R.utils::sourceDirectory("R", modifiedOnly = FALSE) options(dplyr.summarise.inform = FALSE) #--------------------------------------------------------------------------------------------------------------------- ## LOAD DATA ### shift data load here d_orig <- readRDS("impact.rds") country_names <- read.csv("country_names.csv", stringsAsFactors = FALSE) #average per disease d <- d_orig %>% mutate(year = as.numeric(year), impact = as.numeric(impact), focal_burden = as.numeric(focal_burden), baseline_burden = as.numeric(baseline_burden)) %>% group_by(country, year, modelling_group, burden_outcome, touchstone, disease, scenario_type, scenario_description) %>% summarise(impact = mean(impact, na.rm = TRUE), baseline_burden = mean(baseline_burden, na.rm = TRUE), focal_burden = mean(focal_burden, na.rm = TRUE)) %>% ungroup() d <- d %>% filter(grepl("bau|scenario2|scenario7|scenario8", scenario_type)) d <- d %>% mutate(simple_scenario = case_when(grepl("bau", scenario_type) ~ "Business as usual", grepl("scenario2", scenario_type) ~ "Postpone 2020 SIAs until 2021", grepl("scenario7", scenario_type) ~ "50% reduction in RI", grepl("scenario8", scenario_type) ~ "50% reduction in RI, postpone 2020 SIAs until 2021")) d <- d %>% mutate(simple_scenario = factor(simple_scenario, levels = c("Business as usual", "Postpone 2020 SIAs until 2021", "50% reduction in RI", "50% reduction in RI, postpone 2020 SIAs until 2021"))) d <- d %>% filter(!grepl("portnoy", scenario_type)) # choosing Wolfson CFR for measles # get colours disease_pal <- disease_palette(unique(d$disease)) scenario_pal <- rev(c("#EA7580", "#14A7B3","#F7BE9F", "grey50")) #tidy names d <- d %>% ungroup() %>% mutate(disease = case_when(disease == "YF" ~ "Yellow fever", disease == "MenA" ~ "Meningitis A", disease == "Measles" ~ "Measles")) p <- vimpact::get_population(con, touchstone_pop = "202005covid", country_ = unique(d$country)) p <- p %>% rename(population = value) %>% group_by(country, year, gender) %>% summarise(population = sum(population, na.rm = TRUE)) d_pop <- d %>% left_join(p, by = c("year", "country")) ## translate modelling groups d_pop <- d_pop %>% mutate(modelling_group_tidy = case_when(modelling_group == "Cambridge-Trotter" ~ "Cambridge", modelling_group == "KPW-Jackson" ~ "KP", modelling_group == "LSHTM-Jit" ~ "DynaMICE", modelling_group == "PSU-Ferrari" ~ "Penn State", modelling_group == "IC-Garske" ~ "Imperial", modelling_group == "UND-Perkins" ~ "Notre Dame", modelling_group == "McCarthy-ETH" ~ "IDM", modelling_group == "McCarthy-NGA" ~ "IDM")) # remove ETH and UGA for YF d_pop <- d_pop %>% filter(!(country %in% c("ETH", "UGA") & disease == "Yellow fever")) #country names d_pop <- d_pop %>% mutate(country_name = country_names$country_name[match(country, country_names$country)]) #--------------------------------------------------------------------------------------------------------------------- # FIGURES #Deaths per year up to 2030 per disease p1 <- figure_maker_burden_per_mod_dis(d_pop, burden_t = "deaths", scenario_pal, year_end = 2030, dis_name = "Yellow fever", file_name = "deaths_by_year_YF", figheight = 10, figwidth = 16) p2 <- figure_maker_burden_per_mod_dis(d_pop, burden_t = "deaths", scenario_pal, year_end = 2030, dis_name = "Meningitis A", file_name = "deaths_by_year_MeningitisA", figheight = 10, figwidth = 16) p3 <- figure_maker_burden_per_mod_dis(d_pop, burden_t = "deaths", scenario_pal, year_end = 2030, dis_name = "Measles", file_name = "deaths_by_year_Measles", figheight = 10, figwidth = 16) leg <- get_legend(p3) p4 <- cowplot::plot_grid(NULL, p3 + theme(legend.position = "none"), p2 + theme(legend.position = "none"), p1 + theme(legend.position = "none"), leg, ncol = 1, rel_heights = c(0.1,1,1,1,0.1), labels= c("","Measles", "Meningitis A", "Yellow Fever"), hjust = -0.1, vjust = 0.1) ggsave(plot = p4, filename = "deaths_by_year_all.png", height = 12, width = 16) #--------------------------------------------------------------------------------------- #DALYs per year up to 2030 per disease p1 <- figure_maker_burden_per_mod_dis(d_pop, burden_t = "dalys", scenario_pal, year_end = 2030, dis_name = "Yellow fever", file_name = "dalys_by_year_YF", figheight = 10, figwidth = 16) p2 <- figure_maker_burden_per_mod_dis(d_pop, burden_t = "dalys", scenario_pal, year_end = 2030, dis_name = "Meningitis A", file_name = "dalys_by_year_MeningitisA", figheight = 10, figwidth = 16) p3 <- figure_maker_burden_per_mod_dis(d_pop, burden_t = "dalys", scenario_pal, year_end = 2030, dis_name = "Measles", file_name = "dalys_by_year_Measles", figheight = 10, figwidth = 16) leg <- get_legend(p3) p4 <- cowplot::plot_grid(NULL, p3 + theme(legend.position = "none"), p2 + theme(legend.position = "none"), p1 + theme(legend.position = "none"), leg, ncol = 1, rel_heights = c(0.1,1,1,1,0.1), labels= c("","Measles", "Meningitis A", "Yellow Fever"), hjust = -0.1, vjust = 0.1) ggsave(plot = p4, filename = "dalys_by_year_all.png", height = 12, width = 16) #--------------------------------------------------------------------------------------- #Deaths under 5 per year up to 2030 per disease p1 <- figure_maker_burden_per_mod_dis(d_pop, burden_t = "deaths_under5", scenario_pal, year_end = 2030, dis_name = "Yellow fever", file_name = "deaths_by_year_YF_under5", figheight = 10, figwidth = 16) p2 <- figure_maker_burden_per_mod_dis(d_pop, burden_t = "deaths", scenario_pal, year_end = 2030, dis_name = "Meningitis A", file_name = "deaths_by_year_MeningitisA_under5", figheight = 10, figwidth = 16) p3 <- figure_maker_burden_per_mod_dis(d_pop, burden_t = "deaths_under5", scenario_pal, year_end = 2030, dis_name = "Measles", file_name = "deaths_by_year_Measles_under5", figheight = 10, figwidth = 16) leg <- get_legend(p3) p4 <- cowplot::plot_grid(NULL, p3 + theme(legend.position = "none"), p2 + theme(legend.position = "none"), p1 + theme(legend.position = "none"), leg, ncol = 1, rel_heights = c(0.1,1,1,1,0.1), labels= c("","Measles", "Meningitis A", "Yellow Fever"), hjust = -0.1, vjust = 0.1) ggsave(plot = p4, filename = "deaths_by_year_all_under5.png", height = 12, width = 16) #--------------------------------------------------------------------------------------- #Deaths per 100,000 per year up to 2030 per disease p1 <- figure_maker_burden_per_mod_dis_pop(d_pop, burden_t = "deaths", scenario_pal, year_end = 2030, dis_name = "Yellow fever", file_name = "deaths_by_year_pop_YF", figheight = 10, figwidth = 12) p2 <- figure_maker_burden_per_mod_dis_pop(d_pop, burden_t = "deaths", scenario_pal, year_end = 2030, dis_name = "Meningitis A", file_name = "deaths_by_year_pop_MeningitisA", figheight = 10, figwidth = 12) p3 <- figure_maker_burden_per_mod_dis_pop(d_pop, burden_t = "deaths", scenario_pal, year_end = 2030, dis_name = "Measles", file_name = "deaths_by_year_pop_Measles", figheight = 10, figwidth = 12) leg <- get_legend(p3) p4 <- cowplot::plot_grid(NULL, p3 + theme(legend.position = "none"), p2 + theme(legend.position = "none"), p1 + theme(legend.position = "none"), leg, ncol = 1, rel_heights = c(0.1,1,1,1,0.1), labels= c("","Measles", "Meningitis A", "Yellow Fever"), hjust = -0.1, vjust = 0.1) ggsave(plot = p4, filename = "deaths_by_year_pop_all.png", height = 12, width = 16) #--------------------------------------------------------------------------------------- #DALYS per 100,000 per year up to 2030 per disease p1 <- figure_maker_burden_per_mod_dis_pop(d_pop, burden_t = "dalys", scenario_pal, year_end = 2030, dis_name = "Yellow fever", file_name = "dalys_by_year_pop_YF", figheight = 10, figwidth = 12) p2 <- figure_maker_burden_per_mod_dis_pop(d_pop, burden_t = "dalys", scenario_pal, year_end = 2030, dis_name = "Meningitis A", file_name = "dalys_by_year_pop_MeningitisA", figheight = 10, figwidth = 12) p3 <- figure_maker_burden_per_mod_dis_pop(d_pop, burden_t = "dalys", scenario_pal, year_end = 2030, dis_name = "Measles", file_name = "dalys_by_year_pop_Measles", figheight = 10, figwidth = 12) leg <- get_legend(p3) p4 <- cowplot::plot_grid(NULL, p3 + theme(legend.position = "none"), p2 + theme(legend.position = "none"), p1 + theme(legend.position = "none"), leg, ncol = 1, rel_heights = c(0.1,1,1,1,0.1), labels= c("","Measles", "Meningitis A", "Yellow Fever"), hjust = -0.1, vjust = 0.1) ggsave(plot = p4, filename = "dalys_by_year_pop_all.png", height = 12, width = 16) #--------------------------------------------------------------------------------------- # Excess deaths per year up to 2030 as a percentage change from baseline. p1 <- figure_maker_excess_timeline_per_disease_mod(d_pop, burden_t = "deaths", scenario_pal, year_end = 2030, dis_name = "Yellow fever", file_name = "excess_deaths_by_year_YF", figheight = 10, figwidth = 12) p2 <- figure_maker_excess_timeline_per_disease_mod(d_pop, burden_t = "deaths", scenario_pal, year_end = 2030, dis_name = "Meningitis A", file_name = "excess_deaths_by_year_MeningitisA", figheight = 10, figwidth = 12) p3 <- figure_maker_excess_timeline_per_disease_mod(d_pop, burden_t = "deaths", scenario_pal, year_end = 2030, dis_name = "Measles", file_name = "excess_deaths_by_year_Measles", figheight = 10, figwidth = 12) leg <- get_legend(p3) p4 <- cowplot::plot_grid(NULL, p3 + theme(legend.position = "none"), p2 + theme(legend.position = "none"), p1 + theme(legend.position = "none"), leg, ncol = 1, rel_heights = c(0.1,1,1,1,0.1), labels= c("","Measles", "Meningitis A", "Yellow Fever"), hjust = -0.1, vjust = 0.1) ggsave(plot = p4, filename = "excess_deaths_by_year_all.png", height = 12, width = 16) #--------------------------------------------------------------------------------------- #Deaths per 100,000 per year up to 2030 per disease p1 <- figure_maker_burden_per_mod_dis_pop(d_pop, burden_t = "deaths", scenario_pal, year_end = 2030, dis_name = "Yellow fever", file_name = "deaths_by_year_pop_YF", figheight = 10, figwidth = 12) p2 <- figure_maker_burden_per_mod_dis_pop(d_pop, burden_t = "deaths", scenario_pal, year_end = 2030, dis_name = "Meningitis A", file_name = "deaths_by_year_pop_MeningitisA", figheight = 10, figwidth = 12) p3 <- figure_maker_burden_per_mod_dis_pop(d_pop, burden_t = "deaths", scenario_pal, year_end = 2030, dis_name = "Measles", file_name = "deaths_by_year_pop_Measles", figheight = 10, figwidth = 12) leg <- get_legend(p3) p4 <- cowplot::plot_grid(NULL, p3 + theme(legend.position = "none"), p2 + theme(legend.position = "none"), p1 + theme(legend.position = "none"), leg, ncol = 1, rel_heights = c(0.1,1,1,1,0.1), labels= c("","Measles", "Meningitis A", "Yellow Fever"), hjust = -0.1, vjust = 0.1) ggsave(plot = p4, filename = "deaths_by_year_pop_all.png", height = 12, width = 16) #--------------------------------------------------------------------------------------- # Excess deaths per year up to 2030 as a percentage change from baseline. p1 <- figure_maker_excess_timeline_per_disease_mod(d_pop, burden_t = "deaths_under5", scenario_pal, year_end = 2030, dis_name = "Yellow fever", file_name = "excess_deaths_by_year_YF_under5", figheight = 10, figwidth = 12) p2 <- figure_maker_excess_timeline_per_disease_mod(d_pop, burden_t = "deaths_under5", scenario_pal, year_end = 2030, dis_name = "Meningitis A", file_name = "excess_deaths_by_year_MeningitisA_under5", figheight = 10, figwidth = 12) p3 <- figure_maker_excess_timeline_per_disease_mod(d_pop, burden_t = "deaths_under5", scenario_pal, year_end = 2030, dis_name = "Measles", file_name = "excess_deaths_by_year_Measles_under5", figheight = 10, figwidth = 12) leg <- get_legend(p3) p4 <- cowplot::plot_grid(NULL, p3 + theme(legend.position = "none"), p2 + theme(legend.position = "none"), p1 + theme(legend.position = "none"), leg, ncol = 1, rel_heights = c(0.1,1,1,1,0.1), labels= c("","Measles", "Meningitis A", "Yellow Fever"), hjust = -0.1, vjust = 0.1) ggsave(plot = p4, filename = "excess_deaths_by_year_all_under5.png", height = 12, width = 16) #--------------------------------------------------------------------------------------- # survival change per year up to 2030 as a percentage change from baseline. p1 <- figure_maker_survival_timeline_per_disease_mod(d_pop, burden_t = "deaths", scenario_pal, year_end = 2030, dis_name = "Yellow fever", file_name = "survival_change_by_year_YF", figheight = 10, figwidth = 12) p2 <- figure_maker_survival_timeline_per_disease_mod(d_pop, burden_t = "deaths", scenario_pal, year_end = 2030, dis_name = "Meningitis A", file_name = "survival_change_by_year_MeningitisA", figheight = 10, figwidth = 12) p3 <- figure_maker_survival_timeline_per_disease_mod(d_pop, burden_t = "deaths", scenario_pal, year_end = 2030, dis_name = "Measles", file_name = "survival_change_by_year_Measles", figheight = 10, figwidth = 12) leg <- get_legend(p3) p4 <- cowplot::plot_grid(NULL, p3 + theme(legend.position = "none"), p2 + theme(legend.position = "none"), p1 + theme(legend.position = "none"), leg, ncol = 1, rel_heights = c(0.1,1,1,1,0.1), labels= c("","Measles", "Meningitis A", "Yellow Fever"), hjust = -0.1, vjust = 0.1) ggsave(plot = p4, filename = "survival_change_by_year_all.png", height = 12, width = 16) #--------------------------------------------------------------------------------------- #Excess deaths per 100,000 population per year from 2020 to 2030 for the model averaged predictions for Measles, # Meningitis A and Yellow Fever by country.The error bars range from max to min group preditions. figure_maker_excess_country_pop(d_pop, "deaths", scenario_pal, file_name = "excess_deaths_per_pop_by_country", figheight = 10, figwidth = 16) figure_maker_excess_country_pop(d_pop, "dalys", scenario_pal, file_name = "excess_dalys_per_pop_by_country", figheight = 10, figwidth = 16) #--------------------------------------------------------------------------------------- #Excess deaths from 2020 to 2030 for the model predictions for Measles, Meningitis A and Yellow Fever # per country p1 <- figure_maker_excess_per_country_dis_mod(d_pop, "deaths", dis_name = "Yellow fever", scenario_pal, file_name = "excess_deaths_per_country_mod_YF", figwidth = 16, figheight = 10) p2 <- figure_maker_excess_per_country_dis_mod(d_pop, "deaths", dis_name = "Meningitis A", scenario_pal, file_name = "excess_deaths_per_country_mod_MenA", figwidth = 16, figheight = 10) p3 <- figure_maker_excess_per_country_dis_mod(d_pop, "deaths", dis_name = "Measles", scenario_pal, file_name = "excess_deaths_per_country_mod_Measles", figwidth = 16, figheight = 10) leg <- get_legend(p3) shape_leg <- get_legend(figure_maker_excess_per_country_dis_mod(d_pop, "deaths", dis_name = "Measles", scenario_pal, file_name = "excess_deaths_per_country_mod_Measles", figwidth = 16, figheight = 10, shape_leg = TRUE)) p4 <- cowplot::plot_grid(NULL, p3+theme(legend.position = "none"), p2+theme(legend.position = "none"), p1+theme(legend.position = "none"), leg, shape_leg, ncol = 1, rel_heights = c(0.1,1,1,1, 0.1, 0.1), labels= c("","Measles", "Meningitis A", "Yellow Fever"), hjust = -0.1, vjust = 0.1) ggsave(plot = p4, filename = "excess_deaths_per_country_mod.png", height = 12, width = 16) #--------------------------------------------------------------------------------------- #Excess deaths from 2020 to 2030 for the model predictions for Measles, Meningitis A and Yellow Fever # per country p1 <- figure_maker_excess_per_country_dis_mod(d_pop, "dalys", dis_name = "Yellow fever", scenario_pal, file_name = "excess_dalys_per_country_mod_YF", figwidth = 16, figheight = 10) p2 <- figure_maker_excess_per_country_dis_mod(d_pop, "dalys", dis_name = "Meningitis A", scenario_pal, file_name = "excess_dalys_per_country_mod_MenA", figwidth = 16, figheight = 10) p3 <- figure_maker_excess_per_country_dis_mod(d_pop, "dalys", dis_name = "Measles", scenario_pal, file_name = "excess_dalys_per_country_mod_Measles", figwidth = 16, figheight = 10) leg <- get_legend(p3) shape_leg <- get_legend(figure_maker_excess_per_country_dis_mod(d_pop, "dalys", dis_name = "Measles", scenario_pal, file_name = "excess_dalys_per_country_mod_Measles", figwidth = 16, figheight = 10, shape_leg = TRUE)) p4 <- cowplot::plot_grid(NULL, p3+theme(legend.position = "none"), p2+theme(legend.position = "none"), p1+theme(legend.position = "none"), leg, shape_leg, ncol = 1, rel_heights = c(0.1,1,1,1, 0.1, 0.1), labels= c("","Measles", "Meningitis A", "Yellow Fever"), hjust = -0.1, vjust = 0.1) ggsave(plot = p4, filename = "excess_dalys_per_country_mod.png", height = 12, width = 16) #--------------------------------------------------------------------------------------- #Normalised excess deaths from 2020 to 2030 for the model predictions for Measles, Meningitis A and Yellow Fever # per country p1 <- figure_maker_norm_per_country_dis(d_pop, "deaths", dis_name = "Yellow fever", scenario_pal, file_name = "norm_deaths_per_country_YF", figwidth = 16, figheight = 10) p2 <- figure_maker_norm_per_country_dis(d_pop, "deaths", dis_name = "Meningitis A", scenario_pal, file_name = "norm_deaths_per_country_MenA", figwidth = 16, figheight = 10) p3 <- figure_maker_norm_per_country_dis(d_pop, "deaths", dis_name = "Measles", scenario_pal, file_name = "norm_deaths_per_country_Measles", figwidth = 16, figheight = 10) leg <- get_legend(p3) shape_leg <- get_legend(figure_maker_norm_per_country_dis(d_pop, "deaths", dis_name = "Measles", scenario_pal, file_name = "norm_deaths_per_country_Measles", figwidth = 16, figheight = 10, shape_leg = TRUE)) p4 <- cowplot::plot_grid(NULL, p3+theme(legend.position = "none"), p2+theme(legend.position = "none"), p1+theme(legend.position = "none"), leg, shape_leg, ncol = 1, rel_heights = c(0.1,1,1,1, 0.1, 0.1), labels= c("","Measles", "Meningitis A", "Yellow Fever"), hjust = -0.1, vjust = 0.1) ggsave(plot = p4, filename = "norm_deaths_per_country.png", height = 12, width = 16) #--------------------------------------------------------------------------------------- #Normalised excess dalys from 2020 to 2030 for the model predictions for Measles, Meningitis A and Yellow Fever # per country p1 <- figure_maker_norm_per_country_dis(d_pop, "dalys", dis_name = "Yellow fever", scenario_pal, file_name = "norm_dalys_per_country_YF", figwidth = 16, figheight = 10) p2 <- figure_maker_norm_per_country_dis(d_pop, "dalys", dis_name = "Meningitis A", scenario_pal, file_name = "norm_dalys_per_country_MenA", figwidth = 16, figheight = 10) p3 <- figure_maker_norm_per_country_dis(d_pop, "dalys", dis_name = "Measles", scenario_pal, file_name = "norm_dalys_per_country_Measles", figwidth = 16, figheight = 10) leg <- get_legend(p3) shape_leg <- get_legend(figure_maker_norm_per_country_dis(d_pop, "dalys", dis_name = "Measles", scenario_pal, file_name = "norm_dalys_per_country_Measles", figwidth = 16, figheight = 10, shape_leg = TRUE)) p4 <- cowplot::plot_grid(NULL, p3+theme(legend.position = "none"), p2+theme(legend.position = "none"), p1+theme(legend.position = "none"), leg, shape_leg, ncol = 1, rel_heights = c(0.1,1,1,1, 0.1, 0.1), labels= c("","Measles", "Meningitis A", "Yellow Fever"), hjust = -0.1, vjust = 0.1) ggsave(plot = p4, filename = "norm_dalys_per_country.png", height = 12, width = 16) #--------------------------------------------------------------------------------------- #Excess deaths in under5s figure_maker_under5(d_pop) dev.off() #--------------------------------------------------------------------------------------------------------------------- # SAVE TABLES # plain impact overall tmp <- table_maker_impact(d_pop, burden_t = "deaths", year_end= 2030, per_pop = FALSE) write.csv(tmp, "excess_deaths_2020_2030.csv", row.names = FALSE) tmp <- table_maker_impact(d_pop, burden_t = "deaths_under5", year_end= 2030, per_pop = FALSE) write.csv(tmp, "excess_deaths_under5_2020_2030.csv", row.names = FALSE) tmp <- table_maker_impact(d_pop, burden_t = "deaths", year_end= 2030, per_pop = TRUE) write.csv(tmp, "excess_deaths_per_pop_2020_2030.csv", row.names = FALSE) tmp <- table_maker_impact(d_pop, burden_t = "dalys", year_end= 2030, per_pop = FALSE) write.csv(tmp,"excess_dalys_2020_2030.csv", row.names = FALSE) tmp <- table_maker_impact(d_pop, burden_t = "dalys_under5", year_end= 2030, per_pop = FALSE) write.csv(tmp,"excess_dalys_under5_2020_2030.csv", row.names = FALSE) tmp <- table_maker_impact(d_pop, burden_t = "dalys", year_end= 2030, per_pop = TRUE) write.csv(tmp,"excess_dalys_per_pop_2020_2030.csv", row.names = FALSE) # per disease and country per pop lapply(unique(d_pop$disease), FUN = function(x){table_maker_impact_per_dis_country(d_pop, dis_name = x, year_end = 2030, "deaths", per_pop = TRUE) %>% write.csv(paste0("excess_deaths_per_pop_", x, "_country_2000_2030.csv"), row.names = FALSE)}) # per disease and country lapply(unique(d_pop$disease), FUN = function(x){table_maker_impact_per_dis_country(d_pop, dis_name = x, year_end = 2030, "deaths", per_pop = FALSE) %>% write.csv(paste0("excess_deaths_", x, "_country_2000_2030.csv"), row.names = FALSE)}) # per disease and year per pop lapply(unique(d_pop$disease), FUN = function(x){table_maker_impact_per_dis_year(d_pop, dis_name = x, year_end = 2030, "deaths", per_pop = TRUE) %>% write.csv(paste0("excess_deaths_per_pop_", x, "_year_2000_2030.csv"), row.names = FALSE)}) # per disease and year lapply(unique(d_pop$disease), FUN = function(x){table_maker_impact_per_dis_year(d_pop, dis_name = x, year_end = 2030, "deaths", per_pop = FALSE) %>% write.csv(paste0("excess_deaths_", x, "_year_2000_2030.csv"), row.names = FALSE)}) # percentage change from baseline tmp <- table_maker_percent_change(d_pop, burden_t = "deaths", year_end = 2030) write.csv(tmp, "percent_change_deaths_2020_2030.csv", row.names = FALSE) tmp <- table_maker_percent_change(d_pop, burden_t = "dalys", year_end = 2030) write.csv(tmp, "percent_change_dalys_2020_2030.csv", row.names = FALSE) #percentage change overall tmp <- table_maker_percent_change_all(d_pop, burden_t = "deaths", year_end = 2030) write.csv(tmp, "percent_change_deaths_all_2020_2030.csv", row.names = FALSE) tmp <- table_maker_percent_change_all(d_pop, burden_t = "dalys", year_end = 2030) write.csv(tmp, "percent_change_dalys_all_2020_2030.csv", row.names = FALSE)