diff --git a/.gitignore b/.gitignore
index f649fbe..916aced 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,5 +6,5 @@
 .DS_Store
 data/
 glittr_stats_files/
-
+_freeze/
 /.quarto/
diff --git a/_freeze/index/execute-results/html.json b/_freeze/index/execute-results/html.json
deleted file mode 100644
index ef7774c..0000000
--- a/_freeze/index/execute-results/html.json
+++ /dev/null
@@ -1,16 +0,0 @@
-{
-  "hash": "d580c88c9f70559d1e94573ba7c90ccb",
-  "result": {
-    "markdown": "---\ntitle: \"Glittr stats\"\nformat: \n  html:\n    code-fold: true\n---\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(httr2)\nlibrary(ggplot2)\nlibrary(dplyr)\nlibrary(ggbreak)\nlibrary(cowplot)\n```\n:::\n\n\nCreate a file named `.env.sh` and add your GitHub PAT (variable named `PAT` ) and google api key (named `GOOGLE_API_KEY`) in there, e.g.:\n\n\n::: {.cell}\n\n```{.bash .cell-code}\n# this is an example, store it as .env.R:\nexport AT=\"ghp_aRSRESCTZII20Lklser3H\"\nexport GOOGLE_API_KEY=\"AjKSLE5SklxuRsxwPP8s0\"\n```\n:::\n\n\nNow source this file to get the keys as objects:\n\n\n::: {.cell}\n\n```{.bash .cell-code}\nsource .env\n```\n:::\n\n\nGet environment variables as R objects:\n\n\n::: {.cell}\n\n```{.r .cell-code}\npat <- Sys.getenv(\"PAT\")\ngoogle_api_key <- Sys.getenv(\"GOOGLE_API_KEY\")\n```\n:::\n\n\n## Set colors\n\nThese are the same colors as on glittr.org\n\n\n::: {.cell}\n\n```{.r .cell-code}\nglittr_cols <- c(\n  \"Scripting and languages\" = \"#3a86ff\",\n  \"Computational methods and pipelines\" = \"#fb5607\",\n  \"Omics analysis\" = \"#ff006e\",\n  \"Reproducibility and data management\" = \"#ffbe0b\",\n  \"Statistics and machine learning\" = \"#8338ec\",\n  \"Others\" = \"#000000\")\n```\n:::\n\n\n## Parse repository data\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# get all repositories content as nested list\nparsed <- request(\"https://glittr.org/api/repositories\") |>\n  req_perform() |> resp_body_json()\n\n# extract relevant items as dataframe\nrepo_info_list <- lapply(parsed$data, function(x) data.frame(\n  repo = x$name,\n  author_name = x$author$name,\n  stargazers = x$stargazers,\n  recency = x$days_since_last_push,\n  url = x$url,\n  license = ifelse(is.null(x$license), \"none\", x$license),\n  main_tag = x$tags[[1]]$name,\n  main_category = x$tags[[1]]$category\n))\n\nrepo_info <- do.call(rbind, repo_info_list)\n\n# create a column with provider (either github or gitlab)\nrepo_info$provider <- ifelse(grepl(\"github\", repo_info$url), \"github\", \"gitlab\")\n\n# create a factor for categories for sorting\nrepo_info$main_category <- factor(repo_info$main_category,\n                                  levels = names(glittr_cols))\n\n# category table to keep order the same in the plots\ncat_table <- table(category = repo_info$main_category)\ncat_table <- sort(cat_table)\n```\n:::\n\n\nNumber of repositories: 568\n\n## Get contributors info\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# take long time to run, so try to use cache results if no repos have been \n# added in the meantime\n\n# check if data/n_contributors.rds exists\nif(file.exists(\"data/n_contributors.rds\")) {\n  n_contributors <- readRDS(\"data/n_contributors.rds\")\n}\n\n# get contributors info only from github repos\nrepo_info_gh <- repo_info[repo_info$provider == \"github\", ]\n\n# get contributor info from github api if update is needed\nif(!identical(sort(repo_info_gh$repo), sort(names(n_contributors)))) {\n  dir.create(\"data\", showWarnings = FALSE)\n  n_contributors <- sapply(repo_info_gh$repo, function(x) {\n    \n    # get repo contributors\n    resp <- request(\"https://api.github.com/repos/\") |>\n      req_url_path_append(x) |>\n      req_url_path_append(\"contributors\") |>\n      req_url_query(per_page = 1) |>\n      req_headers(\n        Accept = \"application/vnd.github+json\",\n        Authorization = paste(\"Bearer\", pat),\n        `X-GitHub-Api-Version` = \"2022-11-28\",\n      ) |>\n      req_perform() \n    \n    link_url <- resp_link_url(resp, \"last\")\n    if(is.null(link_url)) {\n      return(1)\n    } else {\n      npages <- strsplit(link_url, \"&page=\")[[1]][2] |> as.numeric()\n      return(npages)\n    }\n  })\n  \n  # overwrite rds file\n  saveRDS(n_contributors, \"data/n_contributors.rds\")\n}\n\nrepo_info_gh$contributors <- n_contributors[repo_info_gh$repo]\n```\n:::\n\n\n## Get country information\n\n\n::: {.cell}\n\n:::\n\n\n- Number of authors: 263\n- Number of countries: 25\n\n## Parse tag data\n\n\n::: {.cell}\n\n```{.r .cell-code}\nparsed <- request(\"https://glittr.org/api/tags\") |>\n  req_perform() |> resp_body_json()\n\ntag_dfs <- list()\nfor(i in seq_along(parsed)) {\n  category <- parsed[[i]]$category\n  name <- sapply(parsed[[i]]$tags, function(x) x$name)\n  repositories <- sapply(parsed[[i]]$tags, function(x) x$repositories)\n  tag_dfs[[category]] <- data.frame(name, category, repositories)\n}\n\ntag_df <- do.call(rbind, tag_dfs) |> arrange(repositories)\n```\n:::\n\n\nNumber of tags/topics: 55\n\n## Categories\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncat_count_plot <- table(category = repo_info$main_category) |>\n  as.data.frame() |>\n  ggplot(aes(x = reorder(category, Freq), y = Freq, fill = category)) +\n  geom_bar(stat = \"identity\") +\n  scale_fill_manual(values = glittr_cols) +\n  coord_flip() +\n  theme_classic() +\n  ggtitle(\"Categories\") +\n  theme(legend.position = \"none\",\n        axis.title.y = element_blank()) +\n  ylab(\"Number of repositories\")\n\nprint(cat_count_plot)\n```\n\n::: {.cell-output-display}\n![](index_files/figure-html/unnamed-chunk-10-1.png){width=672}\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\ncategory_count <- table(category = repo_info$main_category) |> as.data.frame()\nknitr::kable(category_count)\n```\n\n::: {.cell-output-display}\n|category                            | Freq|\n|:-----------------------------------|----:|\n|Scripting and languages             |  314|\n|Computational methods and pipelines |   54|\n|Omics analysis                      |   84|\n|Reproducibility and data management |   51|\n|Statistics and machine learning     |   40|\n|Others                              |   23|\n:::\n:::\n\n\n## Licensing\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlic_freq_data <- table(license = repo_info$license,\n                       main_category = repo_info$main_category) |>\n  as.data.frame()\n\nlic_freq_data$main_category <- factor(lic_freq_data$main_category,\n                                     levels = names(cat_table))\n\nlic_freq_plot <- lic_freq_data |>\n  ggplot(aes(x = reorder(license, Freq), y = Freq, fill = main_category)) +\n  geom_bar(stat = \"identity\") +\n  coord_flip() +\n  scale_fill_manual(values = glittr_cols) +\n  theme_classic() +\n  ggtitle(\"License type\") +\n  ylab(\"Number of repositories\") +\n  theme(legend.position = \"none\",\n        axis.title.y = element_blank())\n\nprint(lic_freq_plot)\n```\n\n::: {.cell-output-display}\n![](index_files/figure-html/unnamed-chunk-12-1.png){width=672}\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nrepo_info$license |>\n  table() |>\n  as.data.frame() |>\n  mutate(perc = round(Freq/nrow(repo_info)*100, 1)) |>\n  arrange(desc(Freq)) |>\n  knitr::kable()\n```\n\n::: {.cell-output-display}\n|Var1         | Freq| perc|\n|:------------|----:|----:|\n|other        |  211| 37.3|\n|none         |  172| 30.4|\n|mit          |   59| 10.4|\n|cc-by-sa-4.0 |   30|  5.3|\n|gpl-3.0      |   26|  4.6|\n|cc-by-4.0    |   23|  4.1|\n|cc0-1.0      |   22|  3.9|\n|apache-2.0   |   10|  1.8|\n|bsd-3-clause |    7|  1.2|\n|agpl-3.0     |    2|  0.4|\n|artistic-2.0 |    2|  0.4|\n|unlicense    |    1|  0.2|\n|wtfpl        |    1|  0.2|\n:::\n:::\n\n\n## Authors\n\n\n::: {.cell}\n\n```{.r .cell-code}\nauthor_freq <- table(author_name = repo_info$author_name, \n                     main_category = repo_info$main_category) |>\n  as.data.frame()\n\nauthor_freq$main_category <- factor(author_freq$main_category,\n                                     levels = names(cat_table))\n\nrepos_per_author <- table(repo_info$author_name)\n\nlf_authors <- names(repos_per_author)[repos_per_author < 5]\n\nauthor_freq_plot <- author_freq |>\n  filter(!author_name %in% lf_authors) |>\n  arrange(Freq) |>\n  ggplot(aes(x = reorder(author_name, Freq), y = Freq, fill = main_category)) +\n  geom_bar(stat = \"identity\") +\n  coord_flip() +\n  ggtitle(\"Author or organization\") +\n  ylab(\"Number of repositories\") +\n  scale_fill_manual(values = glittr_cols) +\n  annotate(geom = \"text\", x = 2, y = 30,\n           label = paste(\"Authors with < 5 repos: \",\n                         length(lf_authors)),\n           color=\"black\") +\n  theme_classic() +\n  theme(legend.position = \"none\",\n        axis.title.y = element_blank())\n\nprint(author_freq_plot)\n```\n\n::: {.cell-output-display}\n![](index_files/figure-html/unnamed-chunk-14-1.png){width=672}\n:::\n:::\n\n\n## Tags\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntag_freq_plot <- tag_df |>\n  filter(repositories > 10) |>\n  ggplot(aes(x = reorder(name, repositories),\n             y = repositories, fill = category)) +\n  geom_bar(stat = \"identity\") +\n  coord_flip() +\n  scale_fill_manual(values = glittr_cols) +\n  ggtitle(\"Tags with > 10 repositories\") +\n  ylab(\"Number of repositories\") +\n  annotate(geom = \"text\", x = 2, y = 150,\n           label = paste(\"Total number of tags: \",\n                         nrow(tag_df)),\n           color=\"black\") +\n  theme_classic() +\n  theme(legend.position = \"none\",\n        axis.title.y = element_blank())\n\nprint(tag_freq_plot)\n```\n\n::: {.cell-output-display}\n![](index_files/figure-html/unnamed-chunk-15-1.png){width=672}\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\ntag_df |>\n  filter(repositories > 10) |>\n  arrange(desc(repositories)) |>\n  knitr::kable(row.names = FALSE)\n```\n\n::: {.cell-output-display}\n|name                       |category                            | repositories|\n|:--------------------------|:-----------------------------------|------------:|\n|R                          |Scripting and languages             |          230|\n|Python                     |Scripting and languages             |           81|\n|Transcriptomics            |Omics analysis                      |           74|\n|RNA-seq                    |Omics analysis                      |           65|\n|Statistics                 |Statistics and machine learning     |           54|\n|Next generation sequencing |Omics analysis                      |           48|\n|Data science               |Statistics and machine learning     |           45|\n|Machine learning           |Statistics and machine learning     |           41|\n|Genomics                   |Omics analysis                      |           37|\n|Unix/Linux                 |Scripting and languages             |           34|\n|Single-cell sequencing     |Omics analysis                      |           34|\n|Data management            |Reproducibility and data management |           34|\n|Reproducibility            |Reproducibility and data management |           29|\n|FAIR data                  |Reproducibility and data management |           28|\n|General                    |Others                              |           28|\n|Data visualization         |Scripting and languages             |           27|\n|Variant analysis           |Omics analysis                      |           23|\n|Version control            |Scripting and languages             |           21|\n|Containerization           |Computational methods and pipelines |           18|\n|Workflows                  |Computational methods and pipelines |           17|\n|Shiny                      |Scripting and languages             |           15|\n|Metagenomics               |Omics analysis                      |           15|\n|Docker                     |Computational methods and pipelines |           13|\n|Julia                      |Scripting and languages             |           12|\n|Nextflow                   |Computational methods and pipelines |           12|\n|ChIP-seq                   |Omics analysis                      |           11|\n:::\n:::\n\n\n## Contributors boxplot\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrepo_info_gh$main_category <- factor(repo_info_gh$main_category,\n                                     levels = names(cat_table))\n\ncontributors_plot <- repo_info_gh |>\n  ggplot(aes(x = main_category, y = contributors, fill = main_category)) +\n  geom_violin(scale = \"width\") +\n  geom_boxplot(width = 0.1, col = \"darkgrey\") +\n  coord_flip() +\n  ggtitle(\"Contributors\") +\n  ylab(\"Number of contributors\") +\n  scale_y_sqrt() +\n  scale_fill_manual(values = glittr_cols) +\n  theme_bw() +\n  theme(legend.position = \"none\",\n        axis.title.y = element_blank(),\n        plot.margin = margin(t = 5, r = 10, b = 5, l = 10))\n\nprint(contributors_plot)\n```\n\n::: {.cell-output-display}\n![](index_files/figure-html/unnamed-chunk-17-1.png){width=672}\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nnna_contr <- repo_info_gh$contributors\nparam1 <- sum(nna_contr > 10)/length(nna_contr)\n# 27.3% have more than 10 contributors\nparam2 <- sum(nna_contr > 1)/length(nna_contr)\n# 78.6% have more than one contributor\n# 115 repos with only one contributor\nparam3 <- sum(nna_contr <= 5)/length(nna_contr)\n```\n:::\n\n\n- More than 10 contributors: 25.6%\n- More than 1 contributor: 78.8%\n- Between 1 and 5 contributors: 59.9%\n\n## Countries\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncountry_freq <- table(country = repo_info$country, \n                      main_category = repo_info$main_category) |>\n  as.data.frame()\n\ncountry_freq$main_category <- factor(country_freq$main_category,\n                                     levels = names(cat_table))\n\ncountry_freq_plot <- country_freq |>\n  filter(country != \"undefined\") |>\n  ggplot(aes(x = reorder(country, Freq), y = Freq, fill = main_category)) +\n  geom_bar(stat = \"identity\") +\n  coord_flip() +\n  ggtitle(\"Country\") +\n  ylab(\"Number of repositories\") +\n  scale_fill_manual(values = glittr_cols) +\n  annotate(geom = \"text\", x = 2, y = 70,\n           label = paste(\"Repos with undefined country: \",\n                         sum(repo_info$country == \"undefined\")),\n           color=\"black\") +\n  theme_classic() +\n  theme(legend.position = \"none\",\n        axis.title.y = element_blank())\n\nprint(country_freq_plot)\n```\n\n::: {.cell-output-display}\n![](index_files/figure-html/unnamed-chunk-19-1.png){width=672}\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nrepo_info$country |> table() |> as.data.frame() |> arrange(desc(Freq)) |> knitr::kable()\n```\n\n::: {.cell-output-display}\n|Var1           | Freq|\n|:--------------|----:|\n|undefined      |  244|\n|United States  |  132|\n|Switzerland    |   27|\n|Canada         |   26|\n|Sweden         |   21|\n|United Kingdom |   19|\n|Australia      |   15|\n|Germany        |   12|\n|France         |   11|\n|Netherlands    |   11|\n|Portugal       |   11|\n|Belgium        |   10|\n|Spain          |    8|\n|Denmark        |    4|\n|Italy          |    3|\n|Bulgaria       |    2|\n|Ireland        |    2|\n|Argentina      |    1|\n|China          |    1|\n|Finland        |    1|\n|India          |    1|\n|Luxembourg     |    1|\n|Norway         |    1|\n|Poland         |    1|\n|Ukraine        |    1|\n:::\n:::\n\n\n\n## Summary plot\n\n\n::: {.cell}\n\n```{.r .cell-code}\npdf(\"grid_plot_fig1.pdf\", width = 10, height = 10)\nplot_grid(cat_count_plot, contributors_plot, \n          tag_freq_plot, author_freq_plot,  \n          lic_freq_plot, country_freq_plot,\n          ncol = 2, labels = LETTERS[1:6],\n          rel_heights = c(2,3,3))\ndev.off()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nquartz_off_screen \n                2 \n```\n:::\n:::\n",
-    "supporting": [
-      "index_files"
-    ],
-    "filters": [
-      "rmarkdown/pagebreak.lua"
-    ],
-    "includes": {},
-    "engineDependencies": {},
-    "preserve": {},
-    "postProcess": true
-  }
-}
\ No newline at end of file
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diff --git a/_freeze/site_libs/clipboard/clipboard.min.js b/_freeze/site_libs/clipboard/clipboard.min.js
deleted file mode 100644
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--- a/_freeze/site_libs/clipboard/clipboard.min.js
+++ /dev/null
@@ -1,7 +0,0 @@
-/*!
- * clipboard.js v2.0.11
- * https://clipboardjs.com/
- *
- * Licensed MIT © Zeno Rocha
- */
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