From 756e2add1883257ca4ba4ce40209ab66745a3928 Mon Sep 17 00:00:00 2001 From: Tylar Date: Wed, 8 Jan 2025 17:09:05 -0500 Subject: [PATCH] Built site for gh-pages --- .nojekyll | 2 +- doi-reports/doi-report-template.html | 682 ++++++++++++++++++ index.html | 36 + index_files/figure-html/unnamed-chunk-5-1.png | Bin 0 -> 44310 bytes regional_report/regional_reports.html | 16 +- search.json | 9 +- site_libs/reactwidget-2.0.0/react-tools.js | 1 + .../reactwidget-2.0.0/react-tools.umd.cjs | 1 - sitemap.xml | 22 +- 9 files changed, 749 insertions(+), 20 deletions(-) create mode 100644 doi-reports/doi-report-template.html create mode 100644 index_files/figure-html/unnamed-chunk-5-1.png create mode 100644 site_libs/reactwidget-2.0.0/react-tools.js delete mode 100644 site_libs/reactwidget-2.0.0/react-tools.umd.cjs diff --git a/.nojekyll b/.nojekyll index 16722a5..f76b678 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -e88e6270 \ No newline at end of file +18a73d1b \ No newline at end of file diff --git a/doi-reports/doi-report-template.html b/doi-reports/doi-report-template.html new file mode 100644 index 0000000..124a6b6 --- /dev/null +++ b/doi-reports/doi-report-template.html @@ -0,0 +1,682 @@ + + + + + + + + + +SE US – MBON Data Report + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ +
+
+

SE US

+
+ + + +
+ + + + +
+ + + +
+ + +
+
+get doi citations & print them +
library(glue)
+library(here)
+library(httr)
+library(jsonlite)
+library(tidyr)
+
+source(here("R/getCleanedData.R"))
+df <- getCleanedData()
+
+# Step 1: Split comma-separated DOIs into multiple rows
+df <- df %>%
+  separate_rows(doi, sep = ",") %>%
+  filter(!is.na(doi) & doi != "")  # Remove empty or NA DOIs
+
+# Initialize a list to store citing DOIs for each doi in the dataframe
+all_citing <- list()
+
+# Loop through each DOI in the dataframe
+for (i in seq_along(df$doi)) {
+  doi <- df$doi[i]
+  
+  # Skip if DOI is empty or NA
+  if (is.na(doi) || doi == "") {
+    message(glue("Skipping empty DOI at row {i}."))
+    next
+  }
+  
+  # Create the URL for the OpenCitations API
+  opcit <- glue("https://opencitations.net/index/coci/api/v1/citations/{doi}")
+
+    # Make the API request
+  response <- tryCatch({
+    GET(opcit)
+  }, error = function(e) {
+    message(glue("Error fetching data for DOI: {doi}, skipping."))
+    NULL
+  })
+  
+  # Check if response is valid
+  if (is.null(response) || http_status(response)$category != "Success") {
+    message(glue("Invalid response for DOI: {doi}, skipping."))
+    next
+  }
+  
+  # Parse JSON content
+  result <- tryCatch({
+    content(response, "parsed", type = "application/json")
+  }, error = function(e) {
+    message(glue("Error parsing JSON for DOI: {doi}, skipping."))
+    NULL
+  })
+  
+  # Extract citing DOIs if result is valid
+  if (!is.null(result) && length(result) > 0) {
+    citing <- lapply(result, function(x) x[['citing']])
+    citing <- unlist(citing)
+    
+    # Store in the list
+    all_citing[[doi]] <- citing
+    
+    # Print the citing DOIs
+    print(glue("Citing DOIs for {doi}:"))
+    print(citing)
+  } else {
+    message(glue("No citing DOIs found for {doi}."))
+  }
+}
+
+
+
Citing DOIs for 10.6073/pasta/89b63c4b49b80fb839613e9d389d9902:
+[1] "10.1038/s43247-021-00177-9"
+
+
+
Citing DOIs for 10.6073/pasta/06c6b9983a5f0a44349e027a002f5040:
+[1] "10.1101/2022.07.05.498812"
+
+
+ + + +
+ +
+ + + + + \ No newline at end of file diff --git a/index.html b/index.html index bc4c31c..075fd9e 100644 --- a/index.html +++ b/index.html @@ -305,6 +305,42 @@

MBON Data Report(s)

+
+
+show counts for standard publication metrics +
library(ggplot2)
+library(dplyr)
+library(tidyr)
+
+source(here("R/getCleanedData.R"))
+data <- getCleanedData()
+
+# Select columns to analyze
+columns_to_check <- c(
+  "erddap_link", "ncei_link", "code_link", "obis_link", "doi", "web_link"
+)
+
+# Count non-empty values for each column
+summary_df <- data %>%
+  summarise(across(all_of(columns_to_check), ~ sum(!is.na(.) & . != ""))) %>%
+  pivot_longer(cols = everything(), names_to = "Column", values_to = "NonEmptyCount")
+
+# Plot the bar chart
+ggplot(summary_df, aes(x = Column, y = NonEmptyCount)) +
+  geom_bar(stat = "identity", fill = "steelblue") +
+  labs(title = "N Datasets Published by Repository",
+       x = "Publication Type",
+       y = "Number of Datasets") +
+  theme_minimal()
+
+
+
+
+

+
+
+
+
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diff --git a/search.json b/search.json index 0b501de..680704e 100644 --- a/search.json +++ b/search.json @@ -39,7 +39,14 @@ "href": "index.html", "title": "MBON Data Report(s)", "section": "", - "text": "setup\nif (!requireNamespace(\"librarian\", quietly = TRUE)) {\n # If not installed, install the package\n install.packages(\"librarian\")\n}\n\nlibrarian::shelf(\n dplyr,\n ggplot2,\n here,\n networkD3,\n tm,\n wordcloud\n)\n\n\n\n\nread in data from csv\nsource(here(\"R/getCleanedData.R\"))\ndata <- getCleanedData()\n\n\n\n\ncreate word cloud from titles & descriptions\ncombined_text <- paste(data$`Dataset.title`, data$`Dataset.summary`, collapse = \" \")\ncorpus <- Corpus(VectorSource(combined_text))\n\n# Clean the text data\ntoSpace <- content_transformer(function(x, pattern) gsub(pattern, \" \", x))\ncorpus <- tm_map(corpus, toSpace, \"/\")\ncorpus <- tm_map(corpus, toSpace, \"@\")\ncorpus <- tm_map(corpus, toSpace, \"\\\\|\")\n\ncorpus <- tm_map(corpus, content_transformer(tolower))\ncorpus <- tm_map(corpus, removePunctuation)\ncorpus <- tm_map(corpus, removeNumbers)\ncorpus <- tm_map(corpus, removeWords, stopwords(\"english\"))\ncorpus <- tm_map(corpus, stripWhitespace)\n\n# Create a Term-Document Matrix\ndtm <- TermDocumentMatrix(corpus)\nmatrix <- as.matrix(dtm)\nword_freqs <- sort(rowSums(matrix), decreasing = TRUE)\ndf <- data.frame(word = names(word_freqs), freq = word_freqs)\n\n# Generate the word cloud\nwordcloud(words = df$word, freq = df$freq, min.freq = 1, \n max.words = 100, random.order = FALSE, \n colors = brewer.pal(8, \"Dark2\"))\n\n\n\n\n\n\n\n\n\n\n\ncreate a sankey diagram\nsource(\"R/yes_no_values.R\") # load consts used below\ndata <- data %>%\n mutate(\n RA = ifelse(tolower(RA) %in% no_values, \"\", RA),\n erddap = ifelse(tolower(erddap) %in% yes_values, \"in ERDDAP\", erddap),\n erddap = ifelse(tolower(erddap) %in% no_values, \"\", erddap),\n obis = ifelse(tolower(obis) %in% no_values, \"\", obis),\n obis = ifelse(tolower(obis) %in% yes_values, \"in OBIS\", obis),\n ncei = ifelse(tolower(ncei) %in% no_values, \"\", ncei),\n ncei = ifelse(tolower(ncei) %in% yes_values, \"in NCEI\", ncei)\n )\n\n# Create links data frames for each column pair and merge them together\nlinks <- rbind(\n data.frame(\n source = data$RA,\n target = data$erddap,\n value = 1\n ), \n data.frame(\n source = data$RA,\n target = data$obis,\n value = 1\n ),\n data.frame(\n source = data$RA,\n target = data$ncei,\n value = 1\n )\n) %>%\n filter_all(all_vars(. != \"\")) %>% # drop anything that is empty\n rbind(\n data.frame(\n source = data$mbon.project,\n target = data$RA,\n value = 1\n )\n )\n# Create nodes data frame\nnodes <- data.frame(\n name = c(as.character(links$source), as.character(links$target)) %>% unique()\n)\n\n# Reformat the links with IDs\nlinks$IDsource <- match(links$source, nodes$name) - 1\nlinks$IDtarget <- match(links$target, nodes$name) - 1\n\n# Make the Network\np <- networkD3::sankeyNetwork(Links = links, Nodes = nodes,\n Source = \"IDsource\", Target = \"IDtarget\",\n Value = \"value\", NodeID = \"name\",\n sinksRight = FALSE)\np" + "text": "setup\nif (!requireNamespace(\"librarian\", quietly = TRUE)) {\n # If not installed, install the package\n install.packages(\"librarian\")\n}\n\nlibrarian::shelf(\n dplyr,\n ggplot2,\n here,\n networkD3,\n tm,\n wordcloud\n)\n\n\n\n\nread in data from csv\nsource(here(\"R/getCleanedData.R\"))\ndata <- getCleanedData()\n\n\n\n\ncreate word cloud from titles & descriptions\ncombined_text <- paste(data$`Dataset.title`, data$`Dataset.summary`, collapse = \" \")\ncorpus <- Corpus(VectorSource(combined_text))\n\n# Clean the text data\ntoSpace <- content_transformer(function(x, pattern) gsub(pattern, \" \", x))\ncorpus <- tm_map(corpus, toSpace, \"/\")\ncorpus <- tm_map(corpus, toSpace, \"@\")\ncorpus <- tm_map(corpus, toSpace, \"\\\\|\")\n\ncorpus <- tm_map(corpus, content_transformer(tolower))\ncorpus <- tm_map(corpus, removePunctuation)\ncorpus <- tm_map(corpus, removeNumbers)\ncorpus <- tm_map(corpus, removeWords, stopwords(\"english\"))\ncorpus <- tm_map(corpus, stripWhitespace)\n\n# Create a Term-Document Matrix\ndtm <- TermDocumentMatrix(corpus)\nmatrix <- as.matrix(dtm)\nword_freqs <- sort(rowSums(matrix), decreasing = TRUE)\ndf <- data.frame(word = names(word_freqs), freq = word_freqs)\n\n# Generate the word cloud\nwordcloud(words = df$word, freq = df$freq, min.freq = 1, \n max.words = 100, random.order = FALSE, \n colors = brewer.pal(8, \"Dark2\"))\n\n\n\n\n\n\n\n\n\n\n\ncreate a sankey diagram\nsource(\"R/yes_no_values.R\") # load consts used below\ndata <- data %>%\n mutate(\n RA = ifelse(tolower(RA) %in% no_values, \"\", RA),\n erddap = ifelse(tolower(erddap) %in% yes_values, \"in ERDDAP\", erddap),\n erddap = ifelse(tolower(erddap) %in% no_values, \"\", erddap),\n obis = ifelse(tolower(obis) %in% no_values, \"\", obis),\n obis = ifelse(tolower(obis) %in% yes_values, \"in OBIS\", obis),\n ncei = ifelse(tolower(ncei) %in% no_values, \"\", ncei),\n ncei = ifelse(tolower(ncei) %in% yes_values, \"in NCEI\", ncei)\n )\n\n# Create links data frames for each column pair and merge them together\nlinks <- rbind(\n data.frame(\n source = data$RA,\n target = data$erddap,\n value = 1\n ), \n data.frame(\n source = data$RA,\n target = data$obis,\n value = 1\n ),\n data.frame(\n source = data$RA,\n target = data$ncei,\n value = 1\n )\n) %>%\n filter_all(all_vars(. != \"\")) %>% # drop anything that is empty\n rbind(\n data.frame(\n source = data$mbon.project,\n target = data$RA,\n value = 1\n )\n )\n# Create nodes data frame\nnodes <- data.frame(\n name = c(as.character(links$source), as.character(links$target)) %>% unique()\n)\n\n# Reformat the links with IDs\nlinks$IDsource <- match(links$source, nodes$name) - 1\nlinks$IDtarget <- match(links$target, nodes$name) - 1\n\n# Make the Network\np <- networkD3::sankeyNetwork(Links = links, Nodes = nodes,\n Source = \"IDsource\", Target = \"IDtarget\",\n Value = \"value\", NodeID = \"name\",\n sinksRight = FALSE)\np\n\n\n\n\n\n\n\n\nshow counts for standard publication metrics\nlibrary(ggplot2)\nlibrary(dplyr)\nlibrary(tidyr)\n\nsource(here(\"R/getCleanedData.R\"))\ndata <- getCleanedData()\n\n# Select columns to analyze\ncolumns_to_check <- c(\n \"erddap_link\", \"ncei_link\", \"code_link\", \"obis_link\", \"doi\", \"web_link\"\n)\n\n# Count non-empty values for each column\nsummary_df <- data %>%\n summarise(across(all_of(columns_to_check), ~ sum(!is.na(.) & . != \"\"))) %>%\n pivot_longer(cols = everything(), names_to = \"Column\", values_to = \"NonEmptyCount\")\n\n# Plot the bar chart\nggplot(summary_df, aes(x = Column, y = NonEmptyCount)) +\n geom_bar(stat = \"identity\", fill = \"steelblue\") +\n labs(title = \"N Datasets Published by Repository\",\n x = \"Publication Type\",\n y = \"Number of Datasets\") +\n theme_minimal()" + }, + { + "objectID": "doi-reports/doi-report-template.html", + "href": "doi-reports/doi-report-template.html", + "title": "SE US", + "section": "", + "text": "get doi citations & print them\nlibrary(glue)\nlibrary(here)\nlibrary(httr)\nlibrary(jsonlite)\nlibrary(tidyr)\n\nsource(here(\"R/getCleanedData.R\"))\ndf <- getCleanedData()\n\n# Step 1: Split comma-separated DOIs into multiple rows\ndf <- df %>%\n separate_rows(doi, sep = \",\") %>%\n filter(!is.na(doi) & doi != \"\") # Remove empty or NA DOIs\n\n# Initialize a list to store citing DOIs for each doi in the dataframe\nall_citing <- list()\n\n# Loop through each DOI in the dataframe\nfor (i in seq_along(df$doi)) {\n doi <- df$doi[i]\n \n # Skip if DOI is empty or NA\n if (is.na(doi) || doi == \"\") {\n message(glue(\"Skipping empty DOI at row {i}.\"))\n next\n }\n \n # Create the URL for the OpenCitations API\n opcit <- glue(\"https://opencitations.net/index/coci/api/v1/citations/{doi}\")\n\n # Make the API request\n response <- tryCatch({\n GET(opcit)\n }, error = function(e) {\n message(glue(\"Error fetching data for DOI: {doi}, skipping.\"))\n NULL\n })\n \n # Check if response is valid\n if (is.null(response) || http_status(response)$category != \"Success\") {\n message(glue(\"Invalid response for DOI: {doi}, skipping.\"))\n next\n }\n \n # Parse JSON content\n result <- tryCatch({\n content(response, \"parsed\", type = \"application/json\")\n }, error = function(e) {\n message(glue(\"Error parsing JSON for DOI: {doi}, skipping.\"))\n NULL\n })\n \n # Extract citing DOIs if result is valid\n if (!is.null(result) && length(result) > 0) {\n citing <- lapply(result, function(x) x[['citing']])\n citing <- unlist(citing)\n \n # Store in the list\n all_citing[[doi]] <- citing\n \n # Print the citing DOIs\n print(glue(\"Citing DOIs for {doi}:\"))\n print(citing)\n } else {\n message(glue(\"No citing DOIs found for {doi}.\"))\n }\n}\n\n\nCiting DOIs for 10.6073/pasta/89b63c4b49b80fb839613e9d389d9902:\n[1] \"10.1038/s43247-021-00177-9\"\n\n\nCiting DOIs for 10.6073/pasta/06c6b9983a5f0a44349e027a002f5040:\n[1] \"10.1101/2022.07.05.498812\"" }, { "objectID": "regional_report/regional_reports.html", diff --git a/site_libs/reactwidget-2.0.0/react-tools.js b/site_libs/reactwidget-2.0.0/react-tools.js new file mode 100644 index 0000000..87dc5ba --- /dev/null +++ b/site_libs/reactwidget-2.0.0/react-tools.js @@ -0,0 +1 @@ +(function(s,u){typeof exports=="object"&&typeof module<"u"?u(exports):typeof define=="function"&&define.amd?define(["exports"],u):(s=typeof globalThis<"u"?globalThis:s||self,u(s.reactR={}))})(this,function(s){"use strict";function u(n,t){if(typeof t=="string")return t;if(t.name[0]===t.name[0].toUpperCase()&&!n.hasOwnProperty(t.name))throw new Error("Unknown component: "+t.name);for(let i in t.attribs)g(t.attribs[i])&&(t.attribs[i]=u(n,t.attribs[i]));for(var a=n.hasOwnProperty(t.name)?n[t.name]:t.name,r=[a,t.attribs],e=0;e https://marinebon.github.io/data_reports/regional_report/regional_reports/Southern California Bight.html - 2024-09-12T17:39:32.707Z + 2025-01-08T22:08:52.975Z https://marinebon.github.io/data_reports/regional_report/regional_reports/All - this is a global dataset.html - 2024-09-12T17:39:32.632Z + 2025-01-08T22:08:52.923Z https://marinebon.github.io/data_reports/regional_report/regional_reports/SE US.html - 2024-09-12T17:39:32.702Z + 2025-01-08T22:08:52.972Z https://marinebon.github.io/data_reports/regional_report/regional_reports/Coastal New England.html - 2024-09-12T17:39:32.689Z + 2025-01-08T22:08:52.965Z https://marinebon.github.io/data_reports/index.html - 2024-09-10T18:01:01.423Z + 2025-01-08T22:08:33.594Z + + + https://marinebon.github.io/data_reports/doi-reports/doi-report-template.html + 2025-01-08T22:05:18.445Z https://marinebon.github.io/data_reports/regional_report/regional_reports.html @@ -30,18 +34,18 @@ https://marinebon.github.io/data_reports/regional_report/regional_reports/Arctic.html - 2024-09-12T17:39:32.638Z + 2025-01-08T22:08:52.926Z https://marinebon.github.io/data_reports/regional_report/regional_reports/Gulf of Maine.html - 2024-09-12T17:39:32.697Z + 2025-01-08T22:08:52.970Z https://marinebon.github.io/data_reports/regional_report/regional_reports/Central California.html - 2024-09-12T17:39:32.683Z + 2025-01-08T22:08:52.928Z https://marinebon.github.io/data_reports/regional_report/regional_reports/GLOS MBON.html - 2024-09-12T17:39:32.693Z + 2025-01-08T22:08:52.967Z