seabbs

nots %>% ggplot(aes(x = year, y = prop_decrease, col = prop_decrease)) +
  geom_hline(yintercept = 0, linetype = 1, alpha = 0.5) +
  geom_hline(yintercept = 0.09, linetype = 2, alpha = 0.5) +
  geom_point(size = 2) +
  scale_y_percent() +
  geom_smooth(alpha = 0.2) +
  scale_colour_viridis_c() +
  theme_minimal()

seabbs

  age_groups <- tibble(age_groups = c("15-19 years",
                                      "20-24 years", "25-29 years"), key = 1)

  nots_by_age <- nots %>%
    mutate(key = 1) %>%
    left_join(age_groups, by = "key") %>%
    select(-key, -prev_year_m, -measure, -prop_decrease_sample) %>%
    spread(key = "age_groups", value = "prop_decrease_sample")

seabbs

cluster <- create_cluster(CORES) %>% 
  cluster_library(c("PACKAGE_NAME")) %>% 
  cluster_copy(CUSTOM_VARIABLE)
  
  then use parition and collect

seabbs

install.packages("devtools")
devtools::install_github("seabbs/getTBinR")
getTBinR::plot_tb_burden(metric = "e_inc_100k", countries = "United Kingdom", facet = "country", interactive = TRUE)

seabbs

      ## Likelihood test p values for categorical variables
      model_uni <- outcomes %>% map(function(.) model_outcome_uni(., variable = 'ageatvac', LRT = TRUE))
      model_adj <- outcomes %>% map(function(.) model_outcome_adj(., variable = 'ageatvac', LRT = TRUE))
   
      ageatvac_lrt_uni <- map2(model_ageatvac_uni, model_uni, function(x, y) pool.compare(x, y, data = imp_df, method = "likelihood")) %>% 
    map(function(x) ifelse(x < 0.001, "0.001", pretty_round(x, digits = 3))) 
      ageatvac_lrt_adj <- map2(model_ageatvac_adj, model_adj, function(x, y) pool.compare(x, y, data = imp_df, method = "likelihood")$pvalue) %>% 
        map(function(x) ifelse(x < 0.001, "0.001", pretty_round(x, digits = 3)))
      
      

seabbs

# install.packages("getTBinR")
library(getTBinR)
# install.packages("tidyverse")
library(tidyverse)

# Get the data
tb_burden <- get_tb_burden(download_data = TRUE, save = TRUE)
dict <- get_data_dict(download_data = TRUE, save = TRUE)

# Look up variables with mortality in their definition

seabbs

# install.packages("getTBinR")
library(getTBinR)
# install.packages("tidyverse")
library(tidyverse)

# Get the data
tb_burden <- get_tb_burden(download_data = TRUE, save = TRUE)
dict <- get_data_dict(download_data = TRUE, save = TRUE)

# Look up variables with mortality in their definition

seabbs

# install.packages("getTBinR")
library(getTBinR)
# install.packages("tidyverse")
library(tidyverse)
# install.packages("gridExtra")
library(gridExtra)

# Get the data
tb_burden <- get_tb_burden(download_data = TRUE, save = TRUE)
dict <- get_data_dict(download_data = TRUE, save = TRUE)

seabbs

FLOAT(SCRIPT_REAL("qbinom(0.001, .arg2, .arg1 / .arg2) / .arg2", 
WINDOW_SUM(SUM([prob_Accepted]), -[No. of days for rolling average] + 1, 0), 
WINDOW_SUM(COUNT([prob_Accepted]), -[No. of days for rolling average] + 1, 0)))

seabbs

mapStates = map("state", fill = TRUE, plot = FALSE)
leaflet(data = mapStates) %>% addTiles() %>%
  addPolygons(fillColor = topo.colors(10, alpha = NULL), stroke = FALSE)