Yesterday I asked this question about drawing a line that connects two given points using add_lines() from plotly. That makes me think about some other visualization aspects I want to give to my graph: to connect two given points and extend the line across the whole x-axis.
This is my current database:
dput(sma)
structure(list(time = structure(c(1640808000, 1640822400, 1640836800,
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4578L, 4151L, 8128L, 7304L, 3698L, 7376L, 5582L), SMA_5 = c(NA,
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0.123482, 0.122528, 0.121984, 0.122136, 0.121998, 0.121596, 0.122742,
0.124202, 0.125342, 0.126212, 0.127186, 0.1277, 0.127356, 0.127002,
0.126642, 0.126148, 0.125574, 0.125958, 0.125802, 0.125132, 0.124782,
0.124218, 0.122916, 0.122476, 0.12285, 0.12272, 0.122708, 0.123098,
0.123374, 0.12319, 0.123502, 0.122134, 0.11936, 0.11572, 0.112708,
0.109252, 0.108084, 0.108016, 0.108672, 0.107818, 0.107206, 0.106202,
0.104506, 0.10308, 0.102674, 0.102718, 0.102804, 0.103562, 0.103574,
0.102532, 0.102166, 0.10151, 0.100698, 0.10059, 0.101538, 0.102222,
0.102716, 0.103432, 0.104144, 0.104684, 0.10304, 0.10163, 0.100382,
0.09956, 0.098846, 0.099442, 0.100358, 0.101074, 0.101054, 0.10139,
0.101828, 0.102146, 0.102936, 0.103768, 0.104446, 0.104832, 0.105502,
0.10527, 0.1047, 0.10382, 0.103046, 0.101878, 0.101166, 0.100308,
0.100066, 0.100132, 0.100226, 0.100292, 0.101244, 0.102746, 0.105258,
0.107942, 0.110686, 0.112502, 0.11302, 0.112266, 0.111448, 0.110338,
0.110208, 0.110936, 0.111886, 0.112456, 0.112214, 0.111354, 0.110138,
0.109372, 0.109908, 0.111176, 0.112382, 0.113758, 0.114852, 0.11479,
0.1142, 0.113968, 0.113432, 0.112462, 0.11073, 0.109462, 0.108324,
0.10721, 0.106768, 0.106932, 0.105738, 0.103126, 0.100896, 0.098138,
0.095374, 0.093886, 0.092018, 0.08953, 0.085102, 0.0805, 0.076072,
0.073234, 0.07107, 0.070764, 0.071486, 0.072422, 0.073066, 0.072932,
0.073816, 0.073168, 0.071954, 0.07024, 0.069176, 0.067772, 0.067602,
0.06768, 0.068532, 0.069632, 0.07023, 0.070624, 0.071236, 0.071966,
0.072356, 0.072982, 0.073814, 0.07456, 0.07479, 0.074112, 0.073448,
0.07287, 0.072914, 0.07294, 0.073786, 0.074552, 0.074678, 0.074084,
0.074372, 0.074564, 0.074824, 0.075358, 0.076312, 0.076854, 0.076996,
0.077262, 0.07778, 0.078058, 0.078506, 0.07919, 0.079204, 0.078308,
0.077534, 0.075826, 0.074156, 0.07282, 0.072444, 0.07215, 0.07254,
0.07334, 0.074114, 0.074584, 0.075082, 0.07549, 0.075762, 0.076102,
0.07631, 0.076578, 0.07615, 0.075796, 0.07487, 0.07396, 0.07295,
0.072634, 0.07234, 0.072362, 0.072874, 0.073342, 0.074192, 0.074988,
0.076066, 0.076846, 0.077678, 0.078722, 0.079846), SMA_10 = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, 0.122434, 0.122682, 0.122806,
0.122778, 0.122618, 0.122702, 0.122539, 0.122635, 0.123093, 0.123739,
0.124105, 0.124391, 0.125221, 0.125779, 0.126172, 0.126427, 0.126667,
0.126637, 0.126657, 0.126402, 0.125887, 0.125465, 0.124896, 0.124437,
0.124139, 0.123991, 0.123751, 0.123463, 0.123007, 0.122925, 0.12302,
0.123111, 0.122421, 0.121229, 0.119547, 0.117949, 0.116377, 0.115109,
0.113688, 0.112196, 0.110263, 0.108229, 0.107143, 0.106261, 0.105876,
0.105246, 0.104962, 0.104503, 0.104034, 0.103327, 0.102603, 0.102442,
0.102157, 0.10213, 0.102082, 0.102035, 0.102194, 0.102113, 0.102065,
0.102367, 0.103111, 0.102631, 0.102173, 0.101907, 0.101852, 0.101765,
0.101241, 0.100994, 0.100728, 0.100307, 0.100118, 0.100635, 0.101252,
0.102005, 0.102411, 0.102918, 0.10333, 0.103824, 0.104103, 0.104234,
0.104133, 0.103939, 0.10369, 0.103218, 0.102504, 0.101943, 0.101589,
0.101052, 0.100729, 0.100776, 0.101406, 0.102695, 0.104084, 0.105489,
0.106873, 0.107883, 0.108762, 0.109695, 0.110512, 0.111355, 0.111978,
0.112076, 0.111952, 0.111276, 0.110781, 0.110537, 0.110629, 0.111182,
0.111695, 0.111868, 0.111948, 0.112112, 0.112349, 0.112688, 0.113175,
0.113595, 0.113657, 0.11276, 0.111831, 0.111146, 0.110321, 0.109615,
0.108831, 0.1076, 0.105725, 0.104053, 0.102453, 0.101153, 0.099812,
0.097572, 0.095213, 0.09162, 0.087937, 0.084979, 0.082626, 0.0803,
0.077933, 0.075993, 0.074247, 0.07315, 0.072001, 0.07229, 0.072327,
0.072188, 0.071653, 0.071054, 0.070794, 0.070385, 0.069817, 0.069386,
0.069404, 0.069001, 0.069113, 0.069458, 0.070249, 0.070994, 0.071606,
0.072219, 0.072898, 0.073378, 0.073234, 0.073215, 0.073342, 0.073737,
0.073865, 0.073949, 0.074, 0.073774, 0.073499, 0.073656, 0.074175,
0.074688, 0.075018, 0.075198, 0.075613, 0.07578, 0.076043, 0.076569,
0.077185, 0.07768, 0.078093, 0.078233, 0.078044, 0.077796, 0.077166,
0.076673, 0.076012, 0.075376, 0.074842, 0.074183, 0.073748, 0.073467,
0.073514, 0.073616, 0.074015, 0.074551, 0.075108, 0.075447, 0.07583,
0.07582, 0.075779, 0.075486, 0.075135, 0.074764, 0.074392, 0.074068,
0.073616, 0.073417, 0.073146, 0.073413, 0.073664, 0.074214, 0.07486,
0.07551, 0.076457, 0.077417), SMA_20 = c(NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.1232695,
0.1235365, 0.1240135, 0.1242785, 0.124395, 0.1245645, 0.124603,
0.124636, 0.124875, 0.1250705, 0.124996, 0.124928, 0.1250585,
0.125108, 0.1251555, 0.125209, 0.125209, 0.12505, 0.124832, 0.1246635,
0.1244535, 0.124288, 0.1236585, 0.122833, 0.121843, 0.12097,
0.120064, 0.119286, 0.1183475, 0.1175605, 0.1166415, 0.11567,
0.114782, 0.113745, 0.1127115, 0.1115975, 0.1106695, 0.109806,
0.108861, 0.1077615, 0.106433, 0.1053355, 0.10465, 0.1041955,
0.103979, 0.1036405, 0.103578, 0.103308, 0.1030495, 0.102847,
0.102857, 0.1025365, 0.102165, 0.1020185, 0.101967, 0.1019, 0.1017175,
0.1015535, 0.1013965, 0.101337, 0.1016145, 0.101633, 0.1017125,
0.101956, 0.1021315, 0.1023415, 0.1022855, 0.102409, 0.1024155,
0.1022705, 0.1021255, 0.102287, 0.102471, 0.1026115, 0.1024575,
0.1024305, 0.1024595, 0.102438, 0.102416, 0.102505, 0.1027695,
0.103317, 0.103887, 0.1043535, 0.1046885, 0.104913, 0.1051755,
0.1053735, 0.1056205, 0.1060655, 0.106692, 0.1073855, 0.108018,
0.1083825, 0.108827, 0.10921, 0.1096955, 0.1104385, 0.1111035,
0.1116115, 0.111963, 0.112094, 0.1121505, 0.111982, 0.111978,
0.112066, 0.112143, 0.111971, 0.111763, 0.111507, 0.1111345,
0.1108635, 0.11059, 0.110144, 0.10945, 0.108824, 0.108055, 0.1069565,
0.1058215, 0.104359, 0.102767, 0.1006175, 0.0983839999999999,
0.0962894999999999, 0.0941754999999999, 0.0921764999999999, 0.0901929999999999,
0.0885729999999999, 0.0870294999999999, 0.0853609999999999, 0.0836069999999999,
0.0819549999999999, 0.0801319999999999, 0.0785834999999999, 0.0771394999999999,
0.0756769999999999, 0.0743634999999999, 0.0731889999999999, 0.0720319999999999,
0.0712679999999999, 0.0707024999999999, 0.0706454999999999, 0.0707199999999999,
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0.0735169999999999, 0.0737044999999999, 0.0739514999999999, 0.0741799999999999,
0.0744674999999999, 0.0747389999999999, 0.0748644999999999, 0.0750214999999999,
0.0751714999999999, 0.0753419999999999, 0.0756679999999999, 0.0761339999999999,
0.0764604999999999, 0.0765309999999999, 0.0764969999999999, 0.0763894999999999,
0.0762264999999999, 0.0760274999999999, 0.0759724999999999, 0.0760134999999999,
0.0759314999999999, 0.0759204999999999, 0.0758499999999999, 0.0757789999999999,
0.0757059999999999, 0.0755904999999999, 0.0756119999999999, 0.0755599999999999,
0.0754114999999999, 0.0753359999999999, 0.0750014999999999, 0.0747634999999999,
0.0744764999999999, 0.0743244999999999, 0.0741899999999999, 0.0742034999999999,
0.0743094999999999, 0.0743619999999999, 0.0744319999999999, 0.0744879999999999,
0.0746164999999999, 0.0747214999999999, 0.0748499999999999, 0.0749974999999999,
0.0751369999999999, 0.0754244999999999, 0.0757424999999999)), row.names = c(NA,
-225L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x00000000105f1ef0>)
What I want is to connect the two highest highs and expand the line across all the x-axis. Here's the code to get that data:
highs <- arrange(sma, desc(high))%>%
slice(1:2)
highs
time open high low close volume trades SMA_5 SMA_10 SMA_20
1: 2022-01-01 10:00:00 0.12488 0.13202 0.12429 0.12839 52294930 27559 0.124202 0.123093 NA
2: 2022-01-03 02:00:00 0.12455 0.12930 0.12431 0.12859 32763165 14885 0.125958 0.126657 0.124875
So basically I have 0.13202 from January 1st and 0.12930 from January 3rd as my highest highs. With some code such as:
add_lines(inherit = F, data = highs, x = ~time, y = ~high,
name = "Higher highs trendline", line = list(color = "seagreen", width = 2.5, dash = "dot"))
I can draw the line exactly for the two points in my highs object. But what I want is that this line goes across all my graph. In short, given two points I want to draw a trendline the same way we used to do on basic algebra. Main code for my graph is:
sma %>% plot_ly(x = ~time, type="candlestick",
open = ~open, close = ~close,
high = ~high, low = ~low) %>%
add_lines(x = ~time, y= ~SMA_5, line = list(color = "gold", width = 2), inherit = F,
name = "SMA 5", showlegend=T)%>%
add_lines(x = ~time, y= ~SMA_10, line = list(color = "deeppink", width = 2), inherit = F,
name = "SMA 10", showlegend=T)%>%
add_lines(x = ~time, y= ~SMA_20, line = list(color = "purple", width = 2), inherit = F,
name = "SMA 20", showlegend=T)%>%
plotly::layout(title = paste0(nombre, " Simple Moving Average, ", tiempo),
xaxis= list(title="Time", rangeslider = list(visible = F)), yaxis = list(title = "Price"),
sliders=list(visible=F)) -> sma_
Any help and orientation will be much appreciated.
You could fit a linear model on the two high points, and predict it's values on the remaining dataset:
fit <- lm(high ~ time, data = highs)
sma %>% plot_ly(x = ~time, type="candlestick",
open = ~open, close = ~close,
high = ~high, low = ~low) %>%
add_lines( x = ~time, y = ~predict(fit,sma))
Related
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",
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All suggestions shall be helpful.
I'm trying to plot a pearson correlation heat map in R on a certain dataset.
The rows and columns of the heat map shall be the same, thus I'm trying to plot a lower triangular plot for it.
The code I'm trying to run is:
cormat_UCS_pearson <- round(cor(t(UCS_pearson)),5)
Where
UCS_perason originally has 58387 columns and two rows, but due to constraints, I'll show
UCS_pearson[,c(1:500)]
structure(c(-0.160788424826038, -0.0257422401750742, -0.0518775542691747,
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NA, NA, -0.0467230775481449, 0.0445367556198534, -0.0258221029555385,
-0.337769379137755, -0.0949717047071838, -0.0653334039042102,
-0.0916244514588163, 0.208385897193025, -0.0139516499838043,
-0.0328095707998725, 0.108756365899433, -0.205946694745602, 0.0444850847582909,
-0.0456047105712402, -0.123080606885036, 0.166004972978278, -0.0540745862900131,
-0.104383649403864, NA, NA, NA, NA, -0.256514651048856, 0.0389502818708297,
-0.155885002949447, -0.145172040814028, -0.0197648643935328,
-0.154228761470774, -0.245465986869158, 0.196050394893124, -0.0629062361879831,
-0.0418851053863066, -0.0883849505943926, 0.167116542912757,
NA, NA, -0.0945325286387913, -0.244297051453914, 0.0994391613316947,
-0.0414589626231581, -0.0021774093388973, 0.0789953240584772,
-0.28851469166179, 0.212678450880014, 0.178853309299823, 0.0160852171949784,
NA, NA, -0.0724021587657935, -0.0726525233071279, 0.0641256540271004,
-0.247109926514703, 0.261007190593326, 0.162365145316339, 0.183695385237096,
-0.160854132104893, 0.0254757957933874, 0.159302424608104, 0.0536504218846581,
0.0947326442990328, -0.0739825582591165, -0.023444526981956,
NA, NA, -0.246445416033503, 0.0421799991254654, 0.171399843165558,
0.121857838781291, 0.131291806491966, -0.0310256969763662, 0.084932656034447,
0.319694044603215, -0.0889318830147494, 0.244658060764321, -0.0962644796304246,
-0.0406925583175, 0.0719899653174018, 0.186970645556985, -0.269454328187128,
0.115075337197967, NA, NA, 0.124683035292236, 0.0203697993225934,
-0.188138124518597, 0.0454961850788923, -0.0506301523232337,
0.283829223238744, NA, NA, 0.012953293999785, -0.199688812112181,
-0.0414576280113129, 0.195648813114567, -0.178532798762864, 0.0495794181231349,
-0.284278159207787, 0.119933984376387, -0.0736119199628618, -0.0519048539144688,
-0.193631407386575, -0.289684365032212, 0.0521560438562125, -0.122037909970877,
-0.136878270640725, 0.149803202303949, 0.0153790015443654, 0.0202694351487697,
NA, NA, NA, NA, NA, NA, -0.0756117605914394, 0.148175208233554,
-0.271295578246216, 0.409493126095501, -0.01568142586576, -0.0648911279001933,
NA, NA, 0.0671711556674623, 0.0618219987103883, 0.133024970982326,
-0.193030566840229, -0.0158360602199258, -0.0866160392870411,
-0.0675782565096752, -0.0751017710958831, -0.220393224448939,
-0.00966063181064768, -0.0996194859966963, -0.0991640377156096,
0.0726333418651898, -0.103658323178436, -0.27364637703548, 0.273019847922917,
-0.0781696117636672, 0.129417247633593, 0.0297980677565262, -0.134352011645225,
-0.118604292468149, -0.190775713471729, -0.16647638154017, -0.0762085246815682,
-0.0891311686769857, -0.163661512890215, 0.31722215300791, -0.275034100117617,
-0.129169755185721, -0.00241222587250762, -0.107647650941447,
0.127604415704785, -0.166092508902277, -0.195686403184036, 0.0318623953980768,
0.141089701919201, NA, NA, -0.0961135302259641, -0.0798572664410973,
NA, NA, -0.0461883921074545, 0.0834756138899552, -0.08487553609808,
0.00963537949551373, -0.165936066953134, 0.0111744666718458,
-0.0908197261353577, -0.156010494847943, -0.260674035822964,
-0.114751621595064, NA, NA, NA, NA, -0.0136054096164949, -0.0185447185244471,
-0.122981079778463, -0.0111990675185473, -0.182298509501045,
-0.388317916380626, -0.153758412306888, 0.261778719392714, -0.217254978152196,
0.191612183539808, NA, NA, -0.136777432491356, -0.114496580430642,
-0.02969812077836, -0.268652077750056, -0.0508640036811775, -0.000711744011523578,
0.317425038435027, 0.134985042790569, NA, NA), .Dim = c(2L, 500L
), .Dimnames = list(c("tumor_Mes", "tumor_Epi"), 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",
"AC004009.2", "AC004009.3", "AC004012.1", "AC004014.3", "AC004014.4",
"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", "AC004070.1",
"AC004074.4", "AC004076.5", "AC004076.7", "AC004076.9", "AC004079.1",
"AC004108.1", "AC004112.4", "AC004112.5", "AC004112.7", "AC004125.3",
"AC004129.7", "AC004129.9", "AC004156.3", "AC004158.1", "AC004158.3",
"AC004159.1", "AC004160.4", "AC004166.6", "AC004221.2", "AC004231.2",
"AC004237.1", "AC004257.1", "AC004381.6", "AC004381.7", "AC004383.3",
"AC004386.3", "AC004386.4", "AC004447.2", "AC004448.2", "AC004448.5",
"AC004449.6", "AC004453.1", "AC004453.8", "AC004458.1", "AC004460.1",
"AC004461.4", "AC004470.1", "AC004471.10", "AC004471.9", "AC004477.1",
"AC004485.3", "AC004490.1", "AC004492.1", "AC004510.3", "AC004520.1",
"AC004535.2", "AC004538.3", "AC004540.4", "AC004540.5", "AC004541.1",
"AC004543.1", "AC004543.2", "AC004549.6", "AC004552.1", "AC004593.3",
"AC004595.1", "AC004603.4", "AC004623.2", "AC004623.3", "AC004637.1",
"AC004655.1", "AC004656.1", "AC004673.1", "AC004687.1", "AC004691.1",
"AC004691.5", "AC004692.4", "AC004692.5", "AC004699.1", "AC004702.2",
"AC004744.3", "AC004745.1", "AC004754.3", "AC004769.1", "AC004775.5",
"AC004791.2", "AC004812.1", "AC004813.1", "AC004816.1", "AC004819.1",
"AC004824.1", "AC004824.2", "AC004832.1", "AC004837.1", "AC004837.3",
"AC004837.4", "AC004837.5", "AC004840.8", "AC004846.1", "AC004850.1",
"AC004854.4", "AC004854.5", "AC004862.6", "AC004866.1", "AC004866.3",
"AC004869.2", "AC004869.3", "AC004870.3", "AC004870.4", "AC004870.5",
"AC004875.1", "AC004878.2", "AC004878.8", "AC004893.10", "AC004893.11",
"AC004895.1", "AC004895.4", "AC004899.3", "AC004901.1", "AC004906.3",
"AC004911.2", "AC004915.1", "AC004920.2", "AC004920.3", "AC004924.1",
"AC004932.1", "AC004938.5", "AC004941.3", "AC004941.5", "AC004943.1",
"AC004945.1", "AC004946.1", "AC004947.2", "AC004951.5", "AC004951.6",
"AC004953.1", "AC004967.7", "AC004969.1", "AC004980.1", "AC004980.10",
"AC004980.11", "AC004980.7", "AC004980.8", "AC004980.9", "AC004984.1",
"AC004985.12", "AC004987.10", "AC004987.9", "AC004988.1", "AC005000.1"
)))
This gives me
cormat_UCS_pearson as
structure(c(1, NA, NA, 1), .Dim = c(2L, 2L), .Dimnames = list(
c("tumor_Mes", "tumor_Epi"), c("tumor_Mes", "tumor_Epi")))
Since the values are either 1 or NAs, I cannot move ahead to plot a graph for the same.
Any suggestion to help circumvent this shall be helpful.
The initial steps of my codes produced certain NAs and I had to rectify them in order to get the correct plot.
I'm trying to fit some curves from the data below with a mono-exponential "decay". Graphical display is not as important as is pulling out the time constant. the y-axis is pA and the x is time in seconds.
dput(stackover_data)
structure(list(Time = c(0.09990001, 0.19990001, 0.29990001, 0.39990001,
0.49990001, 0.59990001, 0.69990001, 0.79990001, 0.89990001, 0.99990001,
1.09990001, 1.19990001, 1.29990001, 1.39990001, 1.49990001, 1.59990001,
1.69990001, 1.79990001, 1.89990001, 1.99990001, 2.09990001, 2.19990001,
2.29990001, 2.39990001, 2.49990001, 2.59990001, 2.69990001, 2.79990001,
2.89990001, 2.99990001, 3.09990001, 3.19990001, 3.29990001, 3.39990001,
3.49990001, 3.59990001, 3.69990001, 3.79990001, 3.89990001, 3.99990001,
4.09990001, 4.19990001, 4.29990001, 4.39990001, 4.49990001, 4.59990001,
4.69990001, 4.79990001, 4.89990001, 4.99990001, 5.09990001, 5.19990001,
5.29990001, 5.39990001, 5.49990001, 5.59990001, 5.69990001, 5.79990001,
5.89990001, 5.99990001, 6.09990001, 6.19990001, 6.29990001, 6.39990001,
6.49990001, 6.59990001, 6.69990001, 6.79990001, 6.89990001, 6.99990001,
7.09990001, 7.19990001, 7.29990001, 7.39990001, 7.49990001, 7.59990001,
7.69990001, 7.79990001, 7.89990001, 7.99990001, 8.09990001, 8.19990001,
8.29990001, 8.39990001, 8.49990001, 8.59990001, 8.69990001, 8.79990001,
8.89990001, 8.99990001, 9.09990001, 9.19990001, 9.29990001, 9.39990001,
9.49990001, 9.59990001, 9.69990001, 9.79990001, 9.89990001, 9.99990001,
10.09990001, 10.19990001, 10.29990001, 10.39990001, 10.49990001,
10.59990001, 10.69990001, 10.79990001, 10.89990001, 10.99990001,
11.09990001, 11.19990001, 11.29990001, 11.39990001, 11.49990001,
11.59990001, 11.69990001, 11.79990001, 11.89990001, 11.99990001,
12.09990001, 12.19990001, 12.29990001, 12.39990001, 12.49990001,
12.59990001, 12.69990001, 12.79990001, 12.89990001, 12.99990001,
13.09990001, 13.19990001, 13.29990001, 13.39990001, 13.49990001,
13.59990001, 13.69990001, 13.79990001, 13.89990001, 13.99990001,
14.09990001, 14.19990001, 14.29990001, 14.39990001, 14.49990001,
14.59990001, 14.69990001, 14.79990001, 14.89990001, 14.99990001,
15.09990001, 15.19990001, 15.29990001, 15.39990001, 15.49990001,
15.59990001, 15.69990001, 15.79990001, 15.89990001, 15.99990001,
16.09990001, 16.19990001, 16.29990001, 16.39990001, 16.49990001,
16.59990001, 16.69990001, 16.79990001, 16.89990001, 16.99990001,
17.09990001, 17.19990001, 17.29990001, 17.39990001, 17.49990001,
17.59990001, 17.69990001, 17.79990001, 17.89990001, 17.99990001,
18.09990001, 18.19990001, 18.29990001, 18.39990001, 18.49990001,
18.59990001, 18.69990001, 18.79990001, 18.89990001, 18.99990001,
19.09990001, 19.19990001, 19.29990001, 19.39990001, 19.49990001,
19.59990001, 19.69990001, 19.79990001, 19.89990001, 19.99990001
), `Trace 1` = c(-3.08656892325052, 9.36821982641837, 8.13806079083122,
10.7039590839898, 7.25670468903547, 4.31122291688919, 1.77905971163193,
-6.27606834721828, -8.65955381985049, -10.1445673910916, -10.6649772153892,
-6.52301948183154, -11.9757817854835, -0.976254254762154, 0.237467076202677,
-11.8114896779541, -11.0022757370468, -16.1845427042923, 2.70565927469852,
-18.9048281652216, -22.153682283437, -4363.32044948884, -3470.59111611883,
-2877.51064886248, -2253.51505229908, -1876.62974792002, -1541.14546478629,
-1288.26617158403, -1232.52313999354, -1042.98549163259, -938.795146277054,
-810.913567086442, -736.390770574588, -682.044521632168, -638.575324886466,
-596.587948389699, -629.282103146111, -569.200610245336, -587.387817942122,
-521.939307772762, -533.776693538631, -514.782451411908, -445.949395199026,
-451.026618716539, -461.669600651513, -442.372477296489, -426.132547857502,
-452.471101919398, -449.377497412324, -436.817659066873, -487.464805660851,
-442.915455035179, -458.666741489705, -454.990437793055, -455.173154690614,
-426.885702219019, -456.985443835707, -408.869318611773, -442.418742303429,
-407.42593099033, -414.538253423006, -316.036755461507, -248.395118743017,
-198.708015370115, -183.88480278352, -160.041754268138, -139.020785864805,
-123.701641615743, -137.253323547789, -124.240619047461, -121.512859107349,
-113.719386521321, -102.98429740535, -118.721098087137, -109.789471870234,
-122.145399997255, -109.542467905009, -96.6725326170008, -108.16233877188,
-94.1092968366083, -88.0987495250118, -89.2245425850472, -96.3495439993499,
-110.340434956898, -98.8777265479938, -88.0674013452629, -102.755317774957,
-91.0752842494157, -107.959830375198, -78.8424385901398, -93.7743443479161,
-98.3591889757604, -72.3214090579355, -85.6296201608712, -112.596656084088,
-115.518068650615, -110.973655632476, -78.8021307215932, -85.6443070182152,
-93.1744356389988, -100.483987323044, -87.6672104421484, -83.6481077757535,
-82.1465876740715, -86.7838666454595, -75.9066755520263, -86.6416980998645,
-88.9405806921788, -74.0592581080291, -86.3433338300531, -93.5114839187431,
-91.1875849041866, -82.7018083540351, -102.859075734953, -82.1494206590809,
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"Trace 1", "Trace 2", "Trace 3"), row.names = c(1000L, 2000L,
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I've tried doing lm(y~x)but it doesn't seem to get the right answer (verified the right answer in Igor) and obviously this is because its a linear model and not a exponential. Any all suggestions are welcomed. I'm struggling on this!
Thanks all!
names(DF) <- make.names(names(DF))
plot(Trace.1 ~ Time, data = DF)
#remove the initial values that clearly don't follow the model
DF1 <- DF[-seq_len(which((diff(DF$Trace.1) < -1e3))),]
plot(Trace.1 ~ Time, data = DF1)
fit <- nls(Trace.1 ~ SSasymp(Time, Asym, R0, lrc), data = DF1)
summary(fit)
coef(fit)
help("SSasymp") #for an explanation of the parameters
lines(DF1$Time, predict(fit))
I apologize in advance for the specificity of this question, I'm hoping my neuroscience jargon doesn't confuse things.
I have current traces from single cell recordings and I need to fit a tau from the peak to 4 seconds after the peak out on each trace. In reality, this is just an exponential decay.
So what I'm looking for is a ggplot overlay of the fit (so really a fit line over the period requested.) something along the lines of stat_smooth()
My data looks like follow:
dput(stackover_data)
structure(list(Time = c(0.09990001, 0.19990001, 0.29990001, 0.39990001,
0.49990001, 0.59990001, 0.69990001, 0.79990001, 0.89990001, 0.99990001,
1.09990001, 1.19990001, 1.29990001, 1.39990001, 1.49990001, 1.59990001,
1.69990001, 1.79990001, 1.89990001, 1.99990001, 2.09990001, 2.19990001,
2.29990001, 2.39990001, 2.49990001, 2.59990001, 2.69990001, 2.79990001,
2.89990001, 2.99990001, 3.09990001, 3.19990001, 3.29990001, 3.39990001,
3.49990001, 3.59990001, 3.69990001, 3.79990001, 3.89990001, 3.99990001,
4.09990001, 4.19990001, 4.29990001, 4.39990001, 4.49990001, 4.59990001,
4.69990001, 4.79990001, 4.89990001, 4.99990001, 5.09990001, 5.19990001,
5.29990001, 5.39990001, 5.49990001, 5.59990001, 5.69990001, 5.79990001,
5.89990001, 5.99990001, 6.09990001, 6.19990001, 6.29990001, 6.39990001,
6.49990001, 6.59990001, 6.69990001, 6.79990001, 6.89990001, 6.99990001,
7.09990001, 7.19990001, 7.29990001, 7.39990001, 7.49990001, 7.59990001,
7.69990001, 7.79990001, 7.89990001, 7.99990001, 8.09990001, 8.19990001,
8.29990001, 8.39990001, 8.49990001, 8.59990001, 8.69990001, 8.79990001,
8.89990001, 8.99990001, 9.09990001, 9.19990001, 9.29990001, 9.39990001,
9.49990001, 9.59990001, 9.69990001, 9.79990001, 9.89990001, 9.99990001,
10.09990001, 10.19990001, 10.29990001, 10.39990001, 10.49990001,
10.59990001, 10.69990001, 10.79990001, 10.89990001, 10.99990001,
11.09990001, 11.19990001, 11.29990001, 11.39990001, 11.49990001,
11.59990001, 11.69990001, 11.79990001, 11.89990001, 11.99990001,
12.09990001, 12.19990001, 12.29990001, 12.39990001, 12.49990001,
12.59990001, 12.69990001, 12.79990001, 12.89990001, 12.99990001,
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13.59990001, 13.69990001, 13.79990001, 13.89990001, 13.99990001,
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14.59990001, 14.69990001, 14.79990001, 14.89990001, 14.99990001,
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16.59990001, 16.69990001, 16.79990001, 16.89990001, 16.99990001,
17.09990001, 17.19990001, 17.29990001, 17.39990001, 17.49990001,
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19.59990001, 19.69990001, 19.79990001, 19.89990001, 19.99990001
), `Trace 1` = c(-3.08656892325052, 9.36821982641837, 8.13806079083122,
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If you
plot(stackover_data$Time,stackover_data$'Trace 1',type='l'
you can see from time = 2 is where the peak is and I'm looking to fit it from 2 seconds to 6 seconds in ggplot. ( yes I know I used base graphics for the example, its just easier to plot one graph in that vs ggplot2.
Thanks all!
library(ggplot)
# rename the variables, so there's no space in them (is easier in aes())
names(neurons)[2:4] <- paste0('Trace', 1:3)
Now we make a plot, where we use a selection of the data based on the lowest point in the trace:
ggplot(neurons, aes(x = Time, y = Trace1)) +
geom_line() +
geom_smooth(data = subset(neurons, Time >= Time[which.min(Trace1)] &
Time < Time[which.min(Trace1)] + 4)) +
theme_classic() +
xlim(0, 10)
Results in:
Note that the ggplot equivalent of your simple base plot is this:
qplot(neurons$Time, neurons$Trace1, geom = 'line')
Basically, just as easy ;)
This question already has answers here:
Remove NA values from a vector
(8 answers)
Closed 8 years ago.
I have a vector which stores over 1000 values. The first 50 values are NAs, how can I get rid of it?
c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
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1.5883, 1.591, 1.5944, 1.5793, 1.577, 1.575, 1.5645, 1.5769,
1.5665, 1.5737, 1.5665, 1.5639, 1.5504, 1.5402, 1.536, 1.5158,
1.5246, 1.5215, 1.5102, 1.5183, 1.5117, 1.4945, 1.4945, 1.5135,
1.495, 1.4772, 1.4832, 1.4793, 1.4785, 1.4589, 1.4965, 1.4865,
1.4872, 1.4835, 1.5037, 1.4815, 1.4745, 1.4815, 1.4835, 1.478,
1.4753, 1.475, 1.4775, 1.477, 1.4707, 1.4661, 1.4684, 1.4626,
1.4558, 1.467, 1.463, 1.4568, 1.453, 1.4478, 1.4275, 1.4035,
1.399, 1.4065, 1.4083, 1.4062, 1.4001, 1.3924, 1.3915, 1.4133,
1.4032, 1.4015, 1.3908, 1.41, 1.4095, 1.4482, 1.483, 1.4862,
1.524, 1.4861, 1.5, 1.4815, 1.4938, 1.5, 1.486, 1.484, 1.451,
1.424, 1.417, 1.4235, 1.409, 1.4164, 1.432, 1.4435, 1.4728, 1.493,
1.4685, 1.47, 1.466, 1.4526, 1.4815, 1.4875, 1.5215, 1.5105,
1.5063, 1.5318, 1.537, 1.5345, 1.5374, 1.538, 1.5362, 1.569,
1.5625, 1.569, 1.5795, 1.5945, 1.589, 1.594, 1.5845, 1.5875,
1.567, 1.5885, 1.5995, 1.597, 1.5795, 1.599, 1.6002, 1.6015,
1.5935, 1.5955, 1.6005, 1.5945, 1.576, 1.5705, 1.5818, 1.596,
1.5625, 1.5575, 1.5665, 1.579, 1.5775, 1.569, 1.5675, 1.554,
1.5605, 1.566, 1.567, 1.5875, 1.5945, 1.598, 1.6092, 1.617, 1.6142,
1.6195, 1.637, 1.6265, 1.6345, 1.635, 1.639, 1.6305, 1.6325,
1.6325, 1.6195, 1.6325, 1.6135, 1.6115, 1.6055, 1.615, 1.5935,
1.573, 1.579, 1.586, 1.585, 1.611, 1.6365, 1.643, 1.6465, 1.656,
1.6545, 1.6555, 1.6545, 1.6595, 1.6575, 1.6615, 1.6587, 1.63,
1.628, 1.633, 1.6365, 1.6285, 1.614, 1.6195, 1.6335, 1.6455,
1.646, 1.643, 1.6475, 1.6355, 1.672, 1.6625, 1.668, 1.665, 1.661,
1.6665, 1.662, 1.6615, 1.6645, 1.654, 1.6335, 1.6375, 1.6305,
1.6265, 1.6425, 1.6315, 1.629, 1.618, 1.6085, 1.596, 1.6105,
1.5965, 1.611, 1.617, 1.6065, 1.6035, 1.6035, 1.578, 1.5925,
1.606, 1.6147, 1.5995, 1.595, 1.6035, 1.606, 1.582, 1.567, 1.5805,
1.5855, 1.5815, 1.5845, 1.5815, 1.5715, 1.5775, 1.5795, 1.5825,
1.6055, 1.6084, 1.6135, 1.616, 1.602, 1.6165, 1.624, 1.624, 1.6145,
1.6255, 1.6378, 1.63, 1.6315, 1.607, 1.5868, 1.5895, 1.5927,
1.5948, 1.6004, 1.626, 1.6195, 1.6225, 1.637, 1.629, 1.6235,
1.628, 1.6375, 1.6605, 1.6568, 1.681, 1.6895, 1.6955, 1.6925,
1.7095, 1.7032, 1.6987, 1.692, 1.704, 1.6976, 1.6965, 1.696,
1.698, 1.7091, 1.707, 1.721, 1.7286, 1.7204, 1.7165, 1.7241,
1.7205, 1.7037, 1.7053, 1.6975, 1.7075, 1.72, 1.7245, 1.7243,
1.7185, 1.7385, 1.7402, 1.712, 1.7057, 1.71, 1.712, 1.6975, 1.7,
1.7115, 1.721, 1.7158, 1.7132, 1.6904, 1.6965, 1.6782, 1.6865,
1.6767, 1.686, 1.679, 1.6868, 1.6665, 1.6645, 1.6738, 1.677,
1.658, 1.6445, 1.623, 1.611, 1.6075, 1.6177, 1.5985, 1.5935,
1.612, 1.6085, 1.5935, 1.6047, 1.6092, 1.608, 1.6187, 1.6325,
1.6443, 1.645, 1.6295, 1.6178, 1.6133, 1.6335, 1.6265, 1.623,
1.6255, 1.6221, 1.6215, 1.601, 1.604, 1.5935, 1.604, 1.6145,
1.6137, 1.6285, 1.6377, 1.647, 1.663, 1.676, 1.673, 1.682, 1.6794,
1.6788, 1.6774, 1.694, 1.6965, 1.6937, 1.6957, 1.691, 1.6842,
1.696, 1.6925, 1.691, 1.6908, 1.6865, 1.7023, 1.706, 1.7095,
1.7145, 1.7032, 1.7005, 1.7027, 1.7082, 1.7118, 1.707, 1.7148,
1.7165, 1.7211, 1.7208, 1.7062, 1.7045, 1.704, 1.7055, 1.6985,
1.706, 1.7125, 1.7163, 1.7078, 1.705, 1.7125, 1.7077, 1.701,
1.6935, 1.6965, 1.7019, 1.7008, 1.7175, 1.735, 1.7365, 1.7371,
1.7398, 1.7399, 1.7397, 1.7333, 1.7308, 1.7398, 1.7366, 1.752,
1.7505, 1.7553, 1.7487, 1.744, 1.7358, 1.7474, 1.7504, 1.7528,
1.748, 1.7441, 1.7273, 1.7444, 1.727, 1.7343, 1.7314, 1.736,
1.763, 1.7658, 1.7603, 1.7534, 1.7517, 1.7438, 1.7255, 1.7219,
1.734, 1.718, 1.7275, 1.7269, 1.7279, 1.7306, 1.7055, 1.7069,
1.709, 1.7037, 1.7088, 1.7198, 1.7184, 1.7155, 1.707, 1.6893,
1.6779, 1.6898, 1.6963, 1.691, 1.68, 1.6961, 1.6979, 1.6885,
1.685, 1.6726, 1.668, 1.6728, 1.6675, 1.6788, 1.6695, 1.6952,
1.6985, 1.7071, 1.7151, 1.7181, 1.7134, 1.708, 1.7188, 1.7135,
1.7088, 1.7124, 1.7202, 1.701, 1.694, 1.6882, 1.6947, 1.6794,
1.6801, 1.6727, 1.6697, 1.657, 1.6501, 1.6461, 1.662, 1.6682,
1.6661, 1.6579, 1.6705, 1.6735, 1.6708, 1.6671, 1.674, 1.6683,
1.6596, 1.6551, 1.6463, 1.6456, 1.6478, 1.6447, 1.6438, 1.6444,
1.6466, 1.6435, 1.6428, 1.6515, 1.6665, 1.6692, 1.6695, 1.6698,
1.6721, 1.6672, 1.65, 1.6443, 1.6354, 1.6319, 1.6299, 1.5986,
1.6009, 1.6006, 1.6035, 1.5972, 1.5758, 1.5849, 1.5825, 1.5915,
1.5946, 1.5965, 1.5826, 1.5685, 1.5745, 1.5741, 1.54, 1.5235,
1.5383, 1.5457, 1.5558, 1.5428, 1.5569, 1.5662, 1.576, 1.5955,
1.5849, 1.5865, 1.576, 1.5795, 1.5888, 1.5743, 1.5814, 1.5792,
1.581, 1.5822, 1.5811, 1.582, 1.5745, 1.5872, 1.5557, 1.5533,
1.5552, 1.5603, 1.5456, 1.5396, 1.5309, 1.5377, 1.5411, 1.5462,
1.5615, 1.5831, 1.5805, 1.5783, 1.577, 1.5602, 1.5537, 1.5426,
1.5486, 1.5562, 1.5419, 1.5435, 1.5493, 1.5374, 1.5467, 1.5376,
1.5528, 1.5508, 1.5449, 1.5488, 1.5443, 1.5551, 1.547, 1.5467,
1.5461, 1.5515, 1.5573, 1.5519, 1.5426, 1.5444, 1.5411, 1.55,
1.5466, 1.5421, 1.5423, 1.5201, 1.5036, 1.5003, 1.5009, 1.5016,
1.4954, 1.497, 1.492, 1.4953, 1.4972, 1.5094, 1.5077, 1.4994,
1.4945, 1.5146, 1.5235, 1.5172, 1.5097, 1.53, 1.5348, 1.528,
1.5453, 1.5454, 1.551, 1.548, 1.5539, 1.5594, 1.5536, 1.5537,
1.5582, 1.5589, 1.5643, 1.5612, 1.5703, 1.5722, 1.5778, 1.5741,
1.5725, 1.571, 1.5777, 1.5773, 1.5728, 1.5728, 1.5691, 1.5718,
1.5714, 1.5738, 1.572, 1.5703, 1.5805, 1.5783, 1.5768, 1.5737,
1.5507, 1.55, 1.5539, 1.559, 1.5535, 1.5575, 1.5539, 1.5341,
1.5354, 1.5295, 1.5327, 1.5282, 1.532, 1.53, 1.5303, 1.5087,
1.5095, 1.5127, 1.5183, 1.5147, 1.5119, 1.5099, 1.5148, 1.5228,
1.52, 1.525, 1.5309, 1.5355, 1.5312, 1.5291, 1.5241, 1.5184,
1.5137, 1.5084, 1.4914, 1.4887, 1.4729, 1.4796, 1.4679, 1.4727,
1.4749, 1.458, 1.4644, 1.465, 1.4601, 1.4329, 1.4028, 1.3926,
1.3855, 1.3904, 1.4145, 1.4053, 1.4136, 1.3926, 1.3882, 1.3865,
1.3973, 1.4125, 1.4061, 1.4015, 1.4128, 1.4087, 1.3997, 1.3773,
1.4107, 1.3685, 1.3723, 1.3854, 1.3835, 1.3763, 1.3811, 1.4055,
1.401, 1.4048, 1.3892, 1.39, 1.3715, 1.3677, 1.3542, 1.3704,
1.3766, 1.3699, 1.365, 1.3811, 1.3734, 1.3823, 1.3902, 1.3753,
1.3746, 1.3697, 1.3711, 1.3646, 1.3701, 1.3906, 1.4135, 1.4433,
1.4466, 1.4414, 1.4368, 1.4597, 1.4404, 1.4482, 1.4362, 1.439,
1.4043, 1.3829, 1.3886, 1.3899, 1.413, 1.4233, 1.406, 1.4074,
1.4188, 1.4074, 1.4198, 1.3973, 1.4029, 1.4044, 1.3974, 1.4106,
1.4007, 1.3991, 1.3924, 1.3921, 1.3845, 1.3877, 1.3942, 1.3846,
1.3884, 1.3891, 1.3841, 1.3782, 1.3817, 1.3833, 1.3816, 1.3935,
1.3988, 1.402, 1.4042, 1.3923, 1.391, 1.3963, 1.3883, 1.3887,
1.3767, 1.3865, 1.3837, 1.393, 1.3849, 1.3863, 1.3792, 1.3807,
1.3805, 1.3967, 1.3915, 1.3928, 1.4048, 1.4067, 1.4073, 1.423,
1.4334, 1.4341, 1.4749, 1.4756, 1.4771, 1.4702, 1.4723, 1.4852,
1.4791, 1.4803, 1.4786, 1.4615, 1.4714, 1.4758, 1.4695, 1.4633,
1.4647, 1.4631, 1.479, 1.475, 1.481, 1.4717, 1.4714, 1.4908,
1.4895, 1.4875, 1.4873, 1.4876, 1.4675, 1.44, 1.4175, 1.4278,
1.4413, 1.4268, 1.4212, 1.423, 1.4299, 1.4393, 1.4363, 1.4301,
1.427, 1.4119, 1.4176, 1.4249, 1.4223, 1.4291, 1.4195, 1.4168,
1.4235, 1.4141, 1.3979, 1.3851, 1.387, 1.3936, 1.4017, 1.4006,
1.4053, 1.4083, 1.4164, 1.4191, 1.4155, 1.4134, 1.4167, 1.4166,
1.4161, 1.4123, 1.4195, 1.4145, 1.4024, 1.4095, 1.4048, 1.4125,
1.4079, 1.4085, 1.4136, 1.4165, 1.4358, 1.4338, 1.4368, 1.4453,
1.4451, 1.4381, 1.4363, 1.4432, 1.4416, 1.448, 1.4442, 1.4485,
1.4499, 1.4418, 1.4426, 1.4318, 1.4355, 1.4434, 1.4402, 1.4402,
1.4333, 1.4313, 1.4319, 1.4313, 1.4347, 1.4403, 1.4565, 1.4375,
1.4403, 1.4432, 1.4383, 1.437, 1.4407, 1.4466, 1.4576, 1.4646,
1.4696, 1.479, 1.4755, 1.4773, 1.4803, 1.4763, 1.4912, 1.4859,
1.4899, 1.4879, 1.4933, 1.4872, 1.4693, 1.4718, 1.4773, 1.4762,
1.4774, 1.4725, 1.4782, 1.4692, 1.4713, 1.4617, 1.443, 1.4533,
1.4513, 1.4516, 1.4513, 1.4471, 1.4528, 1.4614, 1.4697, 1.4759,
1.4758, 1.4789, 1.475, 1.4789, 1.4836, 1.4823, 1.4805, 1.4718,
1.4709, 1.476, 1.4742, 1.4762, 1.4775, 1.4763, 1.4753, 1.4777,
1.4758, 1.4875, 1.4781, 1.4764, 1.4832, 1.4818, 1.4792, 1.4805,
1.4826, 1.4778, 1.4918, 1.4997, 1.5013, 1.5037, 1.5104, 1.5096,
1.5073, 1.5121, 1.5057, 1.5164, 1.518, 1.5219, 1.5295, 1.5288,
1.5213, 1.5336, 1.5336, 1.5328, 1.5273, 1.5266, 1.5213, 1.5183,
1.5168, 1.5249, 1.5336, 1.5368, 1.5341, 1.5368, 1.5262, 1.5347,
1.5415, 1.5387, 1.5405, 1.5414, 1.5414, 1.5483, 1.5453, 1.5323,
1.5275, 1.5265, 1.5318, 1.5293, 1.5301, 1.5345, 1.536, 1.5363,
1.5363, 1.5353, 1.5229, 1.5177, 1.5148, 1.5207, 1.5244, 1.5289,
1.5315, 1.5322, 1.5287, 1.5218, 1.5222, 1.5235, 1.527, 1.5252,
1.5235, 1.528, 1.5278, 1.5233, 1.5246, 1.5217, 1.5226, 1.5184,
1.4922, 1.4865, 1.4917, 1.4885, 1.4863, 1.4897, 1.4877, 1.4784,
1.4809, 1.4808, 1.4774, 1.4728, 1.4736, 1.4779, 1.4813, 1.4843,
1.4829, 1.4845, 1.4784, 1.4753, 1.4767, 1.4834, 1.4856, 1.4935,
1.4882, 1.4893, 1.4817, 1.4959, 1.4874, 1.4783, 1.4777, 1.476,
1.4802, 1.4778, 1.4869, 1.4844, 1.4823, 1.4848, 1.4885, 1.4918,
1.508, 1.5098, 1.5122, 1.5124, 1.5095, 1.5143, 1.5084, 1.514,
1.5149, 1.5134, 1.5132, 1.5102, 1.5217, 1.5243, 1.5253, 1.5258,
1.527, 1.5306, 1.5296, 1.5304, 1.5272, 1.5285, 1.5298, 1.5315,
1.5293, 1.5405, 1.5375, 1.5437, 1.543, 1.5362, 1.535, 1.5234,
1.5227, 1.5234, 1.5203, 1.5103, 1.5073, 1.5132, 1.5165, 1.5132,
1.5168, 1.5164, 1.5078, 1.5059, 1.499, 1.4991, 1.5068, 1.5093,
1.5083, 1.5017, 1.5028, 1.4977, 1.499, 1.5023, 1.5192, 1.5276,
1.5261, 1.5316, 1.5371, 1.5424, 1.5665, 1.5627, 1.5547, 1.5404,
1.5566, 1.5539, 1.5412, 1.5439, 1.5466, 1.5534, 1.5454, 1.5544,
1.5601, 1.5539, 1.5536, 1.5553, 1.5564, 1.5552, 1.5567, 1.5411,
1.5418, 1.5575, 1.5649, 1.5628, 1.5714, 1.5757, 1.5841, 1.5895,
1.594, 1.5895, 1.5925, 1.6113, 1.624, 1.6244, 1.6407, 1.639,
1.6268, 1.6376, 1.6511, 1.6436, 1.6394, 1.6377, 1.6429, 1.6415,
1.6484, 1.6501, 1.6722, 1.6565, 1.6721, 1.6832, 1.6871, 1.6862,
1.6995, 1.6914, 1.694, 1.6896, 1.6831, 1.6697, 1.6758, 1.689,
1.6897, 1.6882, 1.6934, 1.7094, 1.7113, 1.7185, 1.7189, 1.7032,
1.7055, 1.7014, 1.7012, 1.6993, 1.6901, 1.6782, 1.6815, 1.6867,
1.6834, 1.6882, 1.6907, 1.6922, 1.6775, 1.6762, 1.6684, 1.6697,
1.6703, 1.6683, 1.6758, 1.711, 1.711, 1.7197, 1.7169, 1.7241,
1.7233, 1.7339, 1.7286, 1.7257, 1.7162, 1.7023, 1.7112, 1.7124,
1.717, 1.7235, 1.7273, 1.7248, 1.7317, 1.7186, 1.7285, 1.732,
1.7231, 1.7194, 1.7075, 1.6934, 1.7004, 1.6972, 1.7006, 1.6983,
1.6941, 1.7072, 1.6949, 1.695, 1.6933, 1.6907, 1.6911, 1.7012,
1.6968, 1.6967, 1.7056, 1.7262, 1.7261, 1.7294, 1.7289, 1.7289,
1.7085, 1.7167, 1.7148, 1.724, 1.7378, 1.7295, 1.7338, 1.727,
1.7247, 1.7321, 1.7224, 1.724, 1.7235, 1.7286, 1.7363, 1.7426,
1.7401, 1.7511, 1.749, 1.7539, 1.7448, 1.7572, 1.7619, 1.7512,
1.7696, 1.7909, 1.8003, 1.7974, 1.7922, 1.7907, 1.7964, 1.8127,
1.8273, 1.8306, 1.8372, 1.8445, 1.8291, 1.8377, 1.8368, 1.8567,
1.8639, 1.879, 1.8745, 1.8733, 1.8531, 1.8544, 1.8624, 1.8316,
1.8461, 1.8183, 1.8223, 1.8364, 1.8568, 1.842, 1.8173, 1.8202,
1.8005, 1.8104, 1.7964, 1.8002, 1.8114, 1.8328, 1.8184, 1.8182,
1.8068, 1.8103, 1.8111, 1.8015, 1.7915, 1.7699, 1.7632, 1.7681,
1.7686, 1.7708, 1.7713, 1.793, 1.7954, 1.7717, 1.7678, 1.7647,
1.7589, 1.7662, 1.7756, 1.7671, 1.7625, 1.7622, 1.7539, 1.754,
1.739, 1.7499, 1.7506, 1.7564, 1.7511, 1.7443, 1.7687, 1.7714,
1.7851, 1.7815, 1.7747, 1.7783, 1.7599, 1.7248, 1.7416, 1.7231,
1.7211, 1.7362, 1.721, 1.7232, 1.7209, 1.7055, 1.7116, 1.7113,
1.7238, 1.7233, 1.7308, 1.7336, 1.7252, 1.7327, 1.7314, 1.748,
1.7353, 1.7437, 1.7593, 1.7642, 1.7639, 1.7774, 1.7746, 1.7684,
1.7704, 1.7828, 1.7884, 1.7946, 1.783, 1.7733, 1.7732, 1.7702,
1.7846, 1.7718, 1.7725, 1.7688, 1.7804, 1.7733, 1.7724, 1.7724,
1.7836, 1.7915, 1.7981, 1.8037, 1.8185, 1.8226, 1.8257, 1.8166,
1.8203, 1.8185, 1.8213, 1.8228, 1.8312, 1.8271, 1.8367, 1.8408,
1.8273, 1.8025, 1.7805, 1.7891, 1.7891, 1.8054, 1.8233, 1.828,
1.8223, 1.819, 1.8082, 1.7888, 1.7934, 1.8138, 1.8087, 1.8205,
1.8097, 1.8243, 1.8186, 1.8248, 1.8196, 1.8243, 1.8218, 1.7988,
1.8019, 1.8117, 1.8131, 1.8148, 1.8122, 1.8089, 1.8187, 1.829,
1.8322, 1.827, 1.8312, 1.8366, 1.8278, 1.8167, 1.8208, 1.8138,
1.826, 1.8273, 1.8343, 1.8282, 1.8295, 1.8266, 1.8233, 1.8279,
1.8446, 1.8494, 1.8497, 1.849, 1.8473, 1.8427, 1.8388, 1.8244,
1.8176, 1.8176, 1.823, 1.8037, 1.797, 1.8076, 1.8075, 1.8024,
1.7904, 1.7917, 1.7987, 1.7953, 1.7896, 1.7951, 1.7938, 1.7945,
1.7793, 1.7815, 1.7728, 1.7648, 1.7638, 1.7688, 1.7765, 1.7747,
1.7778, 1.7779, 1.7814, 1.7892, 1.7845, 1.77, 1.7575, 1.7574,
1.7672, 1.7721, 1.7816, 1.778, 1.7828, 1.7803, 1.7818, 1.7719,
1.7637, 1.7731, 1.7775, 1.7796, 1.7966, 1.8041, 1.8044, 1.8132,
1.798, 1.79, 1.7899, 1.7829, 1.7944, 1.8009, 1.8015, 1.7955,
1.8098, 1.8133, 1.805, 1.8185, 1.8221, 1.8185, 1.808, 1.8122,
1.8179, 1.8292, 1.8183, 1.8036, 1.7988, 1.803, 1.792, 1.7881,
1.7785, 1.7876, 1.7907, 1.7926, 1.7788, 1.7824, 1.7754, 1.7736,
1.7746, 1.7786, 1.7844, 1.7832, 1.7719, 1.7682, 1.7727, 1.7796,
1.7813, 1.7747, 1.7844, 1.7969, 1.7938, 1.7973, 1.8004, 1.7995,
1.8023, 1.7964, 1.7993, 1.8076, 1.8076, 1.7764, 1.7635, 1.7493,
1.7508, 1.7244, 1.7346, 1.725, 1.7291, 1.7242, 1.692, 1.6878,
1.7031, 1.687, 1.6918, 1.681, 1.6915, 1.6826, 1.6849, 1.6873,
1.6788, 1.6711, 1.6806, 1.678, 1.6714, 1.6502, 1.6366, 1.6348,
1.6389, 1.6134, 1.6084, 1.6339, 1.6493, 1.6374, 1.6445, 1.624,
1.6232, 1.6288, 1.6421, 1.6482, 1.639, 1.6353, 1.6449, 1.6583,
1.6572, 1.6505, 1.6562, 1.6507, 1.6588, 1.6693, 1.6581, 1.6623,
1.6875, 1.684, 1.6783, 1.6927, 1.6892, 1.6668, 1.6694, 1.6676,
1.68, 1.6887, 1.7044, 1.7045, 1.7047, 1.7036, 1.7084, 1.6908,
1.678, 1.673, 1.6741, 1.6755, 1.6764, 1.671, 1.6679, 1.6595,
1.6536, 1.6523, 1.6591, 1.6672, 1.6645, 1.6643, 1.6732, 1.6753,
1.6742, 1.6803, 1.679, 1.6722, 1.672, 1.6656, 1.6576, 1.6598,
1.6688, 1.6759, 1.693, 1.6957, 1.6945, 1.6712, 1.6716, 1.6875,
1.6865, 1.6852, 1.6904, 1.6885, 1.6887, 1.6916, 1.6899, 1.7003,
1.713, 1.7222, 1.7302, 1.7225, 1.7238, 1.7324, 1.7329, 1.7387,
1.7308, 1.7276, 1.7312, 1.7342, 1.7406, 1.7494, 1.7412, 1.7429,
1.7627, 1.7729, 1.7812, 1.7856, 1.7698, 1.7817, 1.7889, 1.7899,
1.7915, 1.8061, 1.8026, 1.7972, 1.7964, 1.7854, 1.8057, 1.7872,
1.7871, 1.793, 1.7768, 1.7803, 1.7902, 1.7936, 1.7948, 1.7909,
1.7989, 1.8221, 1.825, 1.8174, 1.8116, 1.809, 1.8131, 1.8242,
1.8175, 1.8117, 1.8053, 1.8149, 1.8031, 1.8126, 1.8091, 1.8232,
1.827, 1.8429, 1.8354, 1.8439, 1.8458, 1.8373, 1.8447, 1.8367,
1.8394, 1.8467, 1.8487, 1.8508, 1.8437, 1.8316, 1.8146, 1.813,
1.8105, 1.8235, 1.8324, 1.8404, 1.8279, 1.8322, 1.834, 1.8352,
1.8408, 1.8529, 1.8499, 1.8435, 1.8667, 1.8701, 1.8705, 1.8755,
1.869, 1.8882, 1.8892, 1.8981, 1.8995, 1.8759, 1.8752, 1.8646,
1.864, 1.8746, 1.8778, 1.8961, 1.8958, 1.8833, 1.8956, 1.8978,
1.8969, 1.8905, 1.8688, 1.8856, 1.892, 1.8966, 1.9088, 1.9138,
1.9118, 1.9082, 1.913, 1.9214, 1.9195, 1.9291, 1.9214, 1.9162,
1.9089, 1.9141, 1.9224, 1.8788, 1.8626, 1.8642, 1.8686, 1.8353,
1.8405, 1.8428, 1.8254, 1.8275, 1.8344, 1.8341, 1.8172, 1.8108,
1.8141, 1.8279, 1.8225, 1.8349, 1.8373, 1.8508, 1.8506, 1.8615,
1.8561, 1.8405, 1.8369, 1.8487, 1.8586, 1.8707, 1.8758, 1.8708,
1.8721, 1.8517, 1.8462, 1.8263, 1.8446, 1.849, 1.849, 1.8452,
1.8517, 1.8678, 1.8828, 1.8752, 1.8815, 1.8804, 1.88, 1.8788,
1.8671, 1.8622, 1.875, 1.8686, 1.8727, 1.8628, 1.8438, 1.8366,
1.8282, 1.828, 1.8311, 1.8268, 1.8223, 1.8404, 1.8383, 1.823,
1.8174, 1.8141, 1.797, 1.803, 1.8116, 1.8149, 1.8095, 1.8293,
1.8319, 1.8472, 1.8577, 1.8577, 1.8609, 1.8641, 1.8641, 1.8662,
1.8726, 1.8786, 1.886, 1.8799, 1.8744, 1.8796, 1.8954, 1.8977,
1.8975, 1.8819, 1.8958, 1.9003, 1.8938, 1.9002, 1.9182, 1.9184,
1.9333, 1.9433, 1.9424, 1.9406, 1.9491, 1.9532, 1.9126, 1.9124,
1.9088, 1.9217, 1.9244, 1.9334, 1.945, 1.9489, 1.9212, 1.9375,
1.9395, 1.9386, 1.9403, 1.9261, 1.9326, 1.9417, 1.9468, 1.9513,
1.9265, 1.8985, 1.8921, 1.8899, 1.9009, 1.9055, 1.8993, 1.904,
1.9042, 1.932, 1.9397, 1.9349, 1.9367, 1.9373, 1.9423, 1.9514,
1.9433, 1.9535, 1.9789, 1.996, 1.9991, 2.0104, 2.0119, 1.9873,
1.9963, 1.9994, 1.9828, 1.9759, 1.9848, 1.9843, 1.9957, 1.9886,
1.9883, 1.9836, 1.9854, 1.9815, 1.9422, 1.9485, 1.9636, 1.9983,
2.0228, 2.0315, 2.0203, 2.0241, 2.0336, 2.0347, 2.0428, 2.0434,
2.0184, 2.0279, 2.0231, 2.0314, 2.0185, 2.0097, 2.0197, 2.0174,
2.0137, 2.0342, 2.0156, 2.0131, 2.0152, 2.0223, 2.0517, 2.0467,
2.044, 2.0451, 2.0488, 2.0254, 2.0394, 2.0403, 2.038, 2.0313,
2.0419, 2.0469, 2.0456, 2.0482, 2.0605, 2.0795, 2.0818, 2.0818,
2.0815, 2.109, 2.1178, 2.1455, 2.1464, 2.1531, 2.1543, 2.1936,
2.1923, 2.1908, 2.1884, 2.1675, 2.1464, 2.1644, 2.1571, 2.1344,
2.1609, 2.1952, 2.1886, 2.1908, 2.1637, 2.1522, 2.1554, 2.1567,
2.1124, 2.1083, 2.0975, 2.109, 2.1007, 2.0716, 2.0708, 2.0397,
2.0435, 2.0514, 2.0568, 2.0505, 2.036, 2.0396, 2.0532, 2.0357,
2.0368, 2.0472, 2.0672, 2.0819, 2.0886, 2.0873, 2.0734, 2.0677,
2.0534, 2.0404, 2.0554, 2.0591, 2.0448, 2.0465, 2.0605, 2.053,
2.0568, 2.0711, 2.0915, 2.0869, 2.0923, 2.098, 2.1162, 2.1157,
2.0976, 2.0962, 2.078, 2.0758, 2.0825, 2.1097, 2.1108, 2.1132,
2.1403, 2.1627, 2.1526, 2.1532, 2.1738, 2.1789, 2.152, 2.1616,
2.1635, 2.1418, 2.1453, 2.1385, 2.1521, 2.1642, 2.1776, 2.188,
2.1665, 2.1614, 2.1751, 2.182, 2.1892, 2.1999, 2.1733, 2.1739,
2.2071, 2.2274, 2.2455, 2.2574, 2.2695, 2.2724, 2.2623, 2.2706,
2.2662, 2.2892, 2.2913, 2.3078, 2.2932, 2.2183, 2.2398, 2.2223,
2.2194, 2.2152, 2.2164, 2.2184, 2.2318, 2.2409, 2.2495, 2.249,
2.2507, 2.2528, 2.2381, 2.2678, 2.2757, 2.3048, 2.3014, 2.3075,
2.3295, 2.3352, 2.3397, 2.3363, 2.3603, 2.3594, 2.3209, 2.3147,
2.3075, 2.2806, 2.274, 2.2648, 2.2711, 2.2724, 2.2841, 2.2772,
2.2651, 2.274, 2.2794, 2.2758, 2.2894, 2.2938, 2.304, 2.313,
2.3194, 2.3226, 2.3289, 2.304, 2.2877, 2.2741, 2.2468, 2.2405,
2.1992, 2.2101, 2.2111, 2.1982, 2.2024, 2.2279, 2.2298, 2.2339,
2.204, 2.1793, 2.1835, 2.195, 2.1569, 2.1398, 2.118, 2.1057,
2.1018, 2.1128, 2.0833)
And even shorter:
v <- na.omit(v)
Try this:
new.v <- v[ !is.na( v ) ]
Either is.na or na.omit are good enough for this situation
x <- c(1,NA,2,NA, 3) # a vector with NA
x[!is.na(x)] # a vector without NA
[1] 1 2 3
as.numeric(na.omit(x))
[1] 1 2 3
Actually as.numeric applied to na.omit is not necessary as you can tell from Dirk's answer :)