複数のグループにわたる年次から月次への爆発

Question

私は毎月の時系列に吹き飛ばす必要があるいくつかの株式の年間財務データを持っています。私はそれを毎月のタイムシリーズに吹き込み、異なる年末の株式を比較し、潜在的に月間の価格を使って、私がどこか別のものを持っているかどうかを計算することができます。

私の月次データは、この（以下dput）のようになります。私が欲しいもの

date  | value | sec_id | metric 
----------------------------------------------- 
2012-06-29 | 2.44376 | 1676 | ROE 
2013-06-28 | 1.73054 | 1676 | ROE 
2014-06-30 | 1.68171 | 1676 | ROE 
2012-05-31 | 2.07297 | 1572 | ROE 
2013-05-30 | 1.77073 | 1572 | ROE 
2012-06-29 | 5.44376 | 1676 | EPS_GROWTH 
2013-06-28 | -0.3054 | 1676 | EPS_GROWTH 
2014-06-30 | 3.68171 | 1676 | EPS_GROWTH 
2012-05-31 | -1.7297 | 1572 | EPS_GROWTH 
2013-05-30 | 6.77073 | 1572 | EPS_GROWTH 

は月次ベースで表示されるように前方にすべてのデータポイントをロールすることです：

date  | value | sec_id | metric 
------------------------------------------------- 
2012-06-29 | 2.44376 | 1676 | ROE 
2012-07-31 | 2.44376 | 1676 | ROE 
2012-08-31 | 2.44376 | 1676 | ROE 
    ...    ... 
2013-05-31 | 2.44376 | 1676 | ROE 
2013-06-28 | 1.73054 | 1676 | ROE 
    ...    ... 

などセキュリティ/メトリックの組み合わせごとに複雑な問題は、年末に一貫性がないことです。たとえば、2000年には、1社が5月末から6月末に切り替わります。

Rでこれを行うために滑らかで、ループのない方法を知っている人はいますか？

これは、同様の（より単純な）question I'd asked beforeの拡張バージョンです。そこでの最善の答えはpurrrパッケージでしたが、私が思いつくことができる最良のアイデアはネストされたfor-loopsです。

ここに私のDFデータフレームのおもちゃのバージョンです：

library(tidyverse) # because it's a tibble with lubridate dates 
df = structure(list(sec_id = c(1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1572L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L, 1676L), 
date = structure(c(6908, 7273, 7638, 8002, 8369, 8734, 9099, 9464, 9829, 10193, 10560, 10925, 11291, 11656, 12020, 12384, 12752, 13117, 13482, 13847, 14211, 14578, 14943, 15308, 15674, 16038, 16402, 16769, 17135, 6784, 7151, 7516, 7881, 8247, 8611, 8975, 9342, 9708, 10073, 10438, 10802, 11200, 11565, 11929, 12293, 12661, 13026, 13391, 13756, 14120, 14487, 14852, 15217, 15583, 15947, 16311, 16678, 17044, 6175, 6542, 6908, 7273, 7638, 8002, 8369, 8734, 9099, 9464, 9829, 10193, 10560, 10925, 11291, 11656, 12020, 12384, 12752, 13117, 13482, 13847, 14211, 14578, 14943, 15308, 15674, 16038, 16402, 16769, 17135, 6420, 6784, 7151, 7516, 7881, 8247, 8611, 8975, 9342, 9708, 10073, 10438, 10802, 11200, 11565, 11929, 12293, 12661, 13026, 13391, 13756, 14120, 14487, 14852, 15217, 15583, 15947, 16311, 16678, 17044), class = "Date"),  
value = c(0.291022, 0.197242, -0.470581, -0.406716, -3.294938, 1.22421, 3.140976, 0.250468, 0.091548, -0.100863, 0.058375, 0.24784, 0.178765, 0.099276, 0.25472, -0.033291, 0.124165, 0.050947, 0.243008, 0.1205, -0.239625, -0.231221, 0.365649, 0.163779, 0.024976, 0.08388, 0.154777, 0.016473, -0.272928, 0.237446, 0.391304, 0.064583, 0.286497, -0.571342, 1.05039, -0.001038, -0.018711, -0.162076, -0.599241, -4.071504, -0.37761, 1.694085, 0.045113, -0.064748, 0.098901, 0.823333, 0.793419, 0.759225, 0.329818, 0.199564, -0.616418, 1.164773, 0.877078, -0.325099, -0.294199, 0.272016, -0.706077, -2.57027, 0.129316, 0.131234, 0.143554, 0.131058, 0.099229, 0.057275, -0.121562, 0.051187, 0.15589, 0.188605, 0.186155, 0.154765, 0.153816, 0.166873, 0.181998, 0.183937, 0.211225, 0.186216, 0.177731, 0.161529, 0.187527, 0.199608, 0.136866, 0.10001, 0.13546, 0.148756, 0.143177, 0.144643, 0.153461, 0.140774, 0.099231, 0.117254, 0.141094, 0.166662, 0.154047, 0.172634, 0.062807, 0.145724, 0.13481, 0.11031, 0.087178, 0.033243, -0.122207, -0.22856, 0.171272, 0.187948, 0.186266, 0.154494, 0.238273, 0.352929, 0.494341, 0.50497, 0.459309, 0.178825, 0.270483, 0.43428, 0.254988, 0.149881, 0.187119, 0.048877, -0.111647), 
metric = c("EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "EPS_GROWTH", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE", "ROE")), 
class = c("tbl_df", "tbl", "data.frame"), 
row.names = c(NA, -119L), .Names = c("sec_id", "date", "value", "metric")) 

EDIT：私のソリューションこれまで（私の〜100kの列与えられた10分程度かかります）

library(tidyverse) 
if (exists("factors.monthly")) rm(factors.monthly) 
for (s in unique(df$sec_id)) { 
    for (m in unique(df$metric)) { 
     # cat(s, " : ", m, "\n") 
     x = df %>% filter(sec_id == s) %>% filter(metric == m) %>% mutate(metric = as.character(metric)) %>% select(sec_id, date, value = metric_value, metric) 
     if (nrow(x) > 0) { 
      y = x %>% 
      mutate(date = ceiling_date(date, 'month'), date = map2(date, lead(date - 1, default = last(date)), seq, by = 'month')) %>% 
      unnest() %>% 
      mutate(date = date - 1) 

      if (exists("factors.monthly")) { 
       factors.monthly = rbind(factors.monthly, y) 
      } else { 
       factors.monthly = y 
      } 
     } 
    } 
} 

Answer 1

私はこれがあなたの望むものだと思います。それは十分に速くなければなりません。

df %>% 
    group_by(sec_id, metric) %>% 
    mutate(date = ceiling_date(date, 'month'), 
      date = map2(date, lead(date - 1, default = last(date)), seq, by = 'month')) %>% 
    unnest() %>% 
    mutate(date = date - 1) %>% 
    arrange(sec_id, metric, date) 
# Source: local data frame [1,386 x 4] 
# Groups: sec_id, metric [4] 
# 
# sec_id value  metric  date 
#  <int> <dbl>  <chr>  <date> 
# 1 1572 0.291022 EPS_GROWTH 1988-11-30 
# 2 1572 0.291022 EPS_GROWTH 1988-12-31 
# 3 1572 0.291022 EPS_GROWTH 1989-01-31 
# 4 1572 0.291022 EPS_GROWTH 1989-02-28 
# 5 1572 0.291022 EPS_GROWTH 1989-03-31 
# 6 1572 0.291022 EPS_GROWTH 1989-04-30 
# 7 1572 0.291022 EPS_GROWTH 1989-05-31 
# 8 1572 0.291022 EPS_GROWTH 1989-06-30 
# 9 1572 0.291022 EPS_GROWTH 1989-07-31 
# 10 1572 0.291022 EPS_GROWTH 1989-08-31 
# # ... with 1,376 more rows 

私はあなたが `group_by`を使用して` dplyr`ではなくを使用している、特に"Grouped Operations" section of this introduction.