Setting WD and loading in data

setwd("/Users/Joeyc//Documents/School/Fall 2020/Data 110/Datasets")
getwd()

## [1] "C:/Users/Joeyc/Documents/School/Fall 2020/Data 110/Datasets"

GHG =read.csv("greenhousegas_gni2018.csv")

Load in libraries

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggthemes)

## Warning: package 'ggthemes' was built under R version 4.0.3

library(plotly)

## Warning: package 'plotly' was built under R version 4.0.2

## Loading required package: ggplot2

## Warning: package 'ggplot2' was built under R version 4.0.3

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

library(ggplot2)
library(RColorBrewer)

## Warning: package 'RColorBrewer' was built under R version 4.0.3

library(rworldmap)

## Warning: package 'rworldmap' was built under R version 4.0.3

## Loading required package: sp

## Warning: package 'sp' was built under R version 4.0.3

## ### Welcome to rworldmap ###

## For a short introduction type :   vignette('rworldmap')

head(GHG)

##   Country.Code Country.Name         IncomeGroup GNI.Per.Capita..USD.
## 1          AFG  Afghanistan          Low income                  550
## 2          ALB      Albania Upper middle income                 4860
## 3          DZA      Algeria Upper middle income                 4060
## 4          AGO       Angola Lower middle income                 3370
## 5          ARG    Argentina Upper middle income                12370
## 6          ARM      Armenia Upper middle income                 4230
##   GHG.Per.Capita change population
## 1           0.84   91.1   37172386
## 2           3.09    9.2    2866376
## 3           5.67   34.5   42228429
## 4           2.63   38.5   30809762
## 5           8.66   10.9   44494502
## 6           2.90   11.0    2951776

str(GHG)

## 'data.frame':    179 obs. of  7 variables:
##  $ Country.Code        : chr  "AFG" "ALB" "DZA" "AGO" ...
##  $ Country.Name        : chr  "Afghanistan" "Albania" "Algeria" "Angola" ...
##  $ IncomeGroup         : chr  "Low income" "Upper middle income" "Upper middle income" "Lower middle income" ...
##  $ GNI.Per.Capita..USD.: int  550 4860 4060 3370 12370 4230 23630 53190 49260 4050 ...
##  $ GHG.Per.Capita      : num  0.84 3.09 5.67 2.63 8.66 ...
##  $ change              : num  91.1 9.2 34.5 38.5 10.9 11 98.3 2.6 -12.3 21.3 ...
##  $ population          : int  37172386 2866376 42228429 30809762 44494502 2951776 105845 24992369 8847037 9942334 ...

summary(GHG)

##  Country.Code       Country.Name       IncomeGroup        GNI.Per.Capita..USD.
##  Length:179         Length:179         Length:179         Min.   :  280       
##  Class :character   Class :character   Class :character   1st Qu.: 1925       
##  Mode  :character   Mode  :character   Mode  :character   Median : 5470       
##                                                           Mean   :13985       
##                                                           3rd Qu.:17045       
##                                                           Max.   :83580       
##  GHG.Per.Capita       change         population       
##  Min.   : 0.420   Min.   :-34.30   Min.   :1.032e+05  
##  1st Qu.: 1.895   1st Qu.: -1.00   1st Qu.:2.828e+06  
##  Median : 4.000   Median : 20.40   Median :9.588e+06  
##  Mean   : 6.879   Mean   : 22.96   Mean   :4.195e+07  
##  3rd Qu.: 8.735   3rd Qu.: 42.50   3rd Qu.:3.029e+07  
##  Max.   :59.000   Max.   :136.20   Max.   :1.393e+09

any(is.na(GHG))

## [1] FALSE

sum(is.na(GHG))

## [1] 0

colSums(is.na(GHG))

##         Country.Code         Country.Name          IncomeGroup 
##                    0                    0                    0 
## GNI.Per.Capita..USD.       GHG.Per.Capita               change 
##                    0                    0                    0 
##           population 
##                    0

ggplot(data=GHG, mapping = aes(x = IncomeGroup, y = GHG.Per.Capita)) +
  geom_jitter() +
  geom_boxplot(alpha = 0.7) +
  theme_economist_white()+
  labs ( y= "GreenHouse Gas Per Capita", x= "Income Group") +
  ggtitle("Greenhouse Gas Per Capita Broken Down By Income Group")

We can see the higher the income group the more likely it is they have higher Greenhouse gas emissions per capita. Although, as we can see many of the outliers for the middle-income countries are above the mean of the high-income countries.

Let’s take a look at a world map of the distributions of Greenhouse Gases around the world to see how the countries compare.

But before starting we need to change GHG.Per.Capita Variable to a more suitable name becasue this variable will populate as the title of our world Map.

colnames(GHG)[colnames(GHG)=="GHG.Per.Capita"] = "Greenhouse_Gas_Per_Capita"

World_Map = joinCountryData2Map(GHG, nameJoinColumn = "Country.Name", joinCode = "NAME")

## 174 codes from your data successfully matched countries in the map
## 5 codes from your data failed to match with a country code in the map
## 69 codes from the map weren't represented in your data

mapCountryData(World_Map, 
               nameColumnToPlot = "Greenhouse_Gas_Per_Capita",
               catMethod= "fixedwidth",
               colourPalette = brewer.pal(10, "Spectral"),
               numCats = 10)

## Warning in rwmGetClassBreaks(dataCategorised, catMethod = catMethod, numCats = numCats, : classification method should be set to one of :fixedWidth diverging quantiles pretty logFixedWidth categorical 
## setting to fixedWidth as default

It is tough to tell the difference between a lot of the countries because we have some outliers in the High-income category. So I decide to make one without the worst GHG countries.

GHG_Under_30 = filter(GHG, GHG$Greenhouse_Gas_Per_Capita < 30)

World_Map_2 = joinCountryData2Map(GHG_Under_30, nameJoinColumn = "Country.Name", joinCode = "NAME")

## 170 codes from your data successfully matched countries in the map
## 5 codes from your data failed to match with a country code in the map
## 73 codes from the map weren't represented in your data

mapCountryData(World_Map_2, 
               nameColumnToPlot = "Greenhouse_Gas_Per_Capita",
               catMethod= "fixedwidth",
               colourPalette = brewer.pal(10, "Spectral"),
               numCats = 10)

## Warning in rwmGetClassBreaks(dataCategorised, catMethod = catMethod, numCats = numCats, : classification method should be set to one of :fixedWidth diverging quantiles pretty logFixedWidth categorical 
## setting to fixedWidth as default

As we can see, this map is a lot more clear than the other maps. The only countries we excluded were Qater, Curacao, Trinidad and Tobago, and Kuwait. All countries that were way too small anyway to see on the map. Now the country with the highest GHG per Capita is Austrialia.

plot_ly(data = GHG,
        x=~population,
        y=~Greenhouse_Gas_Per_Capita,
        mode="markers",
        color = GHG$IncomeGroup,
        hoverinfo = "text",
        text = paste("Population", GHG$Country.Name,
                     "<br>",
                     "Greenhouse Gas Per Capita", GHG$Greenhouse_Gas_Per_Capita)) %>%
  layout(title = "Comparing Greenhouse Gas Emissions Per Capita To Popualtion",
         yaxis=list(title="Greenhouse Gas Emissions Per Capita"),
         xaxis=list(title="Population") )

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plot.ly/r/reference/#scatter

Creating a df without the outliers. So beforehand it was tough to tell the difference between a lot of the points so I made another chart without a couple of countries that made the chart taught to read.

GHG_Under_30_Under_Pop = filter(GHG_Under_30, GHG_Under_30$population < 98423595)

plot_ly(data = GHG_Under_30_Under_Pop,
        x=~population,
        y=~Greenhouse_Gas_Per_Capita,
        mode="markers",
        color = GHG_Under_30_Under_Pop$IncomeGroup,
        hoverinfo = "text",
        text = paste("Population", GHG_Under_30_Under_Pop$Country.Name,
                     "<br>",
                     "Greenhouse Gas Per Capita", GHG_Under_30_Under_Pop$Greenhouse_Gas_Per_Capita)) %>%
  layout(title = "Comparing Greenhouse Gas Emissions Per Capita To Popualtion",
         yaxis=list(title="Greenhouse Gas Emissions Per Capita"),
         xaxis=list(title="Population") )

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plot.ly/r/reference/#scatter

Although this map is missing some countries, we refined our search to max it a lot easier to read.

Essay Questions

The topic of the data, any variables included, what kind of variables they are, where the data came from and how you cleaned it up (be detailed and specific, using proper terminology where appropriate). Be sure to explain why you chose this topic and dataset – what meaning does it have for you?

The topic I am looking to research is the Greenhouse gas emissions of countries in 2018. The variables that are included are; country name, Income Group, GNI per capita, GHG per capita, population, and change in GHG emissions from the prior year. GNI is defined as Gross National income. Which is calculated by GDP + (Money flowing from foreign countries - money flowing to foreign countries). I took the data from your datasets that were provided. I chose this topic because the increase in GHG emissions is slowly speeding up the effects of global warming around the world and I wanted to see how each country is affecting this. Identifying the areas around the world with not only the highest GHG emissions but the highest per capita can help us target the problem countries. This is important to me because I want to keep the Earth healthy and help enrich biodiversity around the world.

Incorporate background research about this topic. This background information will include information you find in an article, website, or book. Please source this background information within the essay or if you have multiple sources, include a bibliography. I am not particular about the format of this bibliography. If you need help finding articles, I am happy to help you and/or show you how to search the MC Library Database.

Greenhouse gases can be defined by several pollutants including, Carbon Dioxide, Methane, Nitrous Oxide, and fluorinated gases. These gases affect the atmosphere in different ways, but all are detrimental to life as we know it. (https://www.epa.gov/ghgemissions/overview-greenhouse-gases) If we want to keep a vast majority of our ecosystems alive we need to cut our emissions drastically. With only a small increase in the Earth’s temperature, this will cause drastic changes across the world. We have already started to see many causes. Draughts and an increase in sea levels are already being seen. Wildlife is being affected as well. The rise in sea temperatures has cause sea turtles to have an overpopulation of females. “Research shows that if a turtle’s eggs incubate below 81.86 Fahrenheit, the turtle hatchlings will be male. If the eggs incubate above 87.8° Fahrenheit, however, the hatchlings will be female. Temperatures that fluctuate between the two extremes will produce a mix of male and female baby turtles” (https://oceanservice.noaa.gov/facts/temperature-dependent.html#:~:text=Research%20shows%20that%20if%20a,male%20and%20female%20baby%20turtles.) Too many of one sex will cause the species to go extinct. One species going extinct has detrimental effects on the food chain causing a worse chain reaction. That will throw off the balance of an ecosystem.

What the visualization represents, any interesting patterns or surprises that arise within the visualization, and anything that could have been shown that you could not get to work or that you wished you could have included.

As a leader in manufactoring, I was surprised to see the US, not in the top 10 of GHG emissions per Capita in the world. I am assuming it is due to the large rural population in the middle of the country to help the numbers go down. Similar to China being ranked 42nd. Even with its large urban population, they have a largely rural population to the west of the coast line cities. The largest offenders of the GHG emissions per capita are the smaller urbanized countries without many regulations.

Final Project

Joey Cartwright

12/15/2020

Setting WD and loading in data

Load in libraries

We can see the higher the income group the more likely it is they have higher Greenhouse gas emissions per capita. Although, as we can see many of the outliers for the middle-income countries are above the mean of the high-income countries.

Let’s take a look at a world map of the distributions of Greenhouse Gases around the world to see how the countries compare.

But before starting we need to change GHG.Per.Capita Variable to a more suitable name becasue this variable will populate as the title of our world Map.

It is tough to tell the difference between a lot of the countries because we have some outliers in the High-income category. So I decide to make one without the worst GHG countries.

As we can see, this map is a lot more clear than the other maps. The only countries we excluded were Qater, Curacao, Trinidad and Tobago, and Kuwait. All countries that were way too small anyway to see on the map. Now the country with the highest GHG per Capita is Austrialia.

Creating a df without the outliers. So beforehand it was tough to tell the difference between a lot of the points so I made another chart without a couple of countries that made the chart taught to read.

Although this map is missing some countries, we refined our search to max it a lot easier to read.

Essay Questions

The topic of the data, any variables included, what kind of variables they are, where the data came from and how you cleaned it up (be detailed and specific, using proper terminology where appropriate). Be sure to explain why you chose this topic and dataset – what meaning does it have for you?

What the visualization represents, any interesting patterns or surprises that arise within the visualization, and anything that could have been shown that you could not get to work or that you wished you could have included.