Plots for a wine data visualisation

wine_plots.R

# script that outputs a graph
library(tidyverse)
library(firatheme)
wine <- read_delim("https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data", 
  delim = ",",
  col_names = c(
    "Cultivar", "Alcohol", "Malic acid", "Ash", "Alcalinity of ash", "Magnesium",
    "Total phenols", "Flavanoids", "Nonflavanoid phenols", "Proanthocyanins",
    "Color intensity", "Hue", "OD280/OD315", "Proline"
  )
)

wine_long <- 
  wine %>% 
  mutate(Cultivar = as_factor(Cultivar)) %>% 
  pivot_longer(-Cultivar)

wine_long %>% 
  filter(name != "Total phenols") %>% 
  ggplot(aes(x = value, fill = Cultivar, colour = Cultivar)) +
  geom_density(alpha = 0.8, colour = "black") +
  geom_rug() +
  facet_wrap(~name, scales = 'free') +
  theme_fira() +
  scale_fill_fira() +
  scale_colour_fira() +
  labs(
    x = "", 
    y = "", 
    title = "Chemical properties of three different wine cultivars"
  )

ggsave("wine_plot.pdf", device = cairo_pdf, width = 12, height = 8)

pca <- prcomp(wine[,-1], scale. = TRUE)
pc12 <- pca$x[,1:2]

as_tibble(pc12) %>%
  set_names("x", "y") %>% 
  mutate(Cultivar = as_factor(wine$Cultivar)) %>% 
  ggplot(aes(x = x, y = y, colour = Cultivar, fill = Cultivar)) +
  geom_polygon(stat = "density_2d", alpha = 0.1, colour = NA, contour = TRUE) +
  geom_point() +
  coord_fixed() +
  theme_fira() +
  scale_colour_fira() +
  scale_fill_fira() +
  xlim(-7, 7) +
  labs(x = "First principal component",
       y = "Second principal component")

ggsave("pca_plot.pdf", device = cairo_pdf, width = 12, height = 6)