0

I'm searching since a couple of hours without being able to find out how to get rows by a specific value of the last data frame column.

I have this data:

          X1           X2           X3           X4          X5      X6
1    48.17695413  39.43730616   3.10972063   2.61426454   6.3099154  1
2    -8.65390666  46.85736629  30.87960405  44.27429466 -41.1696044  1
3    30.77856742  21.74027874  48.19661027 -21.80938770  36.6618114 -1
4   -24.59780029  21.06814152 -39.33865333 -42.97304431  -4.8804663  1
5     7.27249086  34.76682615 -45.85831994  13.88918648  47.3615198  1
6    38.67101648  38.26274828  14.08033108  49.82309644 -33.6286389  1
7   -25.51283904 -45.70014705 -26.42990421  28.78326026  35.1519014 -1
8    -2.39959008  -2.97487159  17.50174041  12.75989751 -45.0535343  1
9    37.15204622  31.15702239   7.78025487  42.89215957  22.0417423  1
10  -47.73408335 -15.17439773  27.97187380  35.77547702  32.3852375 -1
11   -1.02035564  26.50974274 -30.74983917 -22.68985184 -38.8585380  1
12   22.58425138 -19.56519226   1.71576410 -34.55656213   2.9134512 -1
13   -9.38323175 -19.29783276  16.63345252  40.99594080 -29.0354038  1
14  -27.58411688  48.17471695  46.65622143 -30.48450849  14.9938999 -1
15   38.80037898 -47.36718861  37.63894415 -16.63851268  40.3090175 -1
16   43.51794997  18.72671039  37.09440767 -34.12630649 -15.1023072 -1
17  -32.74301919  11.90051064   2.80043937  46.51653604 -28.9494123  1
18   35.68157670  14.81230436 -36.31750911  47.51496385 -37.1951638  1
19  -40.50665190 -34.03052327  29.38828175   1.34482600 -34.8719438 -1
20   41.07689315  43.92234944   2.65074784 -11.53928787  32.2066362  1
21   17.66030733 -14.92802922  12.11998039  15.00645671  21.2442750 -1
22  -33.64110310  10.86618125 -39.01640368  40.54307269  39.6113877  1
23   38.51638250  22.13161306 -12.81533381  33.42758568   2.4696089  1
24  -45.89782895  24.45276037  21.59339122   0.08909106 -12.7604788 -1
25  -29.12297575 -19.68773385   2.79124135  31.15364150 -18.4448177  1
26   38.31280286 -30.47305727  26.44094897  -4.80549014  25.7998981 -1
27   16.18606397 -31.02221894 -49.26810868 -25.00526297   6.1112312 -1
28   48.40843189 -21.66230679  -0.12366320   3.17854472   3.8587339  1
29   40.21567111 -48.13758477  24.93614273  28.69090671 -26.4871368  1
30    1.51516791 -26.42521115 -12.80261928  16.00497463 -29.2357545  1
31  -13.19148520  41.22625608 -19.86476800  32.65111602  35.2801044  1
32   20.27577299 -25.47924414  21.95390093  -1.85122907  29.2785393 -1
33    5.61452187  -5.35398889  10.64601119 -44.01104380  17.1113494 -1
34  -44.78640135  -3.47331611  -1.96771566 -12.24545513 -43.1549257  1
35   25.08181627  33.58752318  28.18229569 -11.75732762  19.3758997 -1
36   14.11425164   0.61068691  35.01117583  32.70457520 -39.7158644  1
37   47.13524659   5.07630450  21.24833809 -47.87322641 -16.3222777 -1
38  -30.68369164  44.48609874  17.87844354  38.41511346 -15.1628380  1
39   -9.82401448  47.94880943 -27.94965848  13.46387044   4.4655846  1
40  -16.53469554  13.49646898  27.13568562  48.88138534  13.3870898  1
41    5.66302277  -5.13428820  28.43233454 -38.00809828  34.1122602 -1
42    0.26656424 -30.29644771  -9.31532411   4.33435896  -6.6514673 -1
43  -23.25400757  43.53289178  31.26810214  26.17365187  10.1966180  1
44  -10.83291732  19.52698252 -46.98810957 -26.03460227   3.7786703  1
45   40.12858390  38.52749658  47.63697251 -43.28443427  -2.9563767 -1
46  -21.25995888  11.08579396  17.64257259  49.27916457  14.6470747  1
47  -14.52012877   4.73049677  29.67983205  11.74713625   0.9333332 -1
48   32.01571801  26.66981509  -4.45819930 -21.25811025  13.7524510  1
49  -44.85126981 -28.82791162 -40.01033327  -7.61150781  33.2228162 -1
50   37.97772161 -20.30672238 -43.07964963  30.13702892 -15.0164150  1
51   29.08064781  15.27835699 -37.16766285  15.21206819  -2.2740492  1
52  -40.48949617 -10.51356671  41.26806469 -29.10868146 -34.6600571 -1
53   30.29430094 -46.88648379 -18.59240613 -22.08509911  -5.0840363 -1
54    2.14905366 -12.34249272  15.41978475 -23.87309705  49.7638849 -1
55  -10.86263228  37.23807719 -38.22960216  24.74888896  39.6630126  1
56   40.46251560  -7.42908975   5.45871421  32.46898202  26.3232591  1
57  -49.35276681  48.47594881  -6.54060557  13.13420576 -30.6234515  1
58  -32.55195227 -24.04979744  -0.90411811 -28.68508857 -14.3195416 -1
59  -41.40694165 -32.71186997 -49.91184238 -47.44685916 -30.7997680 -1
60  -40.25409685 -38.63885684  28.98155658  17.04148985 -29.1016112 -1
61    6.55163422  47.65563791   6.50914682 -43.26901732  12.7081363 -1
62  -34.60404077   0.70960103  40.22226434  22.20810365   4.2410096 -1
63  -34.34207600  19.25329904  41.07820110   5.01101012 -11.3040819 -1
64  -26.09685140  26.89760709   0.13924714   6.23782547  30.3378282 -1
65    2.77869620   3.46297752 -16.93740098 -49.87237649  10.3518355 -1
66  -34.43377707 -11.54497443  -0.03308267  18.72712739 -44.7833285  1
67   15.03299868  -7.64621769  49.49413652 -30.31192285  33.8391314 -1
68  -18.59508867 -25.63164262 -22.74283541  34.89008134  21.4274962  1
69   36.03547243  -1.33956145   8.09449207 -15.73835583 -42.1537299  1
70  -12.76628897 -40.29422191   5.18926801   4.31100223  -2.7201807 -1
71   29.70183801 -47.01430625  -0.10015091 -16.11912933  29.4462418 -1
72   19.68328992 -43.62236075  39.62997815 -47.02085708  46.5351474 -1
73   13.60548099 -14.21226230 -16.40881621  -7.07651307  30.7896358 -1
74   48.97985228  28.08398050 -21.35308585  -7.14836787  22.5584449  1
75   40.56481351  44.48666607  39.73892389 -28.47008093 -37.2993243  1
76   38.65095733 -25.91175342 -42.96530839  28.70831673  30.8613458  1
77  -29.94483940  -8.39585150 -28.69421113 -26.08410181 -33.0075214  1
78   -8.54073463  28.14237990  20.12624827   3.81252319 -38.4828388  1
79   48.69534343 -32.77915958   8.73959693  -4.19653771  37.0420157 -1
80  -11.14250922 -41.17423620 -49.40661241 -20.27474060  25.6862722 -1
81  -31.04337221  11.66699962   5.31555978 -49.68183776  16.6878605 -1
82  -46.18124976 -40.82516073 -38.52873622 -43.34530456 -30.9348033 -1
83   29.78327069 -16.52878316  19.70788015  22.03853386  -6.8582143  1
84  -34.10644981 -24.50293470   3.68809036 -24.28773544  38.5350600 -1
85   -0.10980293  33.79236367  30.77957788 -44.64550817 -15.9467584 -1
86   42.79020275 -49.23286503 -27.00992972 -34.40842158  31.3848205 -1
87   41.49881038 -24.64161024 -22.96349106  20.57055852  14.4795177  1
88  -37.18016476  -6.11188293  -9.10155319  -1.89142874 -45.2628603  1
89   -2.62336845   3.08700614 -15.49178469  26.82823380 -32.2975439  1
90   -4.11448206  30.86272427 -46.10658828  44.78534460  46.8005464  1
91  -45.66761896 -32.70136176  24.77845098  34.32040859  39.1300678 -1
92   31.28242453  40.31769738 -20.26872914 -10.96235495 -22.1892530  1
93    3.80620165  46.78701940   4.35569431  45.98498959 -40.7786838  1
94    0.81615241  24.83391126   9.15402786 -19.10655624 -35.2444648  1
95  -41.83242584 -21.35343621  41.83379223  41.67253454  13.9536310 -1
96   47.49085810  -3.14880335 -37.31183154   9.95015414  -0.4881163  1
97   35.54580712  32.30499793  31.31646449 -11.14545153   8.4006399 -1
98   41.21926124  40.27996780  39.20314582  30.17564565 -32.0637980  1
99   35.71568001  -9.81678010  38.08503181 -39.24719978 -25.6364958 -1
100 -26.34892268  48.57874219 -46.59255797  14.88185874 -25.8851135  1
101 -10.52907093 -18.00235135   5.27036686  31.21748420 -34.4811461  1
102 -29.36565380  30.24585389  40.90974440  26.51184569 -17.7345452  1
103   7.87903350  -1.09457127 -15.66868282  -9.03650313   9.9299264 -1
104  42.60331560  23.64356695  25.10587261 -20.22202369  32.7391773 -1
105  34.14003735 -12.71724263  44.14021997 -25.71712218 -31.9221933 -1
106 -12.44853460 -47.72728265  48.57131951   7.67443904  33.7671637 -1
107 -14.09033376  14.33033464 -32.60425429 -38.24150169   6.7293922 -1
108  34.07870485 -46.51382137  49.84296632 -40.89916174 -43.6066909 -1
109  33.01124896 -49.23768658  14.13645993 -44.37299115   4.8207535 -1
110  10.36037307 -33.63043843  21.71480323 -28.38937459  17.0479339 -1
111  -5.79975462 -31.78398826 -11.77024557  -1.45170258 -34.1461023  1
112 -17.11579286 -36.18001689 -20.23241029   5.67694043  42.8745132 -1
113 -38.41568830   3.69573398 -40.29819805  33.50354005  45.8279202  1
114 -29.81968126  -1.95940533 -20.59776224 -36.95698555  29.9862751 -1
115   2.68202946  18.95755879 -24.05982686 -25.90978143   9.7582864  1
116  42.61640511  27.26011581 -46.40585079   4.26192286  29.0983045  1
117   3.67996979  49.15965132  27.85841639 -26.41195238 -43.4398819  1
118  48.34243569   2.41912773   7.61683735 -29.50917231 -19.5587220  1
119  49.62098389  35.17561462   7.30517844  12.31159559  45.7202338  1
120  -0.02593545  19.94858689  17.98142134 -39.22495318   5.3603052 -1
121 -22.92342477  40.02773573 -23.85643139  12.96254296  20.0602848  1
122 -21.57715647 -17.06917104 -29.41318592  36.47456393   2.7248362  1
123  29.80646805 -27.48751035  31.44713799  11.61041714 -44.3767835  1
124 -17.70907494   0.09368618 -49.75451597 -37.48356637  -6.3979823  1
125  28.88035215 -14.24206311  -0.16729170 -42.50496686 -26.9064285 -1
126  35.68704489  -5.52500989  38.28872927  13.51765934 -23.1683312  1
127  -2.63194633   0.26762190 -12.58921986 -28.77510269  31.0379969 -1
128  30.89340176  29.63612082 -19.92898253 -46.91745718  13.4511064 -1
129 -35.64908991  -0.62351443  -1.92955756 -11.38650151 -26.3827307  1
130  15.95162002  12.78902963  34.69599115  21.07257789  -0.7049875  1
131   4.76377490 -44.94423338   1.10263422  46.68685843 -21.0213374  1
132  -7.37550976  14.72977384  -6.50357553  29.58541936  12.0437405  1
133 -35.41488789  15.30446517 -34.03278021   6.36947788   3.5355144  1
134  35.85945931  32.76696808  46.98049189  16.64510823   2.3072411  1
135  16.23364908 -36.40773527  35.59254617  21.82013777  -8.8757714 -1
136  26.42379180   9.73426250  20.27802824 -36.71915454 -14.1613290 -1
137 -36.27642491 -31.46302165 -36.49763539  -9.67619771  15.6194962 -1
138 -47.50820657 -39.16183484 -38.03864804   5.07058196  15.7445797 -1
139 -21.84303377  10.98107803  -0.40645790  41.45426797 -28.7873543  1
140  -0.95827512  -1.69135577  32.42284888 -19.45907820  25.6286883 -1
141 -25.38794791 -18.17971012 -35.69542712 -14.09463407  28.4907040 -1
142  26.73252139  25.03299059 -12.28499415  30.17292826 -10.5327087  1
143 -43.16460560 -29.86155320 -38.54692092 -45.51337271   2.9275517 -1
144   8.24473461  21.64336434 -27.36689942 -27.98296309  21.6617776 -1
145   4.97203793  33.32681707 -38.52586835 -10.99964115 -36.9242265  1
146 -31.53733450  -1.02430454  40.17001574  18.02365675 -48.1134572  1
147 -12.77233372  24.44318922  -2.01369112 -29.32467558 -36.7925263  1
148  -2.59054168   9.29519264 -43.52171838  -4.17696589  17.9192101  1
149  44.38525802  16.66222662  14.42328799  42.99000488  36.5686033  1
150  24.20129897  -6.10695339   3.54532897   5.21023085   8.3394838 -1
151 -25.18552805  32.60349229   2.03504197   6.99553278 -38.9368815  1
152  11.56381064 -21.92995299  15.23446254 -35.43917607 -41.3730330 -1
153 -13.90532325  20.56872472 -42.20650226 -38.18344893  15.1799403 -1
154  -4.53937219 -15.48650358  30.36769438 -48.65573135  -4.2165786 -1
155 -19.33582416  37.94649800  12.92351044  12.55487665 -23.2021390  1
156 -27.08828198 -41.94863634  19.97831776 -31.80869636 -23.1660966 -1
157 -19.96743444  39.69741114 -13.42580225 -48.12533744 -46.1491481  1
158   9.16460447  12.19791272   3.40255061  46.28932201 -13.4358461  1
159  21.10104361 -36.03372062  19.19042345  36.96155972  38.7752557 -1
160 -13.60133037  33.48209793  25.34574629 -39.59111779  26.5550812 -1
161 -13.08967145   9.11082048   2.15857571  -6.17023725  21.4346305 -1
162  11.66161026 -45.03933019 -16.59182839  18.57721794  41.4250354 -1
163  41.77852059  45.44966395  22.19019849 -35.77017984 -16.8267572  1
164 -21.51823819 -45.14922628  14.55832121   0.94149860 -11.4418946 -1
165   7.77473154 -22.52768062  -6.71764023  -2.76005429  21.3051374 -1
166  24.17912751  11.42646645  41.03107578  38.10855299  -4.4167988  1

And what I want to do is to get all of the rows when X6 == -1 (by example). Also, the name and the number of column can change, so I need to find the name of the last column. I did it like that:

simple_learner <- function(df)
{
    lastColName <- colnames(df)[ncol(df)]
    #....
}

So now, I tried to get the value like that => out <- df[df$lastColName == -1, ], however, I get this input...

> simple_learner <- function(df)
+ {
+   lastColName <- colnames(df)[ncol(df)]
+   as.numeric(df$lastColName)
+   out <- df[df$lastColName == -1, ]
+   out
+ }
> 
> simple_learner(read.csv("Exercise-4.csv"))
[1] X1 X2 X3 X4 X5 X6
<0 rows> (or 0-length row.names)

Why?

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4
  • 1
    try df[df[, lastColName] == -1, ] Commented Mar 1, 2017 at 2:33
  • 1
    Maybe you can use filter{dplyr}, filter(df, X6==-1) Commented Mar 1, 2017 at 2:44
  • 1
    @VidaWang - the point is that X6 will be different each time, so in your case it will be something like filter_(df, lastColumn == -1) Commented Mar 1, 2017 at 2:51
  • @SymbolixAU I got it, try eval(parse(paste0("filter(df,",lastcolname,"==-1)"))) Commented Mar 1, 2017 at 2:55

3 Answers 3

Reset to default
1

Try

out <- df[df[,ncol(df)] == -1, ]

or

out <- df[df[,lastColName] == -1, ]

in your function.

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1 Comment

Perfect, I lost so much of time for such a small mistake... thank you a lot, our time can move forward now !
0

Kindly go through the following solution:

Get the last column index using following R code (I am dealing with iris dataset):

 lastcolumn<-max(ncol(iris))
 lastcolumn
  [1] 5

Now, retrieve the rows by the specific value of the last data frame column using following code (I am retrieving the rows with last column having value virginica):

iris[iris[,lastcolumn]=="virginica",]

Hope it works for you.

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0

I had a silimar problem, finally solved it by looking at the help page of dplyr::filter. At the end it gives a nice example:

# To refer to column names that are stored as strings, use the `.data` pronoun:
vars <- c("mass", "height")
cond <- c(80, 150)
starwars %>%
filter(
  .data[[vars[[1]]]] > cond[[1]],
  .data[[vars[[2]]]] > cond[[2]]
)

In your example above, this could lead to:

df %>%
  filter(.data[[colnames(df)[[ncol(df)]]]]== -1)
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