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r - 当`dplyr`中的所有列都是NA时过滤数据框

问题描述

这肯定是一个简单的问题（如果有人知道答案），但我仍然找不到关于 SO 的指导：我有一个数据框，其中有很多行，这些行只包含NA所有列（在lead操作之后）。我想删除这些行：

df <- structure(list(line = c("0001", NA, "0002", NA, "0003", NA, "0004", 
                              NA, "0005", NA), 
                     speaker = c(NA, NA, "ID16.C-U", NA, NA, NA, "ID16.B-U", NA, NA, NA), 
                     utterance = c("7.060", NA, "  ah-ha,", NA, "0.304", NA, "  °°yes°°", NA, "7.740", NA), 
                     timestamp = c(NA, "00:00:00.000 - 00:00:07.060", NA, "00:00:07.060 - 00:00:07.660", NA, 
                                   "00:00:07.660 - 00:00:07.964", NA, "00:00:07.964 - 00:00:08.610", NA, 
                                   "00:00:08.610 - 00:00:16.350")), row.names = c(NA, 10L), class = "data.frame")

但这都不是：

df %>%
  mutate(timestamp = lead(timestamp)) %>%
  filter(across(everything(), ~!is.na(.)))

这也不起作用：

df %>%
  mutate(timestamp = lead(timestamp)) %>%
  rowwise() %>%
  filter(c_across(everything(), ~!is.na(.)))

解决方案是什么？

预期：

  line  speaker utterance                   timestamp
1 0001     <NA>     7.060 00:00:00.000 - 00:00:07.060
3 0002 ID16.C-U    ah-ha, 00:00:07.060 - 00:00:07.660
5 0003     <NA>     0.304 00:00:07.660 - 00:00:07.964
7 0004 ID16.B-U   °°yes°° 00:00:07.964 - 00:00:08.610
9 0005     <NA>     7.740 00:00:08.610 - 00:00:16.350

标签： rdplyr

解决方案

这行得通吗？

df <- df %>% mutate(timestamp = lead(timestamp))
df[rowSums(is.na(df))!=ncol(df),]

伪tidyverse版本：

df %>% 
  dplyr::mutate(timestamp = dplyr::lead(timestamp)) %>% 
  dplyr::filter(rowSums(is.na(.))!=ncol(.))

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