比較R中的多列

Question

我想比較不同大小的列中的數據R。

這是我的數據集。

pp_value_1 pp_value_2 pp_value_3 pp_filename nn_value_1 nn_value_2 nn_value_3 nn_filename mm_value_1 mm_value_2 mm_value_3 mm_filename 
17 73 53 CC3 5 29 53 AA2 11 56 34 AA2 
129 516 34 BB5 44 217 42 BB1 36 190 39 BB1 
107 436 44 AA3 29 147 53 CC7 30 155 31 CC1 
57 244 53 BB6 21 108 53 BB2 14 77 61 BB4 
57 227 29 AA1 21 104 39 AA6 9 48 44 BB6 
80 318 47 AA2 18 89 47 CC3 37 200 44 DD3 
128 529 56 BB4 43 222 54 CC1 36 202 50 CC3 
31 127 53 CC1 7 38 53 DD4    
18 73 47 CC2 

我用了duplicated功能，具有TRUE和FALSE結果追加列，然後grepped結果，但沒有奏效。這裏是我的代碼：

v=duplicated(data$pp_filename, data$filename, data$filename) 
b=cbind(data, dup=v) 
dupl=(b[grep("FALSE", b$dup),]) 

而這正是我想要的（以適當的形式再次）獲得：

pp_value_1 pp_value_2 pp_value_3 pp_filename nn_value_1 nn_value_2 nn_value_3 nn_filename mm_value_1 mm_value_2 mm_value_3 mm_filename 
17 73 53 CC3 18 89 47 CC3 11 56 34 AA2 
80 318 47 AA2 43 222 54 CC1 30 155 31 CC1 
31 127 53 CC1 5 29 53 AA2 36 202 50 CC3 

Answer 1

這更多的是爲了簡化操作而對數據格式的建議。 我猜，你的數據的一個更好的格式應該是：

spl = split.default(DF, substring(names(DF), 1, 2)) 
lDF = do.call(rbind, 
       lapply(seq_along(spl), 
        function(i) 
          setNames(cbind(names(spl)[i], 
              spl[[i]][complete.cases(spl[[i]]), ]), 
            c("type", gsub("^(.*?)_", "", names(spl[[i]])))))) 
lDF 
# type value_1 value_2 value_3 filename 
#1 mm  11  56  34  AA2 
#2 mm  36  190  39  BB1 
#3 mm  30  155  31  CC1 
#4 mm  14  77  61  BB4 
#5 mm  9  48  44  BB6 
#6 mm  37  200  44  DD3 
#7 mm  36  202  50  CC3 
#8 nn  5  29  53  AA2 
#9 nn  44  217  42  BB1 
#.... 

然後，您可以繼續（至少從我從這個問題理解）與：

commons = Reduce(intersect, split(lDF$filename, lDF$type)) 
lDF[lDF$filename %in% commons, ]          
# type value_1 value_2 value_3 filename 
#1 mm  11  56  34  AA2 
#3 mm  30  155  31  CC1 
#7 mm  36  202  50  CC3 
#8 nn  5  29  53  AA2 
#13 nn  18  89  47  CC3 
#14 nn  43  222  54  CC1 
#16 pp  17  73  53  CC3 
#21 pp  80  318  47  AA2 
#23 pp  31  127  53  CC1 

如果你想要的格式你會發現，有一些解決方法可以與你一起。例如爲：

res = lDF[lDF$filename %in% commons, ] 
tmp = split(res[-1], res[[1]]) 
do.call(cbind, 
     lapply(seq_along(tmp), 
       function(i) 
        setNames(tmp[[i]], 
          paste(names(tmp)[i], names(tmp[[i]]), sep = "_")))) 

「DF」是：

DF = structure(list(pp_value_1 = c(17L, 129L, 107L, 57L, 57L, 80L, 
128L, 31L, 18L), pp_value_2 = c(73L, 516L, 436L, 244L, 227L, 
318L, 529L, 127L, 73L), pp_value_3 = c(53L, 34L, 44L, 53L, 29L, 
47L, 56L, 53L, 47L), pp_filename = structure(c(9L, 5L, 3L, 6L, 
1L, 2L, 4L, 7L, 8L), .Label = c("AA1", "AA2", "AA3", "BB4", "BB5", 
"BB6", "CC1", "CC2", "CC3"), class = "factor"), nn_value_1 = c(5L, 
44L, 29L, 21L, 21L, 18L, 43L, 7L, NA), nn_value_2 = c(29L, 217L, 
147L, 108L, 104L, 89L, 222L, 38L, NA), nn_value_3 = c(53L, 42L, 
53L, 53L, 39L, 47L, 54L, 53L, NA), nn_filename = structure(c(1L, 
3L, 7L, 4L, 2L, 6L, 5L, 8L, NA), .Label = c("AA2", "AA6", "BB1", 
"BB2", "CC1", "CC3", "CC7", "DD4"), class = "factor"), mm_value_1 = c(11L, 
36L, 30L, 14L, 9L, 37L, 36L, NA, NA), mm_value_2 = c(56L, 190L, 
155L, 77L, 48L, 200L, 202L, NA, NA), mm_value_3 = c(34L, 39L, 
31L, 61L, 44L, 44L, 50L, NA, NA), mm_filename = structure(c(1L, 
2L, 5L, 3L, 4L, 7L, 6L, NA, NA), .Label = c("AA2", "BB1", "BB4", 
"BB6", "CC1", "CC3", "DD3"), class = "factor")), .Names = c("pp_value_1", 
"pp_value_2", "pp_value_3", "pp_filename", "nn_value_1", "nn_value_2", 
"nn_value_3", "nn_filename", "mm_value_1", "mm_value_2", "mm_value_3", 
"mm_filename"), class = "data.frame", row.names = c(NA, -9L)) 

Answer 2

對我來說，簡單的操作標似乎比grep的更合適。但我依然找到了一個藉口來使用grep。

b <- read.table(header = TRUE, text = " 
pp_value_1 pp_value_2 pp_value_3 pp_filename nn_value_1 nn_value_2 nn_value_3 nn_filename mm_value_1 mm_value_2 mm_value_3 mm_filename 
17 73 53 CC3 5 29 53 AA2 11 56 34 AA2 
129 516 34 BB5 44 217 42 BB1 36 190 39 BB1 
107 436 44 AA3 29 147 53 CC7 30 155 31 CC1 
57 244 53 BB6 21 108 53 BB2 14 77 61 BB4 
57 227 29 AA1 21 104 39 AA6 9 48 44 BB6 
80 318 47 AA2 18 89 47 CC3 37 200 44 DD3 
128 529 56 BB4 43 222 54 CC1 36 202 50 CC3 
31 127 53 CC1 7 38 53 DD4 18 73 47 CC2 
") 
# Any duplicated values among the "xx_filename" columns? 
filenameCols <- grep("_filename", names(b))  
v <- apply(b[,filenameCols], 1, FUN = anyDuplicated) 
# Save rows with colliding xx_filename columns 
dupl <- b[v != 0,]