02_progresso.qmd

---
title: "02: Avanços no melhoramento do linho no Sul do Brasil: Resultados do Programa de Melhoramento NEPEM/UFSC"
---

```{r global_options, include = FALSE}
knitr::opts_chunk$set(cache = FALSE,
                      comment = "##",
                      collapse = TRUE,
                      warning = FALSE,
                      message = FALSE)

```


# Pacotes

```{r warning=FALSE, message=FALSE}
library(rio)
library(tidyverse)
library(ggpubr)
library(ggridges)
```

# Dados
```{r}
df <- import_list("data/progresso.xlsx")
df$`2024` <- 
  df$`2024` |> 
  filter(RGPLA > 1.5)

junto <- 
  bind_rows(df) |> 
  mutate(ANO = as.factor(ANO))

dfmean <-
  junto |>
  group_by(ANO) |>
  summarise(across(AP:RGPLA, \(x){mean(x, na.rm = TRUE)})) |> 
  mutate(ANO = as.factor(ANO))


```


# Estatística descritiva
```{r}
junto  |> 
  group_by(ANO) |> 
  summarise(
    n = n(),
    min = min(RGPLA, na.rm = TRUE),
    max = max(RGPLA, na.rm = TRUE),
    media = mean(RGPLA, na.rm = TRUE),
    dp = sd(RGPLA, na.rm = TRUE),
    cv = dp / media * 100
  )

```


# Teste t
```{r}
# Calculate n per group
df_n <- 
  junto  |> 
  group_by(ANO) |> 
  summarise(n = n(), .groups = 'drop')

box <-
  ggplot(junto, aes(x = ANO, y = RGPLA))+
  geom_boxplot(aes(fill = ANO),
               outliers = FALSE) +
  geom_jitter(width = 0.2, alpha = 0.15) +
  stat_summary(fun = mean,
               geom = "point",
               shape = 5,
               fill = "red",
               size = 2,
               stroke = 1) +
  stat_compare_means(method = "t.test",
                     method.args = list(var.equal = TRUE),
                     comparisons = list(c("2022", "2023"),
                                        c("2022", "2024"),
                                        c("2023", "2024"))) +
  geom_text(data = df_n,
            aes(x = ANO, y = 0, label = paste0("n = ", n)),
            color = "gray50",
            nudge_y = -1,
            inherit.aes = FALSE) +
  ggthemes::theme_stata(base_size = 14) +
  theme(axis.text.y  = element_text(angle = 0),
        axis.text.x = element_blank(),
        axis.title.x = element_blank()) +
  ylim(c(0, 10)) +
  coord_flip() +
  labs(x = "Ano de cultivo",
       y = "Rendimento de grãos por planta (g)",
       fill = "")
```


# Densidade
```{r}
dfmean <-
  junto |>
  group_by(ANO) |>
  summarise(media = mean(RGPLA, na.rm = TRUE))

dfmean
dens <-
  ggplot(junto, aes(x = RGPLA, y = ANO, group = ANO, fill = ANO)) +
  geom_density_ridges(alpha = 0.7) +
  xlim(c(0, 10)) +
  geom_vline(data = dfmean,
             aes(xintercept = media,
                 color = ANO),
             show.legend = FALSE) +
  ggthemes::theme_stata(base_size = 14) +
  theme(axis.text.y  = element_text(angle = 0)) +
  labs(x = "Rendimento de grãos por planta (g)",
       y = "Ano de cultivo",
       fill = "") 


library(patchwork)
box /dens +
  plot_annotation(tag_levels = "a") + 
  plot_layout(guides = "collect",
              heights = c(0.3, 0.7)) & 
  theme(legend.position = "bottom")

# ggsave("figs/ganho.jpg",
#        width = 10, 
#        height = 6)

```




# Rendimento de Grãos
```{r}
df2024 <- 
  df$`2024` |> 
  mutate(check = ifelse(GEN == "ST_PIONEIRA", "Check", "Genótipos NEPEM"))


df2024 |> 
  group_by(check) |> 
  summarise(mean(RGPLA))

ggplot(df2024, aes(x = reorder(GEN, -RGPLA), y = RGPLA, fill = check)) +
  geom_col(color = "black") +
  facet_wrap(~TIPO, 
             scales = "free_x") +
  geom_hline(yintercept = 4.17,
             linetype = 2,
             color = "red") +
  scale_fill_manual(values = c("salmon", "#1AB281")) +
  coord_radial(start = -0.5 * pi, end = 0.5 * pi, inner.radius = 0.3) +
  scale_y_continuous(breaks = 0:6) +
  ggthemes::theme_stata(base_size = 14) +
  theme(panel.grid.major.x = element_line(),
        plot.caption = element_text(hjust = 1, size = 12, face = "italic"),
        plot.title = element_text(hjust = 0),
        plot.subtitle = element_text(hjust = 0, size = 12, face = "italic")) +
  labs(x = "",
       y = "",
       fill = "",
       title = "Rendimento de grãos por planta (g) em genótipos de linhaça Marrom e Dourada",
       subtitle = "Safra 2024",
       caption = "Programa de Melhoramento NEPEM/UFSC")

```


# Section info
```{r}
sessionInfo()
```