Fix warnings about global bindings and join statement output

R/generate-estimate-details.R

@@ -29,26 +29,24 @@ generate_estimate_details <- function(df, train_data, dv, ...) {
   orig_max <- group_stats$orig_max
 
 
-  suppressWarnings(
-    base_data %>%
-      gather(key = field, value = value, -c(1, 2, 3)) %>%
-      mutate(value = as.character(.data$value)) %>%
-      left_join(group_stats_data, by = c("field", "value")) %>%
-      group_by(.data$unique_id) %>%
-      mutate(complete = sum(!is.na(.data$factor_avg))) %>%
-      ungroup() %>%
-      drop_na(.data$factor_avg:.data$field_wt) %>%
-      #arrange(desc(.data$field_wt)) %>%
-      mutate(factor_avg_wt = .data$factor_avg * .data$field_wt) %>%
-      group_by(.data$unique_id) %>%
-      mutate(estimate = weighted.mean(.data$factor_avg, .data$field_wt)) %>%
-      ungroup() %>%
-      mutate(
-        rescale_estimate = rescale_mid(
-          x = .data$estimate,
-          to = c(orig_min, orig_max),
-          mid = group_stats$grand_avg
-        )
+  base_data %>%
+    gather(key = "field", value = "value", -c(1, 2, 3)) %>%
+    mutate(value = as.character(.data$value)) %>%
+    left_join(group_stats_data, by = c("field", "value")) %>%
+    group_by(.data$unique_id) %>%
+    mutate(complete = sum(!is.na(.data$factor_avg))) %>%
+    ungroup() %>%
+    drop_na(.data$factor_avg:.data$field_wt) %>%
+    #arrange(desc(.data$field_wt)) %>%
+    mutate(factor_avg_wt = .data$factor_avg * .data$field_wt) %>%
+    group_by(.data$unique_id) %>%
+    mutate(estimate = weighted.mean(.data$factor_avg, .data$field_wt)) %>%
+    ungroup() %>%
+    mutate(
+      rescale_estimate = rescale_mid(
+        x = .data$estimate,
+        to = c(orig_min, orig_max),
+        mid = group_stats$grand_avg
       )
-  )
+    )
 }

R/plot-group-split.R

@@ -82,7 +82,7 @@ summarize_over_under_split <- function(df,
     ungroup() %>%
     mutate(
       split_ord = ifelse(
-        str_detect(split_ord, base_group), "bar", "point")
+        str_detect(.data$split_ord, base_group), "bar", "point")
     ) %>%
     pivot_wider(
       names_from = .data$split_ord,
@@ -100,7 +100,7 @@ summarize_over_under_split <- function(df,
       has_point = max(!is.na(.data$split_point))
     ) %>%
     ungroup() %>%
-    fill(split_bar, split_point, .direction = "updown") %>%
+    fill(.data$split_bar, .data$split_point, .direction = "updown") %>%
     group_by(
       .data$field, .data$value,
       .data$has_bar, .data$has_point,

R/summarize-factors.R

@@ -21,6 +21,8 @@
 #' @export
 #' @examples
 #' summarize_factors_all_fields(iris, dv = Sepal.Length)
+#'
+#' # Can also return a list object
 #' summarize_factors_all_fields(iris, dv = Sepal.Length, return = "list")
 summarize_factors_all_fields <- function(df,
                          ...,
@@ -62,7 +64,7 @@ summarize_factors_all_fields <- function(df,
   agg_data <-
     factor_stats %>%
     filter(!is.na(.data$value)) %>%
-    left_join(field_stats) %>%
+    left_join(field_stats, by = "field") %>%
     mutate(
       rescale_factor_avg = rescale_mid(
         x = .data$factor_avg,
@@ -146,7 +148,7 @@ summarize_factors_one_field <- function(var, df, avg_fn) {
 summarize_one_field <- function(var, df) {
   df %>%
     select(value = var, .data$y_outcome) %>%
-    do(glance(lm(.data$y_outcome ~ .data$value, data = .))) %>%
+    do(glance(lm(.data$y_outcome ~ .data$value, data = .data))) %>%
     mutate(field = var) %>%
     select(
       .data$field,

docs/pkgdown.yml

@@ -2,5 +2,5 @@ pandoc: 2.14.0.3
 pkgdown: 1.6.1
 pkgdown_sha: ~
 articles: {}
-last_built: 2022-01-16T05:00Z
+last_built: 2022-01-16T17:39Z