按組 Dplyr/data.table 的滾動平均值

Question

我有一個包含 350k 行的大型 data.frame。 目前我正在嘗試在data.table 中創建lag_close 列的滾動平均值。 由於我在dplyr比在data.table更流利， dplyr我嘗試使用dplyr創建一個滾動平均值，它給我一個錯誤。

df = df %>% na.omit()%>%  group_by(ticker) %>% mutate(avg10 = rollapplyr(lag_close, 10, mean))

錯誤：列avg10長度必須為 577（組大小）或 1，而不是 567

我的下一個嘗試是走 data.table 路線，因為我看到人們取得了成功。 沒有返回錯誤，但是輸出很奇怪而且不准確。

df = as.data.table(df)
df[, laggedRets := rollapplyr(lag_close, 10, mean, na.rm = T), by = ticker]
tail(df, 10)
ticker ret.adjusted.prices ret.closing.prices lag_close         jump laggedRets
 1:   MELI         0.002731904        0.002731904    501.48  0.020174791    507.849
 2:   MELI         0.007258614        0.007258614    502.85  0.001966823    507.528
 3:   MELI        -0.002606134       -0.002606134    506.50 -0.011437339    507.085
 4:   MELI         0.018132218        0.018132218    505.18  0.003832865    506.282
 5:   MELI        -0.020628408       -0.020628408    514.34 -0.014373574    505.262
 6:   MELI         0.001568257        0.001568257    503.73  0.003428456         NA
 7:   MELI         0.018155879        0.018155879    504.52 -0.002957652         NA
 8:   MELI        -0.022815765       -0.022815765    513.68  0.002663470         NA
 9:   MELI        -0.007132015       -0.007132015    501.96 -0.005273231         NA
10:   MELI        -0.012801487       -0.012801487    498.38 -0.016467802         NA

head(df, 10)
  ticker ret.adjusted.prices ret.closing.prices lag_close         jump laggedRets
 1:     AA        0.0496011100       0.0496011100     28.83  0.006568736         NA
 2:     AA        0.0128883014       0.0128883014     30.26 -0.002647255         NA
 3:     AA        0.0009787928       0.0009787928     30.65  0.001304239         NA
 4:     AA       -0.0391134289      -0.0391134289     30.68  0.006497716         NA
 5:     AA        0.0508819539       0.0508819539     29.48  0.011802531     31.154
 6:     AA        0.0319560684       0.0319560684     30.98  0.003222691     31.752
 7:     AA        0.0334689407       0.0334689407     31.97  0.031709630     32.268
 8:     AA       -0.0009080811      -0.0009080811     33.04 -0.006680893     32.769
 9:     AA       -0.0112086950      -0.0112086950     33.01 -0.001212398     33.330
10:     AA        0.0664829064       0.0664829064     32.64  0.009452792     34.132

似乎 data.table 的兩側都有 NA，但事實並非如此。 而且數字也不准確。 我不確定我錯過了什么。 這是一個示例，以便你們可以測試一下。

df =structure(list(ticker = c("AA", "AA", "AA", "AA", "AA", "AA", 
"AA", "AA", "AA", "AA", "AA", "AA", "AA", "AA", "AA", "AA", "AA", 
"AA", "AA", "AA", "AABA", "AABA", "AABA", "AABA", "AABA", "AABA", 
"AABA", "AABA", "AABA", "AABA", "AABA", "AABA", "AABA", "AABA", 
"AABA", "AABA", "AABA", "AABA", "AABA", "AABA", "AAL", "AAL", 
"AAL", "AAL", "AAL", "AAL", "AAL", "AAL", "AAL", "AAL", "AAL", 
"AAL", "AAL", "AAL", "AAL", "AAL", "AAL", "AAL", "AAL", "AAL", 
"AAOI", "AAOI", "AAOI", "AAOI", "AAOI", "AAOI", "AAOI", "AAOI", 
"AAOI", "AAOI", "AAOI", "AAOI", "AAOI", "AAOI", "AAOI", "AAOI", 
"AAOI", "AAOI", "AAOI", "AAOI", "AAPL", "AAPL", "AAPL", "AAPL", 
"AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", 
"AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", 
"ABBV", "ABBV", "ABBV", "ABBV", "ABBV", "ABBV", "ABBV", "ABBV", 
"ABBV", "ABBV", "ABBV", "ABBV", "ABBV", "ABBV", "ABBV", "ABBV", 
"ABBV", "ABBV", "ABBV", "ABBV"), lag_close = c(28.83, 30.26, 
30.65, 30.68, 29.48, 30.98, 31.969999, 33.040001, 33.009998, 
32.639999, 34.810001, 35.419998, 35.66, 36.290001, 37.5, 36.529999, 
36.299999, 36.669998, 36.549999, 36.450001, 38.900002, 40.060001, 
41.34, 41.23, 41.34, 42.299999, 42.59, 42.110001, 42.27, 41.990002, 
42.029999, 42.09, 42.049999, 42.400002, 43.900002, 44.939999, 
44.549999, 44.419998, 43.93, 44.07, 46.299999, 46.700001, 45.889999, 
46.209999, 47.080002, 48.48, 48.639999, 48.099998, 47.650002, 
46.75, 47.639999, 47.259998, 48, 46.939999, 47.540001, 47.91, 
49.59, 46.950001, 44.900002, 44.25, 23.1, 23.51, 22.809999, 22.42, 
22.24, 22.870001, 22.719999, 29.860001, 28.219999, 30.440001, 
30.84, 30.48, 30.58, 31.290001, 32, 31.709999, 31.280001, 31.15, 
30.41, 30.76, 116.150002, 116.019997, 116.610001, 117.910004, 
118.989998, 119.110001, 119.75, 119.25, 119.040001, 120, 119.989998, 
119.779999, 120, 120.080002, 119.970001, 121.879997, 121.940002, 
121.949997, 121.629997, 121.349998, 62.41, 63.290001, 63.77, 
63.790001, 64.209999, 64.07, 61.139999, 61.279999, 61.990002, 
61.860001, 61.66, 61.380001, 61.150002, 60.959999, 60.580002, 
61.380001, 61.27, 60, 60.43, 61.110001)), row.names = c(NA, -120L
), class = "data.frame", .Names = c("ticker", "lag_close"))

Answer 1

這是data.table()方法，第 1-9 行為 NA。

dt <- as.data.table(df)
dt[, laggedRets := frollmean(lag_close, 10, na.rm = T), by = ticker]

此外，尚不清楚是否應在滾動平均值之前刪除NA 。 如果是這種情況，您將使用：

dt <- as.data.table(df)
dt[!is.na(lag_close), laggedRets := frollmean(lag_close, 10), by = ticker]

Answer 2

您可以使用purrr包中的map並將其應用於1:n() ：

df = df %>% 
  na.omit() %>% 
  group_by(ticker) %>% 
  mutate(avg10 = map_dbl(1:n(), ~mean(lag_close[(max(.x-9, 1)):.x], na.rm =T))

當然，您必須決定對少於 10 個觀察值的前 9 行應該發生什么。 在我的解決方案中，第 1 行到第 9 行包含最后 1 到 9 個觀察值的平均值。