diff --git a/DESCRIPTION b/DESCRIPTION
index e497a14..49f0d07 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -17,12 +17,16 @@ Imports:
     ggplot2,
     purrr,
     tibble,
-    curl
+    curl,
+    rlang,
+    stringr,
+    tidyr,
+    wordcloud2
 Suggests: 
     testthat (>= 3.0.0),
     rmarkdown,
     knitr
-URL: https://github.com/rOpenGov/finna
+URL: https://ropengov.github.io/finna/
 BugReports: https://github.com/rOpenGov/finna/issues
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.2
diff --git a/NAMESPACE b/NAMESPACE
index 3c8f6ec..f7ed2dd 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -8,12 +8,21 @@ export(refine_metadata)
 export(search_finna)
 export(visualize_author_distribution)
 export(visualize_format_distribution)
+export(visualize_format_distribution_pie)
+export(visualize_format_library_correlation)
 export(visualize_library_distribution)
 export(visualize_subject_distribution)
+export(visualize_title_year_heatmap)
 export(visualize_top_20_titles)
+export(visualize_word_cloud)
 export(visualize_year_distribution)
+export(visualize_year_distribution_line)
 import(dplyr)
 import(ggplot2)
+import(rlang)
+import(stringr)
+import(tidyr)
+import(wordcloud2)
 importFrom(curl,curl_download)
 importFrom(httr,GET)
 importFrom(httr,content)
diff --git a/R/search_finna.R b/R/search_finna.R
index 938c3c6..4ee9fb7 100644
--- a/R/search_finna.R
+++ b/R/search_finna.R
@@ -57,6 +57,9 @@ search_finna <- function(query = NULL,#lookfor
   page <- 1
   records_per_page <- 100  # Fetch 100 records per page for efficiency
 
+  # Initialize resultCount
+  result_count <- 0
+
   while (total_fetched < limit) {
     # Calculate the remaining number of records to fetch
     remaining_to_fetch <- min(records_per_page, limit - total_fetched)
@@ -66,7 +69,7 @@ search_finna <- function(query = NULL,#lookfor
       lookfor = query,
       type = type,
       `field[]` = fields,
-      `filter[]` = filters,
+      # `filter[]` = filters,
       `facet[]` = facets,
       `facetFilter[]` = facetFilters,
       sort = sort,
@@ -76,6 +79,13 @@ search_finna <- function(query = NULL,#lookfor
       prettyPrint = prettyPrint
     )
 
+    # Add filters to the query parameters (handle each filter as filter[])
+    if (!is.null(filters)) {
+      for (i in seq_along(filters)) {
+        query_params[[paste0('filter[', i, ']')]] <- filters[i]
+      }
+    }
+
     # Execute the GET request and handle potential errors
     response <- tryCatch(
       httr::GET(base_url, query = query_params),
@@ -90,6 +100,11 @@ search_finna <- function(query = NULL,#lookfor
       # Parse the JSON content of the response
       search_results <- httr::content(response, "parsed")
 
+      # Extract resultCount only from the first page
+      if (page == 1) {
+        result_count <- search_results$resultCount
+      }
+
       # Extract and structure relevant data from the search results
       records <- search_results$records
       if (is.null(records) || length(records) == 0) {
@@ -163,5 +178,8 @@ search_finna <- function(query = NULL,#lookfor
   # Attach the language attribute to the tibble
   attr(tibble_results, "language") <- lng
   #cat("Data retrieved from Finna API (https://www.finna.fi) - metadata licensed under CC0.\n")
+  #return(tibble_results)
+  attr(tibble_results, "result_count") <- result_count
   return(tibble_results)
+
 }
diff --git a/R/visualizeFinna.R b/R/visualizeFinna.R
index 796658a..ee745d9 100644
--- a/R/visualizeFinna.R
+++ b/R/visualizeFinna.R
@@ -23,6 +23,41 @@ visualize_year_distribution <- function(year_data) {
     )
 }
 
+#' Visualize Year Distribution (Line Plot)
+#'
+#' Creates a line plot showing the distribution of records by year.
+#'
+#' @param metadata A tibble containing refined Finna metadata, with a "Year" column.
+#' @return A ggplot2 object representing the line plot.
+#' @import ggplot2
+#' @import dplyr
+#' @export
+#' @examples
+#' library(finna)
+#' sibelius_data <- search_finna("sibelius")
+#' refined_data <- refine_metadata(sibelius_data)
+#' visualize_year_distribution_line(refined_data)
+visualize_year_distribution_line <- function(metadata) {
+  # Convert the Year to numeric
+  metadata <- metadata %>%
+    mutate(Year = as.numeric(Year)) %>%
+    filter(!is.na(Year))
+
+  # Count the number of records by year
+  year_distribution <- metadata %>%
+    count(Year, sort = TRUE)
+
+  # Plot the year distribution as a line plot
+  ggplot(year_distribution, aes(x = Year, y = n)) +
+    geom_line(color = "steelblue", size = 1) +
+    theme_minimal() +
+    labs(
+      title = "Yearly Distribution of Records",
+      x = "Year",
+      y = "Number of Records"
+    )
+}
+
 #' Visualize Top-20 Titles by Count
 #'
 #' Creates a bar plot showing the top-20 most frequent titles and their counts.
@@ -31,6 +66,7 @@ visualize_year_distribution <- function(year_data) {
 #' @return A ggplot2 object showing the bar plot of the top-20 titles.
 #' @import ggplot2
 #' @import dplyr
+#' @import stringr
 #' @export
 #' @examples
 #' library(finna)
@@ -38,9 +74,12 @@ visualize_year_distribution <- function(year_data) {
 #' refined_data <- refine_metadata(sibelius_data)
 #' visualize_top_20_titles(refined_data)
 visualize_top_20_titles <- function(metadata) {
-  # Convert titles to lowercase for case-insensitive comparison
+  # Clean the titles: Convert to lowercase, remove punctuation, and trim whitespace
   metadata <- metadata %>%
-    mutate(Title = tolower(Title))
+    mutate(Title = tolower(Title),  # Convert to lowercase
+           Title = stringr::str_trim(Title),  # Trim leading and trailing whitespace
+           Title = stringr::str_squish(Title),  # Remove extra spaces between words
+           Title = stringr::str_replace_all(Title, "[[:punct:]]", ""))  # Remove punctuation
 
   # Calculate the top-20 titles
   top_titles <- metadata %>%
@@ -59,6 +98,52 @@ visualize_top_20_titles <- function(metadata) {
     )
 }
 
+#' Visualize Heatmap of Titles by Year
+#'
+#' Creates a heatmap showing the most frequent titles and their occurrence over time.
+#'
+#' @param metadata A tibble containing refined Finna metadata, with "Title" and "Year" columns.
+#' @return A ggplot2 object showing the heatmap of title frequency by year.
+#' @import ggplot2
+#' @import dplyr
+#' @export
+#' @examples
+#' library(finna)
+#' sibelius_data <- search_finna("sibelius")
+#' refined_data <- refine_metadata(sibelius_data)
+#' visualize_title_year_heatmap(refined_data)
+visualize_title_year_heatmap <- function(metadata) {
+  # Clean the data
+  metadata <- metadata %>%
+    mutate(Title = tolower(Title),  # Convert to lowercase
+           Title = stringr::str_trim(Title),  # Trim leading/trailing whitespace
+           Year = as.numeric(Year)) %>% # Convert year to numeric
+    filter(!is.na(Year), !is.na(Title)) %>%
+    count(Title, Year)
+
+  # Filter top 20 titles by total count
+  top_titles <- metadata %>%
+    group_by(Title) %>%
+    summarise(total = sum(n)) %>%
+    top_n(20, total) %>%
+    pull(Title)
+
+  # Filter metadata to include only top titles
+  metadata_filtered <- metadata %>% filter(Title %in% top_titles)
+
+  # Plot the heatmap
+  ggplot(metadata_filtered, aes(x = Year, y = reorder(Title, n), fill = n)) +
+    geom_tile() +
+    scale_fill_gradient(low = "white", high = "steelblue") +
+    theme_minimal() +
+    labs(
+      title = "Heatmap of Top 20 Titles by Year",
+      x = "Year",
+      y = "Title",
+      fill = "Count"
+    )
+}
+
 
 #' Visualize Distribution by Formats
 #'
@@ -95,6 +180,41 @@ visualize_format_distribution <- function(metadata) {
     )
 }
 
+#' Visualize Correlation Between Formats and Libraries
+#'
+#' Creates a heatmap showing the correlation between formats and libraries.
+#'
+#' @param metadata A tibble containing refined Finna metadata, with "Formats" and "Library" columns.
+#' @return A ggplot2 object showing the heatmap of format-library correlation.
+#' @import ggplot2
+#' @import dplyr
+#' @export
+#' @examples
+#' library(finna)
+#' sibelius_data <- search_finna("sibelius")
+#' refined_data <- refine_metadata(sibelius_data)
+#' visualize_format_library_correlation(refined_data)
+visualize_format_library_correlation <- function(metadata) {
+  # Clean and count format-library combinations
+  format_library_dist <- metadata %>%
+    mutate(Formats = tolower(Formats),
+           Library = tolower(Library)) %>%
+    count(Formats, Library) %>%
+    filter(!is.na(Formats), !is.na(Library))
+
+  # Plot the heatmap
+  ggplot(format_library_dist, aes(x = Formats, y = Library, fill = n)) +
+    geom_tile() +
+    scale_fill_gradient(low = "white", high = "darkorange") +
+    theme_minimal() +
+    theme(axis.text.x = element_text(angle = 45, hjust = 1)) +  # Rotate x-axis labels
+    labs(
+      title = "Correlation Between Formats and Libraries",
+      x = "Format",
+      y = "Library",
+      fill = "Count"
+    )
+}
 
 #' Visualize Distribution by Libraries
 #'
@@ -234,5 +354,72 @@ visualize_subject_distribution <- function(metadata) {
     )
 }
 
+#' Visualize Word Cloud of Titles or Subjects
+#'
+#' Creates a word cloud showing the frequency of words in titles or subjects.
+#'
+#' @param metadata A tibble containing refined Finna metadata, with "Title" or "Subjects" column.
+#' @param column The column to visualize as a word cloud (e.g., "Title" or "Subjects").
+#' @return A word cloud plot of the most frequent words.
+#' @import wordcloud2
+#' @import dplyr
+#' @import tidyr
+#' @import rlang
+#' @export
+#' @examples
+#' library(finna)
+#' music_data <- search_finna("music")
+#' refined_data <- refine_metadata(music_data)
+#' visualize_word_cloud(refined_data, "Title")
+visualize_word_cloud <- function(metadata, column = "Title") {
+  # Convert the column name to symbol
+  column <- rlang::ensym(column)
+
+  # Clean and split words
+  words <- metadata %>%
+    select(!!column) %>%
+    mutate(!!column := tolower(!!column)) %>%
+    tidyr::separate_rows(!!column, sep = " ") %>%
+    count(!!column, sort = TRUE) %>%
+    filter(!is.na(!!column), nchar(!!column) > 1)  # Remove NA and short words
+
+  # Create word cloud
+  wordcloud2::wordcloud2(data = words)
+}
+
+
+
+#' Visualize Format Distribution as Pie Chart
+#'
+#' Creates a pie chart showing the distribution of records by formats.
+#'
+#' @param metadata A tibble containing refined Finna metadata, with a "Formats" column.
+#' @return A ggplot2 object showing the pie chart of format distribution.
+#' @import ggplot2
+#' @import dplyr
+#' @export
+#' @examples
+#' library(finna)
+#' sibelius_data <- search_finna("sibelius")
+#' refined_data <- refine_metadata(sibelius_data)
+#' visualize_format_distribution_pie(refined_data)
+visualize_format_distribution_pie <- function(metadata) {
+  # Clean and count the format distribution
+  format_distribution <- metadata %>%
+    mutate(Formats = tolower(Formats)) %>%
+    count(Formats, sort = TRUE)
+
+  # Plot the pie chart
+  ggplot(format_distribution, aes(x = "", y = n, fill = Formats)) +
+    geom_bar(stat = "identity", width = 1) +
+    coord_polar("y") +
+    theme_void() +  # Remove background and axis
+    labs(
+      title = "Distribution of Formats",
+      fill = "Formats"
+    )
+}
+
+
 
 
diff --git a/custom.css b/custom.css
deleted file mode 100644
index 5e21ff2..0000000
--- a/custom.css
+++ /dev/null
@@ -1,52 +0,0 @@
-# Define the custom CSS
-css_content <- "
-/* Make sure the table takes up the full width and content wraps */
-table {
-    width: 100%;
-    border-collapse: collapse; /* Ensures that borders are collapsed into a single border */
-    word-wrap: break-word; /* Ensure long words are wrapped */
-}
-
-th, td {
-    padding: 10px; /* Add padding to table cells */
-    text-align: left; /* Align text to the left */
-    vertical-align: top; /* Align text to the top for multi-line cells */
-    border-bottom: 1px solid #ddd; /* Add a border under each cell */
-    word-break: break-word; /* Break long words to fit in the cell */
-}
-
-/* Add some space between the columns */
-th, td {
-    padding: 8px 12px;
-}
-
-/* Make the header row stand out */
-th {
-    background-color: #f2f2f2; /* Light gray background for headers */
-    font-weight: bold;
-}
-
-/* Add a hover effect for table rows */
-tr:hover {
-    background-color: #f5f5f5; /* Light gray background on hover */
-}
-
-/* Make the table responsive by allowing horizontal scrolling */
-.table-wrapper {
-    overflow-x: auto;
-    -webkit-overflow-scrolling: touch; /* For smooth scrolling on mobile devices */
-}
-
-/* Ensure long URLs or text don't overflow */
-td {
-    white-space: pre-wrap; /* Preserve spaces and wrap text */
-}
-
-/* Optional: add some color to every other row for readability */
-tr:nth-child(even) {
-    background-color: #f9f9f9;
-}
-"
-
-# Write the CSS content to a file
-writeLines(css_content, "pkgdown/assets/custom.css")
diff --git a/FinnaR.Rproj b/finna.Rproj
similarity index 100%
rename from FinnaR.Rproj
rename to finna.Rproj
diff --git a/man/visualize_format_distribution_pie.Rd b/man/visualize_format_distribution_pie.Rd
new file mode 100644
index 0000000..0ab3e51
--- /dev/null
+++ b/man/visualize_format_distribution_pie.Rd
@@ -0,0 +1,23 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/visualizeFinna.R
+\name{visualize_format_distribution_pie}
+\alias{visualize_format_distribution_pie}
+\title{Visualize Format Distribution as Pie Chart}
+\usage{
+visualize_format_distribution_pie(metadata)
+}
+\arguments{
+\item{metadata}{A tibble containing refined Finna metadata, with a "Formats" column.}
+}
+\value{
+A ggplot2 object showing the pie chart of format distribution.
+}
+\description{
+Creates a pie chart showing the distribution of records by formats.
+}
+\examples{
+library(finna)
+sibelius_data <- search_finna("sibelius")
+refined_data <- refine_metadata(sibelius_data)
+visualize_format_distribution_pie(refined_data)
+}
diff --git a/man/visualize_format_library_correlation.Rd b/man/visualize_format_library_correlation.Rd
new file mode 100644
index 0000000..fed48d2
--- /dev/null
+++ b/man/visualize_format_library_correlation.Rd
@@ -0,0 +1,23 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/visualizeFinna.R
+\name{visualize_format_library_correlation}
+\alias{visualize_format_library_correlation}
+\title{Visualize Correlation Between Formats and Libraries}
+\usage{
+visualize_format_library_correlation(metadata)
+}
+\arguments{
+\item{metadata}{A tibble containing refined Finna metadata, with "Formats" and "Library" columns.}
+}
+\value{
+A ggplot2 object showing the heatmap of format-library correlation.
+}
+\description{
+Creates a heatmap showing the correlation between formats and libraries.
+}
+\examples{
+library(finna)
+sibelius_data <- search_finna("sibelius")
+refined_data <- refine_metadata(sibelius_data)
+visualize_format_library_correlation(refined_data)
+}
diff --git a/man/visualize_title_year_heatmap.Rd b/man/visualize_title_year_heatmap.Rd
new file mode 100644
index 0000000..08a8ee8
--- /dev/null
+++ b/man/visualize_title_year_heatmap.Rd
@@ -0,0 +1,23 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/visualizeFinna.R
+\name{visualize_title_year_heatmap}
+\alias{visualize_title_year_heatmap}
+\title{Visualize Heatmap of Titles by Year}
+\usage{
+visualize_title_year_heatmap(metadata)
+}
+\arguments{
+\item{metadata}{A tibble containing refined Finna metadata, with "Title" and "Year" columns.}
+}
+\value{
+A ggplot2 object showing the heatmap of title frequency by year.
+}
+\description{
+Creates a heatmap showing the most frequent titles and their occurrence over time.
+}
+\examples{
+library(finna)
+sibelius_data <- search_finna("sibelius")
+refined_data <- refine_metadata(sibelius_data)
+visualize_title_year_heatmap(refined_data)
+}
diff --git a/man/visualize_word_cloud.Rd b/man/visualize_word_cloud.Rd
new file mode 100644
index 0000000..e65b41b
--- /dev/null
+++ b/man/visualize_word_cloud.Rd
@@ -0,0 +1,25 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/visualizeFinna.R
+\name{visualize_word_cloud}
+\alias{visualize_word_cloud}
+\title{Visualize Word Cloud of Titles or Subjects}
+\usage{
+visualize_word_cloud(metadata, column = "Title")
+}
+\arguments{
+\item{metadata}{A tibble containing refined Finna metadata, with "Title" or "Subjects" column.}
+
+\item{column}{The column to visualize as a word cloud (e.g., "Title" or "Subjects").}
+}
+\value{
+A word cloud plot of the most frequent words.
+}
+\description{
+Creates a word cloud showing the frequency of words in titles or subjects.
+}
+\examples{
+library(finna)
+music_data <- search_finna("music")
+refined_data <- refine_metadata(music_data)
+visualize_word_cloud(refined_data, "Title")
+}
diff --git a/man/visualize_year_distribution_line.Rd b/man/visualize_year_distribution_line.Rd
new file mode 100644
index 0000000..9210ba9
--- /dev/null
+++ b/man/visualize_year_distribution_line.Rd
@@ -0,0 +1,23 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/visualizeFinna.R
+\name{visualize_year_distribution_line}
+\alias{visualize_year_distribution_line}
+\title{Visualize Year Distribution (Line Plot)}
+\usage{
+visualize_year_distribution_line(metadata)
+}
+\arguments{
+\item{metadata}{A tibble containing refined Finna metadata, with a "Year" column.}
+}
+\value{
+A ggplot2 object representing the line plot.
+}
+\description{
+Creates a line plot showing the distribution of records by year.
+}
+\examples{
+library(finna)
+sibelius_data <- search_finna("sibelius")
+refined_data <- refine_metadata(sibelius_data)
+visualize_year_distribution_line(refined_data)
+}
diff --git a/pkgdown/_pkgdown.yml b/pkgdown/_pkgdown.yml
index ad2ce3f..c80fdd8 100644
--- a/pkgdown/_pkgdown.yml
+++ b/pkgdown/_pkgdown.yml
@@ -1,4 +1,4 @@
-url: ~
+url: https://ropengov.github.io/finna/
 
 template:
   # Use Bootstrap 5 for the site styling
diff --git a/vignettes/articles/Fennica.Rmd b/vignettes/articles/Fennica.Rmd
new file mode 100644
index 0000000..597172a
--- /dev/null
+++ b/vignettes/articles/Fennica.Rmd
@@ -0,0 +1,59 @@
+---
+title: "Fennica in Finna"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Fennica in Finna}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+## Fennica 
+
+To search Fennica data in Finna
+
+```{r}
+library(finna)
+fennica <- search_finna("*",filters=c('collection:"FEN"'))
+print(fennica)
+```
+
+## Bulk data
+
+In order to download the whole data we can add the parameter `limit = Inf`
+as `search_finna("*",filters=c('collection:"FEN"'), limit = Inf)`
+
+## Checking the total counts
+
+search the whole data and it total search of counts in the the interval between 
+some years for example between the years 1809-1917 as follows:
+
+```{r}
+library(finna)
+fennica <- search_finna("*",filters = c('collection:"FEN"', 'search_daterange_mv:"[1809 TO 1918]"'))
+print(fennica)
+```
+
+we can check the whole data count
+
+```{r}
+library(finna)
+fennica <- search_finna("*",filters = c('collection:"FEN"', 'search_daterange_mv:"[1809 TO 1918]"'))
+result_count <- attr(fennica, "result_count")
+print(result_count)
+```
+
+## Visualization for fennica
+
+We can use any of the functions provided to visualize the data 
+```{r}
+library(finna)
+fennica <- search_finna("*",filters = c('collection:"FEN"', 'search_daterange_mv:"[1809 TO 1918]"'))
+refined_data <- refine_metadata(fennica)
+visualize_author_distribution(refined_data)
+```
+
+```{r}
+fennica <- search_finna("*",filters = c('collection:"FEN"', 'search_daterange_mv:"[1809 TO 1918]"'))
+refined_data <- refine_metadata(fennica)
+visualize_word_cloud(refined_data, "Title")
+```
diff --git a/vignettes/articles/refinemetadata.Rmd b/vignettes/articles/refinemetadata.Rmd
index 350bb8c..de6c9e7 100644
--- a/vignettes/articles/refinemetadata.Rmd
+++ b/vignettes/articles/refinemetadata.Rmd
@@ -2,7 +2,7 @@
 title: "Refine, integrate and analyse Finna metadata"
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{Introduction to finna}
+  %\VignetteIndexEntry{Refine, integrate and analyse Finna metadata}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---
@@ -52,6 +52,13 @@ analysis_results <- analyze_metadata(refined_data)
 visualize_year_distribution(analysis_results$year_distribution)
 ```
 
+#### 1.1 Line plot of yearly distribution
+```{r}
+library(finna)
+sibelius_data <- search_finna("sibelius")
+refined_data <- refine_metadata(sibelius_data)
+visualize_year_distribution_line(refined_data)
+```
 
 ### 2. **Applying the `visualize_top_20_titles()` Function**
 
@@ -65,6 +72,14 @@ top_20_titles_plot <- visualize_top_20_titles(refined_data)
 print(top_20_titles_plot)
 ```
 
+#### 2.1 Visualize Heatmap of Titles by Year
+```{r warning=FALSE}
+library(finna)
+sibelius_data <- search_finna("sibelius")
+refined_data <- refine_metadata(sibelius_data)
+visualize_title_year_heatmap(refined_data)
+```
+
 ### 3. **Applying the `visualize_format_distribution()` Function**
 
 This function visualizes the distribution of the records by format.
@@ -76,6 +91,13 @@ format_distribution_plot <- visualize_format_distribution(refined_data)
 # To display the plot
 print(format_distribution_plot)
 ```
+#### 3.1 Visualize Format Distribution as Pie Chart
+```{r}
+library(finna)
+sibelius_data <- search_finna("sibelius")
+refined_data <- refine_metadata(sibelius_data)
+visualize_format_distribution_pie(refined_data)
+```
 
 ### 4. **Applying the `visualize_library_distribution()` Function**
 
@@ -88,6 +110,17 @@ library_distribution_plot <- visualize_library_distribution(refined_data)
 # To display the plot
 print(library_distribution_plot)
 ```
+#### 4.1 **Visualize Correlation Between Formats and Libraries**
+
+This function shows the distribution of the records by library.
+
+```{r}
+library(finna)
+sibelius_data <- search_finna("sibelius")
+refined_data <- refine_metadata(sibelius_data)
+visualize_format_library_correlation(refined_data)
+```
+
 
 ### 5. **Applying the `visualize_author_distribution()` Function**
 
@@ -112,4 +145,12 @@ subject_distribution_plot <- visualize_subject_distribution(refined_data)
 # To display the plot
 print(subject_distribution_plot)
 ```
+#### 6.1 **Visualize Word Cloud of Titles or Subjects**
 
+This function visualizes the distribution of the records by subject.
+
+```{r}
+music_data <- search_finna("music")
+refined_data <- refine_metadata(music_data)
+visualize_word_cloud(refined_data, "Title")
+```
diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd
index af6e4f2..c3e6038 100644
--- a/vignettes/vignette.Rmd
+++ b/vignettes/vignette.Rmd
@@ -11,14 +11,18 @@ vignette: >
 
 This vignette provides an overview of how to use the finna package.
 
-To make a simple search use the following code. I we need a bulk download we use
-`search_finna("sibelius", limit = Inf)` where we add the term `limit = Inf`.
+To make a simple search use the following code.
 
 ```{r}
 library(finna)
 record <- search_finna("sibelius")
 print(record)
 ```
+ 
+## Bulk search 
+ 
+ I we need a bulk download we use
+`search_finna("sibelius", limit = Inf)` where we add the term `limit = Inf`.
 
 ## Free text search
 ```{r}
@@ -257,20 +261,14 @@ print(record)
 
 ### Narrowing the search 
 
-When narrowing search do not use codes as a combination as follows
-
-`search_finna("trump", filters = c('{!geofilt sfield=location_geo pt=61.663987171517796,24.17263895273209 d=212.53603751769646}',author_facet:"Häkkinen, Hannu"')) 
-` 
-or
-
-`search_finna("trump", filters = c('{!geofilt sfield=location_geo pt=61.663987171517796,24.17263895273209 d=100}'), facetFilters = c('author_facet:"Häkkinen, Hannu"'))
-`
-rather use specific search as follows 
+When narrowing search you can  use codes as a combination as follows
 
 ```{r}
-record <- search_finna("trump", filters = c('~author_facet:"Häkkinen, Hannu"'))
+record <- search_finna("trump", filters = c('{!geofilt sfield=location_geo pt=61.663987171517796,24.17263895273209 d=212.53603751769646},author_facet:"Häkkinen,Hannu"'))
 print(record)
 ```
+
+
 ### specific search 
 
 ```{r}
@@ -281,14 +279,32 @@ record <- search_finna("era:'2010-luku'", filters = c('building:"0/3AMK/"'))
 ### search without removing duplication
 
 In order to search data without removing duplication
-[example](https://www.finna.fi/Search/Results?filter%5B%5D=finna.deduplication%3A%220%22&dfApplied=1&lookfor=era%3A%222010-luku%22&type=AllFields)
+[example](https://www.finna.fi/Search/Results?dfApplied=1&lookfor=era%3A%222010-luku%22&type=AllFields&filter[]=~building%3A%220%2F3AMK%2F%22&filter[]=finna.deduplication:%220%22)
+
 ```{r}
-record <- search_finna("era:'2010-luku'", filters = c('finna.deduplication:"0"'))
+record <- search_finna('era:"2010-luku"', filters = c('~building:"0/3AMK/"', 'finna.deduplication:"1"'))
 print(record)
 ```
-removing duplication can be done as follows
+We can confirm this as follows by checking the count
+
+```{r}
+record <- search_finna('era:"2010-luku"', filters = c('~building:"0/3AMK/"', 'finna.deduplication:"1"'))
+result_count <- attr(record, "result_count")
+print(result_count)
+```
+
+Removing duplication can be done as follows
+
 ```{r}
-record <- search_finna("era:'2010-luku'", filters = c('finna.deduplication:"1"'))
+record <- search_finna('era:"2010-luku"', filters = c('~building:"0/3AMK/"', 'finna.deduplication:"0"'))
 print(record)
 ```
 
+To confirm this we can check the count 
+
+```{r}
+record <- search_finna('era:"2010-luku"', filters = c('~building:"0/3AMK/"', 'finna.deduplication:"0"'))
+result_count <- attr(record, "result_count")
+print(result_count)
+```
+