根据不同的分组条件创建列答案

【问题标题】：Create a column based on different group conditions根据不同的分组条件创建列
【发布时间】：2021-06-07 16:35:34
【问题描述】：

我有一个包含不同列的数据集。好像是这样的

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)

基本上我需要创建一个额外的列，名为above threshold，根据以下条件，这将是TRUE/FALSE：

要成为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

否则为FALSE

基本上需要 3 列或更多列为 TRUE，最后一列为 TRUE。输出看起来像这样（阈值以上 == TRUE 被涂成绿色）：

我该如何设置？ - 我希望这很清楚，但如果不是，请询问！

非常感谢！

【问题讨论】：

标签： r if-statement select multiple-columns

【解决方案1】：

我们创建一个命名list（或命名vector），遍历除“PatientID”之外的列，提取具有列名（cur_column()）的list元素，创建一个新的逻辑列通过在.names中添加后缀_new，然后使用rowSums检查每行的TRUE个数是否大于等于3来创建'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

【讨论】：

感谢阿克伦！ - 您能否确认这考虑了高于阈值的 3 列或更多列？我在任何地方都看不到>3。而且，第一个应该是 A:B 对吧？谢谢！！
@Lili 只需将across 更改为across(A:G
@TarJae 您的代码在 A:G 之间循环，然后单独提取 A:F。不清楚
@TarJae 你是说df %>% mutate(x = rowSums(cbind(A >= 900, B >= 900, C>=1000, D >= 1000, E >=1005, G >=1005), na.rm = TRUE) >=3)
很高兴我的问题能激发更多的问题！！哇哦！ :)

【解决方案2】：

最后，在神话般的 akrun 的大力帮助下，这里是解决方案：

df %>% mutate(above_treshold = rowSums(cbind(A >= 990, B >= 990, C>=1000, D >= 1000, E >=1005, G >=1005), na.rm = TRUE) >=3)

输出：

   PatientID        A        B        C        D        E        G above_treshold
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

【讨论】：

【解决方案3】：

基础

使用@akrun 想法

standard <- c(A = 990, B = 990, C = 1000, D = 1000, E = 1005, G = 1005)
tmp <- sweep(df[, -1], MARGIN = 2, STATS = standard, FUN = `>=`)

df$res <- apply(tmp, 1, function(x) sum(x, na.rm = TRUE) >= 3)

   PatientID        A        B        C        D        E        G   res
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

【讨论】：