klitoschr / memecard

Snapshot and Randomness Selection for Meme Card 4

Data

fn <- "snapshot/15036969.csv"
snapshot <- utils::read.delim(fn, sep=",", colClasses=c("character", "character", "integer", "character"))
summary(snapshot)

   address            token_id            balance          contract        
 Length:2471        Length:2471        Min.   :  1.000   Length:2471       
 Class :character   Class :character   1st Qu.:  1.000   Class :character  
 Mode  :character   Mode  :character   Median :  1.000   Mode  :character  
                                       Mean   :  1.264                     
                                       3rd Qu.:  1.000                     
                                       Max.   :111.000                     

Seed

block <- 15040420 # https://etherscan.io/block/15040420
block_hash <- "0x2a7c399ce5c1be2f915f1a38630567a68c106c50b267fc232464da2c9944b913"
# Convert block hash to ASCII
# Take the sum over integer vector to set seed
seed <- sum(utf8ToInt(block_hash))
cat("Seed:", seed, "\n")

Seed: 4458 

Code

# Filter snapshot by contracts
# Order addresses (ascending)
# Replicate address by balance
pool <- function(df, contracts) {
  df %>%
  dplyr::filter(contract %in% contracts) %>%
  dplyr::arrange(address) %>%
  dplyr::select(address, balance) %>%
  purrr::pmap(function(address, balance) {
    base::rep(address, balance)
  }) %>%
  unlist()
}

base::set.seed(seed)

airdrop_gradient <-
  snapshot %>%
  pool(contracts=c("0x0c58ef43ff3032005e472cb5709f8908acb00205")) %>%
  base::sample(size=10, replace=FALSE)
cat(airdrop_gradient, sep="\n")

0x82139687faae8a29851902783e02e699de0e0846
0xa32f90b21d11561d31ff604745907acc77fb67e3
0xc26012491b9dfb2e6f2cb0305673e212721d5950
0x69cb3b1de24e08f1cfc2994171b6c6930498f750
0x07b4529ba0dd7472590629035d347db1bdb4b70b
0x32d53a21813debfcb33a2b1ff2a396bd3a06f818
0xee958e45f3464d712b8830deb5875c8ac105f698
0x896b94f4f27f12369698c302e2049cae86936bbb
0x8f8b4759dc93ca55bd6997df719f20f581f10f5c
0xbbdd72fcce73c2626719be00259ddffef0d5673d

airdrop_memes <-
  snapshot %>%
  pool(contracts=c("0x33fd426905f149f8376e227d0c9d3340aad17af1")) %>%
  base::sample(size=20, replace=FALSE)
cat(airdrop_memes, sep="\n")

0xdcab53e3c8a2c37cc5e2509c2db0caa6c04f3ce0
0xf83fc0d27d92233e789e4d13d452d72404be6fe3
0x34b93462e65303f3857460971584fd0d908f2f45
0xfb03b80a8cc225ac92996c54c6cf523a255ddb28
0xa743c8c57c425b84cb2ed18c6b9ae3ad21629cb5
0xc6400a5584db71e41b0e5dfbdc769b54b91256cd
0x8889ebb11295f456541901f50bcb5f382047caac
0x7a3c82c6b4ad6ee11ecf7f395431ed1135d6e46f
0xe64777dcdca11fd8bde96ed05e7560ae789504b6
0x31331ec182777b3e3cf127f4709a8aaa8f76e549
0x30607f2c34d2e847c41fee6b29c3301082031350
0x0887773b5f43c58f0da7bd0402fc2d49482eb845
0xe9b5a2529ad21454cb5e4d172dcb4bc501789463
0x0887773b5f43c58f0da7bd0402fc2d49482eb845
0x9f281c5b04c091096ac468a9388f0ee6b0b8b1f5
0xae0d16586e5d60d334624c115216a52b9b1a0335
0x0187c9a182736ba18b44ee8134ee438374cf87dc
0xc02e6b0d0c1a5d8cd26beeba0fe8d76c5d2f19b9
0xf16005be19a8cb12e02888123f4ea749c1b7f322
0x0887773b5f43c58f0da7bd0402fc2d49482eb845

allow_gradient <-
  snapshot %>%
  pool(contracts=c("0x0c58ef43ff3032005e472cb5709f8908acb00205")) %>%
  .[!(. %in% airdrop_gradient)]
cat(allow_gradient, sep="\n")

0x04df8d02f912d34fef12a1b0488ee56fd6f7416c
0x0a98f97e89743406a8611e1b4219a073b60ffed3
0x0a98f97e89743406a8611e1b4219a073b60ffed3
0x0a98f97e89743406a8611e1b4219a073b60ffed3
0x129c6695dfe7a906bd8fda202d26dfff601f83a4
0x1566ae673ae80725bcce901b486c336e6acef465
0x1c29dcaa0cad96ca3f60d414c7e2e47c99cd7bdd
0x22fbaa2dd20b848084545515fb04b9c846942779
0x248458947c120ca057ec028b3fe7e4b3f26fdb3d
0x28b8d4f7516a112e2e2fd462293a1c27cde327a7
0x2924196a2ec71ea4ae1b1357381eccdcee6c18f2
0x2be9f18c46df11f05b581131af4d339c20c7254e
0x3cb63b82d778105e43f064ed739b0655f1f0fb87
0x3cb63b82d778105e43f064ed739b0655f1f0fb87
0x45855a3f4404aa08ffe14a366c75663f4ded2fac
0x477ea7a022e51b6eb0dcb6d802fb5f0cfc3b4a81
0x48464efe55fbcae8ae0c992b306afcf21d4910cf
0x53006f95def268f88dc1b8216654ab56f3afd052
0x575f6540c16a72696c14a17fa64f049992d661ab
0x5fdb5fdb61bc2975f3be446c5ae9c5df490f55d2
0x61d9d9cc8c3203dab7100ea79ced77587201c990
0x64f7de90dc79d775703bbec66a1591c7a26a22f0
0x665654f2d2a151be2d0f8e3697e6ce780f732af2
0x670a840848eba22eb2a090fd65e1ff60c3ba9e5b
0x68e5952c3f194251106478bad560661f4f803759
0x699990a8e7ada9e92c932d6e8fb365024fc74b43
0x69e68074f1aada957edd39c5eae0069973343f30
0x6b52e83941eb10f9c613c395a834457559a80114
0x6d1db4a7e83dae0eee7e95d421722d46d2a7e94b
0x6f0735bf1e6c69030d6990cdd580345b370eb50a
0x6f113b0c8c7266d7514e4986e3d6aaf013b6754f
0x6f113b0c8c7266d7514e4986e3d6aaf013b6754f
0x729fcbb6e1289c88ce5113ec1c83a48a8e3c9f2f
0x73bcb8c5e30bf85806aade7fc36f16c6b80fd3b9
0x7546c60ae8d65dc6dd7a0f61c169818059ef49db
0x76db02500f7631d57bc2dcdca9d4cf782b99e119
0x8476b6a8aa0b4037e69e79f116f662aa0096b0c0
0x86f0a82dfd9745ec23bc8a72c819193e74962eb3
0x8ba68cfe71550efc8988d81d040473709b7f9218
0x8bace3a49a375027868cdd34e84521eed1f1b01d
0x8ea76483c888f5bda7d96cab9839488f691daf78
0x982d3c5223f6b5794fccb3208eb164d042cf2526
0x982d3c5223f6b5794fccb3208eb164d042cf2526
0x9dbd781eeba135ad2a779926880adc89196a3265
0xa71000e72d38e6a84d7190f59fd3dfc73931c0e8
0xa743c8c57c425b84cb2ed18c6b9ae3ad21629cb5
0xab2056903a7b62bac46f45a3d7a70ac799ca88cb
0xae0d16586e5d60d334624c115216a52b9b1a0335
0xae72c6a6fad9fa9d82d089e1ebf73b3043855425
0xb5374cac6cad6b025246f19d20b0d4151b640558
0xb6b4a02dca517564eb98790ff67d42b5b37a3d4e
0xb6cf25f5cf8a1e1727d988facdd47f1dfc492caf
0xba4575ea27041d99e6614ec02318f1e23a623fe2
0xbba3ced54477c12fdf16d7009771affc7a8c9ba1
0xbc30e90dc528ece58c1a51b6fb6d572838416489
0xbc30e90dc528ece58c1a51b6fb6d572838416489
0xc2419841dcb9a0f8906d06463ae24e00e1470846
0xc6400a5584db71e41b0e5dfbdc769b54b91256cd
0xc6400a5584db71e41b0e5dfbdc769b54b91256cd
0xc6400a5584db71e41b0e5dfbdc769b54b91256cd
0xc6400a5584db71e41b0e5dfbdc769b54b91256cd
0xc6400a5584db71e41b0e5dfbdc769b54b91256cd
0xc6400a5584db71e41b0e5dfbdc769b54b91256cd
0xc6400a5584db71e41b0e5dfbdc769b54b91256cd
0xc6400a5584db71e41b0e5dfbdc769b54b91256cd
0xc6400a5584db71e41b0e5dfbdc769b54b91256cd
0xc6400a5584db71e41b0e5dfbdc769b54b91256cd
0xc6400a5584db71e41b0e5dfbdc769b54b91256cd
0xc762b1081c56b3fa487c7372f7284d9558a84859
0xcaa1c396e70384db58dd33be74b26fb119e98c3a
0xcabd5f77ca9d48f4ef9793f20de42ad39ce93979
0xcabd5f77ca9d48f4ef9793f20de42ad39ce93979
0xd40b63bf04a44e43fbfe5784bcf22acaab34a180
0xd40b63bf04a44e43fbfe5784bcf22acaab34a180
0xdb561a899557404581e6180fe6d4178577dc117b
0xdcaa90d9f3b75cda80764326f6594b58d0585d21
0xe359ab04cec41ac8c62bc5016c10c749c7de5480
0xe3b41ae8785e4107cc69f988042ff4a66a367fac
0xe9b5a2529ad21454cb5e4d172dcb4bc501789463
0xea39c551834d07ee2ee87f1ceff843c308e089af
0xea39c551834d07ee2ee87f1ceff843c308e089af
0xef5ab90a44b68d4f5e3f6be6af4bedb12cd2c66e
0xf2c5f1fd977dbd6de9d04bc4e62dff722d4bb1a1
0xf598aadf12a2fc4709b5db0c4c169715efaf2038
0xfd22004806a6846ea67ad883356be810f0428793
0xfd22004806a6846ea67ad883356be810f0428793

allow_memes <-
  snapshot %>%
  pool(contracts=c("0x33fd426905f149f8376e227d0c9d3340aad17af1")) %>%
  .[!(. %in% airdrop_memes)] %>%
  base::sample(size=75, replace=FALSE)
cat(allow_memes, sep="\n")

0x54913cc8ea17731d62589039dd0152f306473843
0x159e5998669ec6628a6b670c2ef1ddbd93084698
0x8f6504b48c66eea4e9fbf64fe220e02f1cc4934e
0x4470e0f5a0a3969cb0d5aba71ad4e6c759dfc824
0x71cb0bbb26b37e30148203d05ec34ce88fe5c899
0xe359ab04cec41ac8c62bc5016c10c749c7de5480
0xc72c604a60fd660256be2d7f4ebec13ce7f50daf
0x8774be790cb9e12d5edaf2eb8a3f6c89410a497d
0x7d93f170dfd65d14d58682678b7a0d171f287c93
0x53006f95def268f88dc1b8216654ab56f3afd052
0xd69e257ae6088b717ae6d2ddec9297703b4fb725
0xbd751cac1fb27d930a6a0394d97c77908d94ad5d
0x67a3a43a55bd9cbfa5d696a7ef2d57e9e0869fdd
0xfe3b3f0d64f354b69a5b40d02f714e69ca4b09bd
0x9ae72c2af70b18da6088f8d95aaf35ae43534a8d
0x4d8cb131e2835855e37eb277943f308054f4242f
0x8ba68cfe71550efc8988d81d040473709b7f9218
0xaf5c021754ab82bf556bc6c90650de21cf92d1c7
0x8ba68cfe71550efc8988d81d040473709b7f9218
0x296c442ac3b32bfeb4bc0c64ead7984d5712b989
0x1b8532be318e881a6d073b6b24aa584d76d017fe
0xa32f90b21d11561d31ff604745907acc77fb67e3
0x55ac811cae3ae5867a7bdf5acaf9e7b3dc86ee9c
0x8ba68cfe71550efc8988d81d040473709b7f9218
0xea056794debdb1060d187e5887f0afdd2a897c71
0x04d0c64d8b303586af5cf6bb37db16bd7b78c43d
0xc15b378f95c8e803d7f67db8a9df85183cd5a8a4
0x5997ec7d664cfafb88a86840165d58ce7e638832
0xd1afbe4da2a5adc6faf30fcbea9e4eea0ba8c70a
0x37d4bf98610d7da1be398d4b20c20dff5b82f833
0x089fce8be711cabc806d69b1d0d5aebd52a06455
0xd7f624d0a67d09171b9555e2f43bde7acb16d79e
0x5e3118e91b0a02a5e2b92e49700c63434e94f0b2
0xf141a37978d4b14a4a63936d4b3aaac479fef943
0xa71000e72d38e6a84d7190f59fd3dfc73931c0e8
0x4748ce48e8979e7ab09b2e8df95a1bec7c65b869
0xa81f3639275eecf79247b7ad455cebcf4dda3d87
0x32ffe815277ff53dd2a73557664e229899e6501e
0x4e1829da30cd7ae2f3e9915cb8c9f3f203ac7d83
0x50187b3d4b2ec543032abc09ab54313cd1febb10
0x62d7cd7b7ed4ea69845172507457e7b831656b89
0xc458e1a4ec03c5039fbf38221c54be4e63731e2a
0x389458f93e387fc568ca4568c231a64ffd0456d2
0x42647285714407c7e4664be52e3a4ab38a2a7ef1
0xeaaeeb152d21122a8782bbb046edf48d0d9a389d
0x15237b9fc8c244246abee701f07e42185c5111c3
0xe50ae5655d227767ec3974a11da7e4f67476b96f
0x7ab2f82adf8b48852cf49034e7e242b45b15ef44
0xd18c33ef15f878dce373458a7da93da4f3f9ce0c
0xfbfd507f49a08b9b5f0a71231f69a38864d4b9ba
0xe23e0bd6e320c87367b0e4b797b42dc9b4fe7ca0
0x631cd42eb1063d1fe2b49a78744f3094702edbab
0x47dc3a7aec5c0e1f4b2c71303a4b1eaa0bee3e4c
0x2da903666829f302b0501f76144339213259c260
0x1379aaccf761490ceba36b6ec572e5dfca48273a
0xad74b6eaefae55e9e73e5189e3873ffaa17d45c2
0x7f6ca49d1e50671a586a76bb082dd6b3f73fef17
0xf6dbff32fe8308fd30def6c8629dffa5c315245a
0x3c554c31c2f1938bdde970ac9add36f6264b55bd
0xdd4eb4a4e80f8390ea158fd2cb14cb667b5acdc5
0xf1e1c701b49b1dc2405a9e8ef40f9f88802b80fa
0xc35c8d48553f02d0cded9a1eac241c4d628762f1
0xb7abe0f0a1c31a88fdcdef71033cf7ae7d12f2d3
0xf2133ed357943e193714bfa507841361862dcbc6
0xccc9bdd130f0c03fa5d12b9a85e9e66b087457ec
0x50187b3d4b2ec543032abc09ab54313cd1febb10
0xa837c1e2b87aab4d28fee399c6593b66422fd5d2
0x620f761d45a8b2f9b81079758ec002aeb8d92489
0xeaaeeb152d21122a8782bbb046edf48d0d9a389d
0x54913cc8ea17731d62589039dd0152f306473843
0xa5ed8da239f400141427800da33b602a039f2254
0xdf36b093cbc06522b68105c3d9141db188e980b6
0xeabed8538923d8b8e0616938f8dc657f3cdf74c6
0x61d9d9cc8c3203dab7100ea79ced77587201c990
0x8ba68cfe71550efc8988d81d040473709b7f9218

allow_intern <-
  snapshot %>%
  pool(contracts=c("0x495f947276749ce646f68ac8c248420045cb7b5e")) %>%
  base::sample(size=5, replace=FALSE)
cat(allow_intern, sep="\n")

0x3f849f47f5b372d80407e442f360ad7b17f5fac4
0xbd1ded3bcc8103028c8ebdc61990ca777709b10a
0x726022a9fe1322fa9590fb244b8164936bb00489
0xd3aefe3c531e3e2eb0689206e7d495843c943550
0x2e54b594ff08bad82db35892f1265994c57bf46b

Airdrop

c(airdrop_gradient, airdrop_memes) %>%
data.frame(address = .) %>%
dplyr::group_by(address) %>%
dplyr::summarise(
  amount = n()
) %>%
dplyr::arrange(desc(amount), address) %>%
print(n=Inf)

# A tibble: 28 x 2
   address                                    amount
   <chr>                                       <int>
 1 0x0887773b5f43c58f0da7bd0402fc2d49482eb845      3
 2 0x0187c9a182736ba18b44ee8134ee438374cf87dc      1
 3 0x07b4529ba0dd7472590629035d347db1bdb4b70b      1
 4 0x30607f2c34d2e847c41fee6b29c3301082031350      1
 5 0x31331ec182777b3e3cf127f4709a8aaa8f76e549      1
 6 0x32d53a21813debfcb33a2b1ff2a396bd3a06f818      1
 7 0x34b93462e65303f3857460971584fd0d908f2f45      1
 8 0x69cb3b1de24e08f1cfc2994171b6c6930498f750      1
 9 0x7a3c82c6b4ad6ee11ecf7f395431ed1135d6e46f      1
10 0x82139687faae8a29851902783e02e699de0e0846      1
11 0x8889ebb11295f456541901f50bcb5f382047caac      1
12 0x896b94f4f27f12369698c302e2049cae86936bbb      1
13 0x8f8b4759dc93ca55bd6997df719f20f581f10f5c      1
14 0x9f281c5b04c091096ac468a9388f0ee6b0b8b1f5      1
15 0xa32f90b21d11561d31ff604745907acc77fb67e3      1
16 0xa743c8c57c425b84cb2ed18c6b9ae3ad21629cb5      1
17 0xae0d16586e5d60d334624c115216a52b9b1a0335      1
18 0xbbdd72fcce73c2626719be00259ddffef0d5673d      1
19 0xc02e6b0d0c1a5d8cd26beeba0fe8d76c5d2f19b9      1
20 0xc26012491b9dfb2e6f2cb0305673e212721d5950      1
21 0xc6400a5584db71e41b0e5dfbdc769b54b91256cd      1
22 0xdcab53e3c8a2c37cc5e2509c2db0caa6c04f3ce0      1
23 0xe64777dcdca11fd8bde96ed05e7560ae789504b6      1
24 0xe9b5a2529ad21454cb5e4d172dcb4bc501789463      1
25 0xee958e45f3464d712b8830deb5875c8ac105f698      1
26 0xf16005be19a8cb12e02888123f4ea749c1b7f322      1
27 0xf83fc0d27d92233e789e4d13d452d72404be6fe3      1
28 0xfb03b80a8cc225ac92996c54c6cf523a255ddb28      1

Allow

c(allow_gradient, allow_memes, allow_intern) %>%
data.frame(address = .) %>%
dplyr::group_by(address) %>%
dplyr::summarise(
  amount = n()
) %>%
dplyr::arrange(desc(amount), address) %>%
print(n=Inf)

# A tibble: 135 x 2
    address                                    amount
    <chr>                                       <int>
  1 0xc6400a5584db71e41b0e5dfbdc769b54b91256cd     11
  2 0x8ba68cfe71550efc8988d81d040473709b7f9218      5
  3 0x0a98f97e89743406a8611e1b4219a073b60ffed3      3
  4 0x3cb63b82d778105e43f064ed739b0655f1f0fb87      2
  5 0x50187b3d4b2ec543032abc09ab54313cd1febb10      2
  6 0x53006f95def268f88dc1b8216654ab56f3afd052      2
  7 0x54913cc8ea17731d62589039dd0152f306473843      2
  8 0x61d9d9cc8c3203dab7100ea79ced77587201c990      2
  9 0x6f113b0c8c7266d7514e4986e3d6aaf013b6754f      2
 10 0x982d3c5223f6b5794fccb3208eb164d042cf2526      2
 11 0xa71000e72d38e6a84d7190f59fd3dfc73931c0e8      2
 12 0xbc30e90dc528ece58c1a51b6fb6d572838416489      2
 13 0xcabd5f77ca9d48f4ef9793f20de42ad39ce93979      2
 14 0xd40b63bf04a44e43fbfe5784bcf22acaab34a180      2
 15 0xe359ab04cec41ac8c62bc5016c10c749c7de5480      2
 16 0xea39c551834d07ee2ee87f1ceff843c308e089af      2
 17 0xeaaeeb152d21122a8782bbb046edf48d0d9a389d      2
 18 0xfd22004806a6846ea67ad883356be810f0428793      2
 19 0x04d0c64d8b303586af5cf6bb37db16bd7b78c43d      1
 20 0x04df8d02f912d34fef12a1b0488ee56fd6f7416c      1
 21 0x089fce8be711cabc806d69b1d0d5aebd52a06455      1
 22 0x129c6695dfe7a906bd8fda202d26dfff601f83a4      1
 23 0x1379aaccf761490ceba36b6ec572e5dfca48273a      1
 24 0x15237b9fc8c244246abee701f07e42185c5111c3      1
 25 0x1566ae673ae80725bcce901b486c336e6acef465      1
 26 0x159e5998669ec6628a6b670c2ef1ddbd93084698      1
 27 0x1b8532be318e881a6d073b6b24aa584d76d017fe      1
 28 0x1c29dcaa0cad96ca3f60d414c7e2e47c99cd7bdd      1
 29 0x22fbaa2dd20b848084545515fb04b9c846942779      1
 30 0x248458947c120ca057ec028b3fe7e4b3f26fdb3d      1
 31 0x28b8d4f7516a112e2e2fd462293a1c27cde327a7      1
 32 0x2924196a2ec71ea4ae1b1357381eccdcee6c18f2      1
 33 0x296c442ac3b32bfeb4bc0c64ead7984d5712b989      1
 34 0x2be9f18c46df11f05b581131af4d339c20c7254e      1
 35 0x2da903666829f302b0501f76144339213259c260      1
 36 0x2e54b594ff08bad82db35892f1265994c57bf46b      1
 37 0x32ffe815277ff53dd2a73557664e229899e6501e      1
 38 0x37d4bf98610d7da1be398d4b20c20dff5b82f833      1
 39 0x389458f93e387fc568ca4568c231a64ffd0456d2      1
 40 0x3c554c31c2f1938bdde970ac9add36f6264b55bd      1
 41 0x3f849f47f5b372d80407e442f360ad7b17f5fac4      1
 42 0x42647285714407c7e4664be52e3a4ab38a2a7ef1      1
 43 0x4470e0f5a0a3969cb0d5aba71ad4e6c759dfc824      1
 44 0x45855a3f4404aa08ffe14a366c75663f4ded2fac      1
 45 0x4748ce48e8979e7ab09b2e8df95a1bec7c65b869      1
 46 0x477ea7a022e51b6eb0dcb6d802fb5f0cfc3b4a81      1
 47 0x47dc3a7aec5c0e1f4b2c71303a4b1eaa0bee3e4c      1
 48 0x48464efe55fbcae8ae0c992b306afcf21d4910cf      1
 49 0x4d8cb131e2835855e37eb277943f308054f4242f      1
 50 0x4e1829da30cd7ae2f3e9915cb8c9f3f203ac7d83      1
 51 0x55ac811cae3ae5867a7bdf5acaf9e7b3dc86ee9c      1
 52 0x575f6540c16a72696c14a17fa64f049992d661ab      1
 53 0x5997ec7d664cfafb88a86840165d58ce7e638832      1
 54 0x5e3118e91b0a02a5e2b92e49700c63434e94f0b2      1
 55 0x5fdb5fdb61bc2975f3be446c5ae9c5df490f55d2      1
 56 0x620f761d45a8b2f9b81079758ec002aeb8d92489      1
 57 0x62d7cd7b7ed4ea69845172507457e7b831656b89      1
 58 0x631cd42eb1063d1fe2b49a78744f3094702edbab      1
 59 0x64f7de90dc79d775703bbec66a1591c7a26a22f0      1
 60 0x665654f2d2a151be2d0f8e3697e6ce780f732af2      1
 61 0x670a840848eba22eb2a090fd65e1ff60c3ba9e5b      1
 62 0x67a3a43a55bd9cbfa5d696a7ef2d57e9e0869fdd      1
 63 0x68e5952c3f194251106478bad560661f4f803759      1
 64 0x699990a8e7ada9e92c932d6e8fb365024fc74b43      1
 65 0x69e68074f1aada957edd39c5eae0069973343f30      1
 66 0x6b52e83941eb10f9c613c395a834457559a80114      1
 67 0x6d1db4a7e83dae0eee7e95d421722d46d2a7e94b      1
 68 0x6f0735bf1e6c69030d6990cdd580345b370eb50a      1
 69 0x71cb0bbb26b37e30148203d05ec34ce88fe5c899      1
 70 0x726022a9fe1322fa9590fb244b8164936bb00489      1
 71 0x729fcbb6e1289c88ce5113ec1c83a48a8e3c9f2f      1
 72 0x73bcb8c5e30bf85806aade7fc36f16c6b80fd3b9      1
 73 0x7546c60ae8d65dc6dd7a0f61c169818059ef49db      1
 74 0x76db02500f7631d57bc2dcdca9d4cf782b99e119      1
 75 0x7ab2f82adf8b48852cf49034e7e242b45b15ef44      1
 76 0x7d93f170dfd65d14d58682678b7a0d171f287c93      1
 77 0x7f6ca49d1e50671a586a76bb082dd6b3f73fef17      1
 78 0x8476b6a8aa0b4037e69e79f116f662aa0096b0c0      1
 79 0x86f0a82dfd9745ec23bc8a72c819193e74962eb3      1
 80 0x8774be790cb9e12d5edaf2eb8a3f6c89410a497d      1
 81 0x8bace3a49a375027868cdd34e84521eed1f1b01d      1
 82 0x8ea76483c888f5bda7d96cab9839488f691daf78      1
 83 0x8f6504b48c66eea4e9fbf64fe220e02f1cc4934e      1
 84 0x9ae72c2af70b18da6088f8d95aaf35ae43534a8d      1
 85 0x9dbd781eeba135ad2a779926880adc89196a3265      1
 86 0xa32f90b21d11561d31ff604745907acc77fb67e3      1
 87 0xa5ed8da239f400141427800da33b602a039f2254      1
 88 0xa743c8c57c425b84cb2ed18c6b9ae3ad21629cb5      1
 89 0xa81f3639275eecf79247b7ad455cebcf4dda3d87      1
 90 0xa837c1e2b87aab4d28fee399c6593b66422fd5d2      1
 91 0xab2056903a7b62bac46f45a3d7a70ac799ca88cb      1
 92 0xad74b6eaefae55e9e73e5189e3873ffaa17d45c2      1
 93 0xae0d16586e5d60d334624c115216a52b9b1a0335      1
 94 0xae72c6a6fad9fa9d82d089e1ebf73b3043855425      1
 95 0xaf5c021754ab82bf556bc6c90650de21cf92d1c7      1
 96 0xb5374cac6cad6b025246f19d20b0d4151b640558      1
 97 0xb6b4a02dca517564eb98790ff67d42b5b37a3d4e      1
 98 0xb6cf25f5cf8a1e1727d988facdd47f1dfc492caf      1
 99 0xb7abe0f0a1c31a88fdcdef71033cf7ae7d12f2d3      1
100 0xba4575ea27041d99e6614ec02318f1e23a623fe2      1
101 0xbba3ced54477c12fdf16d7009771affc7a8c9ba1      1
102 0xbd1ded3bcc8103028c8ebdc61990ca777709b10a      1
103 0xbd751cac1fb27d930a6a0394d97c77908d94ad5d      1
104 0xc15b378f95c8e803d7f67db8a9df85183cd5a8a4      1
105 0xc2419841dcb9a0f8906d06463ae24e00e1470846      1
106 0xc35c8d48553f02d0cded9a1eac241c4d628762f1      1
107 0xc458e1a4ec03c5039fbf38221c54be4e63731e2a      1
108 0xc72c604a60fd660256be2d7f4ebec13ce7f50daf      1
109 0xc762b1081c56b3fa487c7372f7284d9558a84859      1
110 0xcaa1c396e70384db58dd33be74b26fb119e98c3a      1
111 0xccc9bdd130f0c03fa5d12b9a85e9e66b087457ec      1
112 0xd18c33ef15f878dce373458a7da93da4f3f9ce0c      1
113 0xd1afbe4da2a5adc6faf30fcbea9e4eea0ba8c70a      1
114 0xd3aefe3c531e3e2eb0689206e7d495843c943550      1
115 0xd69e257ae6088b717ae6d2ddec9297703b4fb725      1
116 0xd7f624d0a67d09171b9555e2f43bde7acb16d79e      1
117 0xdb561a899557404581e6180fe6d4178577dc117b      1
118 0xdcaa90d9f3b75cda80764326f6594b58d0585d21      1
119 0xdd4eb4a4e80f8390ea158fd2cb14cb667b5acdc5      1
120 0xdf36b093cbc06522b68105c3d9141db188e980b6      1
121 0xe23e0bd6e320c87367b0e4b797b42dc9b4fe7ca0      1
122 0xe3b41ae8785e4107cc69f988042ff4a66a367fac      1
123 0xe50ae5655d227767ec3974a11da7e4f67476b96f      1
124 0xe9b5a2529ad21454cb5e4d172dcb4bc501789463      1
125 0xea056794debdb1060d187e5887f0afdd2a897c71      1
126 0xeabed8538923d8b8e0616938f8dc657f3cdf74c6      1
127 0xef5ab90a44b68d4f5e3f6be6af4bedb12cd2c66e      1
128 0xf141a37978d4b14a4a63936d4b3aaac479fef943      1
129 0xf1e1c701b49b1dc2405a9e8ef40f9f88802b80fa      1
130 0xf2133ed357943e193714bfa507841361862dcbc6      1
131 0xf2c5f1fd977dbd6de9d04bc4e62dff722d4bb1a1      1
132 0xf598aadf12a2fc4709b5db0c4c169715efaf2038      1
133 0xf6dbff32fe8308fd30def6c8629dffa5c315245a      1
134 0xfbfd507f49a08b9b5f0a71231f69a38864d4b9ba      1
135 0xfe3b3f0d64f354b69a5b40d02f714e69ca4b09bd      1

Versioning

R.version$version.string

[1] "R version 4.0.4 (2021-02-15)"

packageVersion("tidyverse")

[1] '1.3.0'