Distance between two locationsSource:
step_geodist creates a specification of a
recipe step that will calculate the distance between
points on a map to a reference location.
step_geodist( recipe, lat = NULL, lon = NULL, role = "predictor", trained = FALSE, ref_lat = NULL, ref_lon = NULL, is_lat_lon = TRUE, log = FALSE, name = "geo_dist", columns = NULL, skip = FALSE, id = rand_id("geodist") )
A recipe object. The step will be added to the sequence of operations for this recipe.
- lon, lat
Selector functions to choose which variables are used by the step. See
selections()for more details.
For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.
A logical to indicate if the quantities for preprocessing have been estimated.
- ref_lon, ref_lat
Single numeric values for the location of the reference point.
A logical: Are coordinates in latitude and longitude? If
TRUEthe Haversine formula is used and the returned result is meters. If
FALSEthe Pythagorean formula is used. Default is
TRUEand for recipes created from previous versions of recipes, a value of
A logical: should the distance be transformed by the natural log function?
A single character value to use for the new predictor column. If a column exists with this name, an error is issued.
A character string of variable names that will be populated (eventually) by the
A logical. Should the step be skipped when the recipe is baked by
bake()? While all operations are baked when
prep()is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using
skip = TRUEas it may affect the computations for subsequent operations.
A character string that is unique to this step to identify it.
An updated version of
recipe with the new step added to the
sequence of any existing operations.
step_geodist uses the Pythagorean theorem to calculate Euclidean
is_lat_lon is FALSE. If
is_lat_lon is TRUE, the Haversine
formula is used to calculate the great-circle distance in meters.
tidy() this step, a tibble with columns
echoing the values of
id is returned.
library(modeldata) data(Smithsonian) # How close are the museums to Union Station? near_station <- recipe( ~ ., data = Smithsonian) %>% update_role(name, new_role = "location") %>% step_geodist(lat = latitude, lon = longitude, log = FALSE, ref_lat = 38.8986312, ref_lon = -77.0062457, is_lat_lon = TRUE) %>% prep(training = Smithsonian) bake(near_station, new_data = NULL) %>% arrange(geo_dist) #> # A tibble: 20 × 4 #> name latitude longitude geo_dist #> <fct> <dbl> <dbl> <dbl> #> 1 National Postal Museum 38.9 -77.0 367. #> 2 Renwick Gallery 38.9 -77.0 932. #> 3 National Museum of the American Indian 38.9 -77.0 1571. #> 4 Smithsonian American Art Museum 38.9 -77.0 1636. #> 5 National Portrait Gallery 38.9 -77.0 1646. #> 6 National Air and Space Museum 38.9 -77.0 1796. #> 7 Hirshhorn Museum and Sculpture Garden 38.9 -77.0 2008. #> 8 National Museum of Natural History 38.9 -77.0 2073. #> 9 Arthur M. Sackler Gallery 38.9 -77.0 2108. #> 10 Arts and Industries Building 38.9 -77.0 2124. #> 11 Smithsonian Institution Building 38.9 -77.0 2193. #> 12 National Museum of African Art 38.9 -77.0 2202. #> 13 Freer Gallery of Art 38.9 -77.0 2266. #> 14 National Museum of American History 38.9 -77.0 2393. #> 15 National Museum of African American Histor… 38.9 -77.0 2611. #> 16 National Zoological Park 38.9 -77.1 5246. #> 17 Anacostia Community Museum 38.9 -77.0 5332. #> 18 Steven F. Udvar-Hazy Center 38.9 -77.4 38111. #> 19 George Gustav Heye Center 40.7 -74.0 324871. #> 20 Cooper Hewitt, Smithsonian Design Museum 40.8 -74.0 334041. tidy(near_station, number = 1) #> # A tibble: 1 × 7 #> latitude longitude ref_latitude ref_longitude is_lat_lon name id #> <chr> <chr> <dbl> <dbl> <lgl> <chr> <chr> #> 1 latitude longitude 38.9 -77.0 TRUE geo_dist geodi…