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


```{r libraries, include = FALSE}
library(tidyverse)
library(p8105.datasets)
library(plotly)
library(flexdashboard)

data("rest_inspec") 

astoria_inspec = rest_inspec %>%
  filter(zipcode %in% c(11101, 11102, 11103, 11104, 11105, 11106)) %>%
  drop_na(inspection_date, score, inspection_type) %>%
  mutate(inspection_date = as.character(inspection_date),
         inspection_date = as.Date(inspection_date, "%Y-%m-%d"),
         year = as.factor(format(inspection_date, "%Y")),
         cuisine_description = ifelse(cuisine_description == 'Café/Coffee/Tea', 'Cafe', cuisine_description),
         cuisine_description = ifelse(cuisine_description == 'Latin (Cuban, Dominican, Puerto Rican, South & Central American)', 'Latin', cuisine_description)) %>%
  separate(inspection_type, into = c("inspection_program", "inspection_type_cat"), sep = "/")
  
```


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

### Trend in Restaurant Inspections by Grade
```{r plot1}
x1 <- list(
  title = "Year"
)
y1 <- list(
  title = "Inspections"
)

astoria_inspec %>%
  drop_na(grade) %>%
  group_by(year, grade) %>%
  summarise(
    inspections = n()
  ) %>%
  mutate(text_label = str_c("Year: ", year, "\nGrade: ", grade, "\nInspections: ", inspections)) %>%
  ungroup() %>%
  plot_ly(x = ~year, y = ~inspections, text = ~text_label, color = ~grade, 
          alpha = .5, type = "scatter", mode = "lines", colors = "viridis") %>%
  layout(xaxis = x1, yaxis = y1)
```


### Proportion of Critical Inspections by Select Cuisine Types

```{r plot2}
x2 <- list(
  title = "Cuisine Type"
)
y2 <- list(
  title = "Critical (%)"
)

astoria_inspec %>%
  group_by(cuisine_description, critical_flag) %>%
  summarise(inspections = n()) %>%
  ungroup() %>%
  pivot_wider(
    names_from = critical_flag,
    values_from = inspections
  ) %>%
  janitor::clean_names() %>%
  mutate(
    total = critical + not_critical,
    percent_critical = round(critical / total * 100,1)
    ) %>%
  filter(total > 200) %>%
  mutate(
    cuisine_description = fct_reorder(as.factor(cuisine_description), percent_critical),
    text_label = str_c("Cuisine Type: ", cuisine_description, "\nTotal Inspections: ", total, "\nPercent Critical: ", percent_critical, "%")
    ) %>%
  plot_ly(x = ~cuisine_description, y = ~percent_critical, text = ~text_label, 
          color = ~cuisine_description, alpha = .5, type = "bar", colors = "viridis") %>% 
  layout(showlegend = FALSE, xaxis = x2, yaxis = y2)
```





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


### Distribution in Inspection Scores by Zip Code
```{r plot3}
x3 <- list(
  title = "Zip Code"
)
y3 <- list(
  title = "Inspection Score"
)

astoria_inspec %>%
  mutate(
    zipcode = as.factor(zipcode)
  ) %>%
  plot_ly(x = ~zipcode, y = ~score, color = ~zipcode, alpha = .5,
          type = "box", colors = "viridis") %>% 
  layout(showlegend = FALSE, xaxis = x3, yaxis = y3)
```