Lets’ look at the NYC restaurant inspection data

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

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

library(plotly)
```

Lets' look at the NYC restaurant inspection data

```{r}
data("rest_inspec")

rest_inspec_2017m = 
  rest_inspec %>% 
  filter(boro == "MANHATTAN",
         str_detect(inspection_date, "2017")) %>% 
  drop_na()
```

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

### Chart A

Boxplot of scores with different violation types in 2017 Manhanttan

```{r}
rest_inspec_2017m %>% 
  mutate(
    violation_code = fct_reorder(violation_code, score)
  ) %>% 
  plot_ly(y = ~ score, color = ~ violation_code, type = "box", colors = "viridis")

```

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

### Chart B

Barplot of numbers of each type of violations in 2017 Manhattan

```{r}
rest_inspec_2017m %>% 
  count(violation_code) %>% 
  mutate(
    violation_code = fct_reorder(violation_code, n)
    ) %>% 
  plot_ly(x = ~violation_code, y = ~n, color = ~violation_code, type = "bar", colors = "viridis")
```

### Chart C

Scatterplot of numbers of 10F violation through year 2012 to year 2017

```{r}
rest_inspec %>% 
  filter(violation_code == "10F") %>% 
  mutate(inspection_year = substr(inspection_date, 1,4)) %>% 
  group_by(inspection_year) %>% 
  mutate(n = n()) %>% 
  plot_ly(x = ~inspection_year, y = ~n, color = ~inspection_year, 
          type = "scatter", mode = "markers")
```