你的数据很混乱，所以你可能想改变它的结构，但是这里有一个方法来恢复你的最终输出。注意它需要dplyr 1.1.0，但是你可以用group_by函数改变.by参数。
第一组mutate只是为了获得一个tidy er Dataframe 。您可以检查here我所说的整洁是什么意思，但这对pivot_wider来说更容易理解。然后，我为每组每行创建一个id，然后为数据创建一个pivot。最后，我删除了所有只包含NA的列（无有用信息），并将每组数据总结在一行中：

library(tidyr)
library(dplyr)
A %>% 
  mutate(EVENT = ifelse(REC == "YES", "REC", "REOP"),
         TYPE = ifelse(if_all(TYPE1:TYPE4, is.na), NA, max.col(across(TYPE1:TYPE4))),
         DATE = REOP_DATE,
         .keep = "unused") %>% 
  mutate(row_n = row_number(), .by = c(ID, EVENT)) %>% 
  pivot_wider(names_from = c("EVENT", "row_n"), values_from = c("DATE", "TYPE"),
              names_glue = "{EVENT}{row_n}_{.value}") %>%
  select(where(~ any(complete.cases(.x)))) %>% 
  summarise(across(everything(), ~ ifelse(all(is.na(.x)), NA, max(.x, na.rm = TRUE))), .by = ID)

# A tibble: 3 × 9
     ID    NR INDX.DATE  REC1_DATE  REOP1_DATE REC2_DATE  REOP2_DATE REOP1_TYPE REOP2_TYPE
  <int> <int> <chr>      <chr>      <chr>      <chr>      <chr>           <int>      <int>
1     1  1234 2012-11-04 2013-12-31 2015-01-01 2015-02-02 2016-01-01          3          1
2     2    NA NA         NA         NA         NA         NA                 NA         NA
3     3    NA NA         NA         NA         NA         NA                 NA         NA

为了解决这个问题，我需要掌握我所有的知识。这个解决方案冗长而不是很优雅：

library(dplyr)
library(tidyr)
library(stringr)
library(glue)

# function to coalesce rowwise
coalesce_by_column <- function(df) {
  return(coalesce(df[1], df[2]))
}

A %>% 
  mutate(REC = ifelse(REC=="YES", REOP_DATE, REC),
         REOP_DATE = ifelse(REC == "NO", REOP_DATE, NA_character_),
         REC = ifelse(REC == "NO", NA_character_, REC)) %>% 
  mutate(across(TYPE1:TYPE4, ~case_when(. == 1 ~ cur_column()), .names = 'new_{col}')) %>%
  unite(REOP_TYPE, starts_with('new'), na.rm = TRUE, sep = ' ') %>% 
  mutate(REOP_TYPE = na_if(REOP_TYPE, "")) %>% 
  pivot_longer(c(REOP_DATE, REC, REOP_TYPE)) %>% 
  arrange(name) %>% 
  group_by(name) %>% 
  mutate(x = cumsum(!is.na(value))) %>% 
  arrange(ID) %>% 
  ungroup() %>% 
  mutate(Col1 = rep(row_number(), each=2, length.out = n())) %>% 
  group_by(Col1) %>% 
  arrange(value, .by_group = TRUE) %>% 
  slice(1) %>% 
  mutate(name = ifelse(name=="REC", paste0(name,x,"_DATE"), str_replace(name, "OP", glue::glue("OP{x}")))) %>%
  ungroup() %>% 
  dplyr::select(-starts_with("TYPE"),-x, -Col1) %>% 
  pivot_wider(names_from = name, values_from = value) %>% 
  group_by(ID) %>% 
  summarise_all(coalesce_by_column)

ID    NR INDX.DATE  REC1_DATE  REC2_DATE  REOP1_DATE REOP2_DATE REOP1_TYPE REOP2_TYPE
  <int> <int> <chr>      <chr>      <chr>      <chr>      <chr>      <chr>      <chr>     
1     1  1234 2012-11-04 2013-12-31 2015-02-02 2015-01-01 2016-01-01 TYPE3      TYPE1     
2     2    NA NA         NA         NA         NA         NA         NA         NA        
3     3    NA NA         NA         NA         NA         NA         NA         NA