基于 5 列中至少 3 列的匹配对分组数据集进行子集

Question

我正在尝试对分组数据集进行子集化，以便代码查看每个组，查看五个特定列，查看行在这些列中是否具有匹配值，如果多行具有 3 个或更多匹配值，它将保留第一个行并删除其他具有 3 个或更多匹配项的行。 NA 不应计入该匹配阈值。

举个例子，假设我按月份对数据集进行分组。我希望代码能够查看 1 月组，识别 A、B、C、D 和 E 列中具有 3 个或更多匹配项的行，如果有两行满足匹配阈值，则删除第二行。因此，在这种情况下，它将删除保留删除第 2 行（因为它与第 1 行有三个匹配项），同时保留所有其他行，因为它们具有 <3 matches when compared head-to-head with one another.

data <- data.frame(
  Month = c("Jan", "Jan", "Jan", "Jan", "Feb"),
  A = c("apple", "apple", "banana", "cherry", "peach"),
  B = c("car", "car", "bike", bike", "plane"),
  C = c(NA, NA, NA, "cold", "NA"),
  D = c("London", "Paris", "Tokyo", "Tokyo", "New York"),
  E = c("earth", "earth", "wind", "fire", NA) 
)

到目前为止，这是我最好的尝试，但我很确定这是错误的。

data_cleaned <- data %>%
  group_by(`Month`) %>%
  filter(n() == 1 | (n() > 1 & row_number() == which.min(rowSums(!is.na(select(., 
                                                                              `A`, `B`, `C`, `D`, `E` ))) >= 3)))

Answer 1

这是一个

data.table

方法

library(data.table)
# set to data.table format
setDT(data)
# set row id's
data[, id := rowid(Month)]
# split to list by Month value
L <- split(data, by = "Month")
# columns to compare
cols <- LETTERS[1:5]

# rbindlist binds the output of the below lapply to a singe data.table
rbindlist(
  lapply(L, function(x) {
    # set the NA-values to @@@ (NA == NA resuklts in FALSE, but @@@ == @@@ is TRUE)
    x[is.na(x)] <- "@@@"
    # create a data.table with all rows to compare to one another
    compare_rows <- CJ(row = 1:nrow(x), compare_row = 1:nrow(x))
    # only check rows below row
    compare_rows <- compare_rows[row < compare_row, ][]
    setkey(compare_rows, row, compare_row)
    # self join to get the count of similar columns
    compare_rows[compare_rows, same := {
      sum(x[i.row, .SD, .SDcols = cols] == x[i.compare_row, .SD, .SDcols = cols])
    }, by = .EACHI][]
    # set the @@@ back to NA
    x[x=="@@@"] <- NA_character_
    # filter rows that have >3 matches with previous rows
    x[!id %in% compare_rows[same > 3, compare_row]]
  }))

#    Month      A     B    C        D     E id
# 1:   Jan  apple   car <NA>   London earth  1
# 2:   Jan banana  bike <NA>    Tokyo  wind  3
# 3:   Jan cherry  bike cold    Tokyo  fire  4
# 4:   Feb  peach plane <NA> New York  <NA>  1

如您所见，Month == "Jan" 的第 2 行被过滤掉了