r - 从列表中子集 n 个元素

Question

我有一个像这样的大名单

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

此列表来自数据框的拆分，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了一个真实的例子，只是为了有相同的变量

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!

Answer 1

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

除非我在做傻事， ldply不保留行名。

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