Summary of bilateral flows, counter-flow and net migration flow
Arguments
- m
A
matrixor data frame of origin-destination flows. Formatrixthe first and second dimensions correspond to origin and destination respectively. For a data frame ensure the correct column names are passed toorig,destandflow.- label
Character string for the prefix of the calculated columns. Can take values
floworstream- orig
Character string of the origin column name (when
mis a data frame rather than amatrix)- dest
Character string of the destination column name (when
mis a data frame rather than amatrix)- flow
Character string of the flow column name (when
mis a data frame rather than amatrix)
Value
A tibble with columns for orig, destination, corridor, flow, counter-flow and net flow in each bilateral pair.
Examples
# using 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_bilat(m)
#> # A tibble: 12 × 8
#> orig dest corridor pair flow counter_flow net_flow interchange
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 B A B -> A A - B 50 100 -50 150
#> 2 C A C -> A A - C 60 30 30 90
#> 3 D A D -> A A - D 20 70 -50 90
#> 4 A B A -> B A - B 100 50 50 150
#> 5 C B C -> B B - C 35 45 -10 80
#> 6 D B D -> B B - D 25 5 20 30
#> 7 A C A -> C A - C 30 60 -30 90
#> 8 B C B -> C B - C 45 35 10 80
#> 9 D C D -> C C - D 20 40 -20 60
#> 10 A D A -> D A - D 70 20 50 90
#> 11 B D B -> D B - D 5 25 -20 30
#> 12 C D C -> D C - D 40 20 20 60
# using data frame
library(dplyr)
library(tidyr)
d <- expand_grid(orig = r, dest = r, sex = c("female", "male")) %>%
mutate(flow = sample(x = 1:100, size = 32))
d
#> # A tibble: 32 × 4
#> orig dest sex flow
#> <chr> <chr> <chr> <int>
#> 1 A A female 48
#> 2 A A male 56
#> 3 A B female 82
#> 4 A B male 17
#> 5 A C female 83
#> 6 A C male 24
#> 7 A D female 22
#> 8 A D male 89
#> 9 B A female 90
#> 10 B A male 73
#> # ℹ 22 more rows
# orig-dest summary of sex-specific flows
d %>%
group_by(sex) %>%
sum_bilat()
#> # A tibble: 24 × 9
#> # Groups: sex [2]
#> orig dest corridor pair sex flow counter_flow net_flow interchange
#> <chr> <chr> <chr> <chr> <chr> <int> <int> <int> <int>
#> 1 A B A -> B A - B female 82 90 -8 172
#> 2 A B A -> B A - B male 17 73 -56 90
#> 3 A C A -> C A - C female 83 72 11 155
#> 4 A C A -> C A - C male 24 96 -72 120
#> 5 A D A -> D A - D female 22 78 -56 100
#> 6 A D A -> D A - D male 89 15 74 104
#> 7 B A B -> A A - B female 90 82 8 172
#> 8 B A B -> A A - B male 73 17 56 90
#> 9 B C B -> C B - C female 60 63 -3 123
#> 10 B C B -> C B - C male 94 10 84 104
#> # ℹ 14 more rows
# use group_by to distinguish orig-dest tables
d %>%
group_by(sex) %>%
sum_bilat()
#> # A tibble: 24 × 9
#> # Groups: sex [2]
#> orig dest corridor pair sex flow counter_flow net_flow interchange
#> <chr> <chr> <chr> <chr> <chr> <int> <int> <int> <int>
#> 1 A B A -> B A - B female 82 90 -8 172
#> 2 A B A -> B A - B male 17 73 -56 90
#> 3 A C A -> C A - C female 83 72 11 155
#> 4 A C A -> C A - C male 24 96 -72 120
#> 5 A D A -> D A - D female 22 78 -56 100
#> 6 A D A -> D A - D male 89 15 74 104
#> 7 B A B -> A A - B female 90 82 8 172
#> 8 B A B -> A A - B male 73 17 56 90
#> 9 B C B -> C B - C female 60 63 -3 123
#> 10 B C B -> C B - C male 94 10 84 104
#> # ℹ 14 more rows