---
title: "Suicide Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    navbar:
      - { title: 'Home', href: ../index.html}
    source: embed
    theme: cosmo
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(viridis)
library(plotly)
library(dplyr)
library(readxl)

options(
  ggplot2.continuous.colour = "viridis",
  ggplot2.continuous.fill = "viridis"
)
scale_colour_discrete = scale_colour_viridis_d
scale_fill_discrete = scale_fill_viridis_d
theme_set(
  theme_minimal() + 
    theme(
      legend.position = "bottom",
      plot.title = element_text(hjust = 0.5)
    )  
)
```

```{r, message=FALSE}
suicide_df = 
  read_excel(
    "./data/suicide_data.xlsx",
    sheet = 1,
    col_names = TRUE) %>% 
  janitor::clean_names() %>% 
  mutate(
    population = (suicide_no / suicide_100k) * 100000, 
    sex = as.factor(sex),
    age = as.factor(age)
  )

average_20years = sum(suicide_df$suicide_no) / sum(suicide_df$population) * 100000

suicide_state_df = 
  read_excel(
    "./data/suicide_data.xlsx",
    sheet = 2,
    range = "A1:D351",
    skip = 1,
    col_names = TRUE) %>% 
  janitor::clean_names() %>% 
  rename(
     suicide_no = deaths,
     suicide_100k = death_rate) %>% 
  mutate(
    population = (suicide_no / suicide_100k) * 100000
  )
  
suicide_means_df =
   read_excel(
    "./data/suicide_data.xlsx",
    sheet = 3,
    col_names = TRUE) %>% 
  janitor::clean_names() %>% 
  pivot_longer(
    firearm:others,
    names_to = "means",
    values_to = "rate"
  ) %>% 
  mutate(
       sex = as.factor(sex),
       means = as.factor(means)) 
  
```


Overall Suicide
=====================================

Column {data-width=500}
-----------------------------------------------------------------------

### US National Suicide Rate, 2000-2020
```{r, message=FALSE}
suicide_plot = suicide_df %>% 
  group_by(year) %>% 
  summarize(
    population = sum(population),
    suicide = sum(suicide_no),
    suicide_100k = (suicide / population) * 100000
  ) %>%
  ggplot(aes(x = year, y = suicide_100k)) +
  geom_line(col = "deepskyblue", size = 1) +
  geom_point(col = "deepskyblue", size = 2) +
  geom_hline(
    yintercept = average_20years, linetype = 2, color = "red", size = 1) +
  scale_x_continuous(breaks = seq(2000, 2020, 5)) + 
  scale_y_continuous(breaks = seq(8, 18, 1)) +
  labs(title = "US National Suicide Rate (per 100K), 2000-2020",
       x = "Year", 
       y = "Suicides per 100k") 

ggplotly(suicide_plot)
   
```

Column {data-width=500}
-------------------------------------
### By State
```{r, message=FALSE}
state_plot = suicide_state_df %>% 
  group_by(state) %>% 
  summarize(
    population = sum(population),
    suicide = sum(suicide_no),
    suicide_100k = (suicide / population) * 100000
  ) %>%
  mutate(
    state = fct_reorder(state, suicide_100k)) %>% 
  ggplot(aes(x = suicide_100k, y = state, fill = state )) +
  geom_bar(stat = "identity") +
  scale_x_continuous(breaks = seq(0, 30, 2)) +
  labs(
    title = "Cumulative Suicide Rate, by State, 2014-2020", 
    x = "Suicides per 100k", 
    y = "State") +
  theme(legend.position = "right")

ggplotly(state_plot)


```

Suicide by Sex
=====================================

Column {data-width=500}
-----------------------------------------------------------------------
### By Sex
```{r, message=FALSE}
total_sex_plot = suicide_df %>% 
  group_by(sex) %>% 
  summarize(
    population = sum(population),
    suicide = sum(suicide_no),
    suicide_100k = (suicide / population) * 100000
  ) %>% 
  ggplot(aes(x = sex, y = suicide_100k, fill = sex )) +
  geom_bar(stat = "identity") +
  scale_y_continuous(breaks = seq(0, 24, 4)) +
  labs(
    title = "National Suicide Rate, by Sex", 
    x = "Sex", 
    y = "Suicides per 100k")

ggplotly(total_sex_plot)

```

Column {data-width=500}
-----------------------------------------------------------------------
### By Sex over Years
```{r}
year_sex_plot = suicide_df %>% 
  group_by(year, sex) %>% 
  summarize(
    population = sum(population),
    suicide = sum(suicide_no),
    suicide_100k = (suicide / population) * 100000
  ) %>% 
  ggplot(aes(x = year, y = suicide_100k, color = sex)) +
  geom_line(size = 1) +
  geom_point(size = 2) +
  scale_y_continuous(breaks = seq(0, 30, 5)) +
  labs(
    title = "Suicide Trend Over Years, by Sex",
    x = "Year",
    y = "Suicides per 100k"
  )

ggplotly(year_sex_plot)
```

Suicide by Age
=====================================

Column {data-width=500}
-----------------------------------------------------------------------
### By Age
```{r, message=FALSE}
total_age_plot = suicide_df %>% 
  group_by(age) %>% 
  summarize(
    population = sum(population),
    suicide = sum(suicide_no),
    suicide_100k = (suicide / population) * 100000
  ) %>% 
  ggplot(aes(x = age, y = suicide_100k, fill = age )) +
  geom_bar(stat = "identity") +
  scale_y_continuous(breaks = seq(0, 20, 2)) +
  labs(
    title = "National Suicide Rate, by Age", 
    x = "Age", 
    y = "Suicides per 100k")

ggplotly(total_age_plot)

```

Column {data-width=500}
-----------------------------------------------------------------------
### By Age over Years
```{r}
year_age_plot = suicide_df %>% 
  group_by(year, age) %>% 
  summarize(
    population = sum(population),
    suicide = sum(suicide_no),
    suicide_100k = (suicide / population) * 100000
  ) %>% 
  ggplot(aes(x = year, y = suicide_100k,  color = age)) +
  geom_line(size = 1) +
  geom_point(size = 2) +
  scale_y_continuous(breaks = seq(0, 24, 2)) +
  labs(
    title = "Suicide Trend Over Years, by Age",
    x = "Year",
    y = "Suicides per 100k"
  )

ggplotly(year_age_plot)
```

Suicide by Means
=====================================

Column {data-width=500}
-----------------------------------------------------------------------
### Female Suicide by Means
```{r, message=FALSE}
female_means_plot = suicide_means_df %>% 
  filter(sex == "female") %>% 
  ggplot(aes(x = year, y = rate,  color = means)) +
  geom_line(size = 1) +
  geom_point(size = 2) +
  scale_y_continuous(breaks = seq(0, 2.5, 0.5)) +
  labs(
    title = "Female Suicide Rates, by Means of Suicide, 2000-2020",
    x = "Year",
    y = "Suicides per 100k"
  )

ggplotly(female_means_plot)

```

Column {data-width=500}
-----------------------------------------------------------------------
### Male Suicide by Means
```{r}
male_means_plot = suicide_means_df %>% 
  filter(sex == "male") %>% 
  ggplot(aes(x = year, y = rate,  color = means)) +
  geom_line(size = 1) +
  geom_point(size = 2) +
  scale_y_continuous(breaks = seq(0, 16, 2)) +
  labs(
    title = "Male Suicide Rates, by Means of Suicide, 2000-2020",
    x = "Year",
    y = "Suicides per 100k"
  )

ggplotly(male_means_plot)
```