Wilcoxon Rank Sum test unequal sample sizes I am comparing two sets of data using the Wilcoxon Rank Sum test. Although the distributions appear to be quite different, the p-value is quite high (0.344).
This is the line of code being used for the test:
WG2013_data$Duration <- c(2, 2, 4, 4, 12, 2, 2)
WG2014_data$Duration <- c(1, 26, 1, 31, 18, 12)
wcx <- tidy(wilcox.test(WG2013_data$Duration, WG2014_data$Duration))


There are 7 samples in the WG2013_dataand 6 samples in the WB2014_data. Could it be that the unequal sample sizes is causing the high p-value? 
Should I set any options as TRUE/FALSE?
I've also tried the Kolmogorov-Smirnov test:
ks <- tidy(ks.test(WG2013_data$Duration, WG2014_data$Duration))

And the Kruskal–Wallis test:
kruskal <- tidy(kruskal.test(Duration ~ Year, data = WG_data))

Both of these tests also show p-values of about 0.34.
Welch's t-test gives a p-value = 0.089, so this at least shows a significant difference in the mean at p < 0.1, but the Whigg 2013 data are not normal according to a qqplot and Shapiro-Wilk test.
Is there another non-parametric test I should try?
 A: The Wilcoxon Rank Sum test (aka Mann-Whitney) works with unequal sample sizes. 
The original paper (referenced below) did some analyses with different sample sizes and showed its consistency and asymptotic normality (see table I, n = 8 on page 54). They also go on to show its robustness for small sample sizes. 
Reference: Mann, Henry B.; Whitney, Donald R. (1947). "On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other". Annals of Mathematical Statistics 18 (1): 50–60.
A: Boxplots are a bad choice to display small data sets. Try a plot like this with one point per data point for better intuition:
Duration1 <- c(2, 2, 4, 4, 12, 2, 2)
Duration2 <- c(1, 26, 1, 31, 18, 12)

plot(x=Duration2, y=jitter(rep(.2, length(Duration2)), 2), ylim=c(0,1), yaxt="n", pch=1)
points(x=Duration1, y=jitter(rep(.8, length(Duration1)), 2), pch=2)
axis(2, at=c(.2, .8), labels=c("dur2", "dur1"))


Now consider, that the test can only see ranks. The smallest value and the three largest values belong to duration 2, the two with value 12 do not add to a difference. It will then become more intuitively valid that the order of the circles and triangles is not very convincing, i. e. $p$ should be large. 
In a comment you asked:

Is there some sort of hybrid between Welch's t-test and the Wilcoxon Rank Sum test?

There is no description of what the hybrid is supposed to do. Comparing means with no normality assumptions? You could try bootstrapping or permutation tests.
Duration1 <- c(2, 2, 4, 4, 12, 2, 2)
Duration2 <- c(1, 26, 1, 31, 18, 12)

true.diff <- mean(Duration1) - mean(Duration2)
bootstrapped.diffs <-replicate(10000, mean(Duration1[sample(1:7, replace=TRUE)])-mean(Duration2[sample(1:6, replace=TRUE)]))

hist(bootstrapped.diffs, breaks = 30)
abline(v = true.diff, col = "red", lwd = 3)
sum(bootstrapped.diffs > 0)/10000

