Question

如果某个观点在NA上有mssi1_5:mssi1_18，我想替换NA一系列变量（mssi1_5）。可能有一种更好的方法来实现这个，而不是我建议的那个（如果你认为有，请告诉我），但我考虑的过程是：

使用mssiMiss

if_else()

如果变量mssi1_5为NA，则对mssiMiss == 0
否则mssiMiss == 1
然后使用mssiMiss作为过滤变量，如果mssiMiss == 0，则替换变量NA中的所有mssi1_5:mssi1_18和0

但是，我无法一步获取新变量mssiMiss。请阅读下面的代码和我遇到的问题。可重现的数据集：

df2 <- structure(list(uci = c("10001h", "10476h", "10484h", "10580h", 
"14280h", "2313n", "2778n", "3063n", "3579h", "3699h", "4090h", 
"4393h", "4412h", "4528h", "4582h", "4683h", "4735h", "4736h", 
"4745h", "4750h", "4756h", "4770h", "4771h", "4832h", "4872h", 
"517n", "6292h", "6309h", "6481h", "6601h", "6704h", "6948h", 
"7020h", "7030h", "7071h", "7160h", "7188h", "7235h", "7266h", 
"7348h", "7746h", "7810h", "8082h", "8119h", "8334h", "8345h", 
"8462h", "8486h", "8518h", "8578h", "8761h", "8799h", "8939h", 
"9046h", "9191h", "9194h", "9222h", "9273h", "9293h", "9448h", 
"9486h", "9757h", "9894h", "10268h", "10431h", "10498h", "10572h", 
"10622h", "10652h", "10660h", "14457h", "2420n", "2966n", "3006n", 
"3766h", "4219h", "4256h", "4366h", "4367h", "4534h", "4538h", 
"4543h", "4569h", "4570h", "4757h", "4769h", "4806h", "4843h", 
"4955h", "4958h", "50n", "601h", "603n", "6315h", "6340h", "6348h", 
"6358h", "6369h", "6379h", "6395h"), ID = c(1, 5, 6, 13, 20, 
28, 32, 36, 44, 48, 55, 69, 72, 80, 92, 107, 114, 115, 116, 117, 
118, 124, 125, 131, 135, 154, 158, 160, 179, 185, 193, 214, 218, 
220, 223, 232, 236, 240, 242, 248, 285, 288, 308, 313, 330, 332, 
341, 345, 350, 354, 369, 372, 379, 389, 403, 404, 405, 412, 413, 
421, 425, 445, 456, 2, 3, 7, 11, 14, 17, 18, 23, 30, 34, 35, 
50, 59, 61, 66, 67, 83, 85, 87, 90, 91, 119, 123, 127, 133, 148, 
149, 153, 156, 157, 162, 165, 166, 167, 169, 170, 173), Class = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2), age = c(14, 17, 
14, 14, 15, 14, 16, 20, 12, 16, 12, 15, 15, 12, 16, 17, 14, 14, 
13, 13, 14, 14, 23, 12, 15, 15, 14, 13, 17, 22, 15, 17, 22, 14, 
15, 15, 23, 15, 17, 12, 24, 15, 13, 13, 14, 17, 13, 21, 14, 14, 
15, 13, 21, 14, 21, 15, 15, 14, 16, 13, 12, 12, 12, 14, 17, 16, 
16, 15, 15, 13, 14, 20, 24, 15, 15, 14, 17, 14, 16, 15, 15, 17, 
14, 15, 13, 19, 19, 14, 16, 16, 22, 21, 23, 19, 15, 15, 14, 14, 
15, 24), sex = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 
1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 
0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 
1), bhsMean = c(0.47, 0.3, 0.16, 0.15, 0.35, 0.06, 0.25, 0.35, 
0.15, 0.35, 0.3, 0, 0.3, 0.38, 0.3, 0.1, 0.2, 0.1, 0.25, 0.2, 
0.3, 0.4, 0.3, 0.4, 0.2, 0.2, 0.05, 0.1, 0.35, 0.1, 0, 0.25, 
0.2, 0.25, 0.05, 0.35, 0.3, 0.25, 0.2, 0.27, 0.35, 0.15, 0.25, 
0.1, 0.2, 0.25, 0.05, 0.1, 0.45, 0.3, 0.36, 0.3, 0.44, 0.15, 
0.2, 0.11, 0.25, 0.2, 0.05, 0.45, 0, 0.4, 0.25, 0.6, 0.6, 0.55, 
0.71, 0.67, 0.5, 0.5, 0.55, 0.68, 0.55, 0.4, 0.68, 0.5, 0.6, 
0.53, 0.6, 0.65, 0.53, 0.65, 0.65, 0.65, 0.6, 0.55, 0.5, 0.55, 
0.6, 0.75, 0.65, 0.45, 0.5, 0.5, 0.65, 0.45, 0.6, 0.65, 0.65, 
0.45), tbMean = c(2.56, 3.89, 2.67, 2.33, 4.89, 1.44, 2.44, 2.44, 
NA, NA, NA, NA, NA, 3.44, 1.22, 3.11, 4, 4.11, 3, 2, 2.78, 2.67, 
3.44, 3.33, 3.33, 3.78, 3.89, 2.11, 4.56, 4, 1, 3.22, 3.33, 2.89, 
1.44, 3.11, 2.67, 3.33, 3.44, 1.33, 2.78, 2.67, 3.33, 2, 2.44, 
3.89, 2.44, 3.78, 3.67, 3.56, 3.56, 3.78, 1.78, 2.11, 3.33, 3.11, 
2.67, 2.44, 3.56, 1.67, NA, 2.67, 4.44, 4.89, 4.56, 3.89, 4.44, 
4.11, 3.67, 3.44, 4.44, 5, 3.78, 4.78, NA, NA, NA, NA, NA, 3.44, 
4, 4.56, 4.11, 4, 3.78, 5.11, 3.56, 2.89, 3.11, 3.11, 4.33, 3.56, 
5.11, 3.33, 4.11, 4.44, 4.67, 4, 4.56, 4.67), pbMean = c(2, 3.67, 
4, 4.5, 2.17, 1, 3.5, 2.33, NA, NA, NA, NA, NA, 1.5, 3.67, 3, 
3.5, 2.5, 2.17, 2, 1, 3.67, 2.33, 1.67, 2, 2, 3.17, 2.17, 1, 
3.83, 1, 2.33, 2.67, 3, 1, 3.33, 2, 3, 1.83, 1.17, 1, 2, 2.33, 
2.17, 2.17, 2.83, 2.67, 2.67, 1, 2.17, 1.67, 3.33, 1.33, 2.17, 
2.17, 1.17, 2.33, 1.83, 2.17, 1, NA, 1.5, 1.2, 3.17, 4.67, 1.33, 
2.83, 2.67, 2, 4.33, 3, 3, 5, 3.33, NA, NA, NA, NA, NA, 4.5, 
1.5, 4, 5.17, 3.33, 3.33, 3.67, 4.5, 2, 3.17, 3.67, 4.83, 4.33, 
3.67, 3.83, 5.17, 3, 2.33, 2.33, 4, 1.33), acssMean = c(2.29, 
1.86, 1.14, 2, 1.14, NA, 2, 3.29, NA, NA, NA, NA, NA, NA, 1.57, 
2.33, 3.43, 0.14, 1.43, 1.57, 2.29, 1.29, 0.29, 1.43, 0.57, 0.43, 
2.29, NA, 2.57, 1.71, 2.43, 1.43, 2.71, 2.29, 2.29, 1.86, 0.86, 
3.71, 1.57, NA, 2.29, 1, 2.71, 2, 0, 1.43, 2.71, NA, NA, NA, 
1.86, NA, 1.83, 2, 3.43, 0, 3.43, 0.86, NA, NA, NA, 2.14, NA, 
3.43, 4, 3.14, 3.29, 2.83, 1.71, 1.86, 2.14, 1.33, 1.71, 1.57, 
NA, NA, NA, NA, NA, 2.71, 1.29, 3.57, 2.29, 0.14, 1.71, 0.14, 
2.86, 2.71, 1.43, 1.71, 0.86, 2.33, 2.43, 1.71, 2.57, 1.14, 3.43, 
2.86, 3.57, 1.86), mssi1_1 = c(NA, 0, 0, 1, 1, 0, 2, 2, 0, 0, 
0, 0, 0, NA, 0, 1, 0, 0, 0, 0, 0, 1, NA, 0, 0, 1, 1, 1, 2, 1, 
0, 0, NA, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 1, 0, 0, 0, 0, 
0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 2, 1, 1, 2, 1, 
1, 1, NA, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 1, 0, 1, 1, 2, 
0, 1, 1, 1, 0, 0, 0, 1, 1), mssi1_2 = c(NA, 1, 0, 1, 1, 0, 1, 
2, 1, 1, 0, 0, 2, NA, 1, 1, 0, 0, 0, 0, 0, 1, NA, 0, 0, 2, 1, 
0, 2, 1, 0, 1, NA, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 
0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 2, 1, 
1, 1, 0, 1, 1, NA, 0, 0, 0, 0, 2, 1, 1, 1, 2, 0, 1, 0, 1, 1, 
0, 0, 2, 0, 1, 1, 1, 0, 0, 0, 1, 1), mssi1_3 = c(NA, 0, 0, 0, 
0, 0, 2, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 
0, 0, 0, 0, 2, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 1, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0), mssi1_4 = c(NA, 
0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 
2, NA, 0, 0, 0, 0, 0, 1, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, NA, 0, 0, 0, 0, 1, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0), mssi1_5 = c(NA, 
NA, NA, NA, NA, NA, 2, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, 1, NA, NA, NA, 1, NA, NA, 1, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 0, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, 0, NA, NA, 
NA, 0, NA, NA, 1, 2, NA, NA, NA, NA, NA, NA, NA, 3, NA, NA, NA, 
2, NA, NA, NA, NA, NA, NA, NA, 2, NA, 1, NA, 2, NA, NA, NA, NA, 
NA), mssi1_6 = c(NA, NA, NA, NA, NA, NA, 1, 1, NA, NA, NA, NA, 
0, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, 1, NA, NA, 
1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
0, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, 
NA, NA, NA, NA, NA, NA, 1, NA, NA, 1, 0, NA, NA, NA, NA, NA, 
NA, NA, 3, NA, NA, NA, 1, NA, NA, NA, NA, NA, NA, NA, 2, NA, 
1, NA, 0, NA, NA, NA, NA, NA), mssi1_7 = c(NA, NA, NA, NA, NA, 
NA, 2, 1, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, 
2, NA, NA, NA, 2, NA, NA, 2, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 2, 0, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, 2, NA, NA, 
2, 1, NA, NA, NA, NA, NA, NA, NA, 2, NA, NA, NA, 1, NA, NA, NA, 
NA, NA, NA, NA, 2, NA, 1, NA, 2, NA, NA, NA, NA, NA), mssi1_8 = c(NA, 
NA, NA, NA, NA, NA, 1, 1, NA, NA, NA, NA, 0, NA, NA, NA, NA, 
NA, NA, NA, NA, 0, NA, NA, NA, 1, NA, NA, 2, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 1, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, 
NA, 3, NA, NA, 1, 1, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, 
1, NA, NA, NA, NA, NA, NA, NA, 0, NA, 2, NA, 1, NA, NA, NA, NA, 
NA), mssi1_9 = c(NA, NA, NA, NA, NA, NA, 2, 2, NA, NA, NA, NA, 
2, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, 1, NA, NA, 
2, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, 
NA, NA, NA, NA, NA, NA, 3, NA, NA, 1, 1, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, NA, NA, 2, NA, 
1, NA, 1, NA, NA, NA, NA, NA), mssi1_10 = c(NA, NA, NA, NA, NA, 
NA, 2, 0, NA, NA, NA, NA, 2, NA, NA, NA, NA, NA, NA, NA, NA, 
0, NA, NA, NA, 1, NA, NA, 2, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 0, 0, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, 
1, 2, NA, NA, NA, NA, NA, NA, NA, 2, NA, NA, NA, 2, NA, NA, NA, 
NA, NA, NA, NA, 0, NA, NA, NA, 1, NA, NA, NA, NA, NA), mssi1_11 = c(NA, 
NA, NA, NA, NA, NA, 3, 1, NA, NA, NA, NA, 2, NA, NA, NA, NA, 
NA, NA, NA, NA, 0, NA, NA, NA, 0, NA, NA, 2, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 0, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, NA, 
NA, 0, NA, NA, 1, 2, NA, NA, NA, NA, NA, NA, NA, 2, NA, NA, NA, 
0, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, 1, NA, NA, NA, 
NA, NA), mssi1_12 = c(NA, NA, NA, NA, NA, NA, 1, 2, NA, NA, NA, 
NA, 3, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, 0, NA, 
NA, 2, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, 0, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 
NA, NA, NA, NA, NA, NA, NA, 2, NA, NA, 1, 1, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, NA, NA, 1, 
NA, NA, NA, 0, NA, NA, NA, NA, NA), mssi1_13 = c(NA, NA, NA, 
NA, NA, NA, 1, 3, NA, NA, NA, NA, 3, NA, NA, NA, NA, NA, NA, 
NA, NA, 0, NA, NA, NA, 1, NA, NA, 2, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 1, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, 3, 
NA, NA, 1, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 
NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, 0, NA, NA, NA, NA, 
NA), mssi1_14 = c(NA, NA, NA, NA, NA, NA, 1, 0, NA, NA, NA, NA, 
0, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, 1, NA, NA, 
1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
0, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, 
NA, NA, NA, NA, NA, NA, 1, NA, NA, 0, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, NA, NA, 1, NA, 
0, NA, 0, NA, NA, NA, NA, NA), mssi1_15 = c(NA, NA, NA, NA, NA, 
NA, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, 
1, NA, NA, NA, 0, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 0, 0, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, 
0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, 
NA, NA, NA, NA, NA, 0, NA, 0, NA, 0, NA, NA, NA, NA, NA), mssi1_16 = c(NA, 
NA, NA, NA, NA, NA, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, NA, 
NA, NA, NA, NA, 0, NA, NA, NA, 0, NA, NA, 0, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 0, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, 
NA, 0, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, 0, NA, NA, NA, NA, NA, NA, NA, 0, NA, 0, NA, 0, NA, NA, NA, 
NA, NA), mssi1_17 = c(NA, NA, NA, NA, NA, NA, 0, 0, NA, NA, NA, 
NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, 3, NA, 
NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, 0, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 
NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, 0, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, 0, 
NA, 0, NA, 0, NA, NA, NA, NA, NA), mssi1_18 = c(NA, NA, NA, NA, 
NA, NA, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, 
NA, 0, NA, NA, NA, 0, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, 0, 0, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, 0, NA, 
NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, 0, NA, 
NA, NA, NA, NA, NA, NA, 0, NA, 0, NA, 0, NA, NA, NA, NA, NA)), row.names = c(NA, 
-100L), class = c("tbl_df", "tbl", "data.frame"))

我使用以下代码：

library(dplyr) 

df2 <- df2 %>%
  select(., mssi1_5) %>%  # replace to your needs
  mutate_if(is.numeric, funs(if_else(is.na(.), 0, 1))) %>%
  bind_cols(df2, mssiMiss = .) # this is throwing an error

虽然这似乎给了我想要的结果（0如果单元格mssi1_5是NA，则1。创建的列名称不是我要求的。相反，列名似乎是重复的并命名为mssi1_51。

dput(head(df2,1))  

structure(list(uci = "10001h", ID = 1, Class = 1, age = 14, sex = 0, 
bhsMean = 0.47, tbMean = 2.56, pbMean = 2, acssMean = 2.29, 
mssi1_1 = NA_real_, mssi1_2 = NA_real_, mssi1_3 = NA_real_, 
mssi1_4 = NA_real_, mssi1_5 = NA_real_, mssi1_6 = NA_real_, 
mssi1_7 = NA_real_, mssi1_8 = NA_real_, mssi1_9 = NA_real_, 
mssi1_10 = NA_real_, mssi1_11 = NA_real_, mssi1_12 = NA_real_, 
mssi1_13 = NA_real_, mssi1_14 = NA_real_, mssi1_15 = NA_real_, 
mssi1_16 = NA_real_, mssi1_17 = NA_real_, mssi1_18 = NA_real_, 
mssi1_51 = 0), row.names = c(NA, -1L), class = c("tbl_df", 
"tbl", "data.frame"))

我当然可以重命名标题，以下内容似乎有效：

df3 <- df2
colnames(df3)[colnames(df3)=="mssi1_51"] <- "mssiMiss"
dput(head(df3, 1))

structure(list(uci = "10001h", ID = 1, Class = 1, age = 14, sex = 0, 
bhsMean = 0.47, tbMean = 2.56, pbMean = 2, acssMean = 2.29, 
mssi1_1 = NA_real_, mssi1_2 = NA_real_, mssi1_3 = NA_real_, 
mssi1_4 = NA_real_, mssi1_5 = NA_real_, mssi1_6 = NA_real_, 
mssi1_7 = NA_real_, mssi1_8 = NA_real_, mssi1_9 = NA_real_, 
mssi1_10 = NA_real_, mssi1_11 = NA_real_, mssi1_12 = NA_real_, 
mssi1_13 = NA_real_, mssi1_14 = NA_real_, mssi1_15 = NA_real_, 
mssi1_16 = NA_real_, mssi1_17 = NA_real_, mssi1_18 = NA_real_, 
mssiMiss = 0, mssi1_52 = 0, mssi1_53 = 0), row.names = c(NA, 
-1L), class = c("tbl_df", "tbl", "data.frame"))

但我觉得这效率低下。我究竟做错了什么？如何获取我想要的列名而不必重命名。有没有更好的方法来创建这个过滤变量？

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Answer 1

df2 %>%
  mutate(mssiMiss = 1L*!is.na(mssi1_5)) %>%
  mutate_at(vars(mssi1_5:mssi1_18),
            funs(if_else(mssiMiss == 0 & is.na(.), 0, .))) %>% str
# Classes 'tbl_df', 'tbl' and 'data.frame': 100 obs. of  28 variables:
#  $ uci     : chr  "10001h" "10476h" "10484h" "10580h" ...
#  $ ID      : num  1 5 6 13 20 28 32 36 44 48 ...
#  $ Class   : num  1 1 1 1 1 1 1 1 1 1 ...
#  $ age     : num  14 17 14 14 15 14 16 20 12 16 ...
#  $ sex     : num  0 0 0 0 0 0 1 0 0 0 ...
#  $ bhsMean : num  0.47 0.3 0.16 0.15 0.35 0.06 0.25 0.35 0.15 0.35 ...
#  $ tbMean  : num  2.56 3.89 2.67 2.33 4.89 1.44 2.44 2.44 NA NA ...
#  $ pbMean  : num  2 3.67 4 4.5 2.17 1 3.5 2.33 NA NA ...
#  $ acssMean: num  2.29 1.86 1.14 2 1.14 NA 2 3.29 NA NA ...
#  $ mssi1_1 : num  NA 0 0 1 1 0 2 2 0 0 ...
#  $ mssi1_2 : num  NA 1 0 1 1 0 1 2 1 1 ...
#  $ mssi1_3 : num  NA 0 0 0 0 0 2 0 0 0 ...
#  $ mssi1_4 : num  NA 0 0 0 0 0 1 2 1 0 ...
#  $ mssi1_5 : num  0 0 0 0 0 0 2 1 0 0 ...
#  $ mssi1_6 : num  0 0 0 0 0 0 1 1 0 0 ...
#  $ mssi1_7 : num  0 0 0 0 0 0 2 1 0 0 ...
#  $ mssi1_8 : num  0 0 0 0 0 0 1 1 0 0 ...
#  $ mssi1_9 : num  0 0 0 0 0 0 2 2 0 0 ...
#  $ mssi1_10: num  0 0 0 0 0 0 2 0 0 0 ...
#  $ mssi1_11: num  0 0 0 0 0 0 3 1 0 0 ...
#  $ mssi1_12: num  0 0 0 0 0 0 1 2 0 0 ...
#  $ mssi1_13: num  0 0 0 0 0 0 1 3 0 0 ...
#  $ mssi1_14: num  0 0 0 0 0 0 1 0 0 0 ...
#  $ mssi1_15: num  0 0 0 0 0 0 0 0 0 0 ...
#  $ mssi1_16: num  0 0 0 0 0 0 0 0 0 0 ...
#  $ mssi1_17: num  0 0 0 0 0 0 0 0 0 0 ...
#  $ mssi1_18: num  0 0 0 0 0 0 0 0 0 0 ...
#  $ mssiMiss: int  0 0 0 0 0 0 1 1 0 0 ...

未按预期创建新变量名称 - R

1 个答案: