The USgas package provides an overview of demand for natural gas in the US in a time-series format. That includes the following datasets:
us_total - The US annual natural gas consumption by state-level between 1997 and 2019, and aggregate level between 1949 and 2019us_monthly - The monthly demand for natural gas in the US between 2001 and 2020us_residential - The US monthly natural gas residential consumption by state and aggregate level between 1989 and 2020Data source: The US Energy Information Administration API
More information about the package datasets available on this vignette.
You can install the released version of USgas from CRAN with:
And the development version from GitHub with:
Plotting the consumption of natural gas in New England states:
data(us_total)
str(us_total)
#> 'data.frame': 1266 obs. of 3 variables:
#> $ year : int 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 ...
#> $ state: chr "Alabama" "Alabama" "Alabama" "Alabama" ...
#> $ y : int 324158 329134 337270 353614 332693 379343 350345 382367 353156 391093 ...
head(us_total)
#> year state y
#> 1 1997 Alabama 324158
#> 2 1998 Alabama 329134
#> 3 1999 Alabama 337270
#> 4 2000 Alabama 353614
#> 5 2001 Alabama 332693
#> 6 2002 Alabama 379343Subsetting the New England states:
ne <- c("Connecticut", "Maine", "Massachusetts",
"New Hampshire", "Rhode Island", "Vermont")
ne_gas <- us_total[which(us_total$state %in% ne),]
ne_wide <- reshape(ne_gas, v.names = "y", idvar = "year",
timevar = "state", direction = "wide")
ne_wide <- ne_wide[order(ne_wide$year), ]
names(ne_wide) <- c("year",ne)
head(ne_wide)
#> year Connecticut Maine Massachusetts New Hampshire Rhode Island Vermont
#> 139 1997 144708 6290 402629 20848 117707 8061
#> 140 1998 131497 5716 358846 19127 130751 7735
#> 141 1999 152237 6572 344790 20313 118001 8033
#> 142 2000 159712 44779 343314 24950 88419 10426
#> 143 2001 146278 95733 349103 23398 95607 7919
#> 144 2002 177587 101536 393194 24901 87805 8367Plotting the states series:
# Set the y and x axis ticks
at_x <- seq(from = 2000, to = 2020, by = 5)
at_y <- pretty(ne_gas$y)[c(2, 4, 6)]
# plot the first series
plot(ne_wide$year, ne_wide$Connecticut,
type = "l",
col = "#073b4c",
frame.plot = FALSE,
axes = FALSE,
panel.first = abline(h = c(at_y), col = "grey80"),
main = "New England Annual Natural Gas Consumption by State",
cex.main = 1.2, font.main = 1, col.main = "black",
xlab = "Source: https://www.eia.gov/",
font.axis = 1, cex.lab= 1,
ylab = "Million Cubic Feet",
ylim = c(min(ne_gas$y, na.rm = TRUE), max(ne_gas$y, na.rm = TRUE)),
xlim = c(min(ne_gas$year), max(ne_gas$year) + 3))
# Add the 5 other series
lines(ne_wide$year, ne_wide$Maine, col = "#1f77b4")
lines(ne_wide$year, ne_wide$Massachusetts, col = "#118ab2")
lines(ne_wide$year, ne_wide$`New Hampshire`, col = "#06d6a0")
lines(ne_wide$year, ne_wide$`Rhode Island`, col = "#ffd166")
lines(ne_wide$year, ne_wide$Vermont, col = "#ef476f")
# Add the y and x axis ticks
mtext(side =1, text = format(at_x, nsmall=0), at = at_x,
col = "grey20", line = 1, cex = 0.8)
mtext(side =2, text = format(at_y, scientific = FALSE), at = at_y,
col = "grey20", line = 1, cex = 0.8)
# Add text
text(max(ne_wide$year) + 2,
tail(ne_wide$Connecticut,1),
"Connecticut",
col = "#073b4c",
cex = 0.7)
text(max(ne_wide$year) + 2,
tail(ne_wide$Maine,1) * 0.95,
"Maine",
col = "#1f77b4",
cex = 0.7)
text(max(ne_wide$year) + 2,
tail(ne_wide$Massachusetts,1),
"Massachusetts",
col = "#118ab2",
cex = 0.7)
text(max(ne_wide$year) + 2,
tail(ne_wide$`New Hampshire`,1) * 1.1,
"New Hampshire",
col = "#06d6a0",
cex = 0.7)
text(max(ne_wide$year) + 2,
tail(ne_wide$`Rhode Island`,1) * 1.05,
"Rhode Island",
col = "#ffd166",
cex = 0.7)
text(max(ne_wide$year) + 2,
tail(ne_wide$Vermont,1),
"Vermont",
col = "#ef476f",
cex = 0.7)