General Information

• I used block quote for my questions(hypothesis) and observations
• Between my base R plot and ggplot plot, I have a code chunk which rearrange power data, especially to remove NAs in year_of_capacity_data. But I hide this code chunk by using echo = FALSE, just because it’s fun and professor wanted to use those options. I’m just writing this just in case you might wonder how the code works
• I appreciate if you tell your observations that I couldn’t figure out in the feedback!

Import Data

Load the data using read.csv

power <- read.csv('C:/Users/Saeah Go/OneDrive/Desktop/Projects/Dataset/global_power_plant_database.csv',
                  na.strings = c(NA,"","NA"),
                  header=T)

My question or hypothesis

For my base R plot, I want to explore which energy source is used at most as primary fuels for each company, especially in 2018. I will figure out what are the first, second, third energy sources. My hypothesis is that renewable energies, for example, solar, wind, biomass, etc., will occur in second or third place because those are the energies people prefer regarding environmental issues. But note that since I just count the number of primary fuels each company rather than measuring the quantity of those energy fuels, the highest energy source does not denote that the source is the most energy produced.

Similarly, with the ggplot plot, I explored the same thing. But in this case, I compared them each year from 2015 to 2018. There were more data before 2015, but before then there were not many fuels, only like Biomass, Hydro, and Solar energies, unlike there are various energy sources after 2015. So I thought comparing all years has no meaning. Thus I decided to analyze only the data with 2015, 2016, 2017, and 2018.

My base R plot

My base R plot

# pick rows with year of capacity data is 2018
power_2018 <- power[(power$year_of_capacity_data == 2018),] 

# convert imported data to factors
power_2018 <- factor(power_2018$primary_fuel) 

# create a plot
plot(x = power_2018, 
     cex.names = 0.75, # reduced the x axis labels' size
     xlab = "Primary Fuel", ylab = "Count",
     main = "Counts of Primary Fuels in 2018",
     col = c("red", "orange", "yellow", "lightgreen", "blue", "purple", "pink", "gray"),
     ylim = c(0,250)) # y-range is: 0-250

# manage legend
legend("topright",
       title ="Fuel Types", # legend title
       bty="n", # no box created
       pch=c(15,15,15,15,15,15,15,15),
       col=c("red", "orange", "yellow", "lightgreen", "blue", "purple", "pink", "gray"),
       legend = c("Biomass", "Coal", "Gas", "Hydro", "Nuclear", "Oil", "Solar", "Wind"))

My ggplot plot

# pick the rows with year of capacity data is 2015, 2016, 2017, and 2018.
power_data <- subset(power,
                     year_of_capacity_data == 2015 |
                     year_of_capacity_data == 2016 |
                     year_of_capacity_data == 2017 |
                     year_of_capacity_data == 2018)

# create the barplot
ggplot(data = power_data, aes(x=primary_fuel, fill = primary_fuel)) +
  geom_bar() + 
  xlab("Primary Fuels") + ylab("Primary Fuels Count") + 
  ggtitle("Primary Fuels (Energy Source) Use Comparison") +
  theme_bw() +
  facet_wrap(year_of_capacity_data~., scales = "free") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        legend.position = "bottom",
        plot.title = element_text(size = 15)) + # adjust the title size
  labs(subtitle = "Investigated the data from 2015 to 2018") + # added subtitle
  guides(group = "none",
         fill = guide_legend(override.aes=list(size = 2), ncol = 5)) +
  scale_fill_discrete(name = " Fuel\nTypes") # legend name