I have a list of 337 XTS objects which looks like this:
> head(blowup.instances)
$`AERI.US.Equity`
AERI.US.Equity
2015-04-24 -0.6363379
$SRPT.US.Equity
SRPT.US.Equity
2013-11-12 -0.6400985
2016-01-15 -0.5485299
$PTCT.US.Equity
PTCT.US.Equity
2016-02-23 -0.616419
When I try to write them to a csv, it does not work:
> write.csv(blowup.instances, "blowupInstances.csv")
Error in (function (..., row.names = NULL, check.rows = FALSE, check.names = TRUE, :
arguments imply differing number of rows: 1, 2, 3, 7, 4, 9, 5, 6, 18, 37, 8, 10, 78, 25, 11, 12, 20, 59, 17, 19, 27, 29, 16, 14, 31, 15, 51, 28, 54
Now I know why this doesn't work, but I want to know a solution to this other than writing out a large data frame full of NA values that I would have to remove in Excel. Any ideas?
If you're going to read your list of xts objects into Excel, you probably need to convert it to a flat file. The code below transforms each xts object into a data frame with each row containing the xts object name, and its dates and values. The map_dfr function from the purrr package in tidyverse is used to loop over the xts objects in the list and combine the results into a single data frame.
library(xts)
library(tidyverse)
#
# combine list of xts objects into a single data frame with equity names and dates
#
df_out <- map_dfr(blowup.instances, function(y) data_frame(Name = names(y), Date = index(y), value=as.vector(coredata(y))) )
#
# write as csv flat file
#
write.csv(df_out, file="blowupInstances.csv", row.names = FALSE)
The data frame written to the file is
df_out
# A tibble: 4 x 3
Name Date value
<chr> <date> <dbl>
1 AERI.US.Equity 2018-06-27 -0.5
2 SRPT.US.Equity 2018-06-26 -0.64
3 SRPT.US.Equity 2018-06-27 -0.55
4 PTCT.US.Equity 2018-06-20 -0.7
where the data a simple example set I made.