Fusione dei dati - Errore in fix.by (by.x, x)
https://stackoverflow.com//questions/22038887
-
21-12-2019 - |
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Sto cercando di unire i dati in R come suggerito in una risposta al mio altro post qui. ancora, ho un errore.
Prima lascia che spiega cosa provo a fare. Ho 100 file (ognuno ha x_i e y_i), voglio unirli in questo modo:
Da:
x1; y1 ; x2 ; y2
1 ; 100 ; 1 ; 150
4 ; 90 ; 2 ; 85
7 ; 85 ; 10 ; 60
10; 80 ;
a
x1; y1 ; x2 ; y2
1 ; 100 ; 1 ; 150
2 ; 100 ; 2 ; 85
4 ; 90 ; 4 ; 85
7 ; 85 ; 7 ; 85
10; 80 ;10 ; 60
Lo script semplice funziona bene sull'esempio del giocattolo:
xx <- read.table(text='x1; y1 ; x2 ; y2
1 ; 100 ; 1 ; 150
4 ; 90 ; 2 ; 85
7 ; 85 ; 10 ; 60
10; 80 ;',sep=';',fill=TRUE,header=TRUE)
dm <- merge(xx[,1:2],xx[,3:4],by=1,all=T)
dm <- dm[!is.na(dm$x1),]
dm$y1 <- zoo::na.locf(dm$y1)
dm$y2 <- zoo::na.locf(dm$y2)
dm
x1 y1 y2
1 1 100 150
2 2 100 85
3 4 90 85
4 7 85 85
5 10 80 60
Ora per i miei dati reali. Ho modificato lo script per assomigliare a questo ma ottengo un errore:
library(zoo)
data 1 = read.table("rundata 1", sep= " ", col.names=c("tm 1","score 1","current 1"))
data 2 = read.table("rundata 2", sep= " ", col.names=c("tm 2","score 2","current 2"))
data 3 = read.table("rundata 3", sep= " ", col.names=c("tm 3","score 3","current 3"))
data 4 = read.table("rundata 4", sep= " ", col.names=c("tm 4","score 4","current 4"))
data 5 = read.table("rundata 5", sep= " ", col.names=c("tm 5","score 5","current 5"))
data 6 = read.table("rundata 6", sep= " ", col.names=c("tm 6","score 6","current 6"))
data 7 = read.table("rundata 7", sep= " ", col.names=c("tm 7","score 7","current 7"))
data 8 = read.table("rundata 8", sep= " ", col.names=c("tm 8","score 8","current 8"))
data 9 = read.table("rundata 9", sep= " ", col.names=c("tm 9","score 9","current 9"))
data 10 = read.table("rundata 10", sep= " ", col.names=c("tm 10","score 10","current 10"))
data 11 = read.table("rundata 11", sep= " ", col.names=c("tm 11","score 11","current 11"))
data 12 = read.table("rundata 12", sep= " ", col.names=c("tm 12","score 12","current 12"))
data 13 = read.table("rundata 13", sep= " ", col.names=c("tm 13","score 13","current 13"))
data 14 = read.table("rundata 14", sep= " ", col.names=c("tm 14","score 14","current 14"))
data 15 = read.table("rundata 15", sep= " ", col.names=c("tm 15","score 15","current 15"))
data 16 = read.table("rundata 16", sep= " ", col.names=c("tm 16","score 16","current 16"))
data 17 = read.table("rundata 17", sep= " ", col.names=c("tm 17","score 17","current 17"))
data 18 = read.table("rundata 18", sep= " ", col.names=c("tm 18","score 18","current 18"))
data 19 = read.table("rundata 19", sep= " ", col.names=c("tm 19","score 19","current 19"))
data 20 = read.table("rundata 20", sep= " ", col.names=c("tm 20","score 20","current 20"))
data 21 = read.table("rundata 21", sep= " ", col.names=c("tm 21","score 21","current 21"))
data 22 = read.table("rundata 22", sep= " ", col.names=c("tm 22","score 22","current 22"))
data 23 = read.table("rundata 23", sep= " ", col.names=c("tm 23","score 23","current 23"))
data 24 = read.table("rundata 24", sep= " ", col.names=c("tm 24","score 24","current 24"))
data 25 = read.table("rundata 25", sep= " ", col.names=c("tm 25","score 25","current 25"))
data 26 = read.table("rundata 26", sep= " ", col.names=c("tm 26","score 26","current 26"))
data 27 = read.table("rundata 27", sep= " ", col.names=c("tm 27","score 27","current 27"))
data 28 = read.table("rundata 28", sep= " ", col.names=c("tm 28","score 28","current 28"))
data 29 = read.table("rundata 29", sep= " ", col.names=c("tm 29","score 29","current 29"))
data 30 = read.table("rundata 30", sep= " ", col.names=c("tm 30","score 30","current 30"))
data 31 = read.table("rundata 31", sep= " ", col.names=c("tm 31","score 31","current 31"))
data 32 = read.table("rundata 32", sep= " ", col.names=c("tm 32","score 32","current 32"))
data 33 = read.table("rundata 33", sep= " ", col.names=c("tm 33","score 33","current 33"))
data 34 = read.table("rundata 34", sep= " ", col.names=c("tm 34","score 34","current 34"))
data 35 = read.table("rundata 35", sep= " ", col.names=c("tm 35","score 35","current 35"))
data 36 = read.table("rundata 36", sep= " ", col.names=c("tm 36","score 36","current 36"))
data 37 = read.table("rundata 37", sep= " ", col.names=c("tm 37","score 37","current 37"))
data 38 = read.table("rundata 38", sep= " ", col.names=c("tm 38","score 38","current 38"))
data 39 = read.table("rundata 39", sep= " ", col.names=c("tm 39","score 39","current 39"))
data 40 = read.table("rundata 40", sep= " ", col.names=c("tm 40","score 40","current 40"))
data 41 = read.table("rundata 41", sep= " ", col.names=c("tm 41","score 41","current 41"))
data 42 = read.table("rundata 42", sep= " ", col.names=c("tm 42","score 42","current 42"))
data 43 = read.table("rundata 43", sep= " ", col.names=c("tm 43","score 43","current 43"))
data 44 = read.table("rundata 44", sep= " ", col.names=c("tm 44","score 44","current 44"))
data 45 = read.table("rundata 45", sep= " ", col.names=c("tm 45","score 45","current 45"))
data 46 = read.table("rundata 46", sep= " ", col.names=c("tm 46","score 46","current 46"))
data 47 = read.table("rundata 47", sep= " ", col.names=c("tm 47","score 47","current 47"))
data 48 = read.table("rundata 48", sep= " ", col.names=c("tm 48","score 48","current 48"))
data 49 = read.table("rundata 49", sep= " ", col.names=c("tm 49","score 49","current 49"))
data 50 = read.table("rundata 50", sep= " ", col.names=c("tm 50","score 50","current 50"))
data 51 = read.table("rundata 51", sep= " ", col.names=c("tm 51","score 51","current 51"))
data 52 = read.table("rundata 52", sep= " ", col.names=c("tm 52","score 52","current 52"))
data 53 = read.table("rundata 53", sep= " ", col.names=c("tm 53","score 53","current 53"))
data 54 = read.table("rundata 54", sep= " ", col.names=c("tm 54","score 54","current 54"))
data 55 = read.table("rundata 55", sep= " ", col.names=c("tm 55","score 55","current 55"))
data 56 = read.table("rundata 56", sep= " ", col.names=c("tm 56","score 56","current 56"))
data 57 = read.table("rundata 57", sep= " ", col.names=c("tm 57","score 57","current 57"))
data 58 = read.table("rundata 58", sep= " ", col.names=c("tm 58","score 58","current 58"))
data 59 = read.table("rundata 59", sep= " ", col.names=c("tm 59","score 59","current 59"))
data 60 = read.table("rundata 60", sep= " ", col.names=c("tm 60","score 60","current 60"))
data 61 = read.table("rundata 61", sep= " ", col.names=c("tm 61","score 61","current 61"))
data 62 = read.table("rundata 62", sep= " ", col.names=c("tm 62","score 62","current 62"))
data 63 = read.table("rundata 63", sep= " ", col.names=c("tm 63","score 63","current 63"))
data 64 = read.table("rundata 64", sep= " ", col.names=c("tm 64","score 64","current 64"))
data 65 = read.table("rundata 65", sep= " ", col.names=c("tm 65","score 65","current 65"))
data 66 = read.table("rundata 66", sep= " ", col.names=c("tm 66","score 66","current 66"))
data 67 = read.table("rundata 67", sep= " ", col.names=c("tm 67","score 67","current 67"))
data 68 = read.table("rundata 68", sep= " ", col.names=c("tm 68","score 68","current 68"))
data 69 = read.table("rundata 69", sep= " ", col.names=c("tm 69","score 69","current 69"))
data 70 = read.table("rundata 70", sep= " ", col.names=c("tm 70","score 70","current 70"))
data 71 = read.table("rundata 71", sep= " ", col.names=c("tm 71","score 71","current 71"))
data 72 = read.table("rundata 72", sep= " ", col.names=c("tm 72","score 72","current 72"))
data 73 = read.table("rundata 73", sep= " ", col.names=c("tm 73","score 73","current 73"))
data 74 = read.table("rundata 74", sep= " ", col.names=c("tm 74","score 74","current 74"))
data 75 = read.table("rundata 75", sep= " ", col.names=c("tm 75","score 75","current 75"))
data 76 = read.table("rundata 76", sep= " ", col.names=c("tm 76","score 76","current 76"))
data 77 = read.table("rundata 77", sep= " ", col.names=c("tm 77","score 77","current 77"))
data 78 = read.table("rundata 78", sep= " ", col.names=c("tm 78","score 78","current 78"))
data 79 = read.table("rundata 79", sep= " ", col.names=c("tm 79","score 79","current 79"))
data 80 = read.table("rundata 80", sep= " ", col.names=c("tm 80","score 80","current 80"))
data 81 = read.table("rundata 81", sep= " ", col.names=c("tm 81","score 81","current 81"))
data 82 = read.table("rundata 82", sep= " ", col.names=c("tm 82","score 82","current 82"))
data 83 = read.table("rundata 83", sep= " ", col.names=c("tm 83","score 83","current 83"))
data 84 = read.table("rundata 84", sep= " ", col.names=c("tm 84","score 84","current 84"))
data 85 = read.table("rundata 85", sep= " ", col.names=c("tm 85","score 85","current 85"))
data 86 = read.table("rundata 86", sep= " ", col.names=c("tm 86","score 86","current 86"))
data 87 = read.table("rundata 87", sep= " ", col.names=c("tm 87","score 87","current 87"))
data 88 = read.table("rundata 88", sep= " ", col.names=c("tm 88","score 88","current 88"))
data 89 = read.table("rundata 89", sep= " ", col.names=c("tm 89","score 89","current 89"))
data 90 = read.table("rundata 90", sep= " ", col.names=c("tm 90","score 90","current 90"))
data 91 = read.table("rundata 91", sep= " ", col.names=c("tm 91","score 91","current 91"))
data 92 = read.table("rundata 92", sep= " ", col.names=c("tm 92","score 92","current 92"))
data 93 = read.table("rundata 93", sep= " ", col.names=c("tm 93","score 93","current 93"))
data 94 = read.table("rundata 94", sep= " ", col.names=c("tm 94","score 94","current 94"))
data 95 = read.table("rundata 95", sep= " ", col.names=c("tm 95","score 95","current 95"))
data 96 = read.table("rundata 96", sep= " ", col.names=c("tm 96","score 96","current 96"))
data 97 = read.table("rundata 97", sep= " ", col.names=c("tm 97","score 97","current 97"))
data 98 = read.table("rundata 98", sep= " ", col.names=c("tm 98","score 98","current 98"))
data 99 = read.table("rundata 99", sep= " ", col.names=c("tm 99","score 99","current 99"))
data 100 = read.table("rundata 100", sep= " ", col.names=c("tm 100","score 100","current 100"))
-> Funziona bene
newdata<- merge(
data1 [,1:2],
data2 [,1:2],
data3 [,1:2],
data4 [,1:2],
data5 [,1:2],
data6 [,1:2],
data7 [,1:2],
data8 [,1:2],
data9 [,1:2],
data10 [,1:2],
data11 [,1:2],
data12 [,1:2],
data13 [,1:2],
data14 [,1:2],
data15 [,1:2],
data16 [,1:2],
data17 [,1:2],
data18 [,1:2],
data19 [,1:2],
data20 [,1:2],
data21 [,1:2],
data22 [,1:2],
data23 [,1:2],
data24 [,1:2],
data25 [,1:2],
data26 [,1:2],
data27 [,1:2],
data28 [,1:2],
data29 [,1:2],
data30 [,1:2],
data31 [,1:2],
data32 [,1:2],
data33 [,1:2],
data34 [,1:2],
data35 [,1:2],
data36 [,1:2],
data37 [,1:2],
data38 [,1:2],
data39 [,1:2],
data40 [,1:2],
data41 [,1:2],
data42 [,1:2],
data43 [,1:2],
data44 [,1:2],
data45 [,1:2],
data46 [,1:2],
data47 [,1:2],
data48 [,1:2],
data49 [,1:2],
data50 [,1:2],
data51 [,1:2],
data52 [,1:2],
data53 [,1:2],
data54 [,1:2],
data55 [,1:2],
data56 [,1:2],
data57 [,1:2],
data58 [,1:2],
data59 [,1:2],
data60 [,1:2],
data61 [,1:2],
data62 [,1:2],
data63 [,1:2],
data64 [,1:2],
data65 [,1:2],
data66 [,1:2],
data67 [,1:2],
data68 [,1:2],
data69 [,1:2],
data70 [,1:2],
data71 [,1:2],
data72 [,1:2],
data73 [,1:2],
data74 [,1:2],
data75 [,1:2],
data76 [,1:2],
data77 [,1:2],
data78 [,1:2],
data79 [,1:2],
data80 [,1:2],
data81 [,1:2],
data82 [,1:2],
data83 [,1:2],
data84 [,1:2],
data85 [,1:2],
data86 [,1:2],
data87 [,1:2],
data88 [,1:2],
data89 [,1:2],
data90 [,1:2],
data91 [,1:2],
data92 [,1:2],
data93 [,1:2],
data94 [,1:2],
data95 [,1:2],
data96 [,1:2],
data97 [,1:2],
data98 [,1:2],
data99 [,1:2],
data100 [,1:2],
by=1,all=T)
-> Fornisce un errore:
Error in fix.by(by.x, x) :
'by' must specify one or more columns as numbers, names or logical
Non capisco questo errore, dal momento che indico 1 no?
resto dello script (resta da testare su 100 ingressi dopo aver risolto il primo errore)
newdata <- newdata[!is.na(newdata$tm1 ),]
newdata <- newdata[!is.na(newdata$tm2 ),]
newdata <- newdata[!is.na(newdata$tm3 ),]
newdata <- newdata[!is.na(newdata$tm4 ),]
newdata <- newdata[!is.na(newdata$tm5 ),]
newdata <- newdata[!is.na(newdata$tm6 ),]
newdata <- newdata[!is.na(newdata$tm7 ),]
newdata <- newdata[!is.na(newdata$tm8 ),]
newdata <- newdata[!is.na(newdata$tm9 ),]
newdata <- newdata[!is.na(newdata$tm10 ),]
newdata <- newdata[!is.na(newdata$tm11 ),]
newdata <- newdata[!is.na(newdata$tm12 ),]
newdata <- newdata[!is.na(newdata$tm13 ),]
newdata <- newdata[!is.na(newdata$tm14 ),]
newdata <- newdata[!is.na(newdata$tm15 ),]
newdata <- newdata[!is.na(newdata$tm16 ),]
newdata <- newdata[!is.na(newdata$tm17 ),]
newdata <- newdata[!is.na(newdata$tm18 ),]
newdata <- newdata[!is.na(newdata$tm19 ),]
newdata <- newdata[!is.na(newdata$tm20 ),]
newdata <- newdata[!is.na(newdata$tm21 ),]
newdata <- newdata[!is.na(newdata$tm22 ),]
newdata <- newdata[!is.na(newdata$tm23 ),]
newdata <- newdata[!is.na(newdata$tm24 ),]
newdata <- newdata[!is.na(newdata$tm25 ),]
newdata <- newdata[!is.na(newdata$tm26 ),]
newdata <- newdata[!is.na(newdata$tm27 ),]
newdata <- newdata[!is.na(newdata$tm28 ),]
newdata <- newdata[!is.na(newdata$tm29 ),]
newdata <- newdata[!is.na(newdata$tm30 ),]
newdata <- newdata[!is.na(newdata$tm31 ),]
newdata <- newdata[!is.na(newdata$tm32 ),]
newdata <- newdata[!is.na(newdata$tm33 ),]
newdata <- newdata[!is.na(newdata$tm34 ),]
newdata <- newdata[!is.na(newdata$tm35 ),]
newdata <- newdata[!is.na(newdata$tm36 ),]
newdata <- newdata[!is.na(newdata$tm37 ),]
newdata <- newdata[!is.na(newdata$tm38 ),]
newdata <- newdata[!is.na(newdata$tm39 ),]
newdata <- newdata[!is.na(newdata$tm40 ),]
newdata <- newdata[!is.na(newdata$tm41 ),]
newdata <- newdata[!is.na(newdata$tm42 ),]
newdata <- newdata[!is.na(newdata$tm43 ),]
newdata <- newdata[!is.na(newdata$tm44 ),]
newdata <- newdata[!is.na(newdata$tm45 ),]
newdata <- newdata[!is.na(newdata$tm46 ),]
newdata <- newdata[!is.na(newdata$tm47 ),]
newdata <- newdata[!is.na(newdata$tm48 ),]
newdata <- newdata[!is.na(newdata$tm49 ),]
newdata <- newdata[!is.na(newdata$tm50 ),]
newdata <- newdata[!is.na(newdata$tm51 ),]
newdata <- newdata[!is.na(newdata$tm52 ),]
newdata <- newdata[!is.na(newdata$tm53 ),]
newdata <- newdata[!is.na(newdata$tm54 ),]
newdata <- newdata[!is.na(newdata$tm55 ),]
newdata <- newdata[!is.na(newdata$tm56 ),]
newdata <- newdata[!is.na(newdata$tm57 ),]
newdata <- newdata[!is.na(newdata$tm58 ),]
newdata <- newdata[!is.na(newdata$tm59 ),]
newdata <- newdata[!is.na(newdata$tm60 ),]
newdata <- newdata[!is.na(newdata$tm61 ),]
newdata <- newdata[!is.na(newdata$tm62 ),]
newdata <- newdata[!is.na(newdata$tm63 ),]
newdata <- newdata[!is.na(newdata$tm64 ),]
newdata <- newdata[!is.na(newdata$tm65 ),]
newdata <- newdata[!is.na(newdata$tm66 ),]
newdata <- newdata[!is.na(newdata$tm67 ),]
newdata <- newdata[!is.na(newdata$tm68 ),]
newdata <- newdata[!is.na(newdata$tm69 ),]
newdata <- newdata[!is.na(newdata$tm70 ),]
newdata <- newdata[!is.na(newdata$tm71 ),]
newdata <- newdata[!is.na(newdata$tm72 ),]
newdata <- newdata[!is.na(newdata$tm73 ),]
newdata <- newdata[!is.na(newdata$tm74 ),]
newdata <- newdata[!is.na(newdata$tm75 ),]
newdata <- newdata[!is.na(newdata$tm76 ),]
newdata <- newdata[!is.na(newdata$tm77 ),]
newdata <- newdata[!is.na(newdata$tm78 ),]
newdata <- newdata[!is.na(newdata$tm79 ),]
newdata <- newdata[!is.na(newdata$tm80 ),]
newdata <- newdata[!is.na(newdata$tm81 ),]
newdata <- newdata[!is.na(newdata$tm82 ),]
newdata <- newdata[!is.na(newdata$tm83 ),]
newdata <- newdata[!is.na(newdata$tm84 ),]
newdata <- newdata[!is.na(newdata$tm85 ),]
newdata <- newdata[!is.na(newdata$tm86 ),]
newdata <- newdata[!is.na(newdata$tm87 ),]
newdata <- newdata[!is.na(newdata$tm88 ),]
newdata <- newdata[!is.na(newdata$tm89 ),]
newdata <- newdata[!is.na(newdata$tm90 ),]
newdata <- newdata[!is.na(newdata$tm91 ),]
newdata <- newdata[!is.na(newdata$tm92 ),]
newdata <- newdata[!is.na(newdata$tm93 ),]
newdata <- newdata[!is.na(newdata$tm94 ),]
newdata <- newdata[!is.na(newdata$tm95 ),]
newdata <- newdata[!is.na(newdata$tm96 ),]
newdata <- newdata[!is.na(newdata$tm97 ),]
newdata <- newdata[!is.na(newdata$tm98 ),]
newdata <- newdata[!is.na(newdata$tm99 ),]
newdata <- newdata[!is.na(newdata$tm100 ),]
newdata$score1 <- zoo::na.locf(newdata$score1 )
newdata$score2 <- zoo::na.locf(newdata$score2 )
newdata$score3 <- zoo::na.locf(newdata$score3 )
newdata$score4 <- zoo::na.locf(newdata$score4 )
newdata$score5 <- zoo::na.locf(newdata$score5 )
newdata$score6 <- zoo::na.locf(newdata$score6 )
newdata$score7 <- zoo::na.locf(newdata$score7 )
newdata$score8 <- zoo::na.locf(newdata$score8 )
newdata$score9 <- zoo::na.locf(newdata$score9 )
newdata$score10 <- zoo::na.locf(newdata$score10 )
newdata$score11 <- zoo::na.locf(newdata$score11 )
newdata$score12 <- zoo::na.locf(newdata$score12 )
newdata$score13 <- zoo::na.locf(newdata$score13 )
newdata$score14 <- zoo::na.locf(newdata$score14 )
newdata$score15 <- zoo::na.locf(newdata$score15 )
newdata$score16 <- zoo::na.locf(newdata$score16 )
newdata$score17 <- zoo::na.locf(newdata$score17 )
newdata$score18 <- zoo::na.locf(newdata$score18 )
newdata$score19 <- zoo::na.locf(newdata$score19 )
newdata$score20 <- zoo::na.locf(newdata$score20 )
newdata$score21 <- zoo::na.locf(newdata$score21 )
newdata$score22 <- zoo::na.locf(newdata$score22 )
newdata$score23 <- zoo::na.locf(newdata$score23 )
newdata$score24 <- zoo::na.locf(newdata$score24 )
newdata$score25 <- zoo::na.locf(newdata$score25 )
newdata$score26 <- zoo::na.locf(newdata$score26 )
newdata$score27 <- zoo::na.locf(newdata$score27 )
newdata$score28 <- zoo::na.locf(newdata$score28 )
newdata$score29 <- zoo::na.locf(newdata$score29 )
newdata$score30 <- zoo::na.locf(newdata$score30 )
newdata$score31 <- zoo::na.locf(newdata$score31 )
newdata$score32 <- zoo::na.locf(newdata$score32 )
newdata$score33 <- zoo::na.locf(newdata$score33 )
newdata$score34 <- zoo::na.locf(newdata$score34 )
newdata$score35 <- zoo::na.locf(newdata$score35 )
newdata$score36 <- zoo::na.locf(newdata$score36 )
newdata$score37 <- zoo::na.locf(newdata$score37 )
newdata$score38 <- zoo::na.locf(newdata$score38 )
newdata$score39 <- zoo::na.locf(newdata$score39 )
newdata$score40 <- zoo::na.locf(newdata$score40 )
newdata$score41 <- zoo::na.locf(newdata$score41 )
newdata$score42 <- zoo::na.locf(newdata$score42 )
newdata$score43 <- zoo::na.locf(newdata$score43 )
newdata$score44 <- zoo::na.locf(newdata$score44 )
newdata$score45 <- zoo::na.locf(newdata$score45 )
newdata$score46 <- zoo::na.locf(newdata$score46 )
newdata$score47 <- zoo::na.locf(newdata$score47 )
newdata$score48 <- zoo::na.locf(newdata$score48 )
newdata$score49 <- zoo::na.locf(newdata$score49 )
newdata$score50 <- zoo::na.locf(newdata$score50 )
newdata$score51 <- zoo::na.locf(newdata$score51 )
newdata$score52 <- zoo::na.locf(newdata$score52 )
newdata$score53 <- zoo::na.locf(newdata$score53 )
newdata$score54 <- zoo::na.locf(newdata$score54 )
newdata$score55 <- zoo::na.locf(newdata$score55 )
newdata$score56 <- zoo::na.locf(newdata$score56 )
newdata$score57 <- zoo::na.locf(newdata$score57 )
newdata$score58 <- zoo::na.locf(newdata$score58 )
newdata$score59 <- zoo::na.locf(newdata$score59 )
newdata$score60 <- zoo::na.locf(newdata$score60 )
newdata$score61 <- zoo::na.locf(newdata$score61 )
newdata$score62 <- zoo::na.locf(newdata$score62 )
newdata$score63 <- zoo::na.locf(newdata$score63 )
newdata$score64 <- zoo::na.locf(newdata$score64 )
newdata$score65 <- zoo::na.locf(newdata$score65 )
newdata$score66 <- zoo::na.locf(newdata$score66 )
newdata$score67 <- zoo::na.locf(newdata$score67 )
newdata$score68 <- zoo::na.locf(newdata$score68 )
newdata$score69 <- zoo::na.locf(newdata$score69 )
newdata$score70 <- zoo::na.locf(newdata$score70 )
newdata$score71 <- zoo::na.locf(newdata$score71 )
newdata$score72 <- zoo::na.locf(newdata$score72 )
newdata$score73 <- zoo::na.locf(newdata$score73 )
newdata$score74 <- zoo::na.locf(newdata$score74 )
newdata$score75 <- zoo::na.locf(newdata$score75 )
newdata$score76 <- zoo::na.locf(newdata$score76 )
newdata$score77 <- zoo::na.locf(newdata$score77 )
newdata$score78 <- zoo::na.locf(newdata$score78 )
newdata$score79 <- zoo::na.locf(newdata$score79 )
newdata$score80 <- zoo::na.locf(newdata$score80 )
newdata$score81 <- zoo::na.locf(newdata$score81 )
newdata$score82 <- zoo::na.locf(newdata$score82 )
newdata$score83 <- zoo::na.locf(newdata$score83 )
newdata$score84 <- zoo::na.locf(newdata$score84 )
newdata$score85 <- zoo::na.locf(newdata$score85 )
newdata$score86 <- zoo::na.locf(newdata$score86 )
newdata$score87 <- zoo::na.locf(newdata$score87 )
newdata$score88 <- zoo::na.locf(newdata$score88 )
newdata$score89 <- zoo::na.locf(newdata$score89 )
newdata$score90 <- zoo::na.locf(newdata$score90 )
newdata$score91 <- zoo::na.locf(newdata$score91 )
newdata$score92 <- zoo::na.locf(newdata$score92 )
newdata$score93 <- zoo::na.locf(newdata$score93 )
newdata$score94 <- zoo::na.locf(newdata$score94 )
newdata$score95 <- zoo::na.locf(newdata$score95 )
newdata$score96 <- zoo::na.locf(newdata$score96 )
newdata$score97 <- zoo::na.locf(newdata$score97 )
newdata$score98 <- zoo::na.locf(newdata$score98 )
newdata$score99 <- zoo::na.locf(newdata$score99 )
newdata$score100 <- zoo::na.locf(newdata$score100 )
write.table(newdata, "outputR")
Sarei grato se qualcuno potesse aiutarmi a risolvere l'errore "By= 1". Ecco i miei dati di dati. Potresti anche notare che non sono abituato a notare Usando loop in R , quindi ho appena copiato tutto 100 volte, probabilmente non il modo più semplice.
- Aggiornamento
Oh, sto davvero pensando che questo sia perché unisci solo accetta due argomenti, quindi dovrei usare:
newdata<- merge(data1[,1:2],data2[,1:2],by=1,all=TRUE)
newdata<- merge(newdata[,1:3],data3[,1:2],by=1,all=TRUE)
Per ciascuno degli elementi ...
Soluzione
L'argomento Merge richiede solo due valori come input, quindi devi farlo separatamente:
newdata<- merge(data1[,1:2],data2[,1:2],by=1,all=TRUE)
newdata<- merge(newdata [,1:3 ],data3 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:4 ],data4 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:5 ],data5 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:6 ],data6 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:7 ],data7 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:8 ],data8 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:9 ],data9 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:10 ],data10 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:11 ],data11 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:12 ],data12 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:13 ],data13 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:14 ],data14 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:15 ],data15 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:16 ],data16 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:17 ],data17 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:18 ],data18 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:19 ],data19 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:20 ],data20 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:21 ],data21 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:22 ],data22 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:23 ],data23 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:24 ],data24 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:25 ],data25 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:26 ],data26 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:27 ],data27 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:28 ],data28 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:29 ],data29 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:30 ],data30 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:31 ],data31 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:32 ],data32 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:33 ],data33 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:34 ],data34 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:35 ],data35 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:36 ],data36 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:37 ],data37 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:38 ],data38 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:39 ],data39 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:40 ],data40 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:41 ],data41 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:42 ],data42 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:43 ],data43 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:44 ],data44 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:45 ],data45 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:46 ],data46 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:47 ],data47 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:48 ],data48 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:49 ],data49 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:50 ],data50 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:51 ],data51 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:52 ],data52 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:53 ],data53 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:54 ],data54 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:55 ],data55 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:56 ],data56 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:57 ],data57 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:58 ],data58 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:59 ],data59 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:60 ],data60 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:61 ],data61 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:62 ],data62 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:63 ],data63 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:64 ],data64 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:65 ],data65 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:66 ],data66 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:67 ],data67 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:68 ],data68 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:69 ],data69 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:70 ],data70 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:71 ],data71 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:72 ],data72 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:73 ],data73 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:74 ],data74 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:75 ],data75 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:76 ],data76 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:77 ],data77 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:78 ],data78 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:79 ],data79 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:80 ],data80 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:81 ],data81 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:82 ],data82 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:83 ],data83 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:84 ],data84 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:85 ],data85 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:86 ],data86 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:87 ],data87 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:88 ],data88 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:89 ],data89 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:90 ],data90 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:91 ],data91 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:92 ],data92 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:93 ],data93 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:94 ],data94 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:95 ],data95 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:96 ],data96 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:97 ],data97 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:98 ],data98 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:99 ],data99 [,1:2],by=1,all=TRUE )
newdata<- merge(newdata [,1:100 ],data100 [,1:2],by=1,all=TRUE )
Altri suggerimenti
Google restituisce questo come primo elenco per l'errore R, Errore in fix.by (by.x, x) e l'ho trovato interessante nessuna risposta ha tentato una soluzione ad anello dell'OP molto lungaCommand Elenco.
Per i futuri lettori che devono unire molti dati di dati, considerare il legame individuale dei singoli dati in un elenco con lapply(), eseguire qualsiasi calcolo necessario, quindi eseguire un Reduce(..., merge) per unire tutti i file di elenco in un unico numero di file.Sotto i processi e fonde tutti i 100 file di Pubblicazione originale:
library(zoo)
dfList <- lapply(c(1:100), function(i) {
df <- read.table(paste0("rundata ", i), sep= " ", col.names=c("tm","score","current"))
df <- df[!is.na(df$tm),]
df$score <- zoo::na.locf(df$score)
colnames(df) <- paste0(colnames(df), i)
return(df)
})
newdata <- Reduce(function(...) merge(..., by=1, all=T), dfList)
write.table(newdata, "outputR")
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