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adds more docs and simplifies order of plots
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Geert van Geest committed Apr 9, 2024
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---
title: "Glittr stats"
format:
html:
code-fold: true
html
---

## Set up the environment

Loading required packages.

```{r}
#| output: false
library(httr2)
Expand Down Expand Up @@ -35,23 +38,25 @@ pat <- Sys.getenv("PAT")
google_api_key <- Sys.getenv("GOOGLE_API_KEY")
```

## Set colors

These are the same colors as on glittr.org
Setting colors. These are the same colors as on glittr.org.

```{r}
glittr_cols <- c(
"Scripting and languages" = "#3a86ff",
"Scripting and languages" = "#3a86ff",
"Computational methods and pipelines" = "#fb5607",
"Omics analysis" = "#ff006e",
"Omics analysis" = "#ff006e",
"Reproducibility and data management" = "#ffbe0b",
"Statistics and machine learning" = "#8338ec",
"Others" = "#000000")
"Statistics and machine learning" = "#8338ec",
"Others" = "#000000")
```

## Parse repository data

Using the glittr.org REST API to get repository metadata, among which the stargazers, recency, category, license and tags.

```{r}
#| code-fold: true
# get all repositories content as nested list
parsed <- request("https://glittr.org/api/repositories") |>
req_perform() |> resp_body_json()
Expand Down Expand Up @@ -86,7 +91,11 @@ Number of repositories: `r nrow(repo_info)`

## Get contributors info

Using the GitHub REST API to get the number of contributors for each repository on glittr.org. This takes a few minutes, so if the contributors haven't changed, it will use a cached version.

```{r}
#| code-fold: true
# take long time to run, so try to use cache results if no repos have been
# added in the meantime
Expand Down Expand Up @@ -136,10 +145,12 @@ repo_info_gh$contributors <- n_contributors[repo_info_gh$repo]

## Get country information

Here we get country information for all authors and organizations. It uses the free text specified at 'location'. Since this can be anything, we use the google REST API to translate that into country.

```{r}
#| echo: false
#| warning: false
#| output: false
#| code-fold: true
# check whether author info exists for caching
if(file.exists("data/author_info.rds")) {
Expand Down Expand Up @@ -220,6 +231,8 @@ repo_info$country[is.na(repo_info$country)] <- "undefined"

## Parse tag data

Here, we create `tag_df` that contains information for each tag by using the glittr.org API.

```{r}
parsed <- request("https://glittr.org/api/tags") |>
req_perform() |> resp_body_json()
Expand All @@ -237,9 +250,14 @@ tag_df <- do.call(rbind, tag_dfs) |> arrange(repositories)

Number of tags/topics: `r nrow(tag_df)`

## Categories
## Number of repositories by category

This is figure 2A in the manuscript.

```{r}
#| label: fig-categories
#| fig-cap: Number of repositories per category
cat_count_plot <- table(category = repo_info$main_category) |>
as.data.frame() |>
ggplot(aes(x = reorder(category, Freq), y = Freq, fill = category)) +
Expand All @@ -255,47 +273,105 @@ cat_count_plot <- table(category = repo_info$main_category) |>
print(cat_count_plot)
```

And a table with the actual numbers

```{r}
#| label: tbl-categories
#| tbl-cap: Number of repositories per category
category_count <- table(category = repo_info$main_category) |> as.data.frame()
knitr::kable(category_count)
```

## Licensing
## Number of contributors per repository separated by category

This is figure 2B in the manuscript.

```{r}
lic_freq_data <- table(license = repo_info$license,
main_category = repo_info$main_category) |>
as.data.frame()
#| label: fig-contributors
#| fig-cap: Number of contributors per repository separated by category
lic_freq_data$main_category <- factor(lic_freq_data$main_category,
repo_info_gh$main_category <- factor(repo_info_gh$main_category,
levels = names(cat_table))
lic_freq_plot <- lic_freq_data |>
ggplot(aes(x = reorder(license, Freq), y = Freq, fill = main_category)) +
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))
print(contributors_plot)
```

And some statistics of contributors.

```{r}
nna_contr <- repo_info_gh$contributors
param1 <- sum(nna_contr > 10)/length(nna_contr)
param2 <- sum(nna_contr > 1)/length(nna_contr)
param3 <- sum(nna_contr <= 5)/length(nna_contr)
```

- More than 10 contributors: `r signif(param1*100, digits = 3) `%
- More than 1 contributor: `r signif(param2*100, digits = 3) `%
- Between 1 and 5 contributors: `r signif(param3*100, digits = 3) `%

## Number of repositories per tag

This is figure 2C in the manuscript.

```{r}
#| label: fig-tags
#| fig-cap: Number of repostories per tag, colored by category.
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) +
theme_classic() +
ggtitle("License type") +
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())
print(lic_freq_plot)
print(tag_freq_plot)
```

And a table with the actual numbers.

```{r}
repo_info$license |>
table() |>
as.data.frame() |>
mutate(perc = round(Freq/nrow(repo_info)*100, 1)) |>
arrange(desc(Freq)) |>
knitr::kable()
#| label: tbl-tags
#| tbl-cap: Number of repositories per tag
tag_df |>
filter(repositories > 10) |>
arrange(desc(repositories)) |>
knitr::kable(row.names = FALSE)
```

## Authors
## Number of repositories by author

This is figure 2D in the manuscript.

```{r}
#| label: fig-author
#| fig-cap: Number of repositories per author colored by category
author_freq <- table(author_name = repo_info$author_name,
main_category = repo_info$main_category) |>
as.data.frame()
Expand Down Expand Up @@ -327,76 +403,72 @@ author_freq_plot <- author_freq |>
print(author_freq_plot)
```

## Tags
And a table with the actual numbers.

```{r}
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())
#| label: tbl-author
#| tbl-cap: Number of repositories per author
print(tag_freq_plot)
```
table(repo_info$author_name) |>
as.data.frame() |>
filter(Freq >= 5) |>
arrange(desc(Freq)) |>
knitr::kable()
```{r}
tag_df |>
filter(repositories > 10) |>
arrange(desc(repositories)) |>
knitr::kable(row.names = FALSE)
```

## Contributors boxplot
## Number of repositories per license

This is figure 2E in the manuscript.

```{r}
repo_info_gh$main_category <- factor(repo_info_gh$main_category,
#| label: fig-license
#| fig-cap: Number of repositories per license
lic_freq_data <- table(license = repo_info$license,
main_category = repo_info$main_category) |>
as.data.frame()
lic_freq_data$main_category <- factor(lic_freq_data$main_category,
levels = names(cat_table))
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") +
lic_freq_plot <- lic_freq_data |>
ggplot(aes(x = reorder(license, Freq), y = Freq, fill = main_category)) +
geom_bar(stat = "identity") +
coord_flip() +
ggtitle("Contributors") +
ylab("Number of contributors") +
scale_y_sqrt() +
scale_fill_manual(values = glittr_cols) +
theme_bw() +
theme_classic() +
ggtitle("License type") +
ylab("Number of repositories") +
theme(legend.position = "none",
axis.title.y = element_blank(),
plot.margin = margin(t = 5, r = 10, b = 5, l = 10))
axis.title.y = element_blank())
print(contributors_plot)
print(lic_freq_plot)
```

And a table with the actual numbers.

```{r}
nna_contr <- repo_info_gh$contributors
param1 <- sum(nna_contr > 10)/length(nna_contr)
# 27.3% have more than 10 contributors
param2 <- sum(nna_contr > 1)/length(nna_contr)
# 78.6% have more than one contributor
# 115 repos with only one contributor
param3 <- sum(nna_contr <= 5)/length(nna_contr)
#| label: tbl-license
#| tbl-cap: Number of repositories per license
repo_info$license |>
table() |>
as.data.frame() |>
mutate(perc = round(Freq/nrow(repo_info)*100, 1)) |>
arrange(desc(Freq)) |>
knitr::kable()
```

- More than 10 contributors: `r signif(param1*100, digits = 3) `%
- More than 1 contributor: `r signif(param2*100, digits = 3) `%
- Between 1 and 5 contributors: `r signif(param3*100, digits = 3) `%

## Countries
## Number of repositories per country

This is figure 2F in the mansucript.

```{r}
#| label: fig-country
#| fig-cap: Number of repositories per country colored by category
#|
country_freq <- table(country = repo_info$country,
main_category = repo_info$main_category) |>
as.data.frame()
Expand All @@ -423,13 +495,24 @@ country_freq_plot <- country_freq |>
print(country_freq_plot)
```

And a table with the actual numbers.

```{r}
repo_info$country |> table() |> as.data.frame() |> arrange(desc(Freq)) |> knitr::kable()
#| label: tbl-country
#| tbl-cap: Number of repositories per country
repo_info$country |>
table() |>
as.data.frame() |>
arrange(desc(Freq)) |>
knitr::kable()
```


## Summary plot

A pdf that containing figure 2 in the manuscript.

```{r}
pdf("grid_plot_fig1.pdf", width = 10, height = 10)
plot_grid(cat_count_plot, contributors_plot,
Expand Down

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