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Sum bilateral data to include aggregate bilateral totals for origin and destination meta areas

Source: R/sum_expand.R
sum_expand.Rd

Expand matrix of data frame of migration data to include aggregate sums for corresponding origin and destination meta regions.

Usage

sum_expand(
  m,
  return_matrix = FALSE,
  guess_order = TRUE,
  area_first = TRUE,
  orig_col = "orig",
  dest_col = "dest",
  flow_col = "flow",
  orig_area_col = "orig_area",
  dest_area_col = "dest_area",
  orig_area = NULL,
  dest_area = NULL
)

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.

return_matrix

Logical to return a matrix. Default FALSE.

guess_order

Logical to return a matrix or data frame ordered by origin and destination with area names at the end of each block. Default TRUE. If FALSE returns matrix or data frame based on alphabetical order of origin and destinations.

area_first

Order area sums to be placed before the origin and destination values. 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)

orig_area_col

Character string of the origin area column name (when m is a data frame rather than a matrix)

dest_area_col

Character string of the destination area column name (when m is a data frame rather than a matrix)

orig_area

Vector of labels for the origin areas of each row of m.

dest_area

Vector of labels for the destination areas of each row of m.

Value

A tibble or matrix with additional row and columns (for matrices) for aggregate sums for origin and destination meta-regions

Examples

##
## from matrix
##
m <- block_matrix(x = 1:16, b = c(2,3,4,2))
m
#>    A1 A2 B1 B2 B3 C1 C2 C3 C4 D1 D2
#> A1  1  1  5  5  5  9  9  9  9 13 13
#> A2  1  1  5  5  5  9  9  9  9 13 13
#> B1  2  2  6  6  6 10 10 10 10 14 14
#> B2  2  2  6  6  6 10 10 10 10 14 14
#> B3  2  2  6  6  6 10 10 10 10 14 14
#> C1  3  3  7  7  7 11 11 11 11 15 15
#> C2  3  3  7  7  7 11 11 11 11 15 15
#> C3  3  3  7  7  7 11 11 11 11 15 15
#> C4  3  3  7  7  7 11 11 11 11 15 15
#> D1  4  4  8  8  8 12 12 12 12 16 16
#> D2  4  4  8  8  8 12 12 12 12 16 16

# requires a vector of origin and destination areas
a <- rep(LETTERS[1:4], times = c(2,3,4,2))
a
#>  [1] "A" "A" "B" "B" "B" "C" "C" "C" "C" "D" "D"
sum_expand(m = m, orig_area = a, dest_area = a)
#> # A tibble: 225 × 3
#>    orig  dest   flow
#>    <chr> <chr> <int>
#>  1 A     A         4
#>  2 A     A1        2
#>  3 A     A2        2
#>  4 A     B        30
#>  5 A     B1       10
#>  6 A     B2       10
#>  7 A     B3       10
#>  8 A     C        72
#>  9 A     C1       18
#> 10 A     C2       18
#> # ℹ 215 more rows

# place area sums after regions
sum_expand(m = m, orig_area = a, dest_area = a, area_first = FALSE)
#> # A tibble: 225 × 3
#>    orig  dest   flow
#>    <chr> <chr> <int>
#>  1 A1    A1        1
#>  2 A1    A2        1
#>  3 A1    A         2
#>  4 A1    B1        5
#>  5 A1    B2        5
#>  6 A1    B3        5
#>  7 A1    B        15
#>  8 A1    C1        9
#>  9 A1    C2        9
#> 10 A1    C3        9
#> # ℹ 215 more rows

##
## from large data frame
##
if (FALSE) {
library(tidyverse)
library(countrycode)

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

# 1990-1995 flow estimates
f %>%
  filter(year0 == 1990) %>%
  mutate(
    orig_area = countrycode(sourcevar = orig, custom_dict = dict_ims,
                            origin = "iso3c", destination = "region"),
    dest_area = countrycode(sourcevar = dest, custom_dict = dict_ims,
                            origin = "iso3c", destination = "region")
  ) %>%
  sum_expand(flow_col = "da_pb_closed", return_matrix = FALSE)

# by group (period)
f %>%
  mutate(
    orig_area = countrycode(sourcevar = orig, custom_dict = dict_ims,
                            origin = "iso3c", destination = "region"),
    dest_area = countrycode(sourcevar = dest, custom_dict = dict_ims,
                            origin = "iso3c", destination = "region")
  ) %>%
  group_by(year0) %>%
  sum_expand(flow_col = "da_pb_closed", return_matrix = FALSE)
}