How to loop through objects in the global environment - R - r

Question

I have looked far and wide for a solution to this issue, but I cannot seem to figure it out. I do not have much experience working with xts objects in R.
I have 40 xts objects (ETF data) and I want to run the quantmod function WeeklyReturn on each of them individually.
I have tried to refer to them by using the ls() function:
lapply(ls(), weeklyReturn)
I have also tried the object() function
lapply(object(), weeklyReturn)
I have also tried using as.xts() in my call to coerce the ls() objects to be used as xts but to no avail.
How can I run this function on every xts object in the environment?
Thank you,

Answer 1

It would be better to load all of your xts objects into a list or create them in a way that returns them in a list to begin with. Then you could do results = lapply(xts.list, weeklyReturn).
To work with objects in the global environment, you could test for whether the object is an xts object and then run weeklyReturn on it if it is. Something like this:
results = lapply(setNames(ls(), ls()), function(i) {
x = get(i)
if(is.xts(x)) {
weeklyReturn(x)
}
})
results = results[!sapply(results, is.null)]
Or you could select only the xts objects to begin with:
results = sapply(ls()[sapply(ls(), function(i) is.xts(get(i)))],
function(i) weeklyReturn(get(i)), simplify=FALSE, USE.NAMES=TRUE)
lapply(ls(), weeklyReturn) doesn't work, because ls() returns the object names as strings. The get function takes a string as an argument and returns the object with that name.

Answer 2

An alternate solution using the tidyquant package. Note that this is data frame based so I will not be working with xts objects. I use two core functions to scale the analysis. First, tq_get() is used to go from a vector of ETF symbols to getting the prices. Second, tq_transmute() is used to apply the weeklyReturn function to the adjusted prices.
library(tidyquant)
etf_vec <- c("SPY", "QEFA", "TOTL", "GLD")
# Use tq_get to get prices
etf_prices <- tq_get(etf_vec, get = "stock.prices", from = "2017-01-01", to = "2017-05-31")
etf_prices
#> # A tibble: 408 x 8
#> symbol date open high low close volume adjusted
#> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 SPY 2017-01-03 227.121 227.919 225.951 225.24 91366500 223.1760
#> 2 SPY 2017-01-04 227.707 228.847 227.696 226.58 78744400 224.5037
#> 3 SPY 2017-01-05 228.363 228.675 227.565 226.40 78379000 224.3254
#> 4 SPY 2017-01-06 228.625 229.856 227.989 227.21 71559900 225.1280
#> 5 SPY 2017-01-09 229.009 229.170 228.514 226.46 46265300 224.3848
#> 6 SPY 2017-01-10 228.575 229.554 228.100 226.46 63771900 224.3848
#> 7 SPY 2017-01-11 228.453 229.200 227.676 227.10 74650000 225.0190
#> 8 SPY 2017-01-12 228.595 228.847 227.040 226.53 72113200 224.4542
#> 9 SPY 2017-01-13 228.827 229.503 228.786 227.05 62717900 224.9694
#> 10 SPY 2017-01-17 228.403 228.877 227.888 226.25 61240800 224.1767
#> # ... with 398 more rows
# Use tq_transmute to apply weeklyReturn to multiple groups
etf_returns_w <- etf_prices %>%
group_by(symbol) %>%
tq_transmute(select = adjusted, mutate_fun = weeklyReturn)
etf_returns_w
#> # A tibble: 88 x 3
#> # Groups: symbol [4]
#> symbol date weekly.returns
#> <chr> <date> <dbl>
#> 1 SPY 2017-01-06 0.0087462358
#> 2 SPY 2017-01-13 -0.0007042173
#> 3 SPY 2017-01-20 -0.0013653367
#> 4 SPY 2017-01-27 0.0098350474
#> 5 SPY 2017-02-03 0.0016159256
#> 6 SPY 2017-02-10 0.0094619381
#> 7 SPY 2017-02-17 0.0154636969
#> 8 SPY 2017-02-24 0.0070186222
#> 9 SPY 2017-03-03 0.0070964211
#> 10 SPY 2017-03-10 -0.0030618336
#> # ... with 78 more rows