如何解决在R中，有没有一种方法可以仅提取部分列名来收集数据帧？
概述
所以，我正在整理数据框。我已经找到解决问题的方法，但是当我处理大型数据集时，效率似乎很低。当前，我的代码收集我的数据框，应用一个单独的函数从指标中分离报价器，然后适当地分散数据。参见下面的示例
数据框
structure(list(date = c("2009-07-01","2009-07-02","2009-07-06","2009-07-07","2009-07-08"),PRED.Open = c(0.5,0.5,0.7,0.7),PRED.High = c(0.5,0.6,PRED.Low = c(0.5,PRED.Close = c(0.5,PRED.Volume = c(0L,300L,200L,0L,0L),PRED.Adjusted = c(0.5,GDM.Open = c(1041.02002,1085.109985,1052.02002,1011.429993,1006.630005),GDM.High = c(1097.790039,1029.290039,GDM.Low = c(1041.02002,1038.540039,995.450012,1005.280029,948.73999),GDM.Close = c(1085.109985,1006.630005,966.22998),GDM.Volume = c(0L,GDM.Adjusted = c(1085.109985,NBL.Open = c(29.885,29.325001,27.370001,27.485001,26.815001),NBL.High = c(30.35,27.545,27.610001,27.18),NBL.Low = c(29.83,28.07,26.825001,26.605,25.745001)),row.names = c(NA,-5L),class = "data.frame")
当前解决方案
df <- df %>% gather(c(2:ncol(df)),key = "ticker",value = "val")
df <- separate(df,col = "ticker",into = c("ticker","metric"),sep = "\\.") %>%
ungroup() %>%
spread(key = "metric",value = "val") %>%
arrange(ticker,date)
所需结果
问题
有没有更有效的方法来实现这一目标？
解决方法
如果您使用pivot_longer 1.0.0版中的tidyr，则可以在一行中完成此操作：
tidyr::pivot_longer(df,cols = -date,names_to = c('ticker','.value'),names_sep = '\\.') %>%
dplyr::arrange(ticker,date)
# A tibble: 15 x 8
# date ticker Open High Low Close Volume Adjusted
# <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
# 1 2009-07-01 GDM 1041.0 1097.8 1041.0 1085.1 0 1085.1
# 2 2009-07-02 GDM 1085.1 1085.1 1038.5 1052.0 0 1052.0
# 3 2009-07-06 GDM 1052.0 1052.0 995.45 1011.4 0 1011.4
# 4 2009-07-07 GDM 1011.4 1029.3 1005.3 1006.6 0 1006.6
# 5 2009-07-08 GDM 1006.6 1006.6 948.74 966.23 0 966.23
# 6 2009-07-01 NBL 29.885 30.35 29.83 NA NA NA
# 7 2009-07-02 NBL 29.325 29.325 28.07 NA NA NA
# 8 2009-07-06 NBL 27.370 27.545 26.825 NA NA NA
# 9 2009-07-07 NBL 27.485 27.610 26.605 NA NA NA
#10 2009-07-08 NBL 26.815 27.18 25.745 NA NA NA
#11 2009-07-01 PRED 0.5 0.5 0.5 0.5 0 0.5
#12 2009-07-02 PRED 0.5 0.6 0.5 0.6 300 0.6
#13 2009-07-06 PRED 0.7 0.7 0.5 0.5 200 0.5
#14 2009-07-07 PRED 0.7 0.7 0.7 0.7 0 0.7
#15 2009-07-08 PRED 0.7 0.7 0.7 0.7 0 0.7
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