This is my transaction data. It shows the transactions made from the accounts in from column to the accounts in to column with the date and the amount information
data
id from to date amount
<int> <chr> <chr> <date> <dbl>
19521 6644 6934 2005-01-01 700.0
19524 6753 8456 2005-01-01 600.0
19523 9242 9333 2005-01-01 1000.0
… … … … …
1056317 7819 7454 2010-12-31 60.2
1056318 6164 7497 2010-12-31 107.5
1056319 7533 7492 2010-12-31 164.1
I want to calculate closeness centrality measure on the networks of transactions made in the last 6 month prior to the date each particular transaction was made and want to save this information as a new column in the original data.
example data I'll use here is:
structure(list(id = c(83324L, 87614L, 88898L, 89874L, 94765L,
100277L, 101587L), from = c("5370", "7816", "8046", "5492", "8756",
"5370", "9254"), to = c("9676", "5370", "5370", "5370", "5370",
"9105", "5370"), date = structure(c(13391, 13400, 13404, 13409,
13428, 13452, 13452), class = "Date"), amount = c(261.1, 16400,
3500, 2700, 19882, 182, 14.6)), row.names = c(NA, -7L), class = "data.frame")
Now, this following code works very well to accomplish this in a small dataset:
library(tnet)
closeness_fnc <- function(data){
accounts <- data[date == max(date),from]
id <- data[date == max(date),id]
# for directed networks
df <- data %>% group_by(from, to) %>% mutate(weights = sum(amount)) %>% select(from, to, weights) %>% distinct
cl <- closeness_w(df, directed = T, gconly=FALSE, alpha = 0.5)
list(
id = id,
closeness_directed = cl[,"n.closeness"][accounts]
)
}
network_data <- data[, closeness_fnc(data[(date >= end_date - 180) & (date <= end_date)]), .(end_date = date)] %>% select(-end_date)
# adding this info into the original data
data <- merge(x = data, y = network_data, by = "id")
So, the output is as I expected:
# data
id from to date amount closeness_directed
<int> <chr> <chr> <date> <dbl> <dbl>
83324 5370 9676 2006-08-31 261.1 1.00000000
87614 7816 5370 2006-09-09 16400.0 0.98744695
88898 8046 5370 2006-09-13 3500.0 0.35329017
89874 5492 5370 2006-09-18 2700.0 0.25176754
94765 8756 5370 2006-10-07 19882.0 0.39233504
100277 5370 9105 2006-10-31 182.0 0.07167582
101587 9254 5370 2006-10-31 14.6 0.02390589
However, since my data has over 1 million rows, this code will take more than a day to complete(it runs for more than 12 hours and hasn't yet finished).
I had a similar running time problem here and I want to apply the same logic to this code. So, I modified my code as follows:
library(tnet)
closeness_fnc <- function(data){
accounts <- data[date == max(date),from]
id <- data[date == max(date),id]
# for directed networks
df <- data %>% group_by(from, to) %>% mutate(weights = sum(amount)) %>% select(from, to, weights) %>% distinct
cl <- closeness_w(df, directed = T, gconly=FALSE, alpha = 0.5)
closeness_directed <- cl[,"n.closeness"][accounts]
closeness_directed <- as.data.frame(closeness_directed)
closeness_directed$from <- rownames(closeness_directed)
rownames(closeness_directed) <- NULL
return(closeness_directed)
}
# this is the approach given in the link I provided:
setDT(data)[, date_minus_180 := date - 180]
data[, ':=' (closeness_directed = data[data, closeness_fnc(data),
on = .(from, date <= date, date >= date_minus_180), by = .EACHI]$closeness_directed
)] %>% select(-date_minus_180)
however, that won't work obviously since
data[data, closeness_fnc(data),
on = .(from, date <= date, date >= date_minus_180), by = .EACHI]
gives the output
from date date closeness_directed from
<chr> <date> <date> <dbl> <chr>
5370 2006-08-31 2006-03-04 0.07167582 5370
5370 2006-08-31 2006-03-04 0.02390589 9254
7816 2006-09-09 2006-03-13 0.07167582 5370
7816 2006-09-09 2006-03-13 0.02390589 9254
8046 2006-09-13 2006-03-17 0.07167582 5370
8046 2006-09-13 2006-03-17 0.02390589 9254
5492 2006-09-18 2006-03-22 0.07167582 5370
5492 2006-09-18 2006-03-22 0.02390589 9254
8756 2006-10-07 2006-04-10 0.07167582 5370
8756 2006-10-07 2006-04-10 0.02390589 9254
1-10 of 14 rows
So, now how can I adjust the code here to solve the problem?
A larger dataset
structure(list(id = c(19521L, 19522L, 19523L, 19524L, 19525L,
19526L, 19527L, 19528L, 19529L, 19530L, 19531L, 0L, 19532L, 19533L,
19534L, 21971L, 21972L, 21973L, 21974L, 21975L, 21976L, 21977L,
21978L, 21979L, 21980L, 21981L, 1L, 21761L, 21762L, 21763L, 21764L,
21765L, 21766L, 21767L, 21982L, 21983L, 21984L, 21768L, 21769L,
21770L, 21771L, 21772L, 21773L, 2L, 21774L, 21775L, 21776L, 21777L,
21778L, 21779L, 21780L, 21781L, 21782L, 3L, 21783L, 21784L, 21785L,
21786L, 21787L, 21788L, 21789L, 21790L, 21791L, 21792L, 21793L,
21794L, 21795L, 21796L, 4L, 21797L, 21798L, 21799L, 21800L, 21801L,
21802L, 21803L, 21804L, 21805L, 21806L, 21807L, 21808L, 21809L,
21810L, 21811L, 21812L, 21813L, 21814L, 21815L, 5L, 21816L, 21817L,
21818L, 21819L, 21820L, 21821L, 21822L, 21823L, 21824L, 21825L,
21826L, 21827L, 21828L, 21829L, 21830L, 6L, 21831L, 21832L, 21833L,
21834L, 21835L, 21836L, 21837L, 21838L, 7L, 21839L, 21840L, 21841L,
21842L, 21843L, 21844L, 21845L, 21846L, 21847L, 21848L, 21849L,
21850L, 21851L, 21852L, 21853L, 21854L, 21855L, 21856L, 21857L,
8L, 21858L, 21859L, 9L, 10L, 21860L, 21861L, 21862L, 21863L,
21864L, 21865L, 21866L, 21867L, 21868L, 21869L, 21870L, 21871L,
21872L, 21873L, 21874L, 21875L, 21876L, 21877L, 21878L, 21879L,
21880L, 21881L, 21882L, 21883L, 21884L, 21885L, 21886L, 21887L,
21888L, 21889L, 21890L, 21891L, 21892L, 21893L, 21894L, 21895L,
21896L, 21897L, 21898L, 21899L, 21900L, 11L, 21901L, 21902L,
21903L, 21904L, 21905L, 21906L, 21907L, 21908L, 21909L, 12L,
21910L, 21911L, 21912L, 21913L, 21914L, 21915L, 21916L, 21917L,
21918L, 21919L, 13L, 21920L, 21921L, 21922L, 21923L, 21924L,
21925L, 21926L, 21927L, 21928L, 21929L, 21930L, 21931L, 21932L,
21933L, 21934L, 21935L, 21936L, 14L, 21937L, 21938L, 21939L,
21940L, 21941L, 21942L, 21957L, 21958L, 21959L, 21960L, 21961L,
21962L, 21963L, 21964L, 15L, 21965L, 21966L, 21967L, 21968L,
21969L, 21970L, 21985L, 21986L, 21987L, 21988L, 21989L, 21990L,
21991L, 21992L, 21993L, 21994L, 21995L, 21996L, 16L, 17L, 21551L,
21552L, 21553L, 21554L, 21555L, 21556L, 21557L, 21558L, 21559L,
21560L, 21561L, 21562L, 21563L, 21564L, 21565L, 21566L, 21567L,
21997L, 21998L, 18L, 21568L, 21569L, 21570L, 21571L, 21572L,
21573L, 21574L, 21575L, 21576L, 21577L, 21578L, 21579L, 21580L,
21581L, 19L, 21582L, 21583L, 21584L, 21585L, 21586L, 21587L,
21588L, 21589L, 21590L, 21591L, 21592L, 20L, 21593L, 21594L,
21595L, 21596L, 21597L, 21598L, 21599L, 21600L, 21601L, 21602L,
21603L, 21604L, 21605L, 21606L, 21L, 21607L, 21608L, 21609L,
21610L, 21611L, 21612L, 21613L, 21614L, 21615L, 21616L, 21617L,
21618L, 21619L, 21620L, 21621L, 21622L, 21623L, 21624L, 21625L,
21626L, 22L, 21627L, 21628L, 21629L, 21630L, 21631L, 21632L,
21633L, 21634L, 21635L, 21636L, 21637L, 21638L, 21639L, 21640L,
21641L, 21642L, 21643L, 21644L, 21645L, 23L, 21646L, 21647L,
21648L, 21649L, 21650L, 21651L, 21652L, 21653L, 21654L, 21655L,
21656L, 21657L, 21658L, 24L, 21659L, 21660L, 21661L, 21662L,
21663L, 21664L, 21665L, 21666L, 21667L, 21668L, 21669L, 25L,
21670L, 21671L, 21672L, 21673L, 21674L, 21675L, 21676L, 21677L,
21678L, 21679L, 21680L, 21681L, 21682L, 21683L, 26L, 21684L,
21685L, 21686L, 21687L, 21688L, 21689L, 21690L, 21691L, 21692L,
21693L, 21694L, 21695L, 21696L, 21697L, 21698L, 21699L, 21700L,
21701L, 21702L, 21703L, 27L, 21704L, 21719L, 21720L, 21721L,
21722L, 21723L, 21724L, 21725L, 21726L, 21727L, 21728L, 21729L,
21730L, 21731L, 21732L, 28L, 21733L, 21734L, 21735L, 21736L,
21737L, 21738L, 21739L, 21740L, 29L, 21741L, 21742L, 21743L,
21744L, 21745L, 21746L, 21747L, 21748L, 21749L, 21750L, 21751L,
21752L, 21753L, 21754L, 21755L, 21756L, 21757L, 21758L, 30L,
31L, 32L, 33L, 34L, 35L, 36L, 37L, 21229L, 21230L, 21231L, 21232L,
21233L, 21234L, 21235L, 21236L, 21237L, 21238L, 21239L, 21240L,
21241L, 21242L, 21243L, 21244L, 21245L, 21246L, 21247L, 21248L,
21249L, 21250L, 21251L, 21252L, 21253L, 21254L, 21255L, 21256L,
21257L, 21258L), from = c("6644", "9843", "9242", "6753", "7075",
"8685", "5513", "6340", "6042", "5587", "7237", "5695", "9582",
"8539", "7939", "9077", "8946", "5591", "8380", "5865", "7867",
"9457", "6968", "7971", "6150", "9361", "9379", "8409", "9740",
"7226", "7531", "6752", "7362", "6661", "5730", "5417", "9049",
"7057", "6252", "9476", "6228", "8896", "7371", "8170", "7122",
"6694", "5450", "9435", "5619", "8289", "9862", "5504", "6555",
"9845", "7537", "9482", "6810", "8257", "8490", "6588", "9652",
"7303", "5852", "5746", "9198", "6917", "8688", "9460", "9640",
"7054", "8628", "7065", "9006", "6832", "6185", "8422", "6914",
"7069", "7848", "8436", "5494", "6375", "5653", "8912", "9794",
"8413", "6527", "9101", "5815", "6923", "8184", "6811", "8130",
"6539", "8643", "6329", "7744", "8211", "9641", "8003", "5599",
"8715", "7108", "9573", "8583", "5648", "6444", "5660", "8191",
"9830", "5931", "7921", "6753", "8314", "7940", "6265", "6604",
"6509", "5618", "5860", "6469", "9525", "5887", "6626", "7145",
"6862", "5741", "9144", "9862", "9163", "7297", "7599", "8427",
"8865", "9418", "8636", "6530", "9155", "6934", "8817", "9028",
"5521", "5943", "7443", "9557", "8239", "6819", "9761", "5983",
"6830", "6368", "5381", "8782", "8008", "9160", "9862", "8008",
"9615", "6920", "6164", "6278", "9729", "8960", "6358", "5939",
"8902", "9522", "7344", "9070", "6594", "8058", "6639", "7896",
"6325", "7804", "9554", "9725", "8475", "7746", "7536", "9671",
"9761", "5415", "6837", "8327", "9061", "8981", "9226", "5862",
"7085", "8925", "6226", "6849", "8432", "9545", "5837", "5440",
"9732", "8695", "7690", "5829", "9373", "7977", "6361", "7320",
"7603", "6303", "7077", "7850", "5792", "9588", "9204", "8648",
"8950", "7106", "6334", "6843", "7060", "9606", "5520", "9725",
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"6374", "9528", "7919", "8532", "6795", "6934", "8162", "9275",
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"7815", "9028", "7932", "6125", "6671", "7800", "9835", "5573",
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"7201", "7253", "6720", "6175", "9201", "5682", "5473", "7173",
"6094", "8810", "5874", "6947", "8462", "6885", "6201"), to = c("6934",
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"6213", "6273", "6248", "7109", "7143", "8041", "8665", "8925",
"9115", "6634", "6671"), date = structure(c(12784, 12784, 12784,
12784, 12785, 12785, 12786, 12786, 12786, 12786, 12786, 12787,
12787, 12787, 12787, 12788, 12788, 12788, 12788, 12789, 12789,
12790, 12790, 12790, 12790, 12790, 12791, 12791, 12791, 12791,
12791, 12791, 12791, 12791, 12791, 12791, 12791, 12792, 12792,
12792, 12792, 12792, 12792, 12793, 12793, 12793, 12793, 12793,
12794, 12794, 12794, 12794, 12794, 12795, 12795, 12795, 12795,
12795, 12795, 12795, 12795, 12796, 12796, 12796, 12796, 12796,
12796, 12796, 12797, 12797, 12797, 12797, 12797, 12797, 12797,
12797, 12798, 12798, 12799, 12800, 12800, 12800, 12801, 12801,
12801, 12802, 12802, 12802, 12803, 12803, 12804, 12804, 12804,
12804, 12804, 12805, 12805, 12805, 12805, 12805, 12806, 12806,
12806, 12806, 12807, 12807, 12807, 12807, 12807, 12807, 12808,
12808, 12808, 12809, 12809, 12809, 12809, 12809, 12809, 12809,
12810, 12810, 12810, 12810, 12810, 12811, 12811, 12811, 12811,
12812, 12812, 12812, 12812, 12813, 12813, 12813, 12814, 12814,
12814, 12814, 12814, 12814, 12814, 12814, 12814, 12814, 12814,
12814, 12814, 12814, 12814, 12814, 12814, 12814, 12814, 12814,
12814, 12814, 12814, 12814, 12814, 12814, 12814, 12814, 12814,
12814, 12814, 12814, 12814, 12814, 12814, 12814, 12814, 12814,
12814, 12814, 12814, 12815, 12815, 12816, 12816, 12816, 12816,
12816, 12816, 12816, 12816, 12816, 12816, 12817, 12817, 12817,
12817, 12817, 12817, 12817, 12818, 12818, 12818, 12818, 12819,
12819, 12819, 12819, 12819, 12819, 12819, 12819, 12819, 12819,
12819, 12819, 12819, 12819, 12819, 12819, 12819, 12819, 12820,
12820, 12820, 12820, 12820, 12820, 12820, 12820, 12820, 12820,
12820, 12820, 12820, 12820, 12820, 12821, 12821, 12821, 12821,
12821, 12821, 12821, 12821, 12821, 12821, 12821, 12821, 12821,
12821, 12821, 12821, 12821, 12821, 12821, 12822, 12822, 12822,
12822, 12822, 12822, 12822, 12822, 12822, 12822, 12822, 12822,
12822, 12822, 12822, 12822, 12822, 12822, 12822, 12822, 12822,
12823, 12823, 12823, 12823, 12823, 12823, 12823, 12823, 12823,
12823, 12823, 12823, 12823, 12823, 12823, 12824, 12824, 12824,
12824, 12824, 12824, 12824, 12824, 12824, 12824, 12824, 12824,
12825, 12825, 12825, 12825, 12825, 12825, 12825, 12825, 12825,
12825, 12825, 12825, 12825, 12825, 12825, 12826, 12826, 12826,
12826, 12826, 12826, 12826, 12826, 12826, 12826, 12826, 12826,
12826, 12826, 12826, 12826, 12826, 12826, 12826, 12826, 12826,
12827, 12827, 12827, 12827, 12827, 12827, 12827, 12827, 12827,
12827, 12827, 12827, 12827, 12827, 12827, 12827, 12827, 12827,
12827, 12827, 12828, 12828, 12828, 12828, 12828, 12828, 12828,
12828, 12828, 12828, 12828, 12828, 12829, 12829, 12830, 12830,
12830, 12830, 12830, 12830, 12831, 12831, 12831, 12831, 12831,
12831, 12832, 12832, 12832, 12832, 12832, 12832, 12832, 12832,
12833, 12833, 12833, 12833, 12833, 12833, 12833, 12834, 12834,
12834, 12834, 12834, 12834, 12834, 12834, 12834, 12834, 12834,
12835, 12835, 12835, 12835, 12835, 12836, 12836, 12836, 12836,
12836, 12837, 12837, 12837, 12837, 12837, 12837, 12837, 12837,
12837, 12837, 12838, 12838, 12838, 12838, 12838, 12838, 12839,
12839, 12839, 12839, 12839, 12839, 12839, 12839, 12839, 12840,
12840, 12840, 12840, 12840, 12840, 12840, 12841, 12841, 12841,
12841, 12841, 12841, 12841, 12841, 12841, 12841, 12841, 12841,
12842, 12842, 12842, 12842, 12842, 12842, 12842, 12842, 12842,
12842, 12842, 12842, 12842, 12842, 12842, 12842, 12842, 12842,
12842, 12842, 12842, 12842, 12842, 12842, 12842, 12842, 12842,
12842, 12842, 12842, 12842, 12842, 12842, 12842, 12842, 12842,
12842, 12842), class = "Date"), amount = c(700, 900, 1000, 600,
400, 1100, 600, 1100, 200, 800, 1000, 700, 300, 800, 800, 5123,
400, 3401, 700, 500, 700, 3242, 500, 400, 5298, 900, 11832, 300,
500, 600, 1100, 600, 300, 800, 400, 6774, 300, 200, 400, 14264,
900, 13851, 17366, 1000, 800, 700, 6007, 500, 400, 6207, 900,
12644, 800, 4276, 6434, 14779, 4507, 6446, 800, 17477, 1100,
5009, 1000, 5718, 800, 13967, 6959, 15914, 200, 4470, 600, 800,
10737, 700, 44749, 1000, 46552, 500, 13156, 1000, 23323, 1100,
200, 300, 10792, 200, 400, 700, 200, 700, 1100, 1000, 700, 500,
1100, 7268, 300, 200, 16125, 400, 14440, 700, 900, 300, 49752,
200, 36518, 500, 900, 300, 900, 1000, 200, 19961, 21899, 12336,
1100, 200, 700, 1100, 900, 1100, 800, 600, 400, 200, 500, 200,
200, 38000, 16983, 1000, 300, 1000, 300, 800, 13.4, 42.7, 34700,
12.6, 47.5, 13.3, 37.1, 17, 11.1, 15.5, 22.2, 55.8, 11.8, 50.1,
45, 15.9, 38.8, 38.2, 20.1, 38.9, 7.1, 107.1, 48, 62.4, 2900,
21.5, 19.1, 14, 19.5, 15.2, 5282, 94.7, 19.4, 28.2, 42.7, 110.2,
0.8, 23.1, 20, 19.6, 2000, 5100, 1100, 200, 11900, 1100, 5500,
7500, 1100, 800, 6000, 200, 600, 800, 25300, 45647, 1000, 700,
600, 7000, 700, 900, 300, 2900, 5224, 30192, 24381, 400, 5123,
23330, 700, 8500, 3191, 23041, 5029, 6238, 3401, 900, 20213,
7618, 19935, 600, 5859, 3375, 12817, 500, 38645, 1600, 10600,
5500, 700, 3217, 14626, 4550, 4356, 6689, 600, 3242, 9612, 5080,
5039, 12785, 4212, 17632, 3395, 200, 3399, 5298, 14493, 28157,
1800, 31348, 5544, 14100, 33045, 1800, 200, 800, 20066, 400,
1000, 27666, 500, 600, 700, 700, 3151, 1000, 6774, 800, 1500,
22452, 1100, 44333, 18347, 200, 600, 6242, 13900, 19746, 400,
48098, 7041, 9100, 10584, 49590, 3021, 500, 14264, 5400, 13851,
17366, 1200, 5072, 1100, 1100, 47831, 12015, 5200, 8905, 23524,
6007, 1000, 300, 22349, 31038, 25200, 43737, 12154, 23736, 24863,
400, 200, 6207, 29700, 14622, 4758, 5810, 12644, 17477, 19588,
27078, 32594, 25609, 20281, 700, 900, 6310, 14319, 14400, 6434,
14779, 4507, 6446, 4276, 9600, 13875, 12043, 4391, 4327, 9000,
6698, 16392, 700, 15263, 1100, 18729, 5009, 3098, 4729, 5718,
700, 500, 24400, 9658, 14963, 13967, 6959, 15914, 9800, 20567,
3058, 600, 18497, 6148, 4470, 400, 10737, 15447, 24009, 44749,
12138, 900, 800, 1000, 900, 4200, 700, 1100, 1300, 8000, 6000,
29511, 200, 900, 600, 6600, 1100, 44162, 600, 24023, 900, 400,
200, 300, 800, 33410, 14800, 400, 800, 500, 500, 19136, 33900,
4100, 10500, 13400, 600, 700, 3700, 1000, 1000, 100, 3300, 800,
9400, 45925, 41740, 500, 6200, 8000, 200, 3100, 500, 300, 31332,
62100, 600, 7100, 28361, 4000, 200, 4500, 900, 900, 900, 1000,
1500, 300, 2500, 2700, 11000, 300, 800, 900, 8900, 23990, 1100,
1400, 800, 10700, 1800, 1100, 10900, 900, 200, 5200, 800, 200,
800, 200, 900, 3900, 900, 600, 900, 18.7, 102, 2.9, 3, 39285,
52.1, 34.1, 18.5, 21.3, 38.3, 160, 110.5, 58.6, 83.4, 34.7, 68.6,
31, 20.3, 5.4, 89.3, 110.6, 61.5, 72.7, 13.7, 20.7, 25.9, 2.9,
50.1, 14, 110, 16.2, 39, 73.8, 23.7, 249, 29.6, 117.3, 199)), row.names = c(NA,
-500L), class = "data.frame")
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