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",
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"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
Related
I've run a PCA using prcomp in R and I am trying to produce a variable plot that has 1) a subset of the variables (arrows) in a different color (black) than the rest of the variables, 2) sort those variables prior to plotting so the black arrows aren't covered up by any of the other arrows, and 3) label the black arrows with their TUXXXX number.
Here is a truncated version of my data:
structure(list(sdev = c(21.7106794138444, 15.6885074594869, 11.9124316528111,
10.155277241318, 9.31528828036412, 7.56876266263865, 7.19938201515987,
5.81620977435434, 5.00785424840699, 4.57228787327195, 4.51525494575488,
3.20601607034873, 2.91477640215067, 2.48737967730048, 2.13230488376163,
1.74923754200417, 1.32745772948038, 1.27373216417502, 0.924437474777366,
0.749074623004602, 2.499709597053e-15), rotation = structure(c(-0.0710441966458092,
0.091894514828866, -0.0892433537986534, -0.269473709517009, -0.270455466278075,
0.217492458575054, 0.104541973199297, 0.198858094257877, 0.0222680112919805,
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0.137427869376807, -0.0385728465064452, -0.210836096156869, 0.295086522896984,
0.0791956927556792, 0.154250304318938, 0.212537035577304, 0.165554637810987,
0.0559168852882808, 0.0389301896007549, 8.50109782205011e-06,
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-0.303600234099745, -0.869244048674518, 2.09285131170145, 1.13594169034256,
0.00257581110260907, 1.28821287823238, -0.603719944771219, -0.0929817189825319,
0.646083878133434, -0.997976432253114, -0.0887921104756663, -0.252335674196718,
0.885171983511843, -0.455664633944459, -0.080944547822266, -1.11153038212543,
0.684953529364659, 0.00107091275654292, -0.0523472212020604,
-0.722665903230519, 0.112695429436832, -0.431332703576418, 2.28632079906497,
0.389976510624337, -0.672770348738164, -0.710872748519771, 1.11097033239239,
-0.117525841496901, -0.38659967304807, -0.311519238694312, -0.58769206070627,
0.880912821150173, -0.223180949008615, -0.0563287542495416, 0.677577799103385,
0.174786309548343, -0.486054451272286, -0.875421020334039, -1.99840144432528e-15,
-2.19269047363468e-15, -2.80331313717852e-15, 3.39658856596259e-15,
4.44089209850063e-16, 4.44089209850063e-16, -2.44249065417534e-15,
1.11022302462516e-15, -6.66133814775094e-16, -4.44089209850063e-15,
-3.5527136788005e-15, -2.33146835171283e-15, -6.43929354282591e-15,
2.44249065417534e-15, -1.4432899320127e-15, -2.19269047363468e-15,
1.0547118733939e-15, -1.11022302462516e-15, -9.99200722162641e-16,
1.33226762955019e-15, -1.55431223447522e-15), .Dim = c(21L, 21L
), .Dimnames = list(c("EDA_01", "EDA_02", "EDA_03", "EDA_04",
"EDA_05", "EDA_06", "EDA_07", "EDA_08", "EDA_09", "NDA_01", "NDA_02",
"NDA_03", "NDA_04", "NDA_05", "NDA_06", "NDA_08", "NDA_09", "NDA_10",
"NDA_11", "NDA_12", "NDA_13"), c("PC1", "PC2", "PC3", "PC4",
"PC5", "PC6", "PC7", "PC8", "PC9", "PC10", "PC11", "PC12", "PC13",
"PC14", "PC15", "PC16", "PC17", "PC18", "PC19", "PC20", "PC21"
)))), class = "prcomp")
Here is the minimal code to reproduce this problem:
library(factoextra)
library(tidyverse)
# create a named factor for coding the coloration of the variables in the plot
markers <- facto_summarize(pca,
element = "var",
result = "contrib",
axes = c(1, 2)) %>%
mutate(candidate = ifelse(name == "TU35976" | name == "TU18317" | name == "TU12311" | name == "TU3565" | name == "TU9890" | name == "TU18316", "1", "0")) %>%
select(name, candidate)
markers_vec <- as.factor(markers$candidate)
names(markers_vec) <- markers$name
(var_cluster_PC12 <- fviz_pca_var(pca,
col.var = markers_vec,
axes = c(1,2),
select.var = list(contrib = 200),
palette = c(
"grey90",
"black"),
label = "none",
) +
theme_bw()
)
Which produces this plot:
This image doesn't quite do it justice, so here is a plot of the full data set that shows how bad the overlap is:
Probably the simplest method, given your particular colour scheme, is to make the arrows all black but use the alpha channel to make the gray ones gray. This means the black arrows will still be completely black even if other arrows are drawn over the top:
fviz_pca_var(pca,
axes = c(1,2),
select.var = list(contrib = 200),
alpha.var = ifelse(markers_vec == 0, 0.2, 1),
label = "none") +
theme_bw()
I am working on a data set with 14 variables. I have used the Arima model with Rolling Origin but applying the rolling origin method on each variable every time is a bit slow and want to automate the process. I have tried to automate the process so that it gives me outputs for 14 models but it gives me an error. Please any help is appreciated.
Data:
structure(list(Date = structure(c(289094400, 297043200, 304992000,
312854400, 320716800, 328665600, 336614400, 344476800, 352252800,
360201600, 368150400, 376012800, 383788800, 391737600, 399686400,
407548800, 415324800, 423273600, 431222400, 439084800, 446947200,
454896000, 462844800, 470707200, 478483200, 486432000, 494380800,
502243200, 510019200, 517968000, 525916800, 533779200, 541555200,
549504000, 557452800, 565315200, 573177600, 581126400, 589075200,
596937600, 604713600, 612662400, 620611200, 628473600, 636249600,
644198400, 652147200, 660009600, 667785600, 675734400), tzone = "UTC", class = c("POSIXct",
"POSIXt")), NORTH = c(4.06976744186047, 5.51675977653633, 7.2799470549305,
4.75015422578655, 4.59363957597172, 3.15315315315317, 1.2008733624454,
-0.377562028047452, -0.108283703302655, 0.650406504065032, 0.969305331179318,
0.106666666666688, 3.09003729355352, 2.11886304909562, 2.32793522267207,
5.68743818001977, -1.46934955545156, 3.95611702127658, 5.19438987619354,
-0.0912012507600199, 2.81677896109541, 3.97412590369087, 1.30118326353028,
3.31553807249226, 1.32872294960955, 2.93700394923507, 0.908853875665812,
1.81241002546971, -1.3414545718222, 4.81772747317361, -3.4743890895067,
4.63823913990992, 0.857370960463727, 1.78620594713658, 0.527472527472524,
-4.05973562947765, -0.136726966764838, 3.16657890117607, 5.95161125667812,
8.01002055498458, 10.5501040737437, 13.4138468987035, 2.93371279497212,
8.84291046495554, -6.87764606265876, 2.90741287990725, 3.71548486856639,
1.23317430567388, -1.1153443739474, 4.31313207880924), YORKSANDTHEHUMBER = c(4.0121120363361,
5.45851528384282, 9.52380952380951, 6.04914933837431, 3.03030303030299,
5.42099192618225, 2.78993435448577, -0.53219797764768, 1.97966827180309,
1.15424973767052, 0.466804979253115, -1.96179659266907, 2.42232754081095,
0.719794344473031, -0.306278713629415, 3.37941628264209, 2.74393263992076,
3.91920555341303, 1.91585099967527, 0.892125625853447, 2.91888477848958,
3.78293078507868, 0.109815847271484, 6.83486625601216, 0.722691730511011,
3.56008625759656, -0.227160867754524, 2.69419041475355, -1.17134094520194,
2.78546324684064, 1.01487759630426, 1.54843356139717, 4.15602836879435,
4.43619773934357, -0.309698451507728, -1.45519947678222, -1.09839057574248,
9.08267346664877, 11.8913598474363, 13.9511229623114, 9.71243848306475,
7.66524473371739, 6.46801731884651, -2.26736490763654, -4.35729847494552,
-2.93870179974964, -7.72353426221536, -7.01127302722023, 2.02543627323513,
2.51245245873873), NORTHWEST = c(6.57894736842105, 6.95256660168939,
6.50060753341436, 5.5904164289789, 4.59211237169096, 4.70041322314051,
2.96003946719288, -1.38955438428365, 0.242954324586984, 2.18128938439167,
-0.853889943073994, -2.15311004784691, 0.929095354523226, 2.51937984496125,
0.189035916824195, 2.21698113207546, 2.51499769266268, 3.5066396578888,
1.77437592415414, 0.948636868643719, 4.60125296308836, 3.95775160859537,
-0.237455720347246, 4.218042765725, 2.79306600771276, 2.22545984338008,
0.709042970141798, 0.258269945161875, 0.663420142564747, 2.23655612423752,
1.69729803867784, 0.792339593378065, 2.82330902522246, 2.20899212700891,
1.48327338701976, -1.78151365931687, 1.8457608174996, 5.06380710500736,
7.57132625044768, 9.28561520321818, 9.51969943135663, 11.3671132539057,
10.5960954085668, -1.43026516363364, 3.55308627832826, 3.99351008518014,
-1.44138713566414, -0.165494414563527, 2.01304344107922, 1.70645628251555
), EASTMIDS = c(4.98489425981872, 8.20143884892085, 6.91489361702127,
5.22388059701494, 5.61465721040189, 4.64465584778958, 2.03208556149733,
0.314465408805028, 2.82131661442007, 0, 2.79471544715448, -0.939199209095414,
-1.14770459081835, 2.97829379101462, -0.68627450980392, 3.40572556762095,
3.42243436754175, 4.89223242719342, 0.730408764905171, 2.10107893242476,
2.31025926242835, 5.01798109893785, 0.382256908497274, 4.64894882982943,
3.04374194526571, 2.25491999264298, 0.651125980286367, 1.40105078809108,
2.87265165133409, 3.59418899472349, 1.76616504051596, 3.78627839708797,
3.9017974572556, 3.85473176612416, 0.0696479874633737, 1.45578980947134,
2.96698585107904, 12.8612275490659, 16.8142463597009, 10.6860102754148,
5.80782620275077, 2.65911542610573, -2.54295171544163, 4.66512121048756,
-3.66911045104132, -1.75382312052187, -3.61743042705271, -5.070772474025,
-1.21063610003222, 1.9530155970429), WESTMIDS = c(4.65838509316771,
4.74777448071216, 8.66855524079319, 6.56934306569344, 3.22896281800389,
3.17535545023698, 0.643086816720257, -1.36923779096303, 1.61962054604351,
2.00364298724953, -0.491071428571428, -2.78151637505608, 0, 2.39963082602676,
0.540784136998647, 1.83774092335275, 4.66989436619718, 1.82498633362771,
2.51909973157134, 0.644511581067457, 3.9503702221333, 3.15724626520867,
0.548671245147809, 4.19837410445824, 3.20983256145349, 1.12526319422872,
1.4028740144042, 0.434226470984247, -0.194389516372279, 2.32714328889485,
1.7360199527435, 3.3224734685978, 4.23339889482064, 5.79267379518974,
4.39964893406187, 0.374237288135615, 4.31199848701807, 13.9164443523531,
18.0050929925879, 6.07502745611839, 3.93976822755839, 4.07004176642259,
3.48434981192908, -1.92610381813166, 0.438451356717408, -0.103780578206083,
-3.0952145377791, -1.72381519612015, -2.02143896779759, 4.40768347678723
), EASTANGLIA = c(6.74525212835624, 8.58895705521476, 8.47457627118643,
10.7291666666667, 4.8447789275635, 4.84522207267835, -0.299529311082601,
1.45922746781116, 0.88832487309645, 0.29350104821803, -0.877926421404701,
1.64487557992411, -2.69709543568468, 3.49680170575694, 3.25504738360115,
2.39425379090184, 2.98519095869059, 4.36691137516082, 3.57868020304568,
1.66275772744776, 3.79450451070863, 4.52162951167727, 2.28203256419209,
4.17054552224914, 3.2439678284182, 4.76643873164257, 0.955633279171614,
2.91614381581101, 0.848198902642676, 5.02010671012167, 2.80551592962435,
5.64292321924145, 4.17550004608719, 9.7903026013095, 5.88709352460008,
3.07862089961185, 8.83080444493668, 14.1609281183215, 14.9330678829839,
-2.38242974223737, 1.8287757399192, 1.22633166874738, -5.71564382892894,
-5.25820956533587, -9.72515856236787, 0.957479010339489, -3.50481300299826,
-3.45549395738277, -0.828308094308001, -0.331408094033985), OUTERSEAST = c(6.7110371602884,
7.53638253638255, 9.47317544707589, 8.56512141280351, 3.82269215128102,
2.11515863689776, 1.64940544687381, -1.73584905660378, 1.34408602150539,
1.78097764304659, 0.446760982874161, -1.26019273535953, 0.150150150150159,
3.11094452773611, 1.4176663031625, 2.54480286738352, 5.56448794127927,
4.89371564797033, 3.88257575757575, 1.85961713764815, 5.54859495256845,
4.29879599796508, 2.00525702517411, 3.63679834232127, 3.44509381728699,
3.46664684309643, 1.93988743863012, 2.50440502760482, 2.96578121060713,
4.47634947134114, 4.50826657576274, 4.92742395824838, 5.38770910645244,
7.13653626341212, 6.15524925576032, 1.08283352245096, 6.66955322492704,
9.69075574665124, 11.4606033194907, 3.4233015677836, 1.10095233565968,
1.65461280649144, -3.58737650679069, -5.85546129756061, -4.98846560711691,
-2.32068359558401, -5.55914140928629, -4.66925504224286, -1.07093896112692,
2.07357059157311), OUTERMET = c(4.54545454545458, 6.58505698607005,
7.36633663366336, 7.08225746956843, 4.3747847054771, 1.68316831683168,
1.00616682895164, -1.28534704370181, 2.01822916666665, 0.797702616464613,
0.949667616334271, -0.940733772342415, 1.10794555238999, 2.19160926737633,
2.84926470588237, 2.62138814417631, 5.02467343976781, 5.65213786241397,
3.22555328833776, 3.73552294786995, 5.05948745510956, 4.28797321179426,
2.86300392436674, 2.60339894216597, 4.28031183318191, 3.43199821714381,
3.34554286721641, 3.04770569170409, 1.65167650683293, 4.62120252591965,
6.34025700005186, 6.1931790459772, 8.10781836281492, 6.14401677315165,
5.88313802952244, 0.112183931227468, 4.21036727396348, 5.85740693754756,
8.61496319123439, 2.24246818616477, 2.39678510128783, 1.57885756155336,
-2.68472955079939, -5.09925369345585, -6.23990242127901, -2.51851513733724,
-2.72874133732908, -5.45172276846427, 0.20833593462305, 2.61721355963614
), LONDON = c(8.11719500480309, 10.3065304309196, 6.32299637535239,
7.65151515151515, 1.30190007037299, 2.1535255296978, -0.204012240734436,
-0.306643952299836, 0.786056049213951, 1.18684299762631, 1.00536193029493,
-2.85335102853352, 2.76639344262296, 2.06048521103356, 1.23738196027352,
2.70183338694115, 3.30410272471031, 5.76322570865546, 4.73255747291176,
1.98428989791171, 6.03563952552197, 4.88977753030802, 2.12581135535556,
4.43247330120026, 5.42986425339366, 3.96781115879828, 3.43247538648888,
4.0668901660281, 4.09587727708534, 4.81707991010573, 7.42869193863026,
6.70069362648866, 6.67699006500675, 7.43184006668679, 5.53177257525084,
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2.42130750605327, 1.85185185185186, 0.928433268858785, 5.95247221157533,
4.38447346525341, 3.30272049904696, 2.25107353730542, 3.86823714688802,
2.04371722787289, 3.04596811639065, 4.19057346270538, 2.45646407565451,
2.17525889239081, 2.83400809716597, 1.58015962290428, 2.77894958869438,
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0.528003461834023), WALES = c(6.09418282548476, 8.35509138381203,
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1.28887158859911, 0.68747598104105, 3.71925360474978, 4.66941979801284,
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), SCOTLAND = c(5.15222482435597, 4.12026726057908, 5.40106951871658,
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1.67315466387184, -2.88259765121672, 2.95678544351781, -0.54123711340205,
4.15355569540591, -1.90510040874357, 0.923946519801462, 4.1035398865513,
-2.3519674449081, -5.50238389546177, 7.24670179766041, 2.75090864790844,
0.446509889559553), UK = c(5.76890543055322, 7.20302836425676,
7.39543442582184, 7.22885986848197, 3.23472252213347, 2.95766398929048,
1.20271423347285, -0.554061107319231, 0.98913965036942, 1.55113136643479,
0.373986300291293, -1.61195434757029, 1.59052858167903, 2.07573082205217,
1.17628969016684, 2.44680851063832, 2.84453345201007, 4.10010457610617,
2.88208396840793, 1.58922558922557, 3.67559326527908, 3.90013106997858,
1.36611181194425, 4.12505691303686, 2.02017257462689, 2.93167985827357,
1.54068234183715, 2.12149379408387, 0.594313861969269, 3.83755588673622,
3.33948434056075, 3.50933756603259, 3.25378570059421, 5.14920870654849,
3.36548010504709, -0.177206541696886, 1.65971553844507, 8.51865098567251,
11.0759984490113, 5.32351247098249, 3.99880682100659, 4.55095927082668,
0.864171188197283, -2.04898834977862, -3.10383660120637, -1.01415357182659,
-2.94496091613858, -4.06343734981687, -0.677156948752485, 1.59717017296902
)), row.names = c(NA, -50L), class = c("tbl_df", "tbl", "data.frame"
))
This manual code works:
y <- data2$NORTH
ourCall <- "predict(arima(x=data,order=c(1,0,0)),n.ahead=h)"
ourValue <- c("pred")
returnedValues1 <- ro(y, h=4, origins = 8,
call=ourCall, value=ourValue)
returnedValues1$actuals
returnedValues1$holdout
returnedValues1$pred
But this doesn't:
y <- data2
y %<>% dplyr::select(-Date)
ourCall <- "predict(arima(x=data,order=c(1,0,0)),n.ahead=h)"
ourValue <- c("pred")
ar_ro_model_4 = apply(y,2,function(x){
return(
list(
returnedValues1 <- ro(y, h=4, origins = 8,
call=ourCall, value=ourValue)
))
} )
ar_ro_model_4
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",
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All suggestions shall be helpful.
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 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.