Update surveys gt

DESCRIPTION

@@ -54,7 +54,7 @@ Imports:
     ggalluvial,
     ggplot2 (>= 3.0.0),
     ggspatial,
-    gtsummary,
+    gtsummary (>= 1.7.0),
     glue,
     here,
     janitor,

inst/rmarkdown/templates/ajs_outbreak/skeleton/skeleton.Rmd

@@ -50,9 +50,6 @@ knitr::opts_chunk$set(echo = FALSE,       # hide all code chunks in output
 
 
 
-## set default NA to - in output, define figure width/height
-options(knitr.kable.NA = "-")
-
 # Ensures the package "pacman" is installed
 if (!require("pacman")) {
      install.packages("pacman") }

inst/rmarkdown/templates/cholera_outbreak/skeleton/skeleton.Rmd

@@ -50,9 +50,6 @@ knitr::opts_chunk$set(echo = FALSE,       # hide all code chunks in output
 
 
 
-## set default NA to - in output, define figure width/height
-options(knitr.kable.NA = "-")
-
 # Ensures the package "pacman" is installed
 if (!require("pacman")) {
      install.packages("pacman") }

inst/rmarkdown/templates/measles_outbreak/skeleton/skeleton.Rmd

@@ -50,9 +50,6 @@ knitr::opts_chunk$set(echo = FALSE,       # hide all code chunks in output
 
 
 
-## set default NA to - in output, define figure width/height
-options(knitr.kable.NA = "-")
-
 # Ensures the package "pacman" is installed
 if (!require("pacman")) {
      install.packages("pacman") }

inst/rmarkdown/templates/meningitis_outbreak/skeleton/skeleton.Rmd

@@ -50,9 +50,6 @@ knitr::opts_chunk$set(echo = FALSE,       # hide all code chunks in output
 
 
 
-## set default NA to - in output, define figure width/height
-options(knitr.kable.NA = "-")
-
 # Ensures the package "pacman" is installed
 if (!require("pacman")) {
      install.packages("pacman") }

inst/rmarkdown/templates/mortality/skeleton/skeleton.Rmd

@@ -43,8 +43,6 @@ knitr::opts_chunk$set(echo = FALSE, error = TRUE, warning = FALSE, message = FAL
                       fig.width = 6*1.25, fig.height = 6)
 
 
-## set default NA to - in output, define figure width/height
-options(knitr.kable.NA = "-")
 
 # Ensures the package "pacman" is installed
 if (!require("pacman")) {
@@ -1649,13 +1647,11 @@ Note the possibility of negative confidence intervals if there are low counts
 ## weighted counts and proportion of dead
 death_props <- survey_design %>% 
   select(died) %>% 
-  tbl_svysummary() %>% 
+  tbl_svysummary(type = died ~ "categorical") %>% 
   ## add the weighted total
   add_n() %>%   
   ## add in confidence intervals
-
-  ## add in deff 
-
+  add_ci() %>% 
   ## modify the column headers
   modify_header(
     list(
@@ -1678,10 +1674,9 @@ CMR <- survey_design %>%
 
 
 During the recall period the weighted number and proportion of deaths in the 
-population was `r inline_text(death_props, variable = "died",  column = "stat_0")`,
+population was `r inline_text(death_props, variable = "died",  column = "stat_0", level = "TRUE")`,
 with a weighted confidence interval of
-`r #inline_text(death_props, variable = "died",  column = "add_stat_1")`, and a 
-design effect of `r #inline_text(death_props, variable = "died",  column = "add_stat_2")`.
+`r inline_text(death_props, variable = "died",  column = "ci_stat_0", level = "TRUE")`.
 This is a crude mortality rate of `r CMR` deaths per 10000 person-days. 
 
 
@@ -1709,9 +1704,7 @@ death_props_strat <- survey_design %>%
   ## add the weighted total
   add_n() %>%   
   ## add in confidence intervals
-
-  ## add in deff 
-
+  add_ci() %>% 
   ## modify the column headers
   modify_header(
     list(
@@ -1737,16 +1730,14 @@ CMR_strat <- survey_design %>%
 ```
 
 
-In District A the weighted number of deaths was 
-`r inline_text(death_props_strat, variable = "died",  column = "stat_1")`, 
-which gives a weighted proportion of 
-`r #death_props_strat %>% pull("district_a ci")`, and a design effect of
-`r #death_props_strat %>% pull("district_a deff") %>% round(digits = 3)`.  
-In comparison, the weighted number of deaths in District B was
-`r inline_text(death_props_strat, variable = "died",  column = "stat_2")`, 
-the weighted proportion was 
-`r #death_props_strat %>% pull("district_b ci")` and the design effect was 
-`r #death_props_strat %>% pull("district_b deff") %>% round(digits = 3)`.  
+In District A the weighted number and proportion of deaths was
+`r inline_text(death_props_strat, variable = "died", column = "stat_1")`, 
+with a weighted confidence interval of
+`r inline_text(death_props_strat, variable = "died", column = "ci_stat_1")`.  
+In comparison, the weighted number and proportion of deaths in District B was
+`r inline_text(death_props_strat, variable = "died", column = "stat_2")`, 
+the weighted confidence interval was 
+`r inline_text(death_props_strat, variable = "died", column = "ci_stat_2")`.  
 The crude mortality rate in District A was 
 `r CMR_strat %>% filter(health_district == "district_a") %>% pull(Mortality)`
 deaths per 10000 person-days and in District B was 
@@ -1861,8 +1852,9 @@ cause_of_death_prop <- survey_design %>%
   # proportions only among those who died
   filter(died) %>% 
   select(cause) %>% 
-  tbl_svysummary()
+  tbl_svysummary() %>% 
   ## add ci 
+  add_ci()
 
 ## weighted cause-specific mortality ratios
 cause_of_death_mort <- survey_design %>% 
@@ -1893,7 +1885,7 @@ cause_of_death_prop %>%
   ## modify the column headers
   modify_header(
     list(
-      stat_0 ~ "**Deaths \n N={round(N, digits = 0)}**", 
+      stat_0 ~ "Deaths \n N={round(N, digits = 0)}", 
       mortality ~ "**Mortality per 10,000 person/days (95% CI)**" 
     )
     ) %>% 
@@ -1929,7 +1921,7 @@ survey_design %>%
   select(cause, health_district) %>%
   tbl_svysummary(by = health_district) %>%   
   ## add in confidence intervals
-
+  add_ci() %>% 
   ## modify the column headers
   modify_header(
     list(
@@ -2033,6 +2025,10 @@ by age group.
 Note that low counts or short observation times may lead to a confidence interval 
 that crosses zero (i.e. negative) for mortality ratios. These should be interpreted
 as if no deaths or recoded to zero (impossible to have negative deaths). 
+Note that confidence intervals would make this table crowded; in addition 
+groups with zero counts result in an error, from the original survey package. 
+If you would like to have confidence intervals we suggest only showing a subset
+of the levels or groups in your data. 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->
 
 ```{r weighted_death_cause_by_age, warning = FALSE}
@@ -2042,7 +2038,7 @@ survey_design %>%
   select(cause, age_group) %>%
   tbl_svysummary(by = age_group) %>%   
   ## add in confidence intervals
-
+  # add_ci() %>%
   ## modify the column headers
   modify_spanning_header(
     all_stat_cols() ~ "**Age group (years)**"
@@ -2079,7 +2075,7 @@ survey_design %>%
   select(cause, sex) %>%
   tbl_svysummary(by = sex) %>% 
   ## add in confidence intervals
-
+  add_ci() %>% 
   ## make variable names bold 
   bold_labels() %>% 
   # change to flextable format
@@ -2214,7 +2210,7 @@ ggplot(data = flow_table,
        aes(axis1 = startcause, axis2 = endcause,
            y = n)) +
   ## define x axis limits and labels
-  scale_x_discrete(limits = c("Start\ncause", "End\ncause")) +
+  scale_x_discrete(limits = c("Start\ncause", "End\ncause"), expand = c(0,0)) +
   ## colour lines by sex 
   geom_alluvium(aes(fill = sex)) +
   ## fill in the label boxes grey
@@ -2313,7 +2309,7 @@ survey_design %>%
   select(cause_illness) %>%
   tbl_svysummary() %>% 
   ## add in confidence intervals
-
+  add_ci() %>% 
   ## make variable names bold 
   bold_labels() %>% 
   # change to flextable format
@@ -2344,7 +2340,7 @@ survey_design %>%
   select(cause_illness, age_group) %>%
   tbl_svysummary(by = age_group) %>%   
   ## add in confidence intervals
-
+  add_ci() %>% 
   ## make variable names bold 
   bold_labels() %>% 
   # change to flextable format
@@ -2375,7 +2371,7 @@ survey_design %>%
   select(cause_illness, sex) %>%
   tbl_svysummary(by = sex) %>%   
   ## add in confidence intervals
-
+  add_ci() %>% 
   ## make variable names bold 
   bold_labels() %>% 
   # change to flextable format
@@ -2408,7 +2404,7 @@ survey_design %>%
   select(no_care_illness) %>%
   tbl_svysummary() %>% 
   ## add in confidence intervals
-
+  add_ci() %>% 
   ## make variable names bold 
   bold_labels() %>% 
   # change to flextable format
@@ -2440,7 +2436,7 @@ survey_design %>%
   select(violent_episodes_number, violence_nature, uniform, place_violence) %>%
   tbl_svysummary() %>% 
   ## add in confidence intervals
-
+  add_ci() %>% 
   ## make variable names bold 
   bold_labels() %>% 
   # change to flextable format
@@ -2473,7 +2469,7 @@ survey_design %>%
   select(fever_now, care_fever) %>%
   tbl_svysummary() %>% 
   ## add in confidence intervals
-
+  add_ci() %>% 
   ## make variable names bold 
   bold_labels() %>% 
   # change to flextable format
@@ -2504,7 +2500,7 @@ survey_design %>%
   select(reason_no_care) %>%
   tbl_svysummary() %>% 
   ## add in confidence intervals
-
+  add_ci() %>% 
   ## make variable names bold 
   bold_labels() %>% 
   # change to flextable format

inst/rmarkdown/templates/nutrition/skeleton/skeleton.Rmd

@@ -79,9 +79,6 @@ For help installing packages, please visit https://r4epis.netlify.com/welcome
 knitr::opts_chunk$set(echo = FALSE, error = TRUE, fig.width = 6*1.25, fig.height = 6)
 
 
-## set default NA to - in output, define figure width/height
-options(knitr.kable.NA = "-")
-
 # Ensures the package "pacman" is installed
 if (!require("pacman")) {
      install.packages("pacman") }
@@ -908,9 +905,9 @@ study_data_cleaned <- study_data_cleaned %>%
   mutate(
     across(
       .cols = c(stunting_haz, 
-                         moderate_stunting_haz, 
-                         severe_stunting_haz), # select the indicator variables
-      .funs = ~if_else(!is.na(flen) & flen == 1, NA, .))) # NA if flagged, else leave as is
+                moderate_stunting_haz, 
+                severe_stunting_haz), # select the indicator variables
+      .fns = ~if_else(!is.na(flen) & flen == 1, NA, .))) # NA if flagged, else leave as is
 
 
 
@@ -1996,7 +1993,9 @@ overall <- survey_design %>%
   select(all_of(MUAC)) %>% 
   ## only show the row with TRUE
   tbl_svysummary(missing = "no", 
-                 value = everything() ~ TRUE)
+                 value = everything() ~ TRUE) %>% 
+  ## add confidence intervals
+  add_ci()
 
 age_strat <- survey_design %>% 
   ## tabulate multiple variables with same values
@@ -2013,7 +2012,8 @@ tbl_merge(list(overall, age_strat)) %>%
     list(
       ## rename the spanning header
       ## you can see what the columns are called by putting in an object and inspecting table_body
-      stat_0_1    ~ "**Overall**", 
+      c(stat_0_1, 
+        ci_stat_0_1) ~ "**Overall**",  
       c(stat_1_2,
         stat_2_2, 
         stat_3_2,
@@ -2052,7 +2052,9 @@ overall <- survey_design %>%
   select(all_of(MUAC)) %>% 
   ## only show the row with TRUE
   tbl_svysummary(missing = "no", 
-                 value = everything() ~ TRUE)
+                 value = everything() ~ TRUE) %>% 
+  ## add confidence intervals
+  add_ci()
 
 age_strat <- survey_design %>% 
   ## only keep children less than 87cm
@@ -2063,15 +2065,16 @@ age_strat <- survey_design %>%
   tbl_svysummary(missing = "no", 
                  value = everything() ~ TRUE, 
                  ## stratify by age group
-                 by = age_group)
+                 by = age_group) 
 
 ## combine the overall and stratified tables 
 tbl_merge(list(overall, age_strat)) %>% 
   modify_spanning_header(
     list(
       ## rename the spanning header
       ## you can see what the columns are called by putting in an object and inspecting table_body
-      stat_0_1    ~ "**Overall**", 
+      c(stat_0_1, 
+        ci_stat_0_1) ~ "**Overall**", 
       c(stat_1_2,
         stat_2_2, 
         stat_3_2,
@@ -2110,7 +2113,9 @@ overall <- survey_design %>%
   select(all_of(HAZ)) %>% 
   ## only show the row with TRUE
   tbl_svysummary(missing = "no", 
-                 value = everything() ~ TRUE)
+                 value = everything() ~ TRUE) %>% 
+  ## add confidence intervals
+  add_ci()
 
 age_strat <- survey_design %>% 
   ## tabulate multiple variables with same values
@@ -2127,7 +2132,8 @@ tbl_merge(list(overall, age_strat)) %>%
     list(
       ## rename the spanning header
       ## you can see what the columns are called by putting in an object and inspecting table_body
-      stat_0_1    ~ "**Overall**", 
+      c(stat_0_1, 
+        ci_stat_0_1) ~ "**Overall**",  
       c(stat_1_2,
         stat_2_2, 
         stat_3_2,
@@ -2165,7 +2171,9 @@ overall <- survey_design %>%
   select(all_of(WAZ)) %>% 
   ## only show the row with TRUE
   tbl_svysummary(missing = "no", 
-                 value = everything() ~ TRUE)
+                 value = everything() ~ TRUE) %>% 
+  ## add confidence intervals
+  add_ci()
 
 age_strat <- survey_design %>% 
   ## tabulate multiple variables with same values
@@ -2182,7 +2190,8 @@ tbl_merge(list(overall, age_strat)) %>%
     list(
       ## rename the spanning header
       ## you can see what the columns are called by putting in an object and inspecting table_body
-      stat_0_1    ~ "**Overall**", 
+      c(stat_0_1, 
+        ci_stat_0_1) ~ "**Overall**",   
       c(stat_1_2,
         stat_2_2, 
         stat_3_2,
@@ -2221,7 +2230,9 @@ overall <- survey_design %>%
   select(all_of(WHZ)) %>% 
   ## only show the row with TRUE
   tbl_svysummary(missing = "no", 
-                 value = everything() ~ TRUE)
+                 value = everything() ~ TRUE) %>% 
+  ## add confidence intervals
+  add_ci()
 
 age_strat <- survey_design %>% 
   ## tabulate multiple variables with same values
@@ -2238,7 +2249,8 @@ tbl_merge(list(overall, age_strat)) %>%
     list(
       ## rename the spanning header
       ## you can see what the columns are called by putting in an object and inspecting table_body
-      stat_0_1    ~ "**Overall**", 
+      c(stat_0_1, 
+        ci_stat_0_1) ~ "**Overall**", 
       c(stat_1_2,
         stat_2_2, 
         stat_3_2,