R 条件应用对所有记录都不起作用

R conditional apply does not work equally for all records

我有以下数据框。 Change_Month 表示 Adv 的所有值都属于 N 类别之前的月份,并且从该月份开始属于 V 类别。

Adv_Code Change_Month Change_Dt     April   May June    July    August  September   October November    December    January     February    March
A201    April           7/4/2017    0       0   0       1       0       0           0       0           0           0           0           0
A198    April           7/4/2017    1       1   0       0       3       0           0       0           0           0           0           0
S1212   May             16/04/2017  0       0   0       0       0       0           0       0           0           0           0           1
S1213   January         4/1/2018    1       0   0       1       1       1           1       0           1           1           2           1

因此对于 A201,所有值都属于 V 类别。 A198 同样如此。 对于 S1212,4 月的值将归入 N 类别并保持在 V 类别。 同样,对于 S1213,4 月至 12 月将归入 N 类别,1 月至 3 月将归入 V 类别。

因此,预期输出为:

Adv_Code Change_Month Change_Dt     April   May June    July    August  September   October November    December    January     February    March N_OPN V_OPN
A201    April           7/4/2017    0       0   0       1       0       0           0       0           0           0           0           0       0   1
A198    April           7/4/2017    1       1   0       0       3       0           0       0           0           0           0           0       0   5
S1212   May             16/04/2017  0       0   0       0       0       0           0       0           0           0           0           1       0   1
S1213   January         4/1/2018    1       0   0       1       1       1           1       0           1           1           2           1       6   4

我尝试使用以下代码:

col <- 4 #Column number from where the months start

df[c("N_OPN", "V_OPN")] <- t(apply(df, 1, function(x) {
  inds <- grep(x[["Change_Month"]], names(x))
  if (as.numeric(substr(x["Change_Dt"], 1, 2)) > 15)
    c(sum(as.numeric(x[col:pmax(col, inds - 1)])), 
      sum(as.numeric(x[inds:ncol(df)])))
  else
    c(sum(as.numeric(x[col:inds])), 
      sum(as.numeric(x[pmin(ncol(df), inds + 1):ncol(df)])))
}))

然而这给出了:

Adv_Code Change_Month Change_Dt     April   May June    July    August  September   October November    December    January     February    March N_OPN V_OPN
A201    April           7/4/2017    0       0   0       1       0       0           0       0           0           0           0           0       0   1
A198    April           7/4/2017    1       1   0       0       3       0           0       0           0           0           0           0       1   5
S1212   May             16/04/2017  0       0   0       0       0       0           0       0           0           0           0           1       0   1
S1213   January         4/1/2018    1       0   0       1       1       1           1       0           1           1           2           1       7   3

我不确定为什么会这样。 有人可以帮我解决这个问题吗?

下面是重现数据帧的代码:

df <- structure(list(Adv_Code = structure(c(2L, 1L, 3L,4L), .Label = c("A198","A201", "S1212","S1213"), class = "factor"),
                        Change_Dt = structure(c(2L,3L, 1L,1L), .Label = c("07/04/2017", "07/04/2017", "16/04/2017","4/1/2018"), class = "factor"), 
                        Change_Month = structure(1:4, .Label = c("April", "April","May","January"), class = "factor"), April = c(0L, 1L, 0L,1L), 
                        May = c(0L, 1L, 0L,0L), June = c(0L, 0L, 0L,0L), July = c(1L, 0L,0L,1L),
                        August = c(0L, 3L, 0L,1L), September = c(0L,0L, 0L,1L), October = c(0L, 0L, 0L,1L), November = c(0L,0L, 0L,0L), 
                        December = c(0L, 0L, 0L,1L), January = c(0L,0L, 0L,1L), February = c(0L, 0L, 0L,2L), March = c(0L,0L, 1L,1L)), class = "data.frame", row.names = c(NA, -4L))

带有 for 循环的选项是

df1 <- df[4:ncol(df)]
j1 <- match(df$Change_Month, names(df1))
N_OPN <- numeric(nrow(df1))
V_OPN <- numeric(nrow(df1))
for(i in seq_len(nrow(df1))) {
        j2 <- j1[i] -1
        N_OPN[i] <- if(j2 == 0) 0 else sum(df1[i, seq_len(j2)])
        V_OPN[i] <- sum(df1[i, (j1[i]:ncol(df1))])
  }

df[c("N_OPN", "V_OPN")] <- list(N_OPN, V_OPN)
df
#  Adv_Code  Change_Dt Change_Month April May June July August September October November December January
#1     A201 07/04/2017        April     0   0    0    1      0         0       0        0        0       0
#2     A198 16/04/2017        April     1   1    0    0      3         0       0        0        0       0
#3    S1212 07/04/2017          May     0   0    0    0      0         0       0        0        0       0
#4    S1213 07/04/2017      January     1   0    0    1      1         1       1        0        1       1
#  February March N_OPN V_OPN
#1        0     0     0     1
#2        0     0     0     5
#3        0     1     0     1
#4        2     1     6     4