https://github.com/vimc/vpd-covid-phase-I
Tip revision: ebff9a24b8b7c9a7c6c5c77f783f2435a57d1d2b authored by Katy on 02 June 2021, 11:23:34 UTC
Better labelling of plots
Better labelling of plots
Tip revision: ebff9a2
script.R
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)
