Shapeburst fill in R, such as QGIS one - r

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,
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5.09753251151245, 5.10109938330993, 5.09849793582722, 5.07494495336925,
5.07080988764074, 5.06593393752919, 5.0570178545545, 5.04930317522926,
5.04965028370247, 5.03932103603408, 5.03359869045073, 5.03575877570973,
5.04996445545867, 5.05312323266116, 5.04180552359106, 5.04031198380313,
5.02579092427195, 5.03503618499052, 5.04721936713988, 5.03864236923495,
5.03872490180845, 5.04824703176136, 5.07061483472144, 5.07482141714894,
5.08997547496051, 5.09027165377294, 5.07826000866815, 5.07381785415854,
5.07845398169697, 5.09312172871684, 5.11501351559203, 5.1293233385605,
5.13173289904335, 5.13146893606745, 5.13202534393623, 5.13705002372908,
5.1735974283617, 5.17266095667648, 5.17583460386657, 5.18781575301386,
5.19415235081345, 5.20450507801015, 5.20864592717777, 5.22217152005677,
5.22503585124321, 5.23447817413034, 5.23970171312708, 5.24525210849422,
5.25459659941887, 5.25918235927066, 5.27032776490957, 5.28425103806027,
5.29365574179721, 5.29513349922747, 5.31334149385775, 5.32247655500953,
5.33508307874945, 5.34960745214753, 5.36661524798002, 5.37166493466641,
5.38843356888882, 5.39248303771976, 5.40058819172399, 5.39781030968894,
5.39134097196814, 5.38564621362848, 5.37762764569384, 5.36333168556138,
5.35945676609893, 5.34578629086773, 5.33253267020127, 5.3239748927582,
5.31044557607408, 5.30189482871711, 5.29907038959209, 5.28341445899148,
5.27532725383275, 5.31719774389754, 5.31824060090511, 5.30408202914543,
5.29809876632614, 5.2791460251824, 5.27506984399261, 5.27195306494281,
5.25523596850409, 46.9798884313746, 46.9888538050022, 46.9801454953065,
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46.9571209151818, 46.9487966066838, 46.9448826367359, 46.9544185588653,
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47.0192677325834, 47.0250585238295, 47.0332929445524, 47.0353468060348,
47.0418951799296, 47.045387379151, 47.0517265604338, 47.0540443055103,
47.0675602807485, 47.0737702977231, 47.0742901793863, 47.0751401377207,
47.0877407393032, 47.098745203204, 47.1081815390237, 47.107799446333,
47.1012678394676, 47.1129385721698, 47.1174701154241, 47.1211669793735,
47.1268669214179, 47.1264502577582, 47.1423675804407, 47.1511936960971,
47.1532953297728, 47.1570230837867, 47.1689990340036, 47.1621918261383,
47.1681736366054, 47.172040111888, 47.1694446993111, 47.1764589306895,
47.1743499830118, 47.17663777769, 47.1761771900856, 47.1746377443819,
47.1677099143489, 47.1643467789408, 47.1566671114945, 47.136787079882,
47.1083682573349, 47.1115626507837, 47.1226747988413, 47.1259298549487,
47.1294654829305, 47.1380445407288, 47.1253236489541, 47.11191863755,
47.1075626006323, 47.0946493618273, 47.0897573261982, 47.0817503099525,
47.0793218042649, 47.078372344588, 47.0808286303993, 47.0766491297749,
47.0765924546739, 47.0737930400279, 47.0605081684056, 47.0609047374266,
47.0586370601355, 47.0462294666576, 47.0269342465651, 47.0158060895345,
47.0124617027808, 47.0102587653578, 47.0018119457904, 46.9991526072624,
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)

Related

GSVA function to calculate ssGSEA score

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",
"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"
), 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",
"AB019438.66", "AB019440.50", "AB019441.29", "ABALON", "ABAT",
"ABBA01017803.1", "ABC12-47043100G14.2", "ABC12-47964100C23.1",
"ABC12-49244600F4.4", "ABC14-1080714F14.1", "ABC7-42391500H16.2",
"ABC7-42418200C9.1", "ABC7-43041300I9.1", "ABC7-481722F1.1",
"ABCA1", "ABCA10", "ABCA11P", "ABCA12", "ABCA13", "ABCA17P",
"ABCA2", "ABCA3", "ABCA4", "ABCA5", "ABCA6", "ABCA7", "ABCA8",
"ABCA9", "ABCA9-AS1", "ABCB1", "ABCB10", "ABCB10P1", "ABCB10P3",
"ABCB10P4", "ABCB11", "ABCB4", "ABCB5", "ABCB6", "ABCB7",
"ABCB8", "ABCB9", "ABCC1", "ABCC10", "ABCC11", "ABCC12",
"ABCC13", "ABCC2", "ABCC3", "ABCC4", "ABCC5", "ABCC5-AS1",
"ABCC6", "ABCC6P1", "ABCC6P2", "ABCC8", "ABCC9", "ABCD1",
"ABCD1P2", "ABCD1P3", "ABCD1P4", "ABCD1P5", "ABCD2", "ABCD3",
"ABCD4", "ABCE1", "ABCF1", "ABCF2", "ABCF2P1", "ABCF2P2",
"ABCF3", "ABCG1", "ABCG2", "ABCG4", "ABCG5", "ABCG8", "ABHD1",
"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",
"ABI2", "ABI3", "ABI3BP", "ABL1", "ABL2", "ABLIM1", "ABLIM2",
"ABLIM3", "ABO", "ABR", "ABRA", "ABRACL", "ABT1", "ABT1P1",
"ABTB1", "ABTB2", "AC000003.1", "AC000029.1", "AC000032.2",
"AC000036.4", "AC000041.10", "AC000041.8", "AC000067.1",
"AC000068.10", "AC000068.5", "AC000068.9", "AC000077.2",
"AC000078.5", "AC000081.2", "AC000089.3", "AC000095.11",
"AC000095.9", "AC000099.1", "AC000110.1", "AC000111.3", "AC000111.4",
"AC000111.5", "AC000111.6", "AC000120.7", "AC000123.2", "AC000123.3",
"AC000123.4", "AC000124.1", "AC000354.1", "AC000362.1", "AC000367.1",
"AC000370.2", "AC000374.1", "AC000403.1", "AC000403.4", "AC001226.7",
"AC002044.1", "AC002044.3", "AC002044.4", "AC002056.3", "AC002056.5",
"AC002059.10", "AC002064.4", "AC002064.5", "AC002064.7",
"AC002066.1", "AC002069.5", "AC002069.6", "AC002070.1", "AC002072.1",
"AC002075.3", "AC002075.4", "AC002076.10", "AC002115.5",
"AC002115.9", "AC002116.7", "AC002116.8", "AC002117.1", "AC002127.2",
"AC002127.4", "AC002128.5", "AC002306.1", "AC002310.10",
"AC002310.12", "AC002310.13", "AC002310.14", "AC002310.17",
"AC002310.7", "AC002314.4", "AC002331.1", "AC002365.5", "AC002366.1",
"AC002366.3", "AC002368.4", "AC002383.2", "AC002386.1", "AC002389.1",
"AC002395.1", "AC002398.11", "AC002398.12", "AC002398.13",
"AC002398.9", "AC002400.1", "AC002401.1", "AC002407.1", "AC002429.1",
"AC002429.4", "AC002429.5", "AC002451.3", "AC002454.1", "AC002456.2",
"AC002463.3", "AC002464.1", "AC002465.2", "AC002467.7", "AC002472.11",
"AC002480.2", "AC002480.3", "AC002480.4", "AC002480.5", "AC002486.2",
"AC002486.3", "AC002511.2", "AC002511.3", "AC002519.6", "AC002519.8",
"AC002523.1", "AC002530.1", "AC002539.1", "AC002539.2", "AC002542.2",
"AC002543.2", "AC002550.5", "AC002550.6", "AC002551.1", "AC002553.4",
"AC002558.1", "AC002978.1", "AC002979.1", "AC002981.1", "AC002984.2",
"AC002985.3", "AC003001.1", "AC003002.4", "AC003002.6", "AC003003.5",
"AC003005.2", "AC003005.4", "AC003006.1", "AC003006.7", "AC003009.1",
"AC003045.1", "AC003075.4", "AC003080.4", "AC003084.2", "AC003088.1",
"AC003090.1", "AC003092.1", "AC003092.2", "AC003101.1", "AC003104.1",
"AC003658.1", "AC003664.1", "AC003666.1", "AC003681.1", "AC003682.16",
"AC003682.17", "AC003688.1", "AC003956.1", "AC003958.2",
"AC003958.6", "AC003968.1", "AC003973.1", "AC003973.3", "AC003973.4",
"AC003973.5", "AC003984.1", "AC003985.1", "AC003986.5", "AC003986.6",
"AC003986.7", "AC003988.1", "AC003989.3", "AC003989.4", "AC003991.3",
"AC004000.1", "AC004000.2", "AC004004.2", "AC004006.2", "AC004009.1",
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"AC004016.1", "AC004019.10", "AC004019.13", "AC004022.7",
"AC004022.8", "AC004041.2", "AC004051.2", "AC004052.1", "AC004053.1",
"AC004053.2", "AC004054.1", "AC004057.1", "AC004062.2", "AC004063.1",
"AC004066.2", "AC004066.3", "AC004067.5", "AC004069.1", "AC004069.2",
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"AC004125.3", "AC004129.7", "AC004129.9", "AC004156.3", "AC004158.1",
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data = structure(list(), .Names = character(0), class = "data.frame", row.names = c("TCGA-N6-A4V9-01A",
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All suggestions shall be helpful.

Spatial weight matrix and spatial lags in panel data

I have a panel dataset C_sf1 of 922 cities and 155 variables that looks like this:
structure(list(FID = c("AT001C1", "AT002C1"), E_pc_2000 = c(10.2,
10.2), E_pc_2001 = c(10.7, 10.7), E_pc_2002 = c(10.8, 10.8),
E_pc_2003 = c(11.5, 11.5), E_pc_2004 = c(11.4, 11.4), E_pc_2005 = c(11.5,
11.5), geometry = structure(list(structure(list(list(structure(c(16.4197,
16.4176, 16.4156, 16.4065, 16.4038, 16.4018, 16.4015, 16.4011,
16.398, 16.3951, 16.3941, 16.394, 16.3946, 16.3948, 16.3944,
16.3921, 16.3917, 16.3914, 16.3907, 16.3889, 16.3887, 16.3894,
16.3894, 16.3889, 16.3885, 16.3866, 16.3858, 16.3834, 16.3826,
16.3802, 16.3797, 16.3797, 16.3799, 16.3815, 16.3826, 16.3838,
16.3833, 16.3846, 16.3838, 16.3853, 16.3855, 16.3801, 16.3793,
16.3784, 16.378, 16.3773, 16.3763, 16.3755, 16.3751, 16.3752,
16.3783, 16.3782, 16.3775, 16.3763, 16.3723, 16.3694, 16.3691,
16.369, 16.3692, 16.3696, 16.3692, 16.3685, 16.3657, 16.3631,
16.3619, 16.3603, 16.3596, 16.3587, 16.3585, 16.3575, 16.3553,
16.3545, 16.3533, 16.3529, 16.3495, 16.3475, 16.3465, 16.3454,
16.3446, 16.3445, 16.3444, 16.3446, 16.3458, 16.348, 16.346,
16.3445, 16.3437, 16.3408, 16.3348, 16.3306, 16.329, 16.3236,
16.3212, 16.3193, 16.3179, 16.316, 16.3136, 16.3099, 16.3074,
16.3056, 16.3043, 16.3031, 16.3015, 16.2998, 16.2962, 16.2918,
16.2888, 16.2881, 16.2866, 16.2865, 16.2869, 16.2884, 16.2898,
16.2909, 16.2909, 16.291, 16.2902, 16.2856, 16.282, 16.2785,
16.2718, 16.2688, 16.2657, 16.2644, 16.2639, 16.2638, 16.2645,
16.2652, 16.268, 16.269, 16.2695, 16.2694, 16.2689, 16.2664,
16.2655, 16.2654, 16.2654, 16.265, 16.264, 16.2635, 16.2595,
16.2582, 16.2578, 16.2569, 16.2564, 16.2518, 16.2488, 16.2474,
16.2454, 16.2439, 16.2427, 16.2413, 16.2387, 16.2374, 16.2373,
16.2376, 16.2387, 16.2397, 16.2402, 16.2398, 16.2367, 16.2335,
16.2301, 16.2264, 16.2242, 16.2221, 16.2198, 16.2166, 16.2149,
16.2137, 16.2104, 16.2085, 16.2067, 16.2057, 16.2049, 16.2046,
16.2051, 16.2051, 16.2047, 16.2009, 16.1996, 16.1978, 16.1961,
16.1959, 16.196, 16.1957, 16.1951, 16.1951, 16.1958, 16.1969,
16.1982, 16.1984, 16.1978, 16.1967, 16.1955, 16.1938, 16.1901,
16.1848, 16.184, 16.184, 16.1839, 16.1841, 16.1844, 16.1847,
16.1854, 16.1856, 16.1869, 16.1886, 16.1932, 16.1951, 16.1965,
16.1975, 16.1978, 16.1965, 16.1961, 16.1962, 16.1968, 16.1971,
16.1977, 16.1994, 16.2008, 16.2035, 16.2057, 16.2067, 16.2077,
16.2085, 16.2073, 16.2065, 16.2063, 16.2054, 16.2042, 16.2027,
16.2037, 16.2086, 16.2088, 16.207, 16.2055, 16.2048, 16.2048,
16.2047, 16.2045, 16.204, 16.2035, 16.2016, 16.1997, 16.1986,
16.1975, 16.1961, 16.1947, 16.193, 16.1913, 16.1906, 16.19,
16.1894, 16.1885, 16.1875, 16.1867, 16.1858, 16.1851, 16.1844,
16.1842, 16.1845, 16.1848, 16.1856, 16.1864, 16.1888, 16.1911,
16.1936, 16.1946, 16.1956, 16.1961, 16.1967, 16.1969, 16.1971,
16.197, 16.1969, 16.1971, 16.198, 16.1988, 16.1992, 16.1996,
16.2006, 16.2016, 16.2032, 16.2048, 16.2105, 16.2114, 16.2181,
16.2238, 16.2239, 16.2226, 16.2197, 16.2193, 16.2179, 16.2154,
16.2137, 16.211, 16.2104, 16.2104, 16.2106, 16.2113, 16.2125,
16.2134, 16.216, 16.2184, 16.2194, 16.2199, 16.2205, 16.2208,
16.2215, 16.2241, 16.2314, 16.2348, 16.2369, 16.2376, 16.2364,
16.2385, 16.2383, 16.2312, 16.2265, 16.2186, 16.2169, 16.2154,
16.2151, 16.216, 16.2193, 16.2209, 16.2235, 16.2245, 16.2291,
16.2307, 16.2328, 16.2348, 16.2377, 16.2418, 16.2456, 16.2484,
16.2496, 16.25, 16.2512, 16.2522, 16.2527, 16.2534, 16.2559,
16.2569, 16.2594, 16.2599, 16.2668, 16.2717, 16.2729, 16.2818,
16.2834, 16.2846, 16.2915, 16.2975, 16.2985, 16.2985, 16.2986,
16.2987, 16.299, 16.2991, 16.2992, 16.2992, 16.2989, 16.3003,
16.3008, 16.3061, 16.3073, 16.3103, 16.3121, 16.3125, 16.3127,
16.314, 16.3146, 16.3178, 16.3183, 16.3187, 16.3197, 16.3206,
16.3222, 16.325, 16.3266, 16.3319, 16.3324, 16.3348, 16.3362,
16.3387, 16.3425, 16.3429, 16.3443, 16.3453, 16.3463, 16.349,
16.351, 16.3524, 16.3527, 16.3546, 16.3548, 16.3564, 16.3585,
16.3587, 16.3605, 16.3649, 16.3688, 16.3731, 16.3814, 16.3816,
16.3818, 16.3848, 16.3857, 16.3873, 16.3936, 16.3945, 16.3971,
16.398, 16.3994, 16.403, 16.405, 16.4081, 16.4088, 16.4099,
16.4109, 16.4115, 16.4119, 16.4119, 16.4121, 16.4123, 16.4139,
16.4186, 16.4192, 16.4208, 16.421, 16.4215, 16.4224, 16.4232,
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47.0185, 47.0176, 47.0177, 47.0189, 47.019, 47.0182, 47.0171,
47.0166, 47.015, 47.013, 47.0125, 47.012, 47.0129, 47.0132,
47.0136, 47.0153, 47.0167, 47.0172, 47.0193, 47.0197, 47.0203,
47.0205, 47.0213, 47.0217, 47.0219, 47.0227, 47.0219, 47.0217,
47.0223, 47.0246, 47.0249, 47.0255, 47.0256, 47.026, 47.0265,
47.0268, 47.0308, 47.0328, 47.0339, 47.0363, 47.0366, 47.0374,
47.0378, 47.0402, 47.0412, 47.0456, 47.0473, 47.0491, 47.0525,
47.0533, 47.0543, 47.057, 47.0589, 47.0608, 47.0622, 47.066,
47.0673, 47.068, 47.0689, 47.07, 47.074, 47.075, 47.0756,
47.0767, 47.0778, 47.0786, 47.079, 47.0807, 47.082, 47.0837,
47.0863, 47.0903, 47.092, 47.0931, 47.0941, 47.0969, 47.0973,
47.0974, 47.0973, 47.0984, 47.0998, 47.1008, 47.1018, 47.1023,
47.1034, 47.1043, 47.1052, 47.1063, 47.1072, 47.1075, 47.1103,
47.1107, 47.1095, 47.1075, 47.1069, 47.1068, 47.1074, 47.1081,
47.1095, 47.1118, 47.1144, 47.1157, 47.1171, 47.1176, 47.1177,
47.1189, 47.1201, 47.1213, 47.1234, 47.1263, 47.127, 47.1289,
47.1292, 47.1289, 47.1288, 47.1286, 47.1245, 47.1271, 47.1272,
47.1295, 47.1295, 47.1292, 47.1285, 47.1276, 47.1269, 47.1269,
47.1285, 47.1305, 47.1309, 47.1309, 47.1305, 47.1278, 47.127,
47.1276, 47.1279, 47.1281, 47.128, 47.128, 47.1277, 47.128,
47.1309, 47.1332, 47.1338, 47.1339, 47.1336, 47.1349), .Dim = c(266L,
2L)))), class = c("XY", "MULTIPOLYGON", "sfg"))), class = c("sfc_MULTIPOLYGON",
"sfc"), precision = 0, bbox = structure(c(xmin = 15.3512,
ymin = 47.012, xmax = 16.5782, ymax = 48.3222), class = "bbox"), crs = structure(list(
input = "WGS 84", 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[\"latitude\",north,\n ORDER[1],\n ANGLEUNIT[\"degree\",0.0174532925199433]],\n AXIS[\"longitude\",east,\n ORDER[2],\n ANGLEUNIT[\"degree\",0.0174532925199433]],\n ID[\"EPSG\",4326]]"), class = "crs"), n_empty = 0L)), row.names = 1:2, class = c("sf",
"data.frame"), sf_column = "geometry", agr = structure(c(FID = NA_integer_,
E_pc_2000 = NA_integer_, E_pc_2001 = NA_integer_, E_pc_2002 = NA_integer_,
E_pc_2003 = NA_integer_, E_pc_2004 = NA_integer_, E_pc_2005 = NA_integer_
), .Label = c("constant", "aggregate", "identity"), class = "factor"))
My task is to calculate the spatial weights matrix and the spatial lags associated with the future dependent variable of my model: log(E).
I built the spatial weights matrix for the wide dataframe.
library(spdep)
coord <- st_coordinates(st_centroid(C_sf1))
nb2.knn1 <- knn2nb(knearneigh(coord,k=1,longlat=T))
all.linkedT <- max(unlist(nbdists(nb2.knn1, coord,longlat=T)))
all.linkedT
nb3 <- dnearneigh(coord, 0, all.linkedT,row.names=C_sf$FDI,longlat=TRUE)
n.nb3 <- nbdists(nb3, coord,longlat=TRUE);n.nb3
n.nb3.gl <- lapply(n.nb3, function(x) 1/x); n.nb3.gl
nb3.listw.gl <- nb2listw(nb3, glist=n.nb3.gl,zero.policy=F)
My problem is that I want to shift to a long format (17k+ observations), keeping the spatial weight matrix. The long format would allow me to have a variable called E (and not E_2001, E_2002, etc.) from which I can calculate the logarithm.
I tried to use the same code for the dataframe C_sf_long (obtained with reshape), but R returns me a spatial weight matrix of 0 distances. The reason is that in the panel dataset, the same coordinates are repeated for each time unit, so distance and neighbour functions only recognize a city as the neighbour of itself.
The code for the spatial lags is the following:
lag.E<- lag.listw(nb2listw(nb3),C_sf1$E)
but, as you can see, C_sf1 does not have the variable E, because it is still in the wide format.
So, I want C_sf1 to be in long format, in order to compute the spatial lags, but still I want to keep the spatial weight matrix computed with C_sf1 in wide format.

How to use " ind.sup" on a MCA analysis?

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

R: merge.xts and specific fill function

After merging two unequal time series I want to fill the blanks with a custom function. Lets say my Series1 is daily data and Series2 is monthly data. So now Series1 has for example 30 data points for one month and Series2 only one. If I make a left join Series 2 has 29 NAs which I don't like. Ideally I would like a fill function so that Series 2 takes always the previous value to fill these 29 days.
So for example if the 31. of January has a value of 10 and the 28th of February a value of 15, February 1-27. should have a value of 10 as well. Of course in the beginning this doesn't work (since the first row is probably also a NA), so the first row should take the value of the first row containing a value at all.
At the moment I have this, but still, all NAs are present:
Test<-merge.xts(Series1, Series2, join="left", fill=function(x) x[index(x)-1,])
Series1:
structure(c(1.51762156049755, 1.52103159497526, 1.51401262063846,
1.5226927459172, 1.52933295052158, 1.52409353403389, 1.52292452830189,
1.5268928035982, 1.53555449785816, 1.54004946727549, 1.54031650339111,
1.53987556561086, 1.53733857383492, 1.52781969068276, 1.5303624813154,
1.53149347601615, 1.53200449185851, 1.53034081463009, 1.52689961175818,
1.52616010353115, 1.52004035586536, 1.52604263206673, 1.53170366207736,
1.53332707472775, 1.5400318381871, 1.53717071341521, 1.53998696583186,
1.53676880222841, 1.53316818056702, 1.53512014787431, 1.54153071688263,
1.53692449355433, 1.53382906453686, 1.53159514756473, 1.5344496294263,
1.53717866027826, 1.53445133065986, 1.53503822351656, 1.5306399132321,
1.53633694255827, 1.53748747380887, 1.54019086070839, 1.54068532372772,
1.53600669892073, 1.53977166385926, 1.53468288606184, 1.53986928104575,
1.54024911693623, 1.5402127262549, 1.54151119402985, 1.53934776549289,
1.53958085476343, 1.53900838497995, 1.53818540787939, 1.53465613216017,
1.53500719942405, 1.53537650054565, 1.53317195624888, 1.53192246131958,
1.53136958262882, 1.53666845974538, 1.53503754022167, 1.53098678960901,
1.52377172091382, 1.52796773627915, 1.52584842623527, 1.52760075397182,
1.52793296089385, 1.52820374854273, 1.52947558770344, 1.52752869440459,
1.52590880810595, 1.51771286513362, 1.52378827099884, 1.52171596056488,
1.52387303280875, 1.52663662867745, 1.53114232706069, 1.52827140549273,
1.52923132443161, 1.52939594909482, 1.53232585173925, 1.53195117573147,
1.53853103261361, 1.53776866137519, 1.54085533920156, 1.5410640956972,
1.54313041923661, 1.54222657292872, 1.54302034987504, 1.54211182336182,
1.54181785998761, 1.5424089337942, 1.53578353604795, 1.53286652078775,
1.53120629370629, 1.53219713608012, 1.53192052980132, 1.53522245762712,
1.53543098889476, 1.53283647523016, 1.5296408481177, 1.52531916716648,
1.52295699845811, 1.52777060191165, 1.52890571231934, 1.5233980665583,
1.52386256533288, 1.51978021978022, 1.52140011865412, 1.51797040169133,
1.51707941929974, 1.52089868588385, 1.52408100748809, 1.52491920394625,
1.52068065032432, 1.52637418914305, 1.52848101265823, 1.52656088306313,
1.52858618908214, 1.53068778514246, 1.52826643894108, 1.52470085470085,
1.51927185710623, 1.52041166380789, 1.51975945017182, 1.52318452637941,
1.51831155433287, 1.51966908661151, 1.52143645470753, 1.52183128444256,
1.52286417239331, 1.52149627623561, 1.52065908330545, 1.51957958976098,
1.52554186145346, 1.52094733242134, 1.51794915836482, 1.51173708920188,
1.51222222222222, 1.5101414692347, 1.5068328319725, 1.50393081761006,
1.50417972831766, 1.50391986062718, 1.50638741635526, 1.50589880276151,
1.51000264387063, 1.50961116475029, 1.50934456435904, 1.50983477576711,
1.51314636283961, 1.50903004140604, 1.51011752231157, 1.50968426638366,
1.50718251520226, 1.50750460809269, 1.50457827082233, 1.50718301061836,
1.51371392834807, 1.51775147928994, 1.51589595375723, 1.51878256100905,
1.51964269437608, 1.52107244513819, 1.51828822238478, 1.51868515287852,
1.52112289685443, 1.52031478770132, 1.5218941402322, 1.51964269437608,
1.51789300712069, 1.51745137247773, 1.51548186148772, 1.51610254538819,
1.51619929213177, 1.51333333333333, 1.51241134751773, 1.51200286635614,
1.51837734821672, 1.5163433908046, 1.49981738495252, 1.50498640072529,
1.5011387446479, 1.49350888500138, 1.4836323284631, 1.48080845540515,
1.47762023908813, 1.47091566935708, 1.44464775846295, 1.46478356566398,
1.46516563624619, 1.47632234837995, 1.48080808080808, 1.47685016405396,
1.48288833837967, 1.48791693466875, 1.48385916780979, 1.48779368575624,
1.4842056932966, 1.48020986745214, 1.48406538215688, 1.48219003370684,
1.4840747090138, 1.48181569592562, 1.47840712792072, 1.48482921511628,
1.48070841239722, 1.47882236069719, 1.47693552738063, 1.47952903398448,
1.47818343722173, 1.48081910042028, 1.47554444841128, 1.47437042328987,
1.47387958352196, 1.46947082767978, 1.47113912651959, 1.47202166064982,
1.47102365047843, 1.47226211849192, 1.47248814529838, 1.46853962839961,
1.46421559878636, 1.46491463305623, 1.46394424090787, 1.47141221037794,
1.46876654314452, 1.46473285134897, 1.46621860629643, 1.45898901098901,
1.45649677590319, 1.4541381128097, 1.45816872969889, 1.46286215978929,
1.46461267605634, 1.46386925795053, 1.46151797603196, 1.46911608093717,
1.47140552169236, 1.4750490108715, 1.47230138938368, 1.47392733410322,
1.47497537827917, 1.47591916674085, 1.48151776966242, 1.47590146376294,
1.47583108715184, 1.47547136091502, 1.47256621169665, 1.47307171853857,
1.47527795353882, 1.47582605564059, 1.46818468184682, 1.46878890272097,
1.48522318688065, 1.48453427065026, 1.48568912373404, 1.4814585908529,
1.48118303373771, 1.47687244262587, 1.47909624621953, 1.48514136031072,
1.48368539325843, 1.47950599606229, 1.47334107350183, 1.47758127902822,
1.47985739750446, 1.48092011412268, 1.47403176869534, 1.48108736475007,
1.47305653710247, 1.46450017661604, 1.4681413589495, 1.46912050964431,
1.46845174973489, 1.47360950944735, 1.46758608573436, 1.46957056292263,
1.47418043421849, 1.47130794416681, 1.47095489568003, 1.47372954349699,
1.47756961155036, 1.47673216132368, 1.47682004001044, 1.47401301518438,
1.47194032439934, 1.47180647406892, 1.47518534670737, 1.47624474053296,
1.47794826830338, 1.48057829646403, 1.48357504805172, 1.48148471615721,
1.47989206128134, 1.47923238696109, 1.47960337479342, 1.47915397336583,
1.47995097180879, 1.47630640813842, 1.47675825125281, 1.47637181928337,
1.47504781777082, 1.47135191275749, 1.47813993915689, 1.47672594142259,
1.47480059602069, 1.47183284845279, 1.46386701662292, 1.47050586381936,
1.46995971273428, 1.46776454099509, 1.46059482834701, 1.45992231638418,
1.46362994350282, 1.4642195358687, 1.46497830514478, 1.46292372881356,
1.46326046879115, 1.46075594141892, 1.4626918018413, 1.46522991013001,
1.46767729569611, 1.46556834030683, 1.46354350123283, 1.46293202005101,
1.46216192405955, 1.46279539664412, 1.46416652028807, 1.46635751159332,
1.46744206538021, 1.46897280168999, 1.46536662843025, 1.46557031043884,
1.46789797713281, 1.46835554770942, 1.4694150120203, 1.46747460345749,
1.46710702490404, 1.46860547847741, 1.46663705019991, 1.4664345652562,
1.46345186781609, 1.46563852813853, 1.46283081925752, 1.45655110310671,
1.45227952506118, 1.45321531791908, 1.4547789396441, 1.45564738292011,
1.45421278931479, 1.45517865219358, 1.45266890970265, 1.45443743716296,
1.45503465888362, 1.45726148569365, 1.45540762356374, 1.45618509746766,
1.45435302779312, 1.45287885766928, 1.45133394664213, 1.45226409852764,
1.45390070921986, 1.45874769797422, 1.45672988399926, 1.46178846689572,
1.4674146797569, 1.4640179910045, 1.46468609865471, 1.47339173024395,
1.47045561296383, 1.47032863849765, 1.47437233538607, 1.47061043494669,
1.47251605591235, 1.47473215132265, 1.47768657420511, 1.47433962264151,
1.47615894039735, 1.47619047619048, 1.47506661591169, 1.47083612680778,
1.47052580800772, 1.4673786407767, 1.46719083673073, 1.46737852664577,
1.4680161147686, 1.47064637280095, 1.46837200079318, 1.47009818506397,
1.46631153201144, 1.46435925090695, 1.46418085731063, 1.46629705281587,
1.47042504706232, 1.47244016287615, 1.46962801741195, 1.46572500987752,
1.47040745514028, 1.46655971122029, 1.46671388101983, 1.46569960713206,
1.46444107233182, 1.45887708649469, 1.45496722138174, 1.4528824285573,
1.45116001194862, 1.4471463022508, 1.44598993785144, 1.45799803729146,
1.45748550083554, 1.45195033727637, 1.44973909618982, 1.44844597927972,
1.45353852185846, 1.45797913446677, 1.45808966861598, 1.46286266924565,
1.45828482731859, 1.4618320610687, 1.46203029706866, 1.46219309400372,
1.46284480219888, 1.46597735105859, 1.46784424709671, 1.4689243417833,
1.46860898567785, 1.47238907188529, 1.47246010120669, 1.47172011661808,
1.46688286163522, 1.46971327918583, 1.47072714749582, 1.47229862475442,
1.47179285222014, 1.46633416458853, 1.46399523903987, 1.46048587010412,
1.45797329143755, 1.45885579937304, 1.45979140267083, 1.46490971205466,
1.46888496270122, 1.46831875607386, 1.46836546846236, 1.46927047823123,
1.46807470421433, 1.462829499457, 1.46497003046084, 1.46442900479499,
1.46273932253314, 1.46406951767233, 1.4673116388156, 1.46543100912033,
1.45540647198106, 1.46271003242606, 1.45876085240726, 1.45973718012054,
1.46011549378487, 1.46333792018872, 1.46617056692451, 1.46380829785127,
1.4638067061144, 1.46371087192653, 1.46229022704837, 1.46666666666667,
1.4661108386464, 1.46767617938264, 1.46891393044492, 1.47142439879272,
1.46808094632906, 1.46796059689847, 1.46733815763739, 1.46692037470726,
1.4646265866378, 1.46480534801416, 1.46492177506642, 1.4623687858982,
1.46242774566474, 1.46307385229541, 1.4626074785043, 1.4633068968979,
1.46385298869144, 1.46180344478217, 1.46254927726675, 1.46241896272285,
1.46647171523646, 1.46721558389397, 1.46642431586388, 1.46720484359233,
1.46822373696872, 1.46890958245719, 1.46962101463806, 1.47268740031898,
1.47340742210756, 1.47341746993938, 1.47524262230145, 1.47560369671072,
1.47479367604653, 1.47198963317384, 1.47108097327483, 1.47302572315084,
1.4712827696618, 1.47083753784057, 1.47290739991913, 1.47313237221494,
1.47367359289893, 1.47733523479678, 1.47741935483871, 1.47505622572071,
1.46778337272634, 1.46253469010176, 1.46209942481512, 1.46357003391224,
1.45595482546201, 1.45030384179627, 1.45351356929109, 1.45500778412039,
1.44706984490476, 1.45556604763404, 1.45198866617693), .indexTZ = "UTC", .indexCLASS = "Date", tclass = "Date", tzone = "UTC", class = c("xts",
"zoo"), index = structure(c(978307200, 978393600, 978480000,
978566400, 978652800, 978912000, 978998400, 979084800, 979171200,
979257600, 979516800, 979603200, 979689600, 979776000, 979862400,
980121600, 980208000, 980294400, 980380800, 980467200, 980726400,
980812800, 980899200, 980985600, 981072000, 981331200, 981417600,
981504000, 981590400, 981676800, 981936000, 982022400, 982108800,
982195200, 982281600, 982540800, 982627200, 982713600, 982800000,
982886400, 983145600, 983232000, 983318400, 983404800, 983491200,
983750400, 983836800, 983923200, 984009600, 984096000, 984355200,
984441600, 984528000, 984614400, 984700800, 984960000, 985046400,
985132800, 985219200, 985305600, 985564800, 985651200, 985737600,
985824000, 985910400, 986169600, 986256000, 986342400, 986428800,
986515200, 986774400, 986860800, 986947200, 987033600, 987120000,
987379200, 987465600, 987552000, 987638400, 987724800, 987984000,
988070400, 988156800, 988243200, 988329600, 988588800, 988675200,
988761600, 988848000, 988934400, 989193600, 989280000, 989366400,
989452800, 989539200, 989798400, 989884800, 989971200, 990057600,
990144000, 990403200, 990489600, 990576000, 990662400, 990748800,
991008000, 991094400, 991180800, 991267200, 991353600, 991612800,
991699200, 991785600, 991872000, 991958400, 992217600, 992304000,
992390400, 992476800, 992563200, 992822400, 992908800, 992995200,
993081600, 993168000, 993427200, 993513600, 993600000, 993686400,
993772800, 994032000, 994118400, 994204800, 994291200, 994377600,
994636800, 994723200, 994809600, 994896000, 994982400, 995241600,
995328000, 995414400, 995500800, 995587200, 995846400, 995932800,
996019200, 996105600, 996192000, 996451200, 996537600, 996624000,
996710400, 996796800, 997056000, 997142400, 997228800, 997315200,
997401600, 997660800, 997747200, 997833600, 997920000, 998006400,
998265600, 998352000, 998438400, 998524800, 998611200, 998870400,
998956800, 999043200, 999129600, 999216000, 999475200, 999561600,
999648000, 999734400, 999820800, 1000080000, 1000166400, 1000252800,
1000339200, 1000425600, 1000684800, 1000771200, 1000857600, 1000944000,
1001030400, 1001289600, 1001376000, 1001462400, 1001548800, 1001635200,
1001894400, 1001980800, 1002067200, 1002153600, 1002240000, 1002499200,
1002585600, 1002672000, 1002758400, 1002844800, 1003104000, 1003190400,
1003276800, 1003363200, 1003449600, 1003708800, 1003795200, 1003881600,
1003968000, 1004054400, 1004313600, 1004400000, 1004486400, 1004572800,
1004659200, 1004918400, 1005004800, 1005091200, 1005177600, 1005264000,
1005523200, 1005609600, 1005696000, 1005782400, 1005868800, 1006128000,
1006214400, 1006300800, 1006387200, 1006473600, 1006732800, 1006819200,
1006905600, 1006992000, 1007078400, 1007337600, 1007424000, 1007510400,
1007596800, 1007683200, 1007942400, 1008028800, 1008115200, 1008201600,
1008288000, 1008547200, 1008633600, 1008720000, 1008806400, 1008892800,
1009152000, 1009238400, 1009324800, 1009411200, 1009497600, 1009756800,
1009843200, 1009929600, 1010016000, 1010102400, 1010361600, 1010448000,
1010534400, 1010620800, 1010707200, 1010966400, 1011052800, 1011139200,
1011225600, 1011312000, 1011571200, 1011657600, 1011744000, 1011830400,
1011916800, 1012176000, 1012262400, 1012348800, 1012435200, 1012521600,
1012780800, 1012867200, 1012953600, 1013040000, 1013126400, 1013385600,
1013472000, 1013558400, 1013644800, 1013731200, 1013990400, 1014076800,
1014163200, 1014249600, 1014336000, 1014595200, 1014681600, 1014768000,
1014854400, 1014940800, 1015200000, 1015286400, 1015372800, 1015459200,
1015545600, 1015804800, 1015891200, 1015977600, 1016064000, 1016150400,
1016409600, 1016496000, 1016582400, 1016668800, 1016755200, 1017014400,
1017100800, 1017187200, 1017273600, 1017360000, 1017619200, 1017705600,
1017792000, 1017878400, 1017964800, 1018224000, 1018310400, 1018396800,
1018483200, 1018569600, 1018828800, 1018915200, 1019001600, 1019088000,
1019174400, 1019433600, 1019520000, 1019606400, 1019692800, 1019779200,
1020038400, 1020124800, 1020211200, 1020297600, 1020384000, 1020643200,
1020729600, 1020816000, 1020902400, 1020988800, 1021248000, 1021334400,
1021420800, 1021507200, 1021593600, 1021852800, 1021939200, 1022025600,
1022112000, 1022198400, 1022457600, 1022544000, 1022630400, 1022716800,
1022803200, 1023062400, 1023148800, 1023235200, 1023321600, 1023408000,
1023667200, 1023753600, 1023840000, 1023926400, 1024012800, 1024272000,
1024358400, 1024444800, 1024531200, 1024617600, 1024876800, 1024963200,
1025049600, 1025136000, 1025222400, 1025481600, 1025568000, 1025654400,
1025740800, 1025827200, 1026086400, 1026172800, 1026259200, 1026345600,
1026432000, 1026691200, 1026777600, 1026864000, 1026950400, 1027036800,
1027296000, 1027382400, 1027468800, 1027555200, 1027641600, 1027900800,
1027987200, 1028073600, 1028160000, 1028246400, 1028505600, 1028592000,
1028678400, 1028764800, 1028851200, 1029110400, 1029196800, 1029283200,
1029369600, 1029456000, 1029715200, 1029801600, 1029888000, 1029974400,
1030060800, 1030320000, 1030406400, 1030492800, 1030579200, 1030665600,
1030924800, 1031011200, 1031097600, 1031184000, 1031270400, 1031529600,
1031616000, 1031702400, 1031788800, 1031875200, 1032134400, 1032220800,
1032307200, 1032393600, 1032480000, 1032739200, 1032825600, 1032912000,
1032998400, 1033084800, 1033344000, 1033430400, 1033516800, 1033603200,
1033689600, 1033948800, 1034035200, 1034121600, 1034208000, 1034294400,
1034553600, 1034640000, 1034726400, 1034812800, 1034899200, 1035158400,
1035244800, 1035331200, 1035417600, 1035504000, 1035763200, 1035849600,
1035936000, 1036022400, 1036108800, 1036368000, 1036454400, 1036540800,
1036627200, 1036713600, 1036972800, 1037059200, 1037145600, 1037232000,
1037318400, 1037577600, 1037664000, 1037750400, 1037836800, 1037923200,
1038182400, 1038268800, 1038355200, 1038441600, 1038528000, 1038787200,
1038873600, 1038960000, 1039046400, 1039132800, 1039392000, 1039478400,
1039564800, 1039651200, 1039737600, 1039996800, 1040083200, 1040169600,
1040256000, 1040342400, 1040601600, 1040688000, 1040774400, 1040860800,
1040947200, 1041206400, 1041292800), tzone = "UTC", tclass = "Date"), .Dim = c(522L,
1L), .Dimnames = list(NULL, "Series1"))
Series2:
structure(c(100, 100.32, 100.57, 100.82, 100.98, 101.01, 101.16,
101.3, 101.75, 102.07, 102.12, 102.3, 102.44, 102.59, 102.62,
102.74, 102.84, 103.09, 103.25, 103.31, 103.35, 103.48, 103.6,
103.72, 103.84, 103.96, 104.1, 104.35, 104.52, 104.69, 104.82,
104.96, 104.9, 105.03, 105.08, 105.27, 105.46, 105.55, 105.78,
105.94, 106.11, 106.36, 106.52, 106.6, 106.7, 106.92, 107.1,
107.27, 107.39, 107.41, 107.54, 107.72, 107.96, 108.13, 108.3,
108.43, 108.56, 108.68, 108.77), .indexCLASS = "Date", tclass = "Date", .indexTZ = "UTC", tzone = "UTC", class = c("xts",
"zoo"), index = structure(c(1010102400, 1010707200, 1011312000,
1011916800, 1012435200, 1012521600, 1013126400, 1013731200, 1014336000,
1014854400, 1014940800, 1015545600, 1016150400, 1016755200, 1017360000,
1017964800, 1018569600, 1019174400, 1019779200, 1020124800, 1020384000,
1020988800, 1021593600, 1022198400, 1022803200, 1023408000, 1024012800,
1024617600, 1025222400, 1025827200, 1026432000, 1027036800, 1027641600,
1028073600, 1028246400, 1028851200, 1029456000, 1030060800, 1030665600,
1031270400, 1031875200, 1032480000, 1033084800, 1033344000, 1033689600,
1034294400, 1034899200, 1035504000, 1036022400, 1036108800, 1036713600,
1037318400, 1037923200, 1038528000, 1039132800, 1039737600, 1040342400,
1040947200, 1041292800), tzone = "UTC", tclass = "Date"), .Dim = c(59L,
1L), .Dimnames = list(NULL, "Series2"))
na.locf() will do the job:
Test<-merge.xts(Series1, Series2, join="left", fill=na.locf())
This function "Last Observation Carried Forward" fills the NA with the last knowne value.
Hope this helps people landing here, years after you asked the question.

How to calculate rolling Geometric Mean since inception in R

How can I calculate the rolling geometric mean of the following xts time-series called RET :
RET <- structure(c(0.235313703701719, 0.0842795890067098, -0.233550157364016,
0.193002483647028, -0.0689226509620672, 0.179534446046147, -0.127248883358903,
-0.0743470123083659, -0.135639827536771, -0.164152686819086,
-0.0204543751350705, 0.0664461087883466, 0.0272380829324071,
0.227128887694961, -0.178562010800951, 0.395443526993695, 0.314881406042804,
0.181818850951155, -0.206752910000026, 0.0263153570715485, -0.175020412517093,
-0.01581249554214, -0.15172107661987, 0.107045745638528, 0.0906084021475712,
0.132978183241953, -0.00909419932203037, 0.0140911196519935,
0.213807819676506, -0.18193702641405, -0.227678899382835, 0.301040730720012,
0.0601578951831836, 0.100516179349791, -0.404945499516844, -0.062500075,
-0.203765311964576, -0.07692312083335, -0.0836064900993851, 0.221099166706208,
-0.0815457861285995, 0.316192911249967, -0.0486118279242403,
-0.173325434709271, 0.59325308527751, -0.00727384538023071, -0.0506951075961164,
0.546153054783499, 0.251190702854411, 0.0541671297506965, -0.231331680522771,
0.343474297824453, -0.0996647581834428, -0.16666622673471, -0.0108547028404369,
0.181845988421045, -0.126238743703789, 0.115831564907218, -0.086580986998594,
-0.100940420552943, -0.206575364601145, 0.131537865642544, -0.165064660739094,
-0.0281081354328821, -0.121059125713363, 0.123960117666879, -0.0262060896817153,
0.233629847113372, -0.0283857077499662, 0.0414875891677511, -0.139489434074546,
-0.291593674635723, -0.0394733826899257, -0.154468409162332,
-0.0228881657209516, 0.0583326253535777, -0.304457954370351,
-0.26778991307437, -0.33333268430204, 0.0600079321234409, 0.438760787944855,
-0.223958720648733, 0.7370589189836, 0.090349969129766, -0.267827471745488,
0.00221064777031615, -0.704447751043575, -0.131282309489721,
0, 0, -0.378290758732488, -0.0216356021418299, 0.116571563338307,
-0.105867472732954, -0.172970309117826, 0.0636995840642282, 0.0364789030575694,
0.0952821024300478, 0.230263394963683, -0.0485614318546264, -0.115799574888004,
-0.0120447695973589, 0.760532897132213, -0.289305826263387, 0.0411514151084162,
0.0673184054668599, -0.233772989321894, -0.0892615248786987,
-0.159921094234639, -0.0449699589101911, 0.59114150445647, -0.00487865744023711,
0.0392264443174142, 0.083080680760974, -0.604569467038781, -0.239333037134679,
-0.225872722618789, -0.474746305159428, -0.490736714052568, 0.228916653763629,
0.0308321929055187, 0.0943914873080436, -0.0712560264787054,
0.0798695334307741, -0.135961317412806, 0.0412890533446434, -0.0991550904983131,
0.300616722621631, -0.0418878938161028, -0.380127803498024, -0.0611938814657424,
0.01369237597809, -0.251729062508919, -0.0775118446872033, -0.314275408058115,
0.0876429361863786, 0.062043712399028, 0.134824568530155, 0.0158774971284649,
0.716661259261616, 0.0709424520436908, -0.227571455891104, -0.176479546620475
), index = structure(c(299401200, 307263600, 315388800, 323251200,
331110000, 339058800, 347011200, 354787200, 362646000, 370594800,
378547200, 386323200, 394182000, 402130800, 410083200, 417859200,
425718000, 433666800, 441532800, 449395200, 457254000, 465116400,
473241600, 480844800, 488703600, 496825200, 504777600, 512553600,
520412400, 528361200, 536313600, 544089600, 551948400, 559897200,
567849600, 575712000, 583570800, 591519600, 599385600, 607248000,
615106800, 622969200, 630835200, 638697600, 646556400, 654418800,
662544000, 670060800, 678006000, 686127600, 694080000, 701942400,
709801200, 717750000, 725702400, 733478400, 741337200, 749286000,
757238400, 765014400, 772873200, 780822000, 788688000, 796550400,
804409200, 812271600, 820137600, 8.28e+08, 835858800, 843980400,
851932800, 859708800, 867567600, 875516400, 883468800, 891244800,
899103600, 907052400, 915004800, 922780800, 930639600, 938588400,
946540800, 954403200, 962262000, 970124400, 977990400, 985852800,
993711600, 1001574000, 1009699200, 1017216000, 1025161200, 1033282800,
1041235200, 1049011200, 1056870000, 1064818800, 1072771200, 1080633600,
1088492400, 1096441200, 1104393600, 1112169600, 1120028400, 1127977200,
1135843200, 1143705600, 1151564400, 1159426800, 1167292800, 1175151600,
1183014000, 1190876400, 1199001600, 1206860400, 1214722800, 1222671600,
1230624000, 1238396400, 1246258800, 1254207600, 1262160000, 1269932400,
1277794800, 1285743600, 1293696000, 1301468400, 1309330800, 1317279600,
1325145600, 1333004400, 1340866800, 1348729200, 1356854400, 1364367600,
1372316400, 1380438000, 1388390400, 1396162800, 1404025200, 1411974000,
1419926400), tclass = c("POSIXlt", "POSIXt")), .indexCLASS = c("POSIXlt",
"POSIXt"), .indexTZ = "", tclass = c("POSIXt", "POSIXct"), tzone = "", class = c("xts",
"zoo"), .Dim = c(143L, 1L), .Dimnames = list(NULL, "-1,-1"))
I have tried using rollapply from library(zoo) & also mean.geometric from library("PerformanceAnalytics) but it is incorrect:
rollapply(RET,1:nrow(RET), mean.geometric)
Transferred from comments.
RET is an xts object and rollapply.xts in the xts package does not support vector widths.
Try this to cause it to use rollapply.zoo in the zoo package:
rollapplyr(as.zoo(RET), 1:nrow(RET), mean.geometric)

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