-
Notifications
You must be signed in to change notification settings - Fork 5
/
Copy pathREADME.Rmd
93 lines (63 loc) · 4.26 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
library(ggplot2)
theme_set(theme_minimal())
```
# spatialsample <a href="https://spatialsample.tidymodels.org"><img src="man/figures/logo.png" align="right" height="138" alt="A hand-drawn map with orange roads, blue rivers, green trees, and brown mountains on a beige background" /></a>
<!-- badges: start -->
[](https://github.com/tidymodels/spatialsample/actions/workflows/R-CMD-check.yaml)
[](https://CRAN.R-project.org/package=spatialsample)
[](https://app.codecov.io/gh/tidymodels/spatialsample?branch=main)
[](https://lifecycle.r-lib.org/articles/stages.html)
<!-- badges: end -->
## Introduction
The goal of spatialsample is to provide functions and classes for spatial resampling to use with [rsample](https://rsample.tidymodels.org/), including:
- [spatial clustering cross-validation](https://doi.org/10.1109/IGARSS.2012.6352393)
- [spatial block cross-validation](https://doi.org/10.1111/ecog.02881)
- [spatially buffered cross-validation](https://doi.org/10.1111/geb.12161)
- [leave-location-out cross-validation](https://doi.org/10.1016/j.envsoft.2017.12.001)
Like [rsample](https://rsample.tidymodels.org/), spatialsample provides building blocks for creating and analyzing resamples of a spatial data set but does not include code for modeling or computing statistics. The resampled data sets created by spatialsample are efficient and do not have much memory overhead.
## Installation
You can install the released version of spatialsample from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("spatialsample")
```
And the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("pak")
pak::pak("tidymodels/spatialsample")
```
## Example
The most straightforward spatial resampling strategy is `spatial_clustering_cv()`, which uses k-means clustering to identify cross-validation folds:
```{r}
library(spatialsample)
set.seed(1234)
folds <- spatial_clustering_cv(boston_canopy, v = 5)
folds
```
In this example, the `boston_canopy` data on tree cover in Boston, MA is resampled with `v = 5`; notice that the resulting partitions do not contain an equal number of observations.
In addition to resampling algorithms, spatialsample provides methods to visualize resamples using [ggplot2](https://ggplot2.tidyverse.org/) through the `autoplot()` function:
```{r 2022-06-12_boston_static, fig.width=7, fig.height=5}
autoplot(folds)
```
We can use the same function to visualize each fold separately:
```{r 2022-06-12_boston-anim, animation.hook="gifski", fig.width=7, fig.height=5}
library(purrr)
walk(folds$splits, function(x) print(autoplot(x)))
```
So far, we've only scratched the surface of the functionality spatialsample provides. For more information, check out the [Getting Started](https://spatialsample.tidymodels.org/articles/spatialsample.html) documentation!
## Contributing
This project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.
- For questions and discussions about tidymodels packages, modeling, and machine learning, please [post on RStudio Community](https://forum.posit.co/new-topic?category_id=15&tags=tidymodels,question).
- If you think you have encountered a bug, please [submit an issue](https://github.com/tidymodels/rules/issues).
- Either way, learn how to create and share a [reprex](https://reprex.tidyverse.org/articles/articles/learn-reprex.html) (a minimal, reproducible example), to clearly communicate about your code.
- Check out further details on [contributing guidelines for tidymodels packages](https://www.tidymodels.org/contribute/) and [how to get help](https://www.tidymodels.org/help/).