README

# Install centr from CRAN
install.packages("centr")

# Or the development version from GitHub:
# install.packages("devtools")
devtools::install_github("ryanzomorrodi/centr")

Usage

The main functions are mean_center and median_center. They were designed for calculation of population weighted centroids, but can be extended to other forms of analyses.

Mean center calculates the geographic average center. One can specify the groups to calculate individual centers for groups and weights for each individual point. It is analagous to the ArcGIS Pro Mean Center tool.

library(centr)
df <- data.frame(
  lon = c(20, 50, 30, 80, 10),
  lat = c(25, 70, 30, 50, 30),
  group1 = c("a", "b", "a", "b", "a"),
  group2 = c(1, 1, 1, 1, 2),
  wt = c(1, 5, 1, 3, 2)
)
x <- sf::st_as_sf(df, coords = c("lon", "lat"), crs = 4326)

mean_center(x, group = c("group1", "group2"), weight = "wt")
#> Simple feature collection with 3 features and 2 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 10 ymin: 27.58952 xmax: 65.92087 ymax: 63.32603
#> Geodetic CRS:  WGS 84
#> # A tibble: 3 × 3
#>   group1 group2            geometry
#>   <chr>   <dbl>         <POINT [°]>
#> 1 a           1 (24.88607 27.58952)
#> 2 a           2             (10 30)
#> 3 b           1 (65.92087 63.32603)

Median center iteratively calculates the point that minimizes distance to all features. One can specify the groups to calculate individual centers for and weights for each individual point. It is analagous to the ArcGIS Pro Median Center tool.

x_transformed <- sf::st_transform(x, crs = "ESRI:102003")

median_center(x_transformed, group = c("group1", "group2"), weight = "wt")
#> Simple feature collection with 3 features and 2 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 4950281 ymin: 4293605 xmax: 9003834 ymax: 8151342
#> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic
#> # A tibble: 3 × 3
#>   group1 group2          geometry
#>   <chr>   <dbl>       <POINT [m]>
#> 1 a           1 (9003834 5545860)
#> 2 a           2 (8226081 4293605)
#> 3 b           1 (4950281 8151342)

Summaries of other attributes can be calculated by passing the summary expressions to ... just as in dplyr::summarise().

mean_center(
  x, 
  group = c("group1", "group2"), 
  weight = "wt",
  total_weight = sum(wt)
)
#> Simple feature collection with 3 features and 3 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 10 ymin: 27.58952 xmax: 65.92087 ymax: 63.32603
#> Geodetic CRS:  WGS 84
#> # A tibble: 3 × 4
#>   group1 group2 total_weight            geometry
#>   <chr>   <dbl>        <dbl>         <POINT [°]>
#> 1 a           1            2 (24.88607 27.58952)
#> 2 a           2            2             (10 30)
#> 3 b           1            8 (65.92087 63.32603)

centr

Installation

Usage