tidyterra FAQs

This article collects frequently asked questions about using tidyterra, with a focus on the integration of terra and ggplot2. You can ask for help or search existing questions using the following links.

You can also ask on 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. These data are made available under the Open Government Licence v3.

About the file

The file holyroodpark.tif represents the DSM¹ of Holyrood Park, Edinburgh (Scotland), including Arthur’s Seat, an extinct volcano, much like the well-known Maungawhau / Mount Eden volcano represented in datasets::volcano.

The original file has been cropped and downsampled for demonstration purposes. holyroodpark.tif is available online in the data-raw folder at https://github.com/dieghernan/tidyterra/tree/main/data-raw.

NA values are shown in gray

This is the default behavior of ggplot2. tidyterra color scales, such as scale_fill_whitebox_c(), have na.value = "transparent" by default, which prevents NA values from being filled².

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

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

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

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

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

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

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

(a) Default ggplot2 color for NA values.

(b) Plot with transparent NA values.

(c) NA values mapped with another color.

Figure 1: NA values in ggplot2.

Labeling contours

Use geom_spatraster_contour_text() for contour labels.

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

holyrood <- "holyroodpark.tif"

r <- rast(holyrood)

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

# With options and aesthetics.

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

# Common labels across the plot.

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),
    # Label selected 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 = "Labeling contours",
    subtitle = "With options: black-and-white plot"
  )

(a) Simple contour labels.

(b) Alternative: labeled contours.

Figure 2: Contour labels with tidyterra.

Other alternatives

With fortify.SpatRaster(), you can use your SpatRaster directly with metR (see Hexagonal grids and other geoms). Use bins, binwidth or breaks to align both labels and lines:

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

# Replicate the previous map with tidyterra and metR.
ggplot(r, aes(x, y)) +
  geom_spatraster_contour(
    data = r,
    aes(
      linewidth = after_stat(level),
      color = after_stat(level)
    ),
    breaks = br,
    # Do not inherit fortified aesthetics.
    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 = "Labeling contours",
    subtitle = "tidyterra and metR: black-and-white plot",
    x = "",
    y = ""
  )

Figure 3: Alternative (metR): contour labeling combining tidyterra and the metR package with customized styling.

Using a different color scale

Since tidyterra builds on ggplot2, refer to ggplot2 documentation on scales:

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

holyrood <- "holyroodpark.tif"

r <- rast(holyrood)

# Hillshade with gray 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) +
  # Use a grayscale palette.
  scale_fill_gradientn(
    colours = grey(0:100 / 100),
    na.value = "transparent"
  ) +
  labs(title = "A hillshade plot with gray colors")

Figure 4: Hillshade plot using grayscale colors to enhance terrain relief.

Can I change the default palette of my maps?

Yes, use options("ggplot2.continuous.fill") to modify the default colors in 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 the previous setting.
options(ggplot2.continuous.fill = tmp)

p

(a) Use the new default palette through options.

(b) Restoring the default palette.

Figure 5: Changing default ggplot2 color palettes.

My map tiles are blurry

Blurriness is typically related to the tile source rather than the package. Most base tiles are usually provided in EPSG:3857, so verify that your tile uses this CRS rather than a different one. If your tile is not in EPSG:3857, it has likely been reprojected, which involves resampling and causes blurriness. To avoid extra resampling, increase the maxcell argument and ensure the ggplot2 map uses EPSG:3857 with ggplot2::coord_sf(crs = 3857):

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

# Get a tile from a point in 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 = "CRS EPSG:4326") +
  theme_void()

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

# The tile was in EPSG:4326.

# Get the tile in EPSG: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 the CRS to EPSG:3857.
  coord_sf(crs = 3857) +
  labs(title = "CRS EPSG:3857") +
  theme_void()

(a) Plot with resampled raster (EPSG:4326).

(b) Plot with native CRS, not resampled (EPSG:3857).

Figure 6: Impact of resampling on tile blurriness.

Avoid degree labels on axes

This is the default behavior in ggplot2, but you can modify it with the datum argument in ggplot2::coord_sf():

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 in the units of the SpatRaster",
    subtitle = paste("EPSG:", st_crs(r)$epsg)
  )

(a) Automatic longitude/latitude axes.

(b) Native coordinate system units.

Figure 7: Degree labels with ggplot2.

Modify the number of axis breaks

Use the scales package to change the axis breaks:

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 breaks on x and y.
ggplot() +
  geom_spatraster(data = r) +
  scale_y_continuous(breaks = scales::breaks_extended(n = 5)) +
  scale_x_continuous(breaks = scales::breaks_extended(n = 5)) +
  labs(title = "Three breaks on x and y axes with scales::breaks_extended()")

(a) Default axis breaks.

(b) Breaks modified using the scales package.

Figure 8: Spatial axis breaks with ggplot2.

Plotting a SpatRaster with color tables

Note

We omitted the legends in this section’s figures to improve readability.

tidyterra provides several methods for handling SpatRaster objects with color tables. This example uses clc_edinburgh.tif, available online in the data-raw folder. It contains data from the CORINE Land Cover dataset (2018) for Edinburgh³.

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

# Get a SpatRaster with a color table.
r_coltab <- rast("clc_edinburgh.tif")

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

r_coltab
#> class       : SpatRaster
#> size        : 196, 311, 1  (nrow, ncol, nlyr)
#> resolution  : 178.8719, 177.9949  (x, y)
#> extent      : -380397.3, -324768.1, 7533021, 7567908  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / Pseudo-Mercator (EPSG:3857)
#> source      : clc_edinburgh.tif
#> color table : 1
#> categories  : label
#> name        :                   label
#> min value   : Continuous urban fabric
#> max value   :           Sea and ocean

# Native handling by terra.
plot(r_coltab, legend = FALSE)

Figure 9: Color tables: native plot with the terra package.

# `autoplot()` method.
autoplot(r_coltab, maxcell = Inf, show.legend = FALSE) +
  labs(title = "autoplot() method")

# `geom_spatraster()` method.
ggplot() +
  geom_spatraster(data = r_coltab, maxcell = Inf, show.legend = FALSE) +
  labs(title = "geom_spatraster() method")

# `scale_fill_coltab()` method.
ggplot() +
  geom_spatraster(
    data = r_coltab,
    use_coltab = FALSE,
    maxcell = Inf,
    show.legend = FALSE
  ) +
  scale_fill_coltab(data = r_coltab) +
  labs(title = "scale_fill_coltab() method")

# Extract named colors and use `scale_fill_manual()`.
cols <- get_coltab_pal(r_coltab)

cols |> head()
#>                    Continuous urban fabric 
#>                                  "#E6004D" 
#>                 Discontinuous urban fabric 
#>                                  "#FF0000" 
#>             Industrial or commercial units 
#>                                  "#CC4DF2" 
#> Road and rail networks and associated land 
#>                                  "#CC0000" 
#>                                 Port areas 
#>                                  "#E6CCCC" 
#>                                   Airports 
#>                                  "#E6CCE6"

# Use the extracted colors.
ggplot() +
  geom_spatraster(
    data = r_coltab,
    use_coltab = FALSE,
    maxcell = Inf,
    show.legend = FALSE
  ) +
  scale_fill_manual(
    values = cols,
    na.value = "transparent",
    na.translate = FALSE
  ) +
  labs(title = "scale_fill_manual() method")

(a) autoplot() method.

(b) geom_spatraster() method.

(c) scale_fill_coltab() method.

(d) Named colors and scale_fill_manual() method.

Figure 10: Color tables: tidyterra methods.

Use with gganimate

You can animate tidyterra maps with gganimate, as shown in this example from @frzambra:

library(gganimate)
library(tidyterra)
library(geodata)
library(ggplot2)

temp <- worldclim_country("che", "tavg", path = ".")

che_cont <- gadm("che", level = 0, path = ".")

temp_m <- crop(temp, che_cont, mask = TRUE)
names(temp_m) <- month.name

anim <- ggplot() +
  geom_spatraster(data = temp_m) +
  scale_fill_viridis_c(
    option = "inferno",
    na.value = "transparent",
    labels = scales::label_number(suffix = "º C")
  ) +
  transition_manual(lyr) +
  theme_bw() +
  theme(
    axis.text = element_blank(),
    axis.ticks = element_blank()
  ) +
  labs(
    title = "Avg temp Switzerland: {current_frame}",
    fill = ""
  )

gganimate::animate(anim, duration = 12, device = "ragg_png")

Figure 11: Animation of average monthly temperatures.

North arrows and scale bars

tidyterra does not provide north arrows or scale bars directly for ggplot2 plots, but 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 = 0.8
  )

Figure 12: Map with north arrow (top right) and scale bar (bottom left) annotations added using ggspatial.

How to overlay a SpatRaster on an RGB tile

Use geom_spatraster_rgb() for the RGB SpatRaster background tile, then add your data layers on top:

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 = "."
)

# Climate data (mean precipitation).
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
  )

Figure 13: Continuous precipitation data overlaid as a semi-transparent layer on an RGB shaded relief tile.

You can create variations with binned legends and filled contours using geom_spatraster_contour_filled():

# Binned legend.
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
  )

(a) Binned precipitation legend.

(b) Contour representation.

Figure 14: Alternative overlay approaches.

Hexagonal grids and other geoms

While SpatRaster cells are inherently rectangular, you can create plots with hexagonal cells using fortify.SpatRaster() and stat_summary_hex(). The final plot requires coordinate adjustment with coord_sf():

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

holyrood <- "holyroodpark.tif"

r <- rast(holyrood)

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

Figure 15: Elevation data aggregated and visualized as hexagonal grid cells.

You do not need to call fortify.SpatRaster() directly because ggplot2 invokes it implicitly when you use ggplot(data = a_spatraster).

Thanks to this extension mechanism, you can use additional geoms and stats from 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 implicit fortify()",
    x = NULL,
    y = NULL
  )

Figure 16: Elevation data represented as points with size and transparency scaled by elevation values.

tidyterra and metR

metR provides ggplot2 extensions, primarily for meteorological data visualization. As shown previously (see Labeling contours), you can combine both packages to create rich, complex plots.

# Load libraries and files.
library(terra)
library(tidyterra)
library(ggplot2)
library(metR)

holyrood <- "holyroodpark.tif"

r <- rast(holyrood)

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")

Figure 17: Relief rendering combining tidyterra for raster plotting and metR for terrain relief representation.