需要从r中的列中分离出变量名
所以我有一个非常糟糕的数据集,我不允许更改。我想使用列Draw_CashFlow,只将某些值放入自己的列中。此外,如果你愿意,我需要将变量都设置为一列周期宽,以便整理 在下面的数据集中,我们有一个列Draw_CashFlow,它以所讨论的变量开始,后跟一个ID列表,然后为下一个变量重复。某些变量可能有NA条目需要从r中的列中分离出变量名,r,dataframe,dplyr,tidyverse,tidyr,R,Dataframe,Dplyr,Tidyverse,Tidyr,所以我有一个非常糟糕的数据集,我不允许更改。我想使用列Draw_CashFlow,只将某些值放入自己的列中。此外,如果你愿意,我需要将变量都设置为一列周期宽,以便整理 在下面的数据集中,我们有一个列Draw_CashFlow,它以所讨论的变量开始,后跟一个ID列表,然后为下一个变量重复。某些变量可能有NA条目 structure(list(Draw_CashFlow = c("Principal", "R01", "R02", "R03", "Workout Recovery Principa
structure(list(Draw_CashFlow = c("Principal", "R01",
"R02", "R03", "Workout Recovery Principal",
"Prepaid Principal", "R01", "R02", "R03",
"Interest", "R01", "R02"), `PERIOD 1` = c(NA,
834659.51, 85800.18, 27540.31, NA, NA, 366627.74, 0, 0, NA, 317521.73,
29175.1), `PERIOD 2` = c(NA, 834659.51, 85800.18, 27540.31, NA,
NA, 306125.98, 0, 0, NA, 302810.49, 28067.8), `PERIOD 3` = c(NA,
834659.51, 85800.18, 27540.31, NA, NA, 269970.12, 0, 0, NA, 298529.92,
27901.36), `PERIOD 4` = c(NA, 834659.51, 85800.18, 27540.31,
NA, NA, 307049.06, 0, 0, NA, 293821.89, 27724.4)), row.names = c(NA,
-12L), class = c("tbl_df", "tbl", "data.frame"))
现在它是一个有限的变量列表,需要本金、回收本金、预付本金和利息,所以我试着做一个循环,看看它是否存在,然后收集,但这是不正确的
在将变量与Draw_CashFlow分开后,我希望前四行类似,忽略变量缩写
ID Period Principal Wrk_Reco_Principal Prepaid_Principal Interest
R01 1 834659.51 NA 366627.74 317521.73
R02 1 85800.18 NA 0.00 29175.10
R03 1 27540.31 NA 0.00 NA
R01 2 834659.51 NA 306125.98 302810.49
注:Wrl_Reco_本金为NA,因为此变量的提款现金流中没有ID。请记住,这应该是为了对抗任何数量的ID而构建的,但是Draw\u CashFlow列中的变量名称始终是相同的。这里有一种方法,它假设以R开头的Draw\u CashFlow值是ID号。您可能需要另一种方法,例如!在变量的%LIST\中提取\u现金流%(如果不起作用)
df %>%
# create separate columns for ID and Variable
mutate(ID = if_else(Draw_CashFlow %>% str_starts("R"),
Draw_CashFlow, NA_character_),
Variable = if_else(!Draw_CashFlow %>% str_starts("R"),
Draw_CashFlow, NA_character_)) %>%
fill(Variable) %>% # Fill down Variable in NA rows from above
select(-Draw_CashFlow) %>%
gather(Period, value, -c(ID, Variable)) %>% # Gather into long form
drop_na() %>%
spread(Variable, value, fill = 0) %>% # Spread based on Variable
mutate(Period = parse_number(Period))
# A tibble: 12 x 5
ID Period Interest `Prepaid Principal` Principal
<chr> <dbl> <dbl> <dbl> <dbl>
1 R01 1 317522. 366628. 834660.
2 R01 2 302810. 306126. 834660.
3 R01 3 298530. 269970. 834660.
4 R01 4 293822. 307049. 834660.
5 R02 1 29175. 0 85800.
6 R02 2 28068. 0 85800.
7 R02 3 27901. 0 85800.
8 R02 4 27724. 0 85800.
9 R03 1 0 0 27540.
10 R03 2 0 0 27540.
11 R03 3 0 0 27540.
12 R03 4 0 0 27540.