When trying to replace values, "missing values are not allowed in subscripted assignments of data frames"

It is due to missingness in H01 variable.

> x <- data.frame(a=c(NA,2:5), b=c(1:5))
> x
   a b
1 NA 1
2  2 2
3  3 3
4  4 4
5  5 5
> x[x$a==2,]$b <- 99
Error in `[<-.data.frame`(`*tmp*`, x$a == 1, , value = list(a = NA_integer_,  : 
  missing values are not allowed in subscripted assignments of data frames

The assignment won't work because x$a has a missing value.

Subsetting first works:

> z <- x[x$a==2,]
> z$b <- 99
> z <- x[x$a==2,]
> z
    a  b
NA NA NA
2   2  2

But that's because the [<- function apparently can't handle missing values in its extraction indices, even though [ can:

> `[<-`(x,x$a==2,,99)
Error in `[<-.data.frame`(x, x$a == 2, , 99) : 
  missing values are not allowed in subscripted assignments of data frames

So instead, trying specifying your !is.na(x$a) part when you're doing the assignment:

> `[<-`(x,!is.na(x$a) & x$a==2,'b',99)
   a  b
1 NA  1
2  2 99
3  3  3
4  4  4
5  5  5

Or, more commonly:

> x[!is.na(x$a) & x$a==2,]$b <- 99
> x
   a  b
1 NA  1
2  2 99
3  3  3
4  4  4
5  5  5

Note that this behavior is described in the documentation:

The replacement methods can be used to add whole column(s) by specifying non-existent column(s), in which case the column(s) are added at the right-hand edge of the data frame and numerical indices must be contiguous to existing indices. On the other hand, rows can be added at any row after the current last row, and the columns will be in-filled with missing values. Missing values in the indices are not allowed for replacement.

You can use ifelse, like so

pe94.person$foo <- ifelse(!is.na(pe94.person$H01) & pe94.person$H01 == 12, 0, pe94.person$H03)

check if foo meets your criteria and then go ahead and assign it to pe94.person$H03 directly. I find it safer to assign it a new variable and usually use that in subsequent analysis.

There might be an NA somewhere in the column that is causing the error. Run the index on a specific column instead of the entire data frame.

movies[movies$Actors == "N/A",] = NA #ERROR
movies$Actors[movies$Actors == "N/A"] = NA #Works