DataCamp

Fill missing values by trading day

You may have noticed that the previous exercise carried the last observation of the prior day forward into the first observation of the following day. Sometimes you may need to start each day without any value from the previous trading day.

This exercise will show you how to fill missing values by trading day, without using the prior day's final value. You will use the same split-lapply-rbind paradigm you used in the Introduction to xts and zoo course.

In case you don't remember the paradigm, the pattern is below. Recall that the do.call(rbind, ...) syntax allows you to pass a list of objects to rbind() instead of having to type all their names by hand.

x_split <- split(x, f = "months")
x_list <- lapply(x_split, cummax)
x_list_rbind <- do.call(rbind, x_list)

Your workspace has a trade_day object that contains the regular series from the previous exercise, but without having any NA filled in.

Create a daily_list object by using split() to put the trade_day data into a list of data for each day.
Now use lapply() to fill the NA for each day's data in the daily_list list.
Finally, use do.call() and rbind() to convert daily_filled to a single xts object named filled_by_trade_day.

Take Hint (-30xp)

Exercise

Fill missing values by trading day

Instructions