str(my_data)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 17 obs. of 8 variables:
$ bbgid : chr "6869 HK" "YZJSGD SP" "YLLG SP" "200869 CH" ...
$ name : chr "YOFC-H" "YANGZIJIANG SHIP" "YANLORD LAND GRO" "YANTAI CHANGYU-B" ...
$ sector : chr "Information Technology" "Industrials" "Real Estate" "Consumer Staples" ...
$ portfolio: num 0 0 0 0.0071 0.013 0.0629 0.0101 0.0399 0.0013 0.0084 ...
$ benchmark: num 0.0006 0.002 0.0007 0.0004 0.0002 0.0001 0.001 0.0004 0.0008 0.0018 ...
$ date : chr "1/31/2018" "1/31/2018" "1/31/2018" "1/31/2018" ...
$ return : num 0.0111 0.0884 0.142 0.0467 0.0133 ...
$ country : chr "CN" "CN" "SG" "CN" ...
str(jan)
'data.frame': 3000 obs. of 15 variables:
$ barrid : Factor w/ 51132 levels "ARGAAB1","ARGAAC4",..: 43300 25132 25228 45045 45734 7535 7479 44276 40286 41347 ...
$ name : Factor w/ 51315 levels " ",..: 41025 34789 34790 11177 27326 8029 29073 5498 4141 17301 ...
$ return : num 0.0234 -0.0791 -0.0867 -0.07 -0.0723 ...
$ date : Date, format: "2010-01-01" "2010-01-01" ...
$ sector : Ord.factor w/ 10 levels "Energy"<"Materials"<..: 1 1 1 1 1 1 1 1 1 1 ...
$ momentum : num -0.052 1.757 1.757 0.382 0.629 ...
$ value : num 1.057 0.524 0.524 0.394 0.394 ...
$ size : num 0.25 -0.265 -0.265 -0.281 -0.074 0.715 0.172 0 -0.213 0.227 ...
$ growth : num 0.972 1.233 1.233 1.105 1.317 ...
$ cap.usd : num 2.72e+10 9.23e+09 9.12e+09 1.27e+10 1.71e+10 ...
$ yield : num 0 2.162 2.162 0.546 0.978 ...
$ country : Factor w/ 55 levels "ARE","ARG","AUS",..: 54 38 38 54 54 11 11 54 54 54 ...
$ currency : Factor w/ 44 levels "AREC","ARGC",..: 43 28 28 43 43 9 9 43 43 43 ...
$ portfolio: num 0 0 0 0 0 0 0 0 0 0 ...
$ benchmark: num 0.001259 0.000427 0.000422 0.000589 0.000791 ...