Le jeudi 23 juillet 2015 15:16:49 UTC+2, Andreas Noack a écrit :
>
> For the example you describe, you can simply use the tools in base, but
> unfortunately I don't think our reader can handle continental style decimal
> comma yet. However, that is easy to search/replace with a dot. Something
> like
>
> cov(diff(log(readdlm("prices.csv", ';'))))
>
> should then do the job.
>
Let's assume we have a csv file of the following form:
"Dates","ES1 Index","VG1 Index","TY1 Comdty","RX1 Comdty","GC1 Comdty"
"1999-01-04",1391.12,3034.53,66.515625,86.2,441.39
"1999-01-05",1404.86,3072.41,66.3125,86.17,440.63
"1999-01-06",1435.12,3156.59,66.4375,86.32,441.7
readcsv("prices.csv", header=true) does its job.
However, I won't be able to pass it to any function that performs any
numerical computation as all values in the first column are strings. Hence,
log(readcsv("prices.csv", header=true)) will return an error.
The read.csv function in R has a handy row.names arguments which - when you
set it equal to 1 - takes the first column of your csv file as a vector of
row-names. Hence, if you evaluate log(read.csv("prices.csv", sep = ";",
row.names=1)) you do not get any error as all values are now numerical +
each row keeps its own row name.
As of now, the only place where I found this handy feature is the
readtimearray function in the TimeSeries.jl package. Is there any other way
in Julia to benefit from this little but handy comfort that I am not yet
aware of?
