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programets

programets is an R package for collecting and analyzing academic impact metrics for NIH-funded research projects. It aggregates data from multiple public sources to help researchers, program managers, and evaluators understand how their projects engage with the broader research community over time.

What Does It Do?

The package provides unified access to:

  • NIH RePORTER - Project metadata, funding details, and associated publications
  • iCite - Citation metrics including Relative Citation Ratio (RCR) for PubMed publications
  • GitHub - Repository metrics (stars, forks, commits, contributors, issues, PRs) for projects tagged with NIH Core Project Numbers
  • Google Analytics - Web traffic and engagement data for project websites
  • Europe PMC - Literature search across millions of publications

Installation

Install the development version from GitHub:

# install.packages("pak")
pak::pak("nih-cfde/programets")

Requirements: - R >= 4.1.0 - For GitHub data: A Personal Access Token is recommended for higher rate limits

Quick Start

1. Query NIH RePORTER for Project Information

Retrieve comprehensive project metadata including publications, funding details, and principal investigators:

library(programets)

# Get project info for one or more NIH Core Project Numbers
proj_info <- get_core_project_info(c("u24ca289073"))
proj_info |> colnames()
#>  [1] "core_project_num"         "found_publication"       
#>  [3] "appl_id"                  "subproject_id"           
#>  [5] "fiscal_year"              "project_num"             
#>  [7] "project_serial_num"       "organization"            
#>  [9] "award_type"               "activity_code"           
#> [11] "is_active"                "project_num_split"       
#> [13] "principal_investigators"  "contact_pi_name"         
#> [15] "program_officers"         "agency_ic_admin"         
#> [17] "agency_ic_fundings"       "cong_dist"               
#> [19] "spending_categories"      "project_start_date"      
#> [21] "project_end_date"         "organization_type"       
#> [23] "geo_lat_lon"              "opportunity_number"      
#> [25] "full_study_section"       "award_notice_date"       
#> [27] "is_new"                   "mechanism_code_dc"       
#> [29] "terms"                    "pref_terms"              
#> [31] "abstract_text"            "project_title"           
#> [33] "phr_text"                 "spending_categories_desc"
#> [35] "agency_code"              "covid_response"          
#> [37] "arra_funded"              "budget_start"            
#> [39] "budget_end"               "cfda_code"               
#> [41] "funding_mechanism"        "direct_cost_amt"         
#> [43] "indirect_cost_amt"        "project_detail_url"      
#> [45] "date_added"               "pmid"                    
#> [47] "pubmed_url"

Available fields include: project title, abstract, funding amounts, dates, PIs, publications (PMIDs), and more.

2. Get GitHub Repository Metrics

If your project repositories are tagged with NIH Core Project Numbers as topics, you can collect engagement metrics:

# Fetch GitHub metrics for repos tagged with your project number
df <- get_github_by_topic(c("u24ca289073"))
#> ! No GitHub token provided. Running without authentication
#> ℹ Use of a Personal Access Token (PAT)is recommended for increased rate limits. Create a token with: usethis::create_github_token()
#> This message is displayed once every 8 hours.
df |> colnames()
#>  [1] "name"          "owner"         "description"   "stars"        
#>  [5] "watchers"      "forks"         "open_issues"   "open_prs"     
#>  [9] "closed_issues" "closed_prs"    "commits"       "contributors" 
#> [13] "tags"          "language"      "license"       "created_at"   
#> [17] "pushed_at"     "updated_at"    "html_url"

Metrics include: stars, watchers, forks, open/closed issues, open/closed PRs, commit count, contributor count, and more.

Tip: Tag your GitHub repositories with your NIH Core Project Number (e.g., u24ca289073) to enable discovery.

3. Get Citation Metrics from iCite

Calculate impact metrics including the Relative Citation Ratio (RCR) for your publications:

# Get citation metrics for PubMed IDs
pmids <- c(26001965, 25015380)
citation_data <- icite(pmids)

# View RCR and other metrics
dplyr::select(citation_data, pmid, title, year, 
              relative_citation_ratio, citation_count)

4. Search Europe PMC for Publications

Query millions of publications with flexible search syntax:

# Search for publications related to CRISPR
crispr_pubs <- epmc_search(query = "crispr", page_limit = 2)

# Search by author
author_pubs <- epmc_search(query = 'AUTH:"Smith J"', page_limit = 1)

5. Access Google Analytics Data

Retrieve web traffic data for project websites (requires authentication):

# Authenticate with Google (first time only)
googleAnalyticsR::ga_auth()

# Get traffic data
traffic <- ga_dataframe(
  property_id = "123456789",
  start_date = "2024-01-01",
  end_date = "2024-12-31",
  metrics = c("activeUsers", "sessions"),
  dimensions = c("date", "country")
)

Authentication

GitHub

For increased API rate limits, set up a Personal Access Token:

# Create a token with: usethis::create_github_token()
# Then use it in your calls:
df <- get_github_by_topic(c("u24ca289073"), token = "your_token_here")

Google Analytics

First-time setup requires authentication:

# Opens browser for Google account authorization
googleAnalyticsR::ga_auth()

# For non-interactive use, see DEVELOPER.md for service account setup

Use Cases

  • Impact Reporting: Generate comprehensive reports combining publication citations, web traffic, and GitHub engagement
  • Trend Analysis: Track how metrics evolve over time in response to publications, presentations, or events
  • Portfolio Management: Compare metrics across multiple projects or funding opportunities
  • Compliance: Document project outputs and community engagement for progress reports

Documentation

Getting Help

Authors

License

MIT