• Data preparation
  • All methods
    • Separated
    • Grouped
  • Selection of methods
  • Remaining methods
  • Used for paper
  • Per Week

This notebook presents the results from the scripts in the subfolder calibration_2weeks_restoftheyear.

The sensors are calibrated using a range of methods using the first 2 weeks of the data at a 2min resolution.

The performances of the calibration is then assessed throughout the year, per month first and then per weeks with the calculations being done by calibration_2weeks_restoftheyear/calibration_2weeks_restoftheyear.r and aggregated by calibration_2weeks_restoftheyear/calibration_2weeks_restoftheyear_agg.r.

source("utilities/utilities.R")
source("utilities/nested_models.r")
source("utilities/variables.r")

input_folder <- paste0(here::here(), "/output/calibration/2weeks_restoftheyear_agg/")

Data preparation

res_ue_per_month_initial <-
  readRDS(paste0(input_folder, "res_ue_per_month_initial.rds")) %>%
  mutate(cutMonth = as.POSIXct(cutMonth))

res_bus_per_month_initial <-
  readRDS(paste0(input_folder, "res_bus_per_month_initial.rds")) %>%
  mutate(cutMonth = as.POSIXct(cutMonth))

res_bus_per_month <-
  readRDS(
    paste0(
      input_folder,
      "results_bus_per_month_pms_calibration_2weeks_restofyear.rds"
    )
  ) %>%
  bind_rows(res_bus_per_month_initial)

res_eu_per_month <-
  readRDS(
    paste0(
      input_folder,
      "results_ue_per_month_pms_calibration_2weeks_restofyear.rds"
    )
  ) %>%
  bind_rows(res_ue_per_month_initial)

res_bus_per_month_sps <-
  readRDS(
    paste0(
      input_folder,
      "results_bus_per_month_sps_calibration_2weeks_restofyear.rds"
    )
  )

res_eu_per_month_sps <-
  readRDS(
    paste0(
      input_folder,
      "results_ue_per_month_sps_calibration_2weeks_restofyear.rds"
    )
  )



res_bus_per_month <- res_bus_per_month %>%
  bind_rows(res_bus_per_month_sps)
res_eu_per_month <- res_eu_per_month %>%
  bind_rows(res_eu_per_month_sps)

res_eu_per_month$method <-
  fct_recode(
    res_eu_per_month$method,
    `MLR_part.` =  "PM25_corr_lm_part_number",
    `MLR_part.+RH` =   "PM25_corr_lm_part_number_rh",
    `LR` = "PM25_corr_LR",
    `MLR_RH` = "PM25_corr_MLR1" ,
    `OLS` =  "PM25_corr_ols",
    `RLM` = "PM25_corr_RLM",
    `RLM_part.+RH`  = "PM25_corr_RLM_rh_part",
    `RLM+RH` =      "PM25_corr_RLM_rh",
    `Koehler_mass+LR` = "PM25_corr_koehler_mass_LR"  ,
    `Koehler_mass+OLS` = "PM25_corr_koehler_mass_ols" ,
    `Laulainen+LR` = "PM25_corr_Laulainen_rh_LR",
    `Laulainen+OLS` =  "PM25_corr_Laulainen_rh_ols",
    `Koehler_mass` = "PM25_corr_koehler_mass",
    `Koehler_size` = "PM25_corr_koehler_size",
    `Koehler_size+LR` = "PM25_corr_koehler_size_LR",
    `Koehler_size+OLS` = "PM25_corr_koehler_size_ols",
    `Laulainen` = "PM25_corr_Laulainen_rh",
    `RLM_part.` = "PM25_corr_RLM_part"
  )


res_eu_per_month$method <-
  fct_relevel(
    res_eu_per_month$method,
    "Initial",
    "OLS",
    "LR",
    "MLR_RH",
    "MLR_part.",
    "MLR_part.+RH",
    "RLM",
    "RLM+RH",
    "RLM_part.",
    "RLM_part.+RH",
    "Koehler_mass",
    "Koehler_mass+LR",
    "Koehler_mass+OLS",
    "Koehler_size",
    "Koehler_size+LR",
    "Koehler_size+OLS",
    "Laulainen",
    "Laulainen+LR",
    "Laulainen+OLS"
  )



res_bus_per_month$method <-
  fct_recode(
    res_bus_per_month$method,
    `MLR_part.` =  "PM25_corr_lm_part_number",
    `MLR_part.+RH` =   "PM25_corr_lm_part_number_rh",
    `LR` = "PM25_corr_LR",
    `MLR_RH` = "PM25_corr_MLR1" ,
    `OLS` =  "PM25_corr_ols",
    `RLM` = "PM25_corr_RLM",
    `RLM_part.+RH`  = "PM25_corr_RLM_rh_part",
    `RLM+RH` =      "PM25_corr_RLM_rh",
    `Koehler_mass+LR` = "PM25_corr_koehler_mass_LR"  ,
    `Koehler_mass+OLS` = "PM25_corr_koehler_mass_ols" ,
    `Laulainen+LR` = "PM25_corr_Laulainen_rh_LR",
    `Laulainen+OLS` =  "PM25_corr_Laulainen_rh_ols",
    `Koehler_mass` = "PM25_corr_koehler_mass",
    `Koehler_size` = "PM25_corr_koehler_size",
    `Koehler_size+LR` = "PM25_corr_koehler_size_LR",
    `Koehler_size+OLS` = "PM25_corr_koehler_size_ols",
    `Laulainen` = "PM25_corr_Laulainen_rh",
    `RLM_part.` = "PM25_corr_RLM_part"
  )

res_bus_per_month <-
  res_bus_per_month[res_bus_per_month$cutMonth != "2021-07-01", ]

res_eu_per_month  <-
  res_eu_per_month[res_eu_per_month$cutMonth != "2021-07-01", ]

res_eu_per_month <-
  res_eu_per_month[!grepl("N2", res_eu_per_month$results.sensor),]

res_bus_per_month <-
  res_bus_per_month[!grepl("N2", res_bus_per_month$results.combination), ]

All methods

Separated

scaleFUN <-
  function(x)
    sprintf("%.1f", x) # to have only one digit for the axis

p <- res_eu_per_month %>%
  dplyr::filter(results.type == "Full") %>%
  dplyr::mutate(sensor_type = ifelse(grepl("SPS", results.sensor), "SPS30", "PMS5003")) %>%
  dplyr::filter(!grepl("PM25_corr", method)) %>%
  ggplot(aes(
    x = as.factor(substr(cutMonth, 1, 7)),
    y = results.RMSE,
    colour = method
  )) +
  stat_boxplot(geom = 'errorbar') +
  geom_boxplot(
    fatten = 0.8,
    outlier.size = 0.8,
    outlier.alpha = 0.5,
    lwd = 0.2,
    outlier.shape = NA
  ) +
  custom_theme +
  facet_wrap( ~ sensor_type) +
  theme(axis.text.x = element_text(
    angle = 90,
    hjust = 0.95,
    vjust = 0.2
  )) +
  ylab(quickText("RMSE (ug/m3)")) +
  xlab("Start of the month") +
  scale_y_continuous(labels = scaleFUN,
                     limits = quantile(res_eu_per_month$results.RMSE, c(0.1, 0.9))) +
  labs(colour = "Calibration method")
#p

p_bus_pms <- res_bus_per_month %>%
  dplyr::filter(results.type == "Full") %>%
  dplyr::mutate(sensor_type = ifelse(grepl("SPS", results.combination), "SPS30", "PMS5003")) %>%
  ggplot(aes(
    x = as.factor(substr(cutMonth, 1, 7)),
    y = results.usb,
    colour = method
  )) +
  stat_boxplot(geom = 'errorbar') +
  geom_boxplot(
    fatten = 0.8,
    outlier.size = 0.8,
    outlier.alpha = 0.5,
    lwd = 0.2,
    outlier.shape = NA
  ) +
  custom_theme +
  xlab("Start of the month") +
  ylab(quickText("Between unit uncertainty (ug/m3)")) +
  facet_wrap( ~ sensor_type) +
  theme(axis.text.x = element_text(
    angle = 90,
    hjust = 0.95,
    vjust = 0.2
  )) +
  scale_y_continuous(labels = scaleFUN,
                     limits = quantile(res_bus_per_month$results.usb, c(0.1, 0.9))) +
  labs(colour = "Calibration method")

#p_bus_pms

legend <- get_legend(# create some space to the left of the legend
  p_bus_pms +
    guides(color = guide_legend(ncol = 1, title = "Calibration method")) +
    theme(legend.box.margin = margin(0, 0, 0, 0)))
pg <-
  plot_grid(
    p + xlab(NULL) + guides(x = "none") + theme(legend.position = "none"),
    p_bus_pms + theme(legend.position = "none"),
    nrow = 2,
    labels = c("(a)", "(b)"),
    label_y = 0.1
  )
pg_l <- plot_grid(pg,
                  legend,
                  ncol = 2,
                  rel_widths = c(0.8, 0.2))

#ggsave2(plot=pg_l, filename = paste0(output_folder, "2weeks_restoftheyear_permonth.png"),
# width  = 190, units = "mm")
pg_l

Grouped

The next graph present the variations of RMSE for all the methods grouped together.

p <- res_eu_per_month %>%
  dplyr::filter(results.type == "Full") %>%
  dplyr::mutate(sensor_type = ifelse(grepl("SPS", results.sensor), "SPS30", "PMS5003")) %>%
  dplyr::filter(!grepl("PM25_corr", method)) %>%
  ggplot(aes(x = as.factor(substr(cutMonth, 1, 7)), y = results.RMSE)) +
  stat_boxplot(geom = 'errorbar') +
  geom_boxplot(
    fatten = 0.8,
    outlier.size = 0.8,
    outlier.alpha = 0.5,
    lwd = 0.2,
    outlier.shape = NA
  ) +
  custom_theme +
  facet_wrap( ~ sensor_type) +
  theme(axis.text.x = element_text(
    angle = 90,
    hjust = 0.95,
    vjust = 0.2
  )) +
  ylab(quickText("RMSE (ug/m3)")) +
  xlab("Start of the month") +
  scale_y_continuous(limits = c(0, 16)) +
  labs(colour = "Calibration method") +
  scale_color_brewer(palette = "Set1")

p

Selection of methods

It is hard to see what is going on, too many methods. Let’s pick one of each for the humidity correction methods.

method_list_ft <-
  c(
    "Koehler_mass+LR",
    "Koehler_size+LR",
    "Laulainen+LR",
    "MLR_part.+RH",
    "LR",
    "RLM_part.+RH",
    "MLR_part.",
    "RLM_part."
  )

res_eu_per_month_ft <- res_eu_per_month %>%
  dplyr::filter(method %in% method_list_ft)

res_bus_per_month_ft <- res_bus_per_month %>%
  dplyr::filter(method %in% method_list_ft)


p <- res_eu_per_month_ft %>%
  dplyr::filter(results.type == "Full") %>%
  dplyr::mutate(sensor_type = ifelse(grepl("SPS", results.sensor), "SPS30", "PMS5003")) %>%
  dplyr::filter(!grepl("PM25_corr", method)) %>%
  ggplot(aes(
    x = as.factor(substr(cutMonth, 1, 7)),
    y = results.RMSE,
    colour = method
  )) +
  stat_boxplot(geom = 'errorbar') +
  geom_boxplot(
    fatten = 0.8,
    outlier.size = 0.8,
    outlier.alpha = 0.5,
    lwd = 0.2,
    outlier.shape = NA
  ) +
  custom_theme +
  facet_wrap( ~ sensor_type) +
  theme(axis.text.x = element_text(
    angle = 90,
    hjust = 0.95,
    vjust = 0.2
  )) +
  ylab(quickText("RMSE (ug/m3)")) +
  xlab("Start of the month") +
  #scale_y_continuous(labels=scaleFUN, limits = quantile(res_eu_per_month$results.RMSE, c(0.1, 0.9))) +
  labs(colour = "Calibration method")
#p

p_bus_pms <- res_bus_per_month_ft %>%
  dplyr::filter(results.type == "Full") %>%
  dplyr::mutate(sensor_type = ifelse(grepl("SPS", results.combination), "SPS30", "PMS5003")) %>%
  ggplot(aes(
    x = as.factor(substr(cutMonth, 1, 7)),
    y = results.usb,
    colour = method
  )) +
  stat_boxplot(geom = 'errorbar') +
  geom_boxplot(
    fatten = 0.8,
    outlier.size = 0.8,
    outlier.alpha = 0.5,
    lwd = 0.2,
    outlier.shape = NA
  ) +
  custom_theme +
  xlab("Start of the month") +
  ylab(quickText("Between unit uncertainty (ug/m3)")) +
  facet_wrap( ~ sensor_type) +
  theme(axis.text.x = element_text(
    angle = 90,
    hjust = 0.95,
    vjust = 0.2
  )) +
  #scale_y_continuous(labels=scaleFUN, limits = quantile(res_bus_per_month$results.usb, c(0.1, 0.9))) +
  labs(colour = "Calibration method")

#p_bus_pms

legend <- get_legend(# create some space to the left of the legend
  p_bus_pms +
    guides(color = guide_legend(ncol = 1, title = "Calibration method")) +
    theme(legend.box.margin = margin(0, 0, 0, 0)))
pg <-
  plot_grid(
    p + xlab(NULL) + guides(x = "none") + theme(legend.position = "none"),
    p_bus_pms + theme(legend.position = "none"),
    nrow = 2,
    labels = c("(a)", "(b)"),
    label_y = 0.1
  )
pg_l <- plot_grid(pg,
                  legend,
                  ncol = 2,
                  rel_widths = c(0.8, 0.2))

#ggsave2(plot=pg_l, filename = paste0(output_folder, "2weeks_restoftheyear_permonth.png"),
# width  = 190, units = "mm")
pg_l

Remaining methods

res_eu_per_month_ft <- res_eu_per_month %>%
  dplyr::filter(!(method %in% method_list_ft))

res_bus_per_month_ft <- res_bus_per_month %>%
  dplyr::filter(!(method %in% method_list_ft))


p <- res_eu_per_month_ft %>%
  dplyr::filter(results.type == "Full") %>%
  dplyr::mutate(sensor_type = ifelse(grepl("SPS", results.sensor), "SPS30", "PMS5003")) %>%
  dplyr::filter(!grepl("PM25_corr", method)) %>%
  ggplot(aes(
    x = as.factor(substr(cutMonth, 1, 7)),
    y = results.RMSE,
    colour = method
  )) +
  stat_boxplot(geom = 'errorbar') +
  geom_boxplot(
    fatten = 0.8,
    outlier.size = 0.8,
    outlier.alpha = 0.5,
    lwd = 0.2,
    outlier.shape = NA
  ) +
  custom_theme +
  facet_wrap( ~ sensor_type) +
  theme(axis.text.x = element_text(
    angle = 90,
    hjust = 0.95,
    vjust = 0.2
  )) +
  ylab(quickText("RMSE (ug/m3)")) +
  xlab("Start of the month") +
  scale_y_continuous(labels = scaleFUN) +
  labs(colour = "Calibration method")
#p

p_bus_pms <- res_bus_per_month_ft %>%
  dplyr::filter(results.type == "Full") %>%
  dplyr::mutate(sensor_type = ifelse(grepl("SPS", results.combination), "SPS30", "PMS5003")) %>%
  ggplot(aes(
    x = as.factor(substr(cutMonth, 1, 7)),
    y = results.usb,
    colour = method
  )) +
  stat_boxplot(geom = 'errorbar') +
  geom_boxplot(
    fatten = 0.8,
    outlier.size = 0.8,
    outlier.alpha = 0.5,
    lwd = 0.2,
    outlier.shape = NA
  ) +
  custom_theme +
  xlab("Start of the month") +
  ylab(quickText("Between unit uncertainty (ug/m3)")) +
  facet_wrap( ~ sensor_type) +
  theme(axis.text.x = element_text(
    angle = 90,
    hjust = 0.95,
    vjust = 0.2
  )) +
  #scale_y_continuous(labels=scaleFUN, limits = quantile(res_bus_per_month$results.usb, c(0.1, 0.9))) +
  labs(colour = "Calibration method")

#p_bus_pms

legend <- get_legend(# create some space to the left of the legend
  p_bus_pms +
    guides(color = guide_legend(ncol = 1, title = "Calibration method")) +
    theme(legend.box.margin = margin(0, 0, 0, 0)))
pg <-
  plot_grid(
    p + xlab(NULL) + guides(x = "none") + theme(legend.position = "none"),
    p_bus_pms + theme(legend.position = "none"),
    nrow = 2,
    labels = c("(a)", "(b)"),
    label_y = 0.1
  )
pg_l <- plot_grid(pg,
                  legend,
                  ncol = 2,
                  rel_widths = c(0.8, 0.2))

#ggsave2(plot=pg_l, filename = paste0(output_folder, "2weeks_restoftheyear_permonth.png"),
# width  = 190, units = "mm")
pg_l

Used for paper

p <- res_eu_per_month %>%
  dplyr::filter(!(
    method %in% c(
      "Koehler_mass",
      "Koehler_size",
      "Laulainen",
      "Koehler_mass+OLS",
      "Koehler_size+OLS",
      "Laulainen+OLS"
    )
  )) %>%
  dplyr::filter(results.type == "Full") %>%
  na.exclude() %>%
  dplyr::mutate(sensor_type = ifelse(grepl("SPS", results.sensor), "SPS30", "PMS5003")) %>%
  ggplot(aes(
    x = as.factor(substr(cutMonth, 1, 7)),
    y = results.RMSE,
    colour = method
  )) +
  #stat_boxplot(geom ='errorbar') +
  geom_boxplot(
    fatten = 1,
    outlier.size = 0.8,
    outlier.alpha = 0.5,
    lwd = 0.5,
    outlier.shape = NA
  ) +
  custom_theme +
  facet_wrap( ~ sensor_type, nrow = 2) +
  theme(axis.text.x = element_text(
    angle = 90,
    hjust = 0.95,
    vjust = 0.2
  )) +
  ylab(quickText("RMSE (ug/m3)")) +
  xlab("Start of the month") +
  scale_y_continuous(limits = c(1, 13)) +
  labs(colour = "Calibration method") #+
# scale_color_brewer(palette="Paired")
pl <-
  p + guides(shape = guide_legend(override.aes = list(size = 0.5))) +
  guides(color = guide_legend(override.aes = list(size = 0.5))) +
  theme(legend.title = element_text(size = 9),
        legend.text = element_text(size = 9))

ggsave2(
  pl,
  filename = paste0(PLOT_OUTPUT_FOLDER, "2weeks_restoftheyear_permonth.png"),
  width  = 190,
  units = "mm"
)

pl

Per Week

res_bus_per_week <-
  readRDS(
    paste0(
      input_folder,
      "results_bus_per_week_pms_calibration_2weeks_restofyear.rds"
    )
  )

res_eu_per_week <-
  readRDS(
    paste0(
      input_folder,
      "results_ue_per_week_pms_calibration_2weeks_restofyear.rds"
    )
  )

res_bus_per_week_sps <-
  readRDS(
    paste0(
      input_folder,
      "results_bus_per_week_sps_calibration_2weeks_restofyear.rds"
    )
  )

res_eu_per_week_sps <-
  readRDS(
    paste0(
      input_folder,
      "results_ue_per_week_sps_calibration_2weeks_restofyear.rds"
    )
  )



res_bus_per_week <- res_bus_per_week %>%
  bind_rows(res_bus_per_week_sps)
res_eu_per_week <- res_eu_per_week %>%
  bind_rows(res_eu_per_week_sps)

res_eu_per_week$method <-
  fct_recode(
    res_eu_per_week$method,
    `MLR_part.` =  "PM25_corr_lm_part_number",
    `MLR_part.+RH` =   "PM25_corr_lm_part_number_rh",
    `LR` = "PM25_corr_LR",
    `MLR_RH` = "PM25_corr_MLR1" ,
    `OLS` =  "PM25_corr_ols",
    `RLM` = "PM25_corr_RLM",
    `RLM_part.+RH`  = "PM25_corr_RLM_rh_part",
    `RLM+RH` =      "PM25_corr_RLM_rh"
  )

res_bus_per_week$method <-
  fct_recode(
    res_bus_per_week$method,
    `MLR_part.` =  "PM25_corr_lm_part_number",
    `MLR_part.+RH` =   "PM25_corr_lm_part_number_rh",
    `LR` = "PM25_corr_LR",
    `MLR_RH` = "PM25_corr_MLR1" ,
    `OLS` =  "PM25_corr_ols",
    `RLM` = "PM25_corr_RLM",
    `RLM_part.+RH`  = "PM25_corr_RLM_rh_part",
    `RLM+RH` =      "PM25_corr_RLM_rh"
  )



p <- res_eu_per_week %>%
  dplyr::filter(!grepl("koehler|Laulainen", method)) %>%
  dplyr::mutate(sensor_type = ifelse(grepl("SPS", results.sensor), "SPS30", "PMS5003")) %>%
  #group_by(cutDate, method) %>%
  #summarise(across(where(is.numeric),~mean(.x,na.rm=T))) %>%
  ggplot(aes(
    x = as.factor(substr(cutWeek, 1, 10)),
    y = results.RMSE,
    colour = method
  )) +
  geom_boxplot(
    fatten = 0.8,
    outlier.size = 0.8,
    outlier.alpha = 0.5,
    lwd = 0.2
  ) +
  custom_theme +
  facet_wrap( ~ sensor_type) +
  theme(axis.text.x = element_text(
    angle = 90,
    hjust = 0.95,
    vjust = 0.2
  )) +
  ylab(quickText("RMSE (ug/m3)")) +
  xlab("Start of the week") +
  scale_y_continuous(labels = scaleFUN) +
  labs(colour = "Calibration method")
#geom_line(data=results_sum,aes(x=cutDate, y = results.expanded_uncertainty,colour=method))
p

ggplotly(p) %>%
  layout(boxmode = "group")
2020-07-132020-07-202020-07-272020-08-032020-08-102020-08-172020-08-242020-08-312020-09-072020-09-142020-09-212020-09-282020-10-052020-10-122020-10-192020-10-262020-11-022020-11-092020-11-162020-11-232020-12-142020-12-212020-12-282021-01-042021-01-112021-01-182021-01-252021-02-012021-02-082021-02-152021-03-012021-03-082021-03-152021-03-222021-03-292021-04-052021-04-122021-04-192021-04-262021-05-032021-05-102021-05-172021-05-242021-05-312021-06-072021-06-142021-06-212021-06-280.05.010.015.02020-07-132020-07-202020-07-272020-08-032020-08-102020-08-172020-08-242020-08-312020-09-072020-09-142020-09-212020-09-282020-10-052020-10-122020-10-192020-10-262020-11-022020-11-092020-11-162020-11-232020-12-142020-12-212020-12-282021-01-042021-01-112021-01-182021-01-252021-02-012021-02-082021-02-152021-03-012021-03-082021-03-152021-03-222021-03-292021-04-052021-04-122021-04-192021-04-262021-05-032021-05-102021-05-172021-05-242021-05-312021-06-072021-06-142021-06-212021-06-28
Calibration methodMLR_part.MLR_part.+RHLRMLR_RHOLSRLMPM25_corr_RLM_partRLM+RHRLM_part.+RHStart of the weeknew textPMS5003SPS30
p_bus_pms <- res_bus_per_week %>%
  dplyr::filter(!grepl("koehler|Laulainen", method)) %>%
  dplyr::filter(results.type == "Full") %>%
  dplyr::mutate(sensor_type = ifelse(grepl("SPS", results.combination), "SPS30", "PMS5003")) %>%
  dplyr::filter(!grepl("80|85", method)) %>%
  dplyr::filter(!grepl("Koehler_size", method)) %>%
  ggplot(aes(
    x = as.factor(substr(cutWeek, 1, 10)),
    y = results.usb,
    colour = method
  )) +
  stat_boxplot(geom = 'errorbar') +
  geom_boxplot(
    fatten = 0.8,
    outlier.size = 0.8,
    outlier.alpha = 0.5,
    lwd = 0.2
  ) +
  geom_hline(yintercept = 5, linetype = "dashed") +
  custom_theme +
  xlab("Start of the week") +
  ylab(quickText("Between unit uncertainty (ug/m3)")) +
  #ggtitle(field)+
  facet_wrap( ~ sensor_type) +
  theme(axis.text.x = element_text(
    angle = 90,
    hjust = 0.95,
    vjust = 0.2
  )) +
  scale_y_continuous(labels = scaleFUN) +
  labs(colour = "Calibration method")

p_bus_pms

legend <- get_legend(# create some space to the left of the legend
  p_bus_pms +
    guides(color = guide_legend(ncol = 1, title = "Calibration method")) +
    #theme(legend.position = "bottom") +
    theme(legend.box.margin = margin(0, 0, 0, 0)))
pg <-
  plot_grid(
    p + xlab(NULL) + guides(x = "none") + theme(legend.position = "none"),
    p_bus_pms + theme(legend.position = "none"),
    nrow = 2,
    labels = c("(a)", "(b)"),
    label_y = 0.1
  )
pg_l <- plot_grid(pg,
                  legend,
                  ncol = 2,
                  rel_widths = c(0.8, 0.2))

ggsave2(
  plot = pg_l,
  filename = paste0(PLOT_OUTPUT_FOLDER, "2weeks_restoftheyear.png"),
  width  = 190,
  units = "mm"
)

pg_l