要格式化R向量和数据帧中的所有小数位，可以使用formattable包的formattable函数，在其中可以指定小数点后的位数。例如，如果我们有一个数字矢量说x，那么可以使用命令formattable（x，format =“ f”，digits = 2）将x中的值格式化为只有2个小数位。
加载formattable包-
library(formattable)
x1<-sample(0:9,200,replace=TRUE) x1输出结果
[1] 5 1 5 5 5 3 3 6 8 6 6 5 5 1 3 9 4 2 1 3 4 5 7 4 0 2 6 3 4 6 9 0 1 4 0 4 8 [38] 4 2 8 9 8 0 6 2 9 1 5 0 9 1 3 7 1 7 2 6 8 8 0 8 4 4 7 8 5 3 0 3 6 4 1 5 7 [75] 0 7 9 5 3 4 4 7 3 2 2 1 4 5 2 4 3 8 7 2 5 3 3 7 8 7 2 0 3 1 6 7 9 8 9 4 2 [112] 8 4 6 9 3 1 0 9 4 4 4 7 9 7 7 6 6 4 1 3 1 8 9 1 6 6 5 7 8 3 9 3 5 4 1 4 0 [149] 7 9 6 4 3 4 1 4 0 4 0 0 9 1 0 4 7 1 2 3 0 0 3 9 5 0 5 9 2 6 8 1 1 1 7 3 1 [186] 6 2 6 8 5 1 4 9 8 8 4 4 1 0 0
x1<-formattable(x1,format="f",digits=2) x1
[1] 5.00 1.00 5.00 5.00 5.00 3.00 3.00 6.00 8.00 6.00 6.00 5.00 5.00 1.00 3.00 [16] 9.00 4.00 2.00 1.00 3.00 4.00 5.00 7.00 4.00 0.00 2.00 6.00 3.00 4.00 6.00 [31] 9.00 0.00 1.00 4.00 0.00 4.00 8.00 4.00 2.00 8.00 9.00 8.00 0.00 6.00 2.00 [46] 9.00 1.00 5.00 0.00 9.00 1.00 3.00 7.00 1.00 7.00 2.00 6.00 8.00 8.00 0.00 [61] 8.00 4.00 4.00 7.00 8.00 5.00 3.00 0.00 3.00 6.00 4.00 1.00 5.00 7.00 0.00 [76] 7.00 9.00 5.00 3.00 4.00 4.00 7.00 3.00 2.00 2.00 1.00 4.00 5.00 2.00 4.00 [91] 3.00 8.00 7.00 2.00 5.00 3.00 3.00 7.00 8.00 7.00 2.00 0.00 3.00 1.00 6.00 [106] 7.00 9.00 8.00 9.00 4.00 2.00 8.00 4.00 6.00 9.00 3.00 1.00 0.00 9.00 4.00 [121] 4.00 4.00 7.00 9.00 7.00 7.00 6.00 6.00 4.00 1.00 3.00 1.00 8.00 9.00 1.00 [136] 6.00 6.00 5.00 7.00 8.00 3.00 9.00 3.00 5.00 4.00 1.00 4.00 0.00 7.00 9.00 [151] 6.00 4.00 3.00 4.00 1.00 4.00 0.00 4.00 0.00 0.00 9.00 1.00 0.00 4.00 7.00 [166] 1.00 2.00 3.00 0.00 0.00 3.00 9.00 5.00 0.00 5.00 9.00 2.00 6.00 8.00 1.00 [181] 1.00 1.00 7.00 3.00 1.00 6.00 2.00 6.00 8.00 5.00 1.00 4.00 9.00 8.00 8.00 [196] 4.00 4.00 1.00 0.00 0.00
x2<-rnorm(80) x2输出结果
[1] -1.29604051 0.46330591 0.78263447 -1.34445199 -0.79244441 0.91579428 [7] -0.20265807 0.19579780 1.54057571 -1.15957283 -0.61925230 -1.14279112 [13] 0.32428064 0.18469994 1.00994660 -0.38836171 0.62360893 -0.84954427 [19] 0.33794565 -0.68543650 1.03641121 -1.39719529 1.19779327 -0.28556066 [25] -1.90227084 -0.14752750 1.59804623 0.11688348 -0.22111880 -0.80496219 [31] 1.94998003 -0.44815429 -1.45236881 0.46414144 1.02714470 -0.35517641 [37] -1.91672346 0.33816514 0.02611275 2.52803119 0.42948063 1.41334339 [43] -0.27168314 -0.26893631 -0.79865763 0.69654229 1.30060073 -2.66033456 [49] -1.05879712 -0.70326509 0.44590006 0.22191097 1.52717953 0.88452961 [55] -0.79603885 1.44314800 0.69391092 -0.27902754 -0.85581926 -2.32344161 [61] 0.19283909 0.80069953 0.66911613 -1.04232630 2.14785749 1.74218280 [67] 2.09058219 -0.03829720 -0.48808007 -2.13874852 0.08684361 0.47503292 [73] -0.27623372 -0.78762974 1.19659235 -0.43385002 -1.00537520 0.23216263 [79] 2.36660415 1.07115586
x2<-formattable(x2,format="f",digits=2) x2
[1] -1.30 0.46 0.78 -1.34 -0.79 0.92 -0.20 0.20 1.54 -1.16 -0.62 -1.14 [13] 0.32 0.18 1.01 -0.39 0.62 -0.85 0.34 -0.69 1.04 -1.40 1.20 -0.29 [25] -1.90 -0.15 1.60 0.12 -0.22 -0.80 1.95 -0.45 -1.45 0.46 1.03 -0.36 [37] -1.92 0.34 0.03 2.53 0.43 1.41 -0.27 -0.27 -0.80 0.70 1.30 -2.66 [49] -1.06 -0.70 0.45 0.22 1.53 0.88 -0.80 1.44 0.69 -0.28 -0.86 -2.32 [61] 0.19 0.80 0.67 -1.04 2.15 1.74 2.09 -0.04 -0.49 -2.14 0.09 0.48 [73] -0.28 -0.79 1.20 -0.43 -1.01 0.23 2.37 1.07
x3<-runif(80,2,10) x3输出结果
[1] 7.081306 9.350415 4.351160 4.605524 2.207032 2.195813 7.951999 9.743476 [9] 6.298917 5.333969 7.378064 5.690359 9.342053 2.498212 6.846488 4.782182 [17] 7.988461 2.716306 8.722352 8.400760 3.900622 7.034838 7.943670 9.448758 [25] 7.083654 4.126258 2.582828 9.135599 7.766064 5.453632 5.884645 7.990329 [33] 3.620905 9.892212 3.845167 8.471849 5.911369 8.215719 7.617544 7.759089 [41] 6.751566 8.302347 4.073309 6.493836 8.066407 9.242996 6.263305 6.255265 [49] 4.220699 5.700430 9.602641 4.467327 7.147127 6.307310 2.621831 6.085484 [57] 9.279844 8.147505 4.756649 9.389553 8.030251 7.569268 5.930407 5.020778 [65] 3.925739 9.593744 4.686891 8.079335 8.041129 2.873302 8.414689 8.780803 [73] 4.486050 3.732873 6.186431 9.517897 5.879295 8.485993 6.257261 9.541974
x3<-formattable(x3,format="f",digits=2) x3
[1] 7.08 9.35 4.35 4.61 2.21 2.20 7.95 9.74 6.30 5.33 7.38 5.69 9.34 2.50 6.85 [16] 4.78 7.99 2.72 8.72 8.40 3.90 7.03 7.94 9.45 7.08 4.13 2.58 9.14 7.77 5.45 [31] 5.88 7.99 3.62 9.89 3.85 8.47 5.91 8.22 7.62 7.76 6.75 8.30 4.07 6.49 8.07 [46] 9.24 6.26 6.26 4.22 5.70 9.60 4.47 7.15 6.31 2.62 6.09 9.28 8.15 4.76 9.39 [61] 8.03 7.57 5.93 5.02 3.93 9.59 4.69 8.08 8.04 2.87 8.41 8.78 4.49 3.73 6.19 [76] 9.52 5.88 8.49 6.26 9.54
iv1<-rnorm(20) iv2<-rnorm(20) resp<-rnorm(20,78,3.5) df1<-data.frame(iv1,iv2,resp) df1输出结果
iv1 iv2 resp 1 1.03398540 0.07254862 75.99465 2 -0.09504870 0.13830547 72.74974 3 1.13174190 2.05943775 77.33238 4 -0.02728544 -2.19835582 74.54968 5 0.53777918 -0.60683616 82.39609 6 -0.14458976 -0.06022428 79.49349 7 -0.71368075 -0.29802388 78.03228 8 -1.62761198 -0.50938475 81.42240 9 -0.03233459 0.94152424 86.12316 10 -2.50062742 -0.05108768 75.94005 11 0.11128679 -0.05781440 72.02964 12 -0.05132032 1.62225539 77.66552 13 -0.49332274 -0.22909814 76.69032 14 -0.09782262 1.08388816 74.82925 15 -1.56938242 0.27308632 82.71712 16 0.13721036 -1.05074764 68.95937 17 0.30667417 -2.62395368 78.83784 18 -0.41926090 1.01394042 75.01351 19 -0.88518665 -0.82639519 83.28899 20 0.89221821 0.61685789 80.80044
df1$iv1<-formattable(df1$iv1,format="f",digits=2) df1$iv2<-formattable(df1$iv2,format="f",digits=2) df1$resp<-formattable(df1$resp,format="f",digits=2) df1输出结果
iv1 iv2 resp 1 1.03 0.07 75.99 2 -0.10 0.14 72.75 3 1.13 2.06 77.33 4 -0.03 -2.20 74.55 5 0.54 -0.61 82.40 6 -0.14 -0.06 79.49 7 -0.71 -0.30 78.03 8 -1.63 -0.51 81.42 9 -0.03 0.94 86.12 10 -2.50 -0.05 75.94 11 0.11 -0.06 72.03 12 -0.05 1.62 77.67 13 -0.49 -0.23 76.69 14 -0.10 1.08 74.83 15 -1.57 0.27 82.72 16 0.14 -1.05 68.96 17 0.31 -2.62 78.84 18 -0.42 1.01 75.01 19 -0.89 -0.83 83.29 20 0.89 0.62 80.80