For loop using if and else function due to 'Not enough finite observation' error in R

I am trying to use the spearman correlation test for different variables per year. I have this:

    year <- c(1986:2013)
    spearman_coef_IBD <- c(NA)
    spearman_coef_inbrecoef <- c(NA)
    spearman_coef_inbrecoef_mate <- c(NA)
    spearman_coef_het <- c(NA)
    spearman_coef_het_mate <- c(NA)
    spearman_fem <- data.frame(year, spearman_coef_IBD,                               spearman_coef_inbrecoef, spearman_coef_inbrecoef_mate, spearman_coef_het, spearman_coef_het_mate)
    for(j in c(1990:2013)) {
       dispdata_w_mates <- subset(dispdata_w_mates_fem, YrFirstBred == j)
       dispdata_w_mates$Pairs <- with(dispdata_w_mates, paste(ID, Mate_ID, sep=" "))
       typeA <- genome$IBD[match(dispdata_w_mates$Pairs, genome$pair1)]
       typeB <- genome$IBD[match(dispdata_w_mates$Pairs, genome$pair2)]
       dispdata_w_mates$relatedness <- ifelse(is.na(typeA), typeB, typeA)
       dispdata_w_mates_w_HET <- merge(dispdata_w_mates, het, by.x='ID', by.y='IID', all.x = TRUE)[, c(1:27, 33)]
       colnames(dispdata_w_mates_w_HET)[28] <- 'HET_ID'
       dispdata_w_mates_w_HET2 <- merge(dispdata_w_mates, het, by.x= 'Mate_ID', by.y='IID', all.x = TRUE)[, c(1:27, 33)]
       colnames(dispdata_w_mates_w_HET2)[28] <- 'HET_MATE'
       dispdata_w_mates_w_inbcoef <- merge(dispdata_w_mates, inb_coef, by.x='ID', by.y='IID')[, c(1:27, 32)]
       colnames(dispdata_w_mates_w_inbcoef)[28] <- 'inbcoef_ID'
       dispdata_w_mates_w_inbcoef2 <- merge(dispdata_w_mates, inb_coef, by.x= 'Mate_ID', by.y='IID')[, c(1:27, 32)]
       colnames(dispdata_w_mates_w_inbcoef2)[28] <- 'inbcoef_MATE'

       spearman_fem[which(spearman_fem$year==j), "spearman_coef_IBD"] <- cor.test(dispdata_w_mates$NatalDispDom, dispdata_w_mates$relatedness, method = "spearman")
       spearman_fem[which(spearman_fem$year==j), "spearman_coef_inbrecoef"] <- cor.test(dispdata_w_mates_w_inbcoef$NatalDispDom, dispdata_w_mates_w_inbcoef$inbcoef_ID, method = "spearman")
       spearman_fem[which(spearman_fem$year==j), "spearman_coef_inbrecoef_mate"] <- cor.test(dispdata_w_mates_w_inbcoef2$NatalDispDom, dispdata_w_mates_w_inbcoef2$inbcoef_MATE, method = "spearman")
       spearman_fem[which(spearman_fem$year==j), "spearman_coef_het"] <- cor.test(dispdata_w_mates_w_HET$NatalDispDom, dispdata_w_mates_w_HET$HET_ID, method = "spearman")
       spearman_fem[which(spearman_fem$year==j), "spearman_coef_het_mate"] <- cor.test(dispdata_w_mates_w_HET2$NatalDispDom, dispdata_w_mates_w_HET2$HET_MATE, method = "spearman")
    }

but it keeps returning this: "Error in cor.test.default(dispdata_w_mates$NatalDispDom, dispdata_w_mates$relatedness, : not enough finite observations"

I know it is because in some years there are not enough values for the spearman test to be conducted, such as in year 1986 there are no values for dispdata_w_mates$relatedness or in 1990, there is only one value for dispdata_w_mates_w_inbcoef$inbcoef_ID. How do I use the if else function to put in NA in the columns where there are no values so that it can continue doing the loop for the other years?