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plotting2.R
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plotting2.R
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# Load required packages and utility scripts
source("packages.R")
source("utils.R")
source("plot_functions.R")
source("parameters.R")
results_name <- "results_df"
most_recent_file <- file.info(list.files("results/", full.names = T)) %>%
as.data.frame() %>%
rownames_to_column()%>%
filter(str_detect(rowname,"_main.fst")) %>%
slice_max(mtime) %>%
pull(rowname)
assign(results_name,read.fst(most_recent_file))
select(-c(adherence_quar,adherence_iso)) %>%
crossing(adherence_iso=seq(0,1,by=0.1),adherence_quar=seq(0,1,by=0.1)) %>%
mutate(adhering_quar=rbinom(n=n(),size = 1,prob = adherence_quar),
adhering_iso=rbinom(n=n(),size = 1,prob = adherence_iso)) %>%
group_by(adherence_quar,adherence_iso,test_to_tracing) %>%
group_split()
plan(multisession, workers = 7)
adherence_results <- adherence_list %>%
future_map(.x=.,.f=calc_overlap,.progress = T)
a <- get(results_name) %>%
mutate(adherence_quar=as.factor(adherence_quar)) %>%
mutate(lft_delivery_time=as.factor(lft_delivery_time),
test_to_tracing=as.factor(test_to_tracing)) %>%
plotting_func(x=.,
x_var=n_tests,
group_var = lft_delivery_time,
col_vars = test_to_tracing)
a$plot+facet_nested(~test_to_tracing,labeller = labeller(lft_delivery_time=function(x)paste(x,"days postage delay"),test_to_tracing=function(x)paste(x,"days tracing delay")))+scale_color_viridis_d(option="magma",begin=0.2,end=0.8,name="LFT postage delay")+scale_y_continuous(limits=c(0.45,1),values = scales::percent)
save_plot(dpi = 400,
device = "png",
prefix = "delays",
base = "_plot",
width = 210,
height = 100)
#####################
#### RISK RATIOS ####
#####################
baseline <- get(results_name) %>%
as_tibble() %>%
filter(quar_dur == 10,
#test_to_tracing==1,
is.na(assay)) %>%
filter(!(is.infinite(inf_start) | is.infinite(inf_end))) %>%
select(ind_idx, max_overlap, inf_end, inf_start) %>%
group_by(ind_idx) %>%
summarise(all=sum(inf_end-inf_start),
baseline_prop=sum(max_overlap)/all) %>%
mutate(name="baseline")
#### vs high baseline ----
get(results_name) %>%
mutate(test_to_tracing=as.factor(test_to_tracing)) %>%
rr_func(x=.,
baseline = baseline,
x_var = n_tests,
col_vars = test_to_tracing,
group_var = assay,
log = T)
fig3 <- a$plot+(b$plot+guides(colour=FALSE)+labs(y=""))
p_ranges_y <- c(10^(ggplot_build(fig3[[1]])$layout$panel_scales_y[[1]]$range$range),
10^(ggplot_build(fig3[[2]])$layout$panel_scales_y[[1]]$range$range))
fig3+plot_annotation(tag_levels = "A")+plot_layout(widths = c(3,2),guides = "collect")&theme(legend.position = "bottom")&ylim(min(p_ranges_y), max(p_ranges_y))
save_plot(dpi = 400,
device = "png",
prefix = "baseline_high",
base = "RR_plot",
width = 210,
height = 120)
baseline <- get(results_name) %>%
mutate(n_missed=ifelse(is.na(n_missed),0,n_missed)) %>%
as_tibble() %>%
filter(n_missed==0,lft_delivery_time==0) %>%
filter(!(is.infinite(inf_start) | is.infinite(inf_end))) %>%
group_by(ind_idx,test_to_tracing,lft_delivery_time,n_tests) %>%
summarise(all=sum(inf_end-inf_start),
baseline_prop=sum(max_overlap)/all) %>%
mutate(name="baseline") %>%
ungroup()
missed_tests <- get(results_name) %>%
filter(lft_delivery_time==0) %>%
mutate(n_missed=ifelse(is.na(n_missed),0,n_missed)) %>%
mutate(n_missed=as.factor(n_missed)) %>%
rr_func(x=.,
baseline = baseline,
x_var = n_tests,
col_vars = test_to_tracing,
group_var = n_missed,
log = T)
missed_tests$plot+labs(y="Transmission potential averted\nvs. 0 tests missed")+scale_color_viridis_d(name="Number of tests missed",begin=0.1,end=0.9)
save_plot(dpi = 400,
device = "png",
prefix = "missed_tests",
base = "RR_plot",
width = 210,
height = 120)
baseline <- get(results_name) %>%
mutate(n_missed=ifelse(is.na(n_missed),0,n_missed)) %>%
as_tibble() %>%
filter(is.na(n_tests),quar_dur==10) %>%
filter(!(is.infinite(inf_start) | is.infinite(inf_end))) %>%
group_by(ind_idx) %>%
summarise(all=sum(inf_end-inf_start),
baseline_prop=sum(max_overlap)/all) %>%
mutate(name="baseline") %>%
ungroup()
missed_tests <- get(results_name) %>%
filter(lft_delivery_time==0) %>%
mutate(n_missed=ifelse(is.na(n_missed),0,n_missed)) %>%
mutate(n_missed=as.factor(n_missed)) %>%
mutate(n_missed=fct_relevel(n_missed,"0")) %>%
rr_func(x=.,
baseline = baseline,
x_var = n_tests,
col_vars = test_to_tracing,
row_vars = lft_delivery_time,
group_var = n_missed,
log = F)
missed_tests$plot+labs(y="Transmission potential averted\nvs. 10 days quarantine")+scale_color_viridis_d(name="Number of tests missed",begin=0.1,end=0.9)
save_plot(dpi = 400,
device = "png",
prefix = "missed_tests",
base = "RR_plot",
width = 210,
height = 120)
get(results_name) %>%
mutate(n_missed=ifelse(is.na(n_missed),0,n_missed)) %>%
mutate(n_missed=as.factor(n_missed)) %>%
mutate(n_tests=as.character(n_tests)) %>%
mutate(n_tests=as.factor(if_else(is.na(n_tests),"10 days quarantine",n_tests)),
n_tests=fct_relevel(n_tests,"3","5","7","10","10 days quarantine")) %>%
filter(quar_dur==10|is.na(quar_dur),lft_delivery_time==0|is.na(lft_delivery_time)) %>%
mutate(n_missed=fct_relevel(n_missed,"0")) %>%
plotting_func(.,x_var=n_tests,group_var = n_missed,col_vars = test_to_tracing)