Missing value where true/false needed while loop r

you pass

total = total + rho.prime(y-b0-b1*x,k)*(y[i]-b0-b1*x[i])

total = total + rho.prime(y-b0-b1*x,k)*(y[i]-b0-b1*x[i])

X1 <- X0 + alpha * df(X0)

X1 <- X0 + alpha * df(X0)

total = total + rho.prime(y-b0-b1*x,k)*(y[i]-b0-b1*x[i])

coerces the

X1 <- X0 + alpha * df(X0)

X1 <- X0 + alpha * df(X0)

X1 <- X0 + alpha * df(X0)

I can't suggest a solution because I have no clue what you are trying to accomplish here.

EDIT (Response to OP's comment).

Notwithstanding @thelatemail's comment, which I agree with wholeheartedly, your new error is rather obscure.

In your earlier version, you were passing a function,

X1 <- X0 + alpha * df(X0)

X1 <- X0 + alpha * df(X0)

X1 <- X0 + alpha * df(X0)

Well, normally this would throw an error telling you that

X1 <- X0 + alpha * df(X0)

X1 <- X0 + alpha * df(X0)

gr.ascent(MSEE,...)

gr.ascent(MSEE,...)

I have a data frame called Data that I want to analyse with some basic statistics such as ACP.
But I found that inside the variable Land use change there is some negative values.
So my goal is to use only variables without those negatives.
I tried to implement a loop to remove rows that contains negatives values of Land use change.
When I try to run it into the console, it works but I can't "knit" the markdown document :

# Removing variables with negatives values for Land use change

for(i in 1:length(Data$Retail)) {
  if(Data$`Land use change`[i]<0) {
    Data <- Data[-c(i), ]
    }
} 

It returns me this error :

Error in if (Data$`Land use change`[i] < 0) { : 
  missing value where TRUE/FALSE needed

I tried to look for informations in other forums, and I found that this error returns when there is a NA value in the if statement. But as you can see, I don't have any missing value inside the Land use change column.

image1430×128 17.9 KB

Any help is welcome !

sidenote, I don't understand the actual conceptual purpose of your code, but as written it throws warnings, and it doesn't make intuitive sense why one would do the thing that gives the warnings.
Therefore I suspect that

fes_eq[i] <- 1-f-exp(-r0*f[i])

should be changed to

fes_eq[i] <- 1-f[i]-exp(-r0*f[i])

so that each fes_eq[i] value equals exactly one thing, and not 1001 things...
but I don't know

In this article, we are going to see how to fix missing values where true/false is needed in R Programming Language.

Why Does This Error Occur?

When we will compare the value with NA(Not available) putting into an “if statement” or “while statement”, then the ”missing value where true/false needed” error occurs is that you are passing an invalid value to “if statement” or “while statement”.  

Example 1: Fixing with variables

How to Reproduce the Error

Here we will get the error because we used the syntax vec[l] != NA. We can not compare the value with NA number, this type of statement(If condition) always returns TRUE or FALSE value.

I received this error message:

Error in if (condition) { : missing value where TRUE/FALSE needed

or

Error in while (condition) { : missing value where TRUE/FALSE needed

What does it mean, and how do I prevent it?

The “missing value where true/false needed” R error results from a failure to properly define the input into an “if statement” or “while statement” as either true or false. If you’re getting this error, you’re probably feeding a column of missing values or na value records into a data science procedure where the expected input is a logical vector (logical values – true / false ). This may be a single row or across the entire dataset. Either way, it will throw an error condition that will pause r code execution. It is an easy error to make, but also easy to correct.

When Does This Error Occur?

You will get this error message if the value you are putting into an “if statement” or “while statement” is not available (NA). When this occurs, these statements cannot process the data resulting in an error message. Here is a simple example of a code that produces this error message. Learning how to use the if statement properly, or different loops and functions in R programming can help you avoid these errors in the future.

# source: R Error: Missing value where true/false needed
 > x = NA
 > if(x) {x}
 Error in if (x) { : missing value where TRUE/FALSE needed

As you can tell the variable “x” has a value of “NA” it has a result it triggered the error message.

But Why Does This Error Occur?

The reason why the “missing value where true/false needed” error message occurs is that you are passing an invalid value to “if statement” or “while statement.” These statements simply check to see if the argument is true or false. If the value it gets is not one of these, it will produce an error message. This is actually one of the simplest error messages to understand. Not only is there a message simple, but it gives meaningful information. This means the error message is useful in helping to understand what is going on.

How Do I Fix This Error?

The fix for this error is quite simple. All you need to do embed your “if statement” or “while statement” in another “if statement” that puts the value through the is.na() function to see if its value is “NA” or not. This will allow you to avoid this error message, as illustrated below.

# solution: Missing value where true/false needed
> x = NA
> if(is.na(x)) {x=FALSE} else {if(x) {x}} 

Now that this simple little check, not only does it avoids the error message, but it provides you a way the correct the error when it occurs. Once the error has been detected, you can use it to define the value of the variable being checked who wrote it is no longer “NA” and causes no further problems. In our example above, because the value of “x” becomes “FALSE.” The result is that we have not just bypassed the error what about you corrected it.

# solution - bypassing error: Missing value where true/false needed
 > x = TRUE
 > if(is.na(x)) {x=FALSE} else {if(x) {x}} 
 [1] TRUE

The second example shows the results of the corrected version if x is true. This is because if x is true, it doesn’t have a value of “NA” and so the first “if statement” is false. This means that it gets passed on to the second “if statement” that detects the value of “x” as being “TRUE” and it prints the value of “x.” This is an easy error message to both understand and correct. Correcting it simply involves detecting “NA” value (missing data) before going through the “if statement.” This can be prevented by using isna to filter out bad case examples within a data frame to avoid blowing up a mathematical operation. Many base R package(s) include the narm option as well, which allows you to specify how the computation will handle missing value(s).

We hope our quick tutorial on fixing the “missing value where true/false needed” R error was helpful, and encourage you to check out more of our site for all of your R programming needs!

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