我在R中有三个不同的数据框，其中包含在确定的日期特定资产的值。我需要将数据帧中的每个值乘以一个特定的乘数，然后将这三个值组合（相加）成一个最终的数据帧。
这是我到目前为止所拥有的：
第一个数据帧（df1）
structure(list(
`CRB IM Equity` = c(0, 0, 0),
`US10 IM Equity` = c(0.25, 0.25, 0.2),
`SYBM GY Equity` = c(0, 0, 0),
`LEM FP Equity` = c(0, 0, 0),
`IUSE LN Equity` = c(0, 0, 0),
`LGCPTREH Index` = c(0.25, 0, 0),
`IJPE LN Equity` = c(0, 0, 0),
`XEIN GY Equity` = c(0, 0, 0),
`IQQR GY Equity` = c(0, 0, 0),
`LTI1TREH Index` = c(0.25, 0, 0),
`XAD1 IM Equity` = c(0, 0, 0),
`IMEU NA Equity` = c(0, 0, 0),
`IHYG LN Equity` = c(0, 0, 0),
`IPRP NA Equity` = c(0, 0, 0),
`JPEIHDEU Index` = c(0, 0, 0.2),
`IBTM IM Equity` = c(0.25, 0.25, 0.2),
`EUNR GY Equity` = c(0, 0, 0),
`MWO FP Equity` = c(0, 0.25, 0.2),
`LTM FP Equity` = c(0, 0.25, 0.2),
`XGLE GY Equity` = c(0, 0, 0),
RiskFree = c(0, 0, 0)),
.Names = c("CRB IM Equity", "US10 IM Equity", "SYBM GY Equity", "LEM FP Equity", "IUSE LN Equity", "LGCPTREH Index", "IJPE LN Equity", "XEIN GY Equity", "IQQR GY Equity", "LTI1TREH Index", "XAD1 IM Equity", "IMEU NA Equity", "IHYG LN Equity", "IPRP NA Equity", "JPEIHDEU Index", "IBTM IM Equity", "EUNR GY Equity", "MWO FP Equity", "LTM FP Equity", "XGLE GY Equity", "RiskFree"
),
row.names = c("2019-03-29", "2019-04-30", "2019-05-31"),
class = "data.frame")
第二个数据帧（df2）：
structure(list(
`US10 IM Equity` = c(0, 0, 0),
`SYBM GY Equity` = c(0.00613496932515337, 0, 0.0109983795832177),
`CRB IM Equity` = c(0.0081799591002045, 0.0327510917030568, 0),
`IUSE LN Equity` = c(0, 0, 0.00219967591664355),
`LEM FP Equity` = c(0, 0, 0),
`LGCPTREH Index` = c(0.6032719, 0.58296943231441, 0.406940044579056),
`IQQR GY Equity` = c(0, 0.00655021834061135, 0.0131980554998613),
`XEIN GY Equity` = c(0, 0, 0.103384768082247),
`IJPE LN Equity` = c(0.0061349693, 0.00873362445414847, 0),
`LTI1TREH Index` = c(0.0061349693, 0.02838427947, 0.002199675916), `IMEU NA Equity` = c(0, 0, 0.00879870366657419),
`XAD1 IM Equity` = c(0.00204498977505112, 0.00873362445414847, 0), `IHYG LN Equity` = c(0.3476482617, 0.266375545, 0.008798703666),
`IPRP NA Equity` = c(0.0102249488, 0.02620087336, 0),
`JPEIHDEU Index` = c(0.0102249488, 0.0283842, 0.267507919757698),
`IBTM IM Equity` = c(0, 0, 0.0043993518332871),
`EUNR GY Equity` = c(0, 0, 0.151777638248405),
`MWO FP Equity` = c(0, 0.0087336244, 0.0131980),
`LTM FP Equity` = c(0, 0, 0),
`XGLE GY Equity` = c(0, 0.00218340611353712, 0.00659902774993064), RiskFree = c(0, 0, 1.11022302462516e-16)),
.Names = c("US10 IM Equity", "SYBM GY Equity", "CRB IM Equity",
"IUSE LN Equity", "LEM FP Equity", "LGCPTREH Index", "IQQR GY Equity",
"XEIN GY Equity", "IJPE LN Equity", "LTI1TREH Index", "IMEU NA Equity",
"XAD1 IM Equity", "IHYG LN Equity", "IPRP NA Equity", "JPEIHDEU Index",
"IBTM IM Equity", "EUNR GY Equity", "MWO FP Equity", "LTM FP Equity",
"XGLE GY Equity", "RiskFree"),
row.names = c("2019-03-29", "2019-04-30", "2019-05-31"),
class = "data.frame")
第三数据帧（df3）：
structure(list(
`US10 IM Equity` = c(0.0449534, 0.0485797, 0.042739),
`SYBM GY Equity` = c(0.033885, 0.031267, 0.057019316),
`CRB IM Equity` = c(0.02968644, 0.02332370, 0.0214887),
`IUSE LN Equity` = c(0.02201, 0.0318220, 0.0163260),
`LEM FP Equity` = c(0.013045, 0.016096, 0.0209666),
`LGCPTREH Index` = c(0.08580, 0.088966, 0.1830580),
`IQQR GY Equity` = c(0.026100, 0.024201, 0.015489),
`XEIN GY Equity` = c(0.0821468, 0.094291, 0.075394),
`IJPE LN Equity` = c(0.036475, 0.039088, 0.032539),
`LTI1TREH Index` = c(0.124383, 0.134396, 0.071760),
`IMEU NA Equity` = c(0.03881, 0.03608789, 0.017015),
`XAD1 IM Equity` = c(0.0569415, 0.012407, 0.0396417),
`IHYG LN Equity` = c(0.021150, 0.0800146, 0.0043337),
`IPRP NA Equity` = c(0.033825, 0.0305668, 0.0410015),
`JPEIHDEU Index` = c(0.0619405, 0.0954634, 0.0755022),
`IBTM IM Equity` = c(0.066880, 0.0417783, 0.089807),
`EUNR GY Equity` = c(0.085595, 0.034687, 0.104800),
`MWO FP Equity` = c(0.03117, 0.0225397, 0.0294995),
`LTM FP Equity` = c(0.0077293, 0.01021646, 0.016242),
`XGLE GY Equity` = c(0.0974537, 0.1042031, 0.0453708),
RiskFree = c(0, 0, 1.11022302462516e-16)),
.Names = c("US10 IM Equity", "SYBM GY Equity", "CRB IM Equity",
"IUSE LN Equity", "LEM FP Equity", "LGCPTREH Index", "IQQR GY Equity",
"XEIN GY Equity", "IJPE LN Equity", "LTI1TREH Index", "IMEU NA Equity",
"XAD1 IM Equity", "IHYG LN Equity", "IPRP NA Equity", "JPEIHDEU Index",
"IBTM IM Equity", "EUNR GY Equity", "MWO FP Equity", "LTM FP Equity",
"XGLE GY Equity", "RiskFree"),
row.names = c("2019-03-29", "2019-04-30", "2019-05-31"),
class = "data.frame")
最终数据框应包含具有相同行名和列名的单元格，并且每个单元格应等于：
(df1 * 0.5) + (df2 * 0.25) + (df3 * 0.25)
我遇到的第一个问题是列的排列顺序不同，其次，我不知道如何将因子乘以的数据帧中的值组合到最终数据帧中
答案 0 :(得分：1)
怎么样
> rws=intersect(intersect(rownames(df1),rownames(df2)),rownames(df3))
> cls=intersect(intersect(colnames(df1),colnames(df2)),colnames(df3))
> df1[rws,cls]*0.5+df2[rws,cls]*0.25+df3[rws,cls]*0.25
CRB IM Equity US10 IM Equity SYBM GY Equity LEM FP Equity
2019-03-29 0.009466600 0.1362384 0.01000499 0.00326125
2019-04-30 0.014018698 0.1371449 0.00781675 0.00402400
2019-05-31 0.005372175 0.1106847 0.01700442 0.00524165
IUSE LN Equity LGCPTREH Index IJPE LN Equity XEIN GY Equity
2019-03-29 0.005502500 0.2972680 0.01065249 0.02053670
2019-04-30 0.007955500 0.1679839 0.01195541 0.02357275
2019-05-31 0.004631419 0.1474995 0.00813475 0.04469469
IQQR GY Equity LTI1TREH Index XAD1 IM Equity IMEU NA Equity
2019-03-29 0.006525000 0.15762949 0.014746622 0.009702500
2019-04-30 0.007687805 0.04069507 0.005285156 0.009021972
2019-05-31 0.007171764 0.01848992 0.009910425 0.006453426
IHYG LN Equity IPRP NA Equity JPEIHDEU Index IBTM IM Equity
2019-03-29 0.092199565 0.01101249 0.01804136 0.1417200
2019-04-30 0.086597536 0.01419192 0.03096190 0.1354446
2019-05-31 0.003283101 0.01025038 0.18575253 0.1235516
EUNR GY Equity MWO FP Equity LTM FP Equity XGLE GY Equity
2019-03-29 0.02139875 0.0077925 0.001932325 0.02436343
2019-04-30 0.00867175 0.1328183 0.127554115 0.02659663
2019-05-31 0.06414441 0.1106744 0.104060500 0.01299246
RiskFree
2019-03-29 0.000000e+00
2019-04-30 0.000000e+00
2019-05-31 5.551115e-17
答案 1 :(得分：1)
您还可以按列对各种data.frame进行排序，然后应用公式：
# to be sure, you can order also rows:
df1 <- df1[order(row.names(df1)),order(names(df1))]
df2 <- df2[order(row.names(df2)),order(names(df2))]
df3 <- df3[order(row.names(df3)),order(names(df3))]
df1*0.5 + df2*0.25 + df3*0.25
CRB IM Equity EUNR GY Equity IBTM IM Equity IHYG LN Equity IJPE LN Equity IMEU NA Equity IPRP NA Equity IQQR GY Equity
2019-03-29 0.009466600 0.02139875 0.1417200 0.092199565 0.01065249 0.009702500 0.01101249 0.006525000
2019-04-30 0.014018698 0.00867175 0.1354446 0.086597536 0.01195541 0.009021972 0.01419192 0.007687805
2019-05-31 0.005372175 0.06414441 0.1235516 0.003283101 0.00813475 0.006453426 0.01025038 0.007171764
IUSE LN Equity JPEIHDEU Index LEM FP Equity LGCPTREH Index LTI1TREH Index LTM FP Equity MWO FP Equity RiskFree SYBM GY Equity
2019-03-29 0.005502500 0.01804136 0.00326125 0.2972680 0.15762949 0.001932325 0.0077925 0.000000e+00 0.01000499
2019-04-30 0.007955500 0.03096190 0.00402400 0.1679839 0.04069507 0.127554115 0.1328183 0.000000e+00 0.00781675
2019-05-31 0.004631419 0.18575253 0.00524165 0.1474995 0.01848992 0.104060500 0.1106744 5.551115e-17 0.01700442
US10 IM Equity XAD1 IM Equity XEIN GY Equity XGLE GY Equity
2019-03-29 0.1362384 0.014746622 0.02053670 0.02436343
2019-04-30 0.1371449 0.005285156 0.02357275 0.02659663
2019-05-31 0.1106847 0.009910425 0.04469469 0.01299246
显然，这非常容易，但是如果三个rownames()中的colnames()和data.frame相同（如问题所述），它将可以正常工作。您可以使用setdiff()函数轻松地对其进行检查。
答案 2 :(得分：0)
因此，使用基r和dplyr的一种方法是分别处理数据帧，然后将日期添加为dplyr::group_by()的分组变量。
通常，如果您对随时间变化的图形感兴趣，我建议使用日期而不是行名作为列。
df1 <- df1*0.5
df1$date <- rownames(df1)
df2 <- df2*0.25
df2$date <- rownames(df2)
df3 <- df3*0.25
df3$date <- rownames(df3)
df_fin <- merge(df1,df2,all=T)
df_fin <- merge(df_fin,df3,all = T)
library(dplyr)
df_fin <- df_fin %>% group_by(date) %>% summarise_all(list(sum=sum) )
> df_fin
# A tibble: 3 x 22
date `CRB IM Equity_~ `US10 IM Equity~ `SYBM GY Equity~ `LEM FP Equity_~ `IUSE LN Equity~ `LGCPTREH Index~ `IJPE LN Equity~
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2019~ 0.00947 0.136 0.0100 0.00326 0.00550 0.297 0.0107
2 2019~ 0.0140 0.137 0.00782 0.00402 0.00796 0.168 0.0120
3 2019~ 0.00537 0.111 0.0170 0.00524 0.00463 0.147 0.00813
# ... with 14 more variables: `XEIN GY Equity_sum` <dbl>, `IQQR GY Equity_sum` <dbl>, `LTI1TREH Index_sum` <dbl>, `XAD1 IM
# Equity_sum` <dbl>, `IMEU NA Equity_sum` <dbl>, `IHYG LN Equity_sum` <dbl>, `IPRP NA Equity_sum` <dbl>, `JPEIHDEU Index_sum` <dbl>,
# `IBTM IM Equity_sum` <dbl>, `EUNR GY Equity_sum` <dbl>, `MWO FP Equity_sum` <dbl>, `LTM FP Equity_sum` <dbl>, `XGLE GY
# Equity_sum` <dbl>, RiskFree_sum <dbl>
>