Design Choices

Graphs were formatted using a Minimalistic Theme to make them easier to understand and visually appealing. Because each graph had 8 Subgroups with distinct colors, the Viridis Scale Fill was utilized to convey consistency in colors while still allowing for obvious differences visually.

First ggplot2 graph

This graph shows the mean score in the Every Day Discrimination Scale, divided up between different groups based on gender, sexual orientation, and race/ethnicity. A bar graph was utilized to highlight the color scheme and show the similarities between groups. In this case, we see that reported discrimination scores are similar among all groups, even when comparing historically oppressed populations among more priviledged groups. These results can be due to the self-reporting nature of the test and how the questions were framed.

ggplot(da35525.0003, aes(x=SUBGROUP, y=DIS_TOT, color=SUBGROUP, fill=SUBGROUP))+coord_flip()+stat_summary(fun.y = "mean", geom = "bar")+scale_color_viridis_d()+scale_fill_viridis_d()+labs(title = "Discrimination Faced by Intersectionality of Identities", x="Individual Subgroups of Race and Sexual Orientation", y="Every Day Discrimination Score")+theme(axis.text.y = element_blank())+theme_minimal()

Second ggplot2 graph

This boxplot demonstrates the differences in stigma faced based on gender, sexual orientation, and race/ethnicity. We find that, contrary to the previous graph, we can view clear differences between historically oppressed groups and priviledged identities. In general, we see that the greater intersectionality and presence of vulnerable identities that a group has, the greater the stigma that they faced based on societal views. For simplicity’s sake and because the color scheme clearly illustrates which color belongs to which SUBGROUP, the x axis text was eliminated.

ggplot(da35525.0003, aes(x=SUBGROUP, y=STIGMA, fill=SUBGROUP, color=SUBGROUP))+geom_boxplot()+theme_minimal()+labs(title = "Stigma Associated with Intersectionality of Identities", x="Individual Subgroups of Race and Sexual Orientation", y="Encountered Stigma Score")+theme(axis.text.x = element_blank())+scale_fill_viridis_d()
## Warning: Removed 5 rows containing non-finite values (stat_boxplot).

Third ggplot2 graph

Building on the previous graphs, this one illustrates risk factors for adverse mental health outcomes based on intersectional group identities in race/ethnicity, gender, and sexual orientation. Findings show that more priviledged groups have greater support from similar others than historically marginalized groups. This can raise concerns on support systems for gay persons of color in the event of emergencies and adverse mental health events since they also face greater stigma than their white counterparts.

 ggplot(da35525.0003, aes(x=SUBGROUP, y=SIMOTHSUP, color=SUBGROUP, fill=SUBGROUP))+coord_flip()+stat_summary(fun.y = "mean", geom = "point")+scale_color_viridis_d()+scale_fill_viridis_d()+labs(title = "Support from Similar Others Based on Identities", x="Individual Subgroups of Race and Sexual Orientation", y="Similar Other Support Score")+theme_minimal()