如何将百分位数（/分位数）值添加到数据框中的列

Question

我的数据集包含从 2009 年到 2021 年每天河流的流量测量值。它分为季节：冬季（十二月、一月、二月）、春季（三月、四月、五月）、夏季（六月、四月、五月）。七月、八月）和秋季（九月、十月、十一月）。

这是我的数据集的示例：

> (chitt_brook_wylye_2)
# A tibble: 4,437 x 7
   river           year season month    date                flow_rate quality
   <chr>          <dbl> <chr>  <chr>    <dttm>                  <dbl> <chr>  
 1 chittern_brook  2009 Winter December 2009-12-01 00:00:00     0.059 Good   
 2 chittern_brook  2009 Winter December 2009-12-02 00:00:00     0.061 Good   
 3 chittern_brook  2009 Winter December 2009-12-03 00:00:00     0.064 Good   
 4 chittern_brook  2009 Winter December 2009-12-04 00:00:00     0.068 Good   
 5 chittern_brook  2009 Winter December 2009-12-05 00:00:00     0.076 Good   
 6 chittern_brook  2009 Winter December 2009-12-06 00:00:00     0.138 Good   
 7 chittern_brook  2009 Winter December 2009-12-07 00:00:00     0.592 Good   
 8 chittern_brook  2009 Winter December 2009-12-08 00:00:00     1.04  Good   
 9 chittern_brook  2009 Winter December 2009-12-09 00:00:00     1.46  Good   
10 chittern_brook  2009 Winter December 2009-12-10 00:00:00     1.7   Good   
# ... with 4,427 more rows

我想找到每年每个季节的第 95 个百分位数、第 5 个百分位数、中位数和平均值，并分别获取第 95 个百分位数、中位数和平均值新数据框中的列。

例如：

> (df)
# A tibble: 49 x 2
   season_label flow_rate_mean  Q95   Q5  flow_rate_median   
   <chr>            <dbl>
 1 Winter 2009     0.453        3     2        4
 2 Spring 2010     0.519        6     3        4 
 3 Summer 2010     0.0627       4     3        6
 4 Autumn 2010     0.0415       6     2        6
 5 Winter 2010     0.0622       8     3        3
 6 Spring 2011     0.188        10    3        2
 7 Summer 2011     0.0499       2     3        2
 8 Autumn 2011     0.0383       2     2        1
 9 Winter 2011     0.0461       5     2        7
10 Spring 2012     0.0925       3     2        8
# ... with 39 more rows

我目前有这个代码，它仅使用前两列创建上述数据框，但我希望它还包括第 95 个百分位数、第 5 个百分位数和中位数。这是可行的还是我需要单独完成然后将其合并到一个数据帧中？

df <- chitt_brook_wylye_2 %>% 
  dplyr::mutate(month = as.numeric(format(date,"%m")),
                year = as.numeric(format(date,"%Y")),
                season_id =  (12*year + month) %/% 3) %>% 
  dplyr::group_by(season_id) %>%
  dplyr::mutate(season_label = paste(season, min(year))) %>%  
  dplyr::group_by(season_id,season_label) %>%
  dplyr::summarise(flow_rate = mean(flow_rate))

可重现的示例和代码：

date <- as.Date(c("2009-12-01","2010-01-01","2010-02-01","2010-03-01","2010-04-01","2010-05-01","2010-06-01","2010-07-01","2010-08-01","2010-09-01","2010-10-01","2010-11-01","2010-12-01"))
        season <- c("Winter","Winter","Winter","Spring","Spring","Spring","Summer","Summer","Summer","Autumn","Autumn","Autumn","Winter")
        var <- c(1,2,3,5,5,5,7,7,7,9,9,9,10)

df <- data.frame(date,season,var) %>% # creating the dataframe
  dplyr::mutate(month = as.numeric(format(date,"%m")),
                year = as.numeric(format(date,"%Y")),
                season_id =  (12*year + month) %/% 3) %>% #generating an identifiant for every season that exists in the data
  dplyr::group_by(season_id) %>% # Grouping by the id
  dplyr::mutate(season_label = paste(min(year),season)) %>% 
  dplyr::group_by(season_id,season_label) %>% ## season_label to keep the newly created label after the arriving summarise
  dplyr::summarise(var = mean(var)) # Computing the mean