Related
I am looking to sort my taxonomic annotations (16S dataset) based off of columns 'sample', 'Family', and 'taxa'. Briefly, there are two sample types; m3ofair and m3NC, and I wish to know the unique ASV identifier in column 'taxa' if the annotation in 'Family' are identical across both sample types.
For example, the annotation 'f__Beijerinckiaceae' in column 'Family' is present in both samples, m3ofair and m3NC. Since this is the case, I would like to know the ASV taxa identifier for all three annotations of 'f__Beijerinckiaceae', which are 2ca928ad9749bb9726c35d6528fefec1, bcc8b318ede81a8b211e7bdd1531baec, and e31f3d32519dc9021ff790f87d76114c.
Thank you in advance!
> dput(example)
structure(list(taxa = structure(c(203L, 150L, 150L, 202L, 175L
), .Label = c("8c54f0af4445cdb1a6a36304a80b5a6d", "e6ff53b2675c61cf25ad3d79917b318e",
"38bbf752453c1c64f2e61966016d45c5", "903bcbbcb2ac130bb32d847ca7d191ed",
"7fc0ccbd86b09190f44369a934b3456c", "03c095b4239af5a7f63d39522b34186b",
"513c1bcb314fbf047f366f84a646c4a2", "0d3dca27868f7cfabbdbe1eb90657340",
"4b36fd18a35f063166412a83379bf797", "a6a6601f3a675697e92bc24d32042929",
"c46477febff8e68314f5baa81f4c082f", "f8753b9f13abb306b57aedce2cf6e7d0",
"230363069727610f8f1546cb314f2d1a", "100f4d61f5eaf8fb7369d9583b32ba46",
"69a70c343722f6851fc2191d4b3c3a1b", "0f222d9f6f748209e5d39cbbbb704c3e",
"06b8e39b562a2b31643b4f2c00d5dec7", "51141f001e298a5e44d99f7d7bdb78e2",
"56c98126dde3abb2d263088db55e12c8", "d8adf2a20249cefe6c627f9c17abb202",
"4e3acf7a1c4dddbc3f68097207cacb66", "fadd6b74d7cc847716a19679be001724",
"9815d07f1b564c9be5aab03db01608da", "bdd1f036611254ef872b334f7da681e6",
"028ecb2ed02be346c5f0347a9a3fba2a", "0310f41e594c49368dde5c5993a7a5d0",
"f510252f2eef402050f8169436cb4501", "3cb9f6500275fabe7c87a599a8c31749",
"f148144695ed0a63b976ea05243aba8c", "9ed070c666e12720a6bb6d3df1b09e45",
"e54bcf1c8ec24811e7c355b8d82fa59e", "c6608772f41c7d2202f2504190f2af8a",
"78732fda4b5a16d2bdddcd553953752c", "46271ec11fc9f127649526834a9fd805",
"dd0eaf58cb0b5378f465b4f59adf79d2", "585ab9ce79a8c90d675a66b9678b2137",
"4ef5dabd754e48c26c66c55b3615e69f", "bc3043f80a5fe9e5faa189fa7b050f61",
"f570f293e6b304776e5dce910a0b96e8", "432f8ae2390df378d7bab37a0026ff03",
"0d959da770b454f90e53757f8ab0893f", "0bc8e7f6bf04c1e745749edeb6d6628d",
"c78a82f200050febc553de67fffef578", "da36821195d462457b39c41aeb611ba1",
"2de266b1ffa8e9ec6383c98096caf8f0", "3845a467d4d557e0f035d045d0d86f70",
"630afd8b62db24d65410329610383a4f", "bfbf3b84b315e741c4345ebb43e2b9cc",
"c85df4c87e8960b573d8ae4bf7f65e15", "6c46511a89ec2a070fef341371ad02b7",
"6307431a0a1a9d90045dc5dccbfc5fe9", "0d1bf49752cd0c9211d28bc8577a9145",
"11630fb56d652f032964d7567e083d40", "04caec30dc35c55d62d69e6125cec2c1",
"854aeab59d32bfddbea09ed0571ca007", "f6436e7085d15342388e67b55d692fb6",
"ecbf086d6ccbe5e8c2a69d0afb144662", "da247df6fe91b74bf38d3ecc1c05b4b0",
"d4c4725af861d3daa18bddefb558cf50", "c06b07ff323a7b0bccf5d0e604b0afab",
"ec8bf881537838f56323ada7de070b19", "1d6f8731081df8944cd50ae875fb9b28",
"8101da5193e402ec2741419cd0515110", "381f3c0f833ce81483700d6dc0b940ef",
"d0484307102691e2482cb79f8784827c", "d0c9e10ee52d3c93502663df3328e2c6",
"2f09559bc1ffcc3ea06ae2766194920b", "3bb215c036a5b86e071708ce88665689",
"d8a6777829870b41d1544fb173608685", "10a4a0714d1b0063c37636f2ec22bbae",
"5740f0ff513cc2099d6beacec9336f45", "763f0806ad9068d1f4092259a970c925",
"50e96bbd1a8267119529843a1acb43a5", "500d6b485cef4c52aad211d6d6e6dbc7",
"4ea5683e2c2a183c11c45dc8fbd0c67c", "7e598ad34909a3fb8ff1623f1ede11a6",
"8659c05e82a2d1399cbc3fd26ab938e4", "9c886b1c9e3e2ec63e0dbbd085996334",
"8da02c7114ba0d9ee40312926e037a8f", "5cb28bd0b2eea6847a6328712217c141",
"a049763053c277b16c2a318f41eb23b4", "0ce84d495f51f4e5997076bddebe65a5",
"576fd9e4a384474af1d78a589118b213", "e74212c56f84c309729e86ad3b126716",
"095249b545f554a6a66a4e378eed9c23", "12f69cdd5a43e9c04fa76fe778493dbc",
"6eaaffd8bd37b95220a3bebdf503be32", "78d6bb358b0ec0c94a8caa7c7a28171c",
"57799c0748039c006705cb9c46ace954", "b335608a8c2309ce02373311b7737b55",
"ff4bc2548279113788b4bf164d2db0d0", "254ba9ed4527552e061da0653ae95392",
"2480590c44af81ddbc2f34d381864702", "e4b865c957d0d23766a0e8e87f6d153d",
"f98cc8f203873fd4078c589a6332e72e", "4eb88a4d445e0987c56d0197301bd4e7",
"c5f7ff7e9d319f880b2c41c4ce6d52de", "15e687c6e5701e1b3b10fc0223d96c59",
"5c3138b23334b1d6b4bf3943db5eae74", "e22be263cc3a006762b66711df3d6a23",
"e209a5a9add3e7a68f273a2f17942555", "b19efe6ae9dabd64b44e12f4f1b88643",
"ea0c2938cf670cfcec8bd497e1767ae8", "ea794ad075da6b9b427ecf13244a6e97",
"414cb56f046e21beb8cf72b9285ab2bd", "6f318147a66f5851564f04aba9092057",
"32b53bb775ee918b4b75ee4c87ecdb55", "f467d11f3c1a16ea55e2ab7ab88a85c2",
"dfb69af34af0dcc91f9596d47ea6882b", "db4c51d23607a40db2843c49a38ee271",
"02047d2e2da6d0e73846b63774619e77", "cb2fe0146e2fbcb101050edb996a0ee2",
"4b88b52dd068a7d432309dec77813d29", "d2da0518d0f3ac5dd8d7df770389fee5",
"5f86daf7f135fee1745dba9e874fe013", "9a2740e3bc27419a895dfb6e0b986854",
"3a2e70f61b311dc0cd2261732a9cb627", "313402c6104b2605890eda8d9fa89fba",
"e33cca4021036f12a70132aed9f9c52d", "d29fe3c70564fc0f69f2c03e0d1e5561",
"eb5f185c6ab0e5f7953654a2a5d12ce7", "89fa7b79c6b2c5019ca0d1bc2f509f0b",
"fcd4f95c05b868060121ff709085bf21", "7c14b01c89686b9e327fc7d3dee3af30",
"0161e22acf37955da1e4b8bafe9f54e6", "ac70e87927f15bf812e8cad1cd9003cb",
"bfbed36e63b69fec4627424163d20118", "3f0ecac3d878543cf67dae94e6309453",
"0b607a251255e7aa0ef0c4e242b3e87c", "3d28edbfcb341fbf3d2fd87056633569",
"fa2102f32f8f05f458f0833df8c78500", "3b588d7e9f41b7e5ea9539d52ccda834",
"646eeb6a205282fe7afb5d73d1af30be", "a3e8652354170f4ddc4b96f65ff42aaf",
"09b2620539f4861b9b2e6d271d4bb319", "a301810850607af459b6de0b8a6dfcfb",
"ee2babc92e666c2593d83df7ad086362", "1054403160f296f2f44fbdf81f31b32c",
"b62d8ace7310a137d1ff25156b20b881", "4043cc4801dcf236434002ac21298d76",
"feb46b2df4fc3958ccd80e768b79502b", "c2dfc10913176eda134cd88ed68e0a72",
"668584d4ca79707c62229c2b9a520634", "b48eec9298fe8ed0fdf2e968ee6cc4af",
"a75e4ea5f1add4aa39f660dd13084fd0", "2a8bd6428138528c12a10c06b52954db",
"e6922be77906565e6bae2a89da15d175", "e21e4030f895965d1846404a94ebcb11",
"d03ede724dcc3efd39db449fd5bced6e", "6639f71cd3265109c47115f4a5913958",
"8c08537b3e07c84c47eb29e4de593a02", "8c1456c47986f85320cdfe1c515ffb4c",
"3e00fb174071bfc76c017d666f43af65", "1f70bd0eeef30f2afcd750b70f7e7066",
"c1efd1322ab8cef2a84ace9e85e37467", "b18b389447d758e7cb173b4b4f2ad960",
"9af604229897c70de75a7740fe13e80d", "c9a7a8206f0d8eda7576e0bb44b058a7",
"ae65f0c53471069e8a19b8d38ffcd3e7", "b19b302de47cc21f8f4cf1464a10b2b3",
"8b9a8bca0a4a02cde893f0bf550e4b68", "2492e30becb2716ccd9557e27900a56d",
"997d9c1623cbaab34cdb0668950d5a08", "1c77f5f31494131fcbb21fd9f52c7618",
"bfbd7ffe99a84ab5d92dc545d1a5a3c7", "e91fee71ae5261d22530ee9afa22dae5",
"59988344137e8b4b1c56e5f886bc294d", "3a4d3ac6f2aeecdba4936b18e28d04e5",
"e48dc10d2f3df1f6795902f9d53625ee", "9259758b55aa71398e2cafd0db077fcd",
"61f0d618a9ccef240bf7f6b65ac3cd64", "955d8fcbb35e8bc04c8df0c6aae34d34",
"35a7084609b22c35a5162738624ef6d0", "bc2942a64ee2fa191613f41f7e0134df",
"e31f3d32519dc9021ff790f87d76114c", "594a57ff90b7936ce2cd8036ad681cef",
"a64788b07ff4d444a278dbdd45b9cda4", "ef1e892b6e84a8b51d80eb7d6037167f",
"ce036ee8825f6a2f95a01b9488144638", "5a4979556bf389cd5ba711b146417234",
"ac809fd715cced98911f73f1dfb1ffb9", "e206fe2391a57543027d38d4ab4355e5",
"780d57e7f37aba0e43ea58166978f258", "9995306be33bb6a067fbd14fd12ab247",
"d867858df4524fe62a46ed814274aaa4", "12c0941446564e8d3cb6c3fb07bebed8",
"d174fd25211450a7f7b6dbae6db61e2c", "491e6acf93836613376f546052749de2",
"be94715fa59eebb39dae561eee3e2f3a", "21bc9a5af6203656e6d1066a13121fb0",
"0b925631436d3775231b60f9d0011cae", "47c2b089e44e1baab72de03674f2526e",
"c4d8bbf6d24dd9c56321087cc3936763", "147f2c2c24c7e1b1c371d42a36816214",
"7c999fc012772119e945f6e87a941e92", "3423114aff9da85e4989c2c268769033",
"82eab43c427e6afb5bf1fd10be00cc4b", "f637087afddcd520f0691150409f4d26",
"54edafdb5df25d839fa4696a3d8a08d9", "bfa4fe32a85423441482e307c7ed5e65",
"34d0e4410278fca5bbc2eac3de76feac", "bcc8b318ede81a8b211e7bdd1531baec",
"2ca928ad9749bb9726c35d6528fefec1", "959a178855242ad3833af85d38ccff45",
"3830e37a7ac6cf83a20dc9b8f225bb69", "9205f49b7a5898bf9dec93aa53ff96ec",
"bf2b830db0f89f72b4bf68394b70fbf5", "cba5161b7eb1f07a14ca874577e69cf3",
"179e215ab39a0a69ca15100615efae28", "beb5157c4f4ffcb17a5aa73fb6efd009",
"38401e17915bca5aab62c763930d1cfb", "1fc7138b0d9568c4588eb34dabc430d3",
"d2c5f354da4c7a6e78626761b1801770", "68089dfb5a592ef9b73c307c746a940e",
"f8e8cca8fe936d9be35ed4fcf790d35b", "e68f3ead96583e125709c8a5ecf20a63",
"06b48070c8d8e4ad997399e958871cea", "aff65597908099c9ee322aff6bec0a68",
"c802baf89ae3088ec5027d645223e3d1", "59afa99ca520b0db55db192cf8be4edb",
"e131d6b9e4f997788460a450a69c3a99", "1b448e00c078f81e49a61933072345d2",
"0f48376fe94d49cef9a1ee1b46af7a4a", "79e9e337b10e2d298bb1b3bde946782d",
"1868b3eae7d694c939f6ee777c98ee82", "909755d7142ea53c7f61c97c3586f26c",
"c1bee78e8c05c7d1abd7122bff925e54", "3cdd97ba69a504117de24626ba790e96",
"bfd74cfd3e6c2b27cffc14334ee55878", "e2603d057e6cc94b2b3c774e5b24c09c",
"a7b2fd6bf0b1c028de90c11becdc1b39", "8bf361e85f0f9dbff5b9d6951fdae8c6",
"0c7b675ddf75b3a6ad648b004352b8c1", "f3ed29d9b4cd4e1ea7861fbe19314125",
"4aafbcb8ea0c9be6130d630345116249", "1d926e22515426e147f62e2d27fb25d5",
"6357497de4b956c00e393b9cbebaf228", "c8c6649cf27d3428a978248cb233b4ea",
"6013a4618dd16c1f100b084d722a817d", "fd188dccbbfa7e8d7aa3b6abe7fe59c4",
"c04b4c2c12d6408b4e741b3b91e91354", "4a43d5d77936e5dc6536931101c8d814",
"55982fe768843d5432c485e19f3ca7ae", "498128a80a796620b238d8ad5cd1d2f9",
"8c2d1b95d59fa2a627dd63d7a36c7483", "2ea78aa082eeffffc40ad0ac7e84dbb8",
"b8b381bb5c8bfaf50f1916a102161d01", "fe6a40c9234f9a57e1db10d0d87e3fa0",
"71727d41e7a3452a196977be7f013cb0"), class = "factor"), sample = c("m3ofair",
"m3NC", "m3ofair", "m3NC", "m3NC"), value = c(0.00110121133246571,
0, 0.00200220242266493, 0.00943847890021202, 0), Kingdom = structure(c(2L,
2L, 2L, 2L, 2L), .Label = c("d__Archaea", "d__Bacteria"), class = "factor"),
Phylum = structure(c(15L, 15L, 15L, 15L, 15L), .Label = c("p__Acidobacteriota",
"p__Actinobacteriota", "p__Armatimonadota", "p__Bacteroidota",
"p__Chloroflexi", "p__Cyanobacteria", "p__Deinococcota",
"p__Desulfobacterota", "p__Firmicutes", "p__Fusobacteriota",
"p__Gemmatimonadota", "p__Myxococcota", "p__Patescibacteria",
"p__Planctomycetota", "p__Proteobacteria", "p__SAR324_clade(Marine_group_B)",
"p__Synergistota", "p__Thermoplasmatota", "p__Verrucomicrobiota",
"p__WPS-2"), class = "factor"), Class = structure(c(4L, 4L,
4L, 4L, 4L), .Label = c("c__Acidimicrobiia", "c__Acidobacteriae",
"c__Actinobacteria", "c__Alphaproteobacteria", "c__Bacilli",
"c__Bacteroidia", "c__Chloroflexia", "c__Clostridia", "c__Coriobacteriia",
"c__CPR2", "c__Cyanobacteriia", "c__Deinococci", "c__Desulfitobacteriia",
"c__Desulfotomaculia", "c__Fimbriimonadia", "c__Fusobacteriia",
"c__Gammaproteobacteria", "c__Gemmatimonadetes", "c__KD4-96",
"c__Ktedonobacteria", "c__Myxococcia", "c__Phycisphaerae",
"c__Planctomycetes", "c__Polyangia", "c__Rhodothermia", "c__SAR324_clade(Marine_group_B)",
"c__Sericytochromatia", "c__Synergistia", "c__Syntrophia",
"c__Thermoanaerobacteria", "c__Thermoanaerobaculia", "c__Thermoleophilia",
"c__Thermoplasmata", "c__TK10", "c__Verrucomicrobiae", "c__Vicinamibacteria",
"c__WPS-2"), class = "factor"), Order = structure(c(72L,
7L, 7L, 72L, 72L), .Label = c("o__Acetobacterales", "o__Acidobacteriales",
"o__Actinomarinales", "o__Actinomycetales", "o__Alteromonadales",
"o__Arctic97B-4_marine_group", "o__Azospirillales", "o__Bacillales",
"o__Bacteroidales", "o__Balneolales", "o__Bifidobacteriales",
"o__Burkholderiales", "o__C0119", "o__Caulobacterales", "o__Chitinophagales",
"o__Chthoniobacterales", "o__Clostridia", "o__Clostridiales",
"o__Corynebacteriales", "o__CPR2", "o__Cyanobacteriales",
"o__Cytophagales", "o__Deinococcales", "o__Desulfitobacteriales",
"o__Desulfotomaculales", "o__Elsterales", "o__Enterobacterales",
"o__Eubacteriales", "o__Fimbriimonadales", "o__Flavobacteriales",
"o__Frankiales", "o__Fusobacteriales", "o__Gaiellales", "o__Gemmatales",
"o__Gemmatimonadales", "o__Haliangiales", "o__Halothiobacillales",
"o__IMCC26256", "o__Isosphaerales", "o__KD4-96", "o__Kiloniellales",
"o__Kineosporiales", "o__Ktedonobacterales", "o__Lachnospirales",
"o__Lactobacillales", "o__Legionellales", "o__Marine_Group_II",
"o__Methylococcales", "o__Micrococcales", "o__Micromonosporales",
"o__Micropepsales", "o__Microtrichales", "o__Myxococcales",
"o__Nitriliruptorales", "o__Nitrococcales", "o__Nitrosococcales",
"o__Oceanospirillales", "o__OPB41", "o__Oscillospirales",
"o__Paenibacillales", "o__Parvibaculales", "o__PeM15", "o__Peptostreptococcales-Tissierellales",
"o__Phycisphaerales", "o__Pirellulales", "o__Polyangiales",
"o__Propionibacteriales", "o__Proteinivoracales", "o__Pseudomonadales",
"o__Pseudonocardiales", "o__Puniceispirillales", "o__Rhizobiales",
"o__Rhodobacterales", "o__Rhodospirillales", "o__Rhodothermales",
"o__Rickettsiales", "o__Salinisphaerales", "o__SAR324_clade(Marine_group_B)",
"o__SAR86_clade", "o__Sericytochromatia", "o__Solibacterales",
"o__Solirubrobacterales", "o__Sphingobacteriales", "o__Sphingomonadales",
"o__Staphylococcales", "o__Streptomycetales", "o__Streptosporangiales",
"o__Synechococcales", "o__Synergistales", "o__Syntrophales",
"o__Thalassobaculales", "o__Thermales", "o__Thermoanaerobacterales",
"o__Thermoanaerobaculales", "o__Thermomicrobiales", "o__Tistrellales",
"o__TK10", "o__Vicinamibacterales", "o__WPS-2", "o__Xanthomonadales"
), class = "factor"), Family = structure(c(17L, 13L, 13L,
17L, 17L), .Label = c("f__67-14", "f__Acetobacteraceae",
"f__Acidobacteriaceae_(Subgroup_1)", "f__Actinomarinaceae",
"f__Actinomycetaceae", "f__Aerococcaceae", "f__Alcaligenaceae",
"f__Alcanivoracaceae1", "f__Algiphilaceae", "f__Alkalibacteraceae",
"f__Anaeromyxobacteraceae", "f__Arctic97B-4_marine_group",
"f__Azospirillaceae", "f__Bacillaceae", "f__Bacteroidetes_vadinHA17",
"f__Balneolaceae", "f__Beijerinckiaceae", "f__Beutenbergiaceae",
"f__Bifidobacteriaceae", "f__Bogoriellaceae", "f__Brevibacteriaceae",
"f__C0119", "f__Carnobacteriaceae", "f__Caulobacteraceae",
"f__Cellulomonadaceae", "f__Chitinophagaceae", "f__Chroococcidiopsaceae",
"f__Chthoniobacteraceae", "f__Clostridiaceae", "f__Comamonadaceae",
"f__Corynebacteriaceae", "f__CPR2", "f__Crocinitomicaceae",
"f__Cyanobiaceae", "f__Cyclobacteriaceae", "f__Deinococcaceae",
"f__Demequinaceae", "f__Dermabacteraceae", "f__Dermacoccaceae",
"f__Desulfitobacteriaceae", "f__Desulfotomaculales", "f__Devosiaceae",
"f__Dietziaceae", "f__Dysgonomonadaceae", "f__Endozoicomonadaceae",
"f__Enterobacteriaceae", "f__Eubacteriaceae", "f__Family_III",
"f__Fimbriimonadaceae", "f__Flavobacteriaceae", "f__Fodinicurvataceae",
"f__Frankiaceae", "f__Fusobacteriaceae", "f__Geminicoccaceae",
"f__Gemmataceae", "f__Gemmatimonadaceae", "f__Geodermatophilaceae",
"f__Haliangiaceae", "f__Halomonadaceae", "f__Halorhodospiraceae",
"f__Hungateiclostridiaceae", "f__Hymenobacteraceae", "f__Hyphomicrobiaceae",
"f__Hyphomonadaceae", "f__Idiomarinaceae", "f__IMCC26256",
"f__Intrasporangiaceae", "f__Isosphaeraceae", "f__JG30-KF-CM45",
"f__Jonesiaceae", "f__Kangiellaceae", "f__KD4-96", "f__Kineosporiaceae",
"f__Ktedonobacteraceae", "f__Labraceae", "f__Lachnospiraceae",
"f__Lactobacillaceae", "f__Legionellaceae", "f__Marine_Group_II",
"f__Marinilabiliaceae", "f__Marinobacteraceae", "f__Marinococcaceae",
"f__Methylomonadaceae", "f__Methylophagaceae", "f__Methylophilaceae",
"f__Methylopilaceae", "f__Microbacteriaceae", "f__Micrococcaceae",
"f__Micromonosporaceae", "f__Micropepsaceae", "f__Microscillaceae",
"f__Moraxellaceae", "f__Morganellaceae", "f__Mycobacteriaceae",
"f__Myxococcaceae", "f__Nakamurellaceae", "f__Nitriliruptoraceae",
"f__Nitrosomonadaceae", "f__Nocardiaceae", "f__Nocardioidaceae",
"f__Nocardiopsaceae", "f__Nostocaceae", "f__Oceanibaculaceae",
"f__OPB41", "f__Oscillospiraceae", "f__Oxalobacteraceae",
"f__Paenibacillaceae", "f__Parvibaculaceae", "f__PeM15",
"f__Peptostreptococcales-Tissierellales", "f__Phaselicystidaceae",
"f__Phycisphaeraceae", "f__Pirellulaceae", "f__Planococcaceae",
"f__Prevotellaceae", "f__Prolixibacteraceae", "f__Promicromonosporaceae",
"f__Proteinivoracales", "f__Pseudomonadaceae", "f__Pseudonocardiaceae",
"f__Rhizobiaceae", "f__Rhizobiales_Incertae_Sedis", "f__Rhodobacteraceae",
"f__Rhodobiaceae", "f__Rhodothermaceae", "f__Rickettsiaceae",
"f__Ruminococcaceae", "f__S25-593", "f__Salisediminibacteriaceae",
"f__SAR116_clade", "f__SAR324_clade(Marine_group_B)", "f__SAR86_clade",
"f__SC-I-84", "f__Sedimentibacteraceae", "f__Sericytochromatia",
"f__Solibacteraceae", "f__Solirubrobacteraceae", "f__Sphingobacteriaceae",
"f__Sphingomonadaceae", "f__Spirosomaceae", "f__Staphylococcaceae",
"f__Stappiaceae", "f__Streptococcaceae", "f__Streptomycetaceae",
"f__Streptosporangiaceae", "f__Synergistaceae", "f__Syntrophaceae",
"f__Syntrophobotulaceae", "f__Thalassospiraceae", "f__Thermaceae",
"f__Thermoanaerobaculaceae", "f__Thermotaleaceae", "f__Thioalkalibacteraceae",
"f__Tistrellaceae", "f__TK10", "f__TRA3-20", "f__uncultured",
"f__Vicinamibacteraceae", "f__Weeksellaceae", "f__WPS-2",
"f__Xanthobacteraceae", "f__Xanthomonadaceae"), class = "factor"),
Genus = structure(c(125L, 28L, 28L, 125L, NA), .Label = c("g__67-14",
"g__Acetobacterium", "g__Acidiphilium", "g__Acinetobacter",
"g__Actinomyces", "g__Actinotalea", "g__Advenella", "g__Aerococcus",
"g__Alcanivorax", "g__Algiphilus", "g__Algoriphagus", "g__Aliidiomarina",
"g__Aliihoeflea", "g__Aliterella", "g__Alkalibacter", "g__Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium",
"g__Altererythrobacter", "g__Amorphus", "g__Amycolatopsis",
"g__Anaerobacillus", "g__Anaerobranca", "g__Anaeromyxobacter",
"g__Aquibacillus", "g__Arctic97B-4_marine_group", "g__Arenimonas",
"g__Arthrobacter", "g__Aureimonas", "g__Azospirillum", "g__Bacillus",
"g__Bacteroidetes_vadinHA17", "g__Bauldia", "g__Bifidobacterium",
"g__Blastococcus", "g__Blastopirellula", "g__Bosea", "g__Brachybacterium",
"g__Bradyrhizobium", "g__Brevibacterium", "g__Brevundimonas",
"g__C0119", "g__Candidatus_Actinomarina", "g__Candidatus_Alysiosphaera",
"g__Candidatus_Solibacter", "g__Candidatus_Udaeobacter",
"g__Cecembia", "g__Chromohalobacter", "g__Chryseobacterium",
"g__Chthoniobacter", "g__Citricoccus", "g__Cloacibacterium",
"g__Clostridium_sensu_stricto_9", "g__Conexibacter", "g__Corynebacterium",
"g__CPR2", "g__Craurococcus-Caldovatus", "g__Curtobacterium",
"g__Dehalobacter", "g__Deinococcus", "g__Demequina", "g__Dermacoccus",
"g__Desulfohalotomaculum", "g__Desulfosporosinus", "g__Devosia",
"g__Dietzia", "g__DSSD61", "g__Dyadobacter", "g__Egicoccus",
"g__Ellin6067", "g__Endozoicomonas", "g__Enhydrobacter",
"g__Ercella", "g__Escherichia-Shigella", "g__Faecalibacterium",
"g__Fermentimonas", "g__Fimbriiglobus", "g__Fimbriimonadaceae",
"g__Finegoldia", "g__Flaviflexus", "g__Flavobacterium", "g__Fluviicola",
"g__Fusicatenibacter", "g__Fusobacterium", "g__Gardnerella",
"g__Gemmatimonas", "g__Geobacillus", "g__Georgenia", "g__Glutamicibacter",
"g__Gracilibacillus", "g__Gracilimonas", "g__Guyparkeria",
"g__Haematobacter", "g__Haliangium", "g__Halolactibacillus",
"g__Halomonas", "g__Henriciella", "g__HIMB11", "g__HSB_OF53-F07",
"g__Hymenobacter", "g__Hyphomicrobium", "g__IMCC26256", "g__Janibacter",
"g__Jatrophihabitans", "g__JCM_18997", "g__Jeotgalibacillus",
"g__JG30-KF-CM45", "g__JG30a-KF-32", "g__JGI-0000079-D21",
"g__Jiella", "g__Jonesia", "g__KD4-96", "g__Kineococcus",
"g__Labrys", "g__Lactobacillus", "g__Lawsonella", "g__Leeuwenhoekiella",
"g__Legionella", "g__Marine_Group_II", "g__Marinilabiliaceae",
"g__Marinobacter", "g__Marinococcus", "g__Martelella", "g__Massilia",
"g__Meiothermus", "g__Methylobacillus", "g__Methylobacterium-Methylorubrum",
"g__Methylophaga", "g__Methylotenera", "g__Micrococcus",
"g__Microvirga", "g__Modestobacter", "g__Mucilaginibacter",
"g__Muricauda", "g__Mycobacterium", "g__Nakamurella", "g__Nesterenkonia",
"g__Nitratireductor", "g__Nitriliruptor", "g__Nitriliruptoraceae",
"g__Nocardia", "g__Nocardioides", "g__Nocardiopsis", "g__Novosphingobium",
"g__NS5_marine_group", "g__Oceanibaculum", "g__Oceanobacillus",
"g__OPB41", "g__Oricola", "g__Ornithinimicrobium", "g__Oryzihumus",
"g__P3OB-42", "g__Paenibacillus", "g__Paludisphaera", "g__Paracoccus",
"g__Paraliobacillus", "g__Parvibaculum", "g__Patulibacter",
"g__Pediococcus", "g__Pedomicrobium", "g__Pelagibacterium",
"g__Pelotomaculum", "g__PeM15", "g__Phaselicystis", "g__Phenylobacterium",
"g__Pir4_lineage", "g__Polaromonas", "g__Prevotella", "g__Prochlorococcus_MIT9313",
"g__Promicromonospora", "g__Proteiniclasticum", "g__Pseudolabrys",
"g__Pseudomonas", "g__Pseudonocardia", "g__Psychrobacter",
"g__Psychroglaciecola", "g__Pusillimonas", "g__Quadrisphaera",
"g__Rhodococcus", "g__Rhodopirellula", "g__Roseisolibacter",
"g__Roseovarius", "g__Rubrivirga", "g__Rummeliibacillus",
"g__S25-593", "g__Saccharomonospora", "g__Saccharopolyspora",
"g__Salegentibacter", "g__Salimesophilobacter", "g__Salinicola",
"g__Salipaludibacillus", "g__SAR116_clade", "g__SAR324_clade(Marine_group_B)",
"g__SAR86_clade", "g__SC-I-84", "g__Scytonema_UTEX_2349",
"g__Sedimentibacter", "g__Sediminibacterium", "g__Sericytochromatia",
"g__SM1A02", "g__Solirubrobacter", "g__Sphingobium", "g__Sphingomonas",
"g__Sporobacter", "g__Staphylococcus", "g__Stappia", "g__Streptococcus",
"g__Streptomyces", "g__Subgroup_10", "g__Synechococcus_CC9902",
"g__Syntrophus", "g__Terrabacter", "g__Thalassospira", "g__Thermoanaerobacterium",
"g__Thermopolyspora", "g__Thermus", "g__Tistrella", "g__TK10",
"g__TRA3-20", "g__uncultured", "g__Vicinamibacteraceae",
"g__Virgibacillus", "g__WCHB1-32", "g__WPS-2", "g__Xanthobacter"
), class = "factor"), Species = structure(c(NA, 55L, 55L,
NA, NA), .Label = c("s__Acetobacteraceae_bacterium", "s__Acinetobacter_venetianus",
"s__Actinomycetales_bacterium", "s__Alcanivorax_pacificus",
"s__Amorphus_suaedae", "s__Aquibacillus_sp.", "s__Bacillus_alcalophilus",
"s__bacterium_Ellin6515", "s__bacterium_enrichment", "s__bacterium_QTYC46b",
"s__Bifidobacterium_bifidum", "s__Blastopirellula_cremea",
"s__Brevibacterium_samyangense", "s__Cellulomonas_sp.", "s__Corynebacterium_glaucum",
"s__Deinococcus_geothermalis", "s__Desulfohalotomaculum_halophilum",
"s__Desulfosporosinus_youngiae", "s__Endozoicomonas_acroporae",
"s__Ercella_succinigenes", "s__Flavobacterium_qiangtangense",
"s__Fluviicola_sp.", "s__Gardnerella_vaginalis", "s__iron-reducing_bacterium",
"s__Jonesia_denitrificans", "s__Lactobacillus_iners", "s__Leeuwenhoekiella_sp.",
"s__marine_sediment", "s__Mesorhizobium_sp.", "s__metagenome",
"s__Nitriliruptor_alkaliphilus", "s__Oryzihumus_terrae",
"s__Pedomicrobium_ferrugineum", "s__Phyllobacteriaceae_bacterium",
"s__planctomycete_str.", "s__Prevotella_histicola", "s__Prevotella_pallens",
"s__Psychrobacter_pulmonis", "s__Rhodobacteraceae_bacterium",
"s__Rhodococcus_sp.", "s__rock_porewater", "s__Saccharopolyspora_rectivirgula",
"s__Sedimentibacter_acidaminivorans", "s__Sphingomonas_metalli",
"s__Streptomyces_specialis", "s__Streptosporangiaceae_str.",
"s__Tistrella_bauzanensis", "s__Triticum_aestivum", "s__uncultured_Acidobacteriaceae",
"s__uncultured_actinobacterium", "s__uncultured_Actinomycetales",
"s__uncultured_Alcaligenes", "s__uncultured_Anaerobacillus",
"s__uncultured_Anaerolineaceae", "s__uncultured_Azospirillum",
"s__uncultured_bacterium", "s__uncultured_Bacteroidetes",
"s__uncultured_Chloroflexi", "s__uncultured_Conexibacter",
"s__uncultured_cyanobacterium", "s__uncultured_Ferrimicrobium",
"s__uncultured_Fimbriimonas", "s__uncultured_Ktedobacteria",
"s__uncultured_Methylocystaceae", "s__uncultured_Nitriliruptorales",
"s__uncultured_planctomycete", "s__uncultured_Porphyromonadaceae",
"s__uncultured_prokaryote", "s__uncultured_Rhodospirillaceae",
"s__uncultured_soil"), class = "factor")), row.names = c(86L,
209L, 210L, 333L, 431L), class = "data.frame")
Assuming you are working with a phyloseq object called physeq you can do the following:
Agglomerate the OTU table to Family level:
p <- tax_glom(physeq, "Family")
Then you can select families that are present in both samples:
families <- tax_table(p)[otu_table(p)[,"m3ofair"] > 0 & otu_table(p)[,"m3NC"] > 0, "Family"]
Now you can select the ASVs with the families you want:
pout <- prune_taxa(tax_table(physeq)[, "Family"] %in% families, physeq)
I need to show the unique Count by Url and then show the Avg, Max, Min time that it took as different columns. Was looking at using either dplyr or sqldf.
here is what I am essentially trying to duplicate
SELECT cs-uri-stem as Url, COUNT(*) as totalRequests,
AVG(time-taken) As avgRequestDuration,
MAX(time-taken) As maxRequestDuration,
MIN(time-taken) As minRequestDuration
FROM '[LOGFILEPATH]'
GROUP BY Url
ORDER By totalRequests DESC
Head of data for reference:
> head(iislog1)
iisdate iistime csUriStem timeTaken
1 2019-05-17 03:05:39 /eACommon/SystemConfigurationService.svc/customBinding 7421
2 2019-05-17 03:07:22 /Services/2015V1/EngService.svc/customBinding 8390
3 2019-05-17 03:16:40 /eACommon/SystemConfigurationService.svc/customBinding 515
4 2019-05-17 03:17:39 /eACommon/SystemConfigurationService.svc/customBinding 505
5 2019-05-17 03:25:22 /Services/2015V1/EngService.svc/customBinding 1385
6 2019-05-17 03:31:16 /eAudIT/Services/SAPv1/EngService.svc/customBinding 1365
structure(list(iisdate = structure(c(1L, 1L, 1L, 1L, 1L, 1L), .Label = "2019-05-17", class = "factor"),
iistime = structure(1:6, .Label = c("03:05:39", "03:07:22",
"03:16:40", "03:17:39", "03:25:22", "03:31:16", "03:44:02",
"04:27:09", "04:27:11", "04:27:19", "04:27:20", "04:27:22",
"04:27:30", "04:27:33", "04:27:36", "04:27:40", "04:27:42",
"04:27:43", "04:27:44", "04:27:45", "04:27:46", "04:27:47",
"04:27:48", "04:27:50", "04:27:51", "04:27:55", "04:27:57",
"04:28:00", "04:28:01", "04:28:02", "04:28:03", "04:28:05",
"04:28:06", "04:28:08", "04:28:10", "04:28:19", "04:28:26",
"04:28:27", "04:28:28", "04:28:29", "04:28:32", "04:28:37",
"04:28:50", "04:28:51", "04:28:53", "04:28:54", "04:28:55",
"04:28:56", "04:28:57", "04:29:00", "04:29:16", "04:29:18",
"04:29:19", "04:29:20", "04:29:21", "04:29:23", "04:29:24",
"04:29:25", "04:29:26", "04:29:29", "04:29:30", "04:29:33",
"04:29:35", "04:29:37", "04:29:38", "04:30:27", "04:30:29",
"04:30:31", "04:30:32", "04:30:40", "04:30:43", "04:30:58",
"04:31:00", "04:31:01", "04:31:02", "04:31:04", "04:31:08",
"04:31:46", "04:31:47", "04:31:48", "04:31:49", "04:31:54",
"04:31:55", "04:31:56", "04:31:58", "04:31:59", "04:32:01",
"04:32:02", "04:32:03", "04:32:04", "04:32:05", "04:32:06",
"04:32:08", "04:32:09", "04:32:11", "04:32:12", "04:32:14",
"04:32:15", "04:32:17", "04:32:20", "04:32:27", "04:32:39",
"04:32:41", "04:32:42", "04:32:45", "04:32:58", "04:33:03",
"04:33:04", "04:33:08", "04:33:10", "04:33:11", "04:33:12",
"04:33:14", "04:33:15", "04:33:17", "04:33:18", "04:33:41",
"04:33:42", "04:33:44", "04:33:49", "04:33:50", "04:33:51",
"04:33:52", "04:33:53", "04:33:55", "04:33:57", "04:33:58",
"04:34:00", "04:34:02", "04:34:04", "04:34:05", "04:34:07",
"04:34:08", "04:34:10", "04:34:12", "04:34:15", "04:34:16",
"04:34:18", "04:34:25", "04:34:28", "04:34:39", "04:34:40",
"04:34:42", "04:34:43", "04:34:46", "04:34:51", "04:34:57",
"04:34:58", "04:35:01", "04:35:02", "04:35:10", "04:35:13",
"04:35:14", "04:35:15", "04:35:16", "04:35:17", "04:35:19",
"04:35:20", "04:35:21", "04:35:22", "04:35:24", "04:35:25",
"04:35:27", "04:35:29", "04:35:30", "04:35:31", "04:35:34",
"04:35:35", "04:35:38", "04:35:40", "04:35:49", "04:36:01",
"04:36:02", "04:36:05", "04:36:26", "04:36:28", "04:36:29",
"04:36:31", "04:36:32", "04:36:33", "04:36:36", "04:36:41",
"04:36:42", "04:36:43", "04:36:44", "04:36:45", "04:36:47",
"04:36:49", "04:36:51", "04:36:53", "04:36:54", "04:36:55",
"04:36:56", "04:36:57", "04:36:58", "04:37:00", "04:37:01",
"04:37:02", "04:37:04", "04:37:07", "04:37:11", "04:37:16",
"04:37:29", "04:37:30", "04:37:31", "04:37:33", "04:37:35",
"04:38:08", "04:38:09", "04:38:17", "04:38:18", "04:38:19",
"04:38:23", "04:38:24", "04:38:25", "04:38:27", "04:38:28",
"04:38:29", "04:38:30", "04:38:31", "04:38:32", "04:38:34",
"04:38:35", "04:38:36", "04:38:41", "04:38:42", "04:38:43",
"04:39:14", "04:39:15", "04:39:16", "04:39:17", "04:39:19",
"04:39:30", "04:39:31", "04:39:44", "04:39:45", "04:39:46",
"04:39:48", "04:39:50", "04:40:00", "04:40:01", "04:40:03",
"04:40:06", "04:40:07", "04:40:08", "04:40:09", "04:40:11",
"04:40:12", "04:40:13", "04:40:14", "04:40:16", "04:40:19",
"04:40:20", "04:40:22", "04:40:24", "05:00:45", "05:07:01",
"05:07:03", "05:07:05", "05:07:35", "05:07:36", "05:07:38",
"05:07:39", "05:07:42", "05:07:44", "05:07:45", "05:07:46",
"05:07:49", "05:08:04", "05:08:05", "05:08:06", "05:08:07",
"05:08:08", "05:08:09", "05:08:10", "05:08:11", "05:08:24",
"05:08:30", "05:08:31", "05:08:37", "05:08:38", "05:08:39",
"05:08:40", "05:08:52", "05:08:58", "05:08:59", "05:09:02",
"05:09:03", "05:11:50", "05:11:52", "05:11:53", "05:11:59",
"05:12:00", "05:12:01", "05:12:02", "05:12:03", "05:12:04",
"05:12:06", "05:12:07", "05:12:08", "05:12:09", "05:12:10",
"05:12:11", "05:13:46", "05:13:47", "05:13:48", "05:13:50",
"05:13:51", "05:13:53", "05:13:55", "05:13:56", "05:13:59",
"05:14:05", "05:14:07", "05:14:08", "05:14:10", "05:14:11",
"05:14:12", "05:14:14", "05:14:16", "05:14:18", "05:14:19",
"05:14:20", "05:14:21", "05:14:22", "05:14:24", "05:14:25",
"05:14:27", "05:14:28", "05:14:29", "05:14:30", "05:14:31",
"05:14:32", "05:14:33", "05:14:34", "05:14:36", "05:14:37",
"05:14:38", "05:14:39", "05:14:40", "05:14:41", "05:14:42",
"05:14:43", "05:14:44", "05:14:45", "05:14:46", "05:14:47",
"05:14:48", "05:14:50", "05:14:51", "05:14:52", "05:14:54",
"05:14:55", "05:14:56", "05:14:57", "05:14:58", "05:14:59",
"05:15:00", "05:15:01", "05:15:02", "05:15:03", "05:15:04",
"05:15:06", "05:15:07", "05:15:08", "05:15:09", "05:15:10",
"05:15:11", "05:15:12", "05:15:13", "05:15:15", "05:15:16",
"05:15:17", "05:15:18", "05:15:19", "05:15:20", "05:15:21",
"05:15:22", "05:15:24", "05:15:25", "05:15:26", "05:15:27",
"05:15:28", "05:15:29", "05:15:31", "05:15:32", "05:15:33",
"05:15:34", "05:15:35", "05:15:36", "05:15:37", "05:15:38",
"05:15:39", "05:15:40", "05:15:41", "05:15:42", "05:15:43",
"05:15:44", "05:15:45", "05:15:46", "05:15:48", "05:15:49",
"05:15:50", "05:15:51", "05:15:52", "05:15:53", "05:15:54",
"05:15:55", "05:15:56", "05:15:57", "05:15:58", "05:15:59",
"05:16:00", "05:16:01", "05:16:03", "05:16:04", "05:16:05",
"05:16:06", "05:16:07", "05:16:09", "05:16:10", "05:16:11",
"05:16:12", "05:16:13", "05:16:14", "05:16:15", "05:16:17",
"05:16:19", "05:16:21", "05:16:22", "05:16:23", "05:16:24",
"05:16:25", "05:16:26", "05:16:28", "05:16:29", "05:16:30",
"05:16:31", "05:16:32", "05:16:34", "05:16:35", "05:16:36",
"05:16:37", "05:16:38", "05:16:39", "05:16:41", "05:16:42",
"05:16:43", "05:16:44", "05:16:45", "05:16:47", "05:16:54",
"05:17:13", "05:17:14", "05:17:15", "05:17:16", "05:17:18",
"05:17:19", "05:17:20", "05:17:21", "05:17:22", "05:17:23",
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"12:06:39", "12:06:40", "12:06:42", "12:06:43", "12:06:44",
"12:06:45", "12:06:47", "12:06:53", "12:06:54", "12:06:55",
"12:06:56", "12:06:57", "12:07:06", "12:07:07", "12:07:08",
"12:07:10", "12:07:12", "12:07:14", "12:07:15", "12:07:18",
"12:07:20", "12:07:27", "12:07:29", "12:07:30", "12:07:31",
"12:07:33", "12:07:34", "12:07:35", "12:07:36", "12:07:38",
"12:07:39", "12:07:40", "12:07:42", "12:07:43", "12:07:44",
"12:07:46", "12:07:47", "12:07:48", "12:07:49", "12:07:50",
"12:07:51", "12:07:53", "12:07:54", "12:07:55", "12:07:56",
"12:07:57", "12:07:58", "12:07:59", "12:08:00", "12:08:01",
"12:08:02", "12:08:03", "12:08:05", "12:08:06", "12:08:07",
"12:08:08", "12:08:10", "12:08:11", "12:08:12", "12:08:13",
"12:08:14", "12:08:15", "12:08:16", "12:08:17", "12:08:18",
"12:08:19", "12:08:21", "12:08:22", "12:08:23", "12:08:24",
"12:08:25", "12:08:26", "12:08:27", "12:08:28", "12:08:29",
"12:08:30", "12:08:31", "12:08:33", "12:08:34", "12:08:35",
"12:08:36", "12:08:37", "12:08:38", "12:08:39", "12:08:40",
"12:08:41", "12:08:43", "12:08:44", "12:08:45", "12:08:46",
"12:08:47", "12:08:48", "12:08:49", "12:08:50", "12:08:51",
"12:08:52", "12:08:54", "12:08:55", "12:08:56", "12:08:57",
"12:08:58", "12:08:59", "12:09:00", "12:09:01", "12:09:02",
"12:09:03", "12:09:04", "12:09:05", "12:09:07", "12:09:08",
"12:09:09", "12:09:10", "12:09:11", "12:09:12", "12:09:13",
"12:09:14", "12:09:15", "12:09:16", "12:09:18", "12:09:19",
"12:09:20", "12:09:21", "12:09:22", "12:09:23", "12:09:24",
"12:09:27", "12:09:28", "12:09:29", "12:09:31", "12:09:32",
"12:09:40", "12:09:41", "12:09:42", "12:09:43", "12:09:44",
"12:09:45", "12:09:46", "12:09:47", "12:09:49", "12:10:04",
"12:10:05", "12:10:06", "12:10:07", "12:10:09", "12:10:10",
"12:10:14", "12:10:15", "12:10:16", "12:10:18", "12:10:19",
"12:10:20", "12:10:21", "12:10:22", "12:10:23", "12:10:24",
"12:10:25", "12:10:26", "12:10:28", "12:10:32", "12:10:35",
"12:10:37", "12:10:38", "12:10:39", "12:10:54", "12:11:09",
"12:11:10", "12:11:11", "12:11:12", "12:11:14", "12:11:15",
"12:11:18", "12:11:20", "12:11:21", "12:17:39", "12:17:40",
"12:17:42", "12:17:45", "12:17:50", "12:17:51", "12:17:52",
"12:17:53", "12:17:55", "12:17:56", "12:17:57", "12:17:59",
"12:18:00", "12:18:02", "12:18:09", "12:18:10", "12:18:14",
"12:18:15", "12:18:24", "12:18:38", "12:18:40", "12:18:41",
"20:39:39", "20:39:41", "20:48:52", "20:48:54", "20:48:55",
"20:49:01", "20:49:02", "20:49:03", "20:49:09"), class = "factor"),
csUriStem = c("/eacommon/systemconfigurationservice.svc/custombinding",
"/services/2015v1/engservice.svc/custombinding", "/eacommon/systemconfigurationservice.svc/custombinding",
"/eacommon/systemconfigurationservice.svc/custombinding",
"/services/2015v1/engservice.svc/custombinding", "/eaudit/services/sapv1/engservice.svc/custombinding"
), timeTaken = c(7421L, 8390L, 515L, 505L, 1385L, 1365L)), row.names = c(NA,
6L), class = "data.frame")
install.packages("sqldf")
library(sqldf)
#create subset of the original data
iislog1 <- iislog %>% select(iisdate,iistime,csUriStem,timeTaken)
#Find Count by Url and then Avg,Max,Min timeTaken for each Url
iislog6 <- sqldf(SELECT csUriStem AS iislog6$baseUrl FROM iislog1,
Count(*) as iislog6$totalRequests,
AVG(timeTaken) AS iislog6$avgRequestDuration,
MAX(timeTaken) AS iislog6$maxRequestDuration,
MIN(timeTaken) AS iislog6$minRequestDuration
GROUP BY iislog6$baseUrl
ORDER By iislog6$totalRequests DESC
)
I think this is what you were trying to do. I mainly used your SQL to translate it into R.
group_by is very powerfull in R and it is easy in use.
install.packages("dplyr")
library(dplyr)
iislog1 <- iislog %>% select(iisdate,iistime,csUriStem,timeTaken)
iislog6 <- group_by(iislog1, csUriStem) %>% summarise(totalRequests = n(),
avgRequestDuration = mean(timeTaken), maxRequestDuration = max(timeTaken),
minRequestDuration = min(timeTaken))
# order by
iislog6 <- arrange(iislog6, totalRequests)
There is something wrong with the dput output in the question so we used the data in the Note at the end.
The SQL syntax in the code in the question was incorrect -- it was a mix of SQL and R but the string must be pure SQL like this:
library(sqldf)
sqldf("SELECT
csUriStem AS baseUrl,
COUNT(*) AS totalRequests,
AVG(timeTaken) AS avgRequestDuration,
MAX(timeTaken) AS maxRequestDuration,
MIN(timeTaken) AS minRequestDuration
FROM iislog1
GROUP BY baseUrl
ORDER BY totalRequests DESC")
Note
This is the data used in reproducible form:
Lines <- "iisdate iistime csUriStem timeTaken
1 2019-05-17 03:05:39 /eACommon/SystemConfigurationService.svc/customBinding 7421
2 2019-05-17 03:07:22 /Services/2015V1/EngService.svc/customBinding 8390
3 2019-05-17 03:16:40 /eACommon/SystemConfigurationService.svc/customBinding 515
4 2019-05-17 03:17:39 /eACommon/SystemConfigurationService.svc/customBinding 505
5 2019-05-17 03:25:22 /Services/2015V1/EngService.svc/customBinding 1385
6 2019-05-17 03:31:16 /eAudIT/Services/SAPv1/EngService.svc/customBinding 1365"
iislog1 <- read.table(text = Lines, as.is = TRUE)
This seems relatively straightforward and possibly doable with scale_x_datetime or scale_x_discrete.
I have a column with time increments like on a stopwatch:
[1] 0:00:01 0:00:02 0:00:03 0:00:04 0:00:05 0:00:06
1800 Levels: 0:00:01 0:00:02 0:00:03 0:00:04 0:00:05 0:00:06 0:00:07 ... 0:30:00
Using ggplot how can I set the x-axis labels for 30 second intervals?
Reproducible data:
structure(list(`somedata$Time` = structure(1:4, .Label = c("0:00:01",
"0:00:02", "0:00:03", "0:00:04", "0:00:05", "0:00:06", "0:00:07",
"0:00:08", "0:00:09", "0:00:10", "0:00:11", "0:00:12", "0:00:13",
"0:00:14", "0:00:15", "0:00:16", "0:00:17", "0:00:18", "0:00:19",
"0:00:20", "0:00:21", "0:00:22", "0:00:23", "0:00:24", "0:00:25",
"0:00:26", "0:00:27", "0:00:28", "0:00:29", "0:00:30", "0:00:31",
"0:00:32", "0:00:33", "0:00:34", "0:00:35", "0:00:36", "0:00:37",
"0:00:38", "0:00:39", "0:00:40", "0:00:41", "0:00:42", "0:00:43",
"0:00:44", "0:00:45", "0:00:46", "0:00:47", "0:00:48", "0:00:49",
"0:00:50", "0:00:51", "0:00:52", "0:00:53", "0:00:54", "0:00:55",
"0:00:56", "0:00:57", "0:00:58", "0:00:59", "0:01:00", "0:01:01",
"0:01:02", "0:01:03", "0:01:04", "0:01:05", "0:01:06", "0:01:07",
"0:01:08", "0:01:09", "0:01:10", "0:01:11", "0:01:12", "0:01:13",
"0:01:14", "0:01:15", "0:01:16", "0:01:17", "0:01:18", "0:01:19",
"0:01:20", "0:01:21", "0:01:22", "0:01:23", "0:01:24", "0:01:25",
"0:01:26", "0:01:27", "0:01:28", "0:01:29", "0:01:30", "0:01:31",
"0:01:32", "0:01:33", "0:01:34", "0:01:35", "0:01:36", "0:01:37",
"0:01:38", "0:01:39", "0:01:40", "0:01:41", "0:01:42", "0:01:43",
"0:01:44", "0:01:45", "0:01:46", "0:01:47", "0:01:48", "0:01:49",
"0:01:50", "0:01:51", "0:01:52", "0:01:53", "0:01:54", "0:01:55",
"0:01:56", "0:01:57", "0:01:58", "0:01:59", "0:02:00", "0:02:01",
"0:02:02", "0:02:03", "0:02:04", "0:02:05", "0:02:06", "0:02:07",
"0:02:08", "0:02:09", "0:02:10", "0:02:11", "0:02:12", "0:02:13",
"0:02:14", "0:02:15", "0:02:16", "0:02:17", "0:02:18", "0:02:19",
"0:02:20", "0:02:21", "0:02:22", "0:02:23", "0:02:24", "0:02:25",
"0:02:26", "0:02:27", "0:02:28", "0:02:29", "0:02:30", "0:02:31",
"0:02:32", "0:02:33", "0:02:34", "0:02:35", "0:02:36", "0:02:37",
"0:02:38", "0:02:39", "0:02:40", "0:02:41", "0:02:42", "0:02:43",
"0:02:44", "0:02:45", "0:02:46", "0:02:47", "0:02:48", "0:02:49",
"0:02:50", "0:02:51", "0:02:52", "0:02:53", "0:02:54", "0:02:55",
"0:02:56", "0:02:57", "0:02:58", "0:02:59", "0:03:00", "0:03:01",
"0:03:02", "0:03:03", "0:03:04", "0:03:05", "0:03:06", "0:03:07",
"0:03:08", "0:03:09", "0:03:10", "0:03:11", "0:03:12", "0:03:13",
"0:03:14", "0:03:15", "0:03:16", "0:03:17", "0:03:18", "0:03:19",
"0:03:20", "0:03:21", "0:03:22", "0:03:23", "0:03:24", "0:03:25",
"0:03:26", "0:03:27", "0:03:28", "0:03:29", "0:03:30", "0:03:31",
"0:03:32", "0:03:33", "0:03:34", "0:03:35", "0:03:36", "0:03:37",
"0:03:38", "0:03:39", "0:03:40", "0:03:41", "0:03:42", "0:03:43",
"0:03:44", "0:03:45", "0:03:46", "0:03:47", "0:03:48", "0:03:49",
"0:03:50", "0:03:51", "0:03:52", "0:03:53", "0:03:54", "0:03:55",
"0:03:56", "0:03:57", "0:03:58", "0:03:59", "0:04:00", "0:04:01",
"0:04:02", "0:04:03", "0:04:04", "0:04:05", "0:04:06", "0:04:07",
"0:04:08", "0:04:09", "0:04:10", "0:04:11", "0:04:12", "0:04:13",
"0:04:14", "0:04:15", "0:04:16", "0:04:17", "0:04:18", "0:04:19",
"0:04:20", "0:04:21", "0:04:22", "0:04:23", "0:04:24", "0:04:25",
"0:04:26", "0:04:27", "0:04:28", "0:04:29", "0:04:30", "0:04:31",
"0:04:32", "0:04:33", "0:04:34", "0:04:35", "0:04:36", "0:04:37",
"0:04:38", "0:04:39", "0:04:40", "0:04:41", "0:04:42", "0:04:43",
"0:04:44", "0:04:45", "0:04:46", "0:04:47", "0:04:48", "0:04:49",
"0:04:50", "0:04:51", "0:04:52", "0:04:53", "0:04:54", "0:04:55",
"0:04:56", "0:04:57", "0:04:58", "0:04:59", "0:05:00", "0:05:01",
"0:05:02", "0:05:03", "0:05:04", "0:05:05", "0:05:06", "0:05:07",
"0:05:08", "0:05:09", "0:05:10", "0:05:11", "0:05:12", "0:05:13",
"0:05:14", "0:05:15", "0:05:16", "0:05:17", "0:05:18", "0:05:19",
"0:05:20", "0:05:21", "0:05:22", "0:05:23", "0:05:24", "0:05:25",
"0:05:26", "0:05:27", "0:05:28", "0:05:29", "0:05:30", "0:05:31",
"0:05:32", "0:05:33", "0:05:34", "0:05:35", "0:05:36", "0:05:37",
"0:05:38", "0:05:39", "0:05:40", "0:05:41", "0:05:42", "0:05:43",
"0:05:44", "0:05:45", "0:05:46", "0:05:47", "0:05:48", "0:05:49",
"0:05:50", "0:05:51", "0:05:52", "0:05:53", "0:05:54", "0:05:55",
"0:05:56", "0:05:57", "0:05:58", "0:05:59", "0:06:00", "0:06:01",
"0:06:02", "0:06:03", "0:06:04", "0:06:05", "0:06:06", "0:06:07",
"0:06:08", "0:06:09", "0:06:10", "0:06:11", "0:06:12", "0:06:13",
"0:06:14", "0:06:15", "0:06:16", "0:06:17", "0:06:18", "0:06:19",
"0:06:20", "0:06:21", "0:06:22", "0:06:23", "0:06:24", "0:06:25",
"0:06:26", "0:06:27", "0:06:28", "0:06:29", "0:06:30", "0:06:31",
"0:06:32", "0:06:33", "0:06:34", "0:06:35", "0:06:36", "0:06:37",
"0:06:38", "0:06:39", "0:06:40", "0:06:41", "0:06:42", "0:06:43",
"0:06:44", "0:06:45", "0:06:46", "0:06:47", "0:06:48", "0:06:49",
"0:06:50", "0:06:51", "0:06:52", "0:06:53", "0:06:54", "0:06:55",
"0:06:56", "0:06:57", "0:06:58", "0:06:59", "0:07:00", "0:07:01",
"0:07:02", "0:07:03", "0:07:04", "0:07:05", "0:07:06", "0:07:07",
"0:07:08", "0:07:09", "0:07:10", "0:07:11", "0:07:12", "0:07:13",
"0:07:14", "0:07:15", "0:07:16", "0:07:17", "0:07:18", "0:07:19",
"0:07:20", "0:07:21", "0:07:22", "0:07:23", "0:07:24", "0:07:25",
"0:07:26", "0:07:27", "0:07:28", "0:07:29", "0:07:30", "0:07:31",
"0:07:32", "0:07:33", "0:07:34", "0:07:35", "0:07:36", "0:07:37",
"0:07:38", "0:07:39", "0:07:40", "0:07:41", "0:07:42", "0:07:43",
"0:07:44", "0:07:45", "0:07:46", "0:07:47", "0:07:48", "0:07:49",
"0:07:50", "0:07:51", "0:07:52", "0:07:53", "0:07:54", "0:07:55",
"0:07:56", "0:07:57", "0:07:58", "0:07:59", "0:08:00", "0:08:01",
"0:08:02", "0:08:03", "0:08:04", "0:08:05", "0:08:06", "0:08:07",
"0:08:08", "0:08:09", "0:08:10", "0:08:11", "0:08:12", "0:08:13",
"0:08:14", "0:08:15", "0:08:16", "0:08:17", "0:08:18", "0:08:19",
"0:08:20", "0:08:21", "0:08:22", "0:08:23", "0:08:24", "0:08:25",
"0:08:26", "0:08:27", "0:08:28", "0:08:29", "0:08:30", "0:08:31",
"0:08:32", "0:08:33", "0:08:34", "0:08:35", "0:08:36", "0:08:37",
"0:08:38", "0:08:39", "0:08:40", "0:08:41", "0:08:42", "0:08:43",
"0:08:44", "0:08:45", "0:08:46", "0:08:47", "0:08:48", "0:08:49",
"0:08:50", "0:08:51", "0:08:52", "0:08:53", "0:08:54", "0:08:55",
"0:08:56", "0:08:57", "0:08:58", "0:08:59", "0:09:00", "0:09:01",
"0:09:02", "0:09:03", "0:09:04", "0:09:05", "0:09:06", "0:09:07",
"0:09:08", "0:09:09", "0:09:10", "0:09:11", "0:09:12", "0:09:13",
"0:09:14", "0:09:15", "0:09:16", "0:09:17", "0:09:18", "0:09:19",
"0:09:20", "0:09:21", "0:09:22", "0:09:23", "0:09:24", "0:09:25",
"0:09:26", "0:09:27", "0:09:28", "0:09:29", "0:09:30", "0:09:31",
"0:09:32", "0:09:33", "0:09:34", "0:09:35", "0:09:36", "0:09:37",
"0:09:38", "0:09:39", "0:09:40", "0:09:41", "0:09:42", "0:09:43",
"0:09:44", "0:09:45", "0:09:46", "0:09:47", "0:09:48", "0:09:49",
"0:09:50", "0:09:51", "0:09:52", "0:09:53", "0:09:54", "0:09:55",
"0:09:56", "0:09:57", "0:09:58", "0:09:59", "0:10:00", "0:10:01",
"0:10:02", "0:10:03", "0:10:04", "0:10:05", "0:10:06", "0:10:07",
"0:10:08", "0:10:09", "0:10:10", "0:10:11", "0:10:12", "0:10:13",
"0:10:14", "0:10:15", "0:10:16", "0:10:17", "0:10:18", "0:10:19",
"0:10:20", "0:10:21", "0:10:22", "0:10:23", "0:10:24", "0:10:25",
"0:10:26", "0:10:27", "0:10:28", "0:10:29", "0:10:30", "0:10:31",
"0:10:32", "0:10:33", "0:10:34", "0:10:35", "0:10:36", "0:10:37",
"0:10:38", "0:10:39", "0:10:40", "0:10:41", "0:10:42", "0:10:43",
"0:10:44", "0:10:45", "0:10:46", "0:10:47", "0:10:48", "0:10:49",
"0:10:50", "0:10:51", "0:10:52", "0:10:53", "0:10:54", "0:10:55",
"0:10:56", "0:10:57", "0:10:58", "0:10:59", "0:11:00", "0:11:01",
"0:11:02", "0:11:03", "0:11:04", "0:11:05", "0:11:06", "0:11:07",
"0:11:08", "0:11:09", "0:11:10", "0:11:11", "0:11:12", "0:11:13",
"0:11:14", "0:11:15", "0:11:16", "0:11:17", "0:11:18", "0:11:19",
"0:11:20", "0:11:21", "0:11:22", "0:11:23", "0:11:24", "0:11:25",
"0:11:26", "0:11:27", "0:11:28", "0:11:29", "0:11:30", "0:11:31",
"0:11:32", "0:11:33", "0:11:34", "0:11:35", "0:11:36", "0:11:37",
"0:11:38", "0:11:39", "0:11:40", "0:11:41", "0:11:42", "0:11:43",
"0:11:44", "0:11:45", "0:11:46", "0:11:47", "0:11:48", "0:11:49",
"0:11:50", "0:11:51", "0:11:52", "0:11:53", "0:11:54", "0:11:55",
"0:11:56", "0:11:57", "0:11:58", "0:11:59", "0:12:00", "0:12:01",
"0:12:02", "0:12:03", "0:12:04", "0:12:05", "0:12:06", "0:12:07",
"0:12:08", "0:12:09", "0:12:10", "0:12:11", "0:12:12", "0:12:13",
"0:12:14", "0:12:15", "0:12:16", "0:12:17", "0:12:18", "0:12:19",
"0:12:20", "0:12:21", "0:12:22", "0:12:23", "0:12:24", "0:12:25",
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"0:12:32", "0:12:33", "0:12:34", "0:12:35", "0:12:36", "0:12:37",
"0:12:38", "0:12:39", "0:12:40", "0:12:41", "0:12:42", "0:12:43",
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"0:23:02", "0:23:03", "0:23:04", "0:23:05", "0:23:06", "0:23:07",
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"0:23:26", "0:23:27", "0:23:28", "0:23:29", "0:23:30", "0:23:31",
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"0:23:50", "0:23:51", "0:23:52", "0:23:53", "0:23:54", "0:23:55",
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"0:25:20", "0:25:21", "0:25:22", "0:25:23", "0:25:24", "0:25:25",
"0:25:26", "0:25:27", "0:25:28", "0:25:29", "0:25:30", "0:25:31",
"0:25:32", "0:25:33", "0:25:34", "0:25:35", "0:25:36", "0:25:37",
"0:25:38", "0:25:39", "0:25:40", "0:25:41", "0:25:42", "0:25:43",
"0:25:44", "0:25:45", "0:25:46", "0:25:47", "0:25:48", "0:25:49",
"0:25:50", "0:25:51", "0:25:52", "0:25:53", "0:25:54", "0:25:55",
"0:25:56", "0:25:57", "0:25:58", "0:25:59", "0:26:00", "0:26:01",
"0:26:02", "0:26:03", "0:26:04", "0:26:05", "0:26:06", "0:26:07",
"0:26:08", "0:26:09", "0:26:10", "0:26:11", "0:26:12", "0:26:13",
"0:26:14", "0:26:15", "0:26:16", "0:26:17", "0:26:18", "0:26:19",
"0:26:20", "0:26:21", "0:26:22", "0:26:23", "0:26:24", "0:26:25",
"0:26:26", "0:26:27", "0:26:28", "0:26:29", "0:26:30", "0:26:31",
"0:26:32", "0:26:33", "0:26:34", "0:26:35", "0:26:36", "0:26:37",
"0:26:38", "0:26:39", "0:26:40", "0:26:41", "0:26:42", "0:26:43",
"0:26:44", "0:26:45", "0:26:46", "0:26:47", "0:26:48", "0:26:49",
"0:26:50", "0:26:51", "0:26:52", "0:26:53", "0:26:54", "0:26:55",
"0:26:56", "0:26:57", "0:26:58", "0:26:59", "0:27:00", "0:27:01",
"0:27:02", "0:27:03", "0:27:04", "0:27:05", "0:27:06", "0:27:07",
"0:27:08", "0:27:09", "0:27:10", "0:27:11", "0:27:12", "0:27:13",
"0:27:14", "0:27:15", "0:27:16", "0:27:17", "0:27:18", "0:27:19",
"0:27:20", "0:27:21", "0:27:22", "0:27:23", "0:27:24", "0:27:25",
"0:27:26", "0:27:27", "0:27:28", "0:27:29", "0:27:30", "0:27:31",
"0:27:32", "0:27:33", "0:27:34", "0:27:35", "0:27:36", "0:27:37",
"0:27:38", "0:27:39", "0:27:40", "0:27:41", "0:27:42", "0:27:43",
"0:27:44", "0:27:45", "0:27:46", "0:27:47", "0:27:48", "0:27:49",
"0:27:50", "0:27:51", "0:27:52", "0:27:53", "0:27:54", "0:27:55",
"0:27:56", "0:27:57", "0:27:58", "0:27:59", "0:28:00", "0:28:01",
"0:28:02", "0:28:03", "0:28:04", "0:28:05", "0:28:06", "0:28:07",
"0:28:08", "0:28:09", "0:28:10", "0:28:11", "0:28:12", "0:28:13",
"0:28:14", "0:28:15", "0:28:16", "0:28:17", "0:28:18", "0:28:19",
"0:28:20", "0:28:21", "0:28:22", "0:28:23", "0:28:24", "0:28:25",
"0:28:26", "0:28:27", "0:28:28", "0:28:29", "0:28:30", "0:28:31",
"0:28:32", "0:28:33", "0:28:34", "0:28:35", "0:28:36", "0:28:37",
"0:28:38", "0:28:39", "0:28:40", "0:28:41", "0:28:42", "0:28:43",
"0:28:44", "0:28:45", "0:28:46", "0:28:47", "0:28:48", "0:28:49",
"0:28:50", "0:28:51", "0:28:52", "0:28:53", "0:28:54", "0:28:55",
"0:28:56", "0:28:57", "0:28:58", "0:28:59", "0:29:00", "0:29:01",
"0:29:02", "0:29:03", "0:29:04", "0:29:05", "0:29:06", "0:29:07",
"0:29:08", "0:29:09", "0:29:10", "0:29:11", "0:29:12", "0:29:13",
"0:29:14", "0:29:15", "0:29:16", "0:29:17", "0:29:18", "0:29:19",
"0:29:20", "0:29:21", "0:29:22", "0:29:23", "0:29:24", "0:29:25",
"0:29:26", "0:29:27", "0:29:28", "0:29:29", "0:29:30", "0:29:31",
"0:29:32", "0:29:33", "0:29:34", "0:29:35", "0:29:36", "0:29:37",
"0:29:38", "0:29:39", "0:29:40", "0:29:41", "0:29:42", "0:29:43",
"0:29:44", "0:29:45", "0:29:46", "0:29:47", "0:29:48", "0:29:49",
"0:29:50", "0:29:51", "0:29:52", "0:29:53", "0:29:54", "0:29:55",
"0:29:56", "0:29:57", "0:29:58", "0:29:59", "0:30:00"), class = "factor"),
student = c("bob", "bob", "bob", "bob"), somemeasure = c(0L,
0L, 1L, 1L)), .Names = c("somedata$Time", "student", "somemeasure"
), row.names = c(NA, 4L), class = "data.frame")
Assuming that your data frame is named df. First, create new column which is POSIXct by pasting together some arbitrary date and original Time column and then converting with as.POSIXct().
Then use function scale_x_datetime() to set breaks and format for labels you want to see.
df$Time2<-as.POSIXct(paste("1960-01-01 ",df$Time))
library(scales)
ggplot(df,aes(Time2,somemeasure))+geom_point()+
scale_x_datetime(breaks=date_breaks("30 sec"),labels = date_format("%M:%S"))
I have a list "hhvrs" of length 2 with names and values. The names of these 2 elements of list are years "1920" and "1929".
$`1920`
Nykvarn - 147 - 211920 Nykvarn - 262 - 211920 ...
1.235629 1.013191 ...
$`1929`
Långed - 125 - 11929 Långed - 126 - 11929 ...
1.316499 1.026785 ...
I also have a data.frame "data" consisting of two years, 1920 and 1929. See dput at the bottom of this post.
I then want to negatively match names (i.e. not include those names present in list above). With other words I want to keep the names in my data frame, in last column uniquezCorrectCG, that are not present in the list above. I then want to calculate efficiencies for each company without the names present in the list.
Here is my code:
hhvrsu=lapply(unique(data$year),function(x){
library(Benchmarking)
datat=data[data$year==x,]
datat2=datat[!(datat$uniquezCorrectCG %in% names(hhvrs[[x]])),]
#
y <- datat2[,"Ouput_ton",drop=FALSE]
rownames(y)=paste(datat2[,5],"-",datat2[,4])
#inputs
x=with(datat2,
cbind(Labour_input_1000_hour,
Capital_input_1000_sek,
Electric_input_Mwh,
Rawmaterial_input_M3))
rownames(x)=paste(datat2[,5],"-",datat2[,4],"-",datat2[,3])
e <- dea(x,y,RTS="vrs")
return(e$eff) }
)
names(hhvrsu)=unique(data$year)
But that fails. For example the company Långed - 125 - 11929 year 1929 is still present in the output of my code, while it should be dropped because Långed - 125 - 11929 is present in the list above...
head(hhvrsu[["1929"]])
Billingsfors - 123 - 11929 Billingsfors - 124 - 11929 Långed - 125 - 11929 Långed - 126 - 11929 Långed - 127 - 11929
0.9975506 1.0000000 1.0000000 1.0000000 1.0000000
Hånsfors - 183 - 21929
0.9928677
But it still works if i do it manually:
datat=data[data$year==1929,]
datat2=datat[!(datat$uniquezCorrectCG %in% names(hhvrs[["1929"]])),]
#
y <- datat2[,"Ouput_ton",drop=FALSE]
rownames(y)=paste(datat2[,5],"-",datat2[,4])
#inputs
x=with(datat2,cbind(Labour_input_1000_hour,Capital_input_1000_sek,Electric_input_Mwh,Rawmaterial_input_M3))
rownames(x)=paste(datat2[,5],"-",datat2[,4],"-",datat2[,3])
e <- dea(x,y,RTS="vrs")
head(e$eff)
Billingsfors - 123 - 11929 Billingsfors - 124 - 11929 Hånsfors - 183 - 21929 Hällefors - 237 - 21929 Grycksbo - 350 - 21929
0.9984071 1.0000000 1.0000000 0.5863832 0.9813024
Brättne - 100 - 31929
0.9915349
in e$eff above Långed - 125 - 11929 is dropped!
EDIT:
It works if I put as.character(x) below instead of simply x
hhvrsu=lapply(unique(data$year),function(x){
library(Benchmarking)
datat=data[data$year==x,]
datat2=datat[!(datat$uniquezCorrectCG %in% names(hhvrs[[**as.character(x)**]])),]
#
y <- datat2[,"Ouput_ton",drop=FALSE]
rownames(y)=paste(datat2[,5],"-",datat2[,4])
#inputs
x=with(datat2,
cbind(Labour_input_1000_hour,
Capital_input_1000_sek,
Electric_input_Mwh,
Rawmaterial_input_M3))
rownames(x)=paste(datat2[,5],"-",datat2[,4],"-",datat2[,3])
e <- dea(x,y,RTS="vrs")
return(e$eff) }
)
names(hhvrsu)=unique(data$year)
Any suggestions?
Dputs:
dput(hhvrs)
structure(list(`1920` = structure(c(1.23562876282578, 1.01319073788091,
1.55783496400001, 1.06191988898698, 1.12744927131341, 1.08504615635299,
1.25725741409574, 2.03370195312046, 1.00667697472372, 1.00260726981462,
1.3050604346423, 1.3594555255334, 1.55671945006842, 1.0072581093466,
1.65164991096899, 2.47385616808447, 1.18471196771314, 1.24186522915967,
1.65133103063843, Inf, 1.16498198151401, 1.07017484481922), .Names = c("Nykvarn - 147 - 211920",
"Nykvarn - 262 - 211920", "Tumba - 68 - 381920", "Byske - 294 - 451920",
"Långed - 127 - 571920", "Väja - 270 - 691920", "Ljusfors - 141 - 731920",
"Skärblacka - 370 - 731920", "Sätra - 152 - 781920", "Krokfors - 129 - 871920",
"Åsen - 207 - 1011920", "Åsen - 208 - 1011920", "Lagerfors - 225 - 10121920",
"Lindefors - 243 - 10281920", "Munksjö - 253 - 10281920", "Qvill - 211 - 10431920",
"Esseltewell - 375 - 10521920", "Esseltewell - 376 - 10521920",
"Ulriksfors - 205 - 10541920", "Sellnäs - 352 - 10541920", "Vivstavarv - 314 - 10751920",
"Älvsborg - 369 - 10791920")), `1929` = structure(c(1.31649939189229,
1.02678542256861, 1.50667886828221, 1.06101596031178, 1.00477142430659,
Inf, 1.00038550231904, 1.10347307305662, 1.53782048667181, 1.80890790261425,
1.06103833744605, 1.00036736526695, 1.01053736983199, 1.01119078294682,
1.00295000872313, 1.01778128036389, 1.22049428994262, 1.15078822074877,
1.00346763843347, 1.2192497185324, 1.03195112444193, 1.71491513543284,
1.00168840525869, 1.00575972592046, 1.105483053952, 1.00427057272637,
1.94482017228275, 1.00388363163126), .Names = c("Långed - 125 - 11929",
"Långed - 126 - 11929", "Långed - 127 - 11929", "Hällefors - 234 - 21929",
"Göteborg-Dals - 156 - 91929", "Papyrus - 280 - 231929", "Sofiehem - 330 - 271929",
"Tollare - 66 - 361929", "Tumba - 68 - 381929", "Alstermo - 4 - 491929",
"Billerud - 106 - 571929", "Fengersfors - 135 - 711929", "Gamlestaden - 153 - 821929",
"Gransholm - 228 - 851929", "Åsen - 207 - 1011929", "Nykvarn - 262 - 1101929",
"Haga - 24 - 10041929", "Ljusne - 218 - 10181929", "Husum - 232 - 10251929",
"Munksjö - 253 - 10281929", "Pauliström - 239 - 10311929", "Qvill - 211 - 10431929",
"Esseltewell - 375 - 10521929", "Ställdalen - 356 - 10531929",
"Kvarnsveden - 343 - 10541929", "Skutskär - 345 - 10541929",
"Sellnäs - 352 - 10541929", "Vivstavarv - 314 - 10751929"))), .Names = c("1920",
"1929"))
Dput data.frame
dput( data[data$year==1929,][1:5,])
structure(list(company_code = c(1L, 1L, 1L, 1L, 1L), company_name = c("AB Billingsfors-Långed",
"AB Billingsfors-Långed", "AB Billingsfors-Långed", "AB Billingsfors-Långed",
"AB Billingsfors-Långed"), year_cg_code = c(11929L, 11929L, 11929L,
11929L, 11929L), plant_code = 123:127, plant_name = c("Billingsfors",
"Billingsfors", "Långed", "Långed", "Långed"), plant_location = c("Billingsfors",
"Billingsfors", "Dals Långed", "Dals Långed", "Dals Långed"),
plant_location_by_municipal = c("Bengtsfors", "Bengtsfors",
"Bengtsfors", "Bengtsfors", "Bengtsfors"), year = c(1929L,
1929L, 1929L, 1929L, 1929L), Output_value_1000_sek = c(720L,
2304L, 531L, 3040L, 2079L), Labour_cost_1000_sek = c(102L,
348L, 93L, 199L, 225L), Capital_cost_1000_sek = c(108L, 468L,
126L, 304L, 180L), Electricity_cost_1000_sek = c(130L, 90L,
10L, 120L, 40L), Raw_material_cost_1000_sek = c(174L, 744L,
177L, 1824L, 1080L), Output_price_1_sek.ton = c(220L, 220L,
220L, 220L, 220L), Output_price__sek.ton = c(196L, 196L,
196L, 196L, 196L), Labour_price_sek.hour = c(1, 1.208333333,
2.657142857, 1.093406593, 2.083333333), Capital_price_interest.rate = c(4.556666667,
4.556666667, 4.556666667, 4.556666667, 4.556666667), Motive_Power_pricekr.MwH = c(43.10344828,
67.61833208, 31.54574132, 93.45794393, 45.14672686), Electricity_price_kr.MwH = c(24.34456929,
24.19354839, 13.88888889, 25.26315789, 22.22222222), Raw_Material_price_kr.m3 = c(14.5,
15.5, 11.8, 19, 12), Mean_raw.material_price = c(14.3, 14.3,
14.3, 14.3, 14.3), Output_capacity_ton = c(6000L, 12000L,
3000L, 9500L, 9000L), Ouput_ton = c(3272L, 10472L, 2413L,
13818L, 9450L), Labour_input_1000_hour = c(102L, 288L, 35L,
182L, 108L), Capital_input_1000_sek = c(2853L, 1975L, 219L,
2634L, 878L), Motive_Power_Mwh = c(3016L, 1331L, 317L, 1284L,
886L), Electric_input_Mwh = c(5340, 3720, 720, 4750, 1800
), Rawmaterial_input_M3 = c(12000, 48000, 15000, 96000, 90000
), Capacity_Utilization = c(54.53333333, 87.26666667, 80.43333333,
145.4526316, 105), Labour_cost_share = c(14.16666667, 15.10416667,
17.51412429, 6.546052632, 10.82251082), Capital_cost_share = c(15,
20.3125, 23.72881356, 10, 8.658008658), Electricity_cost_share = c(18.05555556,
3.90625, 1.883239171, 3.947368421, 1.924001924), Raw_Material_cost_share = c(24.16666667,
32.29166667, 33.33333333, 60, 51.94805195), Labour_productivity = c(1.433165382,
1.624502304, 3.080154233, 3.392008925, 3.909230144), Capital_productivity = c(4.8,
22.1, 45.8, 21.9, 44.8), Power_productivity = c(0.24, 1.73,
1.68, 2.37, 2.35), Electricity_productivity = c(0.303469526,
1.39421497, 1.659846295, 1.440769899, 2.60017364), Raw.material.productivity = c(1.439189112,
1.151527229, 0.849086388, 0.759730866, 0.554210966), uniquezCorrect = c("Billingsfors - 123",
"Billingsfors - 124", "Långed - 125", "Långed - 126", "Långed - 127"
), uniquezCorrectCG = c("Billingsfors - 123 - 11929", "Billingsfors - 124 - 11929",
"Långed - 125 - 11929", "Långed - 126 - 11929", "Långed - 127 - 11929"
)), .Names = c("company_code", "company_name", "year_cg_code",
"plant_code", "plant_name", "plant_location", "plant_location_by_municipal",
"year", "Output_value_1000_sek", "Labour_cost_1000_sek", "Capital_cost_1000_sek",
"Electricity_cost_1000_sek", "Raw_material_cost_1000_sek", "Output_price_1_sek.ton",
"Output_price__sek.ton", "Labour_price_sek.hour", "Capital_price_interest.rate",
"Motive_Power_pricekr.MwH", "Electricity_price_kr.MwH", "Raw_Material_price_kr.m3",
"Mean_raw.material_price", "Output_capacity_ton", "Ouput_ton",
"Labour_input_1000_hour", "Capital_input_1000_sek", "Motive_Power_Mwh",
"Electric_input_Mwh", "Rawmaterial_input_M3", "Capacity_Utilization",
"Labour_cost_share", "Capital_cost_share", "Electricity_cost_share",
"Raw_Material_cost_share", "Labour_productivity", "Capital_productivity",
"Power_productivity", "Electricity_productivity", "Raw.material.productivity",
"uniquezCorrect", "uniquezCorrectCG"), row.names = 6:10, class = "data.frame")
I'd do it a bit different (not using lapply at all). I'd use stack to construct a data.frame from hhvrs as follows, first:
my.df <- stack(hhvrs)[, c("ind"), drop = FALSE]
names(my.df) <- c("year")
my.df <- transform(my.df, uniquezCorrectCG = rownames(my.df))
rownames(my.df) <- NULL
Now check for those entries where year and uniquezCorrectCG are present in data but not in my.df.
data[!duplicated(rbind(my.df, data[, c("year",
"uniquezCorrectCG")]))[-seq_len(nrow(my.df))], ]
I have a large time series data set, which I've used xts to summarize in 30 second periods. Not sure how to make this set easily reproducible but it looks like this
> str(taonedf)
'data.frame': 480 obs. of 2 variables:
$ time : POSIXct, format: "2013-01-06 13:00:29" "2013-01-06 13:00:59" "2013-01-06 13:01:29" ...
$ count: int 20763 12030 22188 12183 21112 11628 21543 12609 20095 12992 ...
> head(taonedf)
time count
1 2013-01-06 13:00:29 20763
2 2013-01-06 13:00:59 12030
3 2013-01-06 13:01:29 22188
4 2013-01-06 13:01:59 12183
5 2013-01-06 13:02:29 21112
6 2013-01-06 13:02:59 11628
I've plotted a normal line plot of this and it works fine.
ggplot(data=taonedf, aes(x=time, y=count/30)) + #
geom_line(color="#009E73") +
scale_y_continuous(name="requests per second", labels = format_format(scientific=FALSE, big.mark=",")) +
scale_x_datetime(name="",labels = date_format("%b %d\n%H:%M") ) +
labs(title=paste("Requests per Second - All Requests",count,sep="\n")) +
theme(legend.position = "none")
I want to add some vline annotations. I've created a second dataframe called EV, it looks like this:
> str(ev)
'data.frame': 10 obs. of 2 variables:
$ dt : POSIXct, format: "2013-01-06 13:45:00" "2013-01-06 14:18:00" "2013-01-06 14:49:00" ...
$ event: Factor w/ 9 levels "Event 1",..: 7 8 3 2 5 6 1 4 2 9
> head(ev)
dt event
1 2013-01-06 13:45:00 Event 1
Now, when I add the vline option I get odd results. I'm using the same date time format between the two so the scale should align.
ggplot(data=taonedf, aes(x=time, y=count/30)) +
geom_line(color="#009E73") +
geom_vline(data=ev,aes(xtintercept=dt))+
scale_y_continuous(name="requests per second", labels = format_format(scientific=FALSE, big.mark=",")) +
scale_x_datetime(name="",labels = date_format("%b %d\n%H:%M") ) +
labs(title=paste("Requests per Second - All Requests",count,sep="\n")) +
theme(legend.position = "none")
What am I missing? This doesn't appear to be that hard. All of the documentation and examples show simple numeric X axis so I'm assuming there is some issue with dates in the X axis but I can't pinpoint it. Any help would be appreciated.
> dput(taonedf)
structure(list(time = structure(c(1357506029.996, 1357506059.999,
1357506089.997, 1357506119.998, 1357506149.998, 1357506179.996,
1357506209.996, 1357506239.993, 1357506269.999, 1357506299.996,
1357506329.998, 1357506359.998, 1357506389.999, 1357506419.998,
1357506449.986, 1357506479.996, 1357506509.99, 1357506539.988,
1357506569.996, 1357506599.999, 1357506629.991, 1357506659.998,
1357506689.999, 1357506719.995, 1357506749.996, 1357506779.998,
1357506809.998, 1357506839.997, 1357506869.996, 1357506899.996,
1357506929.997, 1357506959.994, 1357506989.998, 1357507019.999,
1357507049.999, 1357507079.998, 1357507109.998, 1357507139.999,
1357507169.998, 1357507199.99, 1357507229.999, 1357507259.999,
1357507289.999, 1357507319.998, 1357507349.997, 1357507379.997,
1357507409.999, 1357507439.998, 1357507469.994, 1357507499.996,
1357507529.996, 1357507559.996, 1357507589.995, 1357507619.988,
1357507649.999, 1357507679.994, 1357507709.996, 1357507739.996,
1357507769.994, 1357507799.991, 1357507829.999, 1357507859.999,
1357507889.999, 1357507919.999, 1357507949.999, 1357507979.999,
1357508009.999, 1357508039.999, 1357508069.998, 1357508099.999,
1357508129.999, 1357508159.999, 1357508189.999, 1357508219.998,
1357508249.999, 1357508279.999, 1357508309.999, 1357508339.999,
1357508369.999, 1357508399.999, 1357508429.998, 1357508459.999,
1357508489.999, 1357508519.999, 1357508549.999, 1357508579.999,
1357508609.999, 1357508639.999, 1357508669.999, 1357508699.999,
1357508729.999, 1357508759.998, 1357508789.999, 1357508819.998,
1357508849.999, 1357508879.998, 1357508909.999, 1357508939.996,
1357508969.999, 1357508999.999, 1357509029.999, 1357509059.999,
1357509089.999, 1357509119.999, 1357509149.999, 1357509179.999,
1357509209.999, 1357509239.999, 1357509269.999, 1357509299.999,
1357509329.999, 1357509359.999, 1357509389.999, 1357509419.999,
1357509449.999, 1357509479.999, 1357509509.999, 1357509539.999,
1357509569.976, 1357509599.999, 1357509629.999, 1357509659.999,
1357509689.999, 1357509719.999, 1357509749.996, 1357509779.999,
1357509809.999, 1357509839.999, 1357509869.999, 1357509899.999,
1357509929.999, 1357509959.996, 1357509989.999, 1357510019.997,
1357510049.998, 1357510079.997, 1357510109.999, 1357510139.999,
1357510169.999, 1357510199.999, 1357510229.999, 1357510259.999,
1357510289.999, 1357510319.999, 1357510349.999, 1357510379.999,
1357510409.999, 1357510439.999, 1357510469.999, 1357510499.999,
1357510529.999, 1357510559.999, 1357510589.999, 1357510619.999,
1357510649.999, 1357510679.999, 1357510709.999, 1357510739.983,
1357510769.999, 1357510799.999, 1357510829.999, 1357510859.999,
1357510889.999, 1357510919.999, 1357510949.999, 1357510979.999,
1357511009.997, 1357511039.999, 1357511069.999, 1357511099.999,
1357511129.999, 1357511159.999, 1357511189.999, 1357511219.999,
1357511249.999, 1357511279.999, 1357511309.999, 1357511339.999,
1357511369.999, 1357511399.999, 1357511429.999, 1357511459.999,
1357511489.999, 1357511519.999, 1357511549.999, 1357511579.999,
1357511609.999, 1357511639.999, 1357511669.999, 1357511699.999,
1357511729.999, 1357511759.999, 1357511789.996, 1357511819.999,
1357511849.999, 1357511879.999, 1357511909.999, 1357511939.993,
1357511969.999, 1357511999.998, 1357512029.999, 1357512059.999,
1357512089.999, 1357512119.999, 1357512149.999, 1357512179.998,
1357512209.999, 1357512239.999, 1357512269.999, 1357512299.999,
1357512329.997, 1357512359.993, 1357512389.997, 1357512419.999,
1357512449.999, 1357512479.998, 1357512509.999, 1357512539.999,
1357512569.999, 1357512599.999, 1357512629.999, 1357512659.995,
1357512689.999, 1357512719.999, 1357512749.999, 1357512779.995,
1357512809.999, 1357512839.999, 1357512869.999, 1357512899.999,
1357512929.999, 1357512959.999, 1357512989.997, 1357513019.996,
1357513049.999, 1357513079.999, 1357513109.999, 1357513139.999,
1357513169.999, 1357513199.993, 1357513229.999, 1357513259.999,
1357513289.999, 1357513319.999, 1357513349.998, 1357513379.999,
1357513409.999, 1357513439.999, 1357513469.999, 1357513499.999,
1357513529.999, 1357513559.999, 1357513589.999, 1357513619.999,
1357513649.999, 1357513679.999, 1357513709.999, 1357513739.999,
1357513769.999, 1357513799.998, 1357513829.997, 1357513859.999,
1357513889.999, 1357513919.999, 1357513949.999, 1357513979.998,
1357514009.999, 1357514039.996, 1357514069.999, 1357514099.999,
1357514129.999, 1357514159.999, 1357514189.999, 1357514219.999,
1357514249.999, 1357514279.999, 1357514309.999, 1357514339.993,
1357514369.999, 1357514399.999, 1357514429.999, 1357514459.999,
1357514489.999, 1357514519.999, 1357514549.988, 1357514579.997,
1357514609.999, 1357514639.998, 1357514669.984, 1357514699.999,
1357514729.999, 1357514759.999, 1357514789.999, 1357514819.999,
1357514849.999, 1357514879.999, 1357514909.999, 1357514939.996,
1357514969.999, 1357514999.999, 1357515029.999, 1357515059.998,
1357515089.999, 1357515119.97, 1357515149.998, 1357515179.999,
1357515209.999, 1357515239.999, 1357515269.999, 1357515299.999,
1357515329.999, 1357515359.999, 1357515389.999, 1357515419.999,
1357515449.999, 1357515479.999, 1357515509.999, 1357515539.999,
1357515569.999, 1357515599.999, 1357515629.995, 1357515659.999,
1357515689.999, 1357515719.999, 1357515749.999, 1357515779.999,
1357515809.995, 1357515839.999, 1357515869.999, 1357515899.999,
1357515929.999, 1357515959.999, 1357515989.999, 1357516019.999,
1357516049.999, 1357516079.999, 1357516109.999, 1357516139.999,
1357516169.999, 1357516199.999, 1357516229.999, 1357516259.998,
1357516289.998, 1357516319.999, 1357516349.999, 1357516379.999,
1357516409.999, 1357516439.999, 1357516469.999, 1357516499.999,
1357516529.999, 1357516559.999, 1357516589.999, 1357516619.999,
1357516649.999, 1357516679.999, 1357516709.999, 1357516739.999,
1357516769.999, 1357516799.999, 1357516829.999, 1357516859.999,
1357516889.999, 1357516919.999, 1357516949.999, 1357516979.999,
1357517009.999, 1357517039.999, 1357517069.999, 1357517099.999,
1357517129.999, 1357517159.998, 1357517189.999, 1357517219.999,
1357517249.999, 1357517279.999, 1357517309.999, 1357517339.999,
1357517369.999, 1357517399.998, 1357517429.999, 1357517459.999,
1357517489.999, 1357517519.999, 1357517549.999, 1357517579.999,
1357517609.999, 1357517639.999, 1357517669.999, 1357517699.999,
1357517729.999, 1357517759.999, 1357517789.999, 1357517819.999,
1357517849.999, 1357517879.999, 1357517909.999, 1357517939.999,
1357517969.999, 1357517999.999, 1357518029.999, 1357518059.976,
1357518089.999, 1357518119.998, 1357518149.998, 1357518179.999,
1357518209.987, 1357518239.999, 1357518269.998, 1357518299.991,
1357518329.998, 1357518359.999, 1357518389.994, 1357518419.994,
1357518449.995, 1357518479.999, 1357518509.999, 1357518539.998,
1357518569.983, 1357518599.999, 1357518629.998, 1357518659.994,
1357518689.999, 1357518719.988, 1357518749.999, 1357518779.999,
1357518809.999, 1357518839.999, 1357518869.999, 1357518899.999,
1357518929.999, 1357518959.999, 1357518989.999, 1357519019.999,
1357519049.999, 1357519079.998, 1357519109.999, 1357519139.999,
1357519169.999, 1357519199.999, 1357519229.999, 1357519259.999,
1357519289.999, 1357519319.999, 1357519349.999, 1357519379.999,
1357519409.999, 1357519439.999, 1357519469.999, 1357519499.999,
1357519529.999, 1357519559.999, 1357519589.999, 1357519619.999,
1357519649.999, 1357519679.999, 1357519709.999, 1357519739.999,
1357519769.999, 1357519799.999, 1357519829.997, 1357519859.999,
1357519889.999, 1357519919.999, 1357519949.999, 1357519979.999,
1357520009.999, 1357520039.999, 1357520069.999, 1357520099.999,
1357520129.999, 1357520159.999, 1357520189.999, 1357520219.999,
1357520249.999, 1357520279.999, 1357520309.999, 1357520339.999,
1357520369.999, 1357520399.999), tzone = "", tclass = c("POSIXct",
"POSIXt"), class = c("POSIXct", "POSIXt")), count = c(20763L,
12030L, 22188L, 12183L, 21112L, 11628L, 21543L, 12609L, 20095L,
12992L, 21552L, 12447L, 21113L, 12236L, 21705L, 12018L, 21140L,
11820L, 21571L, 12803L, 21146L, 12081L, 21171L, 12440L, 21353L,
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12325L, 22217L, 12195L, 22405L, 11869L, 21380L, 12145L, 21842L,
12224L, 21793L, 12856L, 34934L, 24073L, 41005L, 33964L, 46240L,
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75830L, 104609L, 51063L, 67046L, 66977L, 82513L, 87228L, 107474L,
141878L, 127290L, 70953L, 98879L, 87814L, 117309L, 113463L, 150979L,
198271L, 170456L, 108325L, 119583L, 111803L, 117067L, 186768L,
226191L, 235546L, 228039L, 165570L, 159472L, 161707L, 137614L,
180049L, 254616L, 302166L, 336723L, 234902L, 202560L, 210679L,
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202234L, 236882L, 217502L, 181157L, 196976L, 201901L, 228233L,
221241L, 220140L, 122623L, 76699L, 105589L, 381687L, 264571L,
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107109L, 129405L, 116093L, 135293L, 119048L, 147364L, 127028L,
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132821L, 129279L, 111905L, 130898L, 133135L, 138201L, 121460L,
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152180L, 168528L, 131228L, 140622L, 145363L, 93070L, 58613L,
82024L, 86640L, 77493L, 71205L, 87641L, 89232L, 99214L, 89311L,
87948L, 90790L, 91326L, 106916L, 97318L, 89452L, 91658L, 82069L,
92559L, 89194L, 81721L, 83490L, 96388L, 90145L, 79861L, 90301L,
77676L, 262966L, 227355L, 256477L, 238905L, 241260L, 206168L,
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334305L, 327489L, 336201L, 374153L, 341485L, 321473L, 308773L,
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9385L, 14537L, 9963L, 15676L, 9011L, 16552L, 9587L, 16802L, 9693L,
15267L, 8946L, 14189L, 9067L, 14359L, 9776L, 167922L, 337364L,
350941L, 362928L, 364922L, 319641L, 348687L, 321356L, 400161L,
334171L, 332829L, 323842L, 397809L, 375694L, 384432L, 356825L,
350846L, 395942L, 359471L, 296926L, 418481L, 322144L, 335658L,
347212L, 334421L, 375769L, 364300L, 317370L, 373192L, 346713L,
356341L, 327225L, 305538L, 347815L, 276914L, 322149L, 303627L,
292363L, 284724L, 305082L, 373363L, 304386L, 438592L, 403579L,
430549L, 450536L, 432445L, 389779L, 434888L, 375010L, 456096L,
577393L, 451122L, 432354L, 425547L, 417729L)), .Names = c("time",
"count"), row.names = c(NA, -480L), class = "data.frame")
> dput(ev)
structure(list(dt = structure(c(1357508700, 1357510680, 1357512540,
1357515360, 1357517220, 1357517700, 1357518000, 1357518000, 1357519140,
1357519140), class = c("POSIXct", "POSIXt"), tzone = ""), event = structure(c(7L,
8L, 3L, 2L, 5L, 6L, 1L, 4L, 2L, 9L), .Label = c("Event 1",
"Event 2", "Event 3",
"Event 4", "Event 5",
"Event 6", "Event 7",
"Event 8", "Event 9"
), class = "factor")), .Names = c("dt", "event"), row.names = c(NA,
-10L), class = "data.frame")
Library Versions:
> sessionInfo()
R version 2.15.1 (2012-06-22)
Platform: x86_64-redhat-linux-gnu (64-bit)
attached base packages:
[1] grid stats graphics grDevices utils datasets methods base
other attached packages:
[1] reshape2_1.2.2 xts_0.9-1 zoo_1.7-9 gdata_2.12.0 data.table_1.8.6 caTools_1.14
[7] scales_0.2.3 ggplot2_0.9.3
Simplied code - this still doesnt work
library(scales)
library(ggplot2)
taonedf<-dget("taonedf") #in this thread
ev<-dget("ev") #in this thread
ggplot(data=taonedf, aes(x=time, y=count/30)) +
geom_line() +
geom_vline(data=ev,aes(xtintercept=as.numeric(dt)))
To get geom_vline() display lines as intended, first, library scales should be loaded. Then use as.numeric() in geom_vline().
library(scales)
+ geom_vline(data=ev,aes(xintercept=as.numeric(dt)))
Two things
You need to wrap the datetimes for the vline in as.numeric
You misspelled xintercept
Fixing those:
library("ggplot2")
library("scales")
ggplot(data=taonedf, aes(x=time, y=count/30)) +
geom_line(color="#009E73") +
geom_vline(data=ev,aes(xintercept=as.numeric(dt)))+
scale_y_continuous(name="requests per second", labels = format_format(scientific=FALSE, big.mark=",")) +
scale_x_datetime(name="",labels = date_format("%b %d\n%H:%M") ) +
labs(title=paste("Requests per Second - All Requests")) +
theme(legend.position = "none")