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-library(httr2)
-library(ggplot2)
-library(dplyr)
-library(ggbreak)
-library(cowplot)
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-Glittr stats
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-Create 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.:
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-# this is an example, store it as .env.R:
-export AT="ghp_aRSRESCTZII20Lklser3H"
-export GOOGLE_API_KEY="AjKSLE5SklxuRsxwPP8s0"Now source this file to get the keys as objects:
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-source .envGet environment variables as R objects:
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-pat <- Sys.getenv("PAT")
-google_api_key <- Sys.getenv("GOOGLE_API_KEY")Set colors
-These are the same colors as on glittr.org
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-glittr_cols <- c(
- "Scripting and languages" = "#3a86ff",
- "Computational methods and pipelines" = "#fb5607",
- "Omics analysis" = "#ff006e",
- "Reproducibility and data management" = "#ffbe0b",
- "Statistics and machine learning" = "#8338ec",
- "Others" = "#000000")Parse repository data
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-# get all repositories content as nested list
-parsed <- request("https://glittr.org/api/repositories") |>
- req_perform() |> resp_body_json()
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-# extract relevant items as dataframe
-repo_info_list <- lapply(parsed$data, function(x) data.frame(
- repo = x$name,
- author_name = x$author$name,
- stargazers = x$stargazers,
- recency = x$days_since_last_push,
- url = x$url,
- license = ifelse(is.null(x$license), "none", x$license),
- main_tag = x$tags[[1]]$name,
- main_category = x$tags[[1]]$category
-))
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-repo_info <- do.call(rbind, repo_info_list)
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-# create a column with provider (either github or gitlab)
-repo_info$provider <- ifelse(grepl("github", repo_info$url), "github", "gitlab")
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-# create a factor for categories for sorting
-repo_info$main_category <- factor(repo_info$main_category,
- levels = names(glittr_cols))
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-# category table to keep order the same in the plots
-cat_table <- table(category = repo_info$main_category)
-cat_table <- sort(cat_table)Number of repositories: 568
-Get contributors info
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-# take long time to run, so try to use cache results if no repos have been
-# added in the meantime
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-# check if data/n_contributors.rds exists
-if(file.exists("data/n_contributors.rds")) {
- n_contributors <- readRDS("data/n_contributors.rds")
-}
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-# get contributors info only from github repos
-repo_info_gh <- repo_info[repo_info$provider == "github", ]
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-# get contributor info from github api if update is needed
-if(!identical(sort(repo_info_gh$repo), sort(names(n_contributors)))) {
- dir.create("data", showWarnings = FALSE)
- n_contributors <- sapply(repo_info_gh$repo, function(x) {
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- # get repo contributors
- resp <- request("https://api.github.com/repos/") |>
- req_url_path_append(x) |>
- req_url_path_append("contributors") |>
- req_url_query(per_page = 1) |>
- req_headers(
- Accept = "application/vnd.github+json",
- Authorization = paste("Bearer", pat),
- `X-GitHub-Api-Version` = "2022-11-28",
- ) |>
- req_perform()
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- link_url <- resp_link_url(resp, "last")
- if(is.null(link_url)) {
- return(1)
- } else {
- npages <- strsplit(link_url, "&page=")[[1]][2] |> as.numeric()
- return(npages)
- }
- })
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- # overwrite rds file
- saveRDS(n_contributors, "data/n_contributors.rds")
-}
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-repo_info_gh$contributors <- n_contributors[repo_info_gh$repo]Get country information
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- Number of authors: 263 -
- Number of countries: 25 -
Parse tag data
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-parsed <- request("https://glittr.org/api/tags") |>
- req_perform() |> resp_body_json()
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-tag_dfs <- list()
-for(i in seq_along(parsed)) {
- category <- parsed[[i]]$category
- name <- sapply(parsed[[i]]$tags, function(x) x$name)
- repositories <- sapply(parsed[[i]]$tags, function(x) x$repositories)
- tag_dfs[[category]] <- data.frame(name, category, repositories)
-}
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-tag_df <- do.call(rbind, tag_dfs) |> arrange(repositories)Number of tags/topics: 55
-Categories
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-cat_count_plot <- table(category = repo_info$main_category) |>
- as.data.frame() |>
- ggplot(aes(x = reorder(category, Freq), y = Freq, fill = category)) +
- geom_bar(stat = "identity") +
- scale_fill_manual(values = glittr_cols) +
- coord_flip() +
- theme_classic() +
- ggtitle("Categories") +
- theme(legend.position = "none",
- axis.title.y = element_blank()) +
- ylab("Number of repositories")
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-print(cat_count_plot)
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-category_count <- table(category = repo_info$main_category) |> as.data.frame()
-knitr::kable(category_count)
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-| category | -Freq | -
|---|---|
| Scripting and languages | -314 | -
| Computational methods and pipelines | -54 | -
| Omics analysis | -84 | -
| Reproducibility and data management | -51 | -
| Statistics and machine learning | -40 | -
| Others | -23 | -
Licensing
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-lic_freq_data <- table(license = repo_info$license,
- main_category = repo_info$main_category) |>
- as.data.frame()
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-lic_freq_data$main_category <- factor(lic_freq_data$main_category,
- levels = names(cat_table))
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-lic_freq_plot <- lic_freq_data |>
- ggplot(aes(x = reorder(license, Freq), y = Freq, fill = main_category)) +
- geom_bar(stat = "identity") +
- coord_flip() +
- scale_fill_manual(values = glittr_cols) +
- theme_classic() +
- ggtitle("License type") +
- ylab("Number of repositories") +
- theme(legend.position = "none",
- axis.title.y = element_blank())
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-print(lic_freq_plot)
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-repo_info$license |>
- table() |>
- as.data.frame() |>
- mutate(perc = round(Freq/nrow(repo_info)*100, 1)) |>
- arrange(desc(Freq)) |>
- knitr::kable()
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-| Var1 | -Freq | -perc | -
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| other | -211 | -37.3 | -
| none | -172 | -30.4 | -
| mit | -59 | -10.4 | -
| cc-by-sa-4.0 | -30 | -5.3 | -
| gpl-3.0 | -26 | -4.6 | -
| cc-by-4.0 | -23 | -4.1 | -
| cc0-1.0 | -22 | -3.9 | -
| apache-2.0 | -10 | -1.8 | -
| bsd-3-clause | -7 | -1.2 | -
| agpl-3.0 | -2 | -0.4 | -
| artistic-2.0 | -2 | -0.4 | -
| unlicense | -1 | -0.2 | -
| wtfpl | -1 | -0.2 | -
Authors
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-author_freq <- table(author_name = repo_info$author_name,
- main_category = repo_info$main_category) |>
- as.data.frame()
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-author_freq$main_category <- factor(author_freq$main_category,
- levels = names(cat_table))
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-repos_per_author <- table(repo_info$author_name)
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-lf_authors <- names(repos_per_author)[repos_per_author < 5]
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-author_freq_plot <- author_freq |>
- filter(!author_name %in% lf_authors) |>
- arrange(Freq) |>
- ggplot(aes(x = reorder(author_name, Freq), y = Freq, fill = main_category)) +
- geom_bar(stat = "identity") +
- coord_flip() +
- ggtitle("Author or organization") +
- ylab("Number of repositories") +
- scale_fill_manual(values = glittr_cols) +
- annotate(geom = "text", x = 2, y = 30,
- label = paste("Authors with < 5 repos: ",
- length(lf_authors)),
- color="black") +
- theme_classic() +
- theme(legend.position = "none",
- axis.title.y = element_blank())
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-print(author_freq_plot)
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Tags
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-tag_freq_plot <- tag_df |>
- filter(repositories > 10) |>
- ggplot(aes(x = reorder(name, repositories),
- y = repositories, fill = category)) +
- geom_bar(stat = "identity") +
- coord_flip() +
- scale_fill_manual(values = glittr_cols) +
- ggtitle("Tags with > 10 repositories") +
- ylab("Number of repositories") +
- annotate(geom = "text", x = 2, y = 150,
- label = paste("Total number of tags: ",
- nrow(tag_df)),
- color="black") +
- theme_classic() +
- theme(legend.position = "none",
- axis.title.y = element_blank())
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-print(tag_freq_plot)
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-tag_df |>
- filter(repositories > 10) |>
- arrange(desc(repositories)) |>
- knitr::kable(row.names = FALSE)
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-| name | -category | -repositories | -
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| R | -Scripting and languages | -230 | -
| Python | -Scripting and languages | -81 | -
| Transcriptomics | -Omics analysis | -74 | -
| RNA-seq | -Omics analysis | -65 | -
| Statistics | -Statistics and machine learning | -54 | -
| Next generation sequencing | -Omics analysis | -48 | -
| Data science | -Statistics and machine learning | -45 | -
| Machine learning | -Statistics and machine learning | -41 | -
| Genomics | -Omics analysis | -37 | -
| Unix/Linux | -Scripting and languages | -34 | -
| Single-cell sequencing | -Omics analysis | -34 | -
| Data management | -Reproducibility and data management | -34 | -
| Reproducibility | -Reproducibility and data management | -29 | -
| FAIR data | -Reproducibility and data management | -28 | -
| General | -Others | -28 | -
| Data visualization | -Scripting and languages | -27 | -
| Variant analysis | -Omics analysis | -23 | -
| Version control | -Scripting and languages | -21 | -
| Containerization | -Computational methods and pipelines | -18 | -
| Workflows | -Computational methods and pipelines | -17 | -
| Shiny | -Scripting and languages | -15 | -
| Metagenomics | -Omics analysis | -15 | -
| Docker | -Computational methods and pipelines | -13 | -
| Julia | -Scripting and languages | -12 | -
| Nextflow | -Computational methods and pipelines | -12 | -
| ChIP-seq | -Omics analysis | -11 | -
Contributors boxplot
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-repo_info_gh$main_category <- factor(repo_info_gh$main_category,
- levels = names(cat_table))
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-contributors_plot <- repo_info_gh |>
- ggplot(aes(x = main_category, y = contributors, fill = main_category)) +
- geom_violin(scale = "width") +
- geom_boxplot(width = 0.1, col = "darkgrey") +
- coord_flip() +
- ggtitle("Contributors") +
- ylab("Number of contributors") +
- scale_y_sqrt() +
- scale_fill_manual(values = glittr_cols) +
- theme_bw() +
- theme(legend.position = "none",
- axis.title.y = element_blank(),
- plot.margin = margin(t = 5, r = 10, b = 5, l = 10))
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-print(contributors_plot)
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-nna_contr <- repo_info_gh$contributors
-param1 <- sum(nna_contr > 10)/length(nna_contr)
-# 27.3% have more than 10 contributors
-param2 <- sum(nna_contr > 1)/length(nna_contr)
-# 78.6% have more than one contributor
-# 115 repos with only one contributor
-param3 <- sum(nna_contr <= 5)/length(nna_contr)-
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- More than 10 contributors: 25.6% -
- More than 1 contributor: 78.8% -
- Between 1 and 5 contributors: 59.9% -
Countries
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-country_freq <- table(country = repo_info$country,
- main_category = repo_info$main_category) |>
- as.data.frame()
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-country_freq$main_category <- factor(country_freq$main_category,
- levels = names(cat_table))
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-country_freq_plot <- country_freq |>
- filter(country != "undefined") |>
- ggplot(aes(x = reorder(country, Freq), y = Freq, fill = main_category)) +
- geom_bar(stat = "identity") +
- coord_flip() +
- ggtitle("Country") +
- ylab("Number of repositories") +
- scale_fill_manual(values = glittr_cols) +
- annotate(geom = "text", x = 2, y = 70,
- label = paste("Repos with undefined country: ",
- sum(repo_info$country == "undefined")),
- color="black") +
- theme_classic() +
- theme(legend.position = "none",
- axis.title.y = element_blank())
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-print(country_freq_plot)
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-repo_info$country |> table() |> as.data.frame() |> arrange(desc(Freq)) |> knitr::kable()
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-| Var1 | -Freq | -
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| undefined | -244 | -
| United States | -132 | -
| Switzerland | -27 | -
| Canada | -26 | -
| Sweden | -21 | -
| United Kingdom | -19 | -
| Australia | -15 | -
| Germany | -12 | -
| France | -11 | -
| Netherlands | -11 | -
| Portugal | -11 | -
| Belgium | -10 | -
| Spain | -8 | -
| Denmark | -4 | -
| Italy | -3 | -
| Bulgaria | -2 | -
| Ireland | -2 | -
| Argentina | -1 | -
| China | -1 | -
| Finland | -1 | -
| India | -1 | -
| Luxembourg | -1 | -
| Norway | -1 | -
| Poland | -1 | -
| Ukraine | -1 | -
Summary plot
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-pdf("grid_plot_fig1.pdf", width = 10, height = 10)
-plot_grid(cat_count_plot, contributors_plot,
- tag_freq_plot, author_freq_plot,
- lic_freq_plot, country_freq_plot,
- ncol = 2, labels = LETTERS[1:6],
- rel_heights = c(2,3,3))
-dev.off()
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-quartz_off_screen
- 2