如何在 rowMeans function 的數據表中傳遞具有多個列名的向量

Question

我有太多變量無法在rowMeans(cbind()) function 中手動列出它們。 自然地，我試圖將它們打包在一個字符向量中，但它不起作用。 我試過eval , .. , mget ，但似乎沒有人能做到這一點

column_names <- as.vector(summary$variables) #this is where I take the column names from (characters)

dataset[ , means  := rowMeans( cbind( eval(column_names) )   , na.rm=TRUE  )]

謝謝

Answer 1

您需要使用.SD和.SDcols來指定相關列； 這是一個基於mtcars的最小可重現示例

library(data.table)
dt <- as.data.table(mtcars)
col_names <- c("mpg", "disp", "drat")
dt[, mean := rowMeans(.SD), .SDcols = col_names]
dt
#mpg cyl  disp  hp drat    wt  qsec vs am gear carb      mean
#1: 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4  61.63333
#2: 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4  61.63333
#3: 22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1  44.88333
#4: 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1  94.16000
#5: 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2 127.28333
#6: 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1  81.95333
#7: 14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4 125.83667
#8: 24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2  58.26333
#9: 22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2  55.84000
#10: 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4  63.57333
#11: 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4  63.10667
#12: 16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3  98.42333
#13: 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3  98.72333
#14: 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3  98.02333
#15: 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4 161.77667
#16: 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4 157.80000
#17: 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4 152.64333
#18: 32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1  38.39333
#19: 30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2  37.01000
#20: 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1  36.40667
#21: 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1  48.43333
#22: 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2 112.08667
#23: 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2 107.45000
#24: 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4 122.34333
#25: 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2 140.76000
#26: 27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1  36.79333
#27: 26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2  50.24333
#28: 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2  43.09000
#29: 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 123.67333
#30: 19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6  56.10667
#31: 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 106.51333
#32: 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2  48.83667
#mpg cyl  disp  hp drat    wt  qsec vs am gear carb      mean

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所以在你的情況下，像

dataset[ , means  := rowMeans(.SD, na.rm = T), .SDcols = column_names]