我正在尝试合并 R 中的数据，如我对其他帖子here.的回答中所建议的那样但是，我有一个错误。
首先让我解释一下我的尝试。我有100个文件（每个都有x_i和y_i），我想以这种方式合并它们：
从：
x1; y1 ; x2 ; y2
1 ; 100 ; 1 ; 150
4 ; 90 ; 2 ; 85
7 ; 85 ; 10 ; 60
10; 80 ;
到
x1; y1 ; x2 ; y2
1 ; 100 ; 1 ; 150
2 ; 100 ; 2 ; 85
4 ; 90 ; 4 ; 85
7 ; 85 ; 7 ; 85
10; 80 ;10 ; 60
简单的脚本在玩具示例中运行良好：
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
现在我的真实数据。我修改脚本看起来像这样，但我收到一个错误：
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"))
- ＆GT;工作正常
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)
- ＆GT;给出错误：
Error in fix.by(by.x, x) :
'by' must specify one or more columns as numbers, names or logical
我不明白这个错误，因为我确实指出1没有？
其余的脚本（在我修复第一个错误后仍需在100个输入上进行测试）
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")
如果有人能帮我解决“by = 1”错误，我将不胜感激。 Here are my datafiles.你可能也注意到我不习惯在 R 中使用循环，所以我只是复制了100次，可能不是最简单的方法。
- UPDATE
哦，我实际上认为这是因为merge只接受两个参数，所以我应该使用：
newdata<- merge(data1[,1:2],data2[,1:2],by=1,all=TRUE)
newdata<- merge(newdata[,1:3],data3[,1:2],by=1,all=TRUE)
每个元素......
答案 0 :(得分：9)
merge参数只接受两个值作为输入，因此您必须单独执行：
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 )
答案 1 :(得分：3)
Google将此作为R错误的第一个列表返回，错误在fix.by（by.x，x）中我发现它很有趣没有回答尝试OP的循环解决方案很长命令列表。
对于需要合并许多数据帧的未来读者，请考虑将单个数据帧绑定到lapply()的列表中，运行任何所需的计算，然后运行Reduce(..., merge)将列表中的所有文件合并为一个宽数据帧。下面处理并合并原始发布的所有100个文件：
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")