us_residential: US Monthly Natural Gas Residential Consumption

In USgas: The Demand for Natural Gas in the US

US Monthly Natural Gas Residential Consumption

Description

The US monthly consumption of natural gas by residential consumers by state and aggregate level between 1989 and 2020

Units: Million Cubic Feet

Usage

us_residential

Format

A data.frame with 3 variables.

date

A Date, the month and year of the observation (the day set by default to 1st of the month)

state

A character, the US state indicator

y

A numeric, the monthly natural gas residential consumption in a million cubic feet

Details

The dataset contains monthly summary of the demand for natural gas by residential consumers in the US by state and total aggregate level. The data is available for the state level between January 1989 and September 2020, and for the US level between January 1973 and September 2020.

Source

US Energy Information Administration (EIA) website.

Examples

data("us_residential")

# Subsetting the total consumption in the US

us_res_total <- us_residential[which(us_residential$state == "U.S."),]

us_res_total <- us_res_total[order(us_res_total$date), ]

head(us_res_total)

at_x <- seq.Date(from = as.Date("1980-01-01"),
                 to = as.Date("2020-01-01"),
                 length.out = 5)

at_y <- pretty(us_res_total$y)[c(2, 4, 6)]

plot(us_res_total$date, us_res_total$y,
     col = "#1f77b4",
     type = "l",
     frame.plot = FALSE,
     axes = FALSE,
     panel.first = abline(h = at_y, col = "grey80"),
     main = "US Natural Gas Residential Consumption",
     xlab = "Source: https://www.eia.gov/",
     ylab = "Million Cubic Feet")



mtext(side =1, text = format(at_x, format = "%Y %b"), at = at_x,
      col = "grey20", line = 1, cex = 0.8)

mtext(side =2, text = format(at_y, scientific = FALSE), at = at_y,
      col = "grey20", line = 1, cex = 0.8)

### Plotting the west cost consumption
# Subsetting the west cost states
wc_gas <- us_residential[which(us_residential$state %in%
                             c("Alaska", "California",
                               "Oregon", "Washington")),]

# Reshape to wide
wc_wide <- reshape(wc_gas, v.names = "y", idvar = "date",
                   timevar = "state", direction = "wide")

names(wc_wide) <- c("date","Alaska", "California",
                    "Oregon", "Washington")
# Reorder the data
wc_wide <- wc_wide[order(wc_wide$date), ]

# Set the plot y and x axis ticks
at_x <- seq.Date(from = as.Date("1990-01-01"),
                 to = as.Date("2020-01-01"),
                 length.out = 4)

at_y <- pretty(wc_gas$y)[c(2, 4, 6)]

# plot the first series
plot(wc_wide$date, wc_wide$Alaska,
     type = "l",
     frame.plot = FALSE,
     axes = FALSE,
     panel.first = abline(h = c(at_y), col = "grey80"),
     main = "West Cost Natural Gas Residential Consumption",
     cex.main = 1, font.main = 1, col.main = "black",
     xlab = "Source: https://www.eia.gov/",
     font.axis = 1, cex.lab= 0.7,
     ylab = "Million Cubic Feet",
     ylim = c(min(wc_gas$y, na.rm = TRUE), max(wc_gas$y, na.rm = TRUE)))

# Add the 3 other series
lines(wc_wide$date, wc_wide$California, col = "#1f77b4")
lines(wc_wide$date, wc_wide$Oregon, col = "#457b9d")
lines(wc_wide$date, wc_wide$Washington, col = "#ef476f")

# Add the y and x axis ticks
mtext(side =1, text = format(at_x, format = "%Y %b"), at = at_x,
      col = "grey20", line = 1, cex = 0.8)

mtext(side =2, text = format(at_y, scientific = FALSE), at = at_y,
      col = "grey20", line = 1, cex = 0.8)

text(tail(wc_wide$date, 10)[1],
     max(wc_gas$y, na.rm = TRUE),
     "California",
     col = "#1f77b4",
     cex = 0.6)

text(tail(wc_wide$date, 10)[1],
     max(wc_gas$y, na.rm = TRUE) - 5000,
     "Washington",
     col = "#ef476f",
     cex = 0.6)

text(tail(wc_wide$date, 10)[1],
     max(wc_gas$y, na.rm = TRUE) - 10000,
     "Oregon",
     col = "#457b9d",
     cex = 0.6)

text(tail(wc_wide$date, 10)[1],
     max(wc_gas$y, na.rm = TRUE) - 15000,
     "Alaska",
     col = "black",
     cex = 0.6)

USgas documentation built on April 16, 2023, 9:19 a.m.