Use Apply family function to create multiple new dataframe

Question

I am new to R programming language and currently I working on some financial data. The problem is a bit complicated to describe so I think it's better to start it step by step.

First here is a small portion of the master dataframe(named:log_return) I am working on:

        Date    AUS.Yield   BRA.Yield    CAN.Yield    CHI.Yield    GER.Yield JAP.Yield
1 2008-01-01           NA          NA           NA           NA           NA        NA
2 2008-01-02           NA          NA           NA           NA           NA        NA
3 2008-01-03 -0.033047602 -0.01239795  0.003828977 -0.017857617 -0.031966192        NA
4 2008-01-04 -0.003922215  0.00198792 -0.008443187  0.006734032 -0.006984895        NA
5 2008-01-05           NA          NA           NA           NA           NA        NA
6 2008-01-06           NA          NA           NA           NA           NA        NA

Then I want to create a time series for each column with specific name, for example:

AUS <- xts(log_return$AUS.Yield, log_return$Date)
BRA <- xts(log_return$BRA.Yield, log_return$Date)....

and so on. Is there anyway I can use for loop or apply functions to create them rather than typing one by one?

I was thinking maybe I can create a list of dataframe with names like AUS, BRA etc and use for loop to assign the timeseries into those names. Not sure whether this is the correct approach or not.

Many thx!!!!!

Answer 1

Here's another way with a more traditional loop:

for (i in 2:length(log_return)) {
  assign(names(log_return[i]), xts(log_return[i], log_return$Date))
}

This will create an xts object for each column name in the data.frame -- that is, an xts object named AUS.Yield, BRA.Yield, etc...

Answer 2

A quick way to create a list of data.frame (zoo objects) with lapply:

library(zoo)
df_list <- lapply(names(df)[-1], function(x){
  zoo(df[,c("Date", x)])
})

Then to access your data.frame one by one:

> df_list[[1]]
  Date       AUS.Yield   
1 2008-01-01 <NA>        
2 2008-01-02 <NA>        
3 2008-01-03 -0.033047602
4 2008-01-04 -0.003922215
5 2008-01-05 <NA>        
6 2008-01-06 <NA> 

Data:

df <- structure(list(Date = c("2008-01-01", "2008-01-02", "2008-01-03", 
"2008-01-04", "2008-01-05", "2008-01-06"), AUS.Yield = c(NA, 
NA, -0.033047602, -0.003922215, NA, NA), BRA.Yield = c(NA, NA, 
-0.01239795, 0.00198792, NA, NA), CAN.Yield = c(NA, NA, 0.003828977, 
-0.008443187, NA, NA), CHI.Yield = c(NA, NA, -0.017857617, 0.006734032, 
NA, NA), GER.Yield = c(NA, NA, -0.031966192, -0.006984895, NA, 
NA), JAP.Yield = c(NA, NA, NA, NA, NA, NA)), .Names = c("Date", 
"AUS.Yield", "BRA.Yield", "CAN.Yield", "CHI.Yield", "GER.Yield", 
"JAP.Yield"), class = "data.frame", row.names = c("1", "2", "3", 
"4", "5", "6"))