I have a dataframe (k by 4). I have ordered one of the four columns in a descending order (from 19 to -9 let'say). I would like to throw away those values that are smaller than 1.5.
I just tried unsuccessfully various combinations of the following code
subset(w, select = -c(columnofinterest, <=1.50))
Can anyone help me?
Thanks a lot!
You can use arrange and filter from dplyr package:
library(dplyr)
w <- data.frame(use_this = round(runif(100, min = -9, max = 19)),
second = runif(100),
third = runif(100),
fourth = runif(100)) %>%
arrange(desc(use_this)) %>%
filter(use_this >= 1.5)
Output:
> w
use_this second third fourth
1 19 0.264306555 0.11234097 0.30149863
2 19 0.574675520 0.50406805 0.71502833
3 19 0.376586752 0.21530618 0.35323250
4 18 0.949974135 0.46726122 0.36008741
5 17 0.339737597 0.11358402 0.04035303
6 16 0.180291264 0.81855913 0.16109650
7 16 0.958398058 0.94827266 0.54693974
8 16 0.297317238 0.28726682 0.63560208
9 16 0.653006870 0.15175848 0.69305851
10 16 0.685338886 0.30493976 0.89360112
11 16 0.493931093 0.52830391 0.68391458
12 16 0.945083084 0.19880501 0.66769341
13 16 0.910927578 0.86032225 0.73062990
14 15 0.662130980 0.19207451 0.44240610
15 15 0.730482762 0.92418574 0.46387086
16 15 0.547101759 0.87847767 0.27973739
17 15 0.487773258 0.05870471 0.40147753
18 15 0.695824922 0.91289504 0.94897518
19 14 0.576095914 0.42914670 0.27707368
20 14 0.156691824 0.02187951 0.31940887
21 13 0.079037019 0.16993999 0.53232350
22 13 0.944372064 0.63485350 0.23548337
23 13 0.016378244 0.42772076 0.76618218
24 13 0.606340182 0.33611591 0.36017352
25 13 0.170346203 0.43325314 0.16285515
26 13 0.605379012 0.95574187 0.23941377
27 12 0.157352454 0.90963650 0.01611328
28 12 0.353934785 0.80058806 0.13782414
29 12 0.464950823 0.81835421 0.12771521
30 12 0.624139506 0.69472154 0.02833191
31 11 0.362033514 0.98849181 0.37684822
32 11 0.067974815 0.24154922 0.49300890
33 11 0.522271380 0.03502680 0.50665790
34 10 0.810183210 0.56598130 0.41279787
35 10 0.609560713 0.46745813 0.34939724
36 10 0.087748839 0.56531646 0.02249387
37 10 0.008262635 0.68432285 0.35648525
38 10 0.757824842 0.57826099 0.89973902
39 10 0.428174539 0.12538288 0.69233083
40 10 0.785175550 0.21516237 0.36578714
41 10 0.631388832 0.63700087 0.40933640
42 10 0.171396873 0.37925970 0.27935731
43 10 0.773437320 0.24710107 0.23902388
44 8 0.443778088 0.77238651 0.08517639
45 8 0.954302451 0.87102748 0.52031446
46 8 0.347608835 0.79912385 0.36169856
47 8 0.839238717 0.54200177 0.52221408
48 8 0.235710838 0.85575923 0.78092366
49 7 0.610772265 0.16833538 0.94704562
50 7 0.242917834 0.02852729 0.87131760
51 7 0.875879507 0.04537683 0.81000861
52 7 0.577880660 0.54259171 0.43301336
53 6 0.541772984 0.06164861 0.62867700
54 6 0.071746509 0.51758874 0.70365933
55 5 0.103953563 0.99147043 0.33944620
56 5 0.504618656 0.95827073 0.65527417
57 5 0.726648637 0.37460291 0.47072657
58 5 0.796268586 0.09644167 0.93960812
59 5 0.796498528 0.68346948 0.23290885
60 5 0.490859592 0.76727730 0.39888256
61 5 0.949232913 0.02954981 0.56672834
62 4 0.360401806 0.62879833 0.31107107
63 4 0.926329930 0.87624801 0.91260914
64 4 0.922783983 0.11524112 0.06240194
65 3 0.518727534 0.23927630 0.37114683
66 3 0.951288192 0.58672287 0.45337659
67 3 0.767943126 0.76102957 0.24347122
68 2 0.786254279 0.39824869 0.58548193
69 2 0.321557042 0.75393236 0.43273743
70 2 0.872124621 0.89918160 0.55623725
71 2 0.242389529 0.85453423 0.78540085
72 2 0.013294874 0.61593974 0.70549476
