Use air format on all files

R/assertions.R

@@ -16,7 +16,8 @@ assert_valid_sex <- function(sex) {
 
 assert_valid_age_in_days <- function(age_in_days) {
   if (!is.numeric(age_in_days)) {
-    stop("Parameter `age_in_days` must be integer values",
+    stop(
+      "Parameter `age_in_days` must be integer values",
       " or values that can be coerced to integers",
       call. = FALSE
     )
@@ -51,26 +52,27 @@ deparse_chr <- function(expr) {
 }
 
 assert_logical <- function(x) {
-  assert_type(is.logical, "logical", x,
-    param_name = deparse_chr(substitute(x))
-  )
+  assert_type(is.logical, "logical", x, param_name = deparse_chr(substitute(x)))
 }
 
 assert_numeric <- function(x) {
-  assert_type(is.numeric, "numeric", x,
-    param_name = deparse_chr(substitute(x))
-  )
+  assert_type(is.numeric, "numeric", x, param_name = deparse_chr(substitute(x)))
 }
 
 assert_character <- function(x) {
-  assert_type(is.character, "character", x,
+  assert_type(
+    is.character,
+    "character",
+    x,
     param_name = deparse_chr(substitute(x))
   )
 }
 
 assert_character_or_numeric <- function(x) {
-  assert_type(function(y) is.character(y) || is.numeric(y),
-    "character or numeric", x,
+  assert_type(
+    function(y) is.character(y) || is.numeric(y),
+    "character or numeric",
+    x,
     param_name = deparse_chr(substitute(x))
   )
 }
@@ -86,8 +88,12 @@ assert_type <- function(type_fun, type_name, x, param_name) {
 assert_values_in_set <- function(x, allowed) {
   param_name <- as.character(substitute(x))
   if (length(allowed) > 0L && any(!(x %in% allowed))) {
-    stop("Some values in ", param_name, " are not valid.",
-      " Accepted values are ", paste0(allowed, collapse = ", "),
+    stop(
+      "Some values in ",
+      param_name,
+      " are not valid.",
+      " Accepted values are ",
+      paste0(allowed, collapse = ", "),
       call. = FALSE
     )
   }

R/prevalence-compute-api.R

@@ -6,23 +6,30 @@ NULL
 #' Computes the sample size by indicator and subset
 #' @noRd
 compute_prevalence_sample_size_by <- function(
-    data, indicator, subset_col_name
+  data,
+  indicator,
+  subset_col_name
 ) {
   UseMethod("compute_prevalence_sample_size_by")
 }
 
 #' Computes prevalence of rates for a given indicator, subset and cutoff
 #' @noRd
 compute_prevalence_estimates_for_column_by <- function(
-    data, indicator_name, subset_col_name, prev_col_name
-){
+  data,
+  indicator_name,
+  subset_col_name,
+  prev_col_name
+) {
   UseMethod("compute_prevalence_estimates_for_column_by")
 }
 
 #' Computes prevalence of zscores by indicator and subset
 #' @noRd
 compute_prevalence_zscore_summaries_by <- function(
-    data, indicator, subset_col_name
+  data,
+  indicator,
+  subset_col_name
 ) {
   UseMethod("compute_prevalence_zscore_summaries_by")
 }

R/prevalence-simple.R

@@ -23,7 +23,10 @@ column_values.simple_design <- function(x, col) {
 
 #' @export
 compute_prevalence_zscore_summaries_by.simple_design <- function(
-    data, indicator, subset_col_name) {
+  data,
+  indicator,
+  subset_col_name
+) {
   zscore_col_name <- prev_zscore_value_column(indicator)
   compute_and_aggregate(
     data,
@@ -42,7 +45,11 @@ compute_prevalence_zscore_summaries_by.simple_design <- function(
 
 #' @export
 compute_prevalence_estimates_for_column_by.simple_design <- function(
-    data, indicator_name, subset_col_name, prev_col_name) {
+  data,
+  indicator_name,
+  subset_col_name,
+  prev_col_name
+) {
   compute_and_aggregate(
     data,
     prev_col_name,
@@ -59,7 +66,10 @@ compute_prevalence_estimates_for_column_by.simple_design <- function(
 
 #' @export
 compute_prevalence_sample_size_by.simple_design <- function(
-    data, indicator, subset_col_name) {
+  data,
+  indicator,
+  subset_col_name
+) {
   column_name <- prev_prevalence_column_name(indicator)
   compute_and_aggregate(
     data,
@@ -75,7 +85,12 @@ compute_prevalence_sample_size_by.simple_design <- function(
 
 #' @importFrom stats aggregate
 compute_and_aggregate <- function(
-    data, value_column, subset_column, compute, empty_data_prototype) {
+  data,
+  value_column,
+  subset_column,
+  compute,
+  empty_data_prototype
+) {
   col_values <- column_values(data, value_column)
   N <- length(col_values)
   grouping <- column_values(data, subset_column)
@@ -164,5 +179,5 @@ sample_size <- function(x, N, empty_data_prototype) {
 sample_se <- function(x, x_mean, n, N) {
   scale <- N / (N - 1)
   x_deviation <- x - x_mean
-  sqrt(scale)/n * sqrt(sum(x_deviation * x_deviation))
+  sqrt(scale) / n * sqrt(sum(x_deviation * x_deviation))
 }

R/prevalence-survey.R

@@ -10,7 +10,10 @@ column_names.survey_design <- function(x) {
 
 #' @export
 compute_prevalence_zscore_summaries_by.survey_design <- function(
-    data, indicator, subset_col_name) {
+  data,
+  indicator,
+  subset_col_name
+) {
   zscore_col_name <- prev_zscore_value_column(indicator)
   zscore_formula <- as.formula(paste0("~", zscore_col_name))
   subset_formula <- as.formula(paste0("~", subset_col_name))
@@ -68,7 +71,10 @@ compute_prevalence_zscore_summaries_by.survey_design <- function(
 
 #' @export
 compute_prevalence_sample_size_by.survey_design <- function(
-    data, indicator, subset_col_name) {
+  data,
+  indicator,
+  subset_col_name
+) {
   expr_name <- paste0("I(!is.na(", prev_prevalence_column_name(indicator), "))")
   prev_formula <- as.formula(paste0("~", expr_name))
   subset_formula <- as.formula(paste0("~", subset_col_name))
@@ -93,7 +99,8 @@ compute_prevalence_sample_size_by.survey_design <- function(
   pop_weighted[is.na(pop_weighted)] <- 0
   pop_unweighted[is.na(pop_unweighted)] <- 0
 
-  stopifnot( # check that subsets come in the right order
+  stopifnot(
+    # check that subsets come in the right order
     all(prev[[subset_col_name]] == unweighted_prev[[subset_col_name]])
   )
   data.frame(
@@ -107,7 +114,11 @@ compute_prevalence_sample_size_by.survey_design <- function(
 
 #' @export
 compute_prevalence_estimates_for_column_by.survey_design <- function(
-    data, indicator_name, subset_col_name, prev_col_name) {
+  data,
+  indicator_name,
+  subset_col_name,
+  prev_col_name
+) {
   subset_formula <- as.formula(paste0("~", subset_col_name))
   prev_col_formula <- as.formula(paste0("~", prev_col_name))
   mean_est_prev <- svyby(