#' ---
#' title: Visualización de datos en R
#' date: 4 mayo 2026
#' author: Elena Quintero
#' execute:
#'   echo: true
#' format:
#'   revealjs:
#'     theme:
#'     - simple
#'     - custom2.scss
#'     highlight-style: a11y
#'     slide-number: true
#'     code-line-numbers: false
#'     self-contained: true
#' ---
#'


library(here)
library(tidyverse)
library(tidylog)


dt <- read_csv(here("data/seeds_clean_data.csv"))



ggplot(data = dt)



ggplot(data = dt,
       aes(x = year, y = fruits))



ggplot(data = dt,
       aes(x = year, y = fruits)) +
  geom_point()



ggplot(dt,
       aes(x = year, y = fruits)) +
  geom_point(color = "coral")



ggplot(dt,
       aes(x = year, y = fruits)) +
  geom_point(color = "coral",
             shape = "triangle",
             size = 4,
             alpha = 0.5)



ggplot(dt,
       aes(x = year, y = fruits,
           color = pollinator_code)) +
  geom_point(shape = "triangle",
             size = 4,
             alpha = 0.5)



ggplot(dt,
       aes(x = year, y = fruits,
           color = year)) +
  geom_point()



ggplot(dt,
       aes(x = year, y = fruits,
           color = year)) +
  geom_point() +
  scale_x_continuous(n.breaks = 3)



ggplot(dt,
       aes(x = year, y = fruits,
           color = year)) +
  geom_point() +
  scale_x_continuous(n.breaks = 3) +
  scale_y_continuous(
          breaks = c(100000, 200000),
          labels = c("100mil",
                     "200mil"))



ggplot(dt,
       aes(x = year, y = fruits,
           color = year)) +
  geom_point() +
  scale_x_reverse()



ggplot(dt,
       aes(x = year, y = fruits,
           color = year,
           size = year)) +
  geom_point() +
  scale_size_binned(range = c(0.1, 8),
                    n.breaks = 10)



ggplot(dt,
       aes(x = year, y = fruits,
           color = fleshy_fruit)) +
  geom_point(alpha = 0.6) +
  scale_color_manual(
      values = c("green3",
                 "coral2"))



ggplot(dt,
       aes(x = year, y = fruits,
           color = year)) +
  geom_point() +
  scale_color_gradient(
    low = "yellow", high = "orange")



ggplot(dt,
       aes(x = year, y = fruits,
           color = year)) +
  geom_point() +
  scale_color_viridis_c()



dt |>
  group_by(year, site) |>
  summarise(mean_fruits = mean(fruits,
                    na.rm = TRUE)) |>
  ggplot(aes(x = year,
             y = mean_fruits)) +
  geom_point() +
  geom_line() +
  facet_wrap(~site, scales = "free")



dt |>
  group_by(year, site, pollinator_code) |>
  summarise(mean_fruits = mean(fruits,
                    na.rm = TRUE)) |>
  ggplot(aes(x = year,
             y = mean_fruits)) +
  geom_point() +
  geom_line() +
  facet_grid(site~pollinator_code,
             scales = "free")

#| echo: false
sort(ls("package:ggplot2", pattern = "^geom_"))

#| message: true
dt_diam <- dt |> filter(!is.na(stem_cm))



ggplot(dt_diam,
       aes(x = stem_cm, y = fruits)) +
  geom_point() +
  geom_hline(yintercept = 700,
             size = 2) +
  geom_vline(xintercept = c(50, 120),
          color = c("red", "blue"))



ggplot(dt_diam,
       aes(x = stem_cm, y = fruits)) +
  geom_point() +
  geom_smooth()



ggplot(dt_diam,
       aes(x = stem_cm, y = fruits)) +
  geom_point() +
  geom_smooth(method = "lm")



ggplot(dt_diam,
       aes(x = stem_cm, y = fruits,
           color = shade_tolerance)) +
  geom_point(alpha = 0.5) +
  geom_smooth(method = "lm")



ggplot(dt_diam,
       aes(x = stem_cm)) +
  geom_histogram()



ggplot(dt_diam,
       aes(x = stem_cm)) +
  geom_histogram(binwidth = 5)



ggplot(dt_diam,
       aes(x = stem_cm)) +
  geom_density()



ggplot(dt_diam,
       aes(x = stem_cm)) +
  geom_density(bw = 1)



ggplot(dt_diam,
         aes(x = species_name)) +
  geom_bar(stat = "count")



dt_diam |>
  group_by(species_name) |>
  summarise(mean_stem = mean(stem_cm)) |>
  ggplot(aes(x = species_name,
             y = mean_stem)) +
  geom_bar(stat = "identity")



ggplot(dt_diam,
       aes(x = species_name,
           y = stem_cm)) +
  geom_bar(stat = "summary",
           fun = "mean")



ggplot(dt_diam,
       aes(x = species_name,
           y = stem_cm)) +
  geom_boxplot()



ggplot(dt_diam,
       aes(x = species_name,
           y = stem_cm)) +
  geom_violin() +
  geom_jitter(alpha = 0.5)



ggplot(dt_diam,
 aes(x = reorder(species_name, stem_cm),
     y = stem_cm)) +
  geom_violin() +
  geom_jitter(alpha = 0.5)



ggplot(dt_diam,
       aes(x = reorder(species_name, stem_cm),
           y = stem_cm)) +
  geom_violin() +
  geom_jitter(alpha = 0.5) +
  theme_linedraw()



ggplot(dt_diam,
       aes(x = reorder(species_name, stem_cm),
           y = stem_cm)) +
  geom_violin() +
  geom_jitter(alpha = 0.5) +
  theme_linedraw() +
  theme(axis.text.x = element_text(
          angle = 90,
          hjust = 1, vjust = 0.5,
          face = "italic"))



ggplot(dt_diam,
       aes(x = reorder(species_name, stem_cm),
           y = stem_cm)) +
  geom_violin() +
  geom_jitter(alpha = 0.5) +
  theme_linedraw() +
  theme(axis.text.x = element_text(
          angle = 90,
          hjust = 1, vjust = 0.5,
          face = "italic"),
        axis.text.y = element_text(
          size = 15, face = "bold"))



ggplot(dt_diam,
       aes(x = reorder(species_name, stem_cm),
           y = stem_cm)) +
  geom_violin() +
  geom_jitter(alpha = 0.5) +
  theme_linedraw() +
  theme(axis.text.x = element_text(
          angle = 90,
          hjust = 1, vjust = 0.5,
          face = "italic"),
        axis.text.y = element_text(
          size = 15, face = "bold")) +
  labs(x = NULL,
       y = "Stem diameter (cm)",
       title = "Diameter of Pinaceae species in USA National Parks")


library(patchwork)

p1 <- ggplot(dt_diam,
       aes(x = stem_cm,
           fill = genus)) +
  geom_density(alpha = 0.3)

p2 <- ggplot(dt,
       aes(x = stem_cm,
           fill = shade_tolerance)) +
  geom_density(alpha = 0.3)

p1 + p2 +
  plot_annotation(
  title = "Diameter distribution",
  tag_levels = 'A') +
  plot_layout(guides = "collect")

# ggsave(p1, width = 20, units = "cm",
#   filename = here("figs/figure_1.pdf"))


ggplot(dt,
       aes(x = growth_form,
           y = fruits)) +
  geom_violin() +
  labs(x = "Tipo de crecimiento",
       y = "Fruits per m2 (log scale)") +
  scale_y_log10()


dt |> filter(genus == "Quercus") |>
  ggplot(aes(x = year,
             y = fruits,
             color = species_name)) +
  geom_point(alpha = 0.4)  +
  geom_smooth() +
  scale_color_viridis_d() +
  labs(x = "Year",
       y = "Fruits per m2",
       color = "Quercus sp.")

dt |> filter(genus == "Quercus") |>
  ggplot(aes(x = year,
             y = fruits,
             color = species_name)) +
  geom_point(alpha = 0.4, size = 2) +
  geom_smooth() +
  scale_color_viridis_d() +
  facet_wrap(~species_name, scales = "free", ncol = 1) +
  theme(legend.position = "none",
        strip.text = element_text(face = "italic")) +
  labs(x = "Year",
       y = "Fruits per m2")


dt |>
  #remove outlier year
  filter(!(site == "BNZ" & year == 1958)) |>
  group_by(site, year) |>
  summarise(mean_fruits = mean(fruits, na.rm = TRUE),
            se_fruits = sd(fruits, na.rm = TRUE)/sqrt(n())) |>
  ggplot(aes(x = year, y = mean_fruits)) +
  geom_point() +
  geom_errorbar(aes(ymin = mean_fruits - se_fruits,
                    ymax = mean_fruits + se_fruits)) +
  geom_line() +
  facet_wrap(~site, scales = "free") +
  theme_bw()


dt |>
  #remove outlier year
  filter(!(site == "BNZ" & year == 1958)) |>
  ggplot(aes(x = year, y = fruits)) +
  geom_point(stat = "summary", fun = "mean") +
  geom_line(stat = "summary", fun = "mean") +
  stat_summary(
    fun.data = "mean_se",
    geom = "errorbar",
    width = 0.2) +
  facet_wrap(~site, scales = "free") +
  theme_bw()



dt_diam |>
  group_by(year) |>
  mutate(fruits = mean(fruits, na.rm = TRUE),
          stem_cm = mean(stem_cm, na.rm = TRUE)) |>
  ggplot(aes(x = year)) +
  geom_line(aes(y = fruits), color = "red") +
  geom_line(aes(y = stem_cm*10), color = "blue") +
  scale_y_continuous(
    name = "Fruits per m2",
    sec.axis = sec_axis(~./10, name = "Stem diameter (cm)")) +
  theme(
    axis.title.y = element_text(color = "red"),
    axis.text.y = element_text(color = "red"),
    axis.title.y.right = element_text(color = "blue"),
    axis.text.y.right = element_text(color = "blue"))