---
title: "Narrative Viz Lab"
author: Mehdi Alaoui ; Marcelo Mesquita ; Devendder Chilliar ; Taewoon Kim ; Liang
  Zhao
date: "November 18, 2017"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    storyboard: true
    vertical_layout: fill
    social: menu
    source_code: embed
---

```{r setup, include=FALSE, message=FALSE, echo=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(flexdashboard)
library(dplyr)
library(reshape2)
library(ggplot2)
library(plotly)
library(ggthemes)
library(GGally)
library(gridExtra)

wdi <- read.csv("WDI Data/WDIData.csv", header = TRUE, stringsAsFactors = FALSE)

wdi <- as.data.frame(wdi)
colnames(wdi)[1] <- 'Country.Name'
colnames(wdi)[5:length(colnames(wdi))] <- gsub(pattern = 'X', replacement = '', x = colnames(wdi)[5:length(colnames(wdi))])
indicator <- c('CO2 emissions from transport (% of total fuel combustion)', 
               'CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)',
               'CO2 emissions from manufacturing industries and construction (% of total fuel combustion)', 
               'CO2 emissions (metric tons per capita)',
               'CO2 emissions from electricity and heat production, total (% of total fuel combustion)',
               'CO2 emissions from gaseous fuel consumption (% of total)',
               'CO2 emissions from liquid fuel consumption (% of total)')
country <- c('United States', 'United Kingdom', 'Germany', 'China', 'Japan')

wdifiltered <- wdi %>% 
                select(1,3,45:61) %>%
                filter(Indicator.Name %in% indicator, Country.Name %in% country) %>%
                melt(id = c('Country.Name', 'Indicator.Name'))



```

### Executive Summary

Global warming is one of the hot topicts in todays' news and politics. Rencently in 2017, almost all the contries that belong to the United Nations Framework Convention or Climate Change, apart from USA and Syria, have sined the Paris agreement, which  deals with greenhouse gas emissions mitigation, adaptation and finance starting in the year 2020. In addition, each country determines, plans and regularly reports its own contribution it should make in order to mitigate global warming. There is no mechanism to force a country to set a specific target by a specific date, but each target should go beyond previously set targets. From 2020 onwards, rates are expected to stat decreasing as the agreement officially starts. In the mean time countries will develop new and cleaner ways to generate energy. 

Some countries, UK and France, are focusing on taxing vehicles according to the size of the car's engine, as a incentive to decrease CO2 emissions. However, as we vizualyze the data for the 5 countries we can notcie that vehicles' CO2 emissions are just a small portion from the total CO2. More than that, the greatest volumes come from generating electricity, and therefore raised a question. Would electric cars be a good way to fight CO2 emissions and global waring?    

The following visuals show us the trends evolving from 2000 - 2014 in selected major countries. We picked United States, Germany, China, United Kingdom, and Japan.These major countries are good representation of the CO2 emissions rate between the selected years we are analyzing.

Our analysis represents the behavior of CO2 emissions from fuel combustion over time, in each of the chosen coutries mentioned above. Also, we analyzed where the CO2 emissions are coming from :
- Transport
- Residential Buildings, Commercial and Public Services 
- Electricity and Heat Production
- CO2 emissions provenient from liquid fuel and gaseous fuel


### CO2 emissions from transport (% of total fuel combustion)

```{r 1, message=FALSE, warning=FALSE, echo=FALSE}
CO2liquidfuel <- wdifiltered %>%
          filter(Indicator.Name == 'CO2 emissions from liquid fuel consumption (% of total)')
ggplot(CO2liquidfuel, aes(x = CO2liquidfuel$value,y=CO2liquidfuel$Country.Name)) +
       geom_point() +
       geom_line() +
       ggtitle("Relationship--> 'Co2 value and distribution limit for each country'") +
       stat_smooth(method = "", formula = y~x, size = 1, col = 'green')
```

***

Data Visualization Result and Conclusion- CO2 emissions from transport (% fo total fuel combustion)

 - From the graph, we could see that the developed countries including United states, United kingdom, Japan and Germany had more than 20% of total fuel combustion used for transportation. The developing country such as China had less than 10% of total fuel combusion used for transportation from 2000 to 2014. It indicated that developing countries use more fuel for running factories in order to growing their economy.
 
 - Among developed countreis, only Japan had the decreasing pattern on their CO2 emissions from transport. This trend was the result of efforts made in Japan's road transport sector to increase vehicle fuel efficiency, improve traffic flow and promote eco-driving, and reduce travel distances.


### CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)

```{r 2, message=FALSE, warning=FALSE, echo=FALSE}
CO2fuel <- wdifiltered %>%
          filter(Indicator.Name == 'CO2 emissions from gaseous fuel consumption (% of total)')
ggplot(CO2fuel, aes(x = CO2fuel$value,y=CO2fuel$Country.Name)) +
       geom_point() +
       geom_line() +
       ggtitle("Relationship--> 'Co2 fuel value and distribution limit for each country'") +
       stat_smooth(method = "", formula = y~x, size = 1, col = 'green')
```

***

Data Visualization Result and Conclusion- CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)

 - In United States and United Kingdom, CO2 emissions from residential buildings and commercial and public services were not much different from 2000 to 2014. But China, Germany and Japan showed different result during that period. Their emissions level is slowly decreasing.
 
 - Electricity is the main source of CO2 emissions in residential buildings and comercial and public services. Energy efficiency improvement contributes the most to CO2 mitigation. The world energy efficency ranking of Germany and Japan was the top among the world. So they could keep reducing the CO2 emissions from residential building and comercial and public servcies. However China had different story. Becasue they are developing country, they spent more fule combustion for running factories.


### CO2 emissions (metric tons per capita)

```{r 3, message=FALSE, warning=FALSE, echo=FALSE}
min_x <- min(as.numeric(as.character(CO2fuel$variable)))
max_x <- max(as.numeric(as.character(CO2fuel$variable)))
ggplot(data=CO2fuel, 
       aes(x = as.numeric(levels(variable))[variable], y = value)) + 
  geom_bar(stat="identity") +
       labs(title = CO2fuel$Indicator.Name[1], x = 'year', y = '%') +
  scale_x_continuous(breaks = round(seq(min_x, max_x, by = 1),0)) +
       
       coord_polar(theta = "x")
```

***

Data Visualization Result and Conclusion- CO2 emissions (metric tons per capita)

 - CO2 emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring.
 
 - From the graph, we could see that only China had increasing pattern from 2000 to 2014. This means that the fuel consumption of developing countries is increasing.


### CO2 emissions from electricity and heat production, total (% of total fuel combustion)

```{r 4, message=FALSE, warning=FALSE, echo=FALSE}
CO2electricity <- wdifiltered %>%
          filter(Indicator.Name == 'CO2 emissions from electricity and heat production, total (% of total fuel combustion)')
min_x <- min(as.numeric(as.character(CO2electricity$variable)))
max_x <- max(as.numeric(as.character(CO2electricity$variable)))
ggplot(data=CO2electricity, 
       aes(x = as.numeric(levels(variable))[variable], y = value)) + 
  geom_bar(stat="identity") +
       labs(title = CO2electricity$Indicator.Name[1], x = 'year', y = '%') +
  scale_x_continuous(breaks = round(seq(min_x, max_x, by = 1),0)) +
       
       coord_polar(theta = "x")
```

***

Data Visualization Result and Conclusion- CO2 emissions from electricity and heat production, total (% of total fuel combustion)

 - CO2 emissions from electricity and heat production is the sum of three IEA categories of CO2 emissions. The first IEA category is the main Activity Producer Electricity and Heat which contains the sum of emissions from main activity producer electricity generation, combined heat and power generation and heat plants. The second IEA category is the unallocated Autoproducers which contains the emissions from the generation of electricity and/or heat by autoproducers. The third IEA category is other Energy Industries contains emissions from fuel combusted in petroleum refineries, for the manufacture of solid fuels, coal mining, oil and gas extraction and other energy-producing industries. 
 
 - We could see from th graph that every countries had alsmost same level of CO2 Emissions from electricity and heat production, total from 2000 to 2014.


### CO2 emissions from gaseous fuel consumption (% of total)

```{r 5, message=FALSE, warning=FALSE, echo=FALSE}
ggplot(CO2electricity, aes(x = CO2electricity$value,y=CO2electricity$Country.Name)) +
       geom_point() +
       geom_line() +
       ggtitle("Relationship--> 'Co2 value from electricity and heat production and distribution limit for each country'") +
       stat_smooth(method = "", formula = y~x+z, size = 1, col = "red")
```

***

Data Visualization Result and Conclusion- CO2 emissions from gaseous fuel consumption (% of total)

 - CO2 emissions from liquid fuel consumption refer mainly to emissions from use of natural gas as an energy source.

 - From the graph, we could find that United Kingdom use natural gas as an energy source more than 30% of total fuel consumption. Other developed countries also use natural gas at least more than 10% of total fuel consumption. Howevery the developing countries, China used natural gas as an nergy source very less. China used only less than 5% of it.  


### CO2 emissions from liquid fuel consumption (% of total)

```{r 6, message=FALSE, warning=FALSE, echo=FALSE}
CO2emissions <- wdifiltered %>%
          filter(Indicator.Name == 'CO2 emissions (metric tons per capita)')
ggplot(CO2emissions, aes(x = CO2emissions$value,y=CO2emissions$Country.Name)) +
       geom_point() +
       geom_line() +
       ggtitle("Relationship--> 'Co2 emissions with distribution limit for each country'") +
       stat_smooth(method = "", formula = y~x, size = 1, col = "red")
```

***

Data Visualization Result and Conclusion- CO2 emissions from liquid fuel consumption (% of total)

 - CO2 emissions from liquid fuel consumption refer mainly to emissions from use of petroleum-derived fuels as an energy source.

 - From the graph, we could see that the developed countries used liquid fuel around 40% of tatal fuel consumption. Howevery the developing country, china used liquid fuel less than 20%. We could find that the developed countries and developing countries had different fuel type consumption pattern from the graph.
```{r 7, message=FALSE, warning=FALSE, echo=FALSE}
CO2residentialcommercial <- wdifiltered %>%
          filter(Indicator.Name == 'CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)')
min_x <- min(as.numeric(as.character(CO2residentialcommercial$variable)))
max_x <- max(as.numeric(as.character(CO2residentialcommercial$variable)))
ggplot(data=CO2residentialcommercial, 
       aes(x = as.numeric(levels(variable))[variable], y = value)) + 
  geom_bar(stat="identity") +
       labs(title = CO2residentialcommercial$Country.Name[1], x = 'year', y = '%') +
  scale_x_continuous(breaks = round(seq(min_x, max_x, by = 1),0)) +
       
       coord_polar(theta = "x")
```

```{r 8, message=FALSE, warning=FALSE, echo=FALSE}
CO2transport <- wdifiltered %>%
          filter(Indicator.Name == 'CO2 emissions from transport (% of total fuel combustion)')
min_x <- min(as.numeric(as.character(CO2transport$variable)))
max_x <- max(as.numeric(as.character(CO2transport$variable)))
ggplot(data=CO2transport, 
       aes(x = as.numeric(levels(variable))[variable], y = value)) + 
  geom_bar(stat="identity") +
       labs(title = CO2transport$Country.Name[1], x = 'year', y = '%') +
  scale_x_continuous(breaks = round(seq(min_x, max_x, by = 1),0)) +
       
       coord_polar(theta = "x")
```