After a classic plot (see our former post) used on Wikipedia, we can explore another data visualization. The CO₂ concentration, one of 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 ?