我有一个数据框，其中第1行和第2行实际上是同名。
V1 V2 V3 V4 V5 V6 V7 V8
1 Bristol 014C Bristol, City of South West England
2 Super Output Area Lower Layer Unitary Authority Region Country
3 All Usual Residents Count Persons Mar11 1654 428234 5288935 53012456
4 Age Under 1 Count Persons Mar11 24 6459 59969 675065
5 Age 1 Count Persons Mar11 17 6150 59165 668662
6 Age 2 Count Persons Mar11 23 5927 59051 663119
...
如何从第1行和第2行中创建组合名？
Bristol 014C\n Bristol, City of\n South West\n England\n
X X.1 X.2 X.3 X.4 Super Output Area Lower Layer Unitary Authority Region Country
3 All Usual Residents Count Persons Mar11 1654 428234 5288935 53012456
...
原始CSV数据集位于：
"Age by Single Year (QS103EW)"
,,,,"Bristol, City of","South West","England"
,,,,"Unitary Authority","Region","Country"
"All Usual Residents","Count","Persons","Mar11",428234,5288935,53012456
"Age Under 1","Count","Persons","Mar11",6459,59969,675065
"Age 1","Count","Persons","Mar11",6150,59165,668662
"Age 2","Count","Persons","Mar11",5927,59051,663119
"Age 3","Count","Persons","Mar11",5698,59230,663574
"Age 4","Count","Persons","Mar11",5399,58679,648029
"Age 5","Count","Persons","Mar11",5005,56756,632090
"Age 6","Count","Persons","Mar11",4784,54333,600847
"Age 7","Count","Persons","Mar11",4582,55049,594673
"Age 8","Count","Persons","Mar11",4240,53364,572874
"Age 9","Count","Persons","Mar11",4226,53187,572148
"Age 10","Count","Persons","Mar11",4174,55365,587056
"Age 11","Count","Persons","Mar11",4326,57859,605569
"Age 12","Count","Persons","Mar11",4362,59970,618918
"Age 13","Count","Persons","Mar11",4474,61139,628858
"Age 14","Count","Persons","Mar11",4367,62559,640528
"Age 15","Count","Persons","Mar11",4408,64003,650826
"Age 16","Count","Persons","Mar11",4486,63827,648677
"Age 17","Count","Persons","Mar11",4436,65108,665447
"Age 18","Count","Persons","Mar11",5650,66351,674980
"Age 19","Count","Persons","Mar11",8061,68788,700335
"Age 20","Count","Persons","Mar11",9611,70266,727389
"Age 21","Count","Persons","Mar11",9412,68414,711470
"Age 22","Count","Persons","Mar11",8860,66641,715130
"Age 23","Count","Persons","Mar11",8364,65065,728435
"Age 24","Count","Persons","Mar11",8124,62780,712897
"Age 25","Count","Persons","Mar11",8066,61533,731640
"Age 26","Count","Persons","Mar11",8514,61882,730870
"Age 27","Count","Persons","Mar11",8051,60924,725203
"Age 28","Count","Persons","Mar11",7947,60960,728376
"Age 29","Count","Persons","Mar11",8174,61860,734792
"Age 30","Count","Persons","Mar11",8019,62908,747536
"Age 31","Count","Persons","Mar11",7956,62286,738069
"Age 32","Count","Persons","Mar11",7166,59382,700814
"Age 33","Count","Persons","Mar11",6726,55917,660464
"Age 34","Count","Persons","Mar11",6551,56298,662338
"Age 35","Count","Persons","Mar11",6277,58203,668403
"Age 36","Count","Persons","Mar11",6152,61097,685626
"Age 37","Count","Persons","Mar11",5939,63688,699168
"Age 38","Count","Persons","Mar11",5987,67826,731914
"Age 39","Count","Persons","Mar11",6080,71550,764005
"Age 40","Count","Persons","Mar11",5892,73098,775472
"Age 41","Count","Persons","Mar11",5638,73194,761698
"Age 42","Count","Persons","Mar11",5584,75361,780374
"Age 43","Count","Persons","Mar11",5428,75596,777994
"Age 44","Count","Persons","Mar11",5765,78289,790396
"Age 45","Count","Persons","Mar11",5651,79060,790748
"Age 46","Count","Persons","Mar11",5650,79232,795338
"Age 47","Count","Persons","Mar11",5379,78975,781209
"Age 48","Count","Persons","Mar11",5337,77057,767090
"Age 49","Count","Persons","Mar11",5059,75259,745430
"Age 50","Count","Persons","Mar11",4931,73367,723908
"Age 51","Count","Persons","Mar11",4685,70689,690689
"Age 52","Count","Persons","Mar11",4693,70449,680476
"Age 53","Count","Persons","Mar11",4687,69254,666006
"Age 54","Count","Persons","Mar11",4323,66674,639016
"Age 55","Count","Persons","Mar11",4257,64482,614577
"Age 56","Count","Persons","Mar11",4165,64033,602320
"Age 57","Count","Persons","Mar11",4146,65515,605276
"Age 58","Count","Persons","Mar11",3984,64223,591365
"Age 59","Count","Persons","Mar11",3840,64945,583454
"Age 60","Count","Persons","Mar11",3847,64877,586619
"Age 61","Count","Persons","Mar11",3932,68392,605525
"Age 62","Count","Persons","Mar11",4004,70719,620903
"Age 63","Count","Persons","Mar11",4280,77954,676509
"Age 64","Count","Persons","Mar11",4015,79565,682721
"Age 65","Count","Persons","Mar11",3113,61314,523808
"Age 66","Count","Persons","Mar11",3262,65965,553369
"Age 67","Count","Persons","Mar11",3175,61203,516594
"Age 68","Count","Persons","Mar11",3083,58555,488921
"Age 69","Count","Persons","Mar11",2589,50403,425462
"Age 70","Count","Persons","Mar11",2539,47363,409195
"Age 71","Count","Persons","Mar11",2668,49314,426526
"Age 72","Count","Persons","Mar11",2591,48034,417526
"Age 73","Count","Persons","Mar11",2383,46204,403761
"Age 74","Count","Persons","Mar11",2322,44547,387121
"Age 75","Count","Persons","Mar11",2234,42240,367663
"Age 76","Count","Persons","Mar11",2233,40936,350111
"Age 77","Count","Persons","Mar11",2072,38185,326669
"Age 78","Count","Persons","Mar11",2060,37834,318178
"Age 79","Count","Persons","Mar11",2098,36471,306724
"Age 80","Count","Persons","Mar11",1925,35511,297352
"Age 81","Count","Persons","Mar11",1889,33113,273007
"Age 82","Count","Persons","Mar11",1730,30617,250274
"Age 83","Count","Persons","Mar11",1464,27746,226334
"Age 84","Count","Persons","Mar11",1530,26106,211806
"Age 85","Count","Persons","Mar11",1386,24105,191681
"Age 86","Count","Persons","Mar11",1257,21970,171121
"Age 87","Count","Persons","Mar11",1173,19873,153717
"Age 88","Count","Persons","Mar11",997,17646,136061
"Age 89","Count","Persons","Mar11",985,16306,123731
"Age 90","Count","Persons","Mar11",830,14560,110027
"Age 91","Count","Persons","Mar11",618,10825,82336
"Age 92","Count","Persons","Mar11",390,6599,49584
"Age 93","Count","Persons","Mar11",325,4989,37630
"Age 94","Count","Persons","Mar11",255,4575,34145
"Age 95","Count","Persons","Mar11",219,3549,26370
"Age 96","Count","Persons","Mar11",151,2863,21040
"Age 97","Count","Persons","Mar11",114,2049,15044
"Age 98","Count","Persons","Mar11",78,1429,10327
"Age 99","Count","Persons","Mar11",57,953,6738
"Age 100 and Over","Count","Persons","Mar11",77,1492,10576
"Age by Single Year, 2011 (QS103EW), Mar11","LastUpdated","30 January 2013"
"Age by Single Year, 2011 (QS103EW), Mar11","Source","Office for National Statistics"
"Age by Single Year (QS103EW)","National Statistics"
"This material is Crown Copyright. You may re-use this information (not including logos) free of charge in any format or medium, under the terms of the Open Government Licence. To view this licence, visit www.nationalarchives.gov.uk/doc/open-government-licence Information Policy Team, The National Archives, Kew, London TW9 4DU, or email:psi@nationalarchives.gsi.gov.uk. When reproducing this material, the source should be acknowledged."
请注意，我用以下内容读取数据：
data <- head(read.csv2( file = 'Age by Single Year, 2011 (QS103EW) (2011).csv', skip = 1, header = FALSE, sep = ',' ), -4)
答案 0 :(得分：1)
假设您的数据在data.txt中以制表符分隔，请先使用nrows参数将列名称读入数据框。
colnames <- read.table('./data.txt', nrows = 2)
然后将数据读入数据框，使用skip参数跳过文本文件的前两行。
df <- read.table('./data.txt', skip = 2)
您可能必须检查/修改colnames以确保条目位于正确的位置且其长度正确。然后使用names设置数据框中对象的名称。
names(df) <- colnames
答案 1 :(得分：0)
一个较小的例子可能使问题更清楚。
假设您的data.frame是DF。然后
newDF <- DF[-c(1:2), ]
dimnames(newDF)[[2]] <- paste(DF[1,], DF[2, ])