我希望rbind两个数据框，在结果data.frame中有一个额外的列，表示哪一行来自data.frame。例如，
top <- mtcars[1:16, ]
bottom <- mtcars[17:32, ]
rbind(top, bottom)
这会给我，
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
但我需要，
mpg cyl disp hp drat wt qsec vs am gear carb which.df
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 top
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 top
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 top
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 top
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 top
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 top
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 top
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 top
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 top
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 top
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 top
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 top
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 top
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 top
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 top
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 top
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 bottom
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 bottom
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 bottom
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 bottom
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 bottom
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 bottom
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 bottom
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 bottom
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 bottom
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 bottom
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 bottom
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 bottom
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 bottom
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 bottom
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 bottom
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 bottom
是否有任何现有的功能可以执行此操作，我有很多data.frame，重复计算行号的文本会很麻烦。
答案 0 :(得分：5)
您可以为此编写一个简单的函数：
top <- mtcars[1:16, ]
bottom <- mtcars[17:32, ]
myBind <- function(df1, df2) {
df1$which.df <- all.names(match.call())[2]
df2$which.df <- all.names(match.call())[3]
rbind(df1, df2)
}
result <- myBind(top, bottom)
结果如下：
result[14:19,]
mpg cyl disp hp drat wt qsec vs am gear carb which.df
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 top
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 top
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 top
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 bottom
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 bottom
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 bottom
为了容纳2个以上的数据帧，您可以使用...而不是df1, df2并迭代函数内的所有参数来设置which.df的值。
答案 1 :(得分：4)
我们可以使用Map
res <- do.call(rbind,Map(cbind, list(top, bottom),
which.df = c("top", "bottom")))
head(res)
# mpg cyl disp hp drat wt qsec vs am gear carb which.df
#Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 top
#Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 top
#Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 top
#Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 top
#Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 top
#Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 top
答案 2 :(得分：2)
dfstack <- function(
...,
indname='which.df',
indfunc=function(...) {
l <- list(...);
nms <- names(l);
nms <- (if (is.null(nms)) NA_character_ else nms)[seq_along(l)];
ifelse(is.na(nms) | nms=='',as.character(substitute(list(...))[-1L]),nms);
}
) do.call(rbind,do.call(Map,c(cbind,list(unname(list(...))),setNames(list(indfunc(...)),indname))));
特点：
'which.df'，但可以使用indname参数覆盖。indfunc() lambda参数参数化用于选择指标值的逻辑。indfunc()会在可用和非空时自动更喜欢命名的可变参数的名称，否则会对参数解析树进行字符串化并使用它。演示：
dfstack();
## NULL
dfstack(top);
## mpg cyl disp hp drat wt qsec vs am gear carb which.df
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 top
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 top
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 top
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 top
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 top
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 top
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 top
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 top
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 top
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 top
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 top
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 top
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 top
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 top
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 top
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 top
dfstack(top,bottom);
## mpg cyl disp hp drat wt qsec vs am gear carb which.df
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 top
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 top
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 top
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 top
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 top
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 top
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 top
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 top
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 top
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 top
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 top
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 top
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 top
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 top
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 top
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 top
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 bottom
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 bottom
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 bottom
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 bottom
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 bottom
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 bottom
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 bottom
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 bottom
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 bottom
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 bottom
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 bottom
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 bottom
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 bottom
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 bottom
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 bottom
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 bottom
这里有一个演示，说明如何有选择地指定命名的变量参数来控制指标值，以及如何为未命名的参数对参数解析树进行字符串化：
dfstack(top+2*top,BOTTOM=bottom);
## mpg cyl disp hp drat wt qsec vs am gear carb which.df
## Mazda RX4 63.0 18 480.0 330 11.70 7.860 49.38 0 3 12 12 top + 2 * top
## Mazda RX4 Wag 63.0 18 480.0 330 11.70 8.625 51.06 0 3 12 12 top + 2 * top
## Datsun 710 68.4 12 324.0 279 11.55 6.960 55.83 3 3 12 3 top + 2 * top
## Hornet 4 Drive 64.2 18 774.0 330 9.24 9.645 58.32 3 0 9 3 top + 2 * top
## Hornet Sportabout 56.1 24 1080.0 525 9.45 10.320 51.06 0 0 9 6 top + 2 * top
## Valiant 54.3 18 675.0 315 8.28 10.380 60.66 3 0 9 3 top + 2 * top
## Duster 360 42.9 24 1080.0 735 9.63 10.710 47.52 0 0 9 12 top + 2 * top
## Merc 240D 73.2 12 440.1 186 11.07 9.570 60.00 3 0 12 6 top + 2 * top
## Merc 230 68.4 12 422.4 285 11.76 9.450 68.70 3 0 12 6 top + 2 * top
## Merc 280 57.6 18 502.8 369 11.76 10.320 54.90 3 0 12 12 top + 2 * top
## Merc 280C 53.4 18 502.8 369 11.76 10.320 56.70 3 0 12 12 top + 2 * top
## Merc 450SE 49.2 24 827.4 540 9.21 12.210 52.20 0 0 9 9 top + 2 * top
## Merc 450SL 51.9 24 827.4 540 9.21 11.190 52.80 0 0 9 9 top + 2 * top
## Merc 450SLC 45.6 24 827.4 540 9.21 11.340 54.00 0 0 9 9 top + 2 * top
## Cadillac Fleetwood 31.2 24 1416.0 615 8.79 15.750 53.94 0 0 9 12 top + 2 * top
## Lincoln Continental 31.2 24 1380.0 645 9.00 16.272 53.46 0 0 9 12 top + 2 * top
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 BOTTOM
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 BOTTOM
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 BOTTOM
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 BOTTOM
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 BOTTOM
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 BOTTOM
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 BOTTOM
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 BOTTOM
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 BOTTOM
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 BOTTOM
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 BOTTOM
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 BOTTOM
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 BOTTOM
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 BOTTOM
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 BOTTOM
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 BOTTOM
以下是如何使用indname和indfunc参数自定义结果的演示：
dfstack(top,bottom,indname='ind',indfunc=function(...) paste0('[',as.character(substitute(list(...))[-1L]),']'));
## mpg cyl disp hp drat wt qsec vs am gear carb ind
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 [top]
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 [top]
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 [top]
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 [top]
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 [top]
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 [top]
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 [top]
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 [top]
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 [top]
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 [top]
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 [top]
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 [top]
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 [top]
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 [top]
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 [top]
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 [top]
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 [bottom]
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 [bottom]
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 [bottom]
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 [bottom]
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 [bottom]
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 [bottom]
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 [bottom]
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 [bottom]
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 [bottom]
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 [bottom]
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 [bottom]
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 [bottom]
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 [bottom]
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 [bottom]
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 [bottom]
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 [bottom]
dfstack(top,bottom,indfunc=function(...) seq_along(list(...)));
## mpg cyl disp hp drat wt qsec vs am gear carb which.df
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 1
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 1
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 1
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 1
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 1
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 1
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 1
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 1
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 1
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 1
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 1
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 1
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 1
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 2
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 2
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 2
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 2
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 2
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 2
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 2
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 2
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 2
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 2
答案 3 :(得分：1)
在使用rbind之前，可以在顶部和底部添加列。
which.df.top<-rep("top",dim(top)[1])
top[which.df]<-which.df.top
which.df.bot<-rep("botom",dim(bottom[1])
bottom[which.df]<-which.df.bot
答案 4 :(得分：1)
我也在尝试解决这个问题，并提出了这个解决方案，
stackDf <- function(..., add.to.col = TRUE){
lst <- list(...)
if(add.to.col) {
nms <- sapply(substitute(list(...))[-1], deparse)
lst <- lapply(seq_along(lst),
function(x) cbind(lst[[x]], names = nms[x]))
}
do.call(rbind, lst)
}
> head(stackDf(top, bottom))
mpg cyl disp hp drat wt qsec vs am gear carb names
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 top
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 top
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 top
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 top
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 top
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 top
> head(stackDf(top, bottom, add.to.col=FALSE))
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1