如何在dplyr分组数据上使用rollmean_R_Group By_Dplyr_Moving Average_Rolling Computation

如何在dplyr分组数据上使用rollmean

如何在dplyr分组数据上使用rollmean,r,group-by,dplyr,moving-average,rolling-computation,R,Group By,Dplyr,Moving Average,Rolling Computation,我希望我的示例数据不会太大 df <- structure(list(date = structure(c(17532, 17563, 17591, 17622, 17652, 17683, 17713, 17744, 17775, 17805, 17836, 17866, 17897, 17928, 17956, 17987, 18017, 18048, 18078, 18109, 18140, 17532, 17563, 17591, 17622, 17652, 17683, 1

我希望我的示例数据不会太大

df <- structure(list(date = structure(c(17532, 17563, 17591, 17622, 
17652, 17683, 17713, 17744, 17775, 17805, 17836, 17866, 17897, 
17928, 17956, 17987, 18017, 18048, 18078, 18109, 18140, 17532, 
17563, 17591, 17622, 17652, 17683, 17713, 17744, 17775, 17805, 
17836, 17866, 17897, 17928, 17956, 17987, 18017, 18048, 18078, 
18109, 18140, 17532, 17563, 17591, 17622, 17652, 17683, 17713, 
17744, 17775, 17805, 17836, 17866, 17897, 17928, 17956, 17987, 
18017, 18048, 18078, 18109, 18140, 17532, 17563, 17591, 17622, 
17652, 17683, 17713, 17744, 17775, 17805, 17836, 17866, 17897, 
17928, 17956, 17987, 18017, 18048, 18078, 18109, 18140, 17532, 
17563, 17591, 17622, 17652, 17683, 17713, 17744, 17775, 17805, 
17836, 17866, 17897, 17928, 17956, 17987, 18017, 18048, 18078, 
18109, 18140, 17532, 17563, 17591, 17622, 17652, 17683, 17713, 
17744, 17775, 17805, 17836, 17866, 17897, 17928, 17956, 17987, 
18017, 18048, 18078, 18109, 18140, 17532, 17563, 17591, 17622, 
17652, 17683, 17713, 17744, 17775, 17805, 17836, 17866, 17897, 
17928, 17956, 17987, 18017, 18048, 18078, 18109, 18140, 17532, 
17563, 17591, 17622, 17652, 17683, 17713, 17744, 17775, 17805, 
17836, 17866, 17897, 17928, 17956, 17987, 18017, 18048, 18078, 
18109, 18140), class = "Date"), Gender = c("Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male"), Age = c("Older", "Older", "Older", "Older", 
"Older", "Older", "Older", "Older", "Older", "Older", "Older", 
"Older", "Older", "Older", "Older", "Older", "Older", "Older", 
"Older", "Older", "Older", "Older", "Older", "Older", "Older", 
"Older", "Older", "Older", "Older", "Older", "Older", "Older", 
"Older", "Older", "Older", "Older", "Older", "Older", "Older", 
"Older", "Older", "Older", "Younger", "Younger", "Younger", "Younger", 
"Younger", "Younger", "Younger", "Younger", "Younger", "Younger", 
"Younger", "Younger", "Younger", "Younger", "Younger", "Younger", 
"Younger", "Younger", "Younger", "Younger", "Younger", "Younger", 
"Younger", "Younger", "Younger", "Younger", "Younger", "Younger", 
"Younger", "Younger", "Younger", "Younger", "Younger", "Younger", 
"Younger", "Younger", "Younger", "Younger", "Younger", "Younger", 
"Younger", "Younger", "Older", "Older", "Older", "Older", "Older", 
"Older", "Older", "Older", "Older", "Older", "Older", "Older", 
"Older", "Older", "Older", "Older", "Older", "Older", "Older", 
"Older", "Older", "Older", "Older", "Older", "Older", "Older", 
"Older", "Older", "Older", "Older", "Older", "Older", "Older", 
"Older", "Older", "Older", "Older", "Older", "Older", "Older", 
"Older", "Older", "Younger", "Younger", "Younger", "Younger", 
"Younger", "Younger", "Younger", "Younger", "Younger", "Younger", 
"Younger", "Younger", "Younger", "Younger", "Younger", "Younger", 
"Younger", "Younger", "Younger", "Younger", "Younger", "Younger", 
"Younger", "Younger", "Younger", "Younger", "Younger", "Younger", 
"Younger", "Younger", "Younger", "Younger", "Younger", "Younger", 
"Younger", "Younger", "Younger", "Younger", "Younger", "Younger", 
"Younger", "Younger"), attribute = c("Feeling A", "Feeling A", 
"Feeling A", "Feeling A", "Feeling A", "Feeling A", "Feeling A", 
"Feeling A", "Feeling A", "Feeling A", "Feeling A", "Feeling A", 
"Feeling A", "Feeling A", "Feeling A", "Feeling A", "Feeling A", 
"Feeling A", "Feeling A", "Feeling A", "Feeling A", "Feeling B", 
"Feeling B", "Feeling B", "Feeling B", "Feeling B", "Feeling B", 
"Feeling B", "Feeling B", "Feeling B", "Feeling B", "Feeling B", 
"Feeling B", "Feeling B", "Feeling B", "Feeling B", "Feeling B", 
"Feeling B", "Feeling B", "Feeling B", "Feeling B", "Feeling B", 
"Feeling A", "Feeling A", "Feeling A", "Feeling A", "Feeling A", 
"Feeling A", "Feeling A", "Feeling A", "Feeling A", "Feeling A", 
"Feeling A", "Feeling A", "Feeling A", "Feeling A", "Feeling A", 
"Feeling A", "Feeling A", "Feeling A", "Feeling A", "Feeling A", 
"Feeling A", "Feeling B", "Feeling B", "Feeling B", "Feeling B", 
"Feeling B", "Feeling B", "Feeling B", "Feeling B", "Feeling B", 
"Feeling B", "Feeling B", "Feeling B", "Feeling B", "Feeling B", 
"Feeling B", "Feeling B", "Feeling B", "Feeling B", "Feeling B", 
"Feeling B", "Feeling B", "Feeling A", "Feeling A", "Feeling A", 
"Feeling A", "Feeling A", "Feeling A", "Feeling A", "Feeling A", 
"Feeling A", "Feeling A", "Feeling A", "Feeling A", "Feeling A", 
"Feeling A", "Feeling A", "Feeling A", "Feeling A", "Feeling A", 
"Feeling A", "Feeling A", "Feeling A", "Feeling B", "Feeling B", 
"Feeling B", "Feeling B", "Feeling B", "Feeling B", "Feeling B", 
"Feeling B", "Feeling B", "Feeling B", "Feeling B", "Feeling B", 
"Feeling B", "Feeling B", "Feeling B", "Feeling B", "Feeling B", 
"Feeling B", "Feeling B", "Feeling B", "Feeling B", "Feeling A", 
"Feeling A", "Feeling A", "Feeling A", "Feeling A", "Feeling A", 
"Feeling A", "Feeling A", "Feeling A", "Feeling A", "Feeling A", 
"Feeling A", "Feeling A", "Feeling A", "Feeling A", "Feeling A", 
"Feeling A", "Feeling A", "Feeling A", "Feeling A", "Feeling A", 
"Feeling B", "Feeling B", "Feeling B", "Feeling B", "Feeling B", 
"Feeling B", "Feeling B", "Feeling B", "Feeling B", "Feeling B", 
"Feeling B", "Feeling B", "Feeling B", "Feeling B", "Feeling B", 
"Feeling B", "Feeling B", "Feeling B", "Feeling B", "Feeling B", 
"Feeling B"), measure_1 = c(0.33, 0.31, 0.31, 0.16, 0.37, 0.29, 
0.27, 0.26, 0.24, 0.38, 0.47, 0.21, 0.32, 0.24, 0.26, 0.38, 0.38, 
0.39, 0.37, 0.3, 0.29, 0.48, 0.45, 0.45, 0.35, 0.49, 0.44, 0.41, 
0.44, 0.35, 0.38, 0.39, 0.55, 0.45, 0.43, 0.38, 0.38, 0.57, 0.47, 
0.51, 0.48, 0.32, 0.27, 0.22, 0.13, 0.02, 0.12, 0.16, 0.15, 0.17, 
0.23, 0.12, 0.31, 0.12, 0.16, 0.16, 0.16, 0.24, 0.06, 0.06, 0.17, 
0.15, 0.14, 0.37, 0.35, 0.2, 0.17, 0.25, 0.2, 0.3, 0.23, 0.26, 
0.14, 0.29, 0.35, 0.14, 0.32, 0.14, 0.14, 0.24, 0.18, 0.24, 0.24, 
0.17, 0.4, 0.3, 0.36, 0.41, 0.38, 0.31, 0.33, 0.43, 0.27, 0.31, 
0.26, 0.29, 0.25, 0.23, 0.38, 0.2, 0.29, 0.26, 0.22, 0.41, 0.25, 
0.45, 0.4, 0.54, 0.51, 0.48, 0.46, 0.4, 0.48, 0.29, 0.33, 0.36, 
0.48, 0.5, 0.32, 0.42, 0.43, 0.35, 0.35, 0.49, 0.44, 0.42, 0.48, 
0.34, 0.44, 0.38, 0.49, 0.27, 0.33, 0.42, 0.31, 0.32, 0.31, 0.38, 
0.46, 0.35, 0.4, 0.36, 0.38, 0.51, 0.41, 0.44, 0.36, 0.7, 0.57, 
0.66, 0.65, 0.57, 0.62, 0.53, 0.52, 0.43, 0.52, 0.53, 0.61, 0.67, 
0.59, 0.57, 0.55, 0.54, 0.67, 0.54, 0.57, 0.57), measure_2 = c(0.5, 
0.47, 0.48, 0.31, 0.54, 0.45, 0.43, 0.42, 0.4, 0.55, 0.66, 0.37, 
0.49, 0.4, 0.42, 0.56, 0.55, 0.57, 0.54, 0.47, 0.45, 0.66, 0.63, 
0.63, 0.52, 0.67, 0.62, 0.58, 0.61, 0.52, 0.55, 0.57, 0.74, 0.63, 
0.61, 0.56, 0.56, 0.77, 0.66, 0.7, 0.67, 0.49, 0.38, 0.32, 0.23, 
0.12, 0.22, 0.26, 0.25, 0.27, 0.34, 0.22, 0.41, 0.21, 0.26, 0.26, 
0.26, 0.34, 0.16, 0.16, 0.27, 0.25, 0.24, 0.48, 0.45, 0.31, 0.27, 
0.36, 0.3, 0.4, 0.34, 0.36, 0.24, 0.39, 0.45, 0.24, 0.43, 0.24, 
0.24, 0.35, 0.28, 0.34, 0.35, 0.27, 0.51, 0.43, 0.48, 0.52, 0.49, 
0.44, 0.46, 0.54, 0.4, 0.44, 0.4, 0.42, 0.39, 0.37, 0.49, 0.34, 
0.42, 0.39, 0.36, 0.52, 0.39, 0.56, 0.51, 0.63, 0.6, 0.58, 0.56, 
0.51, 0.58, 0.42, 0.46, 0.48, 0.58, 0.59, 0.45, 0.52, 0.54, 0.47, 
0.47, 0.58, 0.54, 0.53, 0.7, 0.62, 0.68, 0.64, 0.7, 0.59, 0.62, 
0.67, 0.61, 0.61, 0.61, 0.65, 0.69, 0.63, 0.65, 0.64, 0.64, 0.71, 
0.66, 0.68, 0.63, 0.81, 0.75, 0.8, 0.79, 0.75, 0.77, 0.72, 0.72, 
0.67, 0.72, 0.72, 0.77, 0.8, 0.76, 0.75, 0.73, 0.73, 0.8, 0.73, 
0.75, 0.74)), class = "data.frame", row.names = c(NA, -168L), na.action = structure(169:176, .Names = c("169", 
"170", "171", "172", "173", "174", "175", "176"), class = "omit"))

我读过很多关于rollmean和rollmean的文章，但无法让它处理分组数据。如何使用如此简单的函数编写一行或两行解决方案？

1）使用较小的示例（请在将来提供最少的数据）

给予：

# A tibble: 6 x 5
  group value1 value2 value1_roll value2_roll
  <dbl>  <int>  <int>       <dbl>       <dbl>
1     1      1      7        NA          NA  
2     1      2      8         1.5         7.5
3     1      3      9         2.5         8.5
4     2      4     10        NA          NA  
5     2      5     11         4.5        10.5
6     2      6     12         5.5        11.5

# A tibble: 6 x 3
  group value1 value2
  <dbl>  <dbl>  <dbl>
1     1   NA     NA  
2     1    1.5    7.5
3     1    2.5    8.5
4     2   NA     NA  
5     2    4.5   10.5
6     2    5.5   11.5

或者没有原始的

值

变量：

DF %>%
  group_by(group) %>%
  do(rollmeanr(.[-1], k = 2, fill = NA) %>% as.data.frame) %>%
  ungroup

我格式化了第一个代码块，但我不确定为什么要注释掉底部的代码。有什么原因吗？那你的代码怎么办？谢谢你的代码格式和你的问题。散列出来的代码正在删除数据变量，同时尝试让代码正常工作。我觉得奇怪的是，日期变量不是分组变量的一部分。代码看起来确实有效，但我想使用更简洁的动词，而且我相信我在实现stats:：filter表达式时遇到了困难（我将在下次需要时尝试在这里捕获它）。我应该阅读更多关于该工具的信息。我们可以将其应用于多个相关的数字数据吗？就像我最初的示例一样，例如DF。我编写了一个外部函数来处理这个roller%mutate_，如果（is.numeric，roller）@Michael Bellhouse在您的评论中修改了使用数据。太好了，谢谢您的帮助。我已经接受了你的答复

DF %>%
  group_by(group) %>%
  mutate_at(vars(contains("value")), rollmeanr, k = 2, fill = NA) %>%
  ungroup

# A tibble: 6 x 3
  group value1 value2
  <dbl>  <dbl>  <dbl>
1     1   NA     NA  
2     1    1.5    7.5
3     1    2.5    8.5
4     2   NA     NA  
5     2    4.5   10.5
6     2    5.5   11.5

DF %>%
  group_by(group) %>%
  do(cbind(., roll = rollmeanr(.[-1], k = 2, fill = NA))) %>%
  ungroup

DF %>%
  group_by(group) %>%
  do(rollmeanr(.[-1], k = 2, fill = NA) %>% as.data.frame) %>%
  ungroup