In aknutas/nails-package: Network Analysis Interface for Literature Studies

knitr::opts_chunk$set(echo = TRUE, 
                      message = FALSE, 
                      warning = FALSE,
                      tidy = TRUE)

Introduction

NAILS performs statistical and Social Network Analysis (SNA) on citation data. SNA is a new way for researchers to map large datasets and get insights from new angles by analyzing connections between articles. As the amount of publications grows on any given field, automatic tools for this sort of analysis are becoming increasingly important prior to starting research on new fields. NAILS also provides useful data when performing Systematic Mapping Studies (SMS) in scientific literature. According to Kitchenham et al. performing a SMS can be especially suitable if few literature reviews have been done on the topic and there is a need to get a general overview of the field of interest.

The nails package provides functionality for parsing Web of Science data for quantitative Systematic Mapping Study analysis, and a series of custom statistical and network analysis functions to give the user an overview of literature datasets. The features can be divided into two primary sections: Firstly, statistical analysis, which for example gives an overview of publication frequencies, most published authors and journals. Secondly, the more novel network analysis, which gives further insight into relationship between the interlinked citations and cooperation between authors. For example, the most basic features can use citation network analysis identify the most cited authors and publication forums. Lastly, it provides a few convenience functions to use the topicmodels and stm packages to create Latent Dirichlet allocation -based topic models.

For further details see the following article: Knutas, A., Hajikhani, A., Salminen, J., Ikonen, J., Porras, J., 2015. Cloud-Based Bibliometric Analysis Service for Systematic Mapping Studies. CompSysTech 2015.

Example workflow and report

In this section we present how to load Web of Science data using nails package functions and then to create an example report using ggplot2-based visualizations.

Loading data

Below is an example of how data exported from Web of Science can be loaded and parsed using the nails package functions.

# Setup

# Load packages
devtools::load_all()
require(ggplot2)

# Set ggplot theme
theme_set(theme_minimal(12))

# Load data
literature <- read_wos_data("../tests/testthat/test_data")

# Clean data
literature <- clean_wos_data(literature)

Generating visualizations with knittr

Below we present how to generate example report using nails calls and then using ggplot2 and knittr to generate visual reports.

This report provides an analysis on the records downloaded from Web of Science. The analysis identifies the important authors, journals, and keywords in the dataset based on the number of occurences and citation counts. A citation network of the provided records is created and used to identify the important papers according to their in-degree, total citation count and PageRank scores. The analysis finds also often-cited references that were not included in the original dataset downloaded from the Web of Science.

Reports can also be generated by using the online analysis service, and the source code is available at GitHub. Instructions and links to tutorial videos can be found at the project page. Please consider citing our research paper on bibliometrics at if you publish the analysis results.

# Setup

# Load packages
devtools::load_all()
require(ggplot2)

# Set ggplot theme
theme_set(theme_minimal(12))

The analysed dataset, loaded in section "loading data", consist of r nrow(literature) records with r ncol(literature) variables. More information about the variables can be found at Web of Science.

Publication years

ggplot(literature, aes(YearPublished)) +
  geom_histogram(binwidth = 1, fill = "darkgreen") +
  ggtitle("Year published") +
  xlab("Year") +
  ylab("Article count")


# Calculate relative publication counts
# yearTable <- as.data.frame(table(literature$YearPublished))  
# names(yearDF) <- c("Year", "Freq")      # Fix column names

# Merge to dataframe of total publication numbers (years)
# yearDF <- merge(yearDF, years, by.x = "Year", by.y = "Year", 
#                all.x = TRUE)
# yearDF$Year <- as.numeric(as.character(yearDF$Year))    # factor to numeric
# Calculate published articles per total articles by year
# yearDF$Fraction <- yearDF$Freq / yearDF$Records

Relative publication volume

# ADD PLOT HERE!
print("Placeholder")

Important authors

Sorted by the number of articles published and by the total number of citations.

# Get author network nodes, which contain the required information
author_network <- get_author_network(literature)
author_nodes <- author_network$author_nodes
# Change Id to AuthorFullName
names(author_nodes)[names(author_nodes) == "Id"] <- "AuthorFullName"

# Sort by number of articles by author
author_nodes <- author_nodes[with (author_nodes, order(-Freq)), ]
# Re-order factor levels
author_nodes <- transform(author_nodes, 
                          AuthorFullName = reorder(AuthorFullName, Freq))

ggplot(head(author_nodes, 25), aes(AuthorFullName, Freq)) +
    geom_bar(stat = "identity", fill = "blue") +
    coord_flip() +
    ggtitle("Productive authors") +
    xlab("Author") +
    ylab("Number of articles")

# Reorder AuthorFullName factor according to TotalTimesCited (decreasing order)
author_nodes <- transform(author_nodes,
                          AuthorFullName = reorder(AuthorFullName,
                                                   TotalTimesCited))

# Sort by number of articles by author
author_nodes <- author_nodes[with (author_nodes, order(-TotalTimesCited)), ]

ggplot(head(author_nodes, 25), aes(AuthorFullName, TotalTimesCited)) +
    geom_bar(stat = "identity", fill = "blue") +
    coord_flip() +
    ggtitle("Most cited authors") +
    xlab("Author") + ylab("Total times cited")

Important publications

Sorted by number of published articles in the dataset and by the total number of citations.

# Calculate publication occurences
publications <- as.data.frame(table(literature$PublicationName))

# Fix names
names(publications) <- c("Publication", "Count")

# Trim publication name to maximum of 50 characters for displaying in plot
publications$Publication <- strtrim(publications$Publication, 50)

# Sort descending
publications <- publications[with (publications, order(-Count)), ]

# Reorder factor levels
publications <- transform(publications, Publication = reorder(Publication, Count))


# WHY???
# literature <- merge(literature, citation_sums,
#                    by = "PublicationName" )

ggplot(head(publications, 25), aes(Publication, Count)) +
    geom_bar(stat = "identity", fill = "orange") +
    coord_flip() +
    theme(legend.position = "none") +
    ggtitle("Most popular publications") +
    xlab("Publication") +
    ylab("Article count")

# Calculating total citations for each publication.
citation_sums <- aggregate(literature$TimesCited,
    by = list(PublicationName = literature$PublicationName),
    FUN = sum, na.rm = T)

# Fix column names
names(citation_sums) <- c("PublicationName", "PublicationTotalCitations")

# Trim publication name to maximum of 50 characters for displaying in plot
citation_sums$PublicationName <- strtrim(citation_sums$PublicationName, 50)

# Sort descending and reorder factor levels accordingly
citation_sums <- citation_sums[with (citation_sums, order(-PublicationTotalCitations)), ]
citation_sums <- transform(citation_sums,
                          PublicationName = reorder(PublicationName,
                                                    PublicationTotalCitations))
ggplot(head(citation_sums, 25),
       aes(PublicationName, PublicationTotalCitations)) +
    geom_bar(stat = "identity", fill = "orange") +
    coord_flip() +
    theme(legend.position = "none") +
    ggtitle("Most cited publications") +
    xlab("Publication") + ylab("Total times cited")

Important keywords

Sorted by the number of articles where the keyword is mentioned and by the total number of citations for the keyword.

# Calculating total citations for each keyword

literature_by_keywords <- arrange_by(literature, "AuthorKeywords")



# Sometimes AuthorKeywords column is empty.
# Following if-else hack prevents crashing in those situations,
# either by using KeywordsPlus column or skipping keyword analysis.
if (nrow(literature_by_keywords) == 0) {
  cat("No keywords.")
} else {
    keyword_citation_sum <- aggregate(literature_by_keywords$TimesCited,
                                by = list(AuthorKeywords =
                            literature_by_keywords$AuthorKeywords), FUN = sum,
                            na.rm = T)
    names(keyword_citation_sum) <- c("AuthorKeywords", "TotalTimesCited")

    keywords <- unlist(strsplit(literature$AuthorKeywords, ";"))
    keywords <- trim(keywords)
    keywords <- as.data.frame(table(keywords))
    names(keywords) <- c("AuthorKeywords", "Freq")

    keywords <- merge(keywords, keyword_citation_sum, by = "AuthorKeywords")
    keywords <- keywords[with (keywords, order(-Freq)), ]
    keywords <- transform(keywords, 
                          AuthorKeywords = reorder(AuthorKeywords, Freq))

    ggplot(head(keywords, 25), aes(AuthorKeywords, Freq)) +
    geom_bar(stat = "identity", fill = "purple") +
    coord_flip() +
    ggtitle("Popular keywords") +
    xlab("Keyword") +
    ylab("Number of occurences")
}

if (nrow(literature_by_keywords) > 0) {
  keywords <- keywords[with (keywords, order(-TotalTimesCited)), ]
  keywords <- transform(keywords, AuthorKeywords =
                             reorder(AuthorKeywords, TotalTimesCited))
  ggplot(head(keywords, 25), aes(AuthorKeywords, TotalTimesCited)) +
    geom_bar(stat = "identity", fill = "purple") +
    coord_flip()  +
    ggtitle("Most cited keywords") +
    xlab("Keyword") + ylab("Total times cited")
}

Important papers

The most important papers and other sources are identified below using three importance measures: 1) in-degree in the citation network, 2) citation count provided by Web of Science (only for papers included in the dataset), and 3) PageRank score in the citation network. The top 25 highest scoring papers are identified using these measures separately. The results are then combined and duplicates are removed. Results are sorted by in-degree, and ties are first broken by citation count and then by the PageRank.

When a Digital Object Identifier (DOI) is available, the full paper can be found using Resolve DOI website.

# Extract citation nodes
citation_network <- get_citation_network(literature)
citation_nodes <- citation_network$citation_nodes


# Extract the articles included in the data set and articles not included
# in the dataset
citations_lit <- citation_nodes[citation_nodes$Origin == "literature", ]
citations_ref <- citation_nodes[citation_nodes$Origin == "reference", ]

# Create article strings (document title, reference information and abstract
# separated by "|")
citations_lit$Article <- paste(toupper(citations_lit$DocumentTitle), " | ",
                              citations_lit$FullReference, " | ",
                                      citations_lit$Abstract)

Included in the dataset

These papers were included in the r nrow(literature) records downloaded from the Web of Science.

# Sort citations_lit by TimesCited, decreasing
citations_lit <- citations_lit[with (citations_lit, order(-TimesCited)), ]
# Extract top 25
top_lit <- head(citations_lit, 25)
# Sort by InDegree, decreasing
citations_lit <- citations_lit[with (citations_lit, order(-InDegree)), ]
# Add to list of top 25 most cited papers
top_lit <- rbind(top_lit, head(citations_lit, 25))
# Sort by PageRank, decreasing
citations_lit <- citations_lit[with (citations_lit, order(-PageRank)), ]
# Add to list of most cited and highest InDegree papers
top_lit <- rbind(top_lit, head(citations_lit, 25))
# Remove duplicates
top_lit <- top_lit[!duplicated(top_lit[, "FullReference"]), ]
# Sort top_lit by InDegree, break ties by TimesCited, then PageRank.
top_lit <- top_lit[with (top_lit, order(-InDegree, -TimesCited, -PageRank)), ]
# Print list
knitr::kable(top_lit[, c("Article", "InDegree", "TimesCited","PageRank")])

Not included in the dataset

These papers and other references were not among the r nrow(literature) records downloaded from the Web of Science.

# Sort citations_ref by InDegree, decreasing
citations_ref <- citations_ref[with (citations_ref, order(-InDegree)), ]
# Extract top 25
top_ref <- head(citations_ref, 25)
# Sort by PageRank, decreasing
citations_ref <- citations_ref[with (citations_ref, order(-PageRank)), ]
# Add to list of highes in degree papers (references)
top_ref <- rbind(top_ref, head(citations_ref, 25))
# Remove duplicates
top_ref <- top_ref[!duplicated(top_ref[, "FullReference"]), ]
# Sort by InDegree, break ties by PageRank
top_ref <- top_ref[with (top_ref, order(-InDegree, -PageRank)), ]
# Print results
knitr::kable(top_ref[, c("FullReference", "InDegree", "PageRank")])

Most referenced publications

references <- unlist(strsplit(literature$CitedReferences, ";"))

get_publication <- function(x) {
    publication <- "Not found"
    try(
        publication <- unlist(strsplit(x, ","))[[3]],
        silent = TRUE
    )
    return(publication)
}

refPublications <- sapply(references, get_publication)
refPublications <- sapply(refPublications, trim)
refPublications <- refPublications[refPublications != "Not found"]
refPublications <- as.data.frame(table(refPublications))
names(refPublications) <- c("Publication", "Count")
refPublications <- refPublications[with (refPublications, order(-Count)), ]

refPublications <- transform(refPublications,
                             Publication = reorder(Publication, Count))

ggplot(head(refPublications, 25), aes(Publication, Count)) +
    geom_bar(stat = "identity", fill = "orange") +
    coord_flip() +
    theme(legend.position = "none") +
    ggtitle("Most referenced publications") +
    xlab("Publication") +
    ylab("Count")

Topic Model

Topic modeling is a type of statistical text mining method for discovering common "topics" that occur in a collection of documents. A topic modeling algorithm essentially looks through the abstracts included in the datasets for clusters of co-occurring of words and groups them together by a process of similarity.

The following columns describe each topic detected using LDA topic modeling by listing the ten most characteristic words in each topic.

You can specify K, the number of topics, when calling build_topicmodel_from_literature(literature, K). If left empty, stm::searchK function is used to estimate the number of topics. For performance reasons the search range is between 4 and 12. The number of topics is estimated using the structural topic model library semantic coherence diagnostic values. Raw values are available in output file as kqualityvalues.csv and can be interpreted with stm documentation if necessary (see section 3.4).

The analysis below creates the topic model using the convenience functions and then prints out ten most descriptive words for each discovered topic. See topicmodels documentation on the TopicModel-class on other information and instructions and documentation on build_topicmodel_from_literature how to use the rest of the data the convenience function procides.

topicmodel <- build_topicmodel_from_literature(literature)

topickeywords <- topicmodels::terms(topicmodel$fit, 10)
tw <- data.frame(topickeywords)
colnames(tw) <- gsub('X', 'Topic ', colnames(tw))
knitr::kable(tw, col.names = colnames(tw))

aknutas/nails-package documentation built on Nov. 9, 2023, 3:28 p.m.