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---
title: "Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source: embed
    code_folding: hide
    theme: simplex
---

```{r setup, include=FALSE}
library(tidyverse)
library(plotly)
library(p8105.datasets)
library(flexdashboard)
library(rnoaa)
scale_colour_discrete = scale_colour_viridis_d
scale_fill_discrete = scale_fill_viridis_d 
```

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```{r read}
weather_df = 
  rnoaa::meteo_pull_monitors(
    c("USW00094728", "USC00519397", "USS0023B17S"),
    var = c("PRCP", "SNOW", "SNWD", "TMAX", "TMIN"),
    date_min = "2017-01-01",
    date_max = "2019-12-31") %>%
  mutate(
    name = recode(
      id, 
      USW00094728 = "CentralPark_NY", 
      USC00519397 = "Waikiki_HA",
      USS0023B17S = "Waterhole_WA"),
    tmin = tmin / 10,
    tmax = tmax / 10,
    month = lubridate::floor_date(date, unit = "month")) %>%
    mutate(
      snow = case_when(
      snow <0 ~0,
      snow >= 0 ~snow)) %>% 
  select(name, id, everything())

```


## Plot 1

```{r plot_1}
plot_1 =
weather_df %>% 
  ggplot(aes(x = tmin, y = tmax, color = name)) + 
  geom_point(alpha = .2) + 
  geom_smooth(se = FALSE) + 
  facet_grid(. ~ name) +
  labs(
    title = "Max and Min Temperature associations (°C)",
    x = "T-Min",
    y = "T-Max",
    color = "Name",
    caption = "data from NOAA National Climatic Data Center"
    ) 


ggplotly(plot_1)
```

Column {data-width=400}
-----------------------------------------------------------------------

## Plot 2
```{r plot_2}
plot_2 =
weather_df %>%
  select(name, tmax, tmin) %>% 
  pivot_longer(
    tmax:tmin,
    names_to = "observation", 
    values_to = "temp") %>% 
  ggplot(aes(x = temp, fill = observation)) +
  geom_density(alpha = .5) + 
  facet_grid(~name) +
  labs(
    title = "Max and Min Temperature Distributions)",
    x = "Temperature",
    y = "Frequency",
    caption = "data from NOAA National Climatic Data Center"
    ) 

ggplotly(plot_2)
```

Column {data-width=400}
-----------------------------------------------------------------------

## Plot 3

```{r plot_3}
plot_3 = 
weather_df %>%
  group_by(name, month) %>%
  summarize(mean_tmax = mean(tmax)) %>%
  ggplot(aes(x = month, y = mean_tmax, color = name)) + 
  geom_point() + 
  geom_line() + 
  labs(
    title = "Trend of Mean of Max Temperature",
    x = "Year",
    y = "Mean T-Max(°C)",
    color = "Name"
    ) +
  scale_x_date(
    date_breaks = "2 month"
  ) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))


ggplotly(plot_3) 
```

Column {data-width=400}
-----------------------------------------------------------------------

## Plot 4
```{r plot_4}
plot_4 =
  ggplot(weather_df, aes(x = name, y = tmax, fill = name)) + geom_violin(alpha = 0.5)+
  labs(
    title = "Distribution of Max Temperature",
    x = "Location",
    y = "T-Max(°C)",
    color = "Name"
    )

ggplotly(plot_4)
```

Column {data-width=400}
-----------------------------------------------------------------------