R使用group_by中的pracma pkg中的movavg（）和summarise（）计算加权移动平均值？答案

【问题标题】：R Calculate weighted moving average using movavg() from pracma pkg within group_by and summarise()?R使用group_by中的pracma pkg中的movavg（）和summarise（）计算加权移动平均值？
【发布时间】：2019-08-11 23:21:19
【问题描述】：

我确信有一种方法可以通过在循环中编写一个函数来做到这一点，但是 movavg() 函数正是我想要做的，我只是不知道如何在一个组中应用它数据框。

数据集很大，但我试图取几列（比如，

data <- c("Species", "Tonnes", "Year")

并将每个物种每年的所有行（吨）分组（每年每个物种将有数百行），然后计算连续 10 年的加权移动平均值。所以不是总结（平均=平均（吨）），我有

data %>% 
group_by(Species, Year) %>%
summarise(wma = movavg(x = Tonnes, n = 9, type = "w"))

对于 movavg() 函数，x = 作为数值向量的时间序列，n = 后向窗口长度（所以我认为自从我有 10 年的数据以来是 9 年），类型为“w”表示加权移动平均线。但是我想不出一种有效的方法来让 x 和 n 引用分组变量。

任何帮助将不胜感激！

数据样本：

dput(sample)

structure(list(Species = c("Argyrosomus hololepidotus", "Argyrosomus hololepidotus", 
"Argyrosomus hololepidotus", "Argyrosomus hololepidotus", "Argyrosomus hololepidotus", 
"Argyrosomus hololepidotus", "Argyrosomus hololepidotus", "Argyrosomus hololepidotus", 
"Argyrosomus hololepidotus", "Argyrosomus hololepidotus", "Argyrosomus hololepidotus", 
"Dipturus batis", "Dipturus batis", "Dipturus batis", "Dipturus batis", 
"Dipturus batis", "Dipturus batis", "Dipturus batis", "Dipturus batis", 
"Dipturus batis", "Dipturus batis", "Dipturus batis", "Dipturus batis", 
"Dipturus batis", "Dipturus batis", "Dipturus batis", "Dipturus batis", 
"Epinephelus striatus", "Epinephelus striatus", "Epinephelus striatus", 
"Epinephelus striatus", "Epinephelus striatus", "Epinephelus striatus", 
"Epinephelus striatus", "Epinephelus striatus", "Epinephelus striatus", 
"Epinephelus striatus", "Epinephelus striatus", "Epinephelus striatus", 
"Leucoraja circularis", "Leucoraja circularis", "Leucoraja circularis", 
"Leucoraja circularis", "Leucoraja circularis", "Leucoraja circularis", 
"Leucoraja circularis", "Leucoraja circularis", "Leucoraja circularis", 
"Leucoraja circularis", "Leucoraja circularis", "Leucoraja circularis", 
"Leucoraja circularis", "Leucoraja circularis", "Leucoraja circularis", 
"Leucoraja circularis", "Leucoraja circularis", "Leucoraja circularis", 
"Leucoraja circularis", "Leucoraja circularis", "Leucoraja circularis", 
"Leucoraja circularis", "Leucoraja circularis", "Leucoraja circularis", 
"Leucoraja fullonica", "Leucoraja fullonica", "Leucoraja fullonica", 
"Leucoraja fullonica", "Leucoraja fullonica", "Leucoraja fullonica", 
"Leucoraja fullonica", "Leucoraja fullonica", "Leucoraja fullonica", 
"Leucoraja fullonica", "Leucoraja fullonica", "Leucoraja fullonica", 
"Leucoraja fullonica", "Leucoraja fullonica", "Leucoraja fullonica", 
"Leucoraja fullonica", "Leucoraja fullonica", "Leucoraja fullonica", 
"Leucoraja fullonica", "Leucoraja fullonica", "Leucoraja fullonica", 
"Leucoraja fullonica", "Leucoraja fullonica", "Leucoraja fullonica"
), Year = c(2011, 2012, 2012, 2013, 2013, 2013, 2014, 2014, 2014, 
2015, 2015, 2011, 2011, 2011, 2011, 2012, 2012, 2012, 2013, 2013, 
2013, 2014, 2014, 2014, 2015, 2015, 2015, 2011, 2011, 2012, 2012, 
2013, 2013, 2013, 2014, 2014, 2015, 2015, 2015, 2011, 2011, 2011, 
2011, 2012, 2012, 2012, 2012, 2012, 2013, 2013, 2013, 2013, 2013, 
2014, 2014, 2014, 2014, 2014, 2015, 2015, 2015, 2015, 2015, 2011, 
2011, 2011, 2011, 2011, 2012, 2012, 2012, 2012, 2012, 2013, 2013, 
2013, 2013, 2013, 2014, 2014, 2014, 2014, 2014, 2015, 2015, 2015, 
2015), Country = c("Australia", "Australia", "Congo Rep", "Australia", 
"Congo Rep", "Cuba", "Australia", "Congo Rep", "Cuba", "Australia", 
"Congo Rep", "France", "Iceland", "Norway", "UK", "Iceland", 
"Norway", "UK", "Iceland", "Norway", "UK", "Iceland", "Norway", 
"UK", "France", "Norway", "UK", "Bahamas", "Cuba", "Bahamas", 
"Cuba", "Bahamas", "Colombia", "Cuba", "Bahamas", "Cuba", "Bahamas", 
"Colombia", "Cuba", "Belgium", "France", "Portugal", "Spain", 
"Belgium", "France", "Portugal", "Spain", "UK", "Belgium", "France", 
"Portugal", "Spain", "UK", "Belgium", "France", "Portugal", "Spain", 
"UK", "Belgium", "France", "Portugal", "Spain", "UK", "France", 
"Iceland", "Ireland", "Spain", "UK", "France", "Iceland", "Ireland", 
"Spain", "UK", "France", "Iceland", "Ireland", "Spain", "UK", 
"France", "Iceland", "Ireland", "Spain", "UK", "France", "Ireland", 
"Spain", "UK"), Tonnes = c(106.05352, 156.34223, 126.58993, 186.88017, 
99.93942, 2.11171, 141.38148, 76.62019, 1.05582, 139.68761, 68.84715, 
1.01059, 122.28238, 51.54047, 1.01059, 146.53669, 21.22253, 4.04238, 
154.62159, 16.16959, 1.01061, 221.32109, 13.4511, 1.01058, 0.10106, 
11.40965, 1.48557, 123.76517, 31.19285, 75.46656, 26.16171, 92.57231, 
0.20119, 13.08084, 148.92064, 25.15554, 53.32971, 0.20118, 25.15551, 
121.27182, 45.47698, 41.43453, 38.40273, 127.33538, 55.58288, 
31.32855, 21.22261, 4.04233, 102.07043, 59.62533, 30.31795, 11.11656, 
27.28616, 85.90087, 82.86908, 29.30735, 5.05297, 38.40275, 110.25632, 
60.06995, 21.18214, 3.52698, 44.86048, 184.93939, 17.18016, 8.08486, 
60.63577, 90.9538, 171.80171, 21.22261, 7.07419, 56.59356, 62.65707, 
158.66387, 37.39215, 3.03183, 32.33914, 85.90084, 169.78059, 
14.1483, 2.02121, 18.19081, 55.58289, 195.41931, 2.99138, 12.89521, 
26.33628)), class = c("grouped_df", "tbl_df", "tbl", "data.frame"
), row.names = c(NA, -87L), vars = c("Species", "Year"), drop = TRUE, indices = list(
    0L, 1:2, 3:5, 6:8, 9:10, 11:14, 15:17, 18:20, 21:23, 24:26, 
    27:28, 29:30, 31:33, 34:35, 36:38, 39:42, 43:47, 48:52, 53:57, 
    58:62, 63:67, 68:72, 73:77, 78:82, 83:86), group_sizes = c(1L, 
2L, 3L, 3L, 2L, 4L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 4L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L), biggest_group_size = 5L, labels = structure(list(
    Species = c("Argyrosomus hololepidotus", "Argyrosomus hololepidotus", 
    "Argyrosomus hololepidotus", "Argyrosomus hololepidotus", 
    "Argyrosomus hololepidotus", "Dipturus batis", "Dipturus batis", 
    "Dipturus batis", "Dipturus batis", "Dipturus batis", "Epinephelus striatus", 
    "Epinephelus striatus", "Epinephelus striatus", "Epinephelus striatus", 
    "Epinephelus striatus", "Leucoraja circularis", "Leucoraja circularis", 
    "Leucoraja circularis", "Leucoraja circularis", "Leucoraja circularis", 
    "Leucoraja fullonica", "Leucoraja fullonica", "Leucoraja fullonica", 
    "Leucoraja fullonica", "Leucoraja fullonica"), Year = c(2011, 
    2012, 2013, 2014, 2015, 2011, 2012, 2013, 2014, 2015, 2011, 
    2012, 2013, 2014, 2015, 2011, 2012, 2013, 2014, 2015, 2011, 
    2012, 2013, 2014, 2015)), class = "data.frame", row.names = c(NA, 
-25L), vars = c("Species", "Year"), drop = TRUE))

【问题讨论】：

你能提供dput()的样本数据吗？
当然，好点。另外我之前没有使用过 dput() - 非常有用，谢谢
我的回答没有解决您的问题吗？如果不是，你能告诉我为什么吗？

标签： r dplyr

【解决方案1】：

这是一个可能的解决方案。主要区别在于，这是先汇总到要运行移动平均线然后创建移动平均线的水平。

data %>% 
  group_by(Species, Year) %>% 
  summarise(Tonnes = sum(Tonnes, na.rm = TRUE)) %>% 
  arrange(Year) %>% #Just in case the years were out of order because the movavg assumes chronological order
  mutate(Tonnes_ma = movavg(Tonnes, 3, "w")) #Calculating weighted moving averages

我在这里使用 3 作为n，因为我只有 5 年的数据。

【讨论】：

抱歉响应缓慢，在没有互联网的领域 - 感谢@StevenK的回答，这有效！我没有意识到 movavg 假定按时间顺序排列，首先总结然后变异是有意义的。