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Distance between two locations

Source: R/geodist.R
step_geodist.Rd

step_geodist() creates a specification of a recipe step that will calculate the distance between points on a map to a reference location.

Usage

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,
  keep_original_cols = TRUE,
  skip = FALSE,
  id = rand_id("geodist")
)

Arguments

recipe

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.

role

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.

trained

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.

is_lat_lon

A logical: Are coordinates in latitude and longitude? If TRUE the Haversine formula is used and the returned result is meters. If FALSE the Pythagorean formula is used. Default is TRUE and for recipes created from previous versions of recipes, a value of FALSE is used.

log

A logical: should the distance be transformed by the natural log function?

name

A single character value to use for the new predictor column. If a column exists with this name, an error is issued.

columns

A character string of the selected variable names. This field is a placeholder and will be populated once prep() is used.

keep_original_cols

A logical to keep the original variables in the output. Defaults to FALSE.

skip

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 = TRUE as it may affect the computations for subsequent operations.

id

A character string that is unique to this step to identify it.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Details

step_geodist uses the Pythagorean theorem to calculate Euclidean distances if is_lat_lon is FALSE. If is_lat_lon is TRUE, the Haversine formula is used to calculate the great-circle distance in meters.

Tidying

When you tidy() this step, a tibble is returned with columns latitude, longitude, ref_latitude, ref_longitude, is_lat_lon, name , and id:

latitude

character, name of latitude variable

longitude

character, name of longitude variable

ref_latitude

numeric, location of latitude reference point

ref_longitude

numeric, location of longitude reference point

is_lat_lon

character, the summary function name

name

character, name of resulting variable

id

character, id of this step

Case weights

The underlying operation does not allow for case weights.

References

https://en.wikipedia.org/wiki/Haversine_formula

See also

Other multivariate transformation steps: step_classdist_shrunken(), step_classdist(), step_depth(), step_ica(), step_isomap(), step_kpca_poly(), step_kpca_rbf(), step_kpca(), step_mutate_at(), step_nnmf_sparse(), step_nnmf(), step_pca(), step_pls(), step_ratio(), step_spatialsign()

Examples

data(Smithsonian, package = "modeldata")

# 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…