Error with knn function

Question 1

I suspect that your issue lies in having non-numeric data fields in 'mydades'. The error line:

NA/NaN/Inf in foreign function call (arg 6)

makes me suspect that the knn-function call to the C language implementation fails. Many functions in R actually call underlying, more efficient C implementations, instead of having an algorithm implemented in just R. If you type just 'knn' in your R console, you can inspect the R implementation of 'knn'. There exists the following line:

 Z <- .C(VR_knn, as.integer(k), as.integer(l), as.integer(ntr), 
        as.integer(nte), as.integer(p), as.double(train), as.integer(unclass(clf)), 
        as.double(test), res = integer(nte), pr = double(nte), 
        integer(nc + 1), as.integer(nc), as.integer(FALSE), as.integer(use.all))

where .C means that we're calling a C function named 'VR_knn' with the provided function arguments. Since you have two of the errors

NAs introduced by coercion

I think two of the as.double/as.integer calls fail, and introduce NA values. If we start counting the parameters, the 6th argument is:

as.double(train)

that may fail in cases such as:

# as.double can not translate text fields to doubles, they are coerced to NA-values:
> as.double("sometext")
[1] NA
Warning message:
NAs introduced by coercion
# while the following text is cast to double without an error:
> as.double("1.23")
[1] 1.23

You get two of the coercion errors, which are probably given by 'as.double(train)' and 'as.double(test)'. Since you did not provide us with exact details of how 'mydades' is, here are some of my best guesses (and an artificial multivariate normal distribution data):

library(MASS)
mydades <- mvrnorm(100, mu=c(1:6), Sigma=matrix(1:36, ncol=6))
mydades <- cbind(mydades, sample(LETTERS[1:5], 100, replace=TRUE))

# This breaks knn
mydades[3,4] <- Inf
# This breaks knn
mydades[4,3] <- -Inf
# These, however, do not introduce the coercion for NA-values error message

# This breaks knn and gives the same error; just some raw text
mydades[1,2] <- mydades[50,1] <- "foo"
mydades[100,3] <- "bar"

# ... or perhaps wrongly formatted exponential numbers?
mydades[1,1] <- "2.34EXP-05"

# ... or wrong decimal symbol?
mydades[3,3] <- "1,23" 
# should be 1.23, as R uses '.' as decimal symbol and not ','

# ... or most likely a whole column is non-numeric, since the error is given twice (as.double problem both in training AND test set)
mydades[,1] <- sample(letters[1:5],100,replace=TRUE)

I would not keep both the numeric data and class labels in a single matrix, perhaps you could split the data as:

mydadesnumeric <- mydades[,1:6] # 6 first columns
mydadesclasses <- mydades[,7]

Using calls

str(mydades); summary(mydades)

may also help you/us in locating the problematic data entries and correct them to numeric entries or omitting non-numeric fields.

The rest of the run code (after breaking the data), as provided by you:

N <- nrow(mydades) 
permut <- sample(c(1:N),N,replace=FALSE)
ord <- order(permut)
mydades.shuffled <- mydades[ord,]
prop.train <- 1/3
NOMBRE <- round(prop.train*N)
mydades.training <- mydades.shuffled[1:NOMBRE,]
mydades.test <- mydades.shuffled[(NOMBRE+1):N,]

# 7th column seems to be the class labels
knn(train=mydades.training[,-7],test=mydades.test[,-7],mydades.training[,7],k=5)

Question 2

Great answer by@Teemu.

As this is a well-read question, I will give the same answer from an analytics perspective.

The KNN function classifies data points by calculating the Euclidean distance between the points. That's a mathematical calculation requiring numbers. All variables in KNN must therefore be coerce-able to numerics.

The data preparation for KNN often involves three tasks:
(1) Fix all NA or "" values
(2) Convert all factors into a set of booleans, one for each level in the factor
(3) Normalize the values of each variable to the range 0:1 so that no variable's range has an unduly large impact on the distance measurement.

Question 3

I would also point out that the function seems to fail when using integers. I needed to convert everything into "num" type prior to calling the knn function. This includes the target feature, which most methods in R use the factor type. Thus, as.numeric(my_frame$target_feature) is required.