通常，最常用的舍入是四舍五入到最接近的10或最接近的100，但是有时我们实际上想删除一个值之后的值，而不是舍入。例如，删除小数点后2位的值，这就是这种情况，我们需要四舍五入到下一个10，而不是最接近的10。这可以通过下限函数的帮助来完成，如以下示例所示。
x1<−1.2321412 floor(x1*10)/10输出结果
[1] 1.2
x2<−rnorm(100) x2输出结果
[1] −1.01607202 −0.13627843 −0.05332404 −0.20988562 0.53192788 −0.21490491 [7] 0.93909670 −0.71803829 0.36707998 0.71611459 0.53648436 0.73775330 [13] −0.04830651 −0.66710417 0.66421934 0.03941354 0.10747146 −0.12769608 [19] 0.84294195 −0.35810057 −1.21310936 −0.65778384 −0.48420552 −0.65712008 [25] 1.46669154 0.80618156 −1.45079725 −0.99269474 0.79892560 1.72865301 [31] 0.59923441 −0.55706118 −0.17318689 −0.03198674 −0.16822942 −0.86734324 [37] 0.93642051 0.46739939 −0.58546721 −0.28062626 −0.08280940 −0.02796720 [43] −1.20691923 0.61127461 −0.56228455 1.58841226 2.60447614 −0.10876521 [49] 1.65494022 0.67555885 0.96882488 −1.23490973 0.53605869 0.78852196 [55] 0.54627918 −0.21922728 −1.35104345 0.85517266 −1.63592878 1.04832402 [61] −0.97162092 0.87955671 0.28617432 −0.49176891 −3.02859514 −0.67549573 [67] −0.50120112 2.03164978 0.35270213 −0.20283306 −0.41561950 −0.73609624 [73] 1.17009724 1.03897592 1.31227239 0.49276017 −0.03460787 −0.94871491 [79] −2.06266496 0.84675274 0.20100772 0.06472715 0.17989075 −0.88503103 [85] 0.50389279 −1.26776074 −0.29691182 0.03318311 1.66971094 0.60210033 [91] 1.45134752 −0.49960181 0.38621056 1.23958281 0.16353699 −1.39379050 [97] 0.53588952 −0.05194538 1.74137139 0.65337123
floor(x2*10)/10输出结果
[1] −1.1 −0.2 −0.1 −0.3 0.5 −0.3 0.9 −0.8 0.3 0.7 0.5 0.7 −0.1 −0.7 0.6 [16] 0.0 0.1 −0.2 0.8 −0.4 −1.3 −0.7 −0.5 −0.7 1.4 0.8 −1.5 −1.0 0.7 1.7 [31] 0.5 −0.6 −0.2 −0.1 −0.2 −0.9 0.9 0.4 −0.6 −0.3 −0.1 −0.1 −1.3 0.6 −0.6 [46] 1.5 2.6 −0.2 1.6 0.6 0.9 −1.3 0.5 0.7 0.5 −0.3 −1.4 0.8 −1.7 1.0 [61] −1.0 0.8 0.2 −0.5 −3.1 −0.7 −0.6 2.0 0.3 −0.3 −0.5 −0.8 1.1 1.0 1.3 [76] 0.4 −0.1 −1.0 −2.1 0.8 0.2 0.0 0.1 −0.9 0.5 −1.3 −0.3 0.0 1.6 0.6 [91] 1.4 −0.5 0.3 1.2 0.1 −1.4 0.5 −0.1 1.7 0.6
x3<−rnorm(100,5,1.1) x3输出结果
[1] 5.642503 5.230962 3.465324 4.893007 5.389745 5.130625 6.298657 5.127622 [9] 4.661126 3.961675 5.700836 5.052379 5.335749 5.687224 3.988179 6.265202 [17] 2.574791 5.020560 3.063921 4.538074 5.845454 4.441141 5.132403 5.392912 [25] 4.199037 4.928813 3.790512 5.729748 6.563736 3.527240 4.918002 5.072285 [33] 5.046033 7.535285 4.841451 5.285243 4.454778 5.104764 5.892299 4.777694 [41] 6.236553 4.273940 4.061579 4.243717 4.718178 4.136313 6.205208 3.945021 [49] 5.573720 3.991472 5.448390 3.880832 5.417734 5.198035 4.379759 3.015312 [57] 5.656754 4.730319 4.582328 4.613071 6.431624 4.570875 6.502455 6.362682 [65] 4.105208 3.955992 6.298363 5.305739 5.752251 4.152387 3.685942 5.001143 [73] 4.895666 5.040874 5.049020 5.750919 4.723756 4.280671 4.348718 4.981214 [81] 2.924487 5.078067 5.131206 6.301854 5.105356 3.125473 6.770442 3.368280 [89] 5.438926 3.709470 4.430937 4.175861 7.604855 5.325990 6.279979 5.076079 [97] 3.308669 6.649587 8.070356 5.078593
floor(x3*10)/10输出结果
[1] 5.6 5.2 3.4 4.8 5.3 5.1 6.2 5.1 4.6 3.9 5.7 5.0 5.3 5.6 3.9 6.2 2.5 5.0 [19] 3.0 4.5 5.8 4.4 5.1 5.3 4.1 4.9 3.7 5.7 6.5 3.5 4.9 5.0 5.0 7.5 4.8 5.2 [37] 4.4 5.1 5.8 4.7 6.2 4.2 4.0 4.2 4.7 4.1 6.2 3.9 5.5 3.9 5.4 3.8 5.4 5.1 [55] 4.3 3.0 5.6 4.7 4.5 4.6 6.4 4.5 6.5 6.3 4.1 3.9 6.2 5.3 5.7 4.1 3.6 5.0 [73] 4.8 5.0 5.0 5.7 4.7 4.2 4.3 4.9 2.9 5.0 5.1 6.3 5.1 3.1 6.7 3.3 5.4 3.7 [91] 4.4 4.1 7.6 5.3 6.2 5.0 3.3 6.6 8.0 5.0
x4<−runif(100,2,10) x4输出结果
[1] 6.538808 6.545301 3.835959 8.054628 6.608576 9.910128 8.362813 7.586518 [9] 7.943493 7.505711 3.335142 4.091882 9.723603 5.839466 8.884706 6.643211 [17] 3.478755 6.083539 9.769651 4.829378 9.088905 3.168320 7.366497 6.269922 [25] 9.254521 6.945593 5.668853 9.016039 2.495471 7.628513 2.896996 7.785337 [33] 7.766262 2.443361 5.179641 4.008066 7.348657 2.744335 9.372329 5.142811 [41] 2.290931 9.818736 7.648366 4.074540 8.856767 9.181061 2.529208 6.189395 [49] 2.908754 5.087787 8.965813 4.811136 5.740659 4.255490 4.803441 2.255174 [57] 9.769002 8.929364 4.942824 2.755662 6.349775 2.314670 8.221101 9.565006 [65] 9.035085 7.787345 6.732919 2.133845 9.487212 4.278915 7.723010 3.097007 [73] 8.554924 7.757824 9.573348 9.389591 6.271267 7.636842 2.107888 5.260317 [81] 3.756438 3.833873 5.012878 4.322938 8.414654 8.491856 8.479793 7.557254 [89] 3.338573 2.760560 6.813499 9.596506 2.750367 7.317738 6.151885 4.906555 [97] 8.680140 3.040711 3.920693 7.318239
floor(x4*10)/10输出结果
[1] 6.5 6.5 3.8 8.0 6.6 9.9 8.3 7.5 7.9 7.5 3.3 4.0 9.7 5.8 8.8 6.6 3.4 6.0 [19] 9.7 4.8 9.0 3.1 7.3 6.2 9.2 6.9 5.6 9.0 2.4 7.6 2.8 7.7 7.7 2.4 5.1 4.0 [37] 7.3 2.7 9.3 5.1 2.2 9.8 7.6 4.0 8.8 9.1 2.5 6.1 2.9 5.0 8.9 4.8 5.7 4.2 [55] 4.8 2.2 9.7 8.9 4.9 2.7 6.3 2.3 8.2 9.5 9.0 7.7 6.7 2.1 9.4 4.2 7.7 3.0 [73] 8.5 7.7 9.5 9.3 6.2 7.6 2.1 5.2 3.7 3.8 5.0 4.3 8.4 8.4 8.4 7.5 3.3 2.7 [91] 6.8 9.5 2.7 7.3 6.1 4.9 8.6 3.0 3.9 7.3
x5<−rexp(100) x5输出结果
[1] 0.3693550956 4.4835554843 0.1593435327 1.5926173105 0.3622606342 [6] 0.4774536472 0.3866934655 0.0551065494 0.5390452691 2.3621970472 [11] 0.8336312581 1.0139794843 0.8100316476 0.9223797302 1.5480979568 [16] 0.2938991212 2.1378652504 0.0157313182 0.3185261055 0.1638578176 [21] 0.1943867127 0.8233978414 0.1332402905 0.2755519945 1.3449708027 [26] 0.6198626405 0.2668860275 0.2326414920 1.3924008227 1.0283894147 [31] 0.5750101595 0.6337804292 0.3722617605 2.8044816875 0.6452543922 [36] 0.5045517669 0.0295312004 0.6874382785 0.0276178052 0.5838950933 [41] 0.4059721236 0.7831552615 0.9100687967 0.2333946077 0.0490050911 [46] 1.0237078620 0.4145099246 1.2310644500 1.0741089529 0.8645681506 [51] 0.0524017420 0.9652211631 0.8568648770 0.6978872763 3.9212165380 [56] 0.2542064069 0.0226820768 1.8359337756 1.8138077780 2.5640936853 [61] 0.0122036459 1.8706417232 0.0472460319 1.2739779510 0.6243003448 [66] 0.4667825093 0.0919807909 0.4396443103 3.8503847623 0.2734695016 [71] 0.1047210614 1.9733380600 0.0616727980 0.5205048658 2.7171700987 [76] 3.2596589772 1.2333627948 0.0007526749 0.2288445337 0.9081312192 [81] 1.4141255180 0.3164622360 0.9008373019 3.0270216091 0.2559199218 [86] 0.2828875592 0.0460792963 0.0178526601 0.5076802722 1.4419499970 [91] 0.3998881704 0.5913354810 0.3965095463 1.5288099257 0.7145151570 [96] 1.0150692239 1.8374356142 0.6863701791 5.5953387537 1.2190533842
floor(x5*10)/10输出结果
[1] 0.3 4.4 0.1 1.5 0.3 0.4 0.3 0.0 0.5 2.3 0.8 1.0 0.8 0.9 1.5 0.2 2.1 0.0 [19] 0.3 0.1 0.1 0.8 0.1 0.2 1.3 0.6 0.2 0.2 1.3 1.0 0.5 0.6 0.3 2.8 0.6 0.5 [37] 0.0 0.6 0.0 0.5 0.4 0.7 0.9 0.2 0.0 1.0 0.4 1.2 1.0 0.8 0.0 0.9 0.8 0.6 [55] 3.9 0.2 0.0 1.8 1.8 2.5 0.0 1.8 0.0 1.2 0.6 0.4 0.0 0.4 3.8 0.2 0.1 1.9 [73] 0.0 0.5 2.7 3.2 1.2 0.0 0.2 0.9 1.4 0.3 0.9 3.0 0.2 0.2 0.0 0.0 0.5 1.4 [91] 0.3 0.5 0.3 1.5 0.7 1.0 1.8 0.6 5.5 1.2