r - 根据不同的组条件创建列

Question

我有一个包含不同列的数据集。看起来像那样

df <- data.frame(PatientID = c("0002" ,"0004", "0005", "0006" ,"0009" ,"0010" ,"0018", "0019" ,"0020" ,"0027", "0039" ,"0041" ,"0042", "0043" ,"0044" ,"0045", "0046", "0047" ,"0048" ,"0049", "0055"),
                 A = c(987.805 , 977.146 , 790.809 , 964.315 ,1014.020 , 952.311 , 992.967 , 950.797 , 958.975  ,960.712  ,958.117 , 947.465 , 902.852 , 961.417,  985.124  ,994.178 , 930.141 ,1007.790 , 948.848, 1027.110 , 999.414),
                 B = c(998.988 , 972.606 , 998.680 , 955.037 , 972.941 ,1020.560 , 947.751 ,1029.560 , 955.540 , 911.606 , 964.039   ,    NA,  988.087 , 902.367 , 959.338 ,1029.050 , 925.162 , 987.374 ,1066.400  ,957.512 , 917.597),
                 C = c( 975.634 , 987.140 , 961.810 , 929.466 , 978.166, 1005.820  ,925.752 , 969.469 , 943.398  ,936.034,  965.292 , 996.404 , 920.610 , 967.047  ,986.565 , 913.517 , 893.428 , 921.606 , 976.192 , 929.590  ,950.493), 
D = c(975.634 , 987.140 , 961.810 , 929.466 , 978.166, 1005.820 , 925.752 , 969.469  ,943.398 , 936.034 , 965.292 , 996.404 , 920.610 , 967.047 , 986.565 , 913.517 , 893.428 , 921.606 , 976.192 , 929.590 , 950.493),
E = c(1006.330, 1028.070 , 975.554 , 954.274 ,1005.910  ,949.969 , 992.820 , 977.048  ,934.407 , 948.913 , 944.578 , 917.564 , 975.301,  961.375  ,955.296 , 961.128  ,998.119 ,1009.110 , 994.891 ,1000.170  ,982.763),
G= c(951.684 , 958.990 , 944.432 , 944.654 , 924.680 , 955.927 , 972.674 , 949.384  ,973.348 , 984.392 , 943.894 , 961.468 , 995.368 , 994.997 , 973.175 , 979.454 , 952.605 , 930.744  ,   NA, 1015.150 , 956.507), stringsAsFactors = F)

基本上我需要的是创建一个名为 above threshold 的额外列。 , 那将是 TRUE/FALSE基于以下条件:
成为 TRUE ，患者需要在 6 列(A、B、C、D、E 或 G)中的任意 3 列中具有高于阈值的值，阈值是:

对于 A 和 B -> 990

对于 C 和 D -> 1000

对于 E 和 G -> 1005

否则是 FALSE基本上 3 个或更多列需要为 TRUE，最后一列为 TRUE。输出看起来像这样(高于阈值 == TRUE 被涂成绿色):

我怎么能设置这个？ - 我希望这很清楚，但如果不是，请询问!
非常感谢!

Answer 1

我们创建了一个名为 list (或命名 vector )，遍历除“PatientID”以外的列，提取 list具有列名 ( cur_column() ) 的元素，通过添加后缀 _new 创建一个新的逻辑列在 .names ，然后使用 rowSums检查每行的 TRUE 数量是否大于或等于 3 以创建“above_threshold”

library(dplyr)
lst1 <- list(A = 990, B = 990, C = 1000, D = 1000, E = 1005, G = 1005)

df %>% 
    mutate(across(A:G,  ~ . > lst1[[cur_column()]],
       .names = '{.col}_new'), 
     above_threshold = rowSums(select(cur_data(), ends_with('new')), 
            na.rm = TRUE) >=3) %>%
    select(names(df), above_threshold)

-输出

 PatientID        A        B        C        D        E        G above_threshold
1       0002  987.805  998.988  975.634  975.634 1006.330  951.684           FALSE
2       0004  977.146  972.606  987.140  987.140 1028.070  958.990           FALSE
3       0005  790.809  998.680  961.810  961.810  975.554  944.432           FALSE
4       0006  964.315  955.037  929.466  929.466  954.274  944.654           FALSE
5       0009 1014.020  972.941  978.166  978.166 1005.910  924.680           FALSE
6       0010  952.311 1020.560 1005.820 1005.820  949.969  955.927            TRUE
7       0018  992.967  947.751  925.752  925.752  992.820  972.674           FALSE
8       0019  950.797 1029.560  969.469  969.469  977.048  949.384           FALSE
9       0020  958.975  955.540  943.398  943.398  934.407  973.348           FALSE
10      0027  960.712  911.606  936.034  936.034  948.913  984.392           FALSE
11      0039  958.117  964.039  965.292  965.292  944.578  943.894           FALSE
12      0041  947.465       NA  996.404  996.404  917.564  961.468           FALSE
13      0042  902.852  988.087  920.610  920.610  975.301  995.368           FALSE
14      0043  961.417  902.367  967.047  967.047  961.375  994.997           FALSE
15      0044  985.124  959.338  986.565  986.565  955.296  973.175           FALSE
16      0045  994.178 1029.050  913.517  913.517  961.128  979.454           FALSE
17      0046  930.141  925.162  893.428  893.428  998.119  952.605           FALSE
18      0047 1007.790  987.374  921.606  921.606 1009.110  930.744           FALSE
19      0048  948.848 1066.400  976.192  976.192  994.891       NA           FALSE
20      0049 1027.110  957.512  929.590  929.590 1000.170 1015.150           FALSE
21      0055  999.414  917.597  950.493  950.493  982.763  956.507           FALSE