Data 607 - Final Project
Joseph E. Garcia
December 4, 2014
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## guess_encodingEnergy usage changes our lives like technology does the demand of getting better energy and more efficient has change during the last 25 years. The hope is to get renewable energy and better solution for energy conservation and consumption.
Goals:
-To find global energy usage, energy trading flows and per capital use to better understand the progress for energy efficiency usage.
-We will observe energy consuming by category, and find what new strategy for renewable energy conservation.
-To discover which countries are energy suppliers / consumer, what relationship they bound together.
data<-fread('C:/Users/JGARCIA/Desktop/MS in Data Science/Data 607/Project/Final Project/all_energy_statistics.csv')
head(data)## COUNTRY commodity_transaction YEAR unit
## 1: Austria Additives and Oxygenates - Exports 1996 Metric tons, thousand
## 2: Austria Additives and Oxygenates - Exports 1995 Metric tons, thousand
## 3: Belgium Additives and Oxygenates - Exports 2014 Metric tons, thousand
## 4: Belgium Additives and Oxygenates - Exports 2013 Metric tons, thousand
## 5: Belgium Additives and Oxygenates - Exports 2012 Metric tons, thousand
## 6: Belgium Additives and Oxygenates - Exports 2011 Metric tons, thousand
## quantity quantity_footnotes category
## 1: 5 NA additives_and_oxygenates
## 2: 17 NA additives_and_oxygenates
## 3: 0 NA additives_and_oxygenates
## 4: 0 NA additives_and_oxygenates
## 5: 35 NA additives_and_oxygenates
## 6: 25 NA additives_and_oxygenatesallEnergy<-data[,-6]
allEnergy$commodity_transaction<-gsub(".*-", "", allEnergy$commodity_transaction)
names(allEnergy)[names(allEnergy) == 'commodity_transaction'] <- 'transaction'
allEnergy$transaction <- tolower(allEnergy$transaction)
allEnergy$quantity<-as.integer(allEnergy$quantity)## Warning: NAs introduced by coercion to integer range#The top ten countries
subEnergy<- allEnergy[allEnergy$COUNTRY %in% c("United States","China","Japan","Germany","United Kingdom","India","France","Brazil","Italy","Canada"), ]
#The products that of consume like Coal_Peat_Oil, Oil, Nautal_Gas, Electricity_heat and Biofuels_Waste
energy<-fread('C:/Users/JGARCIA/Desktop/MS in Data Science/Data 607/Project/Final Project/energy.csv')
head(energy)## Coal_Peat_Oil Oil
## 1: hard_coal conventional_crude_oil
## 2: anthracite natural_gas_liquids
## 3: cooking_coal additives_and_oxygenates
## 4: other_bituminius_coal other_hydrocarbons
## 5: brown_coal aviation_gasoline
## 6: sub_bituminius_coal motor_gasoline
## Nautal Gas
## 1: natural_gas_including_lng
## 2: gaswork_gas
## 3: blast_furnace_gas
## 4: other_recovered_gases
## 5:
## 6:
## Electricity & heat
## 1: electricity_net_installed_capacity_of _electric_power_plants
## 2: heat
## 3: direct_use_of_geothermal heat
## 4: direct_use_of_solar_thermal_heat
## 5:
## 6:
## Biofuels & Waste
## 1: fuelwood
## 2: charcoal
## 3: bagasse
## 4: animal_waste
## 5: other_vegetal_material_and_residues
## 6: municipal_wasteCoal_Peat_Oil<-energy[,1]
Oil<-energy[,2]
Nautal_Gas<-energy[,3]
Electricity_heat<-energy[,4]
Biofuels_Waste<-energy[,5]
CPO<-left_join(Coal_Peat_Oil,subEnergy, by = c("Coal_Peat_Oil"="category"))
OIL<-left_join(Oil,subEnergy,by = c("Oil"="category"))
NG<-left_join(Nautal_Gas, subEnergy,by = c("Nautal Gas"="category"))
EH<-left_join(Electricity_heat,subEnergy, by = c("Electricity & heat"="category"))
BW<-left_join(Biofuels_Waste,subEnergy, by = c("Biofuels & Waste"="category"))
impCPO<-CPO[grepl("imports",CPO[,3])==TRUE,]
impOIL<-OIL[grepl("imports",OIL[,3])==TRUE,]
impNG<-NG[grepl("imports",NG[,3])==TRUE,]
impBW<-BW[grepl("imports",BW[,3])==TRUE,]
expCPO<-CPO[grepl("exports",CPO[,3])==TRUE,]
expOIL<-OIL[grepl("exports",OIL[,3])==TRUE,]
expNG<-NG[grepl("exports",NG[,3])==TRUE,]
expBW<-BW[grepl("exports",BW[,3])==TRUE,]Import & Export Net Trades of Countries by different types products of Oil:
Coal Peat Oil Net Trade
impCPO.country<-impCPO %>% group_by(COUNTRY,YEAR) %>% summarise(totalImport=sum(quantity))
expCPO.country<-expCPO %>% group_by(COUNTRY,YEAR) %>% summarise(totalExport=sum(quantity))
netCPO<-merge(impCPO.country,expCPO.country, c("COUNTRY","YEAR"))
netCPO<-mutate(netCPO,netTransation=totalExport - totalImport)ggplot(data=netCPO, aes(x=YEAR, y=netTransation, group = COUNTRY, colour = COUNTRY)) +
geom_line() +
geom_point( size=1, shape=15, fill="white")+
ggtitle("Total Coal_Peat_Oil net trade (Export-Import), Metric tons,thousand") +
scale_y_continuous(labels = comma)
Oil net trade
impOIL.country<-impOIL %>% group_by(COUNTRY,YEAR) %>% summarise(totalImport=sum(quantity))
expOIL.country<-expOIL %>% group_by(COUNTRY,YEAR) %>% summarise(totalExport=sum(quantity))
netOIL<-merge(impOIL.country,expOIL.country, c("COUNTRY","YEAR"))
netOIL<-mutate(netOIL,netTransation=totalExport - totalImport)ggplot(data=netOIL, aes(x=YEAR, y=netTransation, group = COUNTRY, colour = COUNTRY)) +
geom_line() +
geom_point( size=1, shape=15, fill="white")+
ggtitle("Total Oil net trade (Export-Import)")+
scale_y_continuous(labels = comma)
Natural Gas net trade
impNG.country<-impNG %>% group_by(COUNTRY,YEAR) %>% summarise(totalImport=sum(quantity))
expNG.country<-expNG %>% group_by(COUNTRY,YEAR) %>% summarise(totalExport=sum(quantity))
netNG<-merge(impNG.country,expNG.country, c("COUNTRY","YEAR"))
netNG<-mutate(netNG,netTransation=totalExport - totalImport)ggplot(data=netNG, aes(x=YEAR, y=netTransation, group = COUNTRY, colour = COUNTRY)) +
geom_line() +
geom_point( size=1, shape=15, fill="white")+
ggtitle("Total Natural Gas net trade (Export-Import)")+
scale_y_continuous(labels = comma)
Biofuels & Waste net trade (Export-Import)
impBW.country<-impBW %>% group_by(unit,COUNTRY,YEAR) %>% summarise(totalImport=sum(quantity))
expBW.country<-expBW %>% group_by(unit,COUNTRY,YEAR) %>% summarise(totalExport=sum(quantity))
netBW<-merge(impBW.country,expBW.country, c("COUNTRY","YEAR","unit"))
netBW<-mutate(netBW,netTransation=totalExport - totalImport)
ggplot(data=netBW, aes(x=YEAR, y=netTransation, group = COUNTRY, colour = COUNTRY)) +
geom_line() +
geom_point( size=1, shape=15, fill="white")+
ggtitle("Total Biofuels & Waste net trade (Export-Import)")+
facet_grid(. ~ unit) +theme(axis.text.x = element_text(angle = 90, hjust = 1))+
scale_y_continuous(labels = comma)
Electricity Consumption by Countries and Households
electricity1990<-subEnergy %>% filter(YEAR=="1990") %>% filter(category=="total_electricity")
electricity1990<-electricity1990[grepl("households",electricity1990[,2])==TRUE,]
electricity1990<-electricity1990[order(electricity1990$quantity,decreasing = TRUE),]
electricity1990<-electricity1990[order(electricity1990$quantity,decreasing = TRUE),]
electricity2014<-subEnergy %>% filter(YEAR=="2014") %>% filter(category=="total_electricity")
electricity2014<-electricity2014[grepl("households",electricity2014[,2])==TRUE,]
electricity2014<-electricity2014[order(electricity2014$quantity,decreasing = TRUE),]
electricity2014<-electricity2014[order(electricity2014$quantity,decreasing = TRUE),]
e<-merge(electricity2014,electricity1990, c("COUNTRY"))
e<-data.frame(e$COUNTRY,e$quantity.x,e$quantity.y,e$unit.x,e$transaction.x)
colnames(e)<- c("COUNTRY","2014_quantity","1990_quantity","unit","transation")
#add new column "growth rate"
e["1990-2014growth"] <-round((e$`2014_quantity`-e$`1990_quantity`)/e$`1990_quantity`,2)
e<-e[order(e$`2014_quantity`,decreasing = TRUE),]
e## COUNTRY 2014_quantity 1990_quantity unit
## 3 China 717610 48080 Kilowatt-hours, million
## 7 Japan 273938 184148 Kilowatt-hours, million
## 5 India 217405 31983 Kilowatt-hours, million
## 2 Canada 161568 129831 Kilowatt-hours, million
## 4 France 149426 96908 Kilowatt-hours, million
## 1 Brazil 132302 48666 Kilowatt-hours, million
## 6 Italy 64255 52730 Kilowatt-hours, million
## transation 1990-2014growth
## 3 consumption by households 13.93
## 7 consumption by households 0.49
## 5 consumption by households 5.80
## 2 consumption by households 0.24
## 4 consumption by households 0.54
## 1 consumption by households 1.72
## 6 consumption by households 0.22Growth rate by Household 1990-2014
ggplot(data=e, aes(x=reorder(COUNTRY, -`1990-2014growth`), y=`1990-2014growth`,fill=COUNTRY)) +
geom_bar(stat="identity")+
geom_text(aes(label=`1990-2014growth`), vjust=-1, color="black", size=3)+
ggtitle("Electricity Consumption") +
xlab("COUNTRY") + ylab("Growth Rate")
Usage of electricty by houselholds in different country:
url<-("https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_population")
temp <- url %>%
html %>%
html_nodes("table")## Warning: 'html' is deprecated.
## Use 'read_html' instead.
## See help("Deprecated")population<-html_table(temp[1])
population<-data.frame(population)
colnames(population)[2] <- "Country"
population$Country <-gsub("\\[.*","",population$Country)
population$Population<-gsub("[^0-9]", "", population$Population)
population$Population<-as.integer(as.character(population$Population))
population_data<- population[population$Country %in% c("China","Japan","Germany","India","France","Brazil","Italy","Canada"), ]
population_data<-data.frame(population_data$Country,population_data$Population)
colnames(population_data) <- c("COUNTRY","Population")
p<-merge(electricity2014,population_data, c("COUNTRY"))
p["Per_Capital_KW_hr"] <-round(p$quantity/p$Population*1000000,2)
p<-p[order(p$quantity,decreasing = TRUE),]ggplot(data=p, aes(x=reorder(COUNTRY, Per_Capital_KW_hr), y=Per_Capital_KW_hr,fill=COUNTRY)) +
geom_bar(stat="identity")+
geom_text(aes(label=Per_Capital_KW_hr), vjust=-1, color="black", size=3)+
ggtitle("Electricity Consumption by Households") +
xlab("COUNTRY") + ylab("Use-Kilowatt-hours")+
coord_flip()
References:
https://www.google.com/search?biw=1366&bih=608&tbm=isch&sa=1&ei=ejMUXM3rLJDK_QbFxJcQ&q=advantages+and+disadvantages+of+renewable+energy&oq=adrenewable+energy&gs_l=img.1.1.0i7i30l10.148534.148732..151667…0.0..0.151.207.1j1……0….1..gws-wiz-img.4-GwLg71oCI#imgrc=VzQWehjiTCIUjM:
https://www.kaggle.com/unitednations/international-energy-statistics/data
https://www.investopedia.com/articles/investing/022415/worlds-top-10-economies.asp
http://api.eia.gov/series/?api_key=YOUR_API_KEY_HERE&series_id=ELEC.GEN.ALL-US-98.A