These graphs are showing different things.
The difference to bear in mind is that between probability and probability density, which are different beasts.
The "Crystal Ball" tool is treating your 15 values, which are all distinct, as 15 bins for each of which the probability is 1/15 $\approx$ .06666667.
The tool also seems to be purporting to tell you about the relative merits of different distributions, which is a real stretch for this sample size.
The tool is also showing you the fitted probability density for a uniform distribution over the range of the data.
It is pure coincidence that the numbers are even of the same order of magnitude, within a factor of 5 or so.
R is plotting probability density, and consistently. The probability density integrates to 1 over the whole distribution. As a ballpark check, we might approximate your data distribution by a rectangle with height 0.02 and width 50, so we do get 1. But the units of density are probability per unit of measurement, not probability.
Think that areas on a histogram drawn properly show probability, so that those areas are
height in probability/units of measurement $\times$ variable in units of measurement
so that the units of measurement cancel leaving you with pure probability for the area (but, as said, not the height).
I can't identify your units of measurement from your post, but it looks like something economic. If it were measured in dollars or rupees or whatever, the density would be probability per dollar or probability per rupee, and so forth.
There are several related posts on this site. I like this one Can a probability distribution value exceeding 1 be OK? for explaining what density is.
On this evidence, the histogram procedure in "Crystal Ball" is at least a bit sloppy by statistical standards, but I know no more about it than your example here, so I will not comment further.