基于对应列标识符R的数据整形

Question

I'm tried to reshape a two column data frame by collapse the corresponding column values that match in column 2 - in this case ticker symbols to their own unique row while making the contents of column 1 which are the fields of data that correspond to those tickers their own columns. See for example a small sample since it's a data frame with 500 tickers and 4 fields:

                            test22 Ticker
         Current SharePrice $6.57    MFM
                Current NAV $7.11    MFM
  Current Premium/Discount -7.59%    MFM
         52WkAvg SharePrice $6.55    MFM
                52WkAvg NAV $7.21    MFM
 52WkAvg Premium/Discount -9.19%    MFM
        52WkHigh SharePrice $6.88    MFM
               52WkHigh NAV $7.34    MFM
 52WkHigh Premium/Discount -5.88%    MFM
        52WkLow SharePrice $6.05    MFM
               52WkLow NAV $7.03    MFM
52WkLow Premium/Discount -14.43%    MFM
        Current SharePrice $4.84    CXE
               Current NAV $5.21    CXE
 Current Premium/Discount -7.10%    CXE
        52WkAvg SharePrice $4.91    CXE
               52WkAvg NAV $5.29    CXE
 52WkAvg Premium/Discount -7.26%    CXE
       52WkHigh SharePrice $5.31    CXE
              52WkHigh NAV $5.37    CXE
52WkHigh Premium/Discount -1.12%    CXE
        52WkLow SharePrice $4.58    CXE
               52WkLow NAV $5.16    CXE
52WkLow Premium/Discount -11.92%    CXE 

Ideally, the ticker column after the reformatting transformation is a unique row with the ticker at the row name with, in this case, 12 corresponding columns containing the contents of the "test22" column but their own names at this stage aren't import. Help is much appreciated!

Answer 1

I interpreted this problem as turning this long data into a wide format. The hardest part of this problem is separating the number from the description. Once that was done it was using the spread function to convert to wide.

df<-structure(list(test22 = structure(c(24L, 20L, 22L, 6L, 2L, 4L, 
12L, 8L, 10L, 18L, 14L, 16L, 23L, 19L, 21L, 5L, 1L, 3L, 11L, 
7L, 9L, 17L, 13L, 15L), .Label = c("52WkAvg NAV $5.29", "52WkAvg NAV $7.21", 
"52WkAvg Premium/Discount -7.26%", "52WkAvg Premium/Discount -9.19%", 
"52WkAvg SharePrice $4.91", "52WkAvg SharePrice $6.55", "52WkHigh NAV $5.37", 
"52WkHigh NAV $7.34", "52WkHigh Premium/Discount -1.12%", "52WkHigh Premium/Discount -5.88%", 
"52WkHigh SharePrice $5.31", "52WkHigh SharePrice $6.88", "52WkLow NAV $5.16", 
"52WkLow NAV $7.03", "52WkLow Premium/Discount -11.92%", "52WkLow Premium/Discount -14.43%", 
"52WkLow SharePrice $4.58", "52WkLow SharePrice $6.05", "Current NAV $5.21", 
"Current NAV $7.11", "Current Premium/Discount -7.10%", "Current Premium/Discount -7.59%", 
"Current SharePrice $4.84", "Current SharePrice $6.57"), class = "factor"), 
    Ticker = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
    ), .Label = c("CXE", "MFM"), class = "factor")), class = "data.frame", row.names = c(NA, 
-24L))


library(tidyr)

#separate the number for the text
df2<-separate(df, test22, into=c("key", "value"), sep=" (?=[$]*[-\\.0-9]+%*)", extra="merge")

#spread from long to wide
spread(df2, key=key, value=value)

#columns are abridged for clarity
#Ticker 52WkAvg NAV 52WkAvg Premium/Discount 52WkAvg SharePrice 52WkHigh NAV 52WkHigh Premium/Discount 52WkHigh ...
#CXE       $5.29                   -7.26%              $4.91        $5.37                    -1.12%                     
#MFM       $7.21                   -9.19%              $6.55        $7.34                    -5.88%