r - 总结后如何考虑组内更大的日期

Question

我通过分组取平均值，3×3。为此，我使用 summarise 函数。在这种情况下，我想从构成平均值的四个日期中选择最后一个日期。

我尝试选择最大值，但这样我只是选择整个组的最高日期。

test = data.frame(my_groups = c("A", "A", "A", "B", "B", "C", "C", "C",  "A", "A", "A"),
                  measure = c(10, 20, 5, 2, 62 ,2, 5, 4, 6, 7, 25),
                  time= c("20-09-2020", "25-09-2020", "19-09-2020", "20-05-2020", "20-06-2021", 
                                      "11-01-2021", "13-01-2021", "13-01-2021", "15-01-2021", "15-01-2021", "19-01-2021"))
# > test
#    my_groups measure       time
# 1          A      10 20-09-2020
# 2          A      20 25-09-2020
# 3          A       5 19-09-2020
# 4          B       2 20-05-2020
# 5          B      62 20-06-2021
# 6          C       2 11-01-2021
# 7          C       5 13-01-2021
# 8          C       4 13-01-2021
# 9          A       6 15-01-2021
# 10         A       7 15-01-2021
# 11         A      25 19-01-2021

test %>%
  arrange(time) %>%
  group_by(my_groups) %>%
  summarise(mean_3 = rollapply(measure, 3, mean, by = 3, align = "left", partial = F),
            final_data = max(time))

# my_groups mean_3 final_data
#   <chr>       <dbl> <chr>     
# 1 A           12.7  25-09-2020
# 2 A           11.7  25-09-2020
# 3 C           3.67 13-01-2021

在第二行中，我希望日期是 19-01-2021，而不是组 A 的全局最大值，(25-09-2020 )。

有什么关于我如何做到这一点的提示吗？

Answer 1

我有 2 种 dplyr 方法给你。对此不满意，因为当带有 max 和日期的 rollapply 没有在 B 组中找到任何内容时，它默认使用与组中的字符不匹配的 double 值A 和 C。

变异:

test %>%
  arrange(time) %>%
  group_by(my_groups) %>% 
  mutate(final = rollapply(time, 3, max, by = 3, fill = NA, align = "left", partial = F),
         mean_3 = rollapply(measure, 3, mean, by = 3, fill = NA, align = "left", partial = F)) %>% 
  filter(!is.na(final)) %>% 
  select(my_groups, final, mean_3) %>% 
  arrange(my_groups)

# A tibble: 3 x 3
# Groups:   my_groups [2]
  my_groups final      mean_3
  <chr>     <chr>       <dbl>
1 A         19-01-2021  12.7 
2 A         25-09-2020  11.7 
3 C         13-01-2021   3.67

总结没有总结，但代码更清晰:

test %>%
  arrange(time) %>%
  group_by(my_groups) %>% 
  summarise(final = rollapply(time, 3, max, by = 3, fill = NA, align = "left", partial = F),
         mean_3 = rollapply(measure, 3, mean, by = 3, fill = NA, align = "left", partial = F)) %>% 
  filter(!is.na(final))

`summarise()` has grouped output by 'my_groups'. You can override using the `.groups` argument.
# A tibble: 3 x 3
# Groups:   my_groups [2]
  my_groups final      mean_3
  <chr>     <chr>       <dbl>
1 A         19-01-2021  12.7 
2 A         25-09-2020  11.7 
3 C         13-01-2021   3.67

编辑:

从评论中添加了isa的解决方案。 Partial = TRUE 可以做到这一点:

test %>% 
  arrange(time) %>% 
  group_by(my_groups) %>% 
  summarise(mean_3 = rollapply(measure, 3, mean, by = 3, align = "left", partial = F), 
            final_data = rollapply(time, 3, max, by = 3, align = "left", partial = T))

`summarise()` has grouped output by 'my_groups'. You can override using the `.groups` argument.
# A tibble: 3 x 3
# Groups:   my_groups [2]
  my_groups mean_3 final_data
  <chr>      <dbl> <chr>     
1 A          12.7  19-01-2021
2 A          11.7  25-09-2020
3 C           3.67 13-01-2021