---
title: "NYC Weather"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(p8105.datasets)
library(plotly)
library(reshape2)
library(cowplot)
library(magrittr)
data("ny_noaa")
ny_noaa = tibble(ny_noaa)
```




```{r data cleaning, include=FALSE}
ny_noaa_clean = 
  ny_noaa %>% 
  drop_na(prcp, snow, snwd, tmax, tmin) %>% 
  mutate(
    prcp = as.numeric(prcp),
    snow = as.numeric(snow),
    snwd = as.numeric(snwd),
    tmax = as.numeric(tmax),
    tmin = as.numeric(tmin)
    ) %>% 
  group_by(date) %>% 
  summarize(prcp = mean(prcp),
            snow = mean(snow),
            snwd = mean(snwd),
            tmax = mean(tmax),
            tmin = mean(tmin))
ny_noaa_avg = 
  ny_noaa_clean %>% 
  mutate(
    year =  as.integer(strftime(date, "%Y")),
    weekday =  as.integer(strftime(date, "%u")),
    month =  as.integer(strftime(date, "%m")),
    week =  as.integer(strftime(date, "%W")),
    day =  as.integer(strftime(date, "%d"))
    ) %>% 
  mutate(
    weekday = factor(weekday, levels = 1:7, 
                     labels = c("Mon", "Tue", "Wed", "Thr", "Fri", "Sat", "Sun"), 
                     ordered = TRUE),
    month = factor(month, levels = 1:12, labels = month.abb, ordered = TRUE)
  ) %>% 
  group_by(month) %>% 
  mutate(monthweek = 1 + week - min(week))

#since we only need the year in 2010, we can filer the data 
ny_noaa_clean_10 = ny_noaa_avg %>% 
  filter(date >= as.Date("2000-01-01") & date <= as.Date("2010-12-31")) %>% 
  group_by(month,year) %>% 
  mutate(mean_tmax_month = mean(tmax), 
         mean_tmin_month = mean(tmin)) %>% 
  filter( day == 1)
```



Row {data-height=650}
-----------------------------------------------------------------------

### Chart A
```{r}

a <- ny_noaa_clean_10  %>% 
  ggplot(aes(year, prcp)) +
  geom_point(size = 0.5) +
  geom_smooth(method = "loess") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
        axis.title.x = element_blank(),
        legend.position = "none") +
  labs(title = "Monthly precipitation(mm) in 10 years", subtitle = "NYC Precipitation: January 2000 - November 2010", y = "Precipitation(mm)", x = "Year") +
  facet_wrap(~month) +
  NULL

ggplotly(a)
```

### Chart B
```{r}
prcp_2010= 
  ny_noaa_avg %>% 
  filter(year == 2010) %>% 
  ggplot(aes(weekday, monthweek, fill =prcp )) + 
  geom_tile(color = "white") + 
  geom_text(aes(label = day), size = 3) + 
  scale_fill_gradient2(low = "darkgreen", mid = "white", high = "darkred") +
  facet_wrap(~month, nrow = 3) + 
  scale_y_reverse(name = "Week") + 
  xlab("Day") +
  theme_bw() +
  theme(panel.grid = element_blank(),
        axis.text.x = element_text(angle = 30, 
                                   hjust = 1, vjust = 1)) +
  expand_limits(y=c(0.5,6.5)) 
ggplotly(prcp_2010)
```



Row {data-height=650}
-----------------------------------------------------------------------
### Chart C
```{r}
ny_noaa_avg %>%  
  filter(year == 2010) %>%
  mutate(date = as.Date(date)) %>% 
  plot_ly(x = ~date) %>% 
  add_lines(y = ~snow, name = "snowfall") %>% 
  layout(
    title = "2010 NYC daily snowfall")
```

### Chart D
```{r}


colors <- c('#4AC6B7', '#1972A4', '#965F8A', '#FF7070', '#C61951')

fig <- ny_noaa_clean_10%>% 
        filter(year == 2010) %>%
  plot_ly(x = ~tmax, y = ~snow, z = ~prcp, color = ~ month , colors = colors,
             marker = list(symbol = 'circle', sizemode = 'diameter'), sizes = c(5, 150)) %>% 
    layout(
      scene = list(
      xaxis = list(title = "maximum temperature (F)"),
      yaxis = list(title = "snow(mm)"),
      zaxis = list(title = "precipitation(mm)"))
  )



fig

```