man and Error corrections

DESCRIPTION

@@ -39,7 +39,6 @@ Imports:
     GGally,
     ggplot2,
     glue,
-    kableExtra,
     lm.beta,
     lubridate,
     magrittr,
@@ -53,7 +52,6 @@ Imports:
     tibble,
     tidyr,
     tidyselect,
-    flextable,
     sjlabelled,
     gt
 Suggests: 

NAMESPACE

@@ -41,7 +41,6 @@ export(test_icr)
 export(to_correlation_matrix)
 export(unianova)
 export(visualize)
-export(visualize_regress_sbci)
 export(z_scale)
 import(pillar)
 import(rlang)

R/knit_frequencies.R

@@ -1,3 +1,22 @@
+#' knit frequencies table variables
+#'
+#' Knits table for one ore more variables
+#'
+#' @param data a [tibble][tibble::tibble-package]
+#' @param weight Weighting variable. Default = NULL, means all weights are 1.
+#' @param ... List of variables.
+#' @param num_decimals Decimal places of the numeric columns. Default is 0.
+#' @param percent_decimals Decimal places of the percent columns. Default is 0.
+#'
+#' @return a gt-table
+#'
+#' @examples
+#' WoJ |> knit_frequencies(reach, employment)
+#' mtcars |> knit_frequencies(mpg, num_decimal = 1, percent_decimal = 1, cums = FALSE)
+#'
+#' @family categorical
+#'
+
 #' @export
 knit_frequencies <- function(data,
                              ...,
@@ -25,14 +44,14 @@ knit_frequencies <- function(data,
     n_valid <- dt %>%
       dplyr::filter(!is.na(!!var_sym)) %>%
       dplyr::summarise(n_valid = sum(.temp_weight, na.rm = TRUE)) %>%
-      pull(n_valid)
+      dplyr::pull(n_valid)
 
   t <- dt %>%
-    group_by(!!var_sym) %>%
+    dplyr::group_by(!!var_sym) %>%
     dplyr::summarise(n = sum(.temp_weight, na.rm = TRUE), .groups = "drop") %>%
     dplyr::mutate(
     percent = n / n_total,
-    valid_percent = if_else(is.na(!!var_sym), 0, n / n_valid))
+    valid_percent = dplyr::if_else(is.na(!!var_sym), 0, n / n_valid))
 
   if(cums == TRUE){
     t <- t |>
@@ -42,7 +61,7 @@ knit_frequencies <- function(data,
 
   gt <- t |>
     sjlabelled::copy_labels(dt) |>
-    dplyr::mutate(!!var_sym := coalesce(sjlabelled::as_character(!!var_sym), as.character(!!var_sym))) |>
+    dplyr::mutate(!!var_sym := dplyr::coalesce(sjlabelled::as_character(!!var_sym), as.character(!!var_sym))) |>
     gt::gt() |>
     gt::fmt_number(columns = n, decimals = num_decimal, use_seps = FALSE)|>
     gt::fmt_percent(columns = c(percent, valid_percent), decimals = percent_decimal, use_seps = FALSE) |>
@@ -52,7 +71,7 @@ knit_frequencies <- function(data,
       ) |>
     gt::text_transform(
       locations = gt::cells_body(
-        columns = c(cum_valid_percent, valid_percent),
+        columns = c(valid_percent),
         rows = is.na(!!var_sym)
       ),
       fn = function(x) "---"
@@ -65,6 +84,13 @@ knit_frequencies <- function(data,
      gt::cols_label(
        n_cum = "cum n",
        cum_valid_percent = "cum %"
+     )|>
+     gt::text_transform(
+       locations = gt::cells_body(
+         columns = c(cum_valid_percent),
+         rows = is.na(!!var_sym)
+       ),
+       fn = function(x) "---"
      )
  }
 

R/knit_regression.R

@@ -1,12 +1,23 @@
-
-# Visualize as html- or pdf-table with linear model ---
-#
-# @param x a [tdcmm] model
-#
-# @return html-, pdf- or word-table
-#
-# @family tdcmm visualize
-#
+#' Compute linear regression
+#'
+#' Computes linear regression for all independent variables on the specified
+#' dependent variable. Linear modeling of multiple independent variables uses
+#' stepwise regression modeling. If specified, preconditions for
+#' (multi-)collinearity and for homoscedasticity are checked.
+#'
+#' @param x a [tdcmm] model
+#' @param digits the decimal digits
+#' @param CIs show the Confidence intervals or not. Default to TRUE.
+#' @param cap Set the caption. Default is NULL, because in Quarto its better to set the tbl-cap in the chunk options
+#'
+#' @return a gt-table
+#'
+#' @examples
+#' WoJ |> regress(autonomy_selection, ethics_1) |>
+#'   knit_regress_table()
+#' WoJ %>% regress(autonomy_selection, work_experience, trust_government) |>
+#'   knit_regress_table(digits = 3, CIs = FALSE, cap = "Regression on Autonomy Selection")
+#'
 #' @export
 knit_regress_table <- function(x,
                                digits = 2,
@@ -141,6 +152,7 @@ knit_regress_table <- function(x,
  return(x)
   }
 }
+
 ## Visualize swimming BETA confidence intervals
 ## Usefull for comparing BETAs and BETAs vs points like 0
 ##
@@ -151,7 +163,6 @@ knit_regress_table <- function(x,
 ## @family tdcmm visualize
 #
 ## @keywords internal
-#' @export
 visualize_regress_sbci <- function(x,
                                    .design = NULL,
                                    title = NULL){