---
title: "Lab2 - Data Exploration and Analysis Laboratory"
author: "Venkata Sarath Pulipati"
Date: "01/21/2020"
output: 
 flexdashboard::flex_dashboard:
    theme: sandstone
    social: menu
    source_code: embed
    vertical_layout: fill
    orientation: rows
---

```{r, echo = TRUE, include = FALSE, message = FALSE}
library(tidyquant)
library(ggplot2)
library(flexdashboard)
library(dygraphs)
library(dplyr)
library(knitr)
library(tidyr)
library(plyr)
library(rnoaa)
library(DT)
library(plotly)
library(RColorBrewer)
library(ggthemes)
library(ggpubr)
```

Background
===========

### **Overview**  
Row {data-height=200}
----------------------

Change in the climate pattern across the globe, definetly alarms every living being on this planet. This climate change pattern creates a great threat to crop yeilding, inhabital conditions as the air we breath is polluted, rise in temperatures and many more. This dashboard includes visualization on different green house gases levels including carbon dioxide, methane, nitrous oxide, MEI and overall global temperature from 1983 to 2008. The source for this dataset comes from the follwing kaggle link "Rise In Temp".  https://www.kaggle.com/vageeshabudanur/riseintemp-dataset

Greenhouse Gases {.storyboard}
================

Carbon Dioxide, Nitrous Oxide, Methane, MEI {.tabset data-width=1000}
------------------------------

### Carbon Dioxide
```{r}
cc_dataset <- read.csv(file = "climate_change.csv",TRUE, sep = ",", stringsAsFactors = FALSE)
cc_dataset_ymd <- cc_dataset %>%
  mutate(year_month = ymd(paste(cc_dataset$Year, cc_dataset$Month, truncated = 1))) 
scat_plot1 <-  ggplot(cc_dataset_ymd, aes(year_month, CO2))+geom_line()+geom_smooth(method = "lm")+ggtitle("Carbon Dioxide")
scat_plot1
```

### Nitrous Oxide
```{r}
scat_plot2<-  ggplot(cc_dataset_ymd, aes(year_month, N2O))+geom_line()+geom_smooth(method = "lm")+ggtitle("Nitrous Oxide")
scat_plot2
```

### Methane
```{r}
scat_plot3<-  ggplot(cc_dataset_ymd, aes(year_month, CH4))+geom_line()+geom_smooth(method = "lm")+ggtitle("Methane")
scat_plot3
```

Multivariate ENSO Index (MEI)
================
### Climatic observations MEI
```{r}
scat_plot4 <-  ggplot(cc_dataset_ymd, aes(year_month, MEI))+geom_line()+ggtitle("MEI")
scat_plot4
```

Rise in Temperatures
================
```{r}
temp_report <- ggplot(cc_dataset, aes(as.factor(Month), Temp)) + 
  geom_point(aes(color = as.factor(Year))) + 
  geom_line(aes(group = as.factor(Year), 
                color = as.factor(Year)), 
            alpha = 0.7) + 
  labs(title = 'Temperature by month') +
  xlab("Month") +
  ylab("Temperature") +
  theme(axis.text.x = element_text(size = 10,angle = 0,hjust = 0.5))
ggplotly(temp_report)
```

Conclusions
============

### **Observations** 
1. It is clear from the greenhouse gases' reports visualizations, their concentration levels have increased signicantly on this planet. If carbon dioxide levels are increased it would foster on increase in temperature of earth. This is seen on "Rise in Temperatures" report. 

2. Similiarly, increase in nitrous oxide would reflect that farm animals, sewage, fertilizers are released more into the atmosphere than in past. Since nitrous oxide is formed because of the combustion, and actually traps more heat than carbon dioxide.

3. Eventhough low concentrations of methane is not harmful but the rise of methane levels consistenly, will displace oxygen levels in atmosphere. This change pattern would impact by symptoms like heavy breathing, rapid heart rate, fatigue, etc.

### **Conclusions & Questions**
From the visualizations made in this report, it certainly raises few questions that we're responsible. 

1. So the changes in the greenhouse gases levels talks about our activities in daily life. We should be much more conscious about our acitivities. 

2. In the Rise in Temperatures report, all the recent years are at the top part of the report. Will this indicate that our efforts to control climate change is not as expected? 

3. The Multivariate ENSO Index (MEI) is an index formed predominately by studying following 6 elements combindely Sea Surface Temperature (S), Surface Air Temperature (A), Sea Level Pressure (P), Meridional Winds (V), Zonal Winds (U), and total cloudiness fraction of the sky (C). This index here varying constantly over time. Increase in the value indicates the global climate disruptions.