US Weekly Gas Prices

economics
US gas prices over time
Author

Cole Baril

Published

July 1, 2025

Load Libraries

require(pacman)
p_load(tidytuesdayR, trashpanda, magick, tidyverse, janitor, tidytext, extrafont, slider, here, TTR)
options(scipen = 999)

Load Data

tuesdata <- tidytuesdayR::tt_load('2025-07-01')
weekly_gas_prices <- tuesdata$weekly_gas_prices

# Extra cpi data 
cpi <- read_csv(here("2025/2025-07-01 - US Weekly Gas Prices/cpi.csv")) |> 
  mutate(year = year(observation_date)) |> 
  rename("cpi" = 2) |> select(-1)

Data Cleaning

vol_df <- weekly_gas_prices |> 
  filter(grade == "all") |> 
  filter(!formulation %in% c("conventional", "reformulated")) |> 
  mutate(year = year(date)) |> 
  left_join(cpi, by = "year") |> 
  mutate(adjusted_price = price * (144.475 / cpi)) |> 

  mutate(fuel = str_to_title(fuel)) |> 
  arrange(fuel, date) |> 
  drop_na(adjusted_price) |> 

mutate(
  log_return = log(adjusted_price / lag(adjusted_price)),

  vol_12w = slide_dbl(
    log_return, sd,
    .before = 11, .complete = TRUE
  ),

  vol_52w = slide_dbl(
    log_return, sd,
    .before = 51, .complete = TRUE
  ),

  .by = fuel
) |> 

  group_by(fuel) |> 
  mutate(volatility = sd(log_return, na.rm = TRUE)) |> 
  ungroup()

52-Week Rolling Volatility

plot <- ggplot(vol_df, aes(x = date, y = vol_52w, color = fuel)) +
  geom_line(na.rm = TRUE, size = 1) +
  scale_x_date(
    breaks = as.Date(c("1995-01-01", "2000-01-01", "2005-01-01", "2010-01-01", 
                       "2015-01-01", "2020-01-01", "2025-01-01")),
    date_labels = "%Y"
  ) +
  labs(
    title = "52-Week Rolling Volatility of Weekly U.S. Gas Prices",
    subtitle = "Indexed to inflation, all gasoline formulations",
    x = "Date",
    y = "Gasoline Price Volatility (Log Return SD)",
    color = "Fuel Type"
  ) +
  theme_bw(base_size = 20) +
  theme(panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        panel.grid.minor.y = element_blank(),
        strip.text = element_text(face = "bold", size = 14),
        plot.title = element_text(face = "bold", hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5),
        legend.position = "top",
        legend.title.position = "top",
        legend.title = element_text(hjust = 0.5),
        legend.key.width = unit(2.5, "lines"),
        legend.key.height = unit(1, "lines"),
        panel.border = element_blank(),
        axis.line = element_line(colour = "black")) +
  add_caption_cwb(include_data_source = TRUE, data_source = "U.S. Energy Information Administration")

# Save and display images
current_dir <- dirname(knitr::current_input())
plot_name <- "us_gas.png"

ggsave(plot = plot, 
       dpi = "screen",
       width = 20,
       height = 18,
       device = ragg::agg_png,
       filename = file.path(current_dir, plot_name))


# Read the big plot
img <- image_read(file.path(current_dir, plot_name))

# Force 16:9 aspect ratio with minimal padding
# Target size: 1200x675 px (16:9)
img_card <- image_scale(img, "1200x675")           # scale to fit inside 16:9
img_card <- image_extent(
  img_card,
  geometry = "1200x675",
  gravity = "center"
)

# Save as card preview
image_write(img_card, path = file.path(current_dir, "preview.png"))

knitr::include_graphics(
  file.path(current_dir, plot_name)
)

References

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