options
    check_overlap = TRUE,
    label.placer = label_placer_minmax(),
    stroke = 0.3,
    stroke.colour = "white",
    family = "mono",
    fontface = "bold",
    key_glyph = "path"
  ) +
  scale_linewidth_continuous(range = c(0.1, 0.5), breaks = labs) +
  scale_color_gradient(low = "grey50", high = "grey10", breaks = labs) +
  scale_size_continuous(range = c(2, 3), breaks = labs) +
  # Integrate scales
  guides(
    linewidth = guide_legend("meters"),
    size = guide_legend("meters"),
    color = guide_legend("meters")
  ) +
  # Theme and titles
  theme_bw() +
  theme(text = element_text(family = "mono")) +
  labs(
    title = "Labelling contours",
    subtitle = "tidyterra and metR: b/w plot",
    x = "",
    y = ""
  )

Using a different color scale

Since tidyterra leverages on ggplot2, please refer to ggplot2 use of scales:

library(terra)
library(tidyterra)
library(ggplot2)

holyrood <- "holyroodpark.tif"

r <- rast(holyrood)

# Hillshade with grey colors
slope <- terrain(r, "slope", unit = "radians")
aspect <- terrain(r, "aspect", unit = "radians")
hill <- shade(slope, aspect, 10, 340)

ggplot() +
  geom_spatraster(data = hill, show.legend = FALSE) +
  # Note the scale, grey colours
  scale_fill_gradientn(
    colours = grey(0:100 / 100),
    na.value = "transparent"
  ) +
  labs(title = "A hillshade plot with grey colors")

Can I change the default palette of my maps?

Yes, use options("ggplot2.continuous.fill") to modify the default colors on your session.

library(terra)
library(tidyterra)
library(ggplot2)

holyrood <- "holyroodpark.tif"

r <- rast(holyrood)

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


# Set options
tmp <- getOption("ggplot2.continuous.fill") # store current setting
options(ggplot2.continuous.fill = scale_fill_grass_c)

p


# restore previous setting
options(ggplot2.continuous.fill = tmp)


p

My map tiles are blurry

This is probably related with the tile itself rather than the package. Most base tiles are provided in EPSG:3857, so check first if your tile has this CRS and not a different one. Not having EPSG:3857 may be an indication that the tile has been reprojected, implied some sort of sampling that causes the blurriness on your data. Also, modify the parameter maxcell to avoid resampling and force the ggplot2 map to be on EPSG:3857 with ggplot2::coord_sf(crs = 3857):

library(terra)
library(tidyterra)
library(ggplot2)
library(sf)
library(maptiles)

# Get a tile from a point on sf format
p <- st_point(c(-3.166011, 55.945235)) %>%
  st_sfc(crs = 4326) %>%
  st_buffer(500)

tile1 <- get_tiles(p,
  provider = "OpenStreetMap", zoom = 14, cachedir = ".",
  crop = TRUE
)

ggplot() +
  geom_spatraster_rgb(data = tile1) +
  labs(title = "This is a bit blurry...")

st_crs(tile1)$epsg
#> [1] 4326

# The tile was in EPSG 4326

# get tile in 3857
p2 <- st_transform(p, 3857)


tile2 <- get_tiles(p2,
  provider = "OpenStreetMap", zoom = 14, cachedir = ".",
  crop = TRUE
)

st_crs(tile2)$epsg
#> [1] 3857

# Now the tile is EPSG:3857

ggplot() +
  geom_spatraster_rgb(data = tile2, maxcell = Inf) +
  # Force crs to be 3857
  coord_sf(crs = 3857) +
  labs(
    title = "See the difference?",
    subtitle = "Init crs=3857 and maxcell modified"
  )

Avoid degrees labeling on axis

Again, this is the ggplot2 default, but can be modified with ggplot2::coord_sf(datum) argument:

library(terra)
library(tidyterra)
library(ggplot2)
library(sf)

holyrood <- "holyroodpark.tif"

r <- rast(holyrood)

ggplot() +
  geom_spatraster(data = r) +
  labs(
    title = "Axis auto-converted to lon/lat",
    subtitle = paste("But SpatRaster is EPSG:", st_crs(r)$epsg)
  )



# Use datum

ggplot() +
  geom_spatraster(data = r) +
  coord_sf(datum = pull_crs(r)) +
  labs(
    title = "Axis on the units of the SpatRaster",
    subtitle = paste("EPSG:", st_crs(r)$epsg)
  )

Modifying the number of breaks on axis

The best option is to pass your custom breaks to ggplot2::scale_x_continous() or ggplot2::scale_y_continous(). You will need to provide the breaks in lon/lat even if your data is projected. See also ggplot2/issues/4622:

library(terra)
library(tidyterra)
library(ggplot2)
library(sf)

holyrood <- "holyroodpark.tif"

r <- rast(holyrood)

ggplot() +
  geom_spatraster(data = r) +
  labs(title = "Default axis breaks")


# Modify y breaks with extent projected in EPSG:4326

# Get extent

ext <- r %>%
  project("EPSG:4326", mask = TRUE) %>%
  ext() %>%
  as.vector()


ggplot() +
  geom_spatraster(data = r) +
  scale_x_continuous(
    expand = expansion(mult = 0.05),
    breaks = scales::breaks_pretty(n = 3)(ext[c("xmin", "xmax")])
  ) +
  labs(title = "Three breaks on x (¿?)")

Plotting a SpatRaster with color tables

tidyterra has several ways to handle these SpatRaster objects:

library(terra)
library(tidyterra)
library(ggplot2)

# Get a SpatRaster with coltab
r_coltab <- rast(system.file("extdata/cyl_era.tif", package = "tidyterra"))

has.colors(r_coltab)
#> [1] TRUE

r_coltab
#> class       : SpatRaster 
#> dimensions  : 470, 590, 1  (nrow, ncol, nlyr)
#> resolution  : 1000, 1000  (x, y)
#> extent      : -787640.9, -197640.9, 4878097, 5348097  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / Pseudo-Mercator (EPSG:3857) 
#> source      : cyl_era.tif 
#> color table : 1 
#> categories  : era 
#> name        :                  era 
#> min value   : Precambric-Paleozoic 
#> max value   :         Undetermined

# Native handling by terra packages
plot(r_coltab)



# A. autoplot

autoplot(r_coltab, maxcell = Inf) + ggtitle("autoplot method")


# B. geom_spatraster
ggplot() +
  geom_spatraster(data = r_coltab, maxcell = Inf) +
  ggtitle("geom_spatraster method")


# C. Using scale_fill_coltab

g <- ggplot() +
  geom_spatraster(data = r_coltab, use_coltab = FALSE, maxcell = Inf)

g


# But...
g +
  scale_fill_coltab(data = r_coltab) +
  ggtitle("scale_fill_coltab method")


# D. Extract named colors and scale_fill_manual

cols <- get_coltab_pal(r_coltab)

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

scales::show_col(cols)


# And now

g +
  scale_fill_manual(
    values = cols, na.value = "transparent",
    na.translate = FALSE
  ) +
  ggtitle("scale_fill_manual method")

North arrows and scale bar

tidyterra does not provide these graphical objects for ggplot2 plots. However, you can use ggspatial functions (ggspatial::annotation_north_arrow() and ggspatial::annotation_scale()):

library(terra)
library(tidyterra)
library(ggplot2)
library(ggspatial)

holyrood <- "holyroodpark.tif"

r <- rast(holyrood)

autoplot(r) +
  annotation_north_arrow(
    which_north = TRUE,
    pad_x = unit(0.8, "npc"),
    pad_y = unit(0.75, "npc"),
    style = north_arrow_fancy_orienteering()
  ) +
  annotation_scale(
    height = unit(0.015, "npc"),
    width_hint = 0.5,
    pad_x = unit(0.07, "npc"),
    pad_y = unit(0.07, "npc"),
    text_cex = .8
  )

How to overlay a SpatRaster over a RGB tile

This is quite straightforward, just use geom_spatraster_rgb() and after that command just create your layer:

library(terra)
library(tidyterra)
library(ggplot2)
library(sf)
# Get example data
library(maptiles)
library(geodata)


# Area of interest
aoi <- gadm(country = "CHE", path = ".", level = 0) %>%
  project("EPSG:3857")

# Tile
rgb_tile <- get_tiles(aoi,
  crop = TRUE, provider = "Esri.WorldShadedRelief",
  zoom = 8, project = FALSE, cachedir = "."
)

# Clim (mean prec)
clim <- worldclim_country("CHE", var = "prec", path = ".") %>%
  project(rgb_tile) %>%
  mask(aoi) %>%
  terra::mean()

# Labels
cap_lab <- paste0(
  c(
    "Tiles © Esri - Source: Esri",
    "Data: © Copyright 2020-2022, worldclim.org."
  ),
  collapse = "\n"
)
tit_lab <- "Average precipitation in Switzerland"

ggplot(aoi) +
  geom_spatraster_rgb(data = rgb_tile, alpha = 1) +
  geom_spatraster(data = clim) +
  geom_spatvector(fill = NA) +
  scale_fill_whitebox_c(
    palette = "deep", alpha = 0.5,
    labels = scales::label_number(suffix = " mm.")
  ) +
  coord_sf(expand = FALSE) +
  labs(
    title = tit_lab,
    subtitle = "With continuous overlay",
    fill = "Precipitation",
    caption = cap_lab
  )

We can create another variations with binned legends and filled contours (see geom_spatraster_contour_filled()):

# Binned
ggplot(aoi) +
  geom_spatraster_rgb(data = rgb_tile, alpha = 1) +
  geom_spatraster(data = clim) +
  geom_spatvector(fill = NA) +
  scale_fill_whitebox_b(
    palette = "deep", alpha = 0.5,
    n.breaks = 4,
    labels = scales::label_number(suffix = " mm.")
  ) +
  coord_sf(expand = FALSE) +
  labs(
    title = tit_lab,
    subtitle = "With overlay: binned legend",
    fill = "Precipitation",
    caption = cap_lab
  )


# Filled contour
ggplot(aoi) +
  geom_spatraster_rgb(data = rgb_tile, alpha = 1) +
  geom_spatraster_contour_filled(data = clim, bins = 4) +
  geom_spatvector(fill = NA) +
  coord_sf(expand = FALSE) +
  scale_fill_whitebox_d(
    palette = "deep", alpha = 0.5,
    guide = guide_legend(reverse = TRUE)
  ) +
  labs(
    title = tit_lab,
    subtitle = "With overlay and filled contour",
    fill = "Precipitation (mm.)",
    caption = cap_lab
  )

Hexagonal grids (and other geoms)

By concept the cells of a SpatRaster are rectangular, so it is not possible to create a SpatRaster with i.e. hexagonal cells.

But it is possible to create a plot with hexagonal cells thanks to fortify.SpatRaster() and stat_summary_hex(). Additional work is needed to adjust the final plot, specifically it is needed also to use coord_sf():

library(terra)
library(tidyterra)
library(ggplot2)

holyrood <- "holyroodpark.tif"

r <- rast(holyrood)

# With hex grid
ggplot(r, aes(x, y, z = elevation)) +
  stat_summary_hex(
    fun = mean, color = NA, linewidth = 0,
    # Bins size determines the number of cells displayed
    bins = 30
  ) +
  coord_sf(crs = pull_crs(r)) +
  labs(
    title = "Hexagonal SpatRaster",
    subtitle = "Using fortify (implicit) and stat_summary_hex",
    x = NULL, y = NULL
  )

Note that there is no need to make a direct call to fortify.SpatRaster(), since this function is implicitly invoked by ggplot2 when using ggplot(data = a_spatraster).

Thanks to this extension mechanism, it is possible to use additional geoms and stats provided by ggplot2:

# Point plot
ggplot(r, aes(x, y, z = elevation), maxcell = 1000) +
  geom_point(aes(size = elevation, alpha = elevation),
    fill = "darkblue",
    color = "grey50", shape = 21
  ) +
  coord_sf(crs = pull_crs(r)) +
  scale_radius(range = c(1, 5)) +
  scale_alpha(range = c(0.01, 1)) +
  labs(
    title = "SpatRaster as points",
    subtitle = "Using fortify (implicit)",
    x = NULL, y = NULL
  )

tidyterra and metR

metR is a package that also provides ggplot2 extensions, mainly focused on the analysis of meteorological fields. As shown previously (see Labeling contours) it is possible to use both packages to provide rich plots. This section shows some demos.

# load libraries and files
library(terra)
library(tidyterra)
library(ggplot2)
library(metR)

holyrood <- "holyroodpark.tif"

r <- rast(holyrood)

metR::geom_contour_tanaka()

ggplot(r, aes(x, y)) +
  geom_spatraster(data = r, inherit.aes = FALSE) +
  geom_contour_tanaka(aes(z = elevation), breaks = seq(0, 250, 25)) +
  scale_fill_viridis_c(option = "magma") +
  labs(x = "", y = "", title = "tidyterra and metR: tanaka contours")


# Using geom_spatraster_contour_filled
ggplot(r, aes(x, y)) +
  geom_spatraster_contour_filled(
    data = r, inherit.aes = FALSE,
    breaks = seq(0, 250, 50)
  ) +
  geom_contour_tanaka(aes(z = elevation),
    breaks = seq(0, 250, 10),
    sun.angle = -90, dark = "gray50"
  ) +
  scale_fill_viridis_d(option = "cividis") +
  labs(
    x = "", y = "", title = "tidyterra and metR: tanaka contours",
    subtitle = "with geom_contour_filled"
  )

metR::geom_relief()

ggplot(r, aes(x, y)) +
  geom_relief(aes(z = elevation)) +
  geom_spatraster(
    data = r, inherit.aes = FALSE,
    aes(alpha = after_stat(value))
  ) +
  scale_fill_cross_blended_c(breaks = seq(0, 250, 25)) +
  scale_alpha(range = c(1, 0.25)) +
  guides(alpha = "none", fill = guide_legend(reverse = TRUE)) +
  labs(x = "", y = "", title = "tidyterra and metR: reliefs")

Session info

Details
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.4.1 (2024-06-14 ucrt)
#>  os       Windows Server 2022 x64 (build 20348)
#>  system   x86_64, mingw32
#>  ui       RTerm
#>  language en
#>  collate  English_United States.utf8
#>  ctype    English_United States.utf8
#>  tz       UTC
#>  date     2024-06-18
#>  pandoc   3.1.11 @ C:/HOSTED~1/windows/pandoc/31F387~1.11/x64/PANDOC~1.11/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package     * version date (UTC) lib source
#>  backports     1.5.0   2024-05-23 [1] RSPM
#>  bslib         0.7.0   2024-03-29 [1] RSPM
#>  cachem        1.1.0   2024-05-16 [1] RSPM
#>  checkmate     2.3.1   2023-12-04 [1] RSPM
#>  class         7.3-22  2023-05-03 [3] CRAN (R 4.4.1)
#>  classInt      0.4-10  2023-09-05 [1] RSPM
#>  cli           3.6.2   2023-12-11 [1] RSPM
#>  codetools     0.2-20  2024-03-31 [3] CRAN (R 4.4.1)
#>  colorspace    2.1-0   2023-01-23 [1] RSPM
#>  data.table    1.15.4  2024-03-30 [1] RSPM
#>  DBI           1.2.3   2024-06-02 [1] RSPM
#>  desc          1.4.3   2023-12-10 [1] RSPM
#>  digest        0.6.35  2024-03-11 [1] RSPM
#>  dplyr         1.1.4   2023-11-17 [1] RSPM
#>  e1071         1.7-14  2023-12-06 [1] RSPM
#>  evaluate      0.24.0  2024-06-10 [1] RSPM
#>  fansi         1.0.6   2023-12-08 [1] RSPM
#>  farver        2.1.2   2024-05-13 [1] RSPM
#>  fastmap       1.2.0   2024-05-15 [1] RSPM
#>  fs            1.6.4   2024-04-25 [1] RSPM
#>  generics      0.1.3   2022-07-05 [1] RSPM
#>  geodata     * 0.6-1   2024-06-18 [1] Github (rspatial/geodata@50abd54)
#>  ggplot2     * 3.5.1   2024-04-23 [1] RSPM
#>  ggspatial   * 1.1.9   2023-08-17 [1] RSPM
#>  glue          1.7.0   2024-01-09 [1] RSPM
#>  gtable        0.3.5   2024-04-22 [1] RSPM
#>  hexbin        1.28.3  2023-03-21 [1] RSPM
#>  highr         0.11    2024-05-26 [1] RSPM
#>  htmltools     0.5.8.1 2024-04-04 [1] RSPM
#>  htmlwidgets   1.6.4   2023-12-06 [1] RSPM
#>  isoband       0.2.7   2022-12-20 [1] RSPM
#>  jquerylib     0.1.4   2021-04-26 [1] RSPM
#>  jsonlite      1.8.8   2023-12-04 [1] RSPM
#>  KernSmooth    2.23-24 2024-05-17 [3] CRAN (R 4.4.1)
#>  knitr         1.47    2024-05-29 [1] RSPM
#>  labeling      0.4.3   2023-08-29 [1] RSPM
#>  lattice       0.22-6  2024-03-20 [3] CRAN (R 4.4.1)
#>  lifecycle     1.0.4   2023-11-07 [1] RSPM
#>  magrittr      2.0.3   2022-03-30 [1] RSPM
#>  maptiles    * 0.7.0   2024-01-11 [1] RSPM
#>  memoise       2.0.1   2021-11-26 [1] RSPM
#>  metR        * 0.15.0  2024-02-09 [1] RSPM
#>  munsell       0.5.1   2024-04-01 [1] RSPM
#>  pillar        1.9.0   2023-03-22 [1] RSPM
#>  pkgconfig     2.0.3   2019-09-22 [1] RSPM
#>  pkgdown       2.0.9   2024-04-18 [1] any (@2.0.9)
#>  plyr          1.8.9   2023-10-02 [1] RSPM
#>  proxy         0.4-27  2022-06-09 [1] RSPM
#>  purrr         1.0.2   2023-08-10 [1] RSPM
#>  R.cache       0.16.0  2022-07-21 [1] RSPM
#>  R.methodsS3   1.8.2   2022-06-13 [1] RSPM
#>  R.oo          1.26.0  2024-01-24 [1] RSPM
#>  R.utils       2.12.3  2023-11-18 [1] RSPM
#>  R6            2.5.1   2021-08-19 [1] RSPM
#>  ragg          1.3.2   2024-05-15 [1] RSPM
#>  Rcpp          1.0.12  2024-01-09 [1] RSPM
#>  rlang         1.1.4   2024-06-04 [1] RSPM
#>  rmarkdown     2.27    2024-05-17 [1] RSPM
#>  s2            1.1.6   2023-12-19 [1] RSPM
#>  sass          0.4.9   2024-03-15 [1] RSPM
#>  scales        1.3.0   2023-11-28 [1] RSPM
#>  sessioninfo * 1.2.2   2021-12-06 [1] RSPM
#>  sf          * 1.0-16  2024-03-24 [1] RSPM
#>  styler        1.10.3  2024-04-07 [1] RSPM
#>  systemfonts   1.1.0   2024-05-15 [1] RSPM
#>  terra       * 1.7-78  2024-05-22 [1] RSPM
#>  textshaping   0.4.0   2024-05-24 [1] RSPM
#>  tibble        3.2.1   2023-03-20 [1] RSPM
#>  tidyr         1.3.1   2024-01-24 [1] RSPM
#>  tidyselect    1.2.1   2024-03-11 [1] RSPM
#>  tidyterra   * 0.6.1   2024-06-18 [1] local
#>  units         0.8-5   2023-11-28 [1] RSPM
#>  utf8          1.2.4   2023-10-22 [1] RSPM
#>  vctrs         0.6.5   2023-12-01 [1] RSPM
#>  viridisLite   0.4.2   2023-05-02 [1] RSPM
#>  withr         3.0.0   2024-01-16 [1] RSPM
#>  wk            0.9.1   2023-11-29 [1] RSPM
#>  xfun          0.45    2024-06-16 [1] RSPM
#>  yaml          2.3.8   2023-12-11 [1] RSPM
#> 
#>  [1] D:/a/_temp/Library
#>  [2] C:/R/site-library
#>  [3] C:/R/library
#> 
#> ──────────────────────────────────────────────────────────────────────────────