## # A tibble: 3 × 9
## Species var1 var2 cor statistic p conf.low conf.high method
## <fct> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 setosa Sepal.Wid… Sepa… 0.74 7.68 6.71e-10 0.585 0.846 Pears…
## 2 versicolor Sepal.Wid… Sepa… 0.53 4.28 8.77e- 5 0.290 0.702 Pears…
## 3 virginica Sepal.Wid… Sepa… 0.46 3.56 8.43e- 4 0.205 0.653 Pears…
bdims_summary <- iris %>%
group_by(Species) %>%
summarize(N = n(), r = cor(Sepal.Width, Sepal.Length),
mean_hgt = mean(Sepal.Width),
mean_wgt = mean(Sepal.Length),
sd_hgt = sd(Sepal.Width),
sd_wgt = sd(Sepal.Length),
slope = r*(sd_wgt/sd_hgt),
intercept = mean_wgt - (slope*mean_hgt),
r2 = r*r)
bdims_summary
## # A tibble: 3 × 10
## Species N r mean_hgt mean_wgt sd_hgt sd_wgt slope intercept r2
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 setosa 50 0.743 3.43 5.01 0.379 0.352 0.690 2.64 0.551
## 2 versicolor 50 0.526 2.77 5.94 0.314 0.516 0.865 3.54 0.277
## 3 virginica 50 0.457 2.97 6.59 0.322 0.636 0.902 3.91 0.209
ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length)) +
geom_point() +
geom_smooth(method = "lm") +
facet_wrap(~Species) +
ggthemes::theme_few(base_size = 15) +
ggpubr::stat_cor(aes(label = paste(after_stat(rr.label), p.label, sep = "~ `,`~")), color = "brown", label.y.npc = 1) +
ggpubr::stat_regline_equation(color = "brown", label.y.npc = 0.95)
