以下是整个数据的一个小提取，多年来我有数千个符号。 。 .both符号和日期范围从运行变为运行
我有2个动物园系列“return”和“decFac”。
> tail(returns)
AAPL DISCA IBM JNJ KO
2014-12-23 -0.0035479832 0.0137774854 0.004943048 -0.0233164191 0.0145336114
2014-12-24 -0.0047206092 -0.0054309123 -0.002592361 0.0029684238 -0.0006984054
2014-12-26 0.0175226064 -0.0005733945 0.003208447 0.0044836732 0.0004657399
2014-12-29 -0.0007020609 NA NA 0.0025666222 -0.0023303779
2014-12-30 -0.0122776892 NA NA 0.0002847851 -0.0023360686
2014-12-31 -0.0192020576 -0.0219631307 0.002433726 -0.0075263261 -0.0127090448
NKE TXN
2014-12-23 0.0004169359 -0.0007298205
2014-12-24 0.0033288228 0.0014592993
2014-12-26 0.0055922518 -0.0020985205
2014-12-29 NA NA
2014-12-30 NA NA
2014-12-31 -0.0075636285 -0.0086595788
> tail(decFac)
2014-12-23 2014-12-24 2014-12-26 2014-12-29 2014-12-30 2014-12-31
0.02576202 0.02655878 0.02738019 0.02822700 0.02910000 0.03000000
这两个都有“动物园系列从2012-01-04 tp 2014-12-31”的价值（根据R-Studio）
每种数据类型如下：
> sapply(returns, typeof)
AAPL DISCA IBM JNJ KO NKE TXN
"double" "double" "double" "double" "double" "double" "double"
> sapply(decFac, typeof)
[1] "double"
我的目标是让每一只股票在每天的回报中乘以decFac当天
AAPL前5天的预期结果如下：
AAPL
12/23/2014 -0.000091403
12/24/2014 -0.000125374
12/26/2014 0.000479772
12/29/2014 -0.000019817
12/30/2014 -0.000357281
12/31/2014 -0.000576062
答案 0 :(得分：2)
zoo和xts对象将按索引对齐：
library(xts)
time = seq.Date(as.Date('2014-12-23'), as.Date('2014-12-31'), by = 'day')
time = time[c(1,2,4,7:9)]
AAPL = c( -0.0035479832, -0.0047206092, 0.0175226064,
-0.0007020609, -0.0122776892, -0.0192020576 )
DISCA = c( 0.0137774854, -0.0054309123 , -0.0005733945 ,
NA, NA, -0.0219631307 )
IBM = c( 0.004943048, -0.002592361, 0.003208447,
NA, NA, 0.002433726 )
JNJ = c( -0.0233164191, 0.0029684238, 0.0044836732,
0.0025666222, 0.0002847851, -0.0075263261 )
KO = c( 0.0145336114, -0.0006984054, 0.0004657399,
-0.0023303779, -0.0023360686, -0.0127090448)
NKE = c( 0.0004169359, 0.0033288228, 0.0055922518,
NA, NA, -0.0075636285 )
TXN = c( -0.0007298205, 0.0014592993, -0.0020985205,
NA, NA, -0.0086595788 )
decFac_v = c( 0.02576202, 0.02655878, 0.02738019,
0.02822700, 0.02910000, 0.03000000 )
returns_zoo = zoo( cbind(AAPL, DISCA, IBM, JNJ, KO, NKE, TXN), time)
returns = xts( cbind(AAPL, DISCA, IBM, JNJ, KO, NKE, TXN), time)
decFac_zoo = drop( zoo( decFac_v, time ))
decFac = drop( xts( decFac_v, time ))
将zoo或xts对象相乘可以起作用：
returns * decFac
# AAPL DISCA IBM JNJ KO NKE TXN
# 2014-12-23 -9.140321e-05 3.549359e-04 1.273429e-04 -6.006781e-04 3.744152e-04 1.074111e-05 -1.880165e-05
# 2014-12-24 -1.253736e-04 -1.442384e-04 -6.884995e-05 7.883771e-05 -1.854880e-05 8.840947e-05 3.875721e-05
# 2014-12-26 4.797723e-04 -1.569965e-05 8.784789e-05 1.227638e-04 1.275205e-05 1.531169e-04 -5.745789e-05
# 2014-12-29 -1.981707e-05 NA NA 7.244804e-05 -6.577958e-05 NA NA
# 2014-12-30 -3.572808e-04 NA NA 8.287246e-06 -6.797960e-05 NA NA
# 2014-12-31 -5.760617e-04 -6.588939e-04 7.301178e-05 -2.257898e-04 -3.812713e-04 -2.269089e-04 -2.597874e-04
returns_zoo * decFac_zoo
# AAPL DISCA IBM JNJ KO NKE TXN
# 2014-12-23 -9.140321e-05 3.549359e-04 1.273429e-04 -6.006781e-04 3.744152e-04 1.074111e-05 -1.880165e-05
# 2014-12-24 -1.253736e-04 -1.442384e-04 -6.884995e-05 7.883771e-05 -1.854880e-05 8.840947e-05 3.875721e-05
# 2014-12-26 4.797723e-04 -1.569965e-05 8.784789e-05 1.227638e-04 1.275205e-05 1.531169e-04 -5.745789e-05
# 2014-12-29 -1.981707e-05 NA NA 7.244804e-05 -6.577958e-05 NA NA
# 2014-12-30 -3.572808e-04 NA NA 8.287246e-06 -6.797960e-05 NA NA
# 2014-12-31 -5.760617e-04 -6.588939e-04 7.301178e-05 -2.257898e-04 -3.812713e-04 -2.269089e-04 -2.597874e-04
考虑如果要对returns或decFac个对象进行分组会发生什么：
# subsetting
x = zoo( cbind(AAPL, DISCA, IBM, JNJ, KO, NKE, TXN), time)
y = drop( zoo( decFac_v, time))
x * y
# AAPL DISCA IBM JNJ KO NKE TXN
# 2014-12-23 -9.140321e-05 3.549359e-04 1.273429e-04 -6.006781e-04 3.744152e-04 1.074111e-05 -1.880165e-05
# 2014-12-24 -1.253736e-04 -1.442384e-04 -6.884995e-05 7.883771e-05 -1.854880e-05 8.840947e-05 3.875721e-05
# 2014-12-26 4.797723e-04 -1.569965e-05 8.784789e-05 1.227638e-04 1.275205e-05 1.531169e-04 -5.745789e-05
# 2014-12-29 -1.981707e-05 NA NA 7.244804e-05 -6.577958e-05 NA NA
# 2014-12-30 -3.572808e-04 NA NA 8.287246e-06 -6.797960e-05 NA NA
# 2014-12-31 -5.760617e-04 -6.588939e-04 7.301178e-05 -2.257898e-04 -3.812713e-04 -2.269089e-04 -2.597874e-04
x * y[-3] # does not return values corresponding to the third date index
# AAPL DISCA IBM JNJ KO NKE TXN
# 2014-12-23 -9.140321e-05 0.0003549359 1.273429e-04 -6.006781e-04 3.744152e-04 1.074111e-05 -1.880165e-05
# 2014-12-24 -1.253736e-04 -0.0001442384 -6.884995e-05 7.883771e-05 -1.854880e-05 8.840947e-05 3.875721e-05
# 2014-12-29 -1.981707e-05 NA NA 7.244804e-05 -6.577958e-05 NA NA
# 2014-12-30 -3.572808e-04 NA NA 8.287246e-06 -6.797960e-05 NA NA
# 2014-12-31 -5.760617e-04 -0.0006588939 7.301178e-05 -2.257898e-04 -3.812713e-04 -2.269089e-04 -2.597874e-04
x[-3] * y # does not return values corresponding to the third date index
# AAPL DISCA IBM JNJ KO NKE TXN
# 2014-12-23 -9.140321e-05 0.0003549359 1.273429e-04 -6.006781e-04 3.744152e-04 1.074111e-05 -1.880165e-05
# 2014-12-24 -1.253736e-04 -0.0001442384 -6.884995e-05 7.883771e-05 -1.854880e-05 8.840947e-05 3.875721e-05
# 2014-12-29 -1.981707e-05 NA NA 7.244804e-05 -6.577958e-05 NA NA
# 2014-12-30 -3.572808e-04 NA NA 8.287246e-06 -6.797960e-05 NA NA
# 2014-12-31 -5.760617e-04 -0.0006588939 7.301178e-05 -2.257898e-04 -3.812713e-04 -2.269089e-04 -2.597874e-04
x[,-3] * y # does not return values corresponding to the 3rd symbol column
# AAPL DISCA JNJ KO NKE TXN
# 2014-12-23 -9.140321e-05 3.549359e-04 -6.006781e-04 3.744152e-04 1.074111e-05 -1.880165e-05
# 2014-12-24 -1.253736e-04 -1.442384e-04 7.883771e-05 -1.854880e-05 8.840947e-05 3.875721e-05
# 2014-12-26 4.797723e-04 -1.569965e-05 1.227638e-04 1.275205e-05 1.531169e-04 -5.745789e-05
# 2014-12-29 -1.981707e-05 NA 7.244804e-05 -6.577958e-05 NA NA
# 2014-12-30 -3.572808e-04 NA 8.287246e-06 -6.797960e-05 NA NA
# 2014-12-31 -5.760617e-04 -6.588939e-04 -2.257898e-04 -3.812713e-04 -2.269089e-04 -2.597874e-04
考虑如果要扩展日期范围会发生什么：
# expanding time dimension
expanded_time = seq.Date(as.Date('2012-01-04'),
as.Date('2014-12-22'),
by = 'day')
value = rep_len(1, length(expanded_time))
old_returns = xts( cbind(AAPL = value,
DISCA = value,
IBM = value,
JNJ = value,
KO = value,
NKE = value,
TXN = value),
expanded_time)
returns_expanded_time = xts( rbind(old_returns, returns), c(expanded_time, time) )
returns_expanded_time * decFac
# returns only values where the date index of each object matches:
# AAPL DISCA IBM JNJ KO NKE TXN
# 2014-12-23 -9.140321e-05 3.549359e-04 1.273429e-04 -6.006781e-04 3.744152e-04 1.074111e-05 -1.880165e-05
# 2014-12-24 -1.253736e-04 -1.442384e-04 -6.884995e-05 7.883771e-05 -1.854880e-05 8.840947e-05 3.875721e-05
# 2014-12-26 4.797723e-04 -1.569965e-05 8.784789e-05 1.227638e-04 1.275205e-05 1.531169e-04 -5.745789e-05
# 2014-12-29 -1.981707e-05 NA NA 7.244804e-05 -6.577958e-05 NA NA
# 2014-12-30 -3.572808e-04 NA NA 8.287246e-06 -6.797960e-05 NA NA
# 2014-12-31 -5.760617e-04 -6.588939e-04 7.301178e-05 -2.257898e-04 -3.812713e-04 -2.269089e-04 -2.597874e-04
考虑如果要添加其他列会发生什么：
new_column1 = rep_len(1, length(c(expanded_time, time)))
new_column2 = new_column1
returns_expanded_cols = xts(
cbind( rbind(old_returns, returns),
nc1 = new_column1,
nc2 =new_column2),
c(expanded_time, time) )
returns_expanded_cols * decFac
# returns only values where the date index of each object matches,
# including the two new columns, `nc1` and `nc2`
# AAPL DISCA IBM JNJ KO NKE TXN nc1 nc2
# 2014-12-23 -9.140321e-05 3.549359e-04 1.273429e-04 -6.006781e-04 3.744152e-04 1.074111e-05 -1.880165e-05 0.02576202 0.02576202
# 2014-12-24 -1.253736e-04 -1.442384e-04 -6.884995e-05 7.883771e-05 -1.854880e-05 8.840947e-05 3.875721e-05 0.02655878 0.02655878
# 2014-12-26 4.797723e-04 -1.569965e-05 8.784789e-05 1.227638e-04 1.275205e-05 1.531169e-04 -5.745789e-05 0.02738019 0.02738019
# 2014-12-29 -1.981707e-05 NA NA 7.244804e-05 -6.577958e-05 NA NA 0.02822700 0.02822700
# 2014-12-30 -3.572808e-04 NA NA 8.287246e-06 -6.797960e-05 NA NA 0.02910000 0.02910000
# 2014-12-31 -5.760617e-04 -6.588939e-04 7.301178e-05 -2.257898e-04 -3.812713e-04 -2.269089e-04 -2.597874e-04 0.03000000 0.03000000