我有这么大的清单
str(sep)
List of 54
$AK:'data.frame': 5 obs. of 3 variables:
..$nombre : Factor w/ 4510 levels "ABBEVILLE AREA MEDICAL CENTER",..: 3085 40 1119 39 2176
..$heartattack: num [1:5] 13.4 14.5 15.5 15.7 17.7
..$state.name : Factor w/ 54 levels "AK","AL","AR",..: 1 1 1 1 1
$AL:'data.frame': 51 obs. of 3 variables:
..$nombre : Factor w/ 4510 levels "ABBEVILLE AREA MEDICAL CENTER",..: 839 224 3587 1288 3063 4018 4259 221 4158 1531 ...
..$heartattack: num [1:51] 13.3 14.2 14.3 14.5 14.6 14.7 14.7 14.9 15 15 ...
..$state.name : Factor w/ 54 levels "AK","AL","AR",..: 2 2 2 2 2 2 2 2 2 2 ...
$AR:'data.frame': 35 obs. of 3 variables:
..$nombre : Factor w/ 4510 levels "ABBEVILLE AREA MEDICAL CENTER",..: 114 214 843 4216 2612 211 2380 3695 4327 3516 ...
..$heartattack: num [1:35] 11.9 14.4 14.4 14.5 14.5 14.7 14.8 14.9 15.2 15.6 ...
..$state.name : Factor w/ 54 levels "AK","AL","AR",..: 3 3 3 3 3 3 3 3 3 3 ...
$AZ:'data.frame': 45 obs. of 3 variables:
..$nombre : Factor w/ 4510 levels "ABBEVILLE AREA MEDICAL CENTER",..: 2200 521 206 205 1170 4509 3433 202 4084 4391 ...
..$heartattack: num [1:45] 12 12.2 12.8 13.6 13.6 13.7 13.9 14 14.1 14.1 ...
..$state.name : Factor w/ 54 levels "AK","AL","AR",..: 4 4 4 4 4 4 4 4 4 4 ...
#continues
这个列表来自数据框的分割,我想选择每个级别的特定位置并保留3个变量的信息.我试图写一个for循环来做它但我找不到正确的子集方式.
例如,如果您选择位置1,您应该得到
nombre heartattack state.name
POSITION 1 OF $AK$nombre) POS 1 OF $AK$heartattack POS 1 OF $AK$state.name
POSITION 1 OF $AL$nombre POS 1 OF $AL$heartattack POS 1 OF $AL$state.name
POSITION 1 OF....
我用一个较小的列表做一个真实的例子,只是为了拥有相同的变量
list(a=mtcars, b=mtcars[8:16, ])
$a
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
$b
mpg cyl disp hp drat wt qsec vs am gear carb
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
expected2<-rbind(mtcars[2,],mtcars[9,]) #mtcars [9,] is the 2nd position of mtcars[8:16,]
expected4<-rbind(mtcars[4,],mtcars[11,]) #mtcars [11,] is the 4th position of mtcars[8:16,]
expected2
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
expected4
mpg cyl disp hp drat wt qsec vs am gear carb
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
我希望我能说清楚.
先感谢您！
最佳答案 这非常接近：
d <- list(a=mtcars, b=mtcars[8:16, ])
f <- function(dat,i) {
plyr::ldply(dat,function(x) data.frame(pos=i,x[i,])) }
f(d,2)
## .id pos mpg cyl disp hp drat wt qsec vs am gear carb
## 1 a 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 2 b 2 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
f(d,9)
## .id pos mpg cyl disp hp drat wt qsec vs am gear carb
## 1 a 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 2 b 9 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
除非我做一些愚蠢的事情,否则ldply不会保留rownames.
rn <- function(dat,i) {
plyr::laply(dat,function(x) rownames(x)[i])
}
f0 <- f(d,2)
rownames(f0) <- rn(d,2)
## .id pos mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 Wag a 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Merc 230 b 2 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
