Exploring the structure of national consumption
This entry is my first exploration of the structure of national resource consumption
library(ggplot2)# READING IN DATA
## SETTING DIRECTORY FOR EORA DATA ON LOCAL HARD DRIVE
wd<-"G:/Documents/PostDocKVA/Data/Eora" ### data directory
setwd(wd)
dir()## [1] "countries.csv" "country_lookup.csv"
## [3] "Eora26_2011_bp.zip" "Eora26Structure.xlsx"
## [5] "gdppop.csv" "regionmembership.csv"
## [7] "TradeBalance_I-ENERGY.csv" "TradeBalance_I-ENERGY.xlsx"
## [9] "TradeBalance_I-VA.csv" "TradeBalance_I-VA.xlsx"
## [11] "Wiedmann"## READING IN MATERIAL USE DATA - ENERGY DATASET
energy.df<-read.csv("TradeBalance_I-ENERGY.csv",header=TRUE)
head(energy.df)## Country CountryA3 y TerritorialEmissions Imports Exports
## 1 Afghanistan AFG 2011 2120 47872 32
## 2 Albania ALB 2011 76785 38725 8777
## 3 Algeria DZA 2011 1595798 275304 791087
## 4 Andorra AND 2011 0 8185 0
## 5 Angola AGO 2011 568776 174772 120704
## 6 Antigua ATG 2011 0 7192 0
## DirectEmissions Consumption
## 1 949 49959
## 2 11011 106734
## 3 251668 1080016
## 4 0 8185
## 5 255897 622844
## 6 0 7192ggplot(energy.df,aes(y=Consumption,x=y)) + geom_point()
Some of the entries are negative. Let’s remove those.
energy.df<-energy.df[which(energy.df[,"Consumption"]>0),]
ggplot(energy.df,aes(y=Consumption,x=y,group=CountryA3)) + geom_line()
What are the units we are looking at?
unique(sort(energy.df$Country))## [1] Afghanistan Albania
## [3] Algeria Andorra
## [5] Angola Antigua
## [7] Argentina Armenia
## [9] Aruba Australia
## [11] Austria Azerbaijan
## [13] Bahamas Bahrain
## [15] Bangladesh Barbados
## [17] Belarus Belgium
## [19] Belize Benin
## [21] Bermuda Bhutan
## [23] Bolivia Bosnia and Herzegovina
## [25] Botswana Brazil
## [27] British Virgin Islands Brunei
## [29] Bulgaria Burkina Faso
## [31] Burundi Cambodia
## [33] Cameroon Canada
## [35] Cape Verde Cayman Islands
## [37] Central African Republic Chad
## [39] Chile China
## [41] Colombia Congo
## [43] Costa Rica Cote dIvoire
## [45] Croatia Cuba
## [47] Cyprus Czech Republic
## [49] Denmark Djibouti
## [51] Dominican Republic DR Congo
## [53] Ecuador Egypt
## [55] El Salvador Eritrea
## [57] Estonia Ethiopia
## [59] Fiji Finland
## [61] Former USSR France
## [63] French Polynesia Gabon
## [65] Gambia Gaza Strip
## [67] Georgia Germany
## [69] Ghana Greece
## [71] Greenland Guatemala
## [73] Guinea Guyana
## [75] Haiti Honduras
## [77] Hong Kong Hungary
## [79] Iceland India
## [81] Indonesia Iran
## [83] Iraq Ireland
## [85] Israel Italy
## [87] Jamaica Japan
## [89] Jordan Kazakhstan
## [91] Kenya Kuwait
## [93] Kyrgyzstan Laos
## [95] Latvia Lebanon
## [97] Lesotho Liberia
## [99] Libya Liechtenstein
## [101] Lithuania Luxembourg
## [103] Macao SAR Madagascar
## [105] Malawi Malaysia
## [107] Maldives Mali
## [109] Malta Mauritania
## [111] Mauritius Mexico
## [113] Moldova Monaco
## [115] Mongolia Montenegro
## [117] Morocco Mozambique
## [119] Myanmar Namibia
## [121] Nepal Netherlands
## [123] Netherlands Antilles New Caledonia
## [125] New Zealand Nicaragua
## [127] Niger Nigeria
## [129] North Korea Norway
## [131] Oman Pakistan
## [133] Panama Papua New Guinea
## [135] Paraguay Peru
## [137] Philippines Poland
## [139] Portugal Qatar
## [141] Romania Russia
## [143] Rwanda Samoa
## [145] San Marino Sao Tome and Principe
## [147] Saudi Arabia Senegal
## [149] Serbia Seychelles
## [151] Sierra Leone Singapore
## [153] Slovakia Slovenia
## [155] Somalia South Africa
## [157] South Korea South Sudan
## [159] Spain Sri Lanka
## [161] Sudan Suriname
## [163] Swaziland Sweden
## [165] Switzerland Syria
## [167] Taiwan Tajikistan
## [169] Tanzania TFYR Macedonia
## [171] Thailand Togo
## [173] Trinidad and Tobago Tunisia
## [175] Turkey Turkmenistan
## [177] UAE Uganda
## [179] UK Ukraine
## [181] Uruguay USA
## [183] Uzbekistan Vanuatu
## [185] Venezuela Viet Nam
## [187] Yemen Zambia
## [189] Zimbabwe
## 190 Levels: Afghanistan Albania Algeria Andorra Angola ... ZimbabweWe see 190 national units in the dataset - including the “Former USSR”.
Let’s first highlight and then remove that entry.
ggplot(energy.df[which(as.character(energy.df$Country)=="Former USSR"),],aes(y=Consumption,x=y)) + geom_line()
## REMOVING FORMER USSR
energy.df<-energy.df[-which(as.character(energy.df$Country)=="Former USSR"),]To make consumption more comparable let’s calculate per capita consumption by associating population data
#Reading in .csv file with annual gdp and population sizes
setwd(wd)
gdppop.df<-read.csv("gdppop.csv",header=TRUE,skip=1) #skipping the first line which includes a description of the file
## taking a look at the gdp and population file
head(gdppop.df)## Country CountryA3 y GDP val
## 1 Aruba ABW 1970 201904.2 56771
## 2 Aruba ABW 1971 213043.3 57253
## 3 Aruba ABW 1972 224756.6 57730
## 4 Aruba ABW 1973 235870.2 58189
## 5 Aruba ABW 1974 244441.6 58610
## 6 Aruba ABW 1975 255802.9 58983## merging the gdp and population size data onto the energy consumption data frame
energy.df<-merge(energy.df,gdppop.df,by=c("CountryA3","y"),all.x=TRUE)
ggplot(energy.df,aes(y=Consumption/val,x=y,group=CountryA3)) + geom_line()## Warning: Removed 320 rows containing missing values (geom_path).
ggplot(energy.df,aes(y=Consumption/GDP,x=y,group=CountryA3)) + geom_line()