如何合并R中的两列？答案

【问题标题】：How to merge two columns in R?如何合并R中的两列？
【发布时间】：2021-07-19 06:19:44
【问题描述】：

newdf=data.frame(id=c(1,3,2),admission=c("2020-05-18","2020-04-30","2020-05-08"),
                 vent=c("mechanical_vent","self_vent","mechanical_vent"))

newdf$admission=as.Date(newdf$admission)


newdf1=data.frame(id=c(1,3,1,2,1,3,2,2),
                 date=c("2020-05-19","2020-05-02","2020-05-20","2020-05-09","2020-05-21","2020-05-04","2020-05-10","2020-05-11"),
                 vent=c("self_vent","mechanical_vent","mechanical_vent","mechanical_vent","self_vent","mechanical_vent","mechanical_vent","self_vent"))

newdf1$date=as.Date(newdf1$date)

newdf=newdf %>% group_by(id) %>% bind_rows(newdf,newdf1)

newdf$dates=paste(newdf$admission,newdf$date)

我想将录取和日期列合并为日期。我使用了粘贴功能，但它提供了带有 NA 值的输出。我附上了数据集的图像。您能否提出解决此问题的方法？

【问题讨论】：

最终的df应该是什么样子的？
请看图。我想要它，但我想从日期列中删除 NA 。
去掉 NA 是什么意思？删除日期为 NA 的行？用不同的值替换 NA？
您看到“日期”列中的 NA 部分了吗？所以我需要删除那些东西。

标签： r merge

【解决方案1】：

如果您想将日期从admission转移到date，其中date是NA，这将起作用：

newdf %>%
  mutate(across(c(admission, date), ~ as.character(.))) %>%
  mutate(date = ifelse(is.na(date), admission, date))

【讨论】：

【解决方案2】：

我们可以使用pmax:

newdf$dates <- pmax(newdf$admission, newdf$date, na.rm = TRUE)

输出：

      id admission  vent            date       dates     
   <dbl> <date>     <chr>           <date>     <date>    
 1     1 2020-05-18 mechanical_vent NA         2020-05-18
 2     3 2020-04-30 self_vent       NA         2020-04-30
 3     2 2020-05-08 mechanical_vent NA         2020-05-08
 4     1 2020-05-18 mechanical_vent NA         2020-05-18
 5     3 2020-04-30 self_vent       NA         2020-04-30
 6     2 2020-05-08 mechanical_vent NA         2020-05-08
 7     1 NA         self_vent       2020-05-19 2020-05-19
 8     3 NA         mechanical_vent 2020-05-02 2020-05-02
 9     1 NA         mechanical_vent 2020-05-20 2020-05-20
10     2 NA         mechanical_vent 2020-05-09 2020-05-09
11     1 NA         self_vent       2020-05-21 2020-05-21
12     3 NA         mechanical_vent 2020-05-04 2020-05-04
13     2 NA         mechanical_vent 2020-05-10 2020-05-10
14     2 NA         self_vent       2020-05-11 2020-05-11

【讨论】：

【解决方案3】：

你可以使用coalesce -

library(dplyr)

newdf %>% ungroup %>% mutate(dates = coalesce(admission, date))

#      id admission  vent            date       dates     
#   <dbl> <date>     <chr>           <date>     <date>    
# 1     1 2020-05-18 mechanical_vent NA         2020-05-18
# 2     3 2020-04-30 self_vent       NA         2020-04-30
# 3     2 2020-05-08 mechanical_vent NA         2020-05-08
# 4     1 2020-05-18 mechanical_vent NA         2020-05-18
# 5     3 2020-04-30 self_vent       NA         2020-04-30
# 6     2 2020-05-08 mechanical_vent NA         2020-05-08
# 7     1 NA         self_vent       2020-05-19 2020-05-19
# 8     3 NA         mechanical_vent 2020-05-02 2020-05-02
# 9     1 NA         mechanical_vent 2020-05-20 2020-05-20
#10     2 NA         mechanical_vent 2020-05-09 2020-05-09
#11     1 NA         self_vent       2020-05-21 2020-05-21
#12     3 NA         mechanical_vent 2020-05-04 2020-05-04
#13     2 NA         mechanical_vent 2020-05-10 2020-05-10
#14     2 NA         self_vent       2020-05-11 2020-05-11

【讨论】：