knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) library(ggplot2) theme_set(theme_minimal())

The resampled objects created by spatialsample can be used in many of the same ways that those created by rsample can, from making comparisons to evaluating models. These objects can be used together with other parts of the tidymodels framework, but let's walk through a more basic example using linear modeling of housing data from Ames, IA.

data("ames", package = "modeldata")

Let's say that the sale price of these houses depends on the year they were built, their living area (size), and the type of house they are (duplex vs. townhouse vs. single family), along with perhaps interactions between type and house size.

log10(Sale_Price) ~ Year_Built + Gr_Liv_Area + Bldg_Type

This relationship may exhibit spatial autocorrelation across the city of Ames, and we can use a spatial resampling strategy to evaluate such a model. We can create `v = 15`

spatial cross-validation folds with `spatial_clustering_cv()`

, which uses k-means clustering to identify the sets:

library(spatialsample) set.seed(123) folds <- spatial_clustering_cv(ames, coords = c("Latitude", "Longitude"), v = 15) folds

The `folds`

object is an `rset`

object that contains many resamples or `rsplit`

objects in the `splits`

column. The resulting partitions do not necessarily contain an equal number of observations.

Now let's write a function that will, for each resample:

- obtain the
*analysis*set for model fitting - fit a linear model with a interaction term
- predict the
*assessment*set and return both the true and predicted price, on the log scale

# `splits` will be the `rsplit` object compute_preds <- function(splits) { # fit the model to the analysis set mod <- lm(log10(Sale_Price) ~ Year_Built + Bldg_Type * log10(Gr_Liv_Area), data = analysis(splits)) # identify the assessment set holdout <- assessment(splits) # return the assessment set, with true and predicted price tibble::tibble(Longitude = holdout$Longitude, Latitude = holdout$Latitude, Sale_Price = log10(holdout$Sale_Price), .pred = predict(mod, holdout)) }

We can apply this function to just one of the `splits`

.

compute_preds(folds$splits[[7]])

Or we can apply this function to all of the `splits`

, using `purrr::map()`

.

library(purrr) library(dplyr) cv_res <- folds %>% mutate(.preds = map(splits, compute_preds)) cv_res

We can `unnest()`

these results and use yardstick to compute any regression metrics appropriate to this modeling analysis, such as `yardstick::rmse()`

:

library(tidyr) library(yardstick) cv_rmse <- cv_res %>% unnest(.preds) %>% group_by(id) %>% rmse(Sale_Price, .pred) cv_rmse

It looks like the RMSE may vary across the city, so we can join the metrics back up to our results and plot them.

library(ggplot2) cv_res %>% unnest(.preds) %>% left_join(cv_rmse) %>% ggplot(aes(Longitude, Latitude, color = .estimate)) + geom_point(alpha = 0.5) + labs(color = "RMSE") + scale_color_viridis_c()

The area of highest RMSE is close to a more industrial area of Ames, by a large Department of Transportation complex.

**Any scripts or data that you put into this service are public.**

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.