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generate symmetric identifiers for dyadic data

Source: R/aggregate_dyad.R
gen_symm_id.Rd

gen_symm_id creates symmetric identifiers for dyadic data, ensuring that each unique pair of actors receives the same id regardless of order. this is particularly useful for undirected network data where the relationship between actors a and b is identical to the relationship between b and a.

Usage

gen_symm_id(dyad_data, actor1, actor2, time = NULL)

Arguments

dyad_data

a data.frame containing dyadic data with at least two columns representing actors in each dyad. will be coerced to data.frame if a tibble or data.table is provided.

actor1

character string specifying the column name for the first actor in each dyad.

actor2

character string specifying the column name for the second actor in each dyad.

time

character string specifying the column name for time periods. if provided, the time value will be appended to the symmetric id to create unique identifiers for each time period. set to NULL (default) for cross-sectional data.

Value

a character vector of symmetric identifiers with the same length as the number of rows in dyad_data. each id is formatted as:

  • without time: a length-prefixed key for the alphabetically sorted actor pair

  • with time: the same key with the time value appended

Details

the function ensures symmetry by alphabetically sorting actor names before creating the identifier. this guarantees that:

  • the dyad "usa-china" receives the same id as "china-usa"

  • the dyad "brazil-argentina" receives the same id as "argentina-brazil"

  • actor pairs are consistently ordered regardless of input order

when a time column is specified, it's appended to the symmetric id to maintain unique identifiers across different time periods. this allows for proper aggregation of longitudinal dyadic data while preserving temporal variation.

Note

all actor values are converted to character strings before creating ids to ensure consistent sorting behavior across different data types.

ids are intended as opaque keys. do not parse them to recover actor names; keep the original actor columns when those values are needed downstream.

this function is primarily used internally by aggregate_dyad for efficient symmetric aggregation, but can be used independently for creating symmetric dyad identifiers.

Author

shahryar minhas

Examples

# create example dyadic data
trade_df <- data.frame(
    from = c("usa", "china", "russia", "usa", "brazil", "argentina"),
    to = c("china", "usa", "usa", "russia", "argentina", "brazil"),
    trade_value = c(100, 100, 50, 75, 30, 25),
    year = c(2020, 2020, 2021, 2021, 2021, 2021)
)

# generate symmetric ids without time
trade_df$symm_id <- gen_symm_id(trade_df, "from", "to")
print(trade_df[, c("from", "to", "symm_id")])
#>        from        to              symm_id
#> 1       usa     china        5:china|3:usa
#> 2     china       usa        5:china|3:usa
#> 3    russia       usa       6:russia|3:usa
#> 4       usa    russia       6:russia|3:usa
#> 5    brazil argentina 9:argentina|6:brazil
#> 6 argentina    brazil 9:argentina|6:brazil
# note: usa-china and china-usa both get "china_usa"

# generate symmetric ids with time
trade_df$symm_id_time <- gen_symm_id(trade_df, "from", "to", "year")
print(trade_df[, c("from", "to", "year", "symm_id_time")])
#>        from        to year                symm_id_time
#> 1       usa     china 2020        5:china|3:usa|4:2020
#> 2     china       usa 2020        5:china|3:usa|4:2020
#> 3    russia       usa 2021       6:russia|3:usa|4:2021
#> 4       usa    russia 2021       6:russia|3:usa|4:2021
#> 5    brazil argentina 2021 9:argentina|6:brazil|4:2021
#> 6 argentina    brazil 2021 9:argentina|6:brazil|4:2021
# note: usa-china in 2020 gets "china_usa_2020"

# use for aggregation of undirected relationships
trade_df$total_trade <- ave(
    trade_df$trade_value,
    trade_df$symm_id_time,
    FUN = sum
)
print(unique(trade_df[, c("symm_id_time", "total_trade")]))
#>                  symm_id_time total_trade
#> 1        5:china|3:usa|4:2020         200
#> 3       6:russia|3:usa|4:2021         125
#> 5 9:argentina|6:brazil|4:2021          55

# example with longitudinal data
library(netify)
data(icews)
icews_sample <- icews[1:100, ]

# create symmetric ids for conflict events
icews_sample$symm_dyad <- gen_symm_id(
    icews_sample,
    actor1 = "i",
    actor2 = "j",
    time = "year"
)

# check that symmetric pairs get same id
icews_sample[icews_sample$i == "united states" & icews_sample$j == "israel", "symm_dyad"]
#> character(0)
icews_sample[icews_sample$i == "israel" & icews_sample$j == "united states", "symm_dyad"]
#> character(0)