根据R中的模式删除观察值 更新

如何解决根据R中的模式删除观察值 更新

我有一个观察足球受伤情况的数据框。不幸的是，每种伤势我都有几支球队可供选择。这是数据框的一部分：

df_x = data.frame(injury_id=c(250,250,100,328,329,330,15,5106,5106),player_id=c(109,109,39728,2374,26,59016,59016),season=c(2011,2011,2010,2012,2012),inury_from=c("2011-09-13","2011-09-13","2011-03-03","2011-04-21","2010-11-23","2010-10-01","2011-02-24","2012-09-16","2012-09-16"),injury_until=c("2011-09-27","2011-09-27","2011-03-17","2011-08-31","2011-03-14","2010-11-22","2011-02-28","2012-10-28","2012-10-28"),team_id=c(1,2,3,4,5,6,7,8,9),member_since=c("1998-07-01",NA,"2009-07-01","2008-07-01","2002-07-01","2012-07-01","2013-01-01","2011-07-01"))

我的目标是每个伤病编号仅一行。结果应显示以下数据框：

df_result_x = data.frame(injury_id=c(250,7),"2012-07-01"))

为具有多个伤亡编号的观察选择的算法：

• 删除在member_since上具有NA的行。
• 删除所有member_since晚于harmony_until的行。
• 如果保留重复的观测值，请在member_since中选择日期较晚的观测值。

我可以通过管道执行此操作还是必须使用循环？

谢谢。

更新11-10-2020：

df_x2 = data.frame(injury_id=c(250,9,"2011-07-01","2012-12-31"))

解决方法

按'injury_id'分组后，我们可以使用slice

library(dplyr)
df_x %>%
    group_by(injury_id) %>%
    slice(1) %>% 
    ungroup 

或带有distinct

df_x %>%
      distinct(injury_id,.keep_all = TRUE)

或者，如果NA元素的顺序不正确，请在'injury_id'上执行arrange，然后在'member_since'中基于NA元素的逻辑矢量进行处理（这样NA会是最后一个）和Date转换后的“ member_since”，然后使用distinct根据“ injury_id”列选择第一个唯一行

df_x %>%
    arrange(injury_id,is.na(member_since),as.Date(member_since)) %>%
    distinct(injury_id,.keep_all = TRUE)

更新

基于评论

df_x %>%
    filter(!is.na(member_since)) %>%
    mutate(injury_until = as.Date(injury_until),member_since = as.Date(member_since)) %>% 
    mutate(ind = injury_until - member_since) %>% 
    group_by(injury_id)  %>%
    filter(ind == min(ind[ind > 0])) %>%
    select(-ind)

-输出

# A tibble: 7 x 7
# Groups:   injury_id [7]
#  injury_id player_id season inury_from injury_until team_id member_since
#      <dbl>     <dbl>  <dbl> <chr>      <date>         <dbl> <date>      
#1       250       109   2011 2011-09-13 2011-09-27         1 1998-07-01  
#2       100     39728   2010 2011-03-03 2011-03-17         3 2009-07-01  
#3       328      2374   2010 2011-04-21 2011-08-31         4 2008-07-01  
#4       329      2374   2010 2010-11-23 2011-03-14         4 2008-07-01  
#5       330      2374   2010 2010-10-01 2010-11-22         4 2008-07-01  
#6        15        26   2010 2011-02-24 2011-02-28         6 2002-07-01  
#7      5106     59016   2012 2012-09-16 2012-10-28         7 2012-07-01