How to filter out na in R language

This is not directly related to dplyr::filter. However, any comparison involving NA, such as NA==NA, will yield NA as a result.
R is unaware of the context of your analysis.
Essentially, it does not permit comparison operators to treat NA as a valid value.
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Try this:

df %>% filter(!is.na(col1))

In R, null values do not possess a concept of equality. Consequently, the expression NA == NA yields NA. In reality, comparing NA with any object in R will also result in NA. The filter function in dplyr necessitates a boolean argument. Therefore, when it processes col1 and checks for inequality using filter(col1 != NA), the command 'col1 != NA' consistently produces NA values for every row in col1. Since this does not constitute a boolean expression, the filter function fails to evaluate correctly.

Is it possible to generate a list similar to the one below to identify the rows that contain no null values, and subsequently use this list with the dplyr function to filter rows based on their position values?

In this case, na.omit(airquality$Ozone) will yield the values that are not null.

Afterward, can we supply the list of positions to the filtering function?

The dplyr package includes the 'filter()' function for filtering data, but its capabilities extend beyond that. With dplyr, you can execute filtering operations that may be challenging or complex to achieve using SQL or conventional business intelligence tools, all in a straightforward and intuitive manner. For instance, consider a flight dataset where we can eliminate NA values using the filter keyword.

flight %>%  select(FL_DATE, CARRIER, ORIGIN, ORIGIN_CITY_NAME, ORIGIN_STATE_ABR, DEP_DELAY, DEP_TIME, ARR_DELAY, ARR_TIME) %>%  filter(!is.na(ARR_DELAY))