Replacing all negative values from a dataset - r

Question

I have a dataframe with mixed data ranging from variables(or columns) with numerical values to variables(or columns) with factors.
I would like to use the following piece of code in R to replace all negative values with NA and subsequently remove the entire variable if more than 99% of observations for that variable are NA.
The first part should make sure there is no problem when encountering strings.
Would it be possible to simply start with:
mydata$v1[mydata$v1<0] <- NA
But then not specific for v1 and only if the observation is not a string ?
Follow up:
This is how far I got with the explanation provided by #stas g. It does however not seem like any variable was dropped from the df.
#mixed data
df <- data.frame(WVS_Longitudinal_1981_2014_R_v2015_04_18)
dat <- df[,sapply(df, function(x) {class(x)== "numeric" | class(x) ==
"integer"})]
foo <- function(dat, p){
ind <- colSums(is.na(dat))/nrow(dat)
dat[dat < 0] <- NA
dat[, ind < p]
}
#process numeric part of the data separately
ii <- sapply(df, class) == "numeric" | sapply(df, class) == "integer"
dat.num <- foo(as.matrix(df[, ii]), 0.99)
#then stick the two parts back together again
WVS <- data.frame(df[, !ii], dat.num)

Answer 1

impossible to know exactly how to help you without a minimal reproducible example, but assuming you have a sample data below:
#matrix of random normal observations, 20 samples, 5 variables
dat <- matrix(rnorm(100), nrow = 20)
#if entry is negative, replace with 'NA'
dat[dat < 0] <- NA
#threshold for dropping a variable
p <- 0.99
#check how many NAs in each column (proportionally)
ind <- colSums(is.na(dat))/nrow(dat)
#only keep columns where threshold is not exceded
dat <- dat[, ind < p]
if you have non-numeric variables and you are dealing with a data.frame you could do something like this (assuming you don't care about order of columns):
#generate mixed data
dat <- matrix(rnorm(100), nrow = 20) #20 * 50 numeric numbers
df <- data.frame(letters[1 : 20], dat) #combined with one character column
foo <- function(dat, p){
ind <- colSums(is.na(dat))/nrow(dat)
dat[dat < 0] <- NA
dat[, ind < p]
}
#process numeric part of the data separately
ii <- sapply(df, class) == "numeric" #ind of numeric columns
dat.num <- foo(as.matrix(df[, ii]), 0.99) #feed numeric part of data to foo
#then stick the two partw back together again
data.frame(df[, !ii], dat.num)

Answer 2

This approach: Solution by YOLO suggested by #YOLO finally solved the issue:
cleanFun <- function(df){
# set negative values as NA
df[df < 0] <- NA
# faster, vectorized solution
# select numeric columns
num_cols <- names(df)[sapply(df, is.numeric)]
# get name of columns with 99% or more NA values
col_to_remove <- names(df)[colMeans(is.na(df[num_cols]))>=0.99]
# drop those columns
return (df[setdiff(colnames(df),col_to_remove)])
}
your_df <- cleanFun(your_df)