After a classic plot of the Keeling curve (see our former post) used on Wikipedia, we can explore another data visualization. The CO₂ atmospheric concentration, the main cause of the climate warming, is following a seasonal cycle so it could be interesting (or ironic ?) to use a polar plot.
Config and data
We only keep two translations for brevity here…
# Required packages
library(tidyverse)
library(scales)
library(lubridate)
# Translations ------------------------------------------------------------
language <- list(
en_US = list(
locale_lc_time = "en_US.UTF-8",
title = bquote("Monthly mean"~CO[2]~"concentration"),
caption = paste("Data : P. Tans, NOAA/ESRL (www.esrl.noaa.gov/gmd/ccgg/trends/)\nand R. Keeling, Scripps Institution of Oceanography (scrippsco2.ucsd.edu/). Accessed", Sys.Date()),
x = "Year",
y = bquote(CO[2]~"fraction in dry air ("*mu*"mol/mol)"),
x2 = "Month",
y2 = bquote(atop(CO[2]~"fraction in dry air ("*mu*"mol/mol)", "Departure from yearly average")),
title2 = "Seasonal variation"
),
fr_FR = list(
locale_lc_time = "fr_FR.UTF-8",
title = bquote("Moyenne mensuelle de la concentration de"~CO[2]),
caption = paste("données : P. Tans, NOAA/ESRL (www.esrl.noaa.gov/gmd/ccgg/trends/)\net R. Keeling, Scripps Institution of Oceanography (scrippsco2.ucsd.edu/). Accédé le", Sys.Date()),
x = "année",
y = bquote("fraction de"~CO[2]~"dans l'air sec ("*mu*"mol/mol)"),
x2 = "mois",
y2 = bquote(atop("fraction de"~CO[2]~"dans l'air sec ("*mu*"mol/mol)", "en écart à la moyenne annuelle")),
title2 = "Variation saisonnière"
))
# Data --------------------------------------------------------------------
# https://www.esrl.noaa.gov/gmd/ccgg/trends/
co2ml <- read_delim("ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_mlo.txt",
delim = " ",
locale = locale(decimal_mark = "."),
na = c("-99.99", "-1"),
col_types = "iiddddi",
col_names = c("year", "month", "decimal", "co2", "co2_interpol", "co2_trend", "days"),
comment = "#",
trim_ws = TRUE) %>%
group_by(year) %>%
mutate(year_mean = mean(co2_interpol, na.rm = TRUE),
delta = co2_interpol - year_mean,
vdate = ymd(paste0("2015-", month, "-01"))) %>%
ungroup()
Create the plot for each language and save
We use a virtual date to keep the data in the same January-December interval and we add a partial dataframe to smooth the Dec./Jan. transition and build the spiral.
# Polar plot
for (l in names(language)) {
message(l)
current <- language[[l]]
# format the date in local names
Sys.setlocale("LC_TIME", current$locale_lc_time)
p3 <- co2ml %>%
filter(vdate == "2015-01-01") %>%
mutate(vdate = ymd("2015-12-31"),
year = year -1) %>%
bind_rows(co2ml) %>%
ggplot(aes(vdate, co2_interpol, group = year, color = year)) +
geom_line(size = 1.2) +
scale_x_date(breaks = pretty_breaks(12), labels = date_format("%b")) +
scale_color_viridis_c() +
labs(subtitle = current$title,
x = "",
y = current$y,
color = current$x,
title = paste("Mauna Loa", min(co2ml$year), "-", max(co2ml$year)),
caption = current$caption) +
coord_polar() +
theme_bw() +
theme(axis.title.y = element_text(hjust = .85),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.border = element_blank(),
plot.caption = element_text(size = 7))
ggsave(p3, file = paste("co2_mauna_loa_polar", l, Sys.Date(), "wp.svg", sep = "_"), width = 20, height = 20, units = "cm", device = svg)
}
Does this new year look good ? Will the spiral cross its path soon ?
