如何仅将 R 数据框中的一列与数字相乘？
要仅将一列与数字相乘，我们可以简单地使用乘法运算符*，但需要用新值替换原始列。例如，如果我们有一个名为df的数据框，它包含三列x1、x2和x3，并且我们想将第二列x2乘以2，那么它可以作为df$x2<−df$x2*2完成。
示例1
考虑以下数据框-
set.seed(212) x1<−rpois(20,2) x2<−rpois(20,5) x3<−rpois(20,2) df1<−data.frame(x1,x2,x3) df1输出结果
x1 x2 x3 1 1 5 3 2 3 5 1 3 3 6 4 4 1 0 2 5 0 3 1 6 0 7 4 7 4 10 4 8 4 8 3 9 1 7 0 10 2 2 0 11 2 1 1 12 2 7 1 13 3 2 1 14 1 3 3 15 1 3 2 16 3 5 2 17 3 2 1 18 1 4 1 19 2 5 2 20 4 6 2
通过将其中的值乘以2来替换列x2-
df1$x2<−df1$x2*2 df1输出结果
x1 x2 x3 1 1 10 3 2 3 10 1 3 3 12 4 4 1 0 2 5 0 6 1 6 0 14 4 7 4 20 4 8 4 16 3 9 1 14 0 10 2 4 0 11 2 2 1 12 2 14 1 13 3 4 1 14 1 6 3 15 1 6 2 16 3 10 2 17 3 4 1 18 1 8 1 19 2 10 2 20 4 12 2
例2
y1<−rnorm(20) y2<−rnorm(20) y3<−rnorm(20) y4<−rnorm(20) df2<−data.frame(y1,y2,y3,y4) df2输出结果
y1 y2 y3 y4 1 0.17051839 −1.07371818 0.717652086 −0.6692174 2 0.26654381 −0.82881794 1.144774784 1.0708255 3 1.17587680 −0.03197159 −0.257318856 −0.2734330 4 0.79978274 1.14677652 −1.052941918 0.8212265 5 0.36352605 0.95455643 0.002662389 0.8991729 6 −0.52918622 −1.19824723 1.121770768 −0.1345990 7 −1.30278723 0.90339625 −1.637918585 −0.3986243 8 −2.01380274 −0.61700004 1.319289169 1.4223520 9 −0.10499300 −0.99640769 1.508072921 −0.8711021 10 −0.57019817 0.23396114 0.371342290 −0.5071846 11 −0.92964644 −2.82593133 −0.191162636 0.2482026 12 −0.62824719 1.39458991 −0.250602510 −0.5094344 13 −1.16899182 0.48402510 0.849597620 0.5386604 14 −0.12800221 −1.01570468 −1.370769300 0.3641254 15 1.60649960 1.00993852 −0.181644717 0.7057080 16 −0.09581029 −0.40099838 0.392519844 −1.6369244 17 −0.43375271 −0.29316467 −0.233208374 0.1270293 18 −0.96182839 0.54334525 1.550101688 2.0853380 19 −1.50775746 −0.89573880 0.366389303 0.3372866 20 1.90255916 −1.41836692 0.428073142 −0.1576013
通过将其中的值乘以*1来替换列y1-
df2$y1<−df2$y1*(−1) df2输出结果
y1 y2 y3 y4 1 −0.17051839 −1.07371818 0.717652086 −0.6692174 2 −0.26654381 −0.82881794 1.144774784 1.0708255 3 −1.17587680 −0.03197159 −0.257318856 −0.2734330 4 −0.79978274 1.14677652 −1.052941918 0.8212265 5 −0.36352605 0.95455643 0.002662389 0.8991729 6 0.52918622 −1.19824723 1.121770768 −0.1345990 7 1.30278723 0.90339625 −1.637918585 −0.3986243 8 2.01380274 −0.61700004 1.319289169 1.4223520 9 0.10499300 −0.99640769 1.508072921 −0.8711021 10 0.57019817 0.23396114 0.371342290 −0.5071846 11 0.92964644 −2.82593133 −0.191162636 0.2482026 12 0.62824719 1.39458991 −0.250602510 −0.5094344 13 1.16899182 0.48402510 0.849597620 0.5386604 14 0.12800221 −1.01570468 −1.370769300 0.3641254 15 −1.60649960 1.00993852 −0.181644717 0.7057080 16 0.09581029 −0.40099838 0.392519844 −1.6369244 17 0.43375271 −0.29316467 −0.233208374 0.1270293 18 0.96182839 0.54334525 1.550101688 2.0853380 19 1.50775746 −0.89573880 0.366389303 0.3372866 20 −1.90255916 −1.41836692 0.428073142 −0.1576013