![]() # Table 1B is ACF and post-ACF time (data collected prospectively) tb_2011_2018b % mutate (ageg = as.character ( ageg ) ) # this is to get rid of "empty" 0-14 factor labels ( tb_2011_2018b ) <- c (ageg = 'Age (years)', sex = "Sex", diagnosed = "TB type", Facility = "Type of Facility", hiv = "HIV Status", art = "ART status" ) Stab1B <- tableby ( acf ~ sex + diagnosed + facility + hiv + art + ageg, data = tb_2011_2018b, control = mycontrols ) summary ( Stab1B, title = "People with TB 2011.Q2-2018", text = T ) Note that shape files are not included in this repo as they have been extremely “fussy” and I have struggled to get them working properly. non-ACF area # These are the "wide" datasets with TB notifications grouped by quarter and with CNR cnrs_smp_clinic <- readRDS ( here ( "data", "cnrs_smp_clinic.rds" ) ) cnrs_all <- readRDS ( here ( "data", "cnrs_all.rds" ) ) # all form cnrs_d <- readRDS ( here ( "data", "cnrs_d.rds" ) ) # cnrs by method of diagnosis Figure 1: Map includes HIV status, age and sex) tb_2009_2010 <- readRDS (file = here ( "data", "tb_2009_2010.rds" ) ) pop <- readRDS (file = here ( "data", "pop.rds" ) ) # Blantyre City adult population by quarter-year and ACF vs. tb_2011_2018 <- readRDS (file = here ( "data", "tb_2010_2018.rds" ) ) # Each row represents one adult in Blantyre City diagnosed with TB, but with more information per person than the 2009_2010 file (i.e. ![]() Library ( here ) library ( tidyverse ) library ( janitor ) library ( lubridate ) library ( arsenal ) # for tableby acf_start_date <- dmy ( "01 April 2011" ) acf_end_date <- dmy ( "" ) # These are the "long" datasets (one row per person with TB) tb_2009_2018 <- readRDS (file = here ( "data", "tb_2009_2018.rds" ) ) # Each row represents one adult in Blantyre City diagnosed with TB, information on method of TB diagnosis, quarter-year of diagnosis and whether they lived in an ACF or non-ACF area.
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