KNN: "no missing values are allow" -> I do not have missing values - r

Question

I am in a group project for a class and one of the people in my group ran the normalization, as well as creating the test/train sets so that we all have the same sets to work from (we're all utilizing different algorithms). I am assigned with running the KNN algorithm.
We had multiple columns with NA's so those columns were omitted (<-NULL). When attempting to run the KNN I keep getting the error of
Error in knn(train = trainsetne, test = testsetne, cl = ne_train_target, :
no missing values are allowed
I ran which(is.na(dataset$col)) and found:
which(is.na(testsetne$median_days_on_market))
# [1] 8038 8097 8098 8100 8293 8304
When I look through the dataset those cells do not have missing data.
I am wondering if I may get some help with how to either find and fix the "No missing values" or to find a work around (if any).
I am sorry if I am missing something simple. Any help is appreciated.
I have listed the code that we have below:
ne$pending_ratio_yy <- ne$total_listing_count_yy <- ne$average_listing_price_yy <- ne$median_square_feet_yy <- ne$median_listing_price_per_square_feet_yy <- ne$pending_listing_count_yy <- ne$price_reduced_count_yy <- ne$median_days_on_market_yy <- ne$new_listing_count_yy <- ne$price_increased_count_yy <- ne$active_listing_count_yy <- ne$median_listing_price_yy <- ne$flag <- NULL
ne$pending_ratio_mm <- ne$total_listing_count_mm <- ne$average_listing_price_mm <- ne$median_square_feet_mm <- ne$median_listing_price_per_square_feet_mm <- ne$pending_listing_count_mm <- ne$price_reduced_count_mm <- ne$price_increased_count_mm <- ne$new_listing_count_mm <- ne$median_days_on_market_mm <- ne$active_listing_count_mm <- ne$median_listing_price_mm <- NULL
ne$factor_month_date <- as.factor(ne$month_date_yyyymm)
ne$factor_median_days_on_market <- as.factor(ne$median_days_on_market)
train20ne= sample(1:20893, 4179)
trainsetne=ne[train20ne,1:10]
testsetne=ne[-train20ne,1:10]
#This is where I start to come in
ne_train_target <- ne[train20ne, 3]
ne_test_target <- ne[-train20ne, 3]
predict_1 <- knn(train = trainsetne, test = testsetne, cl=ne_train_target, k=145)
# Error in knn(train = trainsetne, test = testsetne, cl = ne_train_target, :
# no missing values are allowed