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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_knit$set(
progress = TRUE,
base.url = "https://raw.githubusercontent.com/dieghernan/tidyterra/main/"
)
knitr::opts_chunk$set(
collapse = TRUE,
tidy = "styler",
comment = "#>",
fig.path = "img/README-",
warning = FALSE,
message = FALSE,
dev = "ragg_png",
dpi = 300,
out.width = "100%"
)
```
# tidyterra <a href="https://dieghernan.github.io/tidyterra/"><img src="man/figures/logo.png" align="right" height="139"/></a>
<!-- badges: start -->
[](https://CRAN.R-project.org/package=tidyterra)
[](https://cran.r-project.org/web/checks/check_results_tidyterra.html)
[](https://CRAN.R-project.org/package=tidyterra)
[](https://doi.org/10.21105/joss.05751)
[](https://github.com/dieghernan/tidyterra/actions/workflows/check-full.yaml)
[](https://github.com/dieghernan/tidyterra/actions/workflows/rhub.yaml)
[](https://app.codecov.io/gh/dieghernan/tidyterra)
[](https://www.codefactor.io/repository/github/dieghernan/tidyterra)
[](https://dieghernan.r-universe.dev/tidyterra)
[](https://www.repostatus.org/#active)
[](https://stackoverflow.com/questions/tagged/tidyterra)
[](https://github.com/dieghernan/tidyterra/actions/workflows/check-terra-devel.yaml)
[](https://github.com/dieghernan/tidyterra/actions/workflows/check-sf-devel.yaml)
[](https://github.com/dieghernan/tidyterra/actions/workflows/check-ggplot2-devel.yaml)
[](https://github.com/dieghernan/tidyterra/actions/workflows/check-dplyr-readr.yaml)
<!-- badges: end -->
The goal of **tidyterra** is to provide common methods of the [**tidyverse**
packages](https://www.tidyverse.org/packages/) for objects created with the
[**terra**](https://CRAN.R-project.org/package=terra) package: `SpatRaster` and
`SpatVector`. It also provides `geoms` for plotting these objects with
[**ggplot2**](https://ggplot2.tidyverse.org/).
Please cite **tidyterra** as:
> Hernangómez, D., (2023). Using the tidyverse with terra objects: the tidyterra
> package. *Journal of Open Source Software*, *8*(91), 5751,
> <https://doi.org/10.21105/joss.05751>.
A BibTeX entry for LaTeX users is:
``` bib
@article{Hernangómez2023,
doi = {10.21105/joss.05751},
url = {https://doi.org/10.21105/joss.05751},
year = {2023},
publisher = {The Open Journal},
volume = {8},
number = {91},
pages = {5751},
author = {Diego Hernangómez},
title = {Using the {tidyverse} with {terra} objects: the {tidyterra} package},
journal = {Journal of Open Source Software}
}
```
## Overview
Full manual of the most recent release of **tidyterra** on **CRAN** is online:
<https://dieghernan.github.io/tidyterra/>
```{r echo=FALSE, results='asis'}
tterra_v <- packageVersion("tidyterra")
l <- unlist(strsplit(as.character(tterra_v), ".", fixed = TRUE))
if (length(l) == 4) {
cat(
"\n",
"You can have a look to the documentation of",
"the dev version in <https://dieghernan.github.io/tidyterra/dev/>"
)
}
```
**tidyverse** methods implemented on **tidyterra** works differently depending
on the type of `Spat*` object:
- `SpatVector`: the methods are implemented using `terra::as.data.frame()`
coercion. Rows correspond to geometries and columns correspond to attributes
of the geometry.
- `SpatRaster`: The implementation on `SpatRaster` objects differs, since the
methods could be applied to layers or to cells. **tidyterra** overall
approach is to treat the layers as columns of a tibble and the cells as rows
(i.e. `select(SpatRaster, 1)` would select the first layer of a
`SpatRaster`).
The methods implemented return the same type of object used as input, unless the
expected behavior of the method is to return another type of object, (for
example, `as_tibble()` would return a `tibble`).
Current methods and functions provided by **tidyterra** are:
| tidyverse method | `SpatVector` | `SpatRaster` |
|---------------------|---------------------|--------------------------------------|
| `tibble::as_tibble()` | ✔️ | ✔️ |
| `dplyr::select()` | ✔️ | ✔️ Select layers |
| `dplyr::mutate()` | ✔️ | ✔️ Create /modify layers |
| `dplyr::transmute()` | ✔️ | ✔️ |
| `dplyr::filter()` | ✔️ | ✔️ Modify cells values and (additionally) remove outer cells. |
| `dplyr::slice()` | ✔️ | ✔️ Additional methods for slicing by row and column. |
| `dplyr::pull()` | ✔️ | ✔️ |
| `dplyr::rename()` | ✔️ | ✔️ |
| `dplyr::relocate()` | ✔️ | ✔️ |
| `dplyr::distinct()` | ✔️ | |
| `dplyr::arrange()` | ✔️ | |
| `dplyr::glimpse()` | ✔️ | ✔️ |
| `dplyr::inner_join()` family | ✔️ | |
| `dplyr::summarise()` | ✔️ | |
| `dplyr::group_by()` family | ✔️ | |
| `dplyr::rowwise()` | ✔️ | |
| `dplyr::count()`, `tally()` | ✔️ | |
| `dplyr::bind_cols()` / `dplyr::bind_rows()` | ✔️ as `bind_spat_cols()` / `bind_spat_rows()` | |
| `tidyr::drop_na()` | ✔️ | ✔️ Remove cell values with `NA` on any layer. Additionally, outer cells with `NA` are removed. |
| `tidyr::replace_na()` | ✔️ | ✔️ |
| `tidyr::fill()` | ✔️ | |
| `tidyr::pivot_longer()` | ✔️ | |
| `tidyr::pivot_wider()` | ✔️ | |
| `ggplot2::autoplot()` | ✔️ | ✔️ |
| `ggplot2::fortify()` | ✔️ to **sf** via `sf::st_as_sf()` | To a **tibble** with coordinates. |
| `ggplot2::geom_*()` | ✔️ `geom_spatvector()` | ✔️ `geom_spatraster()` and `geom_spatraster_rgb()`. |
## :exclamation: A note on performance
**tidyterra** is conceived as a user-friendly wrapper of **terra** using the
**tidyverse** methods and verbs. This approach therefore has a **cost in terms
of performance**.
If you are a **heavy user** of **terra** or you need to work with **big raster
files**, **terra** is much more focused on terms of performance. When possible,
each function of **tidyterra** references to its equivalent on **terra**.
As a rule of thumb if your raster has less than 10.000.000 data slots counting
cells and layers (i.e. `terra::ncell(your_rast)*terra::nlyr(your_rast) < 10e6`)
you are good to go with **tidyterra**.
When plotting rasters, resampling is performed automatically (as `terra::plot()`
does, see the help page). You can adjust this with the `maxcell` parameter.
## Installation
Install **tidyterra** from
[**CRAN**](https://CRAN.R-project.org/package=tidyterra):
```{r, eval=FALSE}
install.packages("tidyterra")
```
You can install the development version of **tidyterra** like so:
```{r, eval=FALSE }
remotes::install_github("dieghernan/tidyterra")
```
Alternatively, you can install **tidyterra** using the
[r-universe](https://dieghernan.r-universe.dev/tidyterra):
```{r, eval=FALSE}
# Enable this universe
install.packages("tidyterra", repos = c(
"https://dieghernan.r-universe.dev",
"https://cloud.r-project.org"
))
```
## Example
### `SpatRasters`
This is a basic example which shows you how to manipulate and plot `SpatRaster`
objects:
```{r example-temp}
library(tidyterra)
library(terra)
# Temperatures
rastertemp <- rast(system.file("extdata/cyl_temp.tif", package = "tidyterra"))
rastertemp
# Rename
rastertemp <- rastertemp %>%
rename(April = tavg_04, May = tavg_05, June = tavg_06)
# Facet all layers
library(ggplot2)
ggplot() +
geom_spatraster(data = rastertemp) +
facet_wrap(~lyr, ncol = 2) +
scale_fill_whitebox_c(
palette = "muted",
labels = scales::label_number(suffix = "º"),
n.breaks = 12,
guide = guide_legend(reverse = TRUE)
) +
labs(
fill = "",
title = "Average temperature in Castille and Leon (Spain)",
subtitle = "Months of April, May and June"
)
# Create maximum differences of two months
variation <- rastertemp %>%
mutate(diff = June - May) %>%
select(variation = diff)
# Add also a overlay of a SpatVector
prov <- vect(system.file("extdata/cyl.gpkg", package = "tidyterra"))
ggplot(prov) +
geom_spatraster(data = variation) +
geom_spatvector(fill = NA) +
scale_fill_whitebox_c(
palette = "deep", direction = -1,
labels = scales::label_number(suffix = "º"),
n.breaks = 5
) +
theme_minimal() +
coord_sf(crs = 25830) +
labs(
fill = "variation",
title = "Variation of temperature in Castille and Leon (Spain)",
subtitle = "Average temperatures in June vs. May"
)
```
**tidyterra** also provide a geom for plotting RGB `SpatRaster` tiles with
**ggplot2**
```{r example-tile}
rgb_tile <- rast(system.file("extdata/cyl_tile.tif", package = "tidyterra"))
plot <- ggplot(prov) +
geom_spatraster_rgb(data = rgb_tile) +
geom_spatvector(fill = NA) +
theme_light()
plot
# Recognizes coord_sf()
plot +
# Change crs and datum (for relabeling graticules)
coord_sf(crs = 3857, datum = 3857)
```
**tidyterra** provides specific scales for plotting hypsometric maps with
**ggplot2**:
```{r hypso, fig.asp=0.65}
asia <- rast(system.file("extdata/asia.tif", package = "tidyterra"))
terra::plot(asia)
ggplot() +
geom_spatraster(data = asia) +
scale_fill_hypso_tint_c(
palette = "gmt_globe",
labels = scales::label_number(),
# Further refinements
breaks = c(-10000, -5000, 0, 2000, 5000, 8000),
guide = guide_colorbar(reverse = TRUE)
) +
labs(
fill = "elevation (m)",
title = "Hypsometric map of Asia"
) +
theme(
legend.position = "bottom",
legend.title.position = "top",
legend.key.width = rel(3),
legend.ticks = element_line(colour = "black", linewidth = 0.3),
legend.direction = "horizontal"
)
```
### `SpatVectors`
This is a basic example which shows you how to manipulate and plot `SpatVector`
objects:
```{r spatvec }
vect(system.file("ex/lux.shp", package = "terra")) %>%
mutate(pop_dens = POP / AREA) %>%
glimpse() %>%
autoplot(aes(fill = pop_dens)) +
scale_fill_whitebox_c(palette = "pi_y_g") +
labs(
fill = "population per km2",
title = "Population density of Luxembourg",
subtitle = "By canton"
)
```
## I need your feedback
Please leave your feedback or open an issue on
<https://github.com/dieghernan/tidyterra/issues>.
## Need help?
Check our [FAQs](https://dieghernan.github.io/tidyterra/articles/faqs.html) or
open a new [issue](https://github.com/dieghernan/tidyterra/issues)!
You can also ask in [Stack Overflow](https://stackoverflow.com/) using the tag
[[tidyterra]](https://stackoverflow.com/questions/tagged/tidyterra).
## Acknowledgement
**tidyterra** **ggplot2** geoms are based on
[**ggspatial**](https://github.com/paleolimbot/ggspatial) implementation, by
[Dewey Dunnington](https://github.com/paleolimbot) and [**ggspatial**
contributors](https://github.com/paleolimbot/ggspatial/graphs/contributors).