For the example dataset iris, I would like to compute a table that gives me the p values for a t-test comparing the species Sentosa and Versicolor to Virginia (i.e. Virginia would be the reference group/control) 

Currently, I've processed the average values for columns (sepal length, sepal width, petal length, petal width) and am trying to do a t-test grouped by species against control. 

as an example output would have these columns: 

c=Sepal Width p value, Sepal length p value, Petal length p value, Petal width p value

Thanks in advance for all your help! 

Edit 1:
Here is what I wrote applied to iris (which doesn't exactly fit). I basically cleaned up my data to only include certain independent variables, which is why I have so the %>%. 

```
iris %>%
  group_by(species) %>%
  addcol = function(iris)%>%
    Sepal.length.p.value = mutate(iris, function(t.test(vars(3), ~./[species == 'Sentosa'])))

```

and basically I did that for each of the independat variables.