Final Report - Advancements in Solar and Wind Energy in the USA

Key Drivers of the Growth of Solar and Wind Electricity Generation in the USA

1. Introduction & Research Question

1.1 Introduction

Renewable energy provides a larger share of electricity in the United States than coal.

Code

us_elec_gen <- tibble(
    Type = c("Renewables", "Natural Gas", "Nuclear", "Coal"),
    Percentage = c(0.23, 0.39, 0.18, 0.19)
)

us_elec_bar <- us_elec_gen %>% 
    ggplot(aes(x = reorder(Type, -Percentage),
               y = Percentage,
               fill = Type)) +
    geom_col(width = 0.5) +
    scale_fill_manual(values = c("Renewables" = "#719847",
                                 "Natural Gas" = "gray",
                                 "Nuclear" = "gray",
                                 "Coal" = "dimgray")) +
    geom_text(aes(label = scales::percent(Percentage)), 
              vjust = -0.3,
              size = 5,
              family = "Ubuntu") +
    scale_y_continuous(expand = expansion(mult = c(0, 0.15)),
                       labels = scales::percent_format()) +
    theme_minimal_hgrid(font_family = "Ubuntu") +
    theme(legend.position = "none",
          axis.text.x = element_text(size = 11)) +
    labs(x = "Energy Type",
         y = "Electricity Share",
         title = "Renewables outnumber coal for electricity\ngeneration in 2022",
         caption = "Data from BCSE")

us_elec_bar

Data source by WEForum (2023), originated from BCSE.

This simple statistic emphasizes the growing prominence of renewable energy in the US, which is likely not to slow down anytime soon as renewables have the fastest growth rate of all energy sources at 12.6% (BCSE). This growth has been rapid: the contribution of renewables has almost doubled in just one decade (BCSE). The urgency of the climate crisis is the likely instigator of not only the United State’s renewable energy transition but also the global growth of renewables. The United States is a global renewable energy leader, ranking as the second highest producer of solar and wind energy in 2021 (IRENA).

Code

# 1. Load and clean data of international solar and wind gen
drop <- c('World', 'Asia', 'Europe', 'North America',
          'European Union', 'South America', 'Eurasia', 'Africa')
internationalPath <- here('data', 'irena_solar_wind_international_raw.csv')
international <- read_csv(internationalPath, skip = 2) %>% 
    rename(Country = 'Region/country/area') %>% 
    pivot_longer(
        names_to = 'Year',
        values_to = 'Generation',
        cols = '2000':'2021') %>% 
    mutate(
        Year = as.numeric(Year)
    ) %>%
    filter(!Country %in% drop)

international %>% 
    write_csv(here('data', 'irena_solar_wind_international_long.csv'))

# 2. International Solar Generation
# 2.1 Solar gen data frame
solar_international <- international %>% 
    filter(Technology == 'Solar')

# 2.2 Top 5 solar producers in 2021
top_5_solar <- solar_international %>% 
    filter(Year == '2021') %>% 
    arrange(desc(Generation)) %>% 
    slice(1:5)

# 2.3 Plot of top 5 solar producers
solar_international_plot <- 
    ggplot(solar_international %>% 
        filter(Country %in% c(top_5_solar$Country)),
        aes(x = Year, y = Generation/1000, group = Country, color = Country)) +
    geom_line(linewidth = 1) +
    geom_text_repel(
        data = top_5_solar,
        aes(label = Country),
        hjust = 0, nudge_x = 1, direction = "y",
        size = 5, segment.color = NA,
        family = "Ubuntu") + 
    scale_x_continuous(
        breaks = seq(2000, 2021, 3),
        expand = expansion(add = c(1, 5))) +
    scale_y_continuous(expand = expand_scale(mult = c(0, 0.05))) +
    labs(x = 'Year',
         y = 'Generation (TWh)',
         title = 'United States is the second highest producer of\nsolar energy',
         subtitle = 'Top 5 producers of solar energy in 2021',
         caption = 'Data from IRENA') +
    scale_color_manual(values = c('#719847','#ccca5d','#b2529d','#4c67a5','#a5734c')) +
    theme_minimal_hgrid(font_family = 'Ubuntu') +
    theme(legend.position = 'none')

ggsave(
    filename = here('figs_manu', '3_1_solar_inter_plot.png'), 
    plot = solar_international_plot,
    width = 8,
    height = 8 / 1.618
)

# 3. International Wind Generation
# 3.1 Wind gen data frame
wind_international <- international %>% 
    filter(Technology == 'Wind')

# 3.2 Top 5 wind producers in 2021
top_5_wind <- wind_international %>% 
    filter(Year == '2021') %>% 
    arrange(desc(Generation)) %>% 
    slice(1:5)

# 3.3 Plot of top 5 wind producers
wind_international_plot <- 
    ggplot(wind_international %>% 
        filter(Country %in% c(top_5_wind$Country)),
        aes(x = Year, y = Generation/1000, group = Country, color = Country)) +
    geom_line(linewidth = 1) +
    geom_text_repel(
        data = top_5_wind,
        aes(label = Country),
        hjust = 0, nudge_x = 1, direction = "y",
        size = 5, segment.color = NA,
        family = "Ubuntu") + 
    scale_x_continuous(
        breaks = seq(2000, 2021, 3),
        expand = expansion(add = c(1, 5))) +
    scale_y_continuous(expand = expand_scale(mult = c(0, 0.05))) +
    labs(x = 'Year',
         y = 'Generation (TWh)',
         title = 'United States is the second highest producer of\nwind energy',
         subtitle = 'Top 5 producers of wind energy in 2021',
         caption = 'Data from IRENA') +
    scale_color_manual(values = c('#719847','#ccca5d','#b2529d','#4c67a5','#a5734c')) +
    theme_minimal_hgrid(font_family = 'Ubuntu') +
    theme(legend.position = 'none')

ggsave(
    filename = here('figs_manu', '3_1_wind_inter_plot.png'), 
    plot = wind_international_plot,
    width = 8,
    height = 8 / 1.618
)

# 4. International summary table
sum_international <- international %>% 
    group_by(Country, Technology) %>%
    summarize(`Total Generation (tWh)` = 
                  sum(Generation, na.rm = TRUE)) %>% 
    rename(`Technology (Solar or Wind)` = Technology)

sum_international_table <- sum_international %>% 
    reactable(
        searchable = TRUE,
        highlight = TRUE,
        filterable = TRUE,
        defaultPageSize = 5,
        showPageSizeOptions = TRUE,
        pageSizeOptions = c(5, 10, 15)
        )

Solar

Wind

World Data

It is likely that the competition among these countries, especially between the USA and China, is a driver for the growth of renewable energy in the USA. In addition to this international competitiveness, national drivers for renewable energy growth include the cost of energy sources, renewable energy policies, and research funding. Exploring the relationship between renewable energy growth and these drivers – and the relationships among the drivers – is important for understanding how nations may more efficiently increase their renewable energy growth. Specifically, in the United States, there is a goal outlined in the Long Term Strategy to reach 100% clean electricity by 2035 as part of the larger plan to reach net-zero emissions by 2050. Therefore, this study explores the strategies the United States has been taking regarding renewable energy growth to meet these goals. Solar and wind energy are the focus of this study because of their potential to contribute to the majority of the renewable energy share. The key findings are:

1. The decreasing cost of solar and wind energy is a contributor to their growth but more so for solar than wind;
2. Policy may not be a direct driver for the growth, but rather a way to incentivize the economic and technical advancements shown by the lack of correlation between policy and generation; and
3. There is a funding-implementation lag as RD&D has a weak, positive correlation with solar and wind growth.

Overall, understanding the growth of a technology is complex due to the technical, economic, and political interactions; however, this study shows how one may go about exploring such data.

1.2 Research Question

Exploring the key drivers of the growth of solar and wind electricity generation in the USA.

2. Data Source Discussion

The data sources focus on wind and solar energy and cover generation and capacity of energy sources, United States policies, and country RD&D spending.

2.1 EIA Data Browser - Solar/Wind Generation & Fossil Cost

Description: This data browser is generated by EIA (2023) (US Energy Information Administration). In this browser, the data can be selected as net generation, consumption, or retail; and the reported data components can be selected based on our purposes. It contains data of different renewable energy sources from 2000 to 2023.

Validity: The data is pre-processed by EIA, but originated from EIA API. The EIA appears to collect its data from “survey respondents,” which could indicate missing data if not all power companies or organizations are reporting data to the EIA, and there is the potential for bias if only certain types of companies, like large corporations, are reporting data and if the survey questions are inherently biased.

2.2 IRENA - Solar/Wind Cost and International Renewable Generation

Description: IRENA (2023), short for International Renewable Energy Agency, also provides data about renewable energy capacity, power generation, and renewable energy balances.

Validity: The data is processed by IRENA and was collected from IRENA members using a questionnaire and the gaps in data were filled using desk research. The renewable energy power capacity statistics are updated in March, and the power generation and balances data sets are updated in July. While IRENA tries to fill in missing data through their own research, the data set may still be incomplete. The questionnaire may introduce biases depending on how the questions are worded.

2.3 DSIRE - Solar/Wind Laws & Incentives

Description: DSIRE (2023) (Database of State Incentives for Renewables and Efficiency) provides state and federal policy related to renewable energy and efficiency.

Validity: This data has been pre-processed by the N.C. Clean Energy Technology Center at N.C. State University and provides summaries of incentives and policies established by the federal government, state governments and U.S. territories, local governments, and larger electric and gas utilities in the USA. The website does not state how N.C. collected this information, but it is likely from publicly available websites and news reports. The data does not include federal funding opportunities for RD&D, demonstration projects, outreach, or inter-governmental programs nor bills that have not been enacted or regulations that have not been fully approved. The scope is limited to local incentives and policies that are especially innovative, and to municipalities and counties with the largest populations, which results in missing data and biases for what qualifies as “innovative.” The data is typically updated at least once a year.

2.4 IEA - Energy Technology RD&D Budgets

Description: This data set from the IEA (2023) contains spending on energy technologies within IEA countries since 1974.

Validity: The data was processed by the IEA and collected from central or federal government budgets, as well as the budgets of state-owned companies, for spending on a range of sectors including energy efficiency, renewables, nuclear power, fossil fuels, hydrogen and fuel cells, and more. There is missing data because not all countries are IEA members and perhaps not all spending is reflected in the budgets, which also could introduce biases.

3. Results

3.1 Solar and Wind Generation

Using EIA data, the generation of solar and wind energy over time was explored.

Code

# 1. Solar generation data processing
solar_gen_path <- here('data', 'eia_solar_gen_raw.csv')
solar_gen <- read_csv(solar_gen_path, skip = 4) %>% 
    clean_names() %>% 
    select('month', 'united_states_thousand_megawatthours') %>% 
    arrange(month) %>% 
    mutate(
        month = dmy(paste('01', month)),
        month = format(month, '%Y-%m'),
        year = as.numeric(substr(month, 1, 4))
    ) %>% 
    filter(year <= 2022) %>% 
    group_by(year) %>% 
    summarise(solar_gen_gwh = sum(united_states_thousand_megawatthours))

write_csv(solar_gen, here('data', 'eia_solar_gen.csv'))

# 2. Wind generation data processing
wind_gen_path <- here('data', 'eia_wind_gen_raw.csv')
wind_gen <- read_csv(wind_gen_path, skip = 4) %>% 
    clean_names() %>% 
    select('month', 'united_states_thousand_megawatthours') %>% 
    arrange(month) %>% 
    mutate(
        month = dmy(paste('01', month)),
        month = format(month, '%Y-%m'),
        year = as.numeric(substr(month, 1, 4))
    ) %>% 
    filter(year <= 2022) %>% 
    group_by(year) %>% 
    summarise(wind_gen_gwh = sum(united_states_thousand_megawatthours))

write_csv(wind_gen, here('data', 'eia_wind_gen.csv'))

# 3. Long format & exporting
solar_wind_gen_longer <- inner_join(solar_gen, wind_gen, by = 'year') %>% 
    pivot_longer(names_to = 'energy_source',
                 values_to = 'gwh',
                 cols = c('solar_gen_gwh', 'wind_gen_gwh'))

write_csv(solar_wind_gen_longer,
          here('data', 'eia_solar_wind_gen_longer.csv'))

# 4. Plot the Solar and Wind Generation over Time
generation_plot <- solar_wind_gen_longer %>% 
    ggplot(aes(x = year,
               y = gwh / 1000,
               color = energy_source)) +
    geom_line(linewidth = 1) +
    geom_point(fill = 'white', size = 2, shape = 21) +
    geom_text(data = subset(solar_wind_gen_longer, year == max(year)),
              aes(label = recode(energy_source, 
                                 solar_gen_gwh = 'Solar', 
                                 wind_gen_gwh = 'Wind')),
              nudge_x = -1.15,
              nudge_y = 10,
              size = 5,
              family = "Ubuntu") +
    labs(x = 'Year',
         y = 'Generation (TWh)',
         color = 'Energy Source',
         title = 'Wind outpaces solar energy generation in the USA\nevery year',
         subtitle = 'Wind generation is more than double solar generation in 2022',
         caption = 'Data from EIA') +
    scale_x_continuous(breaks = seq(2000, 2022, by = 2)) +
    scale_y_continuous(expand = expand_scale(mult = c(0, 0.05))) +
    scale_color_manual(values = c('#ccca5d','#b2529d'))+
    theme_minimal_hgrid(font_family = 'Ubuntu') +
    theme(legend.position = 'none')

ggsave(
    filename = here('figs_manu', '3_2_generation_plot.png'), 
    plot = generation_plot,
    width = 7,
    height = 7 / 1.618
)

generation_plot

The generation of both solar and wind energy follows the expectations of being unimodal and widely spread. Both sources are increasing in annual generation and have yet to plateau, and the generation is widely-spread from about 0 to 150 and over 400 TWh for solar and wind, respectively. Solar generation did not start to grow until 2013, while wind generation has been increasing since 2001. Future exploration of the data can look into the deployment, size, and location of commercial solar and wind projects.

3.2 Cost of Solar and Wind Energy

The cost of solar and wind was the first driver to be explored using data from IRENA.

Code

# 1. Read solar and wind cost data and merge to one
solar_cost <- read_csv(here('data', 'irena_solar_cost.csv'))
wind_cost <- read_csv(here('data', 'irena_wind_cost.csv'))
solar_wind_cost <- left_join(solar_cost, wind_cost, by = 'year')

# 2. Pivot to longer format and export to csv
solar_wind_cost_longer <- solar_wind_cost %>% 
    pivot_longer(names_to = 'energy_source',
                 values_to = 'cost',
                 cols = -year)

write_csv(solar_wind_cost_longer,
          here('data', 'irena_solar_wind_cost_longer.csv'))

# 3. Create plot of solar and wind costs compared to fossil fuels
cost_plot <- solar_wind_cost_longer %>% 
    ggplot(aes(x = year,
               y = cost,
               color = energy_source)) +
    annotate('rect',
             xmin = -Inf, xmax = Inf,
             ymin = 0.05, ymax = 0.177,
             fill = 'gray', alpha = 0.3) +
    annotate('text',
             x = 2013, y = 0.08,
             color = 'dimgray',
             label = 'The shaded area is the range of fossil fuel costs.',
             family = "Ubuntu") +
    geom_line(linewidth = 1) +
    geom_point(fill = 'white', size = 2, shape = 21) +
    geom_text_repel(data = subset(solar_wind_cost_longer,
                                  year == max(year)),
                    aes(label = recode(energy_source, 
                                       solar_cost_usd_per_kwh = 'Solar', 
                                       wind_cost_usd_per_kwh = 'Wind')),
                    nudge_x = 0.5,
                    nudge_y = 0,
                    size = 4.5,
                    family = "Ubuntu") +
    labs(x = 'Year',
         y = 'Cost (USD/kWh, 2022 prices)',
         color = 'Energy Source',
         title = 'The cost of solar has significantly decreased in the USA \n becoming cheaper than wind in 2017',
         caption = 'Data from IRENA') +
    scale_x_continuous(breaks = seq(2010, 2019, by = 1)) +
    scale_y_continuous(limits = c(0, 0.4), breaks = c(0.1, 0.2, 0.3, 0.4), expand = expand_scale(mult = c(0, 0.05)), labels = scales::dollar)+
    scale_color_manual(values = c('#ccca5d','#b2529d'))+
    theme_minimal_hgrid(font_family = 'Ubuntu') +
    theme(legend.position = 'none')

ggsave(
    filename = here('figs_manu', '3_3_cost_plot.png'), 
    plot = cost_plot,
    width = 7,
    height = 7 / 1.618
)

cost_plot

It was expected that the cost for renewables would be multimodal due to yearly variations and changes in administrations, but that the overall trend would be decreasing. In addition, the grouping was expected to range depending on the type of technology, specifically due to the capital cost elasticity. However, neither technology showed a multimodal distribution, rather the costs mainly remained relatively constant or decreased. The cost of solar energy generation shows a decreasing trend from around 0.38 to 0.07 USD/kWh and is more widely-spread than that of wind. Wind costs remained relatively constant around 0.13 to 0.08 USD/kWh. It makes sense that the cost of solar installation has a larger range than wind cost because of the technological solar panel advancements, which have made the capital cheaper, while wind turbines have not seen such progress. Furthermore, it is important to note that the cost of wind energy generation is the average value of on-shore and off-shore: it would be interesting to explore how wind costs vary depending on location within the USA.

The fossil fuel cost range is approximately 0.18 and 0.05 USD/kWh. For solar and wind to be economically competitive with fossil fuels, the costs needed to be similar if not less than that of fossil fuels. Interestingly, wind has been cost competitive with fossil fuels since 2010, but solar became competitive later in 2013.

When comparing the cost and generation trends, the cost competitiveness of solar energy with fossil fuels in 2013 coincides with the beginning of solar generation growth in 2013, which perhaps suggests that cost was the main factor behind solar’s delayed growth compared to wind. Since the cost of wind has remained relatively constant, cost does not seem to be the major contributor to wind’s growth. While solar did become cheaper than wind in 2017, wind continued to have a higher growth rate than solar in subsequent years.

3.3 Policies and Incentives

State policies was the second driver to be explored using data from DSIRE (Database of State Incentives for Renewables & Efficiency). Because the states receive federal funding for programs, like infrastructure, these state level policies likely coincide with federal level policies. Due to this state and federal relationship, congressional and presidential party alignment was explored as well (House), which usually coincides with more federal legislative activity.

Code

# 1. Scrape and save the raw policy data frame
# 1.1 Read the html from local path
laws_html_path <- here('data', 'dsire_laws_raw.html')
laws_html <- read_html(laws_html_path)

# 1.2 Scrape the table as a df
laws_table <- laws_html %>% 
  html_nodes('table') %>% 
  .[[1]] %>% 
  html_table()

# 1.3 Save the df to csv
write_csv(laws_table, here('data', 'dsire_laws_raw.csv'))

# 2. Construct solar and wind policy data frame
# 2.1 Solar laws
solar_laws <- read_csv(here('data', 'dsire_laws_raw.csv')) %>%
    filter(grepl("Solar", Name, ignore.case = TRUE) |
           grepl("Solar", `Policy/Incentive Type`, ignore.case = TRUE)) %>%
    mutate(Created = as.integer(substr(Created, 7, 10))) %>% 
    count(Created) %>% 
    rename(year = Created, count_of_solar_laws = n)

write_csv(solar_laws, here('data', 'dsire_solar_laws.csv'))

# 2.2 Wind laws
wind_laws <- read_csv(here('data', 'dsire_laws_raw.csv')) %>% 
    filter(grepl("Wind", Name, ignore.case = TRUE) |
           grepl("Wind", `Policy/Incentive Type`, ignore.case = TRUE)) %>%
    mutate(Created = as.integer(substr(Created, 7, 10))) %>% 
    count(Created) %>% 
    rename(year = Created, count_of_wind_laws = n)

write_csv(wind_laws, here('data', 'dsire_wind_laws.csv'))

# 3. Plots of solar and wind laws
# 3.1 Judge if congress is the same party as the president
congress_path <- here('data', 'congress.csv')
congress <- read_csv(congress_path)

# 3.2 Solar laws plot
solar_laws_plot <- solar_laws %>% 
    left_join(congress, by = 'year') %>% 
    filter(year <= 2020) %>% 
    ggplot() +
    geom_col(aes(x = year, y = count_of_solar_laws, fill = both)) +
    geom_vline(xintercept = 2000.5, color = 'black', linetype = 'solid') +
    geom_vline(xintercept = 2008.5, color = 'black', linetype = 'solid') +
    geom_vline(xintercept = 2016.5, color = 'black', linetype = 'solid') +
    annotate(
        'text', x = 2000.35, y = 70, color = '#4c67a5', hjust = 1,
        label = 'Clinton', size = 5, family = "Ubuntu") +
    annotate(
        'text', x = 2005, y = 70, color = '#ad2730', hjust = 1,
        label = 'Bush', size = 5, family = "Ubuntu") +
    annotate(
        'text', x = 2014, y = 70, color = '#4c67a5', hjust = 1,
        label = 'Obama', size = 5, family = "Ubuntu") +
    annotate(
        'text', x = 2019.75, y = 70, color = '#ad2730', hjust = 1,
        label = 'Trump', size = 5, family = "Ubuntu") +
    geom_label(data = data.frame(x = 2004.5, y = 50, label = 'Both Congress and the \nPresident are of the \nsame political party'),
      aes(x = x, y = y, label = label),
      size = 3.5, color = '#719847', family = "Ubuntu") +
    geom_curve(data = data.frame(x = 2004.5, xend = 2001.5, y = 42, yend = 10),
               mapping = aes(x = x, xend = xend, y = y, yend = yend),
               color = '#719847', size = 0.5, curvature = 0.1,
               arrow = arrow(length = unit(0.01, "npc"), type = "closed")) +
    geom_curve(data = data.frame(x = 2004.5, xend = 2005, y = 42, yend = 10),
               mapping = aes(x = x, xend = xend, y = y, yend = yend),
               color = '#719847', size = 0.5, curvature = -0.1,
               arrow = arrow(length = unit(0.01, "npc"), type = "closed")) +
    geom_curve(data = data.frame(x = 2004.5, xend = 2009.5, y = 42, yend = 25),
               mapping = aes(x = x, xend = xend, y = y, yend = yend),
               color = '#719847', size = 0.5, curvature = -0.1,
               arrow = arrow(length = unit(0.01, "npc"), type = "closed")) +
    scale_x_continuous(breaks = seq(2000, 2020, by = 2),
                       limits = c(1998, 2020),
                       expand = expand_scale(mult = c(0, 0.05))) +
    scale_y_continuous(breaks = seq(0, 60, by = 10),
                       limits = c(0 , 70),
                       expand = expand_scale(mult = c(0, 0.05))) +
    scale_fill_manual(values = c('#dbd9d6', '#719847')) +
    labs(x = 'Year',
         y = 'Number of Solar Policies',
         title = 'State solar policies appear to coincide with\ndemocratic presidential terms',
         subtitle = 'Alignment of political parties does not appear to impact legislation',
         caption = 'Data from DSIRE') +
    theme_minimal_hgrid(font_family = 'Ubuntu') +
    theme(legend.position = 'none')

ggsave(
    filename = here('figs_manu', '3_4_solar_laws_plot.png'), 
    plot = solar_laws_plot,
    width = 7,
    height = 7 / 1.618
)

# 3.3 Wind policy plot
wind_laws_plot <- wind_laws %>% 
    left_join(congress, by = 'year') %>% 
    filter(year <= 2020) %>% 
    ggplot() +
    geom_col(aes(x = year, y = count_of_wind_laws, fill = both)) +
    geom_vline(xintercept = 2000.5, color = 'black', linetype = 'solid') +
    geom_vline(xintercept = 2008.5, color = 'black', linetype = 'solid') +
    geom_vline(xintercept = 2016.5, color = 'black', linetype = 'solid') +
    annotate(
        'text', x = 2000.35, y = 70, color = '#4c67a5', hjust = 1,
        label = 'Clinton', size = 5, family = "Ubuntu") +
    annotate(
        'text', x = 2005, y = 70, color = '#ad2730', hjust = 1,
        label = 'Bush', size = 5, family = "Ubuntu") +
    annotate(
        'text', x = 2014, y = 70, color = '#4c67a5', hjust = 1,
        label = 'Obama', size = 5, family = "Ubuntu") +
    annotate(
        'text', x = 2019.75, y = 70, color = '#ad2730', hjust = 1,
        label = 'Trump', size = 5, family = "Ubuntu") +
    geom_label(data = data.frame(x = 2004.5, y = 50, label = 'Both Congress and the \nPresident are of the \nsame political party'),
      aes(x = x, y = y, label = label),
      size = 3.5, color = '#719847', family = "Ubuntu") +
    geom_curve(data = data.frame(x = 2004.5, xend = 2001.5, y = 42, yend = 10),
               mapping = aes(x = x, xend = xend, y = y, yend = yend),
               color = '#719847', size = 0.5, curvature = 0.1,
               arrow = arrow(length = unit(0.01, "npc"), type = "closed")) +
    geom_curve(data = data.frame(x = 2004.5, xend = 2005, y = 42, yend = 10),
               mapping = aes(x = x, xend = xend, y = y, yend = yend),
               color = '#719847', size = 0.5, curvature = -0.1,
               arrow = arrow(length = unit(0.01, "npc"), type = "closed")) +
    geom_curve(data = data.frame(x = 2004.5, xend = 2009.5, y = 42, yend = 25),
               mapping = aes(x = x, xend = xend, y = y, yend = yend),
               color = '#719847', size = 0.5, curvature = -0.1,
               arrow = arrow(length = unit(0.01, "npc"), type = "closed")) +
    scale_x_continuous(breaks = seq(2000, 2020, by = 2),
                       limits = c(1998, 2020),
                       expand = expand_scale(mult = c(0, 0.05))) +
    scale_y_continuous(breaks = seq(0, 60, by = 10),
                       limits = c(0 , 70),
                       expand = expand_scale(mult = c(0, 0.05))) +
    scale_fill_manual(values = c('#dbd9d6', '#719847')) +
    labs(x = 'Year',
         y = 'Number of Wind Policies',
         title = 'State wind policies appear to coincide with\ndemocratic presidential terms',
         subtitle = 'Alignment of political parties does not appear to impact legislation',
         caption = 'Data from DSIRE') +
    theme_minimal_hgrid(font_family = 'Ubuntu') +
    theme(legend.position = 'none')

ggsave(
    filename = here('figs_manu', '3_4_wind_laws_plot.png'), 
    plot = wind_laws_plot,
    width = 7,
    height = 7 / 1.618
)

Solar Policy

Wind Policy

It was expected that the number of policies would be multimodal and widely-spread due to changes in administration, both on a federal and state level. The plots for wind and solar policies support these expectations. The largest number of wind and solar policies occurred in 2000, which was near the end of Clinton’s presidential term, so more progressive policies may have been instituted at this time. There is a clear association of policies enacted depending on the presidential party: Clinton and Obama show higher policy counts than Trump and Bush, which makes sense since democrats are more progressive in renewable energy targets, producing a sort-of trickle down effect on the state-level polices. Interestingly, the enactment of these policies does not appear to be influenced by Congress and the president being of the same political party perhaps indicating non-partisan agreement for these policies: increasing solar and wind share of the state’s energy portfolio is not only an environmental benefit but can also provide more independence from the fossil fuel market, create new jobs, and opens the opportunity for the state to be a renewable energy technical leader. Finally, the figures have a high correlation due to both wind and solar being part of the larger renewable energy category and thus mentioned in the same bills, but generally solar policies outnumbered wind policies. Further research should explore how these state policies were distributed by state.

Relating these policies to generation, it makes sense that the largest number of wind and solar policies occurred in 2000 while the highest generation of wind and solar has been in the more recent years because there is usually a policy-implementation lag. Furthermore, the greater number of solar policies likely corresponds to the decreasing cost of solar through subsidies and credits, which corresponds to the more recent growth in solar.

3.4 Energy Research Budgets

The third and final driver to be explored was solar and wind RD&D budgets using data from the iea.

Code

# Store the wider format for later use
solar_wind_budget_wider <- read_csv(
    here('data', 'iea_solar_wind_budget_wider.csv')
    )

# Generate the longer format
solar_wind_budget_longer <- read_csv(
    here('data', 'iea_solar_wind_budget_wider.csv')
    ) %>% 
    pivot_longer(cols = -year,
                 names_to = 'energy_source',
                 values_to = 'rdd_millions_usd')

# Prepare the label data for the plot
label_data <- subset(solar_wind_budget_longer, year == max(year))
label_data$nudge <- ifelse(label_data$energy_source == 
                               'solar_rdd_million_usd', 220, -100)

# Sketch the plot
budget_plot <- solar_wind_budget_longer %>% 
    ggplot(aes(x = year,
               y = rdd_millions_usd,
               color = energy_source)) +
    geom_line(linewidth = 1) +
    geom_text(data = label_data,
               aes(label = recode(energy_source, 
                                  solar_rdd_million_usd = 'Solar', 
                                  wind_rdd_million_usd = 'Wind')),
              nudge_x = 1.75,
              nudge_y = label_data$nudge, size = 5,
              family = "Ubuntu") + 
    labs(x = 'Year',
         y = 'RD&D Budget (Millions, 2022 USD)',
         color = 'Energy Source',
         title = 'Solar and wind energy RD&D budgets follow\nsimilar trends in the USA',
         subtitle = 'Solar budget was higher than wind for all years',
         caption = 'Data from IEA') +
    scale_x_continuous(limits = c(1970, 2018), breaks = seq(1975, 2015, by = 5)) +
    theme_minimal_hgrid(font_family = 'Ubuntu') +
    scale_color_manual(values = c('#ccca5d','#b2529d'))+
    scale_y_continuous(labels = scales::dollar)+
    theme(legend.position = 'none') +
    # Carter box
    geom_label(data = data.frame(x = 1972.6, y = 990, label = 'President Carter\nannounces\nNational Energy\nProgram'),
      aes(x = x, y = y, label = label),
      size = 3.5, color = '#719847', color = 'black',
      family = "Ubuntu") +
    annotate('point', x = 1977 , y = 578.5, color = '#719847', size = 2) +
    annotate('point', x = 1977 , y = 83.0, color = '#719847', size = 2) +
    geom_curve(
        data = data.frame(
          x = 1973, xend = 1977, y = 790, yend = 578.5),
        mapping = aes(x = x, xend = xend, y = y, yend = yend),
        color = '#719847', size = 0.5, curvature = 0.1,
        arrow = arrow(length = unit(0.01, "npc"),
                      type = "closed")) +
    geom_curve(
        data = data.frame(
          x = 1973, xend = 1977, y = 790, yend = 83.0),
        mapping = aes(x = x, xend = xend, y = y, yend = yend),
        color = '#719847', size = 0.5, curvature = 0.1,
        arrow = arrow(length = unit(0.01, "npc"),
                      type = "closed")) +
    # Treasury box
    geom_label(data = data.frame(x = 2007, y = 750, label = 'Section 1603\nProgram begins'),
      aes(x = x, y = y, label = label),
      size = 3.5, color = '#719847', color = 'black',
      family = "Ubuntu") +
    annotate('point', x = 2009 , y = 531.5, color = '#719847', size = 2) +
    annotate('point', x = 2009 , y = 247.7, color = '#719847', size = 2) +
    geom_curve(
        data = data.frame(
          x = 2007, xend = 2009, y = 650, yend = 531.5),
        mapping = aes(x = x, xend = xend, y = y, yend = yend),
        color = '#719847', size = 0.5, curvature = 0.1,
        arrow = arrow(length = unit(0.01, "npc"),
                      type = "closed")) +
    geom_curve(
        data = data.frame(
          x = 2007, xend = 2009, y = 650, yend = 247.7),
        mapping = aes(x = x, xend = xend, y = y, yend = yend),
        color = '#719847', size = 0.5, curvature = 0.1,
        arrow = arrow(length = unit(0.01, "npc"),
                      type = "closed")) 

ggsave(
    filename = here('figs_manu', '3_5_budget_plot.png'), 
    plot = budget_plot,
    width = 7,
    height = 7 / 1.618
)

budget_plot

It was expected that the amount of funding for renewable energy research would also be multimodal and widely-spread because of administration changes. The multimodal expectation was supported by the figure with peaks occurring around 1980, 1995, and 2010 for wind and solar, but solar was more wide-spread, while wind was more tightly-grouped. The boom in solar funding in the late 1970s could be related to the start of the environmental movement as well as President Carter’s announcement of the National Energy Program in 1977. The second major increase in funding around 2010 overlaps with Obama’s administration and the peak in state solar and wind policies (see previous figure). In addition, the research funding boom in 2010 may be due to the Section 1603 Program, which had to do with reimbursements for solar and wind installations, which drives efficiency research (1, 2).

Relating R&D back to generation, there appears to be a funding-implementation lag, as the most funding for solar occurred in the late 1970s and early 1980s, but the decline in solar costs and increase in solar generation is more recent. Although wind has less research funding than solar, it continues to generally have a higher growth rate in generation per year, which may indicate that research is not the primary driver of wind growth.

3.5 Correlations

To support the visual conclusions made between the drivers and generation, the correlation coefficients were calculated. All correlation calculations in this report have the method set as Spearman. The following relationships were expected:

1. The price of renewable energy is negatively correlated with renewable energy generation.
2. The number of promotional policies is positively correlated with renewable energy generation.
3. The amount of funding for renewable research is positively correlated with renewable energy generation.

3.5.1 Solar/Wind Generation vs 3 Drivers

Code

# 1. Merge and save solar and wind gen
merged_solar_wind <- solar_cost %>% 
    full_join(solar_gen, by = 'year') %>%
    full_join(solar_laws, by = 'year') %>%
    full_join(solar_wind_budget_wider, by = 'year') %>%
    full_join(wind_cost, by = 'year') %>%
    full_join(wind_gen, by = 'year') %>%
    full_join(wind_laws, by = 'year') %>% 
    arrange(year)

write_csv(merged_solar_wind, here('data', 'merged_solar_wind.csv'))

# 2. Correlation coefficients between solar generation and other solar related variables:
# cost (solar_cost_usd_per_kwh), laws (count_of_solar_laws), and budget(solar_rdd_million_usd)
cor_solar_gen_solar_cost <- cor(
    merged_solar_wind$solar_gen_gwh,
    merged_solar_wind$solar_cost_usd_per_kwh,
    use = 'pairwise.complete.obs',
    method = 'spearman'
    )
cor_solar_gen_solar_laws <- cor(
    merged_solar_wind$solar_gen_gwh,
    merged_solar_wind$count_of_solar_laws,
    use = 'pairwise.complete.obs',
    method = 'spearman'
    )
cor_solar_gen_solar_rdd <- cor(
    merged_solar_wind$solar_gen_gwh,
    merged_solar_wind$solar_rdd_million_usd,
    use = 'pairwise.complete.obs',
    method = 'spearman'
    )

# 3. Correlation coefficients for wind generation and other wind variables
cor_wind_gen_wind_cost <- cor(
    merged_solar_wind$wind_gen_gwh,
    merged_solar_wind$wind_cost_usd_per_kwh,
    use = 'pairwise.complete.obs',
    method = 'spearman'
    )
cor_wind_gen_wind_laws <- cor(
    merged_solar_wind$wind_gen_gwh,
    merged_solar_wind$count_of_wind_laws,
    use = 'pairwise.complete.obs',
    method = 'spearman'
    )
cor_wind_gen_wind_rdd <- cor(
    merged_solar_wind$wind_gen_gwh,
    merged_solar_wind$wind_rdd_million_usd,
    use = 'pairwise.complete.obs',
    method = 'spearman'
    )

# 4. Create dataframe of correlations
cor_solar_wind <- data.frame(value_1 = c('Solar', 'Solar', 'Solar',
                                         'Wind','Wind', 'Wind'),
                             value_2 = c('Cost', 'Laws', 'Budgets',
                                         'Cost', 'Laws', 'Budgets'),
                             cor = c(cor_solar_gen_solar_cost,
                                     cor_solar_gen_solar_laws,
                                     cor_solar_gen_solar_rdd,
                                     cor_wind_gen_wind_cost,
                                     cor_wind_gen_wind_laws,
                                     cor_wind_gen_wind_rdd))

write_csv(cor_solar_wind, here('data', 'cor_solar_wind.csv'))

# 5. Summary table of the correlations
cor_solar_wind %>% 
    mutate(cor = round(cor, 2),
           cor = cell_spec(
               cor, 'html', 
               color = ifelse(
                   cor > 0.5 | cor < -0.5, 'white', 'black'
                   ),
               background = ifelse(
                   cor > 0.5 | cor < -0.5, 'cornflowerblue', 'white'
                   ))) %>%
    kable(format = 'html',
          escape = FALSE,
          align = c('l', 'l', 'l'),
          col.names = c('Energy Type', 'Compared Variable', 'Correlation'),
          caption = 'Correlations of Solar/Wind Generation vs the Other 3 Variables') %>%
    kable_styling(bootstrap_options = c('striped', 'hover'))

Correlations of Solar/Wind Generation vs the Other 3 Variables

Energy Type	Compared Variable	Correlation
Solar	Cost	-1
Solar	Laws	-0.01
Solar	Budgets	0.46
Wind	Cost	-0.81
Wind	Laws	-0.08
Wind	Budgets	0.29

Support for two of the expected relationships was found: both solar and wind costs were negatively correlated with generation, and solar and wind budgets were positively correlated with generation. Solar and wind laws had an unexpected lack of correlation with generation, possibly indicating a policy-implementation lag.

3.5.2 Correlation Matrix

The main focus of this report was how these factors influenced solar and wind generation growth, but the relationships among these drivers are also an important consideration for synergy. The correlations among all variables were calculated and filtered for those greater than |0.8| indicating a high correlation.

Code

# 1. Merge and save datasets
# 1.1 Solar data
merged_solar <- solar_cost %>%
    full_join(solar_gen, by = 'year') %>%
    full_join(solar_laws, by = 'year') %>%
    full_join(solar_wind_budget_wider %>%
                  select(-wind_rdd_million_usd),
              by = 'year') %>% 
    arrange(year) %>% 
    mutate(log_solar_cost = log(solar_cost_usd_per_kwh),
           log_solar_gen = log(solar_gen_gwh))

write_csv(merged_solar, here('data', 'merged_solar.csv'))

# 1.2 Wind data
merged_wind <- wind_cost %>%
    full_join(wind_gen, by = 'year') %>%
    full_join(wind_laws, by = 'year') %>% 
    full_join(solar_wind_budget_wider %>%
                  select(-solar_rdd_million_usd),
              by = 'year') %>% 
    arrange(year) %>% 
    mutate(log_wind_cost = log(wind_cost_usd_per_kwh),
           log_wind_gen = log(wind_gen_gwh))
write_csv(merged_wind, here('data', 'merged_wind.csv'))

# 2. Correlation matrix for merged data
# 2.1 Solar correlation matrix
cor_matrix_solar <- cor(
    merged_solar[, sapply(merged_solar, is.numeric)],
    use = 'pairwise.complete.obs',
    method = 'spearman'
    ) %>% 
    as.data.frame()

# 2.2 Wind correlation matrix
cor_matrix_wind <- cor(
    merged_wind[, sapply(merged_wind, is.numeric)],
    use = 'pairwise.complete.obs',
    method = 'spearman'
    ) %>% 
    as.data.frame()

# 3. Filter 'high' correlations with threshold of +/- 0.8
# 3.1 High solar correlations
cor_solar_high <- cor_matrix_solar %>%
    mutate(value_1 = row.names(.)) %>%
    gather(value_2, cor, -value_1) %>%
    filter(value_1 != value_2,
         cor > 0.8 | cor < -0.8) %>% 
    mutate(min_var = pmin(value_1, value_2),
           max_var = pmax(value_1, value_2)) %>%
    distinct(min_var, max_var, .keep_all = TRUE) %>%
    select(-min_var, -max_var) %>% 
    arrange(value_1) %>% 
    filter((startsWith(value_1, "log") & startsWith(value_2, "log")) | 
           (!startsWith(value_1, "log") & !startsWith(value_2, "log")))

write_csv(cor_solar_high, here('data', 'cor_solar_high.csv'))

# 3.2 High wind correlations
cor_wind_high <- cor_matrix_wind %>%
    mutate(value_1 = row.names(.)) %>%
    gather(value_2, cor, -value_1) %>%
    filter(value_1 != value_2,
         cor > 0.8 | cor < -0.8) %>% 
    mutate(min_var = pmin(value_1, value_2),
           max_var = pmax(value_1, value_2)) %>%
    distinct(min_var, max_var, .keep_all = TRUE) %>%
    select(-min_var, -max_var) %>% 
    arrange(value_1) %>% 
    filter((startsWith(value_1, "log") & startsWith(value_2, "log")) | 
           (!startsWith(value_1, "log") & !startsWith(value_2, "log")))

write_csv(cor_wind_high, here('data', 'cor_wind_high.csv'))

# 4. Construct the 'high' correlation matrix table
# There are 4 in solar and 3 in wind
# Excluding time series, there are 2 in solar and 1 in wind
cor_matrix_high <- tibble(
    `No.` = seq(1,7),
    `Value 1` = c("Solar Cost", "Solar Gen", "log Solar Gen", "Solar RDD",
                  "Wind Cost", "Wind Gen", "log Wind Gen"),
    `Value 2` = c("Year", "Year", "log Solar Cost", "Solar Cost",
                  "Year", "Year", "log Wind Cost"),
    Correlation = c(round(cor_solar_high[2,3], 2),
                    round(cor_solar_high[3,3], 2),
                    round(cor_solar_high[1,3], 2),
                    round(cor_solar_high[5,3], 2),
                    round(cor_wind_high[2,3], 2),
                    round(cor_wind_high[3,3], 2),
                    round(cor_wind_high[1,3], 2))
)

cor_matrix_high_kable <- cor_matrix_high %>% kable(
    format = 'html', escape = FALSE,
    align = c('c', 'l', 'l', 'c'),
    caption = 'Correlations Matrix of High Correlations') %>%
    kable_styling(bootstrap_options = c('striped', 'hover'))

cor_matrix_high_kable

Correlations Matrix of High Correlations

No.	Value 1	Value 2	Correlation
1	Solar Cost	Year	-1.00
2	Solar Gen	Year	0.98
3	log Solar Gen	log Solar Cost	-1.00
4	Solar RDD	Solar Cost	0.89
5	Wind Cost	Year	-0.81
6	Wind Gen	Year	1.00
7	log Wind Gen	log Wind Cost	-0.81

3.5.3 High Correlation Plots

Of the 7 high correlations, the team chose to skip over the time series correlations since they were previously depicted.

Code

# Plot 1: Log Solar Cost vs Log Solar Generation
cor_plot_1 <- merged_solar_wind %>%
    filter(!is.na(solar_cost_usd_per_kwh) & !is.na(solar_gen_gwh)) %>% 
    ggplot(aes(x = log(solar_cost_usd_per_kwh),
               y = log(solar_gen_gwh / 1000))) +
    geom_point() +
    geom_smooth(method = 'lm', se = FALSE, size = 0.5,
                color = 'cornflowerblue') +
    labs(x = 'Solar Cost: log(USD/kWh)',
         y = 'Solar Generation: log(TWh)',
         title='Log Solar Cost vs Log Solar Generation',
         subtitle = 'Correlation = -1') +
    theme_minimal_grid(font_family = 'Ubuntu')

ggsave(
    filename = here('figs_manu', '3_6_cor_plot_1.png'), 
    plot = cor_plot_1,
    width = 6,
    height = 6 / 1.618
)

# Plot 2: log Wind Cost vs log Wind Generation
cor_plot_2 <- merged_solar_wind %>%
    filter(!is.na(wind_cost_usd_per_kwh) & !is.na(wind_gen_gwh)) %>% 
    ggplot(aes(x = log(wind_cost_usd_per_kwh),
               y = log(wind_gen_gwh / 1000))) +
    geom_point() +
    geom_smooth(method = 'lm', se = FALSE, size = 0.5,
                color = 'cornflowerblue') +
    labs(x = 'Wind Cost: log(USD/kWh)',
         y = 'Wind Generation: log(TWh)',
         title='Log Wind Cost vs Log Wind Generation',
         subtitle = 'Correlation = -0.81') +
    theme_minimal_grid(font_family = 'Ubuntu')

ggsave(
    filename = here('figs_manu', '3_6_cor_plot_2.png'), 
    plot = cor_plot_2,
    width = 6,
    height = 6 / 1.618
)

# Plot 3: Solar RD&D vs Solar Cost
cor_plot_3 <- merged_solar_wind %>%
    filter(!is.na(solar_rdd_million_usd) &
               !is.na(solar_cost_usd_per_kwh)) %>% 
    ggplot(aes(x = solar_rdd_million_usd,
               y = solar_cost_usd_per_kwh)) +
    geom_point() +
    geom_smooth(method = 'lm', se = FALSE, size = 0.5,
                color = 'cornflowerblue') +
    labs(x = 'Solar RD&D (USD)',
         y = 'Solar Cost (USD / kWh)',
         title='Solar RD&D vs Solar Cost',
         subtitle = 'Correlation = 0.89') +
    theme_minimal_grid(font_family = 'Ubuntu')

ggsave(
    filename = here('figs_manu', '3_6_cor_plot_3.png'), 
    plot = cor_plot_3,
    width = 6,
    height = 6 / 1.618
)

Log Solar Cost vs Log Generation

Log Wind Cost vs Log Generation

Solar RD&D vs Cost

These correlation plots indicate the following findings:

1. The logarithm of cost and generation show a strong negative correlation, which is logical due to economies of scale: as solar and wind projects and thus generation increase, the price will fall. Also, as the capital cost get cheaper, there is a higher incentive to build these facilities.
2. Solar RD&D has strong positive correlation with solar costs: cheaper solar energy requires efficient construction of PV cells.

4. Conclusion

Overall, this study highlighted the complexity of understanding the growth of a new technology: technical advancements, economic incentives, and supporting policies need to be aligned. One key finding is that the decreasing cost of solar and wind energy is a contributor to their growth, but this relationship is stronger for solar than for wind highlighting solar’s rapid cost reduction that has spurred a more recent growth in generation. The wind and solar policies have almost no correlation with increased generation, thus the policy-implementation lag and variation in state implementation of solar and wind supportive policies may not be a direct driver for the growth, but rather a way to incentivize the economic and technical advancements that have a more direct impact on generation. Finally, the RD&D budget for solar and wind show a positive correlation with generation, but it is not strong perhaps indicating a funding-implementation lag as RD&D takes years to show a significant impact on technical efficiency and costs and eventually deployment.

Further analysis of these drivers is necessary to provide more significant conclusions about which drivers are more impactful for the United States’ growth in renewables. It is suggested that the USA continue a mix of economic, technical, and political drivers to see a continued growth in not only solar and wind but also other renewable energy sources. Suggested further analysis includes a focus on the distribution and strength of state government policies as well as the location and size of the solar and wind facilities across the US.

5. Attribution

Below is the attribution breakdown for the team:

• The team did 1. Introduction & Research Question and 3. Results together.
• See the breakdown of each member for details of 3. Results.
• Abbey was responsible for:
  • 2. Data Source Discussion,
  • 3.1 Solar and Wind Generation - Updates,
  • 3.2 Cost of Solar and Wind Energy - Updates,
  • 3.3 Policies and Incentives - Lots of Updates,
  • 3.4 Energy Research Budgets - Update with labeling,
  • 4. Conclusion,
  • Updated all text and reviewed code.
• Pingfan was responsible for:
  • 3.1 Solar and Wind Generation - Original Version,
  • 3.2 Cost of Solar and Wind Energy - Original Version & Updates,
  • 3.3 Policies and Incentives - Original Version,
  • 3.4 Energy Research Budgets - Original Version,
  • 3.5 Correlations,
  • 5. Attribution,
  • 6. Appendix,
  • Proofread all codes and plots.

6. Appendix

6.1 Summary Tables

Summary of Solar

Code

# 1. Calculate the summary and export to csv
summary_solar_gen <- summary(solar_gen$solar_gen_gwh)
summary_solar_cost <- summary(solar_cost$solar_cost_usd_per_kwh)
summary_solar_laws <- summary(solar_laws$count_of_solar_laws)
summary_solar_budget <- summary(solar_wind_budget_wider$solar_rdd_million_usd)

summary_solar_df <- data.frame(
    tier = names(summary_solar_gen),
    solar_gen_gwh = round(as.numeric(summary_solar_gen)),
    solar_cost_usd_per_kw = round(as.numeric(summary_solar_cost), 2),
    solar_laws_count = round(as.numeric(summary_solar_laws)),
    solar_budget_million_usd = round(as.numeric(summary_solar_budget))
    )

write_csv(summary_solar_df, here('data', 'summary_solar.csv'))

# 2. Generate the table using kable()
summary_solar_df %>% 
    kable(format = 'html',
          escape = FALSE,
          col.names = c('Tier',
                        'Electricity Generation (GWh)',
                        'Cost (USD/kWh)',
                        'Laws Count',
                        'RD&D Budget (Million USD)'),
          caption = 'Summary of Solar') %>%
    kable_styling(bootstrap_options = c('striped', 'hover'))

Summary of Solar

Tier	Electricity Generation (GWh)	Cost (USD/kWh)	Laws Count	RD&D Budget (Million USD)
Min.	508	0.07	1	18
1st Qu.	584	0.10	4	133
Median	3072	0.15	9	173
Mean	29080	0.17	13	284
3rd Qu.	48979	0.21	21	277
Max.	145598	0.38	65	1168

Summary of Wind

Code

# 1. Calculate the summary and export to csv
summary_wind_gen <- summary(wind_gen$wind_gen_gwh)
summary_wind_cost <- summary(wind_cost$wind_cost_usd_per_kwh)
summary_wind_cost <- summary_wind_cost[!names(summary_wind_cost) == "NA's"]
summary_wind_laws <- summary(wind_laws$count_of_wind_laws)
summary_wind_budget <- summary(solar_wind_budget_wider$wind_rdd_million_usd)

summary_wind_df <- data.frame(
    tier = names(summary_wind_gen),
    wind_gen_gwh = round(as.numeric(summary_wind_gen)),
    wind_cost_usd_per_kw = round(as.numeric(summary_wind_cost), 2),
    wind_laws_count = round(as.numeric(summary_wind_laws)),
    wind_budget_million_usd = round(as.numeric(summary_wind_budget))
    )

write_csv(summary_wind_df, here('data', 'summary_wind.csv'))

# 2. Generate the table using kable()
summary_wind_df %>% 
    kable(format = 'html',
          escape = FALSE,
          col.names = c('Tier',
                        'Electricity Generation (GWh)',
                        'Cost (USD/kWh)',
                        'Laws Count',
                        'RD&D Budget (Million USD)'),
          caption = 'Summary of Wind') %>%
    kable_styling(bootstrap_options = c('striped', 'hover'))

Summary of Wind

Tier	Electricity Generation (GWh)	Cost (USD/kWh)	Laws Count	RD&D Budget (Million USD)
Min.	6737	0.08	1	0
1st Qu.	28554	0.10	2	32
Median	130499	0.12	8	57
Mean	152144	0.11	10	68
3rd Qu.	247475	0.13	10	83
Max.	434812	0.13	55	248

6.2 Dictionary of Correlations

This dictionary contains the correlation datasets.

Code

dict_cor <- data.frame(
    name = c('cor_matrix_high',
             'cor_matrix_solar',
             'cor_matrix_wind',
             'cor_solar_wind',
             'cor_solar_high',
             'cor_wind_high'),
    type = c('A tibble: 7 × 4',
             'A data frame: 6 × 6',
             'A data frame: 6 × 6',
             'A data frame: 8 × 3',
             'A data frame: 5 × 3',
             'A data frame: 2 × 3'),
    description = c('Correlation matrix of high correlation values',
                    'Correlation matrix of all solar related variables',
                    'Correlation matrix of all wind related variables',
                    'Correlation between generation and 3 other variables',
                    'Solar related correlations higher than 0.8',
                    'Wind related correlations higher than 0.8')
) %>% 
    mutate(count = row_number()) %>%
    select(count, everything())
dict_cor %>% 
    kable(format = 'html',
          escape = FALSE,
          align = c('c', 'l', 'l', 'l'),
          col.names = c('No.', 'Name', 'Type', 'Description'),
          caption = 'Dictionary of Correlations') %>%
    kable_styling(bootstrap_options = c('striped', 'hover'))

Dictionary of Correlations

No.	Name	Type	Description
1	cor_matrix_high	A tibble: 7 × 4	Correlation matrix of high correlation values
2	cor_matrix_solar	A data frame: 6 × 6	Correlation matrix of all solar related variables
3	cor_matrix_wind	A data frame: 6 × 6	Correlation matrix of all wind related variables
4	cor_solar_wind	A data frame: 8 × 3	Correlation between generation and 3 other variables
5	cor_solar_high	A data frame: 5 × 3	Solar related correlations higher than 0.8
6	cor_wind_high	A data frame: 2 × 3	Wind related correlations higher than 0.8

6.3 Dictionary of Files

This is a complete dictionary of files. The files can be accessed in the “data” folder. In the dictionary, there are 4 columns:

1. Name - The name of the file.
2. Type - The type of the file. In our case, we have CSV, HTML, and Excel.
3. Source - The source of the file. “Generated” means it is generated by R codes; “Raw” means Its either directly downloaded or scrapped from the website.
4. Description - A brief description of the file.

The dictionary is in alphabetical order.

Code

dict_file <- data.frame(
    name = c('congress',
             'cor_solar_wind',
             'dsire_laws_raw',
             'dsire_solar_laws',
             'dsire_wind_laws',
             'eia_solar_gen_raw',
             'eia_solar_gen',
             'eia_solar_wind_gen_longer',
             'eia_wind_gen_raw',
             'eia_wind_gen',
             'cor_solar_high',
             'cor_wind_high',
             'iea_solar_wind_budget_wider',
             'irena_solar_cost',
             'irena_solar_wind_cost_longer',
             'irena_solar_wind_international_long',
             'irena_solar_wind_international_raw',
             'irena_wind_cost',
             'merged_solar_wind',
             'merged_solar',
             'merged_wind',
             'summary_solar',
             'summary_wind',
             'dsire_laws_raw',
             'irena_costs_raw'),
    type = c('CSV', 'CSV', 'CSV', 'CSV', 'CSV',
             'CSV', 'CSV', 'CSV', 'CSV', 'CSV',
             'CSV', 'CSV', 'CSV', 'CSV', 'CSV',
             'CSV', 'CSV', 'CSV', 'CSV', 'CSV',
             'CSV', 'CSV', 'CSV', 'HTML', 'Excel'),
    source = c('Raw', 'Generated', 'Raw', 'Generated',
               'Generated','Raw', 'Generated', 'Generated',
               'Raw', 'Generated', 'Generated', 'Generated',
               'Raw', 'Generated', 'Generated', 'Generated', 'Raw',
               'Generated', 'Generated', 'Generated', 'Generated',
               'Generated', 'Generated', 'Raw', 'Raw'),
    description = c('Parties of congress and present',
                    'Correlation between generation and 3 other variables',
                    'Laws and incentives count from DSIRE',
                    'Solar laws and incentives count',
                    'Wind laws and incentives count',
                    'Monthly solar electricity generation from EIA',
                    'Annual solar electricity generation',
                    'Longer version of annual solar/wind generation',
                    'Monthly wind electricity generation from EIA',
                    'Annual wind electricity generation',
                    'Solar related correlations higher than 0.8',
                    'Wind related correlations higher than 0.8',
                    'Wider version of solar/wind RD&D budgets from IEA',
                    'Annual average solar cost',
                    'Longer version of solar/wind generation cost',
                    'Longer version of international solar/wind gen',
                    'Wider version of international solad/wind gen',
                    'Annual average wind cost',
                    'Merged dataset of both solar, wind',
                    'Merged dataset of solar',
                    'Merged dataset of wind',
                    'Summary dataset of solar',
                    'Summary dataset of wind',
                    'Scraped law count data from DSIRE',
                    'Renewable power generation cost data from IRENA')
) %>% 
    mutate(count = row_number()) %>%
    select(count, everything())
dict_file %>% 
    kable(format = 'html',
          escape = FALSE,
          align = c('c', 'l', 'l', 'l', 'l'),
          col.names = c('No.', 'Name', 'Type', 'Source', 'Description'),
          caption = 'Dictionary of Data Files') %>%
    kable_styling(bootstrap_options = c('striped', 'hover'))

Dictionary of Data Files

No.	Name	Type	Source	Description
1	congress	CSV	Raw	Parties of congress and present
2	cor_solar_wind	CSV	Generated	Correlation between generation and 3 other variables
3	dsire_laws_raw	CSV	Raw	Laws and incentives count from DSIRE
4	dsire_solar_laws	CSV	Generated	Solar laws and incentives count
5	dsire_wind_laws	CSV	Generated	Wind laws and incentives count
6	eia_solar_gen_raw	CSV	Raw	Monthly solar electricity generation from EIA
7	eia_solar_gen	CSV	Generated	Annual solar electricity generation
8	eia_solar_wind_gen_longer	CSV	Generated	Longer version of annual solar/wind generation
9	eia_wind_gen_raw	CSV	Raw	Monthly wind electricity generation from EIA
10	eia_wind_gen	CSV	Generated	Annual wind electricity generation
11	cor_solar_high	CSV	Generated	Solar related correlations higher than 0.8
12	cor_wind_high	CSV	Generated	Wind related correlations higher than 0.8
13	iea_solar_wind_budget_wider	CSV	Raw	Wider version of solar/wind RD&D budgets from IEA
14	irena_solar_cost	CSV	Generated	Annual average solar cost
15	irena_solar_wind_cost_longer	CSV	Generated	Longer version of solar/wind generation cost
16	irena_solar_wind_international_long	CSV	Generated	Longer version of international solar/wind gen
17	irena_solar_wind_international_raw	CSV	Raw	Wider version of international solad/wind gen
18	irena_wind_cost	CSV	Generated	Annual average wind cost
19	merged_solar_wind	CSV	Generated	Merged dataset of both solar, wind
20	merged_solar	CSV	Generated	Merged dataset of solar
21	merged_wind	CSV	Generated	Merged dataset of wind
22	summary_solar	CSV	Generated	Summary dataset of solar
23	summary_wind	CSV	Generated	Summary dataset of wind
24	dsire_laws_raw	HTML	Raw	Scraped law count data from DSIRE
25	irena_costs_raw	Excel	Raw	Renewable power generation cost data from IRENA

6.4 Dictionary of Solar and Wind Datasets

This is a dictionary of solar and wind related datasets that are generated in this report. The majority of the datasets overlap with the file dictionary, and they have same or similar names.

Code

dict_solar_wind <- data.frame(
    name = c('merged_solar',
             'merged_solar_wind',
             'merged_wind',
             'solar_cost',
             'solar_gen',
             'solar_international',
             'solar_laws',
             'solar_wind_budget_longer',
             'solar_wind_budget_wider',
             'solar_wind_cost',
             'solar_wind_cost_longer',
             'solar_wind_gen_longer',
             'sum_international',
             'summary_solar_df',
             'summary_wind_df',
             'top_5_solar',
             'top_5_wind',
             'us_elec_gen',
             'wind_cost',
             'wind_gen',
             'wind_international',
             'wind_laws'),
    type = c('A tibble: 50 × 7',
             'A tibble: 50 × 10',
             'A tibble: 49 × 6',
             'A tibble: 10 × 2',
             'A tibble: 22 × 2',
             'A tibble: 5,302 x 4',
             'A tibble: 20 × 2',
             'A tibble: 84 × 3',
             'A tibble: 42 × 3',
             'A tibble: 10 × 3',
             'A tibble: 20 × 3',
             'A tibble: 44 × 3',
             'A tibble: 482 x 3',
             'A data frame: 6 x 5',
             'A data frame: 6 x 5',
             'A tibble: 5 × 4',
             'A tibble: 5 × 4',
             'A tibble: 4 × 2',
             'A tibble: 36 × 2',
             'A tibble: 22 × 2',
             'A tibble: 5,302 × 4',
             'A tibble: 17 × 2'),
    description = c('Merged dataset of solar data',
                    'Merged dataset of all solar and wind data',
                    'Merged dataset of wind data',
                    'Annual average solar cost',
                    'Annual solar electricity generation',
                    'International solar generation by country',
                    'Solar laws and incentives count',
                    'Longer version of solar/wind RD&D budgets',
                    'Wider version of solar/wind RD&D budgets',
                    'Solar/wind cost',
                    'Longer version of solar/wind cost',
                    'Longer version of annual solar/wind generation',
                    'Sum of international solar gen by country',
                    'Summary dataset of solar',
                    'Summary dataset of wind',
                    'Solar gen of top 5 countries in 2021',
                    'Wind gen of top 5 countries in 2021',
                    'Percentage of energy source in US in 2022',
                    'Annual average wind cost',
                    'Annual wind electricity generation',
                    'International wind generation by country',
                    'Wind laws and incentives count')
) %>% 
    mutate(count = row_number()) %>%
    select(count, everything())
dict_solar_wind %>% 
    kable(format = 'html',
          escape = FALSE,
          align = c('c', 'l', 'l', 'l'),
          col.names = c('No.', 'Name', 'Type', 'Description'),
          caption = 'Dictionary of Solar and Wind Datasets') %>%
    kable_styling(bootstrap_options = c('striped', 'hover'))

Dictionary of Solar and Wind Datasets

No.	Name	Type	Description
1	merged_solar	A tibble: 50 × 7	Merged dataset of solar data
2	merged_solar_wind	A tibble: 50 × 10	Merged dataset of all solar and wind data
3	merged_wind	A tibble: 49 × 6	Merged dataset of wind data
4	solar_cost	A tibble: 10 × 2	Annual average solar cost
5	solar_gen	A tibble: 22 × 2	Annual solar electricity generation
6	solar_international	A tibble: 5,302 x 4	International solar generation by country
7	solar_laws	A tibble: 20 × 2	Solar laws and incentives count
8	solar_wind_budget_longer	A tibble: 84 × 3	Longer version of solar/wind RD&D budgets
9	solar_wind_budget_wider	A tibble: 42 × 3	Wider version of solar/wind RD&D budgets
10	solar_wind_cost	A tibble: 10 × 3	Solar/wind cost
11	solar_wind_cost_longer	A tibble: 20 × 3	Longer version of solar/wind cost
12	solar_wind_gen_longer	A tibble: 44 × 3	Longer version of annual solar/wind generation
13	sum_international	A tibble: 482 x 3	Sum of international solar gen by country
14	summary_solar_df	A data frame: 6 x 5	Summary dataset of solar
15	summary_wind_df	A data frame: 6 x 5	Summary dataset of wind
16	top_5_solar	A tibble: 5 × 4	Solar gen of top 5 countries in 2021
17	top_5_wind	A tibble: 5 × 4	Wind gen of top 5 countries in 2021
18	us_elec_gen	A tibble: 4 × 2	Percentage of energy source in US in 2022
19	wind_cost	A tibble: 36 × 2	Annual average wind cost
20	wind_gen	A tibble: 22 × 2	Annual wind electricity generation
21	wind_international	A tibble: 5,302 × 4	International wind generation by country
22	wind_laws	A tibble: 17 × 2	Wind laws and incentives count

6.5 Miscellaneous

There are also other datasets that are not included in the above dictionaries:

1. There are 3 plots named as cor_plot_1 through cor_plot_3. These are the correlation plots shown in [3.6.3 High Correlation Plots]. The other plots are shown directly and are not stored as variables or datasets.
2. There are also some other datasets like label_data, laws_html, laws_table, etc. They are used to temporarily store values.

6.6 All Code

Code

knitr::opts_chunk$set(
    comment = "#>",
    fig.align = "center",
    fig.show = "hold",
    fig.width = 7,
    fig.asp = 0.618,
    fig.retina = 3,
    fig.path = "figs_auto/",
    out.width = "90%"
)

library(tidyverse)
library(here)
library(janitor)
library(lubridate)
library(rvest)
library(gridExtra)
library(kableExtra)
library(cowplot)
library(ggrepel)
library(gganimate)
library(plotly)
library(reactable)
us_elec_gen <- tibble(
    Type = c("Renewables", "Natural Gas", "Nuclear", "Coal"),
    Percentage = c(0.23, 0.39, 0.18, 0.19)
)

us_elec_bar <- us_elec_gen %>% 
    ggplot(aes(x = reorder(Type, -Percentage),
               y = Percentage,
               fill = Type)) +
    geom_col(width = 0.5) +
    scale_fill_manual(values = c("Renewables" = "#719847",
                                 "Natural Gas" = "gray",
                                 "Nuclear" = "gray",
                                 "Coal" = "dimgray")) +
    geom_text(aes(label = scales::percent(Percentage)), 
              vjust = -0.3,
              size = 5,
              family = "Ubuntu") +
    scale_y_continuous(expand = expansion(mult = c(0, 0.15)),
                       labels = scales::percent_format()) +
    theme_minimal_hgrid(font_family = "Ubuntu") +
    theme(legend.position = "none",
          axis.text.x = element_text(size = 11)) +
    labs(x = "Energy Type",
         y = "Electricity Share",
         title = "Renewables outnumber coal for electricity\ngeneration in 2022",
         caption = "Data from BCSE")

us_elec_bar
# 1. Load and clean data of international solar and wind gen
drop <- c('World', 'Asia', 'Europe', 'North America',
          'European Union', 'South America', 'Eurasia', 'Africa')
internationalPath <- here('data', 'irena_solar_wind_international_raw.csv')
international <- read_csv(internationalPath, skip = 2) %>% 
    rename(Country = 'Region/country/area') %>% 
    pivot_longer(
        names_to = 'Year',
        values_to = 'Generation',
        cols = '2000':'2021') %>% 
    mutate(
        Year = as.numeric(Year)
    ) %>%
    filter(!Country %in% drop)

international %>% 
    write_csv(here('data', 'irena_solar_wind_international_long.csv'))

# 2. International Solar Generation
# 2.1 Solar gen data frame
solar_international <- international %>% 
    filter(Technology == 'Solar')

# 2.2 Top 5 solar producers in 2021
top_5_solar <- solar_international %>% 
    filter(Year == '2021') %>% 
    arrange(desc(Generation)) %>% 
    slice(1:5)

# 2.3 Plot of top 5 solar producers
solar_international_plot <- 
    ggplot(solar_international %>% 
        filter(Country %in% c(top_5_solar$Country)),
        aes(x = Year, y = Generation/1000, group = Country, color = Country)) +
    geom_line(linewidth = 1) +
    geom_text_repel(
        data = top_5_solar,
        aes(label = Country),
        hjust = 0, nudge_x = 1, direction = "y",
        size = 5, segment.color = NA,
        family = "Ubuntu") + 
    scale_x_continuous(
        breaks = seq(2000, 2021, 3),
        expand = expansion(add = c(1, 5))) +
    scale_y_continuous(expand = expand_scale(mult = c(0, 0.05))) +
    labs(x = 'Year',
         y = 'Generation (TWh)',
         title = 'United States is the second highest producer of\nsolar energy',
         subtitle = 'Top 5 producers of solar energy in 2021',
         caption = 'Data from IRENA') +
    scale_color_manual(values = c('#719847','#ccca5d','#b2529d','#4c67a5','#a5734c')) +
    theme_minimal_hgrid(font_family = 'Ubuntu') +
    theme(legend.position = 'none')

ggsave(
    filename = here('figs_manu', '3_1_solar_inter_plot.png'), 
    plot = solar_international_plot,
    width = 8,
    height = 8 / 1.618
)

# 3. International Wind Generation
# 3.1 Wind gen data frame
wind_international <- international %>% 
    filter(Technology == 'Wind')

# 3.2 Top 5 wind producers in 2021
top_5_wind <- wind_international %>% 
    filter(Year == '2021') %>% 
    arrange(desc(Generation)) %>% 
    slice(1:5)

# 3.3 Plot of top 5 wind producers
wind_international_plot <- 
    ggplot(wind_international %>% 
        filter(Country %in% c(top_5_wind$Country)),
        aes(x = Year, y = Generation/1000, group = Country, color = Country)) +
    geom_line(linewidth = 1) +
    geom_text_repel(
        data = top_5_wind,
        aes(label = Country),
        hjust = 0, nudge_x = 1, direction = "y",
        size = 5, segment.color = NA,
        family = "Ubuntu") + 
    scale_x_continuous(
        breaks = seq(2000, 2021, 3),
        expand = expansion(add = c(1, 5))) +
    scale_y_continuous(expand = expand_scale(mult = c(0, 0.05))) +
    labs(x = 'Year',
         y = 'Generation (TWh)',
         title = 'United States is the second highest producer of\nwind energy',
         subtitle = 'Top 5 producers of wind energy in 2021',
         caption = 'Data from IRENA') +
    scale_color_manual(values = c('#719847','#ccca5d','#b2529d','#4c67a5','#a5734c')) +
    theme_minimal_hgrid(font_family = 'Ubuntu') +
    theme(legend.position = 'none')

ggsave(
    filename = here('figs_manu', '3_1_wind_inter_plot.png'), 
    plot = wind_international_plot,
    width = 8,
    height = 8 / 1.618
)

# 4. International summary table
sum_international <- international %>% 
    group_by(Country, Technology) %>%
    summarize(`Total Generation (tWh)` = 
                  sum(Generation, na.rm = TRUE)) %>% 
    rename(`Technology (Solar or Wind)` = Technology)

sum_international_table <- sum_international %>% 
    reactable(
        searchable = TRUE,
        highlight = TRUE,
        filterable = TRUE,
        defaultPageSize = 5,
        showPageSizeOptions = TRUE,
        pageSizeOptions = c(5, 10, 15)
        )
solar_international_plot
wind_international_plot
sum_international_table
# 1. Solar generation data processing
solar_gen_path <- here('data', 'eia_solar_gen_raw.csv')
solar_gen <- read_csv(solar_gen_path, skip = 4) %>% 
    clean_names() %>% 
    select('month', 'united_states_thousand_megawatthours') %>% 
    arrange(month) %>% 
    mutate(
        month = dmy(paste('01', month)),
        month = format(month, '%Y-%m'),
        year = as.numeric(substr(month, 1, 4))
    ) %>% 
    filter(year <= 2022) %>% 
    group_by(year) %>% 
    summarise(solar_gen_gwh = sum(united_states_thousand_megawatthours))

write_csv(solar_gen, here('data', 'eia_solar_gen.csv'))

# 2. Wind generation data processing
wind_gen_path <- here('data', 'eia_wind_gen_raw.csv')
wind_gen <- read_csv(wind_gen_path, skip = 4) %>% 
    clean_names() %>% 
    select('month', 'united_states_thousand_megawatthours') %>% 
    arrange(month) %>% 
    mutate(
        month = dmy(paste('01', month)),
        month = format(month, '%Y-%m'),
        year = as.numeric(substr(month, 1, 4))
    ) %>% 
    filter(year <= 2022) %>% 
    group_by(year) %>% 
    summarise(wind_gen_gwh = sum(united_states_thousand_megawatthours))

write_csv(wind_gen, here('data', 'eia_wind_gen.csv'))

# 3. Long format & exporting
solar_wind_gen_longer <- inner_join(solar_gen, wind_gen, by = 'year') %>% 
    pivot_longer(names_to = 'energy_source',
                 values_to = 'gwh',
                 cols = c('solar_gen_gwh', 'wind_gen_gwh'))

write_csv(solar_wind_gen_longer,
          here('data', 'eia_solar_wind_gen_longer.csv'))

# 4. Plot the Solar and Wind Generation over Time
generation_plot <- solar_wind_gen_longer %>% 
    ggplot(aes(x = year,
               y = gwh / 1000,
               color = energy_source)) +
    geom_line(linewidth = 1) +
    geom_point(fill = 'white', size = 2, shape = 21) +
    geom_text(data = subset(solar_wind_gen_longer, year == max(year)),
              aes(label = recode(energy_source, 
                                 solar_gen_gwh = 'Solar', 
                                 wind_gen_gwh = 'Wind')),
              nudge_x = -1.15,
              nudge_y = 10,
              size = 5,
              family = "Ubuntu") +
    labs(x = 'Year',
         y = 'Generation (TWh)',
         color = 'Energy Source',
         title = 'Wind outpaces solar energy generation in the USA\nevery year',
         subtitle = 'Wind generation is more than double solar generation in 2022',
         caption = 'Data from EIA') +
    scale_x_continuous(breaks = seq(2000, 2022, by = 2)) +
    scale_y_continuous(expand = expand_scale(mult = c(0, 0.05))) +
    scale_color_manual(values = c('#ccca5d','#b2529d'))+
    theme_minimal_hgrid(font_family = 'Ubuntu') +
    theme(legend.position = 'none')

ggsave(
    filename = here('figs_manu', '3_2_generation_plot.png'), 
    plot = generation_plot,
    width = 7,
    height = 7 / 1.618
)

generation_plot
# 1. Read solar and wind cost data and merge to one
solar_cost <- read_csv(here('data', 'irena_solar_cost.csv'))
wind_cost <- read_csv(here('data', 'irena_wind_cost.csv'))
solar_wind_cost <- left_join(solar_cost, wind_cost, by = 'year')

# 2. Pivot to longer format and export to csv
solar_wind_cost_longer <- solar_wind_cost %>% 
    pivot_longer(names_to = 'energy_source',
                 values_to = 'cost',
                 cols = -year)

write_csv(solar_wind_cost_longer,
          here('data', 'irena_solar_wind_cost_longer.csv'))

# 3. Create plot of solar and wind costs compared to fossil fuels
cost_plot <- solar_wind_cost_longer %>% 
    ggplot(aes(x = year,
               y = cost,
               color = energy_source)) +
    annotate('rect',
             xmin = -Inf, xmax = Inf,
             ymin = 0.05, ymax = 0.177,
             fill = 'gray', alpha = 0.3) +
    annotate('text',
             x = 2013, y = 0.08,
             color = 'dimgray',
             label = 'The shaded area is the range of fossil fuel costs.',
             family = "Ubuntu") +
    geom_line(linewidth = 1) +
    geom_point(fill = 'white', size = 2, shape = 21) +
    geom_text_repel(data = subset(solar_wind_cost_longer,
                                  year == max(year)),
                    aes(label = recode(energy_source, 
                                       solar_cost_usd_per_kwh = 'Solar', 
                                       wind_cost_usd_per_kwh = 'Wind')),
                    nudge_x = 0.5,
                    nudge_y = 0,
                    size = 4.5,
                    family = "Ubuntu") +
    labs(x = 'Year',
         y = 'Cost (USD/kWh, 2022 prices)',
         color = 'Energy Source',
         title = 'The cost of solar has significantly decreased in the USA \n becoming cheaper than wind in 2017',
         caption = 'Data from IRENA') +
    scale_x_continuous(breaks = seq(2010, 2019, by = 1)) +
    scale_y_continuous(limits = c(0, 0.4), breaks = c(0.1, 0.2, 0.3, 0.4), expand = expand_scale(mult = c(0, 0.05)), labels = scales::dollar)+
    scale_color_manual(values = c('#ccca5d','#b2529d'))+
    theme_minimal_hgrid(font_family = 'Ubuntu') +
    theme(legend.position = 'none')

ggsave(
    filename = here('figs_manu', '3_3_cost_plot.png'), 
    plot = cost_plot,
    width = 7,
    height = 7 / 1.618
)

cost_plot
# 1. Scrape and save the raw policy data frame
# 1.1 Read the html from local path
laws_html_path <- here('data', 'dsire_laws_raw.html')
laws_html <- read_html(laws_html_path)

# 1.2 Scrape the table as a df
laws_table <- laws_html %>% 
  html_nodes('table') %>% 
  .[[1]] %>% 
  html_table()

# 1.3 Save the df to csv
write_csv(laws_table, here('data', 'dsire_laws_raw.csv'))

# 2. Construct solar and wind policy data frame
# 2.1 Solar laws
solar_laws <- read_csv(here('data', 'dsire_laws_raw.csv')) %>%
    filter(grepl("Solar", Name, ignore.case = TRUE) |
           grepl("Solar", `Policy/Incentive Type`, ignore.case = TRUE)) %>%
    mutate(Created = as.integer(substr(Created, 7, 10))) %>% 
    count(Created) %>% 
    rename(year = Created, count_of_solar_laws = n)

write_csv(solar_laws, here('data', 'dsire_solar_laws.csv'))

# 2.2 Wind laws
wind_laws <- read_csv(here('data', 'dsire_laws_raw.csv')) %>% 
    filter(grepl("Wind", Name, ignore.case = TRUE) |
           grepl("Wind", `Policy/Incentive Type`, ignore.case = TRUE)) %>%
    mutate(Created = as.integer(substr(Created, 7, 10))) %>% 
    count(Created) %>% 
    rename(year = Created, count_of_wind_laws = n)

write_csv(wind_laws, here('data', 'dsire_wind_laws.csv'))

# 3. Plots of solar and wind laws
# 3.1 Judge if congress is the same party as the president
congress_path <- here('data', 'congress.csv')
congress <- read_csv(congress_path)

# 3.2 Solar laws plot
solar_laws_plot <- solar_laws %>% 
    left_join(congress, by = 'year') %>% 
    filter(year <= 2020) %>% 
    ggplot() +
    geom_col(aes(x = year, y = count_of_solar_laws, fill = both)) +
    geom_vline(xintercept = 2000.5, color = 'black', linetype = 'solid') +
    geom_vline(xintercept = 2008.5, color = 'black', linetype = 'solid') +
    geom_vline(xintercept = 2016.5, color = 'black', linetype = 'solid') +
    annotate(
        'text', x = 2000.35, y = 70, color = '#4c67a5', hjust = 1,
        label = 'Clinton', size = 5, family = "Ubuntu") +
    annotate(
        'text', x = 2005, y = 70, color = '#ad2730', hjust = 1,
        label = 'Bush', size = 5, family = "Ubuntu") +
    annotate(
        'text', x = 2014, y = 70, color = '#4c67a5', hjust = 1,
        label = 'Obama', size = 5, family = "Ubuntu") +
    annotate(
        'text', x = 2019.75, y = 70, color = '#ad2730', hjust = 1,
        label = 'Trump', size = 5, family = "Ubuntu") +
    geom_label(data = data.frame(x = 2004.5, y = 50, label = 'Both Congress and the \nPresident are of the \nsame political party'),
      aes(x = x, y = y, label = label),
      size = 3.5, color = '#719847', family = "Ubuntu") +
    geom_curve(data = data.frame(x = 2004.5, xend = 2001.5, y = 42, yend = 10),
               mapping = aes(x = x, xend = xend, y = y, yend = yend),
               color = '#719847', size = 0.5, curvature = 0.1,
               arrow = arrow(length = unit(0.01, "npc"), type = "closed")) +
    geom_curve(data = data.frame(x = 2004.5, xend = 2005, y = 42, yend = 10),
               mapping = aes(x = x, xend = xend, y = y, yend = yend),
               color = '#719847', size = 0.5, curvature = -0.1,
               arrow = arrow(length = unit(0.01, "npc"), type = "closed")) +
    geom_curve(data = data.frame(x = 2004.5, xend = 2009.5, y = 42, yend = 25),
               mapping = aes(x = x, xend = xend, y = y, yend = yend),
               color = '#719847', size = 0.5, curvature = -0.1,
               arrow = arrow(length = unit(0.01, "npc"), type = "closed")) +
    scale_x_continuous(breaks = seq(2000, 2020, by = 2),
                       limits = c(1998, 2020),
                       expand = expand_scale(mult = c(0, 0.05))) +
    scale_y_continuous(breaks = seq(0, 60, by = 10),
                       limits = c(0 , 70),
                       expand = expand_scale(mult = c(0, 0.05))) +
    scale_fill_manual(values = c('#dbd9d6', '#719847')) +
    labs(x = 'Year',
         y = 'Number of Solar Policies',
         title = 'State solar policies appear to coincide with\ndemocratic presidential terms',
         subtitle = 'Alignment of political parties does not appear to impact legislation',
         caption = 'Data from DSIRE') +
    theme_minimal_hgrid(font_family = 'Ubuntu') +
    theme(legend.position = 'none')

ggsave(
    filename = here('figs_manu', '3_4_solar_laws_plot.png'), 
    plot = solar_laws_plot,
    width = 7,
    height = 7 / 1.618
)

# 3.3 Wind policy plot
wind_laws_plot <- wind_laws %>% 
    left_join(congress, by = 'year') %>% 
    filter(year <= 2020) %>% 
    ggplot() +
    geom_col(aes(x = year, y = count_of_wind_laws, fill = both)) +
    geom_vline(xintercept = 2000.5, color = 'black', linetype = 'solid') +
    geom_vline(xintercept = 2008.5, color = 'black', linetype = 'solid') +
    geom_vline(xintercept = 2016.5, color = 'black', linetype = 'solid') +
    annotate(
        'text', x = 2000.35, y = 70, color = '#4c67a5', hjust = 1,
        label = 'Clinton', size = 5, family = "Ubuntu") +
    annotate(
        'text', x = 2005, y = 70, color = '#ad2730', hjust = 1,
        label = 'Bush', size = 5, family = "Ubuntu") +
    annotate(
        'text', x = 2014, y = 70, color = '#4c67a5', hjust = 1,
        label = 'Obama', size = 5, family = "Ubuntu") +
    annotate(
        'text', x = 2019.75, y = 70, color = '#ad2730', hjust = 1,
        label = 'Trump', size = 5, family = "Ubuntu") +
    geom_label(data = data.frame(x = 2004.5, y = 50, label = 'Both Congress and the \nPresident are of the \nsame political party'),
      aes(x = x, y = y, label = label),
      size = 3.5, color = '#719847', family = "Ubuntu") +
    geom_curve(data = data.frame(x = 2004.5, xend = 2001.5, y = 42, yend = 10),
               mapping = aes(x = x, xend = xend, y = y, yend = yend),
               color = '#719847', size = 0.5, curvature = 0.1,
               arrow = arrow(length = unit(0.01, "npc"), type = "closed")) +
    geom_curve(data = data.frame(x = 2004.5, xend = 2005, y = 42, yend = 10),
               mapping = aes(x = x, xend = xend, y = y, yend = yend),
               color = '#719847', size = 0.5, curvature = -0.1,
               arrow = arrow(length = unit(0.01, "npc"), type = "closed")) +
    geom_curve(data = data.frame(x = 2004.5, xend = 2009.5, y = 42, yend = 25),
               mapping = aes(x = x, xend = xend, y = y, yend = yend),
               color = '#719847', size = 0.5, curvature = -0.1,
               arrow = arrow(length = unit(0.01, "npc"), type = "closed")) +
    scale_x_continuous(breaks = seq(2000, 2020, by = 2),
                       limits = c(1998, 2020),
                       expand = expand_scale(mult = c(0, 0.05))) +
    scale_y_continuous(breaks = seq(0, 60, by = 10),
                       limits = c(0 , 70),
                       expand = expand_scale(mult = c(0, 0.05))) +
    scale_fill_manual(values = c('#dbd9d6', '#719847')) +
    labs(x = 'Year',
         y = 'Number of Wind Policies',
         title = 'State wind policies appear to coincide with\ndemocratic presidential terms',
         subtitle = 'Alignment of political parties does not appear to impact legislation',
         caption = 'Data from DSIRE') +
    theme_minimal_hgrid(font_family = 'Ubuntu') +
    theme(legend.position = 'none')

ggsave(
    filename = here('figs_manu', '3_4_wind_laws_plot.png'), 
    plot = wind_laws_plot,
    width = 7,
    height = 7 / 1.618
)
solar_laws_plot
wind_laws_plot
# Store the wider format for later use
solar_wind_budget_wider <- read_csv(
    here('data', 'iea_solar_wind_budget_wider.csv')
    )

# Generate the longer format
solar_wind_budget_longer <- read_csv(
    here('data', 'iea_solar_wind_budget_wider.csv')
    ) %>% 
    pivot_longer(cols = -year,
                 names_to = 'energy_source',
                 values_to = 'rdd_millions_usd')

# Prepare the label data for the plot
label_data <- subset(solar_wind_budget_longer, year == max(year))
label_data$nudge <- ifelse(label_data$energy_source == 
                               'solar_rdd_million_usd', 220, -100)

# Sketch the plot
budget_plot <- solar_wind_budget_longer %>% 
    ggplot(aes(x = year,
               y = rdd_millions_usd,
               color = energy_source)) +
    geom_line(linewidth = 1) +
    geom_text(data = label_data,
               aes(label = recode(energy_source, 
                                  solar_rdd_million_usd = 'Solar', 
                                  wind_rdd_million_usd = 'Wind')),
              nudge_x = 1.75,
              nudge_y = label_data$nudge, size = 5,
              family = "Ubuntu") + 
    labs(x = 'Year',
         y = 'RD&D Budget (Millions, 2022 USD)',
         color = 'Energy Source',
         title = 'Solar and wind energy RD&D budgets follow\nsimilar trends in the USA',
         subtitle = 'Solar budget was higher than wind for all years',
         caption = 'Data from IEA') +
    scale_x_continuous(limits = c(1970, 2018), breaks = seq(1975, 2015, by = 5)) +
    theme_minimal_hgrid(font_family = 'Ubuntu') +
    scale_color_manual(values = c('#ccca5d','#b2529d'))+
    scale_y_continuous(labels = scales::dollar)+
    theme(legend.position = 'none') +
    # Carter box
    geom_label(data = data.frame(x = 1972.6, y = 990, label = 'President Carter\nannounces\nNational Energy\nProgram'),
      aes(x = x, y = y, label = label),
      size = 3.5, color = '#719847', color = 'black',
      family = "Ubuntu") +
    annotate('point', x = 1977 , y = 578.5, color = '#719847', size = 2) +
    annotate('point', x = 1977 , y = 83.0, color = '#719847', size = 2) +
    geom_curve(
        data = data.frame(
          x = 1973, xend = 1977, y = 790, yend = 578.5),
        mapping = aes(x = x, xend = xend, y = y, yend = yend),
        color = '#719847', size = 0.5, curvature = 0.1,
        arrow = arrow(length = unit(0.01, "npc"),
                      type = "closed")) +
    geom_curve(
        data = data.frame(
          x = 1973, xend = 1977, y = 790, yend = 83.0),
        mapping = aes(x = x, xend = xend, y = y, yend = yend),
        color = '#719847', size = 0.5, curvature = 0.1,
        arrow = arrow(length = unit(0.01, "npc"),
                      type = "closed")) +
    # Treasury box
    geom_label(data = data.frame(x = 2007, y = 750, label = 'Section 1603\nProgram begins'),
      aes(x = x, y = y, label = label),
      size = 3.5, color = '#719847', color = 'black',
      family = "Ubuntu") +
    annotate('point', x = 2009 , y = 531.5, color = '#719847', size = 2) +
    annotate('point', x = 2009 , y = 247.7, color = '#719847', size = 2) +
    geom_curve(
        data = data.frame(
          x = 2007, xend = 2009, y = 650, yend = 531.5),
        mapping = aes(x = x, xend = xend, y = y, yend = yend),
        color = '#719847', size = 0.5, curvature = 0.1,
        arrow = arrow(length = unit(0.01, "npc"),
                      type = "closed")) +
    geom_curve(
        data = data.frame(
          x = 2007, xend = 2009, y = 650, yend = 247.7),
        mapping = aes(x = x, xend = xend, y = y, yend = yend),
        color = '#719847', size = 0.5, curvature = 0.1,
        arrow = arrow(length = unit(0.01, "npc"),
                      type = "closed")) 

ggsave(
    filename = here('figs_manu', '3_5_budget_plot.png'), 
    plot = budget_plot,
    width = 7,
    height = 7 / 1.618
)

budget_plot
# 1. Merge and save solar and wind gen
merged_solar_wind <- solar_cost %>% 
    full_join(solar_gen, by = 'year') %>%
    full_join(solar_laws, by = 'year') %>%
    full_join(solar_wind_budget_wider, by = 'year') %>%
    full_join(wind_cost, by = 'year') %>%
    full_join(wind_gen, by = 'year') %>%
    full_join(wind_laws, by = 'year') %>% 
    arrange(year)

write_csv(merged_solar_wind, here('data', 'merged_solar_wind.csv'))

# 2. Correlation coefficients between solar generation and other solar related variables:
# cost (solar_cost_usd_per_kwh), laws (count_of_solar_laws), and budget(solar_rdd_million_usd)
cor_solar_gen_solar_cost <- cor(
    merged_solar_wind$solar_gen_gwh,
    merged_solar_wind$solar_cost_usd_per_kwh,
    use = 'pairwise.complete.obs',
    method = 'spearman'
    )
cor_solar_gen_solar_laws <- cor(
    merged_solar_wind$solar_gen_gwh,
    merged_solar_wind$count_of_solar_laws,
    use = 'pairwise.complete.obs',
    method = 'spearman'
    )
cor_solar_gen_solar_rdd <- cor(
    merged_solar_wind$solar_gen_gwh,
    merged_solar_wind$solar_rdd_million_usd,
    use = 'pairwise.complete.obs',
    method = 'spearman'
    )

# 3. Correlation coefficients for wind generation and other wind variables
cor_wind_gen_wind_cost <- cor(
    merged_solar_wind$wind_gen_gwh,
    merged_solar_wind$wind_cost_usd_per_kwh,
    use = 'pairwise.complete.obs',
    method = 'spearman'
    )
cor_wind_gen_wind_laws <- cor(
    merged_solar_wind$wind_gen_gwh,
    merged_solar_wind$count_of_wind_laws,
    use = 'pairwise.complete.obs',
    method = 'spearman'
    )
cor_wind_gen_wind_rdd <- cor(
    merged_solar_wind$wind_gen_gwh,
    merged_solar_wind$wind_rdd_million_usd,
    use = 'pairwise.complete.obs',
    method = 'spearman'
    )

# 4. Create dataframe of correlations
cor_solar_wind <- data.frame(value_1 = c('Solar', 'Solar', 'Solar',
                                         'Wind','Wind', 'Wind'),
                             value_2 = c('Cost', 'Laws', 'Budgets',
                                         'Cost', 'Laws', 'Budgets'),
                             cor = c(cor_solar_gen_solar_cost,
                                     cor_solar_gen_solar_laws,
                                     cor_solar_gen_solar_rdd,
                                     cor_wind_gen_wind_cost,
                                     cor_wind_gen_wind_laws,
                                     cor_wind_gen_wind_rdd))

write_csv(cor_solar_wind, here('data', 'cor_solar_wind.csv'))

# 5. Summary table of the correlations
cor_solar_wind %>% 
    mutate(cor = round(cor, 2),
           cor = cell_spec(
               cor, 'html', 
               color = ifelse(
                   cor > 0.5 | cor < -0.5, 'white', 'black'
                   ),
               background = ifelse(
                   cor > 0.5 | cor < -0.5, 'cornflowerblue', 'white'
                   ))) %>%
    kable(format = 'html',
          escape = FALSE,
          align = c('l', 'l', 'l'),
          col.names = c('Energy Type', 'Compared Variable', 'Correlation'),
          caption = 'Correlations of Solar/Wind Generation vs the Other 3 Variables') %>%
    kable_styling(bootstrap_options = c('striped', 'hover'))
# 1. Merge and save datasets
# 1.1 Solar data
merged_solar <- solar_cost %>%
    full_join(solar_gen, by = 'year') %>%
    full_join(solar_laws, by = 'year') %>%
    full_join(solar_wind_budget_wider %>%
                  select(-wind_rdd_million_usd),
              by = 'year') %>% 
    arrange(year) %>% 
    mutate(log_solar_cost = log(solar_cost_usd_per_kwh),
           log_solar_gen = log(solar_gen_gwh))

write_csv(merged_solar, here('data', 'merged_solar.csv'))

# 1.2 Wind data
merged_wind <- wind_cost %>%
    full_join(wind_gen, by = 'year') %>%
    full_join(wind_laws, by = 'year') %>% 
    full_join(solar_wind_budget_wider %>%
                  select(-solar_rdd_million_usd),
              by = 'year') %>% 
    arrange(year) %>% 
    mutate(log_wind_cost = log(wind_cost_usd_per_kwh),
           log_wind_gen = log(wind_gen_gwh))
write_csv(merged_wind, here('data', 'merged_wind.csv'))

# 2. Correlation matrix for merged data
# 2.1 Solar correlation matrix
cor_matrix_solar <- cor(
    merged_solar[, sapply(merged_solar, is.numeric)],
    use = 'pairwise.complete.obs',
    method = 'spearman'
    ) %>% 
    as.data.frame()

# 2.2 Wind correlation matrix
cor_matrix_wind <- cor(
    merged_wind[, sapply(merged_wind, is.numeric)],
    use = 'pairwise.complete.obs',
    method = 'spearman'
    ) %>% 
    as.data.frame()

# 3. Filter 'high' correlations with threshold of +/- 0.8
# 3.1 High solar correlations
cor_solar_high <- cor_matrix_solar %>%
    mutate(value_1 = row.names(.)) %>%
    gather(value_2, cor, -value_1) %>%
    filter(value_1 != value_2,
         cor > 0.8 | cor < -0.8) %>% 
    mutate(min_var = pmin(value_1, value_2),
           max_var = pmax(value_1, value_2)) %>%
    distinct(min_var, max_var, .keep_all = TRUE) %>%
    select(-min_var, -max_var) %>% 
    arrange(value_1) %>% 
    filter((startsWith(value_1, "log") & startsWith(value_2, "log")) | 
           (!startsWith(value_1, "log") & !startsWith(value_2, "log")))

write_csv(cor_solar_high, here('data', 'cor_solar_high.csv'))

# 3.2 High wind correlations
cor_wind_high <- cor_matrix_wind %>%
    mutate(value_1 = row.names(.)) %>%
    gather(value_2, cor, -value_1) %>%
    filter(value_1 != value_2,
         cor > 0.8 | cor < -0.8) %>% 
    mutate(min_var = pmin(value_1, value_2),
           max_var = pmax(value_1, value_2)) %>%
    distinct(min_var, max_var, .keep_all = TRUE) %>%
    select(-min_var, -max_var) %>% 
    arrange(value_1) %>% 
    filter((startsWith(value_1, "log") & startsWith(value_2, "log")) | 
           (!startsWith(value_1, "log") & !startsWith(value_2, "log")))

write_csv(cor_wind_high, here('data', 'cor_wind_high.csv'))

# 4. Construct the 'high' correlation matrix table
# There are 4 in solar and 3 in wind
# Excluding time series, there are 2 in solar and 1 in wind
cor_matrix_high <- tibble(
    `No.` = seq(1,7),
    `Value 1` = c("Solar Cost", "Solar Gen", "log Solar Gen", "Solar RDD",
                  "Wind Cost", "Wind Gen", "log Wind Gen"),
    `Value 2` = c("Year", "Year", "log Solar Cost", "Solar Cost",
                  "Year", "Year", "log Wind Cost"),
    Correlation = c(round(cor_solar_high[2,3], 2),
                    round(cor_solar_high[3,3], 2),
                    round(cor_solar_high[1,3], 2),
                    round(cor_solar_high[5,3], 2),
                    round(cor_wind_high[2,3], 2),
                    round(cor_wind_high[3,3], 2),
                    round(cor_wind_high[1,3], 2))
)

cor_matrix_high_kable <- cor_matrix_high %>% kable(
    format = 'html', escape = FALSE,
    align = c('c', 'l', 'l', 'c'),
    caption = 'Correlations Matrix of High Correlations') %>%
    kable_styling(bootstrap_options = c('striped', 'hover'))

cor_matrix_high_kable
# Plot 1: Log Solar Cost vs Log Solar Generation
cor_plot_1 <- merged_solar_wind %>%
    filter(!is.na(solar_cost_usd_per_kwh) & !is.na(solar_gen_gwh)) %>% 
    ggplot(aes(x = log(solar_cost_usd_per_kwh),
               y = log(solar_gen_gwh / 1000))) +
    geom_point() +
    geom_smooth(method = 'lm', se = FALSE, size = 0.5,
                color = 'cornflowerblue') +
    labs(x = 'Solar Cost: log(USD/kWh)',
         y = 'Solar Generation: log(TWh)',
         title='Log Solar Cost vs Log Solar Generation',
         subtitle = 'Correlation = -1') +
    theme_minimal_grid(font_family = 'Ubuntu')

ggsave(
    filename = here('figs_manu', '3_6_cor_plot_1.png'), 
    plot = cor_plot_1,
    width = 6,
    height = 6 / 1.618
)

# Plot 2: log Wind Cost vs log Wind Generation
cor_plot_2 <- merged_solar_wind %>%
    filter(!is.na(wind_cost_usd_per_kwh) & !is.na(wind_gen_gwh)) %>% 
    ggplot(aes(x = log(wind_cost_usd_per_kwh),
               y = log(wind_gen_gwh / 1000))) +
    geom_point() +
    geom_smooth(method = 'lm', se = FALSE, size = 0.5,
                color = 'cornflowerblue') +
    labs(x = 'Wind Cost: log(USD/kWh)',
         y = 'Wind Generation: log(TWh)',
         title='Log Wind Cost vs Log Wind Generation',
         subtitle = 'Correlation = -0.81') +
    theme_minimal_grid(font_family = 'Ubuntu')

ggsave(
    filename = here('figs_manu', '3_6_cor_plot_2.png'), 
    plot = cor_plot_2,
    width = 6,
    height = 6 / 1.618
)

# Plot 3: Solar RD&D vs Solar Cost
cor_plot_3 <- merged_solar_wind %>%
    filter(!is.na(solar_rdd_million_usd) &
               !is.na(solar_cost_usd_per_kwh)) %>% 
    ggplot(aes(x = solar_rdd_million_usd,
               y = solar_cost_usd_per_kwh)) +
    geom_point() +
    geom_smooth(method = 'lm', se = FALSE, size = 0.5,
                color = 'cornflowerblue') +
    labs(x = 'Solar RD&D (USD)',
         y = 'Solar Cost (USD / kWh)',
         title='Solar RD&D vs Solar Cost',
         subtitle = 'Correlation = 0.89') +
    theme_minimal_grid(font_family = 'Ubuntu')

ggsave(
    filename = here('figs_manu', '3_6_cor_plot_3.png'), 
    plot = cor_plot_3,
    width = 6,
    height = 6 / 1.618
)
cor_plot_1
cor_plot_2
cor_plot_3
# 1. Calculate the summary and export to csv
summary_solar_gen <- summary(solar_gen$solar_gen_gwh)
summary_solar_cost <- summary(solar_cost$solar_cost_usd_per_kwh)
summary_solar_laws <- summary(solar_laws$count_of_solar_laws)
summary_solar_budget <- summary(solar_wind_budget_wider$solar_rdd_million_usd)

summary_solar_df <- data.frame(
    tier = names(summary_solar_gen),
    solar_gen_gwh = round(as.numeric(summary_solar_gen)),
    solar_cost_usd_per_kw = round(as.numeric(summary_solar_cost), 2),
    solar_laws_count = round(as.numeric(summary_solar_laws)),
    solar_budget_million_usd = round(as.numeric(summary_solar_budget))
    )

write_csv(summary_solar_df, here('data', 'summary_solar.csv'))

# 2. Generate the table using kable()
summary_solar_df %>% 
    kable(format = 'html',
          escape = FALSE,
          col.names = c('Tier',
                        'Electricity Generation (GWh)',
                        'Cost (USD/kWh)',
                        'Laws Count',
                        'RD&D Budget (Million USD)'),
          caption = 'Summary of Solar') %>%
    kable_styling(bootstrap_options = c('striped', 'hover'))
# 1. Calculate the summary and export to csv
summary_wind_gen <- summary(wind_gen$wind_gen_gwh)
summary_wind_cost <- summary(wind_cost$wind_cost_usd_per_kwh)
summary_wind_cost <- summary_wind_cost[!names(summary_wind_cost) == "NA's"]
summary_wind_laws <- summary(wind_laws$count_of_wind_laws)
summary_wind_budget <- summary(solar_wind_budget_wider$wind_rdd_million_usd)

summary_wind_df <- data.frame(
    tier = names(summary_wind_gen),
    wind_gen_gwh = round(as.numeric(summary_wind_gen)),
    wind_cost_usd_per_kw = round(as.numeric(summary_wind_cost), 2),
    wind_laws_count = round(as.numeric(summary_wind_laws)),
    wind_budget_million_usd = round(as.numeric(summary_wind_budget))
    )

write_csv(summary_wind_df, here('data', 'summary_wind.csv'))

# 2. Generate the table using kable()
summary_wind_df %>% 
    kable(format = 'html',
          escape = FALSE,
          col.names = c('Tier',
                        'Electricity Generation (GWh)',
                        'Cost (USD/kWh)',
                        'Laws Count',
                        'RD&D Budget (Million USD)'),
          caption = 'Summary of Wind') %>%
    kable_styling(bootstrap_options = c('striped', 'hover'))
dict_cor <- data.frame(
    name = c('cor_matrix_high',
             'cor_matrix_solar',
             'cor_matrix_wind',
             'cor_solar_wind',
             'cor_solar_high',
             'cor_wind_high'),
    type = c('A tibble: 7 × 4',
             'A data frame: 6 × 6',
             'A data frame: 6 × 6',
             'A data frame: 8 × 3',
             'A data frame: 5 × 3',
             'A data frame: 2 × 3'),
    description = c('Correlation matrix of high correlation values',
                    'Correlation matrix of all solar related variables',
                    'Correlation matrix of all wind related variables',
                    'Correlation between generation and 3 other variables',
                    'Solar related correlations higher than 0.8',
                    'Wind related correlations higher than 0.8')
) %>% 
    mutate(count = row_number()) %>%
    select(count, everything())
dict_cor %>% 
    kable(format = 'html',
          escape = FALSE,
          align = c('c', 'l', 'l', 'l'),
          col.names = c('No.', 'Name', 'Type', 'Description'),
          caption = 'Dictionary of Correlations') %>%
    kable_styling(bootstrap_options = c('striped', 'hover'))
dict_file <- data.frame(
    name = c('congress',
             'cor_solar_wind',
             'dsire_laws_raw',
             'dsire_solar_laws',
             'dsire_wind_laws',
             'eia_solar_gen_raw',
             'eia_solar_gen',
             'eia_solar_wind_gen_longer',
             'eia_wind_gen_raw',
             'eia_wind_gen',
             'cor_solar_high',
             'cor_wind_high',
             'iea_solar_wind_budget_wider',
             'irena_solar_cost',
             'irena_solar_wind_cost_longer',
             'irena_solar_wind_international_long',
             'irena_solar_wind_international_raw',
             'irena_wind_cost',
             'merged_solar_wind',
             'merged_solar',
             'merged_wind',
             'summary_solar',
             'summary_wind',
             'dsire_laws_raw',
             'irena_costs_raw'),
    type = c('CSV', 'CSV', 'CSV', 'CSV', 'CSV',
             'CSV',