Often when replacing one character vector with another, the expression will have to deal with missing variables that the previous string doesn’t possess and an error occurs as there are more values than spaces to match. Let’s look at a regular expression in R that tries to match values in two strings:
data.frame(a=c(3, 7, 14),
b=c(4, 4, 5, 12, 13, 18))
Obviously, there are multiple values in the later than in the former we plan to replace. When comparing the two, the expression can’t comprehend the missing value with the extra values, so there’s no comprehension as the algorithm figures the replacement length.
Absolving this issue can be made easy with another regular expression. The if-else statement sets a decision pathway to respond to the situation.
Using the statement, dfa ifelse(is. na(dfa), dfb, dfa), the expression now knows when faced with an empty slot to replace, it as a value to fit into its slot. If there is a value to replace, the statement doesn’t take effect and the set replaces it as normal. A simple solution if looking for a quick fix. If you feel the statement contains factors limiting the effectiveness of the if-then statement, and you are more focused on eliminating the error message, you can simply remove the root of the problem entirely;
dfa[is.na(df$a)] 0
substitutes any missing values with zeroes, filling the missing values and making the string acceptable to replace.
Let’s look at a real-life example with this programmer’s code as part of a census dataset. When looking to replace a line of code with another, this string brought up the warning message in R studio: dfa[is.na(dfa)] 0
Diving deep into the internals of this program seems daunting, bur in reviewing this particular section,
multiLodSim function (GM, GSD, n_samples, n_iterations, p) {
X_after matrix(NA_real_, nrow = n_iterations, ncol = n_samples)
delta matrix(NA_real_, nrow = n_iterations, ncol = n_samples)
mu log(GM)
sigma log(GSD)
lod1 quantile(rlnorm(100000,mu,sigma),p)
lod2 quantile(rlnorm(100000,mu,sigma),(p*0.95))
lod3 quantile(rlnorm(100000,mu,sigma),(p*0.9))
pct_cens numeric(n_iterations)
count 1
while(count = n_iterations) {
sub_samples = n_samples/3 # divide the total sample into third (for 3 lods)
n1 rlnorm(sub_samples,mu,sigma),
the errors are linked to missing values mu and sigma in the empty n-samples value spots.
After changing out a few lines,
n1 = rlnorm(round(sub_samples),mu,sigma)
n2 = rlnorm(round(sub_samples),mu,sigma)
sub_samples3 = n_samples – length(n1)-length(n2)
n3 = rlnorm(subsamples3, mu,sigma),
the program stops looking for undefined values and processes unencumbered.
This was a rather complex example but when handling your own code it’s important to keep track of what your function specifies as a search term. Correcting early in writing can save you a lot of hassle in the long run.