我正在处理一个大型数据集，该数据集的压缩版本如下所示
Col1 Z1 Z2 Z3 Z4 Z5 Z6
0.077 1.6163062 0.7573661 -1.1250619 -1.046367579 -0.9179156 -0.1372472 0.1200780 -0.1669360 -0.8295464 0.6470479
0.01 -0.6611817 -0.4043414 -2.0809741 1.129659352 0.4477784 0.1425769 -1.4063330 -0.2119068 0.1621555 0.6139109
0.033 -0.7142192 -0.1317318 -0.3298212 0.651356337 -0.8588449 1.5542608 0.1376427 -1.0568366 -0.7058332 0.2803810
0.05 -1.0913298 -0.4996984 -0.6667251 -0.022393207 1.2802708 -0.3077195 -0.8905316 1.5384766 -0.8538084 -2.1926031
0.23 -0.1383636 0.3953499 0.6674759 -1.517436558 1.0223679 1.2748456 0.2315481 -0.1999777 -1.2599197 -1.6293457
0.78 0.6727714 0.5522693 0.8231503 -0.006216825 1.3181397 -0.3797453 0.9328081 -0.8693609 -0.7529270 2.1232194
我正在寻找的是有效地将Z columns与Col1
我不想做Col1 * Z1，Col * Z2，Col1 * Z3等。因为我有100列以Z开头的列，所以手动执行此操作效率低下或不可能。
是否可以有效地将Z列中的每个元素与Col1相乘？在此先感谢您的任何建议或帮助。
答案 0 :(得分：0)
您可以使用mutate_at并指定要修改的列
library(dplyr)
df %>%
mutate_at(.vars = vars(starts_with("Z")), .funs = funs(. * Col1))
#> Col1 Z1 Z2 Z3 Z4 Z5
#> 1 0.077 0.124455577 0.058317190 -0.08662977 -0.080570304 -0.070679501
#> 2 0.010 -0.006611817 -0.004043414 -0.02080974 0.011296594 0.004477784
#> 3 0.033 -0.023569234 -0.004347149 -0.01088410 0.021494759 -0.028341882
#> 4 0.050 -0.054566490 -0.024984920 -0.03333626 -0.001119660 0.064013540
#> 5 0.230 -0.031823628 0.090930477 0.15351946 -0.349010408 0.235144617
#> 6 0.780 0.524761692 0.430770054 0.64205723 -0.004849124 1.028148966
#> Z6 Z7 Z8 Z9 Z10
#> 1 -0.010568034 0.009246006 -0.012854072 -0.063875073 0.049822688
#> 2 0.001425769 -0.014063330 -0.002119068 0.001621555 0.006139109
#> 3 0.051290606 0.004542209 -0.034875608 -0.023292496 0.009252573
#> 4 -0.015385975 -0.044526580 0.076923830 -0.042690420 -0.109630155
#> 5 0.293214488 0.053256063 -0.045994871 -0.289781531 -0.374749511
#> 6 -0.296201334 0.727590318 -0.678101502 -0.587283060 1.656111132
df %>%
mutate_at(.vars = vars(starts_with("Z")), .funs = funs(mul = . * Col1))
#> Col1 Z1 Z2 Z3 Z4 Z5
#> 1 0.077 1.6163062 0.7573661 -1.1250619 -1.046367579 -0.9179156
#> 2 0.010 -0.6611817 -0.4043414 -2.0809741 1.129659352 0.4477784
#> 3 0.033 -0.7142192 -0.1317318 -0.3298212 0.651356337 -0.8588449
#> 4 0.050 -1.0913298 -0.4996984 -0.6667251 -0.022393207 1.2802708
#> 5 0.230 -0.1383636 0.3953499 0.6674759 -1.517436558 1.0223679
#> 6 0.780 0.6727714 0.5522693 0.8231503 -0.006216825 1.3181397
#> Z6 Z7 Z8 Z9 Z10 Z1_mul
#> 1 -0.1372472 0.1200780 -0.1669360 -0.8295464 0.6470479 0.124455577
#> 2 0.1425769 -1.4063330 -0.2119068 0.1621555 0.6139109 -0.006611817
#> 3 1.5542608 0.1376427 -1.0568366 -0.7058332 0.2803810 -0.023569234
#> 4 -0.3077195 -0.8905316 1.5384766 -0.8538084 -2.1926031 -0.054566490
#> 5 1.2748456 0.2315481 -0.1999777 -1.2599197 -1.6293457 -0.031823628
#> 6 -0.3797453 0.9328081 -0.8693609 -0.7529270 2.1232194 0.524761692
#> Z2_mul Z3_mul Z4_mul Z5_mul Z6_mul
#> 1 0.058317190 -0.08662977 -0.080570304 -0.070679501 -0.010568034
#> 2 -0.004043414 -0.02080974 0.011296594 0.004477784 0.001425769
#> 3 -0.004347149 -0.01088410 0.021494759 -0.028341882 0.051290606
#> 4 -0.024984920 -0.03333626 -0.001119660 0.064013540 -0.015385975
#> 5 0.090930477 0.15351946 -0.349010408 0.235144617 0.293214488
#> 6 0.430770054 0.64205723 -0.004849124 1.028148966 -0.296201334
#> Z7_mul Z8_mul Z9_mul Z10_mul
#> 1 0.009246006 -0.012854072 -0.063875073 0.049822688
#> 2 -0.014063330 -0.002119068 0.001621555 0.006139109
#> 3 0.004542209 -0.034875608 -0.023292496 0.009252573
#> 4 -0.044526580 0.076923830 -0.042690420 -0.109630155
#> 5 0.053256063 -0.045994871 -0.289781531 -0.374749511
#> 6 0.727590318 -0.678101502 -0.587283060 1.656111132
如果要更新的表很大，使用data.table可能会更快
library(data.table)
dt <- data.table(df)
cols <- names(dt)[-1]
dt[, (cols) := lapply(.SD, function(x) x*Col1), .SDcols = cols]
dt
#> Col1 Z1 Z2 Z3 Z4 Z5
#> 1: 0.077 0.124455577 0.058317190 -0.08662977 -0.080570304 -0.070679501
#> 2: 0.010 -0.006611817 -0.004043414 -0.02080974 0.011296594 0.004477784
#> 3: 0.033 -0.023569234 -0.004347149 -0.01088410 0.021494759 -0.028341882
#> 4: 0.050 -0.054566490 -0.024984920 -0.03333626 -0.001119660 0.064013540
#> 5: 0.230 -0.031823628 0.090930477 0.15351946 -0.349010408 0.235144617
#> 6: 0.780 0.524761692 0.430770054 0.64205723 -0.004849124 1.028148966
#> Z6 Z7 Z8 Z9 Z10
#> 1: -0.010568034 0.009246006 -0.012854072 -0.063875073 0.049822688
#> 2: 0.001425769 -0.014063330 -0.002119068 0.001621555 0.006139109
#> 3: 0.051290606 0.004542209 -0.034875608 -0.023292496 0.009252573
#> 4: -0.015385975 -0.044526580 0.076923830 -0.042690420 -0.109630155
#> 5: 0.293214488 0.053256063 -0.045994871 -0.289781531 -0.374749511
#> 6: -0.296201334 0.727590318 -0.678101502 -0.587283060 1.656111132
由reprex package（v0.2.1.9000）于2018-10-07创建