If your question is to find out by how much the independent variables (C-F) impact Rank, then you may want to treat Rank as a factor (categorical) variable. Ordinary least squares will not help you because the dependent variable is supposed to be continuous meeting certain assumptions. Categorical dependent variables violate those assumptions. Instead, you would want to look to general linear models (GLMs) like logistic and Poisson regressions that work with categorical variables or counts (integers) for dependent variables. As for RPO, you would usually encode the "yes" and "no" with 0 and 1. Otherwise, how would you interpret its numeric impact on a model as a character? From the look of it, GC is also a categorical variable, too, and so is RF. You will want to treat these appropriately (as factors, not as numeric variables, because they're not).
Frankly, the model required to appropriately answer your question is very complex and requires a lot of sophistication to set up and interpret. You may be able to get away with setting Rank as a numeric variable, but there will be some serious issues with the remedial measures required to make your model work right and in how to interpret the results. In all likelihood, a logistic regression of some sort would suit your needs.