---
title: "Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source: embed
---

```{r setup, include=FALSE}
library(tidyverse)
library(dplyr)
library(usmap)
library(ggplot2)
require(maps)
library(plotly)
library(flexdashboard)
```

Column {data-width=650}
-----------------------------------------------------------------------

### Chart A: Where marijuana is legal in the United States?

```{r}
legal_state = read_csv("data/legalization_state.csv") 

legal_state = legal_state %>% 
  rename(full = State)

legal_state = legal_state %>%
  mutate(status = case_when(
    Recreational == "Yes" & Medical == "Yes" ~ "Recreational/Medical", #rec/med
    Recreational == "No" & Medical == "Yes" ~ "Medical", #medical legal
    Recreational == "No" & Medical == "No" ~ "Illegal"#none
  )) 

data(statepop)

mapping_df <-inner_join(statepop, legal_state, by = "full")

mapping_df <- mapping_df %>%
  select(-pop_2015) %>%
  mutate(status = factor(status))

labels = usmapdata::centroid_labels("states")
state_labels <- merge(mapping_df, labels, by = "fips")

map = plot_usmap(regions = "states", data=mapping_df, values = "status",color = "white", labels = TRUE)+
  theme(panel.background = element_rect(colour = "black")) +
  scale_fill_manual(values = c(`Recreational/Medical` = "red", `Medical` = "blue", `Illegal` = "grey" ), name = "Legalized states") + 
  theme(legend.position = "bottom") +
  labs(title = "Map: Legalized status for marijuana in the United States") +
  theme(plot.caption = element_text(hjust = 0, face= "italic"))+
  theme(plot.title = element_text(hjust = 0.5))

map$layers[[2]]$aes_params$size <- 3

ggplotly(map) %>%
  highlight("plotly_hover", selected = attrs_selected(line = list(color = "black"))) %>%
  layout(legend = list(orientation = "h", x = 0.5, y = -0.2))


```


### Chart B: When marijuana legalized in each State?

```{r}
year_df = mapping_df %>%
  select(full,Year_legalized_REC,Year_legalized_MED) %>%
  pivot_longer(
    Year_legalized_REC:Year_legalized_MED,
    names_to = "rec_med", 
    values_to = "year")


legal_year <- year_df %>%
  plot_ly(x = ~ year, y = ~full, color = ~rec_med, type = "scatter", mode = "markers") %>%
  layout(title = "Legalized year of each state",
         yaxis = list(title = "States", tickfont = list(size = 5)), xaxis = list(title = "Legalized year")) %>%
  layout(legend = list(orientation = "h", x = 0.5, y = -0.2))

legal_year
```

Column {data-width=350}
-----------------------------------------------------------------------


### Chart C: Legalization data 

```{r}
legalized_states <- legal_state %>%
  select(-Recreational, -Medical) %>%
  knitr::kable(col.names = c("State", "Legalization Year for Recreational", "Legalization Year for Medical",
                             "Legalized status"))

legalized_states
  
```