Converting a linear model analysis into a data visualization

I am not a coder, per se, but I am working on a project for my university. I have created a code that looks at 7 different types of academic involvement and analyzes how each category impacts a student's graduation rate. What I need to do next is convert it into a visual model, which I have never done before. Any advice on how to do this? It doesn't particularly matter what type of model, just one that communicates the important information.

I've looked into using ggplot2 and a bar graph but I'm able to graph the percentage of students who did/didn't graduate, not the analysis of how it affects their chance of graduating.

Linear_Model <- lm(GraduatedEver ~ Type.of.Participation, data = studentdata) 
Linear_Model 

lm(formula = GraduatedEver ~ Type.of.Participation, data = studentdata)

Coefficients:
                                (Intercept)                Type.of.ParticipationCapstone  
                                     0.3588                                       0.6252  
                  Type.of.ParticipationCBLR                    Type.of.ParticipationCOIL  
                                     0.5457                                       0.3034  
            Type.of.ParticipationField Work              Type.of.ParticipationInternship  
                                     0.6368                                       0.5992  
                   Type.of.ParticipationLLC               Type.of.ParticipationPracticum  
                                     0.2132                                       0.5026  
          Type.of.ParticipationStudy Abroad  Type.of.ParticipationUndergraduate Research  
                                     0.5685                                       0.6071  
              Type.of.ParticipationVeterans  
                                     0.4903 

So, how do I go about transforming this?