使用 data.table 在每组数据后插入一行 NA

Insert a row of NAs after each group of data using data.table

我想在R中的每组数据后添加一行NA。

之前有人问过类似的问题。 Insert a blank row after each group of data.

接受的答案在这种情况下也适用,如下所示。

group <- c("a","b","b","c","c","c","d","d","d","d")
xvalue <- c(16:25)
yvalue <- c(1:10)
df <- data.frame(cbind(group,xvalue,yvalue))
df_new <- as.data.frame(lapply(df, as.character), stringsAsFactors = FALSE)
head(do.call(rbind, by(df_new, df$group, rbind, NA)), -1 )
     group xvalue yvalue
a.1      a     16      1
a.2   <NA>   <NA>   <NA>
b.2      b     17      2
b.3      b     18      3
b.31  <NA>   <NA>   <NA>
c.4      c     19      4
c.5      c     20      5
c.6      c     21      6
c.41  <NA>   <NA>   <NA>
d.7      d     22      7
d.8      d     23      8
d.9      d     24      9
d.10     d     25     10

我怎样才能使用 data.table 来加快速度 data.frame?

你可以试试

df$group <- as.character(df$group)
setDT(df)[, .SD[1:(.N+1)], by=group][is.na(xvalue), group:=NA][!.N]
#     group xvalue yvalue
#1:     a     16      1
#2:    NA     NA     NA
#3:     b     17      2
#4:     b     18      3
#5:    NA     NA     NA
#6:     c     19      4
#7:     c     20      5
#8:     c     21      6
#9:    NA     NA     NA
#10:    d     22      7
#11:    d     23      8
#12:    d     24      9
#13:    d     25     10

或者按照@David Arenburg 的建议

 setDT(df)[, indx := group][, .SD[1:(.N+1)], indx][,indx := NULL][!.N]

或者

 setDT(df)[df[,.I[1:(.N+1)], group]$V1][!.N]

或者可以根据@eddi 的评论进一步简化

 setDT(df)[df[, c(.I, NA), group]$V1][!.N]

我能想到的一种方法是先构造一个向量,如下所示:

foo <- function(x) {
    o = order(rep.int(seq_along(x), 2L))
    c(x, rep.int(NA, length(x)))[o]
}
join_values = head(foo(unique(df_new$group)), -1L)
# [1] "a" NA  "b" NA  "c" NA  "d"

然后是 setkey()join

setkey(setDT(df_new), group)
df_new[.(join_values), allow.cartesian=TRUE]
#     group xvalue yvalue
#  1:     a     16      1
#  2:    NA     NA     NA
#  3:     b     17      2
#  4:     b     18      3
#  5:    NA     NA     NA
#  6:     c     19      4
#  7:     c     20      5
#  8:     c     21      6
#  9:    NA     NA     NA
# 10:     d     22      7
# 11:     d     23      8
# 12:     d     24      9
# 13:     d     25     10