I'm trying to run an R code for calculating the ssGSEA score between a gene expression dataset and a gene list.
The function I'm trying to run is:
GSVAtumor_UCS_Epi<-gsva(file, EM_gene_signature_tumor_KS_Epi_list, method=c("gsva", "ssgsea", "zscore", "plage"))
I'm facing the same error and two warning messages every time I run the code and there is not much information available for it over the internet for the same:
Error in relist(v, part) :
shape of 'skeleton' is not compatible with 'NROW(flesh)'
In addition: Warning messages:
1: In .filterFeatures(expr, method) :
6171 genes with constant expression values throuhgout the samples.
2: In .filterFeatures(expr, method) :
Since argument method!="ssgsea", genes with constant expression values are discarded.
Here EM_gene_signature_tumor_KS_Epi_list is
list(structure(list(...1 = c("KRT19", "AGR2", "RAB25", "CDH1",
"ERBB3", "FXYD3", "SLC44A4", "S100P", "SCNN1A", "GALNT3", "PRSS8",
"ELF3", "CEACAM6", "TMPRSS4", "CLDN7", "TACSTD2", "CLDN3", "EPCAM",
"SPINT1", "TSPAN1", "PLS1", "TMEM30B", "PRR15L", "KRT8", "ST14",
NA, "RBM47", "S100A14", "C1orf106", "NQO1", "TOX3", "PTK6", "TFF1",
"CLDN4", "GPRC5A", "TJP3", "KRT18", "MAP7", "CKMT1A", "ESRP1",
"MUC1", "SPINT2", "ESRP2", "CDS1", "PPAP2C", "CEACAM7", "TTC39A",
"OVOL2", "EHF", "AP1M2", "CEACAM5", "LAD1", "ARHGAP8", "TFF3",
"JUP", "CD24", "TMC5", "MLPH", "ELMO3", "ERBB2", "LLGL2", "DDR1",
"FA2H", "CBLC", "TMPRSS2", "LSR", "PERP", "POF1B", "MYO5C", "RAB11FIP1",
"MAPK13", "KRT7", "CEACAM1", "CXADR", "ATP2C2", "RNF128", "MPZL2",
"EPS8L1", "GALNT7", "CORO2A", "BCAS1", "TPD52", "ARHGAP32", "FUT2",
"OR7E14P", "GALE", "GRHL2", "BIK", "RAPGEFL1", "STYK1", "F11R",
"PKP3", "CYB561", "SH3YL1", "GDF15", "PSCA", "EZR", "TJP2", "FGFR3",
"FUT3", "BSPRY", "TOM1L1", "IRF6", "EPB41L4B", "OCLN", "LRRC1",
"C19orf21", "ABHD11", "EPS8L2", "MYO6", "TSPAN8", "MST1R", "SLC16A5",
"GPR56", "AZGP1", "TOB1", "SLC35A3", "TRPM4", "PHLDA2", "VAMP8",
"SLC22A18", "AKR1B10", "VAV3", "SPAG1", "ABCC3", "SYNGR2", "STAP2",
"C4orf19", "PPL", "PLLP", "DSG2", "HDHD3", "CD2AP", "MANSC1",
"DHCR24", "EPN3", "TUFT1", "GMDS", "EXPH5", "DSP", "SDC4", "IL20RA",
"FAM174B", "PTPRF", "SORD")), row.names = c(NA, -145L), class = c("tbl_df",
"tbl", "data.frame")))
And file is
new("ExpressionSet", experimentData = new("MIAME", name = "",
lab = "", contact = "", title = "", abstract = "", url = "",
pubMedIds = "", samples = list(), hybridizations = list(),
normControls = list(), preprocessing = list(), other = list(),
.__classVersion__ = new("Versions", .Data = list(c(1L, 0L,
0L), c(1L, 1L, 0L)))), assayData = <environment>, phenoData = new("AnnotatedDataFrame",
varMetadata = structure(list(labelDescription = "state"), row.names = "state", class = "data.frame"),
data = structure(list(state = c("TCGA-N6-A4V9-01A", "TCGA-QM-A5NM-01A",
"TCGA-N8-A4PM-01A", "TCGA-NG-A4VU-01A", "TCGA-NG-A4VW-01A",
"TCGA-N8-A4PN-01A", "TCGA-N5-A4RA-01A", "TCGA-N6-A4VD-01A",
"TCGA-N7-A4Y8-01A", "TCGA-N6-A4VE-01A", "TCGA-N5-A59F-01A",
"TCGA-N9-A4PZ-01A", "TCGA-N6-A4VF-01A", "TCGA-NF-A5CP-01A",
"TCGA-N6-A4VC-01A", "TCGA-N5-A4RF-01A", "TCGA-N8-A4PL-01A",
"TCGA-ND-A4WA-01A", "TCGA-N5-A4RU-01A", "TCGA-N9-A4Q3-01A",
"TCGA-NA-A4R1-01A", "TCGA-N7-A4Y5-01A", "TCGA-N5-A4RV-01A",
"TCGA-QN-A5NN-01A", "TCGA-N9-A4Q1-01A", "TCGA-N5-A59E-01A",
"TCGA-NA-A4QW-01A", "TCGA-N5-A4RM-01A", "TCGA-NF-A4X2-01A",
"TCGA-N5-A4RN-01A", "TCGA-N5-A4RS-01A", "TCGA-N8-A4PQ-01A",
"TCGA-N9-A4Q4-01A", "TCGA-NA-A4QY-01A", "TCGA-N5-A4RJ-01A",
"TCGA-N5-A4RD-01A", "TCGA-NA-A5I1-01A", "TCGA-NA-A4R0-01A",
"TCGA-NA-A4QV-01A", "TCGA-N7-A4Y0-01A", "TCGA-N5-A4R8-01A",
"TCGA-NF-A4WU-01A", "TCGA-N6-A4VG-01A", "TCGA-N8-A4PI-01A",
"TCGA-N8-A4PO-01A", "TCGA-N8-A4PP-01A", "TCGA-ND-A4WF-01A",
"TCGA-NA-A4QX-01A", "TCGA-N9-A4Q7-01A", "TCGA-N5-A4RO-01A",
"TCGA-N5-A4RT-01A", "TCGA-NF-A4WX-01A", "TCGA-N7-A59B-01A",
"TCGA-ND-A4W6-01A", "TCGA-N8-A56S-01A", "TCGA-ND-A4WC-01A"
)), row.names = c("TCGA-N6-A4V9-01A", "TCGA-QM-A5NM-01A",
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"TCGA-N7-A4Y8-01A", "TCGA-N6-A4VE-01A", "TCGA-N5-A59F-01A",
"TCGA-N9-A4PZ-01A", "TCGA-N6-A4VF-01A", "TCGA-NF-A5CP-01A",
"TCGA-N6-A4VC-01A", "TCGA-N5-A4RF-01A", "TCGA-N8-A4PL-01A",
"TCGA-ND-A4WA-01A", "TCGA-N5-A4RU-01A", "TCGA-N9-A4Q3-01A",
"TCGA-NA-A4R1-01A", "TCGA-N7-A4Y5-01A", "TCGA-N5-A4RV-01A",
"TCGA-QN-A5NN-01A", "TCGA-N9-A4Q1-01A", "TCGA-N5-A59E-01A",
"TCGA-NA-A4QW-01A", "TCGA-N5-A4RM-01A", "TCGA-NF-A4X2-01A",
"TCGA-N5-A4RN-01A", "TCGA-N5-A4RS-01A", "TCGA-N8-A4PQ-01A",
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"TCGA-NF-A4WU-01A", "TCGA-N6-A4VG-01A", "TCGA-N8-A4PI-01A",
"TCGA-N8-A4PO-01A", "TCGA-N8-A4PP-01A", "TCGA-ND-A4WF-01A",
"TCGA-NA-A4QX-01A", "TCGA-N9-A4Q7-01A", "TCGA-N5-A4RO-01A",
"TCGA-N5-A4RT-01A", "TCGA-NF-A4WX-01A", "TCGA-N7-A59B-01A",
"TCGA-ND-A4W6-01A", "TCGA-N8-A56S-01A", "TCGA-ND-A4WC-01A"
), class = "data.frame"), dimLabels = c("sampleNames", "sampleColumns"
), .__classVersion__ = new("Versions", .Data = list(c(1L,
1L, 0L)))), featureData = new("AnnotatedDataFrame", varMetadata = structure(list(
labelDescription = character(0)), row.names = character(0), class = "data.frame"),
data = structure(list(), .Names = character(0), class = "data.frame", row.names = c("5_8S_rRNA",
"5S_rRNA", "7SK", "A1BG", "A1BG-AS1", "A1CF", "A2M", "A2M-AS1",
"A2ML1", "A2ML1-AS1", "A2ML1-AS2", "A2MP1", "A3GALT2", "A4GALT",
"A4GNT", "AA06", "AAAS", "AACS", "AACSP1", "AADAC", "AADACL2",
"AADACL2-AS1", "AADACL3", "AADACL4", "AADACP1", "AADAT",
"AAED1", "AAGAB", "AAK1", "AAMDC", "AAMP", "AANAT", "AAR2",
"AARD", "AARS", "AARS2", "AARSD1", "AARSP1", "AASDH", "AASDHPPT",
"AASS", "AATBC", "AATF", "AATK", "AATK-AS1", "AB015752.3",
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"ABCA1", "ABCA10", "ABCA11P", "ABCA12", "ABCA13", "ABCA17P",
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"ABHD10", "ABHD11", "ABHD11-AS1", "ABHD12", "ABHD12B", "ABHD13",
"ABHD14A", "ABHD14A-ACY1", "ABHD14B", "ABHD15", "ABHD15-AS1",
"ABHD16A", "ABHD16B", "ABHD17A", "ABHD17AP1", "ABHD17AP3",
"ABHD17AP4", "ABHD17AP6", "ABHD17AP9", "ABHD17B", "ABHD17C",
"ABHD2", "ABHD3", "ABHD4", "ABHD5", "ABHD6", "ABHD8", "ABI1",
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varMetadata = structure(list(labelDescription = character(0)), row.names = character(0), class = "data.frame"),
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"TCGA-QM-A5NM-01A", "TCGA-N8-A4PM-01A", "TCGA-NG-A4VU-01A",
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All suggestions shall be helpful.
Related
I've got the following MEs file after performing WGCNA on genes.
structure(list(MEdarkturquoise = -0.126475329197849, MEtan = -0.233526198026173,
MEdarkgrey = -0.086222455739712, MEsalmon = 0.0096490189937994,
MEviolet = -0.074214462874189, MEdarkolivegreen = 0.0351479764276791,
MEdarkred = 0.0669868352219114, MEskyblue = 0.0952261185132281,
MEskyblue3 = 0.135145582896583, MEsteelblue = 0.11287454763798,
MEmidnightblue = 0.0651813145953543, MEcyan = 0.209007602108883,
MEsienna3 = 0.44443648889449, MEyellowgreen = -0.103085026146051,
MElightcyan = -0.031750256452673, MEorange = -0.0126799698551899,
MEblack = -0.0634155967712594, MEdarkmagenta = 0.0211453409000538,
MEdarkorange = -0.00210726593791533, MEwhite = 0.133020812016434,
MEblue = -0.197746247022991, MEgrey60 = -0.0996015659492354,
MEdarkgreen = -0.0916627743675571, MEred = -0.0309829945285656,
MEgrey = 0.152772139607461), row.names = "TCGA.02.0054.01", class = "data.frame")
> dput(MEs[1:25,])
structure(list(MEdarkturquoise = c(-0.126482404651362, 0.00859685106988051,
-0.139119978025995, -0.121760256216002, -0.114050357589663, -0.16158166178197,
-0.169089521235389, 0.134388350128016, -0.128728040505512, 0.0933639502568886,
-0.116285533417, -0.0715164710720025, -0.050281653395796, -0.0712583935347317,
-0.116848802717176, -0.1394796603133, -0.131958454866075, -0.167862157710569,
0.0781961256653059, -0.0874083890994826, -0.142602528126273,
-0.132749359004561, 0.0530754944359762, -0.112556115187184, -0.126475329197849
), MEtan = c(-0.167883113654055, -0.0109845487812259, -0.246272322275879,
-0.155118397135028, -0.187094786264526, -0.208897816323153, 0.00918849047141923,
0.0660987731811726, -0.179682091342798, 0.0361927428937938, -0.168337427405039,
-0.0872997940891031, 0.0487147246178567, -0.104997421215032,
-0.166612765418889, -0.27972731849213, -0.269614736192796, -0.218686589132102,
0.0186266000013494, -0.115752853428279, -0.234180322169165, -0.234468472122879,
0.0853896953163215, -0.119401273395233, -0.233526198026173),
MEdarkgrey = c(-0.100329095271737, -0.0784894235648148, -0.0879486442773462,
-0.0995049018784005, -0.0714818317916746, -0.0976149187829668,
0.159506068630208, -0.0078547447048395, -0.0919187341851776,
-0.0183352208123751, -0.0802716031327569, -0.0921892597579533,
0.122475915845315, -0.105142453664124, -0.107464669408259,
-0.0838230654624859, -0.0643026477240475, -0.0991970944971272,
-0.0299497021490766, -0.0925869123215248, -0.0801263954108516,
-0.0801822804978344, 0.0483614999202892, -0.113020942570186,
-0.086222455739712), MEsalmon = c(-0.0358095654745313, -0.0534749789951331,
0.00945930449870025, -0.0402326557250527, 0.0141791776591814,
-0.0193044312130539, 0.0176549615193692, 0.0196918377830713,
-0.0161165396744247, 0.0489395668597594, -0.0157533935456657,
-0.0365864961052464, 0.00284816428633602, -0.0390827955707751,
-0.0331282498660023, 0.0160144463637484, 0.0456439557518989,
-0.0170910975438849, 0.0278516976939946, -0.0461529642613651,
0.000932542725999325, 0.0306652565937469, -0.00572936088385097,
-0.047929378464284, 0.0096490189937994), MEviolet = c(-0.0698514607272664,
-0.0625444441558566, -0.0505951048105294, -0.0449811807220172,
-0.0441953669134818, -0.0841615937800127, -0.0221491455239211,
-0.0267981445053516, -0.0794779460694139, -0.0302013048014515,
-0.0353669499138048, -0.0017331573094048, 0.0874838415896361,
-0.047762725972725, -0.0669664745407823, -0.0712203584491592,
-0.0295047546287239, -0.08493075962853, 0.0272695404925932,
-0.0525343912541236, -0.0847673835546743, -0.0538857173401459,
-0.0328162107969099, -0.0966668580989554, -0.074214462874189
), MEdarkolivegreen = c(0.0377820765118535, -0.126289848165377,
0.0457552036312885, -0.0720376769423904, 0.0674183078192647,
0.0872226089549376, 0.0671641745702857, 0.0986229412529974,
0.124873710183385, -0.0145322250064643, 0.0835047870659539,
0.0275515952723316, 0.114534984742249, 0.0174957637942426,
0.0638552985728309, 0.0778140457135465, 0.251550135774411,
0.0286025658766736, -0.0492443633949548, 0.0329210913327464,
0.0787017703166302, 0.0615020315780856, -0.0184948705908313,
-0.0954877380526897, 0.0351479764276791), MEdarkred = c(0.272876545608831,
0.000123183429299067, 0.0534538229798448, 0.0966098482194768,
0.0376326716689517, 0.822582158679362, -0.0248835680553132,
-0.0383904231751435, 0.0136807003192766, -0.0248358334970572,
0.0339322151406484, 0.0716557037805583, -0.0539693528926393,
0.106398558847861, 0.0923427494085759, 0.169664971210157,
0.161648440649969, 0.0190084156548717, 0.000138628237731107,
0.000585214632906398, 0.102262784369053, 0.0534505728224905,
-0.059973432049819, 0.0144335767114106, 0.0669868352219114
), MEskyblue = c(0.0300817906913275, -0.0283955827490865,
0.382808148350357, 0.0367108628998102, 0.119370910675356,
0.0412642225896619, -0.00346854861171808, -0.0143470278284632,
0.107636689352283, -0.0236684849544009, 0.0917438787357169,
0.0145447693950731, -0.0382373445817665, 0.0191434123578404,
0.0375158618776065, 0.249299207645228, 0.120963274601968,
0.146633570220078, -0.00679395818159729, 0.0233447602384917,
0.0792772300611593, 0.78202446362806, -0.0543273832838903,
0.0108020494546566, 0.0952261185132281), MEskyblue3 = c(0.0544999124430812,
-0.0415537322590853, 0.148498774122011, 0.045015742997482,
0.0728228727907875, 0.0763671830934277, 0.000625753326390824,
-0.0432869954615651, 0.0859576301808161, -0.0427299484684677,
0.092614515678829, 0.014278460672561, -0.0339679894655559,
0.0742750317248251, 0.0683990062576902, 0.147094858821046,
0.187211235984884, 0.817070341622215, -0.00380348023692627,
0.0271100286319751, 0.105519330253122, 0.25743166485415,
-0.0622649562610468, 0.0191289446807367, 0.135145582896583
), MEsteelblue = c(0.0496514378945234, -0.0119028777509424,
0.0876026179283109, 0.875168424597013, 0.113111751301955,
0.0512419512431396, -0.00415862479258981, -0.0287581359673004,
0.0358772065144821, -0.0233613685194822, 0.0245461112657854,
0.0338562304377162, -0.0315874613922273, 0.030394516232703,
0.0555994037601733, 0.160637881942329, 0.169118254533511,
0.0451606357014289, -0.0145989327568692, 0.0252714145226534,
0.0839064560962295, 0.159428227426239, -0.0573138459939694,
0.0174199687282867, 0.11287454763798), MEmidnightblue = c(0.261366892829522,
0.023044681576429, 0.0343889442489335, 0.132848541119855,
0.0451576879950088, 0.0252827715295774, -0.0337324509562343,
-0.0317626799281452, 0.00867375015130735, -0.0355601371114707,
-0.00444014493366716, 0.0524201198652068, -0.0465892341718296,
0.53368866448374, 0.40299004894058, 0.226878425330428, 0.288891850461152,
0.0034111639191928, -0.0218911490874513, 0.0428414277129276,
0.127759568873117, 0.0230208606028655, -0.0486763743391364,
-0.000393935612089922, 0.0651813145953543), MEcyan = c(0.14712352890204,
-0.0225389566680152, 0.310239714084796, 0.266828682037704,
0.274085380006878, 0.0198325328800958, -0.0271481393021675,
-0.024480016276833, 0.361036065881631, -0.0310804478509462,
0.104903141200727, -0.00844783885620602, -0.0423320596946433,
0.217939441329577, 0.168597379421396, 0.174848423174792,
0.17116192322786, 0.145536587676942, -0.0085695012138382,
0.262571866404432, 0.116885684620837, 0.298472250629788,
-0.0455865890454148, -0.0198367637146339, 0.209007602108883
), MEsienna3 = c(0.0364613522451339, -0.0150044936323261,
0.0859786195398347, 0.106731994744255, 0.3676154185844, 0.0176360451265258,
-0.0163146904234233, -0.0235733528486296, 0.0142637017953146,
-0.0313496090372881, 0.0554025449971393, -0.00886560890289934,
-0.0299155299683427, 0.0802190450199411, 0.0531084986705855,
0.132177853079289, 0.115753358405972, 0.04683928504613, -0.0224627603109401,
0.483305710073052, 0.0502197126088502, 0.0753150865546981,
-0.0487254418019003, -0.000161377685444758, 0.44443648889449
), MEyellowgreen = c(0.0115359152267024, 0.135057278002594,
-0.163986652388384, 0.0330253189145193, -0.0646237426605537,
-0.182159469390447, -0.0763758202479116, 0.00753002768998373,
-0.175745378130036, -0.0866930520900523, -0.127613619675027,
-0.0832229515648747, -0.0379533464493758, 0.227986268308109,
0.103786513478538, -0.111497522959011, 0.0543128016169437,
-0.168835985541294, -0.0443617981956915, 0.0497044203429988,
-0.0846269823241523, -0.161465337778303, -0.0345036685471383,
-0.0961578498644668, -0.103085026146051), MElightcyan = c(0.0496191101107336,
0.130845431420586, 0.0520264149008036, 0.0854617949112739,
0.0153143285568461, 0.0138577418822467, -0.146526829901649,
-0.0388445912643835, 0.16637132352514, -0.036790968426816,
0.136179898679897, 0.0325362917451082, -0.0862509925988111,
0.134280378238923, 0.0481757419670939, -0.0678217105237136,
-0.0980229321521949, 0.0660774319688092, 0.0686963951231444,
0.127712350792151, -0.0437788600734283, 0.0878538693816119,
-0.0370044484031624, 0.0774977578587763, -0.031750256452673
), MEorange = c(0.13722224885649, 0.348756408712165, -0.0800936623582766,
0.197824913378728, -0.0205788858009016, -0.126818727577524,
-0.0865505994454585, -0.0596441543425443, -0.0197970499720824,
-0.0463019712805513, -0.0334182781567987, 0.122694391378806,
-0.0578620147308185, 0.136041922205044, 0.0626293391953756,
0.0316228992508401, 0.051048170154005, -0.0938622475936843,
-0.0136841309266074, 0.29538347151955, 0.0672243499977651,
-0.081023944076598, -0.0707293257868871, 0.0650574935030528,
-0.0126799698551899), MEblack = c(0.0669111171346558, 0.0837057669788319,
-0.164559526573629, 0.014702020473466, -0.123047193449949,
-0.0291251094597731, -0.156231277020043, -0.0447495590042895,
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0.0501740645662331, -0.0848560252717673, 0.162625652137377,
0.0936054374640849, -0.0220460671613258, -0.0311431986113967,
-0.174860138606855, 0.0508889727241686, 0.013307684883397,
-0.0389735225998367, -0.146220072707352, -0.072458305736215,
-0.0163412223207896, -0.0634155967712594), MEdarkmagenta = c(0.0981791947286194,
0.138464419108534, -0.138443536660989, 0.0735849457821136,
-0.0989132475093273, 0.208435014282372, -0.196962446030651,
-0.0187764726316799, 0.0279066357560171, 0.107299478700084,
-0.0836441885372265, 0.0346409967849602, -0.0962524864123106,
0.142618931084639, 0.0750906041071312, 0.0800820894150817,
-0.0388956049610418, -0.0862962942007109, 0.125617514770139,
0.109800363695881, -0.0603003835784385, 0.00858662657497652,
0.0529975055493171, 0.0427784887634529, 0.0211453409000538
), MEdarkorange = c(-0.0389613203162392, 0.0812780201455584,
0.0750911866557248, 0.00750993227102546, -0.00170616028160571,
0.0678048499345556, 0.0631589127461073, -0.00903712074086093,
0.10349869021427, -0.0824956520330546, 0.0616051849769035,
-0.15434803287655, -0.029264436424772, 0.0201543005265066,
-0.0670127845248307, -0.0516851755136453, -0.4145788128165,
0.131425827634174, -0.0302549933173993, 0.01867736005915,
-0.0125070668068291, 0.0698915480466011, 0.0784835008404346,
0.0203252856416219, -0.00210726593791533), MEwhite = c(0.0290935398852734,
0.0422842196727377, 0.0695406138759151, 0.120865984617736,
0.159270489022687, 0.175941528117507, 0.0559958673176756,
-0.0960882619193223, 0.0681820267892018, -0.0899077237106928,
0.0624521908300418, -0.0619794085515802, -0.0976850447934499,
0.0868355955174896, 0.00534865194514006, -0.0489642356559877,
-0.234375104882794, 0.171063805316584, -0.128968065240522,
-0.0264488709945445, -0.0163951034436283, 0.0578930638008963,
-0.08895666012489, 0.0709316976503018, 0.133020812016434),
MEblue = c(-0.0438160100250189, 0.0205864110567424, -0.089574297986365,
-0.0228697246325195, -0.0478684201028521, 0.0315178886185285,
0.0997019543929526, 0.0522665035834491, 0.111573175132434,
-0.039766523067667, 0.0554814245199621, -0.0916893423364658,
0.0705662146799376, 0.0156334233571506, -0.107396855651202,
-0.302131304020196, -0.247578337048839, 0.043361259025573,
-0.0104425537088264, 0.0443072405388741, -0.198865108634832,
-0.0361698433062441, 0.0821076401458689, -0.0978577689161953,
-0.197746247022991), MEgrey60 = c(-0.00136880839558318, -0.0275833699067186,
-0.039882440735729, -0.0525869028464486, -0.00912419686053642,
0.0928788723567378, 0.0734655244111471, 0.132922206619663,
0.117756463773648, -0.0416205429323441, 0.0878530983399299,
-0.0600973853406165, 0.140077921831975, 0.0160988482647548,
-0.0553621242005487, -0.131331494202082, -0.142532621940606,
0.100721216689661, -0.100883207816199, 0.0597345076546271,
-0.105322855790332, 0.039735865024859, 0.0952310616043726,
-0.106615569178441, -0.0996015659492354), MEdarkgreen = c(0.0274020843334667,
0.0436385774800176, 0.0947005842374533, 0.0575400257478901,
0.120931034418584, 0.174210568563443, 0.13572478502157, -0.0311255678482926,
0.194745284926632, -0.102137593170246, 0.261056084621793,
-0.0403664444724046, 0.0787608796858007, 0.134775433556818,
0.017729034036735, -0.101597869034928, -0.121335386372983,
0.167218651293673, -0.0509483741931395, 0.17923174058135,
-0.0861912933809606, 0.0965421906363849, -0.00511227897621758,
-0.0575646092870182, -0.0916627743675571), MEred = c(0.00684939198719674,
0.013557838077012, 0.191954503171545, 0.0132835577290769,
0.0456079404330869, 0.19777154880118, 0.197929991500107,
-0.0505822456570982, 0.253001323274958, -0.0981601294837628,
0.192635505850396, -0.0522121852056791, 0.124362208531841,
-0.0849374786734087, -0.124823316491311, -0.0593347866019663,
-0.160654145093744, 0.277260325248897, -0.0887701479804753,
-0.00466328615275768, 0.0153802201669976, 0.0480954401161867,
0.0642172097701228, 0.00333365773780607, -0.0309829945285656
), MEgrey = c(-0.00533499927967739, -0.0866434191241955,
0.133801184209863, -0.0124432897797533, 0.0922499906965556,
0.0953795979215922, 0.0548824355446993, -0.0283109788329285,
-0.00931881479985485, 0.0184344848243561, 0.0313248766224585,
0.106731086518589, -0.000578668939631188, -0.0923485894819629,
0.0387579566695894, 0.209642899356913, 0.292344147721993,
0.08801305387923, 0.000499791539282575, -0.070360812595406,
0.145743119891815, 0.120846559493449, -0.0820460485075853,
0.0804668976442401, 0.152772139607461)), row.names = c("TCGA.02.0003.01",
"TCGA.02.0004.01", "TCGA.02.0007.01", "TCGA.02.0009.01", "TCGA.02.0010.01",
"TCGA.02.0011.01", "TCGA.02.0014.01", "TCGA.02.0016.01", "TCGA.02.0021.01",
"TCGA.02.0023.01", "TCGA.02.0024.01", "TCGA.02.0025.01", "TCGA.02.0026.01",
"TCGA.02.0027.01", "TCGA.02.0028.01", "TCGA.02.0033.01", "TCGA.02.0034.01",
"TCGA.02.0038.01", "TCGA.02.0039.01", "TCGA.02.0043.01", "TCGA.02.0046.01",
"TCGA.02.0047.01", "TCGA.02.0048.01", "TCGA.02.0051.01", "TCGA.02.0054.01"
), class = "data.frame")
I now want to extract the genes present in each individual module. What shall be the most effective way for the same? I tried searching from the tutorial, but it didn't work out.
Please help me extract the genes from individual modules specific to my case?
I'd like to know if there's a way in R to create a shapeburst fill such as the one you can create in QGIS. Here's an example:
In my case with R, I'd like to apply this effect to the central territory:
ggplot(stack) +
geom_sf()
Thanks if you have any idea!
Data:
stack <- structure(list(EPCI = c("200000925", "200069540", "200070894",
"200070902", "242100410", "242101509"), geometry = structure(list(
structure(list(list(structure(c(5.1735974283617, 5.13705002372908,
5.13202534393623, 5.1222023440224, 5.11839072561264, 5.11644969100171,
5.1029081794839, 5.0950620658008, 5.09692391455608, 5.09963215687193,
5.11042077651943, 5.10887005786876, 5.09977697374407, 5.10235285384242,
5.11609803515252, 5.11535076399997, 5.11523097143535, 5.10835046174538,
5.12060840937552, 5.12068476967575, 5.11938757754232, 5.10201274112028,
5.09895039412222, 5.10740752499771, 5.11444778610704, 5.12337818763651,
5.14323633111868, 5.13846251504936, 5.13844797361896, 5.15732954077117,
5.16194343894565, 5.17657520913029, 5.16803209441542, 5.19709934734259,
5.19932006451528, 5.19349718863177, 5.18884164386742, 5.18081948053972,
5.17832117515058, 5.20447750561387, 5.20611181829902, 5.23496619843428,
5.24582337506913, 5.25580164328686, 5.26807118674955, 5.28841280212112,
5.28999580819349, 5.32961015278703, 5.32708689317271, 5.32034685881108,
5.32505959236611, 5.31200132312281, 5.32264580583079, 5.32778997868823,
5.316571627811, 5.30805586678404, 5.30505079986893, 5.29074238594877,
5.27575536688688, 5.28519022527792, 5.2614183980798, 5.25459659941887,
5.24525210849422, 5.23970171312708, 5.23447817413034, 5.22503585124321,
5.22217152005677, 5.20864592717777, 5.20450507801015, 5.19415235081345,
5.18781575301386, 5.17583460386657, 5.17266095667648, 5.1735974283617,
47.1423675804407, 47.1264502577582, 47.1268669214179, 47.1401426839133,
47.1385377201833, 47.1354831568858, 47.1392064249828, 47.1478597301388,
47.1546713329326, 47.1699590360691, 47.1817316540889, 47.1886675137587,
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47.2400348750727, 47.2525720644016, 47.255086016093, 47.2629752298219,
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47.2506809263479, 47.2489545246627, 47.2515150493153, 47.25953233523,
47.2725525882941, 47.2806381653613, 47.29734585002, 47.2966536480023,
47.2974247124954, 47.302410909839, 47.2951404098266, 47.2937941959612,
47.295644701492, 47.2865094407212, 47.283271624433, 47.2805647961309,
47.2766699699782, 47.2673935016772, 47.2503307419648, 47.2397936303344,
47.2361135424544, 47.2287865547147, 47.2175732667888, 47.2205892015503,
47.2180186663939, 47.2172856715349, 47.2092587924421, 47.2017236256144,
47.1800969703936, 47.1761771900856, 47.17663777769, 47.1743499830118,
47.1764589306895, 47.1694446993111, 47.172040111888, 47.1681736366054,
47.1621918261383, 47.1689990340036, 47.1570230837867, 47.1532953297728,
47.1511936960971, 47.1423675804407), .Dim = c(74L, 2L)))), class = c("XY",
"MULTIPOLYGON", "sfg")), structure(list(list(structure(c(5.25580164328686,
5.24582337506913, 5.23496619843428, 5.20611181829902, 5.20100229930494,
5.18658736299213, 5.1683211451952, 5.1594960272113, 5.1421687050214,
5.13129503678413, 5.11891950742839, 5.10353091494866, 5.08246363009285,
5.06875416871612, 5.05504858978677, 5.05557819430982, 5.05120126830834,
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5.16121061029462, 5.16181181654592, 5.17456227866114, 5.18137015196538,
5.17886127833726, 5.18158589768812, 5.18456590739242, 5.18215140774103,
5.17159997839231, 5.17128490858267, 5.16202731701095, 5.16592383516234,
5.16029570341947, 5.17556011058898, 5.20327492495206, 5.23521926380278,
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5.24454742021653, 5.25140171949523, 5.27314980682815, 5.26807118674955,
5.25580164328686, 47.2937941959612, 47.2951404098266, 47.302410909839,
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47.3192417226877, 47.3173663324485, 47.3108625336976, 47.3063174964036,
47.3027135825277, 47.295644701492, 47.2937941959612), .Dim = c(54L,
2L)))), class = c("XY", "MULTIPOLYGON", "sfg")), structure(list(
list(structure(c(5.04180552359106, 5.03256414245053,
5.01958963535669, 5.00058371561759, 4.98121558904593,
4.97807762856607, 4.96493283628928, 4.95601181916994,
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4.82558381395774, 4.82230985832488, 4.83277001661117,
4.8415467800823, 4.85545071269338, 4.85790622668886,
4.86230524162067, 4.8699079457462, 4.87573461445667,
4.89637318450673, 4.90001528182637, 4.90673729102348,
4.93017159325173, 4.93790996323201, 4.93857979103788,
4.9483148515113, 4.9534087943808, 4.97100220064239, 4.99951275138935,
5.02409978038541, 5.02157629844929, 5.02282769600011,
5.02196164823738, 5.02508956961358, 5.03045113367058,
5.04512332562591, 5.06728768796758, 5.07353962090774,
5.08570378051162, 5.10835046174538, 5.11523097143535,
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47.1947678399604, 47.1946584855389, 47.1919585277174,
47.1886675137587, 47.1817316540889, 47.1699590360691,
47.1546713329326, 47.1478597301388, 47.1392064249828,
47.1354831568858, 47.1385377201833, 47.1401426839133,
47.1268669214179, 47.1211669793735, 47.1174701154241,
47.1129385721698, 47.1012678394676, 47.107799446333,
47.1081815390237, 47.098745203204, 47.0877407393032,
47.0751401377207, 47.0742901793863, 47.0737702977231,
47.0675602807485, 47.0540443055103, 47.0517265604338,
47.045387379151, 47.0418951799296, 47.0353468060348,
47.0332929445524, 47.0250585238295, 47.0192677325834), .Dim = c(99L,
2L)))), class = c("XY", "MULTIPOLYGON", "sfg")), structure(list(
list(structure(c(5.40058819172399, 5.39248303771976,
5.38843356888882, 5.37166493466641, 5.36661524798002,
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46.9980261141844, 46.9893415761815, 46.9798884313746), .Dim = c(98L,
2L)))), class = c("XY", "MULTIPOLYGON", "sfg"))), class = c("sfc_MULTIPOLYGON",
"sfc"), precision = 0, bbox = structure(c(xmin = 4.7639489853556,
ymin = 46.9448826367359, xmax = 5.51853896591882, ymax = 47.4493248620917
), class = "bbox"), crs = structure(list(input = "WGS84", wkt = "GEOGCRS[\"WGS 84\",\n DATUM[\"World Geodetic System 1984\",\n ELLIPSOID[\"WGS 84\",6378137,298.257223563,\n LENGTHUNIT[\"metre\",1]]],\n PRIMEM[\"Greenwich\",0,\n ANGLEUNIT[\"degree\",0.0174532925199433]],\n CS[ellipsoidal,2],\n AXIS[\"geodetic latitude (Lat)\",north,\n ORDER[1],\n ANGLEUNIT[\"degree\",0.0174532925199433]],\n AXIS[\"geodetic longitude (Lon)\",east,\n ORDER[2],\n ANGLEUNIT[\"degree\",0.0174532925199433]],\n ID[\"EPSG\",4326]]"), class = "crs"), n_empty = 0L)), row.names = c(NA,
6L), class = c("sf", "data.table", "data.frame"), sf_column = "geometry", agr = structure(c(EPCI = NA_integer_), .Label = c("constant",
"aggregate", "identity"), class = "factor"))
Using the ggfx package:
ggplot() +
geom_sf(data = stack[2:6,]) +
with_shadow(geom_sf(data = stack[1,]),
sigma = 1,
x_offset = 5,
y_offset = 5)
I have a homework assignment where I need to take a CSV file based around population data around the United States and do some data analysis on the data inside. I need to find the data that exists for my state and for starters run a Linear Regression Analysis to predict the size of the population.
I've been studying R for a few weeks now, went through a LinkedIn Learning training, as well as 2 different trainings on pluralsight about R. I have also tried searching for how to do a Linear Regression Analysis in R and I find plenty of examples for how to do it when the data is perfectly laid out in a table in just the right way to Analyze.
The CSV file is laid out so that each state is defined on a single line/row so I used the filter function to grab just the data for my State and put it into a variable.
Within that dataset the population data is defined across several columns with the most important data being the Population Estimates for each year from 2010 to 2018.
library(tidyverse)
population.data <- read_csv("nst-est2018-alldata.csv")
mn.state.data <- filter(population.data, NAME == "Minnesota")
I'm looking for some help to get headed in the right direction my thought is that I will need to create to containers of data 1 having each year from 2010 to 2018 and one that contains the population data for each of those years. And then use the xyplot function with those two containers? If you have some experience in this area please help me think this through I'm not looking for anybody to do the assignment for me just want some help trying to think it through.
Edit: Here is the results of the
dput(head(population.data))
command:
structure(list(SUMLEV = c("010", "020", "020", "020", "020",
"040"), REGION = c("0", "1", "2", "3", "4", "3"), DIVISION = c("0",
"0", "0", "0", "0", "6"), STATE = c("00", "00", "00", "00", "00",
"01"), NAME = c("United States", "Northeast Region", "Midwest Region",
"South Region", "West Region", "Alabama"), CENSUS2010POP = c(308745538L,
55317240L, 66927001L, 114555744L, 71945553L, 4779736L), ESTIMATESBASE2010
= c(308758105L,
55318430L, 66929743L, 114563045L, 71946887L, 4780138L), POPESTIMATE2010 =
c(309326085L,
55380645L, 66974749L, 114867066L, 72103625L, 4785448L), POPESTIMATE2011 =
c(311580009L,
55600532L, 67152631L, 116039399L, 72787447L, 4798834L), POPESTIMATE2012 =
c(313874218L,
55776729L, 67336937L, 117271075L, 73489477L, 4815564L), POPESTIMATE2013 =
c(316057727L,
55907823L, 67564135L, 118393244L, 74192525L, 4830460L), POPESTIMATE2014 =
c(318386421L,
56015864L, 67752238L, 119657737L, 74960582L, 4842481L), POPESTIMATE2015 =
c(320742673L,
56047587L, 67869139L, 121037542L, 75788405L, 4853160L), POPESTIMATE2016 =
c(323071342L,
56058789L, 67996917L, 122401186L, 76614450L, 4864745L), POPESTIMATE2017 =
c(325147121L,
56072676L, 68156035L, 123598424L, 77319986L, 4875120L), POPESTIMATE2018 =
c(327167434L,
56111079L, 68308744L, 124753948L, 77993663L, 4887871L), NPOPCHG_2010 =
c(567980L,
62215L, 45006L, 304021L, 156738L, 5310L), NPOPCHG_2011 = c(2253924L,
219887L, 177882L, 1172333L, 683822L, 13386L), NPOPCHG_2012 = c(2294209L,
176197L, 184306L, 1231676L, 702030L, 16730L), NPOPCHG_2013 = c(2183509L,
131094L, 227198L, 1122169L, 703048L, 14896L), NPOPCHG_2014 = c(2328694L,
108041L, 188103L, 1264493L, 768057L, 12021L), NPOPCHG_2015 = c(2356252L,
31723L, 116901L, 1379805L, 827823L, 10679L), NPOPCHG_2016 = c(2328669L,
11202L, 127778L, 1363644L, 826045L, 11585L), NPOPCHG_2017 = c(2075779L,
13887L, 159118L, 1197238L, 705536L, 10375L), NPOPCHG_2018 = c(2020313L,
38403L, 152709L, 1155524L, 673677L, 12751L), BIRTHS2010 = c(987836L,
163454L, 212614L, 368752L, 243016L, 14227L), BIRTHS2011 = c(3973485L,
646265L, 834909L, 1509597L, 982714L, 59689L), BIRTHS2012 = c(3936976L,
637904L, 830701L, 1504936L, 963435L, 59070L), BIRTHS2013 = c(3940576L,
635741L, 830869L, 1504799L, 969167L, 57936L), BIRTHS2014 = c(3963195L,
632433L, 836505L, 1525280L, 968977L, 58907L), BIRTHS2015 = c(3992376L,
634515L, 837968L, 1545722L, 974171L, 59637L), BIRTHS2016 = c(3962654L,
628039L, 831667L, 1541342L, 961606L, 59388L), BIRTHS2017 = c(3901982L,
616552L, 816177L, 1519944L, 949309L, 58259L), BIRTHS2018 = c(3855500L,
609336L, 804431L, 1499838L, 941895L, 57216L), DEATHS2010 = c(598691L,
110848L, 140785L, 228706L, 118352L, 11073L), DEATHS2011 = c(2512442L,
470816L, 586840L, 962751L, 492035L, 48818L), DEATHS2012 = c(2501531L,
460985L, 584817L, 960575L, 495154L, 48364L), DEATHS2013 = c(2608019L,
480032L, 605188L, 1011093L, 511706L, 50847L), DEATHS2014 = c(2582448L,
470196L, 597078L, 1006057L, 509117L, 49692L), DEATHS2015 = c(2699826L,
488881L, 626494L, 1052360L, 532091L, 51820L), DEATHS2016 = c(2703215L,
480331L, 619471L, 1058173L, 545240L, 51662L), DEATHS2017 = c(2779436L,
501022L, 620556L, 1092949L, 564909L, 53033L), DEATHS2018 = c(2814013L,
506909L, 621030L, 1109152L, 576922L, 53425L), NATURALINC2010 = c(389145L,
52606L, 71829L, 140046L, 124664L, 3154L), NATURALINC2011 = c(1461043L,
175449L, 248069L, 546846L, 490679L, 10871L), NATURALINC2012 = c(1435445L,
176919L, 245884L, 544361L, 468281L, 10706L), NATURALINC2013 = c(1332557L,
155709L, 225681L, 493706L, 457461L, 7089L), NATURALINC2014 = c(1380747L,
162237L, 239427L, 519223L, 459860L, 9215L), NATURALINC2015 = c(1292550L,
145634L, 211474L, 493362L, 442080L, 7817L), NATURALINC2016 = c(1259439L,
147708L, 212196L, 483169L, 416366L, 7726L), NATURALINC2017 = c(1122546L,
115530L, 195621L, 426995L, 384400L, 5226L), NATURALINC2018 = c(1041487L,
102427L, 183401L, 390686L, 364973L, 3791L), INTERNATIONALMIG2010 =
c(178835L,
45723L, 25158L, 68742L, 39212L, 928L), INTERNATIONALMIG2011 = c(792881L,
206686L, 116948L, 285343L, 183904L, 4716L), INTERNATIONALMIG2012 =
c(858764L,
207584L, 120995L, 344198L, 185987L, 5874L), INTERNATIONALMIG2013 =
c(850952L,
194103L, 126681L, 329897L, 200271L, 5111L), INTERNATIONALMIG2014 =
c(947947L,
222685L, 134310L, 365281L, 225671L, 3753L), INTERNATIONALMIG2015 =
c(1063702L,
227275L, 142759L, 429088L, 264580L, 4685L), INTERNATIONALMIG2016 =
c(1069230L,
236718L, 144859L, 436795L, 250858L, 5950L), INTERNATIONALMIG2017 =
c(953233L,
215872L, 126013L, 404582L, 206766L, 3190L), INTERNATIONALMIG2018 =
c(978826L,
229700L, 127583L, 418418L, 203125L, 3344L), DOMESTICMIG2010 = c(0L,
-32918L, -50873L, 90679L, -6888L, 1238L), DOMESTICMIG2011 = c(0L,
-159789L, -186896L, 335757L, 10928L, -2239L), DOMESTICMIG2012 = c(0L,
-205314L, -181285L, 336615L, 49984L, 59L), DOMESTICMIG2013 = c(0L,
-216273L, -123814L, 293443L, 46644L, 2641L), DOMESTICMIG2014 = c(0L,
-274391L, -182730L, 373439L, 83682L, -755L), DOMESTICMIG2015 = c(0L,
-339996L, -234823L, 452879L, 121940L, -1553L), DOMESTICMIG2016 = c(0L,
-372953L, -228200L, 442633L, 158520L, -1977L), DOMESTICMIG2017 = c(0L,
-316879L, -161387L, 364465L, 113801L, 2065L), DOMESTICMIG2018 = c(0L,
-292928L, -157048L, 345132L, 104844L, 5718L), NETMIG2010 = c(178835L,
12805L, -25715L, 159421L, 32324L, 2166L), NETMIG2011 = c(792881L,
46897L, -69948L, 621100L, 194832L, 2477L), NETMIG2012 = c(858764L,
2270L, -60290L, 680813L, 235971L, 5933L), NETMIG2013 = c(850952L,
-22170L, 2867L, 623340L, 246915L, 7752L), NETMIG2014 = c(947947L,
-51706L, -48420L, 738720L, 309353L, 2998L), NETMIG2015 = c(1063702L,
-112721L, -92064L, 881967L, 386520L, 3132L), NETMIG2016 = c(1069230L,
-136235L, -83341L, 879428L, 409378L, 3973L), NETMIG2017 = c(953233L,
-101007L, -35374L, 769047L, 320567L, 5255L), NETMIG2018 = c(978826L,
-63228L, -29465L, 763550L, 307969L, 9062L), RESIDUAL2010 = c(0L,
-3196L, -1108L, 4554L, -250L, -10L), RESIDUAL2011 = c(0L, -2459L,
-239L, 4387L, -1689L, 38L), RESIDUAL2012 = c(0L, -2992L, -1288L,
6502L, -2222L, 91L), RESIDUAL2013 = c(0L, -2445L, -1350L, 5123L,
-1328L, 55L), RESIDUAL2014 = c(0L, -2490L, -2904L, 6550L, -1156L,
-192L), RESIDUAL2015 = c(0L, -1190L, -2509L, 4476L, -777L, -270L
), RESIDUAL2016 = c(0L, -271L, -1077L, 1047L, 301L, -114L), RESIDUAL2017 =
c(0L,
-636L, -1129L, 1196L, 569L, -106L), RESIDUAL2018 = c(0L, -796L,
-1227L, 1288L, 735L, -102L), RBIRTH2011 = c(12.79898857, 11.646389369,
12.449493906, 13.0753983, 13.564866164, 12.455601786), RBIRTH2012 =
c(12.589173852,
11.454833676, 12.353389372, 12.900715293, 13.172754439, 12.287820829
), RBIRTH2013 = c(12.511116578, 11.384582534, 12.318197145, 12.770698648,
13.1250523, 12.012410502), RBIRTH2014 = c(12.493440163, 11.301146646,
12.363692308, 12.814734, 12.993051496, 12.179749675), RBIRTH2015 =
c(12.493175596,
11.324209532, 12.357461907, 12.843808208, 12.92441189, 12.301816868
), RBIRTH2016 = c(12.309933949, 11.20434042, 12.242454436, 12.663079639,
12.619264908, 12.222387438), RBIRTH2017 = c(12.039095529, 10.996948983,
11.989119413, 12.357287884, 12.333939366, 11.962999487), RBIRTH2018 =
c(11.820984126,
10.863177115, 11.789576855, 12.078306222, 12.128940451, 11.720998206
), RDEATH2011 = c(8.0928244199, 8.4846099623, 8.7504877826, 8.3388830191,
6.7917918366, 10.187095914), RDEATH2012 = c(7.9990857588, 8.2779015368,
8.6968381072, 8.2343067033, 6.7700904074, 10.060744313), RDEATH2013 =
c(8.2803198685,
8.5962112289, 8.9723230665, 8.5807898649, 6.9298356343, 10.542582104
), RDEATH2014 = c(8.1408206164, 8.4020820365, 8.8249187702, 8.4524499397,
6.8267702932, 10.274434632), RDEATH2015 = c(8.4484528254, 8.7250748685,
9.2388679994, 8.7443343664, 7.0592978512, 10.689339673), RDEATH2016 =
c(8.3975028099,
8.5692003816, 9.1188486402, 8.6935469035, 7.1552465339, 10.632332792
), RDEATH2017 = c(8.5756150392, 8.9363320099, 9.1155717285, 8.8857783149,
7.3396052849, 10.889883997), RDEATH2018 = c(8.6277792774, 9.0371195009,
9.1016891619, 8.9320830002, 7.4291216994, 10.944391939), RNATURALINC2011 =
c(4.7061641498,
3.161779407, 3.6990061239, 4.7365152812, 6.7730743272, 2.2685058724
), RNATURALINC2012 = c(4.5900880929, 3.1769321388, 3.656551265,
4.66640859, 6.402664032, 2.2270765159), RNATURALINC2013 = c(4.2307967093,
2.7883713049, 3.3458740787, 4.1899087829, 6.1952166656, 1.4698283977
), RNATURALINC2014 = c(4.3526195469, 2.89906461, 3.5387735378,
4.3622840605, 6.1662812026, 1.9053150433), RNATURALINC2015 =
c(4.0447227708,
2.5991346635, 3.1185939072, 4.0994738414, 5.8651140389, 1.6124771946
), RNATURALINC2016 = c(3.912431139, 2.6351400388, 3.123605796,
3.969532736, 5.4640183742, 1.5900546466), RNATURALINC2017 =
c(3.4634804902,
2.0606169731, 2.8735476848, 3.4715095687, 4.9943340813, 1.0731154898
), RNATURALINC2018 = c(3.1932048488, 1.8260576141, 2.687887693,
3.1462232219, 4.6998187519, 0.7766062675), RINTERNATIONALMIG2011 =
c(2.5539481982,
3.7247036946, 1.7438348531, 2.4715029092, 2.5385138982, 0.9841112772
), RINTERNATIONALMIG2012 = c(2.7460490726, 3.7275831375, 1.7993217139,
2.9505576333, 2.5429438207, 1.2219173785), RINTERNATIONALMIG2013 =
c(2.7017267715,
3.4759149144, 1.8781318506, 2.7997195452, 2.7121923767, 1.0597112344
), RINTERNATIONALMIG2014 = c(2.988275652, 3.9792291689, 1.9851256285,
3.0689308523, 3.0260314993, 0.7759790947), RINTERNATIONALMIG2015 =
c(3.3285982753,
4.0561842059, 2.1052580818, 3.5654043717, 3.5102060089, 0.9664136698
), RINTERNATIONALMIG2016 = c(3.3215493142, 4.2230961065, 2.1323795548,
3.5885415898, 3.2920380658, 1.2245437674), RINTERNATIONALMIG2017 =
c(2.9410856198,
3.8503376372, 1.8510505744, 3.2892897676, 2.6864164429, 0.6550398799
), RINTERNATIONALMIG2018 = c(3.0010858795, 4.0950670621, 1.8698304564,
3.3695510667, 2.6156748143, 0.685035969), RDOMESTICMIG2011 = c(0,
-2.879569389, -2.786843372, 2.9081645678, 0.1508443529, -0.467223314
), RDOMESTICMIG2012 = c(0, -3.686820778, -2.69589683, 2.8855541222,
0.6834160664, 0.0122732593), RDOMESTICMIG2013 = c(0, -3.872925953,
-1.835626629, 2.4903472978, 0.6316815776, 0.5475831286), RDOMESTICMIG2014
= c(0,
-4.903180146, -2.700781819, 3.1374707924, 1.1220952977, -0.156105573
), RDOMESTICMIG2015 = c(0, -6.067919504, -3.462920156, 3.7630900106,
1.6177886489, -0.320350145), RDOMESTICMIG2016 = c(0, -6.653555548,
-3.359190761, 3.6365043774, 2.0802759896, -0.40687782), RDOMESTICMIG2017 =
c(0,
-5.651919379, -2.370672066, 2.963134779, 1.4785645494, 0.4240305179
), RDOMESTICMIG2018 = c(0, -5.222289092, -2.301663494, 2.7793734944,
1.350093835, 1.1713623417), RNETMIG2011 = c(2.5539481982, 0.845134306,
-1.043008519, 5.379667477, 2.6893582511, 0.516887963), RNETMIG2012 =
c(2.7460490726,
0.0407623599, -0.896575116, 5.8361117555, 3.2263598871, 1.2341906378
), RNETMIG2013 = c(2.7017267715, -0.397011039, 0.0425052219,
5.2900668429, 3.3438739543, 1.6072943629), RNETMIG2014 = c(2.988275652,
-0.923950977, -0.71565619, 6.2064016447, 4.148126797, 0.6198735214
), RNETMIG2015 = c(3.3285982753, -2.011735298, -1.357662074,
7.3284943823, 5.1279946578, 0.6460635248), RNETMIG2016 = c(3.3215493142,
-2.430459441, -1.226811206, 7.2250459672, 5.3723140554, 0.8176659475
), RNETMIG2017 = c(2.9410856198, -1.801581742, -0.519621492,
6.2524245465, 4.1649809923, 1.0790703978), RNETMIG2018 = c(3.0010858795,
-1.12722203, -0.431833037, 6.1489245611, 3.9657686492, 1.8563983107
)), .Names = c("SUMLEV", "REGION", "DIVISION", "STATE", "NAME",
"CENSUS2010POP", "ESTIMATESBASE2010", "POPESTIMATE2010",
"POPESTIMATE2011",
"POPESTIMATE2012", "POPESTIMATE2013", "POPESTIMATE2014",
"POPESTIMATE2015",
"POPESTIMATE2016", "POPESTIMATE2017", "POPESTIMATE2018", "NPOPCHG_2010",
"NPOPCHG_2011", "NPOPCHG_2012", "NPOPCHG_2013", "NPOPCHG_2014",
"NPOPCHG_2015", "NPOPCHG_2016", "NPOPCHG_2017", "NPOPCHG_2018",
"BIRTHS2010", "BIRTHS2011", "BIRTHS2012", "BIRTHS2013", "BIRTHS2014",
"BIRTHS2015", "BIRTHS2016", "BIRTHS2017", "BIRTHS2018", "DEATHS2010",
"DEATHS2011", "DEATHS2012", "DEATHS2013", "DEATHS2014", "DEATHS2015",
"DEATHS2016", "DEATHS2017", "DEATHS2018", "NATURALINC2010",
"NATURALINC2011",
"NATURALINC2012", "NATURALINC2013", "NATURALINC2014", "NATURALINC2015",
"NATURALINC2016", "NATURALINC2017", "NATURALINC2018",
"INTERNATIONALMIG2010",
"INTERNATIONALMIG2011", "INTERNATIONALMIG2012", "INTERNATIONALMIG2013",
"INTERNATIONALMIG2014", "INTERNATIONALMIG2015", "INTERNATIONALMIG2016",
"INTERNATIONALMIG2017", "INTERNATIONALMIG2018", "DOMESTICMIG2010",
"DOMESTICMIG2011", "DOMESTICMIG2012", "DOMESTICMIG2013",
"DOMESTICMIG2014",
"DOMESTICMIG2015", "DOMESTICMIG2016", "DOMESTICMIG2017",
"DOMESTICMIG2018",
"NETMIG2010", "NETMIG2011", "NETMIG2012", "NETMIG2013", "NETMIG2014",
"NETMIG2015", "NETMIG2016", "NETMIG2017", "NETMIG2018", "RESIDUAL2010",
"RESIDUAL2011", "RESIDUAL2012", "RESIDUAL2013", "RESIDUAL2014",
"RESIDUAL2015", "RESIDUAL2016", "RESIDUAL2017", "RESIDUAL2018",
"RBIRTH2011", "RBIRTH2012", "RBIRTH2013", "RBIRTH2014", "RBIRTH2015",
"RBIRTH2016", "RBIRTH2017", "RBIRTH2018", "RDEATH2011", "RDEATH2012",
"RDEATH2013", "RDEATH2014", "RDEATH2015", "RDEATH2016", "RDEATH2017",
"RDEATH2018", "RNATURALINC2011", "RNATURALINC2012", "RNATURALINC2013",
"RNATURALINC2014", "RNATURALINC2015", "RNATURALINC2016",
"RNATURALINC2017",
"RNATURALINC2018", "RINTERNATIONALMIG2011", "RINTERNATIONALMIG2012",
"RINTERNATIONALMIG2013", "RINTERNATIONALMIG2014", "RINTERNATIONALMIG2015",
"RINTERNATIONALMIG2016", "RINTERNATIONALMIG2017", "RINTERNATIONALMIG2018",
"RDOMESTICMIG2011", "RDOMESTICMIG2012", "RDOMESTICMIG2013",
"RDOMESTICMIG2014",
"RDOMESTICMIG2015", "RDOMESTICMIG2016", "RDOMESTICMIG2017",
"RDOMESTICMIG2018",
"RNETMIG2011", "RNETMIG2012", "RNETMIG2013", "RNETMIG2014", "RNETMIG2015",
"RNETMIG2016", "RNETMIG2017", "RNETMIG2018"), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame"))
In order to help you out, an example data using dput(head(population.data)) would be helpful. Based on your comments, your data is in what is called 'wide' format, meaning each observation is contained in a column, rather than a row (pupulation 2010, population 2011 etc.).
As i hinted in my comment, a sub-goal within statistical modelling is always to clean and reshape data to a proper format, that will work for running models. In this case the problem is that your format is in an incorrect shape. The most common is likely melting to long format via the reshape2 or data.table package as explained in this link. I personally prefer the data.table package, as it seems to have better large scale performance. Their usage however is identical.
Lets say you have a column 'NAME' for states and 9 columns for population estimates (2010 population estimates, 2011 population estimates and so on), we could then convert these columns into a long format, using melt from either of the two suggested packages (They are identical in use)
require(data.table)
value_columns <- paste(2010:2018, "Population Estimates")
population.data_long <- melt(population.data, id.vars = "NAME",
measure.vars = value_columns, #Columns containing values we (that are grouped by their column names)
variable.name = 'Year (Population Estimate)', #Name of the column which tells us [(Year) Population Estimate]
value.name = 'Population Estimate') #Name of the column with values
population.data_long$year <- as.integer(substr(population.data_long$`Year (Population Estimate)`, 1, 4)) #Create a year column in a bit of a hacky way
Note i have ignored any additional columns, and these should be included in your melt statement. From here on a linear regression should follow any standard example that you have found.
I cant run my MCa with a ind.sup, I dont know why. Here's my code :
library(FactoMineR)
data_ACM <- data[80000:81000,]
res.AMC <- MCA(data_ACM, ncp = 6, graph = F, ind.sup = 700)
Error in eigen(crossprod(t(X), t(X)), symmetric = TRUE) :
infinite or missing values in 'x'
dpu(data[1:10,]) returns this :
"SOHDZET", "SOHDZF", "SOHDZFT", "SOKFXD", "SOKFXF", "SOM31BL4B1D",
"SONFZD", "SONFZF", "SOVJZC", "SOVJZE", "SOVJZET", "SOVJZF",
"SOVJZFT", "SOVJZJ", "SOZAAZ", "SP01", "SP21", "SP308", "SP41",
"SP4DWC", "SP61", "SP81", "SR11", "SR1F0H", "SR1G0H", "SR1H06",
"SR1J0H", "SR1J22", "SR1J32", "SR1L22", "SR1L32", "SR1M22",
"SR270E", "SR31", "SR4DWC", "SR51", "SR81", "SR8J32", "SR8L32",
"SREU0H", "SRH1ELC", "SRH1ELCD", "SS11", "SS31", "SS51",
"SS55", "SS71", "SS91", "ST12", "ST72", "ST785E", "STA",
"STADRX01AG", "STADRX01SG", "STAFNX01AG", "STAFNX01SG", "STAGZX01SG",
"STAHLX01AG", "STAHLX01SG", "STAHUX01AG", "STAHUX01SG", "STAV",
"STB", "STBV", "STC", "STCDCA", "STCV", "SU31", "SU35", "SU55",
"SU55A", "SV10", "SV10EM", "SV1V12", "SV31", "SVA", "SVAV",
"SW11", "SW31", "SW51", "SW52X400", "SW52X700", "SW53K2",
"SW53K200", "SW53K9", "SW53K900", "SW5441", "SW5449", "SW58K2",
"SW58K200", "SW58K3", "SW58K7", "SW58K9", "SW58K900", "SW5952",
"SW5953", "SW5957", "SW5959", "SW59G200", "SW59G300", "SW59G700",
"SW59G900", "SW6142", "SW6149", "SW61P2", "SW61P200", "SW61P9",
"SW61P900", "SW61S200", "SW61S900", "SW6542", "SW6549", "SW65P2",
"SW65P200", "SW65P9", "SW65P900", "SW65S200", "SW65S900",
"SW6942", "SW71", "SW7140", "SW7141", "SW7144", "SW7146",
"SW7362", "SW743200", "SW743900", "SW7462", "SW7469", "SW793200",
"SW793700", "SW793900", "SW7962", "SW7964", "SW7967", "SW7969",
"SW8042", "SW8142", "SX11", "SX31", "SX71", "SXBHW6", "SXBHY6",
"SXHMP6", "SXHMZ6", "SXHNZ6", "SXHNZT", "SY11", "SY31", "SY33",
"SY34", "SY53", "SY54", "SY71", "SYN1E", "SZ58K3", "SZ58K7",
"SZ5953", "SZ5957", "SZ59G300", "SZ59G700", "SZ7144", "SZ7146",
"SZ793700", "SZ7964", "SZ7967", "SZ799400", "T11", "T12",
"T1X000", "T1X004", "T1X300", "T1X305", "T1X805", "T1XE05",
"T1XF05", "T27WWT270", "T27WWT271", "T27ZRT270", "T27ZRT271",
"T2DG1", "T2DH1", "T2DH2", "T2DJ1", "T2DJ2", "T2W300", "T2W305",
"T2W800", "T2W805", "T2WE05", "T2WF05", "T31DD03", "T31EE01",
"T32AA", "T32AA01", "T32AA02", "T32AA03", "T32AA04", "T32BB",
"T32BB01", "T32BB02", "T32BB05", "T32BB06", "T32CC", "T32CC01",
"T32CC02", "T32CC03", "T32CC04", "T32DD", "T32EE", "T3408",
"T3419", "T3435", "T3448", "T3467", "T3DB1", "T3DE1", "T3DE2",
"T3LA1", "T3LA2", "T3X000", "T3X100", "T3X300", "T3X305",
"T3X800", "T3X805", "T3XE05", "T3XF05", "T4BBD", "T4BBG",
"T4BCD", "T4BCE", "T4BCF", "T4DCA", "T4DCD", "T4DCT", "T4DCX",
"T4DCY", "T4X000", "T4X100", "T4X305", "T4X405", "T4X800",
"T4X805", "T4XE05", "T4XF05", "T4XG05", "T5408", "T5419",
"T5435", "T5448", "T5808", "T5819", "T5835", "T5848", "T5X000",
"T5X200", "T5X205", "T5X300", "T5X305", "T5X405", "T5X800",
"T5X805", "T5XF05", "T5XG05", "T6EB1", "T6EB4", "T6EB6",
"T6EB7", "T6ED5", "T6EE2", "T6MB1", "T6MB6", "T6MB7", "T6MC1",
"T6MD5", "T72", "T7JA1", "T7W200", "T7W205", "T7W300", "T7W405",
"T7WG05", "T824833", "T82W433", "T82W434", "T8344", "T8358A",
"T8378A", "T8380", "T8390A", "T8394", "T83T4", "T844433",
"T844434", "T844533", "T844544", "T845034", "T845043", "T845044",
"T8458", "T847433", "T847443", "T847833", "T8478A", "T8480",
"T8488", "T849033", "T849043", "T8490A", "T8494", "T8494B43",
"T8494B44", "T84T4", "T84T443", "T84T444", "T84T543", "T84T544",
"T84T833", "T84T843", "T855", "T857", "T85T4", "T85T8", "T9408",
"T9419", "T9435", "T9448", "T9508", "T9519", "T9535", "T9548",
"T9808", "T9819", "T9835", "T9848", "T9PG2", "TA", "TA12",
"TA12F2", "TA12J5", "TA12L2", "TA12L5", "TA13008", "TA13035",
"TA13068", "TA1308", "TA1335", "TA1368", "TA14", "TA16008",
"TA16035", "TA16068", "TA1608", "TA1635", "TA1668", "TA2",
"TA22", "TA23H", "TA23MB", "TA23MQG", "TA2422", "TA2422A",
"TA28MB", "TA32408", "TA32419", "TA32468", "TA40", "TA40AKS",
"TA40AMS", "TA43035", "TA43068", "TA4308", "TA4319", "TA4335",
"TA4368", "TA4408", "TA4419", "TA4435", "TA4468", "TA60AMS",
"TA60BLMB", "TA72L2", "TA72L5", "TA8408", "TA8419", "TA8435",
"TA8468", "TA9408", "TA9419", "TA9435", "TA9468", "TA94E08",
"TA94E19", "TA94E35", "TA94E68", "TAA408", "TAA419", "TAA435",
"TAA468", "TAA8408", "TAA9408", "TAA94E08", "TAAA408", "TAAP11",
"TAAW10", "TABCAS", "TABCDS", "TAC408", "TAC419", "TAC468",
"TAD308", "TAD3G08", "TAF408", "TAF419", "TAF435", "TAF468",
"TAJ3G008", "TAJ3G08", "TAK1367", "TAK4367", "TAKD467", "TAKU467",
"TAKV467", "TAM3019", "TAM3035", "TAM3068", "TAM319", "TAM335",
"TAM368", "TAM6019", "TAM6035", "TAM6068", "TAM619", "TAM635",
"TAM668", "TAN308", "TAN419", "TAN435", "TAN468", "TAN619",
"TAN635", "TAN668", "TANT30", "TAS335", "TAS3G08", "TATB",
"TATF", "TATHA", "TATHB", "TATJA", "TATJB", "TAU8470", "TAUA470",
"TAW4035", "TAW4068", "TAW6035", "TAW6068", "TAX100", "TAX300",
"TAZ100", "TAZ300", "TB", "TB2", "TB2412", "TB367", "TBAABS",
"TBABBS", "TBAP12", "TBAV10", "TBBCBS", "TBC1NPLY", "TBC1NRLY",
"TBCAAS", "TBCADS", "TBCAES", "TBCBAS", "TBCBDS", "TBCBES",
"TBDCAS", "TBDCDS", "TBDCES", "TBE1OVM", "TBE1OVN", "TBE2PZN",
"TBE2UZN", "TBE4TWN", "TBE4TYN", "TBE4TYNC", "TBE5IWN", "TBE5IWNC",
"TBE5TWM", "TBE5TWN", "TBE5TWNC", "TBE5TYN", "TBGMSLV", "TBGMTLVI",
"TBGNSLV", "TBGNSLVC", "TBGNSLY", "TBGNTLVI", "TBIF5P11M51AZ1",
"TBIF5P21M52CZ1", "TBNT30", "TBUR20", "TBX200", "TBX205",
"TBX300", "TBX305", "TBX405", "TBXE05", "TBXG05", "TBZ200",
"TBZ205", "TBZ300", "TBZ305", "TBZ405", "TBZ800", "TBZE05",
"TBZG05", "TC2", "TCAP11", "TCAP12", "TCAV10", "TCTG05",
"TCY405", "TCYG05", "TD2", "TD367", "TD4HSJ", "TD4HTH", "TDABKS",
"TDABTS", "TDABUS", "TDAP11", "TDAV10", "TDAW10", "TDAW10M",
"TDAW10U", "TDFBAS", "TDGCKS", "TDGCTS", "TDGCUS", "TDHCAS",
"TDSY61", "TDUHZJ", "TDX8ZA", "TDXFVJ", "TE2", "TE51", "TEABKL",
"TEABTL", "TEAP12", "TEBBAL", "TECCKL", "TECCTL", "TEDCAL",
"TENT30", "TESY61", "TF08II51", "TF08J551", "TF08M351", "TF35II51",
"TF35J551", "TF35M351", "TF408", "TF419", "TF435", "TF448",
"TF48II51", "TF48J551", "TF48M351", "TFABAL", "TFABKL", "TFABSL",
"TFABTL", "TFABUL", "TFAP12", "TFAY10", "TFBCAL", "TFBCKL",
"TFBCSL", "TFBCTL", "TFBCUL", "TG08GCA1", "TG08GCS1", "TG08ICA1",
"TG08ICS1", "TG08M351", "TG08M3A1", "TG2", "TG35GCA1", "TG35GCS1",
"TG35ICA1", "TG35ICS1", "TG35M351", "TG35M3A1", "TG408",
"TG419", "TG435", "TG448", "TG48GCA1", "TG48GCS1", "TG48ICA1",
"TG48ICS1", "TG48M351", "TG48M3A1", "TGABAL", "TGABKL", "TGABSL",
"TGABTE", "TGABTL", "TGBN", "TGCCAL", "TGCCKL", "TGCCSL",
"TGCCTE", "TGCCTL", "TGFCAL", "TGFCTL", "TGFCUL", "TH308",
"TH348", "TJAC9S", "TJAN91", "TJAN9S", "TJBBAL", "TJBBTE",
"TJBBTL", "TJBBUL", "TJBCAL", "TJBCTE", "TJBCTL", "TJBCUL",
"TK308", "TK348", "TKACAL", "TKACTE", "TKACTL", "TKACUL",
"TLB310", "TLEF5D14A63ZZ1", "TLEF5D24M62ZZ1", "TLEF5D31M62ZZ1",
"TLEF5D51M66ZZ1", "TLEF5D61D7", "TLEF5P31M64ZZ1", "TLEF5P41D71ZZ1",
"TLEF5P44D71ZZ1", "TM4BUL", "TM4DBC", "TP260", "TPAD", "TPADJ",
"TPAE", "TPAEJ", "TQF8D62M53AZ1", "TQF8D82A51AZ1", "TR160",
"TR160T", "TR467", "TR567", "TR7969", "TR9145", "TR9245",
"TS08ICS1", "TS08MCS1", "TS31", "TS48ICS1", "TS6142", "TS6149",
"TS61E2", "TS61P2", "TS61P200", "TS61P9", "TS61P900", "TS61S200",
"TS61S900", "TS65E2", "TS65P2", "TS65P200", "TS65P9", "TS65P900",
"TS65S200", "TS65S900", "TS7202", "TS7206", "TS7432", "TS743200",
"TS7439", "TS743900", "TS7462", "TS7469", "TS7932", "TS793200",
"TS793900", "TS7962", "TS7969", "TS90C5", "TS90K5", "TS9145",
"TS91J500", "TS91K5", "TS91K500", "TS9245", "TS92K5", "TS92K500",
"TS94K2", "TS94K5", "TS97C2", "TS97C5", "TT132", "TT408",
"TT419", "TT435", "TT448", "TT508", "TT519", "TT535", "TT548",
"TT808", "TT819", "TT835", "TT848", "TTFN44", "TU308", "TU319",
"TU348", "TU408", "TU419", "TU435", "TU448", "TU467", "TU508",
"TU519", "TU535", "TU548", "TU567", "TU808", "TU819", "TU835",
"TU848", "TVUF", "TVUR20", "TVUR20U", "TW308", "TW319", "TW348",
"TW3T08", "TW3T19", "TW3T35", "TW3T48", "TWUR20", "TX31",
"TX71", "TY260", "TY260T", "U11", "U11T", "U2", "U2MAC",
"U2NAC", "U6UA", "U6UB", "U6UC", "U6UE", "U6UF", "U6UG",
"U6UJ", "U6UJT", "U6UK", "U6UR", "U6UR9", "U6UT", "U6UT9",
"U6UU", "U6UU9", "U6UW", "U6UWT", "U857", "U858", "U859533",
"U85T", "U9C1G6", "U9C1GH", "U9C1K6", "U9C1KH", "U9C2G6",
"U9C2K6", "U9CYG6", "U9CYK6", "U9VCK5", "UA5FV81P", "UA5FWC",
"UA5FWC1", "UA5FXH1P", "UA6FYC", "UA71", "UA9HR8", "UA9HR82PS",
"UA9HR8P", "UA9HR8PS", "UA9HZC", "UA9HZC1", "UA9HZH1P", "UA9HZHP",
"UARFJHP", "UARHB8P", "UARHE8", "UARHHA", "UARHJH1P", "UARHJHP",
"UARHRJ", "UB11", "UB31", "UB51", "UB53", "UB71", "UB91",
"UC11", "UC31", "UC71", "UC91", "UD11", "UD1Y11", "UD51",
"UD5FS0", "UD5FV81P", "UD5FWC", "UD5FXH1P", "UD5FXHP", "UD6FYC",
"UD71", "UD91", "UD9HR8", "UD9HR82PS", "UD9HR8P", "UD9HR8PS",
"UD9HZC", "UD9HZC1", "UD9HZC1CU1", "UD9HZH1P", "UD9HZH1PCU1",
"UD9HZHP", "UDC1G6", "UDC1K6", "UDC2G6", "UDC2K6", "UDC3G6",
"UDC3K6", "UDCAG5", "UDCAK5", "UDCCG5", "UDCCK5", "UDCEG5",
"UDCEK5", "UDCGG5", "UDCGK5", "UDCJG5", "UDCJK5", "UDCMG5",
"UDCMK5", "UDCNG5", "UDCNK5", "UDCSG6", "UDCSK6", "UDCUG5",
"UDCUG6", "UDCUK5", "UDCUK6", "UDCVG5", "UDCVK5", "UDCYG6",
"UDCYK6", "UDRFJHP", "UDRHB8P", "UDRHE8", "UDRHHA", "UDRHJH1P",
"UDRHJHP", "UDRHRJ", "UE11", "UE31", "UE51", "UE9HR8", "UE9HR8P",
"UE9HZC1", "UE9HZH1P", "UF11", "UF1Y32", "UF31", "UF51",
"UF8U52", "UF8UL2", "UF91", "UG31", "UG51", "UH11", "UH31",
"UH51", "UK11", "UK31", "UK51", "UL91", "UM11", "UM51", "UM71",
"UM91", "UMC7D2", "UN10", "UN13", "UN1A22", "UN71", "UN8B42",
"UN8D32", "UN8F42", "UN91", "UP11", "UR11", "UR31", "UR51",
"UR91", "URMD21", "US31", "US91", "USD135", "USD145", "USD1F5",
"USD1K5", "USD1W5", "USDBL5", "USDUK5", "USDUW5", "UT31",
"UT71", "UU11", "UU51", "UU6M", "UU71", "UU91", "UV51", "UV71",
"UV91", "UW71", "UW91", "UX11", "UX31", "UX51", "UX71", "UX91",
"UXC1", "UY11", "UY31", "UY51", "UY71", "UY91", "UZ10BC",
"UZ68BC", "UZA2BC", "UZA8BC", "UZBABD", "UZJ100LGNAEKW",
"V1DKS", "V1DVS", "V1JKS", "V1JVS", "V23CGRHE", "V23WGNXE",
"V23WGNXES", "V24WGNXF", "V24WGNXFS", "V24WNDF", "V24WNDFS",
"V24WNHF", "V24WNHFS", "V25WGNX", "V25WGRX", "V26WGNX", "V26WGNXS",
"V2DAC", "V2JAC", "V2W200", "V2W300", "V36MM01", "V36NN01",
"V36PP01", "V36RR01", "V36SS01", "V36TT01", "V37CC03", "V37DD03",
"V37EE01", "V37FF01", "V37GG01", "V3X208", "V3X308", "V3XG08",
"V43WGRXE", "V44WGNXF", "V44WGNXFS", "V44WNHF", "V45WGRX",
"V46WGNX", "V46WGRX", "V46WNH", "V46WNHS", "V4X208", "V4X300",
"V4X308", "V4X408", "V4XE08", "V4XG08", "V4Y208", "V4Y408",
"V4YG08", "V521L22HCR167", "V521SLLDA5865", "V521SLLDAR165",
"V5W208", "V5W408", "V5WG08", "V6419", "V6435", "V64WMN",
"V7419", "V7435", "V9X300", "VA", "VA51", "VA71", "VA91",
"VABHSH", "VABHVB", "VABHXH", "VAJ", "VAJI", "VAJIA", "VAL",
"VAM", "VAN", "VANA", "VB11", "VB31", "VB71", "VB84035",
"VB84035M", "VB84069", "VB84069M", "VB8435", "VB8469", "VB86035",
"VB86035M", "VB86069", "VB86069M", "VB8635", "VB8669", "VB94035",
"VB94069", "VB9435", "VB9469", "VB96035", "VB96069", "VB9635",
"VB9669", "VBBHSH", "VBBHVB", "VBBHXH", "VBW4035", "VBW4069",
"VBW435", "VBW469", "VBW6035", "VBW6069", "VBW635", "VBW669",
"VBY4035", "VBY4069", "VBY6035", "VBY6069", "VBZ4035", "VBZ4069",
"VBZ6035", "VBZ6069", "VBZ6E035", "VBZ6E069", "VC11", "VC31",
"VC419", "VC435", "VC51", "VC619", "VC635", "VC91", "VCA419",
"VCA435", "VCA619", "VCA635", "VCDZ", "VCM36V", "VD0419",
"VD0619", "VD11", "VD21SDDAAN735", "VD21SEEAAN735", "VD31",
"VD419", "VD435", "VD51", "VD619", "VD635", "VD71", "VD91",
"VDGCS", "VDGDS", "VDL", "VDM", "VDNS", "VDPA", "VDPAF",
"XWYWMX", "XWYWTP", "XWYWTX", "XWYWXT", "XWZLUR", "XX11",
"XX31", "XX51", "XY11", "XY31", "XY51", "Y3AA", "Y3AB", "Y3AC",
"Y3ACA", "Y3AE", "Y3AF", "Y3AG", "Y3AH", "Y3AHA", "Y3AL",
"Y3AP", "Y3AR", "Y3AS", "Y3AT", "Y3AW", "Y3AX", "Y3AY", "Y3AZ",
"Y3CN", "Y4CW", "Y4CZ", "Y4GB", "Y4GG", "Y4GM", "Y4GN", "Y4GR",
"Y4GU", "Y4GV", "Y4GW", "Y4GX", "Y4GY", "Y4GZ", "Y4MZ", "Y4NW",
"Y4NX", "Y4NY", "Y4NZ", "Y4RM", "Y4RN", "Y4TD", "Y4TN", "Y4TR",
"Y4TS", "Y4TT", "Y4TU", "Y4TV", "Y4TX", "Y4WC", "Y4WG", "Y4WH",
"Y4WK", "Y51AA01", "Y51BB0", "Y51BB01", "Y51CC01", "Y51DD01",
"Y51FF0", "Y51HEE0", "Y51HEE01", "Y910", "YA01", "YA1MFA",
"YA1MFB", "YA1MRA", "YA2MFA", "YA2MFB", "YA2MRA", "YA3MFA",
"YA3MFB", "YA3MRA", "YA41", "YA61", "YA81", "YA9S", "YAAMFA",
"YAAMFAAX", "YAAMFB", "YAAMFBBX", "YAAMPA", "YAAMRA", "YABMFA",
"YABMFAAX", "YABMFB", "YABMFBBX", "YABMPA", "YABMRA", "YASMFA",
"YASMFB", "YASMPA", "YASMRA", "YATMFA", "YATMFB", "YATMPA",
"YATMRA", "YAUMPA", "YAUMRA", "YB01", "YB1MFA", "YB1MFB",
"YB1MFC", "YB1MRB", "YB2MFA", "YB2MFB", "YB2MFC", "YB2MRB",
"YB2R", "YB3MFA", "YB3MFB", "YB3MFC", "YB3MRB", "YB4R", "YB61",
"YB81", "YBAMAA", "YBAMAAAX", "YBAMAB", "YBAMABAX", "YBAMAC",
"YBAMACAX", "YBAMADAX", "YBAMCB", "YBAMCBAX", "YBAMCC", "YBAMCCAX",
"YBAMDBAX", "YBAMDCAX", "YBAMFA", "YBAMFAAX", "YBAMFB", "YBAMFBAX",
"YBAMFBBX", "YBAMFC", "YBAMFCBX", "YBAMFCCX", "YBAMGBAX",
"YBAMHBAX", "YBAMHCAX", "YBAMRB", "YBB5D11M6", "YBB5P11M6",
"YBB5P21M5", "YBB5P31M5", "YBB5P51A4", "YBBMAA", "YBBMAB",
"YBBMABAX", "YBBMAC", "YBBMACAX", "YBBMADAX", "YBBMCB", "YBBMCBAX",
"YBBMCC", "YBBMCCAX", "YBBMDBAX", "YBBMDC", "YBBMDCAX", "YBBMFA",
"YBBMFAAX", "YBBMFB", "YBBMFBAX", "YBBMFBBX", "YBBMFC", "YBBMFCBX",
"YBBMFCCX", "YBBMGBAX", "YBBMHBAX", "YBBMHCAX", "YBBMPB",
"YBBMRB", "YBC5D31M6", "YBCP11M6", "YBCP41M6", "YBDMAB",
"YBDMAC", "YBDMADAX", "YBDMDC", "YBDMDCAX", "YBDMFA", "YBDMFB",
"YBDMFBAX", "YBDMFBBX", "YBDMFC", "YBDMFCBX", "YBDMFCCX",
"YBDMHCAX", "YBDMPB", "YBPMFB", "YBPMFC", "YBPMPB", "YBSMFA",
"YBSMFB", "YBSMFC", "YBSMRB", "YBTMFA", "YBTMFB", "YBTMFC",
"YBTMPB", "YBTMRB", "YBUMFA", "YBUMFB", "YBUMFC", "YBUMPB",
"YBUMRB", "YC1MFA", "YC1MFB", "YC1MFC", "YC21", "YC2MFA",
"YC2MFB", "YC2MFC", "YC3MFB", "YC3MFC", "YC41", "YC81", "YCAMAB",
"YCAMABAX", "YCAMAC", "YCAMACAX", "YCAMACAXL", "YCAMAD",
"YCAMADAX", "YCAMCB", "YCAMCBAX", "YCAMCC", "YCAMCCAX", "YCAMCCAXL",
"YCAMDB", "YCAMDBAX", "YCAMDC", "YCAMDCAX", "YCAMFB", "YCAMFBAX",
"YCAMFBBX", "YCAMFC", "YCAMFCBX", "YCAMFCCX", "YCAMGCAX",
"YCAMHC", "YCAMHCAX", "YCBMAB", "YCBMABAX", "YCBMAC", "YCBMACAX",
"YCBMACAXL", "YCBMAD", "YCBMADAX", "YCBMCB", "YCBMCBAX",
"YCBMCC", "YCBMCCAX", "YCBMCCAXL", "YCBMDB", "YCBMDBAX",
"YCBMDC", "YCBMDCAX", "YCBMFB", "YCBMFBAX", "YCBMFBBX", "YCBMFC",
"YCBMFCBX", "YCBMFCCX", "YCBMGC", "YCBMGCAX", "YCBMHC", "YCBMHCAX",
"YCDMAB", "YCDMABAX", "YCDMAC", "YCDMACAX", "YCDMACAXL",
"YCDMAD", "YCDMADAX", "YCDMCB", "YCDMCC", "YCDMCCAX", "YCDMCCAXL",
"YCDMDB", "YCDMDC", "YCDMDCAX", "YCDMFB", "YCDMFBAX", "YCDMFBBX",
"YCDMFC", "YCDMFCBX", "YCDMFCCX", "YCPMFB", "YCPMFC", "YCSMFB",
"YCSMFC", "YCTMFA", "YCTMFB", "YCTMFC", "YCUMFB", "YCUMFC",
"YD2MFC", "YD3MFC", "YDBMACAX", "YDBMACAXL", "YDBMBCAX",
"YDBMCCAXL", "YDBMDCAXL", "YDBMFCBX", "YDBMFCCX", "YDBMFCCXL",
"YDBMGCAXL", "YDBMHCAXL", "YDDMACAX", "YDDMACAXL", "YDDMCCAXL",
"YDDMDCAXL", "YDDMFCBX", "YDDMFCBXL", "YDDMFCCX", "YDDMFCCXL",
"YDDMHCAXL", "YDPMFC", "YDTMFC", "YDUMFC", "YE01", "YE41",
"YE81", "YF61", "YF81", "YG01", "YG41", "YM", "YM11", "YM31",
"YM91", "YN", "YN1P", "YN2B", "YN2BCF", "YN2BDG", "YN2C",
"YN2M", "YN2MC", "YN2MD", "YN4M", "YN5B", "YN5M", "YN9P",
"YN9S", "YNA", "YNSF5DH1M6", "YNSF5P71M5", "YNSF5P91M6",
"YP", "YP11", "YP31", "YP51", "YP91", "YPA", "YS", "YS31",
"YS3B55D", "YS3F55E", "YS3F55L", "YS3F59E", "YS3GG4JA6A01",
"YS3GG4LA6F01", "YS3GG4LM6F02", "YS3GG4ZA6A01", "YS3GG4ZA6F01",
"YS3GG4ZM6A01", "YS3GG4ZM6F01", "YS51", "YS91", "YT11", "YT54Y",
"YT71", "YT91", "YU11", "YU31", "YU51", "YU71", "YV1PW10BD",
"YV1PW68BC", "YV1PW79B0", "YV1PW79B1", "YV1PWA2BC", "YV1PWA8B1",
"YV1PWA8BC", "YV1PWA8BD", "YV1PWARBC", "YV1PZ68BC", "YV1PZA2BC",
"YV1PZA8B4", "YV1PZA8BC", "YV71", "YV91", "YY23P", "YYCAD2M",
"Z10", "Z12", "Z12AA01", "Z12AA02", "Z12BB01", "Z16AMJ",
"Z16AMN", "Z34AA01", "Z34AA02", "Z34AA03", "Z34AA04", "Z34AA05",
"Z34BB01", "Z34BB02", "Z34BB03", "Z51AA01", "Z51BB01", "Z51CC01",
"ZA31", "ZAAMFA/BX", "ZAAMFAAX", "ZAAMFABX", "ZAAMFBAX",
"ZAAMNABX", "ZAAMPA", "ZAAMRA", "ZADA", "ZADAB", "ZADAC",
"ZADB", "ZADBB", "ZADBC", "ZADD", "ZADDB", "ZADE", "ZADEB",
"ZADFT", "ZADH", "ZADJ", "ZADM", "ZADN", "ZAKE", "ZAKET",
"ZAKF", "ZAKH", "ZAKJ", "ZALAT", "ZALB", "ZALC", "ZALF",
"ZALG", "ZALH", "ZALHT", "ZALJ", "ZAPAPA", "ZAPMFAAX", "ZAPMFAAY",
"ZAPMNAAX", "ZAPMPA", "ZARMFAAX", "ZARMFABX", "ZARMFBAX",
"ZARMNAAX", "ZARMPA", "ZARMRA", "ZATMFAAX", "ZATMFABX", "ZATMFBAX",
"ZATMNAAX", "ZAXZ", "ZAZA", "ZAZAT", "ZAZB", "ZAZBT", "ZAZC",
"ZAZD", "ZAZE", "ZAZF", "ZAZH", "ZAZJ", "ZAZK", "ZAZL", "ZAZT",
"ZAZTT", "ZAZU", "ZAZY", "ZAZZ", "ZB31", "ZB51", "ZB71",
"ZB8S", "ZB8Y", "ZB8Z", "ZBAMAAAX", "ZBAMABAX", "ZBAMDAAX",
"ZBAMDBAX", "ZBAMDCAX", "ZBAMFAAX", "ZBAMFABX", "ZBAMFBAX",
"ZBAMNAAX", "ZBAMNBAX", "ZBAMRB", "ZBC", "ZBD", "ZBPANBAX",
"ZBPANCAX", "ZBPARB", "ZBPMABAY", "ZBPMACAY", "ZBPMBBAY",
"ZBPMBCAY", "ZBPMCBAX", "ZBPMDCAY", "ZBPMFAAY", "ZBPMFABY",
"ZBPMFBAY", "ZBPMGBAY", "ZBPMGCAY", "ZBPMHCAY", "ZBPMNAAX",
"ZBPMNABY", "ZBPMNBAY", "ZBPMNCAX", "ZBPMNCAY", "ZBPMRB",
"ZBPWFAAY", "ZBPWFABY", "ZBPWNBAY", "ZBRMAAAX", "ZBRMABAX",
"ZBRMACAX", "ZBRMCBAX", "ZBRMDAAX", "ZBRMDBAX", "ZBRMDCAX",
"ZBRMFAAX", "ZBRMFABX", "ZBRMFBAX", "ZBRMGBAX", "ZBRMGCAX",
"ZBRMHCAX", "ZBRMNAAX", "ZBRMNBAX", "ZBRMNCAX", "ZBRMRB",
"ZBTMFAAX", "ZBTMFABX", "ZBTMFBAX", "ZBTMNAAX", "ZBTMNBAX",
"ZBTMNCAX", "ZBZMACAY", "ZBZMDCAY", "ZBZMFAAY", "ZBZMFABY",
"ZBZMFBAY", "ZBZMGCAY", "ZBZMHCAY", "ZBZMNBAY", "ZBZMNCAY",
"ZC31", "ZC51", "ZCBHZH", "ZCBHZW", "ZCPANBAX", "ZCPANBBX",
"ZCPANCAX", "ZCPANCBX", "ZCPMABAY", "ZCPMACAY", "ZCPMAGAY",
"ZCPMBBAY", "ZCPMBCAY", "ZCPMCBAY", "ZCPMCCAY", "ZCPMDBAY",
"ZCPMDCAY", "ZCPMFAAY", "ZCPMFABY", "ZCPMGCAY", "ZCPMHCAX",
"ZCPMHCAY", "ZCPMNBAY", "ZCPMNBBY", "ZCPMNCAY", "ZCPMNCBY",
"ZCPWABAY", "ZCPWACAY", "ZCPWCBAY", "ZCPWCCAY", "ZCPWDBAY",
"ZCPWDCAY", "ZCPWNBAY", "ZCPWNCAY", "ZCRMABAX", "ZCRMACAX",
"ZCRMAGAX", "ZCRMCBAX", "ZCRMCCAX", "ZCRMDBAX", "ZCRMDCAX",
"ZCRMFAAX", "ZCRMFABX", "ZCRMGCAX", "ZCRMHCAX", "ZCRMNBAX",
"ZCRMNBBX", "ZCRMNCAX", "ZCRMNCBX", "ZCTMCBAX", "ZCTMCCAX",
"ZCTMNBAX", "ZCTMNBBX", "ZCTMNCAX", "ZCTMNCBX", "ZCZMABAY",
"ZCZMACAY", "ZCZMAGAY", "ZCZMCBAY", "ZCZMCCAY", "ZCZMDBAY",
"ZCZMDCAY", "ZCZMDGAY", "ZCZMFAAY", "ZCZMFABY", "ZCZMGCAY",
"ZCZMGGAY", "ZCZMHCAY", "ZCZMHGAY", "ZCZMNBAY", "ZCZMNBBY",
"ZCZMNCAY", "ZCZMNCBY", "ZD11", "ZD31", "ZD51", "ZD71", "ZE2",
"ZE31", "ZF111", "ZF31", "ZFANF6EK6", "ZFANF6EKA", "ZG31",
"ZH51", "ZH71", "ZHNC9L", "ZHNE9L", "ZM31", "ZN31", "ZN57",
"ZN67", "ZN8M", "ZP31", "ZR31", "ZR71", "ZRHNYH", "ZRHNYW",
"ZT14", "ZT24", "ZT35", "ZT37", "ZT38", "ZT45", "ZT47", "ZT48",
"ZT55", "ZT57", "ZT58", "ZT65", "ZT67", "ZT68", "ZV18", "ZV30",
"ZV38", "ZV41", "ZV81", "ZW41", "ZW61", "ZW81", "ZZT220LAEMNKW",
"ZZT220LALMNKW", "ZZT221LAEMEKW", "ZZT221LAEPEKW", "ZZT221LALMEKW",
"ZZT221LALPEKW", "ZZT230LBLFGHW", "ZZT231LBLFVFW", "ZZT251LALMEKW",
"ZZT251LALMNKW", "ZZT251LALPEKW", "ZZW30LAKMQHW"), class = "factor")), .Names
= c("Lib_Marque__MRQ_",
"Lib_Modele__MOD_", "Lib_Carrosserie__CAR_", "Lib_Energie__ENE_",
"Puiss_Fiscale", "Nb_Places", "Nb_Cylindres", "Cylindree", "Vitesse_Max",
"Puiss_Reelle_kW", "Puiss_Reelle_DIN", "Regime_Puiss", "Couple_Max",
"Regime_Couple_Max", "Nb_Rapports", "Longueur", "Largeur", "Hauteur",
"Empattement", "Voie_AV", "Voie_AR", "Poids_Vide", "PTAC", "Charge_Utile",
"Valeur_Neuf_Orig_EUR", "Code_SRA"), row.names = c(NA, 10L), class =
"data.frame")
Thank you for your kind help
I am interest to write a list of lists in a text file in the same patters as it is. The data I would like to write is shown
structure(list(alpha = structure(list(coord = structure(c(-12.2866476198535,
-18.316117409566, -8.967429617903, 1.12428419143426, 5.64841344065713,
5.44808832719262, 5.4933681771463, 1.9309844060162, 0.233398747299152,
-2.99825174446503, -0.614368425069781, 2.46564289448005, 4.66090502549971,
5.85072710241625, 6.05039548299675, 3.76632919408699, 1.41357656882162,
-0.903298741189705, -2.04803182906018, -4.29159971822507, -10.9370577335933,
-10.4408842089726, -3.96875616934913, 0.121960788365283, 6.18374509958836,
8.14853675618862, 9.88793476169869, 3.88115658226667, -2.54678957335455,
-6.47283540567184, -4.77245571184901, -0.627696099407765, 1.88975367842293,
3.5575754911426, 6.77903556061373, 5.65640773119659), .Dim = c(18L,
2L), .Dimnames = list(c("alpha0", "alpha7", "alpha14", "alpha21",
"alpha28", "alpha35", "alpha42", "alpha49", "alpha56", "alpha63",
"alpha70", "alpha77", "alpha84", "alpha91", "alpha98", "alpha105",
"alpha112", "alpha119"), NULL)), totalVar = 224.006168211038), .Names = c("coord",
"totalVar")), cdc15 = structure(list(coord = structure(c(3.47162630576928,
8.48782177704198, 15.0137638851927, 10.7906288581053, 3.63385217951696,
5.35572208423149, -3.25353199133528, -1.56135110102186, -7.75612902660608,
0.336703958433859, 0.369082417850372, 5.34503555916275, 1.92967854766031,
6.51852381743397, -1.21720901946083, 4.5170731102192, -3.30408507300723,
-0.984750968271877, -8.40102749295709, -8.46082456852006, -12.9415452444904,
-5.24176034595145, -6.55825327910539, -6.08904438989054, -1.86350110076595,
-11.4352897317111, -7.97410132086123, 4.0084226414636, 5.73268492795077,
9.73019959676426, 8.39274636758967, 9.64829450668746, 4.37500530928532,
-4.46583186497905, -7.38893631815797, -7.96186328098463, -5.8246279470231,
-3.7664732488773, -0.197403598498376, 5.49542607598301, 4.32441586772294,
6.97775742830444, 4.22579465801243, 1.89421538493436, -2.49167768466654,
-4.00933035006213, -4.2725035868669, -3.15342273124391), .Dim = c(24L,
2L), .Dimnames = list(c("cdc15_10", "cdc15_30", "cdc15_50", "cdc15_70",
"cdc15_80", "cdc15_90", "cdc15_100", "cdc15_110", "cdc15_120",
"cdc15_130", "cdc15_140", "cdc15_150", "cdc15_160", "cdc15_170",
"cdc15_180", "cdc15_190", "cdc15_200", "cdc15_210", "cdc15_220",
"cdc15_230", "cdc15_240", "cdc15_250", "cdc15_270", "cdc15_290"
), NULL)), totalVar = 465.172014273611), .Names = c("coord",
"totalVar")), cdc28 = structure(list(coord = structure(c(-17.0546131306391,
-3.66229994382873, 11.6887094745458, 9.1742388638346, 8.52829507051842,
5.07577055316834, 4.08395116454314, -0.173829127948164, -4.55958318577516,
-2.26498682123389, -1.96917640175427, 1.2139570540714, 0.949348894924083,
1.42543768864397, -1.34172880845612, -4.09831002297555, -7.01518132163873,
-6.11841279145655, -7.46318485493823, -11.0977474794828, -6.5158721204813,
0.100407193168119, 2.48089061979771, 6.88321073830055, 8.8512526398517,
6.44139467778423, -1.4938717542132, -6.27905863623537, -4.7271008527727,
-1.28540378171629, 2.52270971781573, 5.39065653580633, 7.37698285416012,
4.933147294612), .Dim = c(17L, 2L), .Dimnames = list(c("cdc28_0",
"cdc28_10", "cdc28_20", "cdc28_30", "cdc28_40", "cdc28_50", "cdc28_60",
"cdc28_70", "cdc28_80", "cdc28_90", "cdc28_100", "cdc28_110",
"cdc28_120", "cdc28_130", "cdc28_140", "cdc28_150", "cdc28_160"
), NULL)), totalVar = 434.213382002418), .Names = c("coord",
"totalVar")), elu = structure(list(coord = structure(c(-16.5385661891214,
-7.22079482875697, -3.54426033968934, -0.490137067585021, 6.41505042855706,
8.5763425812589, 7.09765646432215, 5.08639180593248, 2.03913603133563,
4.82068185798214, 3.3960214921007, -2.08018553751718, -4.74563686297203,
-2.81169983584704, -6.14568798086542, 1.07761816321543, -3.65016036562603,
-6.63747020746656, -8.38502803731479, -6.58956439536117, -2.03845851348116,
3.12479766419619, 7.8140081835477, 8.65512130826726, 7.31933446871695,
6.30983959657581, 4.23861782098735, -5.09296770539154), .Dim = c(14L,
2L), .Dimnames = list(c("elu0", "elu30", "elu60", "elu90", "elu120",
"elu150", "elu180", "elu210", "elu240", "elu270", "elu300", "elu330",
"elu360", "elu390"), NULL)), totalVar = 264.546605949057), .Names = c("coord",
"totalVar"))), .Names = c("alpha", "cdc15", "cdc28", "elu"))
The data given above should be written to a text file (list of lists). How to do this ??
Edited The expected output in txt/csv format
$alpha
$alpha$coord
[,1] [,2]
alpha0 -12.2866476 -2.0480318
alpha7 -18.3161174 -4.2915997
alpha14 -8.9674296 -10.9370577
alpha21 1.1242842 -10.4408842
alpha28 5.6484134 -3.9687562
alpha35 5.4480883 0.1219608
alpha42 5.4933682 6.1837451
alpha49 1.9309844 8.1485368
alpha56 0.2333987 9.8879348
alpha63 -2.9982517 3.8811566
alpha70 -0.6143684 -2.5467896
alpha77 2.4656429 -6.4728354
alpha84 4.6609050 -4.7724557
alpha91 5.8507271 -0.6276961
alpha98 6.0503955 1.8897537
alpha105 3.7663292 3.5575755
alpha112 1.4135766 6.7790356
alpha119 -0.9032987 5.6564077
$alpha$totalVar
[1] 224.0062
$cdc15
$cdc15$coord
[,1] [,2]
cdc15_10 3.4716263 -1.8635011
cdc15_30 8.4878218 -11.4352897
cdc15_50 15.0137639 -7.9741013
cdc15_70 10.7906289 4.0084226
cdc15_80 3.6338522 5.7326849
cdc15_90 5.3557221 9.7301996
cdc15_100 -3.2535320 8.3927464
cdc15_110 -1.5613511 9.6482945
cdc15_120 -7.7561290 4.3750053
cdc15_130 0.3367040 -4.4658319
cdc15_140 0.3690824 -7.3889363
cdc15_150 5.3450356 -7.9618633
cdc15_160 1.9296785 -5.8246279
cdc15_170 6.5185238 -3.7664732
cdc15_180 -1.2172090 -0.1974036
cdc15_190 4.5170731 5.4954261
cdc15_200 -3.3040851 4.3244159
cdc15_210 -0.9847510 6.9777574
cdc15_220 -8.4010275 4.2257947
cdc15_230 -8.4608246 1.8942154
cdc15_240 -12.9415452 -2.4916777
cdc15_250 -5.2417603 -4.0093304
cdc15_270 -6.5582533 -4.2725036
cdc15_290 -6.0890444 -3.1534227
$cdc15$totalVar
[1] 465.172
$cdc28
$cdc28$coord
[,1] [,2]
cdc28_0 -17.0546131 -6.1184128
cdc28_10 -3.6622999 -7.4631849
cdc28_20 11.6887095 -11.0977475
cdc28_30 9.1742389 -6.5158721
cdc28_40 8.5282951 0.1004072
cdc28_50 5.0757706 2.4808906
cdc28_60 4.0839512 6.8832107
cdc28_70 -0.1738291 8.8512526
cdc28_80 -4.5595832 6.4413947
cdc28_90 -2.2649868 -1.4938718
cdc28_100 -1.9691764 -6.2790586
cdc28_110 1.2139571 -4.7271009
cdc28_120 0.9493489 -1.2854038
cdc28_130 1.4254377 2.5227097
cdc28_140 -1.3417288 5.3906565
cdc28_150 -4.0983100 7.3769829
cdc28_160 -7.0151813 4.9331473
$cdc28$totalVar
[1] 434.2134
$elu
$elu$coord
[,1] [,2]
elu0 -16.5385662 -6.145688
elu30 -7.2207948 1.077618
elu60 -3.5442603 -3.650160
elu90 -0.4901371 -6.637470
elu120 6.4150504 -8.385028
elu150 8.5763426 -6.589564
elu180 7.0976565 -2.038459
elu210 5.0863918 3.124798
elu240 2.0391360 7.814008
elu270 4.8206819 8.655121
elu300 3.3960215 7.319334
elu330 -2.0801855 6.309840
elu360 -4.7456369 4.238618
elu390 -2.8116998 -5.092968
$elu$totalVar
[1] 264.5466
While you can combine writeLines and capture.output, I prefer the sink function in these cases which simply diverts the R output to a file or connection. So, you should get what you want with:
sink("myfile.txt")
print(mylist)
sink()
where mylist is your list and "myfile.txt" is the text file (including path) you write to.