Summary of regional in-, out-, turnover and net-migration totals from an origin-destination migration flow matrix or data frame.

Usage

sum_region(
  m,
  drop_diagonal = TRUE,
  orig_col = "orig",
  dest_col = "dest",
  flow_col = "flow",
  international = FALSE,
  include_net = TRUE,
  na_rm = TRUE
)

sum_country(
  m,
  drop_diagonal = TRUE,
  orig_col = "orig",
  dest_col = "dest",
  flow_col = "flow",
  include_net = TRUE,
  international = TRUE,
  na_rm = TRUE
)

Arguments

m: A matrix or data frame of origin-destination flows. For matrix the first and second dimensions correspond to origin and destination respectively. For a data frame ensure the correct column names are passed to orig_col, dest_col and flow_col.
drop_diagonal: Logical to indicate dropping of diagonal terms, where the origin and destination are the same, in the calculation of totals. Default TRUE.
orig_col: Character string of the origin column name (when m is a data frame rather than a matrix)
dest_col: Character string of the destination column name (when m is a data frame rather than a matrix)
flow_col: Character string of the flow column name (when m is a data frame rather than a matrix)
international: Logical to indicate if flows are international.
include_net: Logical to indicate inclusion of a net migration total column for each region, in addition to the total in- and out-flows. Default TRUE.
na_rm: Logical to indicate if to remove NA values in m when calcualting in and out migraiton flow totals. Default set to TRUE.

Value

A tibble with total in-, out- and turnover of flows for each region.

Examples

# matrix
r <- LETTERS[1:4]
m <- matrix(data = c(0, 100, 30, 70, 50, 0, 45, 5, 60, 35, 0, 40, 20, 25, 20, 0),
            nrow = 4, ncol = 4, dimnames = list(orig = r, dest = r), byrow = TRUE)
m
#>     dest
#> orig  A   B  C  D
#>    A  0 100 30 70
#>    B 50   0 45  5
#>    C 60  35  0 40
#>    D 20  25 20  0
sum_region(m)
#> # A tibble: 4 × 5
#>   region out_mig in_mig  turn   net
#>   <chr>    <dbl>  <dbl> <dbl> <dbl>
#> 1 A          200    130   330   -70
#> 2 B          100    160   260    60
#> 3 C          135     95   230   -40
#> 4 D           65    115   180    50

if (FALSE) {
# data frame (tidy) format
library(tidyverse)

# download Abel and Cohen (2019) estimates
f <- read_csv("https://ndownloader.figshare.com/files/38016762", show_col_types = FALSE)
f

# single period
f %>%
  filter(year0 == 1990) %>%
  sum_country(flow_col = "da_pb_closed")

# all periods using group_by
f %>%
  group_by(year0) %>%
  sum_country(flow_col = "da_pb_closed")
}