Question

我希望我已经正确设置了问题的格式，因为这是我第一次发帖，对R来说还很陌生。

下面是我目前正在使用的一些运动员运动数据的小样本。

```
      Player   Period      Dist    Date          Type
122 Player_2  Session 4245.9002 31/7/18 Main Training
123 Player_1  Session 4868.2153  2/8/18 Main Training
124 Player_2  Session 4515.1996  2/8/18 Main Training
125 Player_2  Session 3215.8634  7/8/18 Main Training
126 Player_2 Modified  551.8737  7/8/18 Main Training
127 Player_2  Session 4264.7384  9/8/18 Main Training
128 Player_1  Session 4038.1687 16/8/18 Main Training
129 Player_2  Session 4751.6978 16/8/18 Main Training
130 Player_1      RTP 4038.1687 16/8/18 Main Training
131 Player_2 Modified  229.6872 16/8/18 Main Training
132 Player_2 Modified  342.2797 16/8/18 Main Training
133 Player_1  Session 3573.4509 23/8/18 Main Training
134 Player_2  Session 3717.3467 23/8/18 Main Training
reprex()
#> Error: <text>:1:16: unexpected symbol
#> 1:       Player   Period
#>                    ^
```

我想使用dplyr根据多个条件删除数据框的行。具体来说，我想删除包含Session的行，其中有Modified或RTP共享相同的Date。例如，当Player_2完成对Modified的{{1}}培训时，我希望删除该日期的7/8/2018数据。

Session

与``` Player Period Dist Date Type 125 Player_2 Session 3215.8634 7/8/18 Main Training 126 Player_2 Modified 551.8737 7/8/18 Main Training #> Error: <text>:1:16: unexpected symbol #> 1: Player Period #> ^ ```类似，其中16/8/2018和Player_1在当天分别完成了Player_2和Modified培训。

RTP

我过去曾经使用这样的代码过滤数据。

```
      Player   Period      Dist    Date          Type
128 Player_1  Session 4038.1687 16/8/18 Main Training
129 Player_2  Session 4751.6978 16/8/18 Main Training
130 Player_1      RTP 4038.1687 16/8/18 Main Training
131 Player_2 Modified  229.6872 16/8/18 Main Training
132 Player_2 Modified  342.2797 16/8/18 Main Training
#> Error: <text>:1:16: unexpected symbol
#> 1:       Player   Period
#>                    ^
```

但是，我希望删除包含``` db18 <- db18 %>% filter(Period %in% c("Session")) ```或Session的运动员Modified数据，因为它不会“污染”我要执行的分析。想知道如果可能的话我该怎么做。

任何帮助将不胜感激。谢谢。

Answer 1

希望这对您有所帮助。

player <- read.csv("player.csv")
player

player
    Id   Player   Period      Dist     Date          Type
1  122 Player_2  Session 4245.9002 31/07/18 Main Training
2  123 Player_1  Session 4868.2153 02/08/18 Main Training
3  124 Player_2  Session 4515.1996 02/08/18 Main Training
4  125 Player_2  Session 3215.8634 07/08/18 Main Training
5  126 Player_2 Modified  551.8737 07/08/18 Main Training
6  127 Player_2  Session 4264.7384 09/08/18 Main Training
7  128 Player_1  Session 4038.1687 16/08/18 Main Training
8  129 Player_2  Session 4751.6978 16/08/18 Main Training
9  130 Player_1      RTP 4038.1687 16/08/18 Main Training
10 131 Player_2 Modified  229.6872 16/08/18 Main Training
11 132 Player_2 Modified  342.2797 16/08/18 Main Training
12 133 Player_1  Session 3573.4509 23/08/18 Main Training
13 134 Player_2  Session 3717.3467 23/08/18 Main Training

按Player和Date列分组。然后，如果特定的Date具有 Modified(or)RTP和Session。

library(dplyr)
removable <- player %>%  group_by_(.dots = c("Player", "Date")) %>% 
  filter( (sum(Period == 'Session') >= 1) & ((sum(Period == 'Modified') != 0) | (sum(Period == 'RTP') != 0 ))) %>%
  filter(Period == 'Session')

如果有任何player与player$Id匹配，现在从removable$Id数据框中删除行

player <- player[!(player$Id  %in% removable$Id), ]
player

    Id   Player   Period      Dist     Date          Type
1  122 Player_2  Session 4245.9002 31/07/18 Main Training
2  123 Player_1  Session 4868.2153 02/08/18 Main Training
3  124 Player_2  Session 4515.1996 02/08/18 Main Training
5  126 Player_2 Modified  551.8737 07/08/18 Main Training
6  127 Player_2  Session 4264.7384 09/08/18 Main Training
9  130 Player_1      RTP 4038.1687 16/08/18 Main Training
10 131 Player_2 Modified  229.6872 16/08/18 Main Training
11 132 Player_2 Modified  342.2797 16/08/18 Main Training
12 133 Player_1  Session 3573.4509 23/08/18 Main Training
13 134 Player_2  Session 3717.3467 23/08/18 Main Training

Answer 2

一种方法是先使用group_by()函数，然后将过滤器应用于组。在下面的代码中，我使用了group_by()和mutate()来创建要过滤的新列。可能会有更优雅的解决方案，但这可能会让您入门。

df <- tibble::tribble(
~Player,   ~Period,      ~Dist,    ~Date,       
'Player_2',  'Session', 4245.9002, '31/7/18',
'Player_1',  'Session', 4868.2153, '2/8/18',
'Player_2',  'Session', 4515.1996,  '2/8/18',
'Player_2',  'Session', 3215.8634,  '7/8/18',
'Player_2', 'Modified',  551.8737,  '7/8/18',
'Player_2',  'Session', 4264.7384,  '9/8/18',
'Player_1',  'Session', 4038.1687, '16/8/18',
'Player_2',  'Session', 4751.6978, '16/8/18',
'Player_1',      'RTP', 4038.1687, '16/8/18',
'Player_2', 'Modified',  229.6872, '16/8/18',
'Player_2', 'Modified',  342.2797, '16/8/18',
'Player_1',  'Session', 3573.4509, '23/8/18',
'Player_2',  'Session', 3717.3467, '23/8/18'
)

df %>%
  group_by(Player, Date) %>%
  mutate(filter_col = ifelse(all(c('Session','Modified') %in% Period), 'delete', 'keep'),
         filter_col = ifelse(all(c('Session','RTP') %in% Period), 'delete', filter_col)) %>%
  ungroup() %>%
  filter(filter_col == 'keep')

根据R中的多个条件删除数据框中的行

2 个答案: