---
title: "ANLY 512_Lab2"
author: "Abhilasha Vyas"
date: "04/17/2020"
output:
flexdashboard::flex_dashboard:
orientation: rows
storyboard: true
social: menu
vertical_layout: scroll
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
knitr::opts_chunk$set(echo = TRUE, fig.align = "center")
library(ggplot2)
library(reshape2)
library(readr)
library(readxl)
library(dplyr)
library(tidyverse)
library
```
# Introduction {data-navmenu="Project Sections"}
## column {.sidebar}
### **Exploring Global and Country level CO2 Emissions from Human activity**
Human activities since the beginning of the Industrial Revolution have produced a 45% increase in the atmospheric concentration of carbon dioxide.At current emission rates, earth temperatures could increase by 2 °C (3.6°F) by 2036, which the United
Nations designated as the upper limit to avoid "dangerous" levels!
This dashboard provides data exploration and analysis of global territorial CO2 emissions over time( 1960 -2020) in total volume and per capita units
Additionally, this dashboard provides visualization and analysis of CO2 emmision levels by regions and countries highlighting the top 10 countries responsible for highest emissions in 2018
Data Source: http://www.globalcarbonatlas.org/en/content/welcome-carbon-atlas
Row
-----------------------------------------------------------------------
# Global Territorial CO2 Emissions Over Time {data-navmenu="Project Sections"}
### **History of Global territorial CO2 emissions since 1960**
```{r, echo = FALSE, message = FALSE,fig.width=12, fig.height=8}
# get data
region_total_mco2 <- read_excel("C:/Users/vyasa/OneDrive/Documents/region_total_mco2.xlsx")
# reformat data
p1 <- ggplot() + theme_bw() +
geom_line(aes(y = Total, x = Year), size=1.5, data = region_total_mco2,
stat="identity") +
scale_x_continuous(breaks=seq(1960,2020,5)) +
labs(x="Year", y="Total Co2 emission") +
ggtitle("Territorial CO2 levels across the globe over time (MtCO2)")
p1
```
# Country Wise territorial CO2 Emissions over time {data-navmenu="Project Sections"}
### **History of Territorial Carbon Dioxide Emissions for Major Countries**
```{r, echo = FALSE, message = FALSE, fig.width=12, fig.heigth=8}
# get data
co2_timeseries <- read_excel("C:/Users/vyasa/OneDrive/Documents/co2_timeseries.xlsx")
# reformat date
melted.co2_timeseries<-melt(co2_timeseries,id=c("Year"),
variable.name = "Countries",
value.name = "Emissions")
# plot
p2 <- ggplot() + theme_bw() +
geom_line(aes(y = Emissions, x = Year, color = Countries), size=1.5, data = melted.co2_timeseries,
stat="identity") +
scale_x_continuous(breaks=seq(1960,2020,5)) +
labs(x="Year", y="Total Co2 emission") +
ggtitle("Territorial CO2 emission for major countries levels over time (MtCO2)") + scale_fill_discrete(name = "Countries", labels = c("EU28","Canada","China","India","Japan","Russian Federation","South Africa","South Korea","Ukraine","United States of America"))
p2
```
# Regional CO2 emissions in 2018 {data-navmenu="Project Sections"}
### **Total Territorial CO2 Emissions (MtCO2)**
```{r, echo = FALSE, message = FALSE, fig.width=12, fig.height=8}
# get data
region_total_mco2 <- read_excel("C:/Users/vyasa/OneDrive/Documents/region_total_mco2.xlsx")
# reformat data
data1 <- region_total_mco2[which (region_total_mco2$Year == 2018),]
melted.data1<-melt(data1,id=c("Year"),
variable.name = "Regions",
value.name = "Emissions")
# plot
ggplot(melted.data1, aes(x = Regions, y = Emissions))+
geom_bar(
aes(fill = Regions), stat = "identity", color = "white",
position = position_dodge(0.9)
)+
labs(y = "Emissions (MtCO2)",
x = "Regions",
title = "Territorial CO2 emissions per region in 2018")+theme_bw()
```
# Territorial CO2 Emissions Per Person (tCO2) {data-navmenu="Project Sections"}
```{r, echo = FALSE, message = FALSE, fig.width=12, fig.height=8}
# get data
region_tco2 <- read_excel("C:/Users/vyasa/OneDrive/Documents/region_tco2.xlsx")
# reformat data
data2 <- region_tco2[which (region_tco2$Year == 2018),]
melted.data2<-melt(data2,id=c("Year"),
variable.name = "Regions",
value.name = "Emissions")
# plot
ggplot(melted.data2, aes(x = Regions, y = Emissions))+
geom_bar(
aes(fill = Regions), stat = "identity", color = "white",
position = position_dodge(0.9)
)+
labs(y = "Emissions (tCO2)",
x = "Regions",
title = "Territorial CO2 emissions per person per region in 2018")+ theme_bw()
```
# Top 10 MtCo2 Emittior countries in 2018 {data-navmenu="Project Sections"}
```{r,echo = FALSE, message = FALSE, fig.width=12, fig.height =8}
# get data
top_10_mco2 <- read_excel("C:/Users/vyasa/OneDrive/Documents/top_10_mco2.xlsx")
# reformat data
data3 <- top_10_mco2[which (top_10_mco2$Year == 2018),]
melted.data3<-melt(data3,id=c("Year"),
variable.name = "Country",
value.name = "Emissions")
# plot
ggplot(melted.data3, aes(x = Country, y = Emissions))+
geom_bar(
aes(fill = Country), stat = "identity", color = "white",
position = position_dodge(0.9)
)+
labs(y = "Emissions (MtCO2)",
x = "Country",
title = "Ten countries with highest Territorial CO2 emissions in 2018 ")+theme_bw()
```
# Top 10 Per capita CO2 Emittior Countries in 2018 {data-navmenu="Project Sections"}
```{r,echo = FALSE, message = FALSE, fig.width = 12, fig.height=8}
# get data
top_10_tco2 <- read_excel("C:/Users/vyasa/OneDrive/Documents/top_10_tco2.xlsx")
# reformat data
data4 <- top_10_tco2[which (top_10_tco2$Year == 2018),]
melted.data4<-melt(data4,id=c("Year"),
variable.name = "Country",
value.name = "Emissions")
# plot
ggplot(melted.data4, aes(x = Country, y = Emissions))+
geom_bar(
aes(fill = Country), stat = "identity", color = "white",
position = position_dodge(0.9)
)+
labs(y = "Emissions (tCO2)",
x = "Country",
title = "Ten countries with highest Territorial CO2 emissions per person in 2018 ")+theme_bw()
```
Observation {data-navmenu="Project Sections"}
==============================
1. The global Co2 levels have risen steeply since 1960
2. Since beginning rapid industrialization in the early 2000's, China's total CO2 emissions have risen steeply and have quickly overtaken other developing countries such as India.Total emissions in Europe and USa have actually dipped a little in last few years
3. Asia has the highest overall emissions among the world regions
4. Saudi Arabia,a major oil producer leads the way in per capital Co2 emissions
# Top 10 Per capita CO2 Emittior Countries in 2018 {data-navmenu=“Project Sections”}