vio_ses <- ggplot(df, aes(x = PartnerG, y = ARI))
geom_violin(adjust = 1.5) +
geom_boxplot(width = .07, fill = "gray", outlier.colour = NA) +
stat_summary(fun.y = mean, geom = "point", fill = "black", shape = 21, size = 2.5) +
scale_y_continuous(breaks=seq(0,12,2))
library(dplyr)
df %>%
group_by(sex_p) %>%
summarise(group_data = list(ARI)) %>%
wilcox.test(.$group_data[[1]], .$group_data[[2]]) %>%
print()
USER_ID | SEX | AGE | GRADE | siblings | nationality | partner_p | annual_income |
---|
1 | Female | 12 | 4 | 3 | Japanese | Married | |
2 | Female | 14 | 5 | 3 | Japanese | Married | |
3 | Male | 17 | 11 | 1 | Japanese | Married | |
Wilcoxon rank sum test with continuity correction
data: arj_male and arj_female
W = 288837, p-value = 0.4258
alternative hypothesis: true location shift is not equal to 0