如何添加已删除缺失数据的空行答案

【问题标题】：How to add empty rows where missing data has been removed如何添加已删除缺失数据的空行
【发布时间】：2019-11-18 22:45:29
【问题描述】：

我使用lmlist 将公共数据应用于分组数据（Participant）并使用每个参与者的系数（A）创建一个数据框，如下所示：

myCoefficients <- as.data.frame(coef(lmList(Outcome ~ A | Participant, mydata)))

这会产生一个如下所示的数据框：

      (Intercept)            A   
1    11.784913184  0.008224641
2    13.416552668  0.126538988
3     1.255375012 -0.261815119
4    -0.453204283 -0.050068500
5     1.855255007  0.019615941
6    18.233333333  0.266666667
7    10.622690151  0.039481512
8     4.583096557  0.056392969
9   -10.316190476 -0.327619048
10   -0.750918742 -0.011841568
11    0.000000000  0.000000000
12    1.658747938 -0.003200315
13    6.639959940  0.100150225
14    8.543573432  0.111088310
15   -0.409441233  0.075626204
16    0.000000000  0.000000000
17   16.391626950  0.338496534
18   -2.780630438 -0.119811724
19    3.581120944  0.057030482
20   -7.435064935 -0.086580087
21    0.000000000  0.000000000
22   -1.399803872 -0.041049967
23    0.000000000  0.000000000
24    5.748297340  0.073490890
25    2.502387775  0.022385387
26    5.477356181 -0.071399429
27    0.000000000  0.000000000
28    0.000000000  0.000000000
29    0.202822570  0.004236503
30    0.000000000  0.000000000
31    3.191532668  0.018318746
32    5.808669308 -0.089812308
33   15.556690047  0.288999378
34    6.044498212  0.033978540
35    5.384817738  0.088688463
36    2.429605338 -0.119020694
37    9.498121941 -0.068103350
38    1.449211455 -0.038649097
39   18.311828852  0.228294348
40   36.288255223  0.685770751
41   20.607074068  0.097268429
42   12.587294301  0.126299603
43    6.688188926  0.088422840
44    6.820835614  0.051997811
45   -2.063996902 -0.533215333
46   12.255847953  0.066520468
47   -4.818481848 -0.099009901
48   11.449166132  0.105355997
49   13.623012447  0.204422043
50   11.676916534  0.056099996
51    2.514750467 -0.019606775
52    0.125293117 -0.050347142
53    0.000000000  0.000000000
54    5.304979604 -0.175722320
55   10.318437929  0.096100382
56    0.000000000  0.000000000
57   10.607768097 -0.002279945
58   10.333968509  0.137398456
59   -4.513711889 -0.563639297
60    6.721815687  0.006860279
61   -0.718921180 -0.058085796
62   12.354598540  0.192189781
63   20.850979616  0.274787255
65    7.154075137  0.059849368
66    5.020082784 -0.008201748
67    0.229156161 -0.459531014
68    6.602570969  0.038832351
69   18.606677985  0.180531975
70    1.261939931 -0.064992614
71    0.000000000  0.000000000
72    8.565326633  0.105527638
73    6.025134650  0.065914337
74    0.411054480 -0.008632617
75    6.001972711  0.005753740
76   14.423697726  0.102622891
77   -1.782058047 -0.024274406
78   13.461871683  0.196417421
79   -2.490421456 -0.137691571
80    0.986939239  0.006814310
81    0.000000000  0.000000000
82   26.074865546  0.338946141
83    4.721769334 -0.023747076
84    3.491952414  0.055983205
85    8.621555769  0.111749489
86   13.298121427  0.175333515
87    5.075415244 -0.030621479
88    5.427200030 -0.056299149
89    5.784197111  0.052613361
90    2.967869893 -0.024593415
91   11.439869695  0.154194191
92    1.439169713 -0.137264690
93    0.000000000  0.000000000
94    5.696352440  0.077569872
95    2.544640478 -0.024518949
96    3.933483703  0.037261186
97    4.896524416  0.065283756
98    2.135022525 -0.374031801
99    7.190891371  0.083368454
100  23.054124552  0.531790023
101   6.161769255 -0.221620786
102 -14.547148289 -0.265139993
103  12.140619804  0.201380861
104   4.432939593  0.015699761
105  -0.837367221 -0.034716496
106  -0.122268163 -0.033668045
107  -5.696417101 -0.176646920
108  13.010822852  0.085412776
109  19.237564131  0.530002231
110   1.938517087 -0.178676770
111   6.888465629 -0.009047188
112   7.164846545  0.155902843
113  -0.403225806 -0.016129032
114   1.008421194 -0.058635633
115   3.170498084  0.028136973
116   7.475271328  0.008586344
117   9.387123820  0.130438620
118  -4.720329503 -0.102333977
119  -0.690008119 -0.122506379
120   9.928991185 -0.073457395
121  -8.166768363 -0.243770694
122  -0.836936555 -0.071781828
123 -13.756592007 -0.129834974
124  22.520513735  0.360226288
125  -6.268156425 -0.167597765
126  -7.517700552 -0.130074700
127 -14.041414611 -0.273382969
128   0.000000000  0.000000000
129   8.046064474 -0.259777450
130   3.669741697 -0.357933579
131   2.593244581 -0.190890087
132  -8.000000000           NA
133   8.328107184 -0.071265678
134   1.637694105 -0.192730521
135   3.693134192 -0.136592243
136   2.161687299 -0.180897599
137   0.000000000  0.000000000
138   0.721612005 -0.039410582
139   7.749737119  0.030494217
140  -5.808393153 -0.096355605
141  12.282297336  0.080170438
142  -5.316274128 -0.176288295
143  -4.441255140 -0.138249032
144  -1.117341518 -0.083225121
145  -0.752677582 -0.141942632
146   3.407083929 -0.101590819
147   6.265884172  0.002073376
148  -2.148945392 -0.152051430
149  28.415807560  0.554123711
150   8.716573171  0.118457600
151  12.496143959  0.088946015
152  19.149987332  0.217095262
153   0.009304822 -0.011094211
154   7.863488624  0.082204767
155   4.728299224 -0.416372618
156   4.624162598  0.035483138
157   8.492298276  0.117821075
158 -14.046175789 -0.229987520
159   1.183873993  0.017370732
160   0.000000000  0.000000000
161   7.045593921  0.066213394
162   5.513560300  0.031015580
163   4.244824402 -0.064298666
164   1.054528148  0.080908349
165 -11.086497890 -0.221518987
166  13.992083786  0.151239106
167   0.111296142 -0.093810713
168  -1.042661399 -0.045771688
169  16.828913325  0.164294955
170   1.743498818 -0.011820331
171   3.609741954  0.005798782
172  -2.110598319  0.011537828
173  -8.958189217 -0.714852492
174  -7.150518727 -0.141802105
175  10.645382098  0.141322541
176 -14.073564593 -0.445574163
177  16.096282103  0.289202798
178   8.649276860  0.162060950
179   0.000000000  0.000000000
180  -7.084494960 -0.158696118
181  -1.312672577 -0.103255341
182  -3.366945247 -0.223307037
183  14.938014695  0.208146358
184  -0.678981349 -0.185437590
185  10.027063876  0.030983707
186  12.853728050  0.126413533
187  -0.418968692 -0.010474217
188   7.242331839  0.175605381
189  -4.014336918 -0.143369176
190  10.229368645  0.118231260
191  -7.187500000 -0.539772727
192  25.785714286  0.464285714
193  15.189222323  0.136346677
194  12.845133231  0.164776481
195 -22.088160874 -0.266480224
196   0.000000000  0.000000000
197   0.000000000  0.000000000
198   1.362202049 -0.052177290
199 -11.184823000 -0.463622140
200  -0.859435330 -0.035683015
201   2.601550453  0.026826641
202   0.342018669 -0.125041972
204   5.335232543  0.084139393
205  20.000000000           NA
206  -0.375872383 -0.002492522
207  28.872131148  0.863934426
208   3.563577586  0.101939655
209  20.433887877  0.131835967
210  -5.815011124 -0.218884963
211 -11.266979501 -0.353173623
212 -11.173933501 -0.250941029
213  -0.056431693 -0.006460194
214  18.165829146  0.251256281
215   6.369295470  0.060584610
216  -3.322072072 -0.073198198
217  -2.677165354  0.078740157
218   0.898822629 -0.078491433
219   4.393285217 -0.030609615
220   6.165938069  0.054128719
221  15.223880597  0.179104478
222  22.583177570  0.337180008
223  15.386230059  0.304366079
224 -11.271916238 -0.118435454
225   9.214443626  0.087140752
226  13.651131824 -0.015978695
227  10.126607319  0.117705242
228  -4.100042391 -0.123781263
229   9.859298532  0.218189233
230   2.279411765 -0.489430147
231   7.042338217  0.066514042
232  -1.149144869 -0.020812374
233   3.501552079 -0.045179334
234  10.348929473  0.157197118
235  -4.309844723 -0.070597199
236  -0.406851091  0.064296520
237   0.000000000  0.000000000
238 -12.870644391 -0.185441527
239   6.572819573  0.091931533
240 146.000000000  7.800000000
241  59.560466246  1.131617290
242  -2.477952661 -0.017301454
243   7.670426344  0.195818054
244   4.464389692  0.030444692
245   0.000000000  0.000000000
246  -5.090371915 -0.281543274
247   3.318152764  0.031160115
248   2.089063523 -0.111329404
249   4.831575199 -0.003182445
250   3.528881195  0.044187318
251  -7.641223478 -0.186414990
252   8.691099476  0.314136126
253   7.673273132  0.161838865
254  11.152559139  0.074838199
255 -11.197466897 -0.077720207
256   1.761562470  0.020807718
257  12.047270872  0.192994389
258   3.223861983  0.014549990
259  -4.670398010 -0.061300640
260   1.468158966  0.014962892
261   3.374092944  0.074725953
262   0.000000000  0.000000000
263  11.308158651  0.131560838
264  30.000000000           NA
265   9.231769665  0.085299130
266   4.178069353  0.007966261
267  13.004952586  0.161534918
268   5.447510330  0.099796905
269   0.000000000  0.000000000
270   9.579288026  0.115580213
271  16.908915559  0.311615945
272   9.610721530 -0.059055346
273   9.265092417  0.141893316
274  -7.569751715 -0.282050043
275   5.496770989 -0.041728763
276   7.409235108  0.163905534
277  -1.593325458 -0.018606025
278   5.703958450  0.002105559
279  17.006838906  0.118920973
280  -3.320901995  0.143972246
281  30.029290374  0.623751831
282  -2.678965618 -0.022542195
283  24.687500000  0.312500000
284 -15.059602649 -0.238410596
286   0.000000000           NA
287  -2.157762938 -0.045909850
288  -7.806357888 -0.209365920
289  11.782270607  0.287713841
290   4.821551496  0.065395817
291  17.686923508  0.085167915
292   6.617959072  0.108308808
293  14.685806258 -0.169112143
294  11.424153771  0.142710499
295   2.513812155  0.041436464
296  19.254025911  0.275941397
297  11.448286941  0.142535853
298  -5.008457711 -0.162106136
299  -1.435754190 -0.140229741
300  23.478882079  0.311710847

最左侧的索引列包含Participant ID 号。虽然myCoefficients 中只有 297 行，但Participant ID 的范围是 1-300。这是因为，例如，参与者 285 和另外两个在较早阶段被从分析中删除。

如果缺少参与者，我如何添加空行？

例如，参与者 285 在哪里，我想看看这个：

...
283  24.687500000  0.312500000
284 -15.059602649 -0.238410596
285            NA           NA
286   0.000000000           NA
287  -2.157762938 -0.045909850
...

是否有某种方法可以使用索引列，即使 R 不将其识别为列？即：

ncol(myCoefficients)
[1] 2

【问题讨论】：

这些被称为行名，您可以使用rownames 函数引用它们。缺失行的一种方法是创建一个包含所有参与者代码的数据框，然后使用 left_join 将回归结果添加到参与者代码匹配的位置。

标签： r indexing data-manipulation missing-data

【解决方案1】：

正如@Simon Woodward 所提到的，这些是行名，使用tidyverse 我们可以使用complete 用NA 完成缺失的行。

library(tidyverse)

df %>%
  rownames_to_column('row') %>%
  mutate(row = as.integer(row)) %>%
  complete(row = seq(min(row) : max(row))) %>%
  select(-row)

# A tibble: 14 x 2
#       A        B
#     <dbl>    <dbl>
# 1  11.8    0.00822
# 2  13.4    0.127  
# 3   1.26  -0.262  
# 4  NA     NA      
# 5  -0.453 -0.0501 
# 6   1.86   0.0196 
# 7  18.2    0.267  
# 8  10.6    0.0395 
# 9  NA     NA      
#10  NA     NA      
#11   4.58   0.0564 
#12 -10.3   -0.328  
#13  -0.751 -0.0118 
#14   0      0

数据

在这个小数据子集上进行了测试

df <- structure(list(A = c(11.784913184, 13.416552668, 1.255375012, 
-0.453204283, 1.855255007, 18.233333333, 10.622690151, 4.583096557, 
-10.316190476, -0.750918742, 0), B = c(0.008224641, 0.126538988, 
-0.261815119, -0.0500685, 0.019615941, 0.266666667, 0.039481512, 
0.056392969, -0.327619048, -0.011841568, 0)), class = "data.frame", row.names = c("1", 
"2", "3", "5", "6", "7", "8", "11", "12", "13", "14"))

【讨论】：

【解决方案2】：

使用 dplyr 和 tibble：

library(dplyr)
library(tibble)

# list of participants
mydata <- tibble(n = 1:300)

# convert rownames to numeric column
myCoefficients <- myCoefficients %>% 
    rownames_to_column() %>% 
    mutate(rowname = as.numeric(rowname))

# join myCoefficients to participants
result <- left_join(mydata, myCoefficients, by = c("n" = "rowname"))

【讨论】：

【解决方案3】：

这是一种方法：

library(dplyr)

seq_merge <- data.frame(a = seq(1:length(rownames(myCoefficients))))
myCoefficients$a <- rownames(myCoefficients)
final <- merge(seq_merge,myCoefficients,all.x = T) %>% 
  select(-a)

> final
      (Intercept)            A
1    11.784913184  0.008224641
2    13.416552668  0.126538988
3     1.255375012 -0.261815119
4    -0.453204283 -0.050068500
5     1.855255007  0.019615941
6    18.233333333  0.266666667
7    10.622690151  0.039481512
8     4.583096557  0.056392969
9   -10.316190476 -0.327619048
10   -0.750918742 -0.011841568
11    0.000000000  0.000000000
12    1.658747938 -0.003200315
13    6.639959940  0.100150225
14    8.543573432  0.111088310
15   -0.409441233  0.075626204
16    0.000000000  0.000000000
17   16.391626950  0.338496534
18   -2.780630438 -0.119811724
19    3.581120944  0.057030482
20   -7.435064935 -0.086580087
21    0.000000000  0.000000000
22   -1.399803872 -0.041049967
23    0.000000000  0.000000000
24    5.748297340  0.073490890
25    2.502387775  0.022385387
26    5.477356181 -0.071399429
27    0.000000000  0.000000000
28    0.000000000  0.000000000
29    0.202822570  0.004236503
30    0.000000000  0.000000000
31    3.191532668  0.018318746
32    5.808669308 -0.089812308
33   15.556690047  0.288999378
34    6.044498212  0.033978540
35    5.384817738  0.088688463
36    2.429605338 -0.119020694
37    9.498121941 -0.068103350
38    1.449211455 -0.038649097
39   18.311828852  0.228294348
40   36.288255223  0.685770751
41   20.607074068  0.097268429
42   12.587294301  0.126299603
43    6.688188926  0.088422840
44    6.820835614  0.051997811
45   -2.063996902 -0.533215333
46   12.255847953  0.066520468
47   -4.818481848 -0.099009901
48   11.449166132  0.105355997
49   13.623012447  0.204422043
50   11.676916534  0.056099996
51    2.514750467 -0.019606775
52    0.125293117 -0.050347142
53    0.000000000  0.000000000
54    5.304979604 -0.175722320
55   10.318437929  0.096100382
56    0.000000000  0.000000000
57   10.607768097 -0.002279945
58   10.333968509  0.137398456
59   -4.513711889 -0.563639297
60    6.721815687  0.006860279
61   -0.718921180 -0.058085796
62   12.354598540  0.192189781
63   20.850979616  0.274787255
64             NA           NA
65    7.154075137  0.059849368
66    5.020082784 -0.008201748
67    0.229156161 -0.459531014
68    6.602570969  0.038832351
69   18.606677985  0.180531975
70    1.261939931 -0.064992614
71    0.000000000  0.000000000
72    8.565326633  0.105527638
73    6.025134650  0.065914337
74    0.411054480 -0.008632617
75    6.001972711  0.005753740
76   14.423697726  0.102622891
77   -1.782058047 -0.024274406
78   13.461871683  0.196417421
79   -2.490421456 -0.137691571
80    0.986939239  0.006814310
81    0.000000000  0.000000000
82   26.074865546  0.338946141
83    4.721769334 -0.023747076
84    3.491952414  0.055983205
85    8.621555769  0.111749489
86   13.298121427  0.175333515
87    5.075415244 -0.030621479
88    5.427200030 -0.056299149
89    5.784197111  0.052613361
90    2.967869893 -0.024593415
91   11.439869695  0.154194191
92    1.439169713 -0.137264690
93    0.000000000  0.000000000
94    5.696352440  0.077569872
95    2.544640478 -0.024518949
96    3.933483703  0.037261186
97    4.896524416  0.065283756
98    2.135022525 -0.374031801
99    7.190891371  0.083368454
100  23.054124552  0.531790023
101   6.161769255 -0.221620786
102 -14.547148289 -0.265139993
103  12.140619804  0.201380861
104   4.432939593  0.015699761
105  -0.837367221 -0.034716496
106  -0.122268163 -0.033668045
107  -5.696417101 -0.176646920
108  13.010822852  0.085412776
109  19.237564131  0.530002231
110   1.938517087 -0.178676770
111   6.888465629 -0.009047188
112   7.164846545  0.155902843
113  -0.403225806 -0.016129032
114   1.008421194 -0.058635633
115   3.170498084  0.028136973
116   7.475271328  0.008586344
117   9.387123820  0.130438620
118  -4.720329503 -0.102333977
119  -0.690008119 -0.122506379
120   9.928991185 -0.073457395
121  -8.166768363 -0.243770694
122  -0.836936555 -0.071781828
123 -13.756592007 -0.129834974
124  22.520513735  0.360226288
125  -6.268156425 -0.167597765
126  -7.517700552 -0.130074700
127 -14.041414611 -0.273382969
128   0.000000000  0.000000000
129   8.046064474 -0.259777450
130   3.669741697 -0.357933579
131   2.593244581 -0.190890087
132  -8.000000000           NA
133   8.328107184 -0.071265678
134   1.637694105 -0.192730521
135   3.693134192 -0.136592243
136   2.161687299 -0.180897599
137   0.000000000  0.000000000
138   0.721612005 -0.039410582
139   7.749737119  0.030494217
140  -5.808393153 -0.096355605
141  12.282297336  0.080170438
142  -5.316274128 -0.176288295
143  -4.441255140 -0.138249032
144  -1.117341518 -0.083225121
145  -0.752677582 -0.141942632
146   3.407083929 -0.101590819
147   6.265884172  0.002073376
148  -2.148945392 -0.152051430
149  28.415807560  0.554123711
150   8.716573171  0.118457600
151  12.496143959  0.088946015
152  19.149987332  0.217095262
153   0.009304822 -0.011094211
154   7.863488624  0.082204767
155   4.728299224 -0.416372618
156   4.624162598  0.035483138
157   8.492298276  0.117821075
158 -14.046175789 -0.229987520
159   1.183873993  0.017370732
160   0.000000000  0.000000000
161   7.045593921  0.066213394
162   5.513560300  0.031015580
163   4.244824402 -0.064298666
164   1.054528148  0.080908349
165 -11.086497890 -0.221518987
166  13.992083786  0.151239106
167   0.111296142 -0.093810713
168  -1.042661399 -0.045771688
169  16.828913325  0.164294955
170   1.743498818 -0.011820331
171   3.609741954  0.005798782
172  -2.110598319  0.011537828
173  -8.958189217 -0.714852492
174  -7.150518727 -0.141802105
175  10.645382098  0.141322541
176 -14.073564593 -0.445574163
177  16.096282103  0.289202798
178   8.649276860  0.162060950
179   0.000000000  0.000000000
180  -7.084494960 -0.158696118
181  -1.312672577 -0.103255341
182  -3.366945247 -0.223307037
183  14.938014695  0.208146358
184  -0.678981349 -0.185437590
185  10.027063876  0.030983707
186  12.853728050  0.126413533
187  -0.418968692 -0.010474217
188   7.242331839  0.175605381
189  -4.014336918 -0.143369176
190  10.229368645  0.118231260
191  -7.187500000 -0.539772727
192  25.785714286  0.464285714
193  15.189222323  0.136346677
194  12.845133231  0.164776481
195 -22.088160874 -0.266480224
196   0.000000000  0.000000000
197   0.000000000  0.000000000
198   1.362202049 -0.052177290
199 -11.184823000 -0.463622140
200  -0.859435330 -0.035683015
201   2.601550453  0.026826641
202   0.342018669 -0.125041972
203            NA           NA
204   5.335232543  0.084139393
205  20.000000000           NA
206  -0.375872383 -0.002492522
207  28.872131148  0.863934426
208   3.563577586  0.101939655
209  20.433887877  0.131835967
210  -5.815011124 -0.218884963
211 -11.266979501 -0.353173623
212 -11.173933501 -0.250941029
213  -0.056431693 -0.006460194
214  18.165829146  0.251256281
215   6.369295470  0.060584610
216  -3.322072072 -0.073198198
217  -2.677165354  0.078740157
218   0.898822629 -0.078491433
219   4.393285217 -0.030609615
220   6.165938069  0.054128719
221  15.223880597  0.179104478
222  22.583177570  0.337180008
223  15.386230059  0.304366079
224 -11.271916238 -0.118435454
225   9.214443626  0.087140752
226  13.651131824 -0.015978695
227  10.126607319  0.117705242
228  -4.100042391 -0.123781263
229   9.859298532  0.218189233
230   2.279411765 -0.489430147
231   7.042338217  0.066514042
232  -1.149144869 -0.020812374
233   3.501552079 -0.045179334
234  10.348929473  0.157197118
235  -4.309844723 -0.070597199
236  -0.406851091  0.064296520
237   0.000000000  0.000000000
238 -12.870644391 -0.185441527
239   6.572819573  0.091931533
240 146.000000000  7.800000000
241  59.560466246  1.131617290
242  -2.477952661 -0.017301454
243   7.670426344  0.195818054
244   4.464389692  0.030444692
245   0.000000000  0.000000000
246  -5.090371915 -0.281543274
247   3.318152764  0.031160115
248   2.089063523 -0.111329404
249   4.831575199 -0.003182445
250   3.528881195  0.044187318
251  -7.641223478 -0.186414990
252   8.691099476  0.314136126
253   7.673273132  0.161838865
254  11.152559139  0.074838199
255 -11.197466897 -0.077720207
256   1.761562470  0.020807718
257  12.047270872  0.192994389
258   3.223861983  0.014549990
259  -4.670398010 -0.061300640
260   1.468158966  0.014962892
261   3.374092944  0.074725953
262   0.000000000  0.000000000
263  11.308158651  0.131560838
264  30.000000000           NA
265   9.231769665  0.085299130
266   4.178069353  0.007966261
267  13.004952586  0.161534918
268   5.447510330  0.099796905
269   0.000000000  0.000000000
270   9.579288026  0.115580213
271  16.908915559  0.311615945
272   9.610721530 -0.059055346
273   9.265092417  0.141893316
274  -7.569751715 -0.282050043
275   5.496770989 -0.041728763
276   7.409235108  0.163905534
277  -1.593325458 -0.018606025
278   5.703958450  0.002105559
279  17.006838906  0.118920973
280  -3.320901995  0.143972246
281  30.029290374  0.623751831
282  -2.678965618 -0.022542195
283  24.687500000  0.312500000
284 -15.059602649 -0.238410596
285            NA           NA
286   0.000000000           NA
287  -2.157762938 -0.045909850
288  -7.806357888 -0.209365920
289  11.782270607  0.287713841
290   4.821551496  0.065395817
291  17.686923508  0.085167915
292   6.617959072  0.108308808
293  14.685806258 -0.169112143
294  11.424153771  0.142710499
295   2.513812155  0.041436464
296  19.254025911  0.275941397
297  11.448286941  0.142535853

【讨论】：

【解决方案4】：

在 base 中，您可以使用 match 来填充缺失的行，例如：

#Using length of x
y[match(seq_len(nrow(x)), rownames(y)),]
#    i  a
#1   1  5
#2   2  4
#NA NA NA
#4   4  2
#5   5  1

#Using rownames of y
y[match(min(as.numeric(rownames(y))):max(as.numeric(rownames(y))), rownames(y)),]
#    i  a
#1   1  5
#2   2  4
#NA NA NA
#4   4  2
#5   5  1

数据：

x <- data.frame(i=1:5, a=5:1)
y <- x[-3,]
y
#  i a
#1 1 5
#2 2 4
#4 4 2
#5 5 1

【讨论】：