INTRODUCTION
Dataset: EIA Energy Information on Renewable Energy Consumption by Source, January 2013 to August 2017.
Units of raw data are quadrillion BTU.
SET-UP
Load required libraries.
library(knitr)
## Warning: package 'knitr' was built under R version 3.5.3
library(tibble)
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.5.3
RAW DATA
Load dataset2 file into R Studio, name the columns, and scan the first two rows to combine the two row header into one column name.
dataset2 <- read.csv("https://raw.githubusercontent.com/Zchen116/assignment-2/master/renew%20energy2.csv", skip = 1, header = FALSE)
names(dataset2)[1] <- "Sector_and_Source"
names(dataset2)[2] <- "1989"
names(dataset2)[3] <- "1990"
names(dataset2)[4] <- "1991"
names(dataset2)[5] <- "1992"
names(dataset2)[6] <- "1993"
names(dataset2)[7] <- "1994"
names(dataset2)[8] <- "1995"
names(dataset2)[9] <- "1996"
names(dataset2)[10] <- "1997"
names(dataset2)[11] <- "1998"
names(dataset2)[12] <- "1999"
names(dataset2)[13] <- "2000"
names(dataset2)[14] <- "2001"
names(dataset2)[15] <- "2002"
names(dataset2)[16] <- "2003"
names(dataset2)[17] <- "2004"
names(dataset2)[18] <- "2005"
names(dataset2)[19] <- "2006"
names(dataset2)[20] <- "2007"
names(dataset2)[21] <- "2008"
head(dataset2)
## Sector_and_Source 1989 1990 1991 1992 1993 1994 1995
## 1 Total 6.391 6.206 6.238 5.992 6.261 6.153 6.703
## 2 Biomass 3.159 2.735 2.782 2.932 2.908 3.028 3.101
## 3 Biofuels1 0.125 0.111 0.128 0.145 0.169 0.188 0.2
## 4 Waste2 0.354 0.408 0.44 0.473 0.479 0.515 0.531
## 5 Wood and Derived Fuels3 2.68 2.216 2.214 2.313 2.26 2.324 2.37
## 6 Geothermal 0.317 0.336 0.346 0.349 0.364 0.338 0.294
## 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
## 1 7.166 7.175 6.654 6.677 6.26 5.311 5.888 6.141 6.247 6.407 6.825 6.719
## 2 3.157 3.105 2.928 2.963 3.008 2.622 2.701 2.807 3.01 3.117 3.277 3.503
## 3 0.143 0.184 0.201 0.209 0.236 0.253 0.303 0.404 0.5 0.577 0.771 0.991
## 4 0.577 0.551 0.542 0.54 0.511 0.364 0.402 0.401 0.389 0.403 0.397 0.413
## 5 2.437 2.371 2.184 2.214 2.262 2.006 1.995 2.002 2.121 2.136 2.109 2.098
## 6 0.316 0.325 0.328 0.331 0.317 0.311 0.328 0.331 0.341 0.343 0.343 0.349
## 2008
## 1 7.367
## 2 3.852
## 3 1.372
## 4 0.436
## 5 2.044
## 6 0.36
header_rows <- read.csv("https://raw.githubusercontent.com/Zchen116/assignment-2/master/renew%20energy2.csv", nrows=2, header=FALSE)
new_header <- sapply(header_rows, paste, collapse="_")
names(dataset2) <- new_header
dataset2[-2:-10,1:3]
## Sector and Source_Total 1989_6.391 1990_6.206
## 1 Total 6.391 6.206
## 11 Biomass 0.92 0.58
## 12 Wood and Derived Fuels 0.92 0.58
## 13 Geothermal 0.005 0.006
## 14 Solar Thermal/PV4 0.053 0.056
## 15 Commercial 0.102 0.098
## 16 Biomass 0.099 0.094
## 17 Biofuels5 0.001 *
## 18 Waste2 0.022 0.028
## 19 Wood and Derived Fuels3 0.076 0.066
## 20 Geothermal 0.003 0.003
## 21 Hydroelectric Conventional 0.001 0.001
## 22 Solar Thermal/PV - -
## 23 Industrial 1.871 1.717
## 24 Biomass 1.841 1.684
## 25 Biofuels6 0.057 0.05
## 26 Waste2 0.2 0.192
## 27 Wood and Derived Fuels3 1.584 1.442
## 28 Geothermal 0.002 0.002
## 29 Hydroelectric Conventional 0.028 0.031
## 30 Solar Thermal/PV - -
## 31 Wind - -
## 32 Transportation 0.068 0.06
## 33 Biomass 0.068 0.06
## 34 Biofuels7 0.068 0.06
## 35 Electric Power8 3.372 3.689
## 36 Electric Utilities 2.983 3.151
## 37 Biomass 0.02 0.022
## 38 Waste2 0.01 0.013
## 39 Wood and Derived Fuels3 0.01 0.008
## 40 Geothermal 0.197 0.181
## 41 Hydroelectric Conventional 2.765 2.948
## 42 Solar Thermal/PV * *
## 43 Wind * *
## 44 Independent Power Producers 0.389 0.538
## 45 Biomass 0.211 0.295
## 46 Waste2 0.122 0.175
## 47 Wood and Derived Fuels3 0.089 0.12
## 48 Geothermal 0.111 0.145
## 49 Hydroelectric Conventional 0.043 0.066
## 50 Solar Thermal/PV 0.003 0.004
## 51 Wind 0.022 0.029
SUBSET DATA
Create a datasubset with only the Residential Consumption of Renewable Energy rows 11 through 14 from 1989 to 2008.
Replace the first row of the new table with the first row of the raw table to retain the months designation.
dataset2[,1]
## [1] Total Biomass
## [3] Biofuels1 Waste2
## [5] Wood and Derived Fuels3 Geothermal
## [7] Hydroelectric Conventional Solar Thermal/PV4
## [9] Wind Residential
## [11] Biomass Wood and Derived Fuels
## [13] Geothermal Solar Thermal/PV4
## [15] Commercial Biomass
## [17] Biofuels5 Waste2
## [19] Wood and Derived Fuels3 Geothermal
## [21] Hydroelectric Conventional Solar Thermal/PV
## [23] Industrial Biomass
## [25] Biofuels6 Waste2
## [27] Wood and Derived Fuels3 Geothermal
## [29] Hydroelectric Conventional Solar Thermal/PV
## [31] Wind Transportation
## [33] Biomass Biofuels7
## [35] Electric Power8 Electric Utilities
## [37] Biomass Waste2
## [39] Wood and Derived Fuels3 Geothermal
## [41] Hydroelectric Conventional Solar Thermal/PV
## [43] Wind Independent Power Producers
## [45] Biomass Waste2
## [47] Wood and Derived Fuels3 Geothermal
## [49] Hydroelectric Conventional Solar Thermal/PV
## [51] Wind
## 21 Levels: Biofuels1 Biofuels5 Biofuels6 ... Transportation
renewable_res <- dataset2[11:14,]
head(renewable_res)
## Sector and Source_Total 1989_6.391 1990_6.206 1991_6.238 1992_5.992
## 11 Biomass 0.92 0.58 0.61 0.64
## 12 Wood and Derived Fuels 0.92 0.58 0.61 0.64
## 13 Geothermal 0.005 0.006 0.006 0.006
## 14 Solar Thermal/PV4 0.053 0.056 0.058 0.06
## 1993_6.261 1994_6.153 1995_6.703 1996_7.166 1997_7.175 1998_6.654
## 11 0.55 0.52 0.52 0.54 0.43 0.38
## 12 0.55 0.52 0.52 0.54 0.43 0.38
## 13 0.007 0.006 0.007 0.007 0.008 0.008
## 14 0.062 0.064 0.065 0.065 0.065 0.065
## 1999_6.677 2000_6.26 2001_5.311 2002_5.888 2003_6.141 2004_6.247
## 11 0.39 0.42 0.37 0.38 0.4 0.41
## 12 0.39 0.42 0.37 0.38 0.4 0.41
## 13 0.009 0.009 0.009 0.01 0.013 0.014
## 14 0.064 0.061 0.06 0.059 0.058 0.059
## 2005_6.407 2006_6.825 2007_6.719 2008_7.367
## 11 0.43 0.39 0.43 0.45
## 12 0.43 0.39 0.43 0.45
## 13 0.016 0.018 0.022 0.026
## 14 0.061 0.067 0.075 0.088
Build vectors from the raw data so it will be easier to manipulate
Convert the units because Quadrillion BTU (“Quads”) is not commonly used, instead use Giga-Watthours (“GWh”)
Conversion factor: 1 Quad = 293,071.07 GWh
Round the numbers for ease of viewing
Time <- c(new_header)
Geothermal <- round(mapply(`*`, as.numeric(c(renewable_res[1,])), 293071.07), digits = 1)
Solar <- round(mapply(`*`, as.numeric(c(renewable_res[2,])), 293071.07), digits = 1)
Biomass <- round(mapply(`*`, as.numeric(c(renewable_res[3,])) , 293071.07), digits = 1)
Total <- round(mapply(`*`, as.numeric(c(renewable_res[4,])), 293071.07), digits = 1)
Create a dataframe from the original data making the rows vectors
new_data <- data.frame(Time, Geothermal, Solar, Biomass, Total)
head(new_data)
## Time Geothermal Solar Biomass Total
## V1 Sector and Source_Total 2344569 1758426 2637640 3516853
## V2 1989_6.391 8499061 8499061 1758426 3516853
## V3 1990_6.206 8792132 8792132 2051498 4396066
## V4 1991_6.238 9378274 9378274 2051498 4396066
## V5 1992_5.992 8792132 8792132 2051498 4102995
## V6 1993_6.261 9085203 9085203 2051498 4102995
Analysis
Plot the data to visualize the change in renewable energy consumption in the residential sector and to determine if one technology is contributing more than others to thte overall trends.
Plot the total renewable energy consumed in the residential sector using blue.
ggplot(data=new_data, aes(x=Time, y=Total, group=1)) +
geom_line(color="#0066ff", size=1) +
geom_point(color="#0066ff", size=2) +
scale_x_discrete(breaks=c("2013","2014","2015","2016","2017","2018")) +
ggtitle("Monthly Consumption of Total Renewable Energy in GWh for Residential Sector") +
labs(x="January 2013 to August 2018", y="Consumption in GWh") +
theme(axis.title.y = element_text(size=12, color="#666666")) +
theme(axis.text = element_text(size=12, family="Trebuchet MS")) +
theme(plot.title = element_text(size=12, family="Trebuchet MS", face="bold", hjust=0, color="#666666"))
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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Plot the solar component of the renewable energy consumed in the residential sector using red.
ggplot(data=new_data, aes(x=Time, y=Solar, group=1)) +
geom_line(color="#aa0022", size=1) +
geom_point(color="#aa0022", size=2) +
scale_x_discrete(breaks=c("2013","2014","2015","2016","2017","2018")) +
ggtitle("Monthly Consumption of Solar Energy in GWh for Residential Sector") +
labs(x="January 2013 to August 2018", y="Consumption in GWh") +
theme(axis.title.y = element_text(size=12, color="#666666")) +
theme(axis.text = element_text(size=12, family="Trebuchet MS")) +
theme(plot.title = element_text(size=12, family="Trebuchet MS", face="bold", hjust=0, color="#666666"))
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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Conclusion
These two graphs show that monthly renewable energy consumed by the residential sector has been volatile up and down from January 2013 to August 2018. However, the monthly consumption of solar has been a little bit down on the second graph.