I have multiple xts objects stored in a list each with 1000+ rows. They represent stock rolling window regression data. Each element has it's unique Ticker name. Here they are called Stock1, 2 ...etc for testing purposes. Rows are named by date as is the xts format. Each element is equal in dimensions. Each one looks like this:
> tail(testlist$Stock1, n = 3)
(Intercept) rmrf smb hml rmw cma
2014-12-29 0.0003223177 1.010215 -0.02164844 -0.3322500 0.07819563 1.106934
2014-12-30 0.0002631315 1.002356 -0.02351438 -0.3465390 0.05954400 1.118506
2014-12-31 0.0002837304 1.000084 -0.01619536 -0.3494401 0.06121434 1.124845
> tail(testlist$Stock2, n = 3)
(Intercept) rmrf smb hml rmw cma
2014-12-29 0.0003308951 0.7503819 -0.1967255 -0.10242616 -0.2264914 0.8329570
2014-12-30 0.0003051495 0.7409709 -0.1899856 -0.07461764 -0.2240448 0.7921883
2014-12-31 0.0002614874 0.7478099 -0.1833077 -0.06197362 -0.2056615 0.7550211
> tail(testlist$Stock3, n = 3)
(Intercept) rmrf smb hml rmw cma
2014-12-29 -0.0003803988 0.8363603 -0.4153470 0.7459769 -0.7981382 -0.2839360
2014-12-30 -0.0004121386 0.8352243 -0.4224404 0.7405976 -0.8114066 -0.2790438
2014-12-31 -0.0004660716 0.8355641 -0.4343012 0.7571033 -0.8057412 -0.3026019
> tail(testlist$Stock4, n = 3)
(Intercept) rmrf smb hml rmw cma
2014-12-29 -0.0008295692 0.9296299 -0.07776571 0.007084297 -0.1377356 0.8038542
2014-12-30 -0.0007734696 0.9383387 -0.08941983 0.011685507 -0.1092656 0.7863335
2014-12-31 -0.0007591168 0.9391670 -0.08782070 0.015619229 -0.1083707 0.7924232
What i need to do: Merge the rows by name and by aggregating all data in my list to obtain a new set of data. Each should look like this:
Name Date (Intercept) rmrf smb hml rmw cma
Stock1 2014-12-29 0.0003223177 1.010215 -0.02164844 -0.3322500 0.07819563 1.106934
Stock2 2014-12-29 0.0003308951 0.7503819 -0.1967255 -0.10242616 -0.2264914 0.8329570
Stock3 2014-12-29 -0.0003803988 0.8363603 -0.4153470 0.7459769 -0.7981382 -0.2839360
Stock4 2014-12-29 -0.0008295692 0.9296299 -0.07776571 0.007084297 -0.1377356 0.8038542
Each such element should not be a time-series any more. but a static one, with each stock representing it's coeffiecient values at time "t". In terms of size each element should have a number of rows equal to the number of Stocks in the original list.
EDIT as asked by josilber
> dput(list(Stock1=tail(testlist$Stock1, n = 3), Stock2=tail(testlist$Stock2, n = 3)))
structure(list(Stock1 = structure(c(0.000322317700198485, 0.000263131488679374,
0.000283730373928844, 1.01021497011709, 1.00235580055438, 1.00008407331697,
-0.0216484434660844, -0.023514378867335, -0.0161953614672028,
-0.332250031553704, -0.346538978804535, -0.349440052163927, 0.078195628743663,
0.0595439997647003, 0.0612143446991752, 1.1069343396633, 1.11850626745067,
1.12484530131584), class = c("xts", "zoo"), .indexCLASS = "Date", tclass = "Date", .indexTZ = "UTC", tzone = "UTC", index = structure(c(1419811200,
1419897600, 1419984000), tzone = "UTC", tclass = "Date"), .Dim = c(3L,
6L), .Dimnames = list(NULL, c("(Intercept)", "rmrf", "smb", "hml",
"rmw", "cma"))), Stock2 = structure(c(0.000330895099805035, 0.000305149500450527,
0.000261487411574969, 0.750381906747217, 0.740970893865186, 0.747809929767095,
-0.1967254672836, -0.189985607343021, -0.183307667378927, -0.10242615734439,
-0.0746176364711423, -0.0619736225998069, -0.226491384004977,
-0.224044849587752, -0.205661480898329, 0.832956994676299, 0.792188348360969,
0.755021100668421), class = c("xts", "zoo"), .indexCLASS = "Date", tclass = "Date", .indexTZ = "UTC", tzone = "UTC", index = structure(c(1419811200,
1419897600, 1419984000), tzone = "UTC", tclass = "Date"), .Dim = c(3L,
6L), .Dimnames = list(NULL, c("(Intercept)", "rmrf", "smb", "hml",
"rmw", "cma")))), .Names = c("Stock1", "Stock2"))
I am completely in the dark. I have looked at some functions that may come to use: lapply
/ also the merge
function seems to be suitable but it only works on 2 elements.
I will continue to update this post as i search for answers. If anyone has any leads or has done this before and can point in the right direction, thank you!
EDIT
#Flatten data add one more name and put into one data frame
all_coef_data<- do.call(rbind,Map(cbind,
Name=names(testlist),
Date=lapply(testlist,function(x) as.Date(as.POSIXct(c(attr(x,'index')),origin='1970-01-01'))),
lapply(testlist, as.data.frame)
))
A common denominator in each row that i need to get out is the Date. I now split the dataframe by Date using this. The output is a list.
out <- split(all_coef_data , f = all_coef_data$Date )
output:
> head(out$'2011-05-23', n=3)
Name Date (Intercept) rmrf smb hml rmw cma
Stock1.2011-05-23 Stock1 2011-05-23 -4.376389e-04 1.103582 -0.21747611 -0.1879211 -0.05849794 -0.1949192
Stock2.2011-05-23 Stock2 2011-05-23 1.115140e-04 1.198622 0.05422819 0.9998529 0.92141407 -0.8565260
Stock3.2011-05-23 Stock3 2011-05-23 5.457214e-05 1.303025 0.04705294 0.6897673 -0.19708983 -0.8247877
> tail(out$'2011-05-23', n=3)
Name Date (Intercept) rmrf smb hml rmw cma
Stock48.2011-05-23 Stock48 2011-05-23 0.0007354997 0.505054 0.1774544 -0.38934089 0.71775909 0.5189329
Stock49.2011-05-23 Stock49 2011-05-23 0.0004224351 1.304719 0.4511903 -0.64937062 -0.08872941 0.1545058
Stock50.2011-05-23 Stock50 2011-05-23 0.0003851261 1.020434 -0.1107910 -0.03964192 0.09526658 -0.4961902
Sounds like you want to
as.data.frame()
,cbind()
new columnsName
(from list component names) andDate
(from xts row names, which actually come from theindex
attribute), and finallyrbind()
everything together into a single data.frame.If you don't like the new row names, you can wrap the line in
`rownames<-`(...,NULL)
.