SMA2 | Sorce code of Apkinson : android app

This is one way to do it.

By "reducing the length of the code" you're also losing information about frequency that you specified on the time series. You can also set period in the arima function that should lead to the same results.

You could use a diff() method on your Crossover_Long series - since it is already coded as just 0 or 1, when it changes state, you will get a diff of 1 or -1 one row only. Then just look for the non-0 rows and flag accordingly.

You're possibly making this a bit more complicated than it needs to be. You can convert a string to a formula by doing as.formula(string), so simply build your formula as a string, convert it to a formula, then feed it to ARIMA. Here's a reprex:

You do not define current_portfolio anywhere in this code, from what is shown at least.

In order to call current_portfolio it must be defined somewhere above where you call it.

This is why you are getting the "not defined" error.

I am able to figure out another reason for this error.

Along with just 1 level to a factor field(which is not the case in above data), above error appears when you have NA's available in data-set.

In above case, i removed NA's and error disappeared.

It seems that you have an enum that represents types, and a struct with the same name as an enum variant.

Note that enum variant names have nothing to do with any types they might represent.

In addition, reflections do not exist (not in the Java sense) in Rust, so you can't have a value that contains a type name and create that type from it.

However, it is possible to take an enum of unknown value (the type checker cannot constraint the enum values anyway), and return a value based on this enum.

Furthermore, while a method may not return unknown types directly, you can use a Box to wrap a value of unknown type, or create an enum that implements the trait and delegates to sealed implementations.

The following might be closer to what you wanted:

Like so? Or are you looking for something different?

Using dplyr, group_by person then use mutate. This will reset the calculations per person.