R 取消安装数据帧并用NAs填充新行

R 取消安装数据帧并用NAs填充新行,r,dataframe,tidyr,unnest,R,Dataframe,Tidyr,Unnest,假设我有一个嵌套的df,我想取消列的嵌套: df一种方法是将副本更改为NA df1 <- tidyr::unnest(df, nestdf) cols <- c('x', 'y') df1[duplicated(df1[cols]), cols] <- NA df1 # x y a b # <dbl> <dbl> <int> <int> #1 1 2 1 3

假设我有一个嵌套的df,我想取消列的嵌套:


df一种方法是将副本更改为
NA

df1 <- tidyr::unnest(df, nestdf) 
cols <- c('x', 'y')
df1[duplicated(df1[cols]), cols] <- NA
df1

#      x     y     a     b
#  <dbl> <dbl> <int> <int>
#1     1     2     1     3
#2    NA    NA     2     4
#3     3     4     3     5
#4    NA    NA     4     6
#5    NA    NA     5     7

重复行,并与嵌套列表列的
unnest
绑定:


nr您可以先将
x
y
转换为列表:

library(tidyverse)

df <- tibble::tribble(
  ~x, ~y, ~nestdf,
  1,  2,  tibble::tibble(a=1:2, b=3:4),
  3,  4,  tibble::tibble(a=3:5, b=5:7)
)

df %>%
  mutate_at(vars(x:y), ~map2(., nestdf, ~.x[seq(nrow(.y))])) %>%
  unnest(everything())

# A tibble: 5 x 4
#x     y     a     b
#<dbl> <dbl> <int> <int>
#  1     1     2     1     3
#2    NA    NA     2     4
#3     3     4     3     5
#4    NA    NA     4     6
#5    NA    NA     5     7
库(tidyverse)
df%
在(vars(x:y),~map2(,nestdf,~.x[seq(nrow(.y))))%%>处突变
unnest(所有内容())
#一个tibble:5x4
#x y a b
#   
#  1     1     2     1     3
#2钠2 4
#3     3     4     3     5
#4 NA NA 4 6
#5 NA 5 7

您对可能的非最新答案感兴趣吗?这很好,我已经解决了这个问题,这样在没有任何行的情况下它就可以工作了duplicated@HongOoi-谢谢-我欠你一杯啤酒。哎呀,我把它弄坏了,等等on@HongOoi-我想我已经修好了
library(dplyr)
library(tidyr)

df1 <- df %>% mutate(row = row_number()) %>% unnest(nestdf)
cols <- c('x', 'y', 'row')
df1[duplicated(df1[cols]), cols] <- NA
df1 <- select(df1, -row)
nr <- sapply(df$nestdf, nrow) - 1
cbind(
  df[rep(rbind(seq_along(nr), NA), rbind(1, nr)), c("x","y")],
  unnest(df["nestdf"], cols=everything())
)

#   x  y a b
#1  1  2 1 3
#2 NA NA 2 4
#3  3  4 3 5
#4 NA NA 4 6
#5 NA NA 5 7
library(tidyverse)

df <- tibble::tribble(
  ~x, ~y, ~nestdf,
  1,  2,  tibble::tibble(a=1:2, b=3:4),
  3,  4,  tibble::tibble(a=3:5, b=5:7)
)

df %>%
  mutate_at(vars(x:y), ~map2(., nestdf, ~.x[seq(nrow(.y))])) %>%
  unnest(everything())

# A tibble: 5 x 4
#x     y     a     b
#<dbl> <dbl> <int> <int>
#  1     1     2     1     3
#2    NA    NA     2     4
#3     3     4     3     5
#4    NA    NA     4     6
#5    NA    NA     5     7