r-一个tsible对象是否可以有多行具有相同日期和关联的行值

Question

我有一个数据帧，我正在将其转换为tsibble时间序列对象，以便更容易地绘制时间序列图形和操作数据(滚动时间窗口分析(。我每天都会获得新的数据，我想将其附加到表示为df的原始数据帧上，新的传入数据表示为df2。我可以将这些data.frame独立地更改为tsibble对象，但当我先使用rbind()加入它们，然后使用as_tsibble时，我会出现错误。

as_tsibble(final_df, index = date, key = ticker)
Error: A valid tsibble must have distinct rows identified by key and index.
i Please use duplicates() to check the duplicated rows.

在这里设置问题是reprex的代码。

df <- data.frame(ticker = c("UST10Y", "UST2Y", "AAPL", "SPX", "BNO"),
buy_price = c(62.00, 68.00, 37.00, 55.00, 41.00),
sale_price = c(64.00, 71.00, 42.00, 60.00, 45.00),
close_price = c(63.00, 70.00, 38.00, 56.00, 43.00),
date = c(as.Date("April 29th, 2021", "April 29th, 2021", "April 29th, 2021", "April 29th, 2021", "April 29th, 2021")))
df2 <- data.frame(ticker = c("UST10Y", "UST2Y", "AAPL", "SPX", "BNO"),
buy_price = c(63.00, 69.00, 38.00, 53.00, 44.00),
sale_price = c(66.00, 77.00, 47.00, 63.00, 48.00),
close_price = c(65.00, 74.00, 39.00, 55.00, 45.00),
date = c(as.Date("April 30th, 2021", "April 30th, 2021", "April 30th, 2021", "April 30th, 2021", "April 30th, 2021")))
final_df <- rbind(df,df2)
str(final_df)
> 'data.frame': 10 obs. of  5 variables:
as_tsibble(final_df, index = date, key = ticker)

在运行代码as_tsibble(final_df, index = date, key = ticker)时，顺序也更改为字母顺序，而我希望保留原始顺序(另一个问题(。

虽然可以在df和df2上单独创建tsibble，但我无法使用final_df创建tsible。

我是遗漏了什么，还是不可能有一个具有多行相同股票代码名称的tsibble对象

Answer 1

对于时间序列中的每个观测，tsible必须有一个唯一的时间点(index(，其中每个时间序列由key标识。

您为MRE构建的数据集似乎具有这种质量，但迄今为止的转换并没有给您带来所需的结果。例如，df中的索引变量为：

as.Date("April 29th, 2021", "April 29th, 2021", "April 29th, 2021", "April 29th, 2021", "April 29th, 2021")
#> [1] "2021-05-06"

为了正确地解析"；2021年4月29日"；您可以使用{lubridate}包的mdy()功能：

lubridate::mdy("April 29th, 2021", "April 29th, 2021", "April 29th, 2021", "April 29th, 2021", "April 29th, 2021")
#> [1] "2021-04-29" "2021-04-29" "2021-04-29" "2021-04-29" "2021-04-29"

通过修复日期解析，问题得到了解决，我们可以创建tsible。

library(tsibble)
library(lubridate)
df <- data.frame(ticker = c("UST10Y", "UST2Y", "AAPL", "SPX", "BNO"),
buy_price = c(62.00, 68.00, 37.00, 55.00, 41.00),
sale_price = c(64.00, 71.00, 42.00, 60.00, 45.00),
close_price = c(63.00, 70.00, 38.00, 56.00, 43.00),
date = mdy(c("April 29th, 2021", "April 29th, 2021", "April 29th, 2021", "April 29th, 2021", "April 29th, 2021")))
df2 <- data.frame(ticker = c("UST10Y", "UST2Y", "AAPL", "SPX", "BNO"),
buy_price = c(63.00, 69.00, 38.00, 53.00, 44.00),
sale_price = c(66.00, 77.00, 47.00, 63.00, 48.00),
close_price = c(65.00, 74.00, 39.00, 55.00, 45.00),
date = mdy(c("April 30th, 2021", "April 30th, 2021", "April 30th, 2021", "April 30th, 2021", "April 30th, 2021")))
final_df <- rbind(df,df2)
as_tsibble(final_df, index = date, key = ticker)
#> # A tsibble: 10 x 5 [1D]
#> # Key:       ticker [5]
#>    ticker buy_price sale_price close_price date      
#>    <chr>      <dbl>      <dbl>       <dbl> <date>    
#>  1 AAPL          37         42          38 2021-04-29
#>  2 AAPL          38         47          39 2021-04-30
#>  3 BNO           41         45          43 2021-04-29
#>  4 BNO           44         48          45 2021-04-30
#>  5 SPX           55         60          56 2021-04-29
#>  6 SPX           53         63          55 2021-04-30
#>  7 UST10Y        62         64          63 2021-04-29
#>  8 UST10Y        63         66          65 2021-04-30
#>  9 UST2Y         68         71          70 2021-04-29
#> 10 UST2Y         69         77          74 2021-04-30

^{由reprex软件包(v1.0.0(于2021-05-06创建}