relacion_muertes.qmd

---
title: "Relación con muertes"
format: html
editor_options: 
  chunk_output_type: console
---

# Importar datos

```{r}
library(tidyverse)
```


```{r}
# Ruta al directorio donde se encuentran los archivos
path_dir <- "01_data/raw/Data Request MS242_02142023/"

# Leer los archivos .csv que contienen "DTHRED" en su nombre
death_df <- purrr::map_df(
  .x = list.files(
    path = path_dir,
    pattern = "DTHRED.*\\.csv$",
    full.names = TRUE  # Esto asegura que se devuelva la ruta completa al archivo
    ),
  .f = readr::read_csv
)

gbmt_fit_final <- readRDS("01_data/processed/gbmt_fit_final.rds")
```


```{r}
write_csv(death_df, file = "01_data/processed/death_df.csv")
```


```{r}
deaths_by_year <- death_df %>% 
  group_by(ISO2, SALID1, YEAR) %>% 
  summarise(
    deaths = sum(DTHRED3DEATHS, na.rm = TRUE)
  ) %>% 
  ungroup()

deaths <- deaths_by_year %>% 
  group_by(ISO2, SALID1) %>% 
  summarise(
    deaths = sum(deaths, na.rm = TRUE)
  ) %>% 
  ungroup()
```

```{r}
gbmt_log_3 <- gbmt_fit_final %>% 
  filter(scale_name == "logarithmic", ng == 3) %>% 
  slice(1)

gbmt_log_3 <- gbmt_log_3$gbmt_fit_total[[1]]


gbmt_log_4 <- gbmt_fit_final %>% 
  filter(scale_name == "logarithmic", ng == 4) %>% 
  slice(1)

gbmt_log_4 <- gbmt_log_4$gbmt_fit_total[[1]]


gbmt_log <- gbmt_log_3 %>% 
  bind_rows(gbmt_log_4,
            .id = "Clusters") %>% 
  mutate(Clusters = case_match(Clusters,
                               "1" ~ 3,
                               "2" ~ 4))
```

```{r}
gbmt_log_final <- gbmt_log %>% 
  left_join(deaths)
```


```{r}
gbmt_log2 <- gbmt_log %>%
  group_by(Clusters, pubsalid1, SALID1, group) %>% 
  summarise(
    across(
      c(population_imp_norm:bectuareal1ux_imp),
      ~ mean(.x, na.rm = TRUE)
    )
  ) %>% 
  ungroup()

gbmt_log_final <- gbmt_log2 %>% 
  left_join(deaths)

gbmt_log_final2 <- gbmt_log_final %>% 
  mutate(
    deaths_pob = deaths*100000/population_imp
  )
```

```{r}
gbmt_log_final2 %>% 
  group_by(
    Clusters
  ) %>% 
  summarise(
    broom::tidy(
      kruskal.test(deaths_pob ~ group)
    )
  )
```


```{r}
library(ggstatsplot)

grouped_ggbetweenstats(
  data             = gbmt_log_final2,
  x                = group,
  y                = deaths_pob,
  grouping.var     = Clusters,
  # ggsignif.args    = list(textsize = 4, tip_length = 0.01),
  # p.adjust.method  = "bonferroni",
  # palette          = "default_jama",
  package          = "ggsci"
  # plotgrid.args    = list(nrow = 1),
  # annotation.args  = list(title = "Differences in movie length by mpaa ratings for different genres")
)
```

```{r}
ggbetweenstats(
  data  = gbmt_log_final2 %>% filter(Clusters == 3),
  x     = group,
  y     = deaths_pob
)
```

```{r}
ggbetweenstats(
  data  = gbmt_log_final2 %>% filter(Clusters == 4),
  x     = group,
  y     = deaths_pob
)
```