resolved vignette response_prop issues

NAMESPACE

@@ -3,5 +3,6 @@
 export(discretize_density)
 export(estimate_params)
 export(plot_likert_transform)
+export(response_prop)
 export(rlikert)
 export(simulate_likert)

R/utilities.R

@@ -37,11 +37,37 @@ plot_likert_transform <- function(n_items, n_levels, mean=0, sd=1, skew=0) {
   }
 }
 
+#' Calculate Response Proportions
+#'
+#' Returns a table of proportions for each possible response category.
+#'
+#' @param data numeric vector or matrix of responses.
+#' @param n_levels number of response categories.
+#' @return A table of response category proportions.
+#' @examples
+#' data <- c(1, 2, 2, 3, 3, 3)
+#' response_prop(data, n_levels = 3)
+#'
+#' data_matrix <- matrix(c(1, 2, 2, 3, 3, 3), ncol = 2)
+#' response_prop(data_matrix, n_levels = 3)
+#' @export
+response_prop <- function(data, n_levels) {
+  if (is.vector(data)) {
+    tab <- pad_levels(prop.table(table(data)), n_levels)
+  } else {
+    tab <- t(apply(data, 2, function(x_col) {
+      pad_levels(prop.table(table(x_col)), n_levels)
+    }))
+    dimnames(tab) <- list(Item = rownames(tab), Response = colnames(tab))
+  }
+  return(tab)
+}
+
 #' Pad Missing Levels
 #'
-#' Takes a vector of proportions or probabilities across possible responses
-#' and pads the missing levels with zeros up to the specified number
-#' of response categories.
+#' Helper function that takes a vector of proportions or probabilities 
+#' across possible responses and pads the missing levels with zeros up 
+#' to the specified number of response categories.
 #'
 #' @param pr proportions or probabilities across possible responses.
 #' @param n_levels number of response categories.
@@ -204,26 +230,6 @@ cor2cov <- function(corr, s) {
   return(diag(s) %*% corr %*% diag(s))
 }
 
-#' Get Proportion Table
-#'
-#' Returns a table of proportions for each possible response.
-#'
-#' @param data numeric vector or matrix. Responses.
-#' @param K integer. Number of response categories.
-#' @return numeric vector or matrix. Table of proportions.
-#' @noRd
-get_prop_table <- function(data, K) {
-  if (is.vector(data)) {
-    tab <- pad_levels(prop.table(table(data)), K)
-  } else {
-    tab <- t(apply(data, 2, function(x_col) {
-      pad_levels(prop.table(table(x_col)), K)
-    }))
-    dimnames(tab) <- list(Item = rownames(tab), Response = colnames(tab))
-  }
-  return(tab)
-}
-
 #' Generate Random Centered Parameters
 #'
 #' Generates random centered parameters for mu, sd, and skewness.

README.Rmd

@@ -17,10 +17,9 @@ knitr::opts_chunk$set(
 
 <!-- badges: start -->
 [![R-CMD-check](https://github.com/markolalovic/latent2likert/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/markolalovic/latent2likert/actions/workflows/R-CMD-check.yaml)
-[![Codecov test coverage](https://codecov.io/gh/markolalovic/latent2likert/branch/main/graph/badge.svg)](https://app.codecov.io/gh/markolalovic/latent2likert?branch=main)
+[![codecov](https://codecov.io/gh/markolalovic/latent2likert/branch/main/graph/badge.svg?token=HZTG6RUB2J)](https://codecov.io/gh/markolalovic/latent2likert)
 <!-- badges: end -->
 
-
 ## Overview
 
 The R package **latent2likert** is designed to effectively simulate the discretization process inherent to Likert scales while minimizing distortion. It converts continuous latent variables into ordinal categories to generate Likert scale item responses. This is particularly useful for accurately modeling and analyzing survey data that use Likert scales, especially when applying statistical techniques that require metric data.

dev/fix_site.py

@@ -5,32 +5,13 @@
 Script adds:
 
 - `/docs/.nojekyll` for githubpages.
-- meta description to index.html
+- meta description to index.html DONE
 
 After running pkgdown::build_site()
 '''
 
 import os
-import sys
-import fileinput
-
-def add_meta():
-    filename = './docs/index.html'
-    meta_line = '<meta name="description" content="Converting latent variables into Likert scale responses in R. Package latent2likert converts continuous latent variables into ordinal categories to simulate Likert scale item responses." />'
-
-    with open(filename, 'r') as f:
-        in_file = f.readlines()
-
-    out_file = []
-    for line in in_file:
-        out_file.append(line)
-        if '</title>' in line:
-            out_file.append(meta_line)    
-
-    with open(filename, 'w') as f:
-        f.writelines(out_file)    
 
 if __name__ == '__main__':
     os.system("touch ./docs/.nojekyll")
-    add_meta()
     print("Done.")

docs/pkgdown.yml

@@ -3,7 +3,7 @@ pkgdown: 2.0.9
 pkgdown_sha: ~
 articles:
   using_latent2likert: using_latent2likert.html
-last_built: 2024-06-20T21:04Z
+last_built: 2024-06-20T21:59Z
 urls:
   reference: https://markolalovic.github.io/latent2likert/reference
   article: https://markolalovic.github.io/latent2likert/articles