为什么变量名（var_3）不出现在输出中？

我有一个data.frame：

以及以下功能：

library(magrittr)

for (i in lt_1[c(1)]) {
  print(
    x = cbind(i, var_1 = rowSums(i[, 1:2]), 
              var_2 = rowMeans(i[, 1:3]), 
              var_3 = multiply_by(i[1], i[2]))
  )
}

结果是：

         x.1       x.2       x.3 y    var_1     var_2       x.1
7  104.87429  96.05710  81.95041 1 200.9314  94.29393 10073.920
9  105.75781 111.00025 101.53253 1 216.7581 106.09687 11739.144
12 103.89843  97.46638  92.90054 1 201.3648  98.08845 10126.604
14  77.85300 105.56663  90.65902 1 183.4196  91.35955  8218.679
16  99.55066  92.92505 102.91446 1 192.4757  98.46339  9250.750
21 109.18977 103.98106  94.31331 1 213.1708 102.49472 11353.668
29  95.21850 105.69720 103.70019 1 200.9157 101.53863 10064.328

---

为什么变量3在输出中是x.1？

这是因为您将两个数据帧相乘，而不是向量相乘。以下是一个简短的示例：

head(cbind(mtcars, new_col = mtcars[1] * mtcars[2]))

人们希望数据中有一个名为new_col的新列，但是

#                   mpg cyl disp  hp drat    wt  qsec vs am gear carb mpg
#Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 126.0
#Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 126.0
#Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1  91.2
#Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 128.4
#Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 149.6
#Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1 108.6

mpg，因为这是mtcars[1]Try，headcbindmtcars，new_col=mtcars[2]*mtcars[1]的列名，它首先相乘

为了避免这种情况，将数据作为向量获取。所以

head(cbind(mtcars, new_col = mtcars[[1]] * mtcars[[2]]))

#                   mpg cyl disp  hp drat    wt  qsec vs am gear carb new_col
#Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4   126.0
#Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4   126.0
#Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1    91.2
#Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1   128.4
#Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2   149.6
#Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1   108.6

因此，在for循环中使用：

for (i in lt_1[c(1)]) {
  print(
    x = cbind(i, var_1 = rowSums(i[, 1:2]), 
                 var_2 = rowMeans(i[, 1:3]), 
                 var_3 = multiply_by(i[[1]], i[[2]]))
   )
}


#       x.1     x.2     x.3 y  var_1   var_2   var_3
#1   89.510  93.741 113.766 1 183.25  99.006  8390.8
#2   94.791  90.991  98.196 1 185.78  94.660  8625.2
#3  116.232 106.637  84.323 1 222.87 102.398 12394.7
#4   89.299 103.003  97.393 1 192.30  96.565  9198.1
#10  86.656 101.626 118.714 1 188.28 102.332  8806.6
#13 106.344 103.055  93.797 1 209.40 101.065 10959.2
#15 107.936 104.104  97.580 1 212.04 103.207 11236.5
#16  98.476 101.837 111.175 1 200.31 103.829 10028.5
#19  92.650 111.762 101.930 1 204.41 102.114 10354.7
#21 106.193  90.544 100.071 1 196.74  98.936  9615.1
#23  93.143 104.520  90.227 1 197.66  95.963  9735.3
#28  96.806 104.856  92.445 1 201.66  98.036 10150.7
#29 100.845  97.343  97.360 1 198.19  98.516  9816.6
#30  92.315 101.516  92.475 1 193.83  95.436  9371.5

head(cbind(mtcars, new_col = mtcars[[1]] * mtcars[[2]]))

#                   mpg cyl disp  hp drat    wt  qsec vs am gear carb new_col
#Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4   126.0
#Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4   126.0
#Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1    91.2
#Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1   128.4
#Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2   149.6
#Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1   108.6

for (i in lt_1[c(1)]) {
  print(
    x = cbind(i, var_1 = rowSums(i[, 1:2]), 
                 var_2 = rowMeans(i[, 1:3]), 
                 var_3 = multiply_by(i[[1]], i[[2]]))
   )
}


#       x.1     x.2     x.3 y  var_1   var_2   var_3
#1   89.510  93.741 113.766 1 183.25  99.006  8390.8
#2   94.791  90.991  98.196 1 185.78  94.660  8625.2
#3  116.232 106.637  84.323 1 222.87 102.398 12394.7
#4   89.299 103.003  97.393 1 192.30  96.565  9198.1
#10  86.656 101.626 118.714 1 188.28 102.332  8806.6
#13 106.344 103.055  93.797 1 209.40 101.065 10959.2
#15 107.936 104.104  97.580 1 212.04 103.207 11236.5
#16  98.476 101.837 111.175 1 200.31 103.829 10028.5
#19  92.650 111.762 101.930 1 204.41 102.114 10354.7
#21 106.193  90.544 100.071 1 196.74  98.936  9615.1
#23  93.143 104.520  90.227 1 197.66  95.963  9735.3
#28  96.806 104.856  92.445 1 201.66  98.036 10150.7
#29 100.845  97.343  97.360 1 198.19  98.516  9816.6
#30  92.315 101.516  92.475 1 193.83  95.436  9371.5