---
title: "Climate Change Analysis"
author: "Laxman Panthi"
output: 
  flexdashboard::flex_dashboard:
    theme: bootstrap
    orientation: rows
    social: menu
    source_code: embed
html_document:
    df_print: paged
---

```{r global, include=FALSE}

#load required libraries
library(rnoaa)
library(tidyverse)
library(gridExtra)
library(lubridate)
library(rnoaa)
library(ggmap)
library(rbokeh)
library(maps)

#load and get the dataset for sea ice coverage from NOAA
url_1997 <- seaiceeurls(mo = 'Apr', pole = 'N', yr = 1997)
url_2007 <- seaiceeurls(mo = 'Apr', pole = 'N', yr = 2007)
url_2017 <- seaiceeurls(mo = 'Apr', pole = 'N', yr = 2017)

np_1997 <- seaice(url_1997)
np_2007 <- seaice(url_2007)
np_2017 <- seaice(url_2017)

url_1997 <- seaiceeurls(mo = 'Apr', pole = 'S', yr = 1997)
url_2007 <- seaiceeurls(mo = 'Apr', pole = 'S', yr = 2007)
url_2017 <- seaiceeurls(mo = 'Apr', pole = 'S', yr = 2017)

sp_1997 <- seaice(url_1997)
sp_2007 <- seaice(url_2007)
sp_2017 <- seaice(url_2017)

#load and get storm data
st_1997 = storm_data(year='1997')
st_2007 = storm_data(year='2007')
st_2017 = storm_data(year='2017')

df_1997 = select(st_1997,serial_num,iso_time,season,num,basin,sub_basin,name,nature,latitude,longitude,wind.wmo.,pres.wmo.,center,	wind.wmo..percentile	,pres.wmo..percentile,track_type,latitude_for_mapping,longitude_for_mapping,	current.basin)
df_2007 = select(st_2007,serial_num,iso_time,season,num,basin,sub_basin,name,nature,latitude,longitude,wind.wmo.,pres.wmo.,center,	wind.wmo..percentile	,pres.wmo..percentile,track_type,latitude_for_mapping,longitude_for_mapping,	current.basin)
df_2017 = select(st_2017,serial_num,iso_time,iso_time,season,num,basin,sub_basin,name,nature,latitude,longitude,wind.wmo.,pres.wmo.,center,	wind.wmo..percentile	,pres.wmo..percentile,track_type,latitude_for_mapping,longitude_for_mapping,	current.basin)
st=rbind(df_1997,df_2007,df_2017)


st =st[st$wind.wmo.!="-999",]
st =st[st$wind.wmo.!=-1,]
st =st[st$wind.wmo.!=0,]
st =st[st$pres.wmo.!=-1,]
st=st[st$pres.wmo.!=0,]

#load global temperatures data
globalTemp <- read_csv("GlobalTemperatures.csv")%>%
  select(dt,LandAverageTemperature,LandAverageTemperatureUncertainty) %>%
  mutate(year = as.numeric(substring(as.character(dt),0,4))) %>% 
  group_by(year) %>% summarise(average = mean(LandAverageTemperature))

```


INTRODUCTION
============================================================================

## Is Climate Change Real?

We have been talking about climate change and global warming since a long time. There have been so many evidence from around the world around the issues of global warming. Contrary to what the administration of the contrary thinks about the climate change (a hoax?), the data shows that the climate change is real. In this dashboard, efforts has been made to visualize the different aspects of the climate change.




![Climate Change is real!!!](earth.jpg)


SEA ICE COVERAGE
============================================================================

Sidebar {.sidebar}
-----------------------------------------------------------------------------

### Sea Ice Coverage
In this visualization, effort has been made to visualize the ice coverage for north pole and south pole over the last three decades. Compared to last decades, you can definitely see the ice coverage has depleted in both the poles.

Row
----------------------------------------------------------------------------

### Sea Ice Coverage Over Years - North Pole
    
```{r, fig.width=15, fig.height=5}

np1 <- ggplot(np_1997, aes(long, lat, group = group)) +
   geom_polygon(fill = "steelblue") +
   theme_ice() +
  ggtitle("1997")
np2 <- ggplot(np_2007, aes(long, lat, group = group)) +
   geom_polygon(fill = "steelblue") +
   theme_ice() +
  ggtitle("2007")
np3 <- ggplot(np_2017, aes(long, lat, group = group)) +
   geom_polygon(fill = "steelblue") +
   theme_ice() +
  ggtitle("2017")
grid.arrange(np1, np2, np3, nrow = 1)
```


Row
-----------------------------------------------------------------------

### Sea Ice Coverage Over Years - South Pole
    
```{r, fig.width=15, fig.height=5}
sp1 <- ggplot(sp_1997, aes(long, lat, group = group)) +
   geom_polygon(fill = "steelblue") +
   theme_ice() +
  ggtitle("1997")
sp2 <- ggplot(sp_2007, aes(long, lat, group = group)) +
   geom_polygon(fill = "steelblue") +
   theme_ice() +
  ggtitle("2007")
sp3 <- ggplot(sp_2017, aes(long, lat, group = group)) +
   geom_polygon(fill = "steelblue") +
   theme_ice() +
  ggtitle("2017")
grid.arrange(sp1, sp2, sp3, nrow = 1)
```

GLOBAL TEMPERATURES
============================================================================

## Global Temperature over years

Sidebar {.sidebar}
-----------------------------------------------------------------------------

In this visualization, we have visualized the data of global temperature over years.

Row
--------------------------------------------------------------------------

### Yearly Global Temperature
```{r}

ggplot(globalTemp,aes(year,average)) + 
  geom_point(color = "blue") + 
  geom_smooth(color = "red") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_x_continuous(breaks=seq(1750, 2015, 10)) + 
  labs(title = 'Annual Gloabl Average Land Temperature', x = 'Year', y= 'Temperature')

```



STORMS
============================================================================

## STORMS - SPATIAL DATA VISUALIZATION

Sidebar {.sidebar}
-----------------------------------------------------------------------------

In this visualization, effort has been made to visualize the the storm over the last three decades

Row
--------------------------------------------------------------------------
### 1997 STORMS
```{r}

st1 <- figure(width = 1400, height = 250, padding_factor = 0) %>%
  ly_map("world", col = "gray") %>%
  ly_points(longitude_for_mapping, latitude_for_mapping,color=wind.wmo..percentile, data = st[st$season=='1997',], size = 4,
            hover = c(latitude,longitude))

st1
```

Row
-----------------------------------------------------------------------
### 2007 STORMS
```{r}

st2 <- figure(width = 1400, height = 250, padding_factor = 0) %>%
  ly_map("world", col = "gray") %>%
  ly_points(longitude_for_mapping, latitude_for_mapping,color=wind.wmo..percentile, data = st[st$season=='2007',], size = 4,
            hover = c(latitude,longitude))

st2

```

Row
-----------------------------------------------------------------------

### 2017 STORMS
```{r fig.width=9}
 st3 <- figure(width = 1400, height = 250, padding_factor = 0) %>%
  ly_map("world", col = "gray") %>%
  ly_points(longitude_for_mapping, latitude_for_mapping,color=wind.wmo..percentile, data = st[st$season=='2017',], size = 4,
            hover = c(latitude,longitude))

st3

```



CONCLUSION
============================================================================

### Discussion
We made an effort to tie human activity with the effect of the climate change, it's good to see those hand in hand and see an impact on how each and every part of the geography impacts/helps the climate.

### More Research
I think, the whole discussion about the climate change and global warming as well as the visualization raises various questions about the climate change:
1. When will we start feeling the real impact?
2. We have made an effort to tie up the human activity with the impact, how statistically significant is that?
3. Can we tie the climate change with human activities more effectively?
4. Also, is the climate change being discussed more than it should have been, since