### Group search terms into concepts (commented example)

Source: https://elizagrames.github.io/litsearchr/litsearchr_vignette.html

Commented R code showing how to save search terms to a CSV, manually group them, read the grouped CSV, and extract terms for a concept group. This example is not runnable without the CSV file.

```R
# write.csv(searchterms, "./search_terms.csv")
# manually group terms in the csv
# grouped_terms <- read.csv("./search_terms_grouped.csv")
# extract the woodpecker terms from the csv
# woodpecker_terms <- grouped_terms$term[grep("woodpecker", grouped_terms$group)]
# join together a list of manually generated woodpecker terms with the ones from the csv
# woodpeckers <- unique(append(c("woodpecker")), woodpecker_terms)
# repeat this for all concept groups
# then merge them into a list, using the code below as an example
# mysearchterms <- list(woodpeckers, fire)
```

--------------------------------

### Import and deduplicate naive search results with litsearchr

Source: https://elizagrames.github.io/litsearchr/litsearchr_vignette.html

Imports bibliographic files from a directory using litsearchr::import_results and removes duplicates with litsearchr::remove_duplicates. The example uses system.file() to locate example datasets; for your own files, provide the full path to the directory. The remove_duplicates function is called with field='title' and method='string_osa'.

```R
# Note: system.file() is only used to identify where the example datasets are stored
# If litsearchr and its dependencies were successfully installed, this directory exists on your computer

# If you are using your own bibliographic files, you should not use system.file
# You should instead give it the full path (or relative path from your current working directory) to the directory where your files are stored
search_directory <- system.file("extdata", package="litsearchr")

naiveimport <-
  litsearchr::import_results(directory = search_directory, verbose = TRUE)
#> Reading file /tmp/RtmpGp7mOt/temp_libpath605c6719a3ad/litsearchr/extdata/scopus.ris ... done
#> Reading file /tmp/RtmpGp7mOt/temp_libpath605c6719a3ad/litsearchr/extdata/zoorec.txt ... done

naiveresults <-
  litsearchr::remove_duplicates(naiveimport, field = "title", method = "string_osa")
```

--------------------------------

### Write Boolean search with litsearchr::write_search

Source: https://elizagrames.github.io/litsearchr/litsearchr_vignette.html

Creates a list of search term groups and passes them to litsearchr::write_search to generate a Boolean search string in English. The example uses stemming, no closure, exact phrase matching, and returns the search string without writing to a file. The output shows the generated search string with wildcards and quotes.

```R
# Note: these search terms are a shortened example of a full search for illustration purposes only
mysearchterms <-
  list(
    c(
      "picoides arcticus",
      "black-backed woodpecker",
      "cavity-nesting birds",
      "picoides tridactylus",
      "three-toed woodpecker"),
    c(
      "wildfire",
      "burned forest",
      "post-fire",
      "postfire salvage logging",
      "fire severity",
      "recently burned"
    )
  )

my_search <-
  litsearchr::write_search(
    groupdata = mysearchterms,
    languages = "English",
    stemming = TRUE,
    closure = "none",
    exactphrase = TRUE,
    writesearch = FALSE,
    verbose = TRUE
  )
#> [1] "English is written"

# when writing to a plain text file, the extra \ are required to render the * and " properly
# if copying straight from the console, simply find and replace them in a text editor
my_search
#> [1] "((\"picoid* arcticus*\" OR \"black-back* woodpeck*\" OR \"cavity-nest* bird*\" OR \"picoid* tridactylus*\" OR \"three-to* woodpeck*\") AND (wildfir* OR \"burn* forest*\" OR post-fir* OR \"postfir* salvag* logging\" OR \"fire* sever*\" OR \"recent* burn*\"))"
```

--------------------------------

### Build keyword co-occurrence network with litsearchr

Source: https://elizagrames.github.io/litsearchr/litsearchr_vignette.html

Combines extracted keywords, creates a document-feature matrix with create_dfm, and builds a co-occurrence network with create_network. Requires the results from the previous keyword extraction step. The network is built with a minimum of 2 studies and 2 occurrences.

```R
all_keywords <- unique(append(taggedkeywords, rakedkeywords))

naivedfm <-
  litsearchr::create_dfm(
    elements = paste(naiveresults$title, naiveresults$abstract),
    features = all_keywords
  )

naivegraph <-
  litsearchr::create_network(
    search_dfm = naivedfm,
    min_studies = 2,
    min_occ = 2
  )
```

--------------------------------

### Import and deduplicate search results with litsearchr::import_results and remove_duplicates

Source: https://elizagrames.github.io/litsearchr/litsearchr_vignette.html

Imports search results from a directory using litsearchr::import_results and removes duplicate titles using litsearchr::remove_duplicates with the 'string_osa' method. The output shows the files read from the package's extdata directory.

```R
results_directory <- system.file("extdata", package="litsearchr")

retrieved_articles <-
  litsearchr::import_results(directory = results_directory, verbose = TRUE)
#> Reading file /tmp/RtmpGp7mOt/temp_libpath605c6719a3ad/litsearchr/extdata/scopus.ris ... done
#> Reading file /tmp/RtmpGp7mOt/temp_libpath605c6719a3ad/litsearchr/extdata/zoorec.txt ... done

retrieved_articles <- litsearchr::remove_duplicates(retrieved_articles, field="title", method="string_osa")
```

--------------------------------

### Find cutoff and extract search terms with litsearchr

Source: https://elizagrames.github.io/litsearchr/litsearchr_vignette.html

Uses litsearchr::find_cutoff with method='cumulative' and percent=.80 to determine a cutoff strength, then reduces the graph and extracts keywords. The output shows the first 20 search terms from the reduced graph.

```R
cutoff <-
  litsearchr::find_cutoff(
    naivegraph,
    method = "cumulative",
    percent = .80,
    imp_method = "strength"
  )

reducedgraph <-
  litsearchr::reduce_graph(naivegraph, cutoff_strength = cutoff[1])

searchterms <- litsearchr::get_keywords(reducedgraph)

head(searchterms, 20)
#>  [1] "black hill"              "black hills"            
#>  [3] "black-backed woodpecker" "boreal forest"          
#>  [5] "breeding season"         "burned forest"          
#>  [7] "cavity nesters"          "cavity-nesting birds"   
#>  [9] "certhia americana"       "colaptes auratus"       
#> [11] "conifer forests"         "coniferous forest"      
#> [13] "coniferous forests"      "dendroctonus ponderosae"
#> [15] "dryocopus pileatus"      "fire severity"          
#> [17] "food availability"       "forest management"      
#> [19] "habitat selection"       "habitat suitability"
```

--------------------------------

### Extract potential keywords with litsearchr::extract_terms

Source: https://elizagrames.github.io/litsearchr/litsearchr_vignette.html

Uses litsearchr::extract_terms to extract keywords from titles and abstracts (method = "fakerake") and from the keywords field (method = "tagged"). Requires the stopwords namespace. The fakerake method extracts ngrams of length 2 or more, with a minimum frequency of 2.

```R
rakedkeywords <-
  litsearchr::extract_terms(
    text = paste(naiveresults$title, naiveresults$abstract),
    method = "fakerake",
    min_freq = 2,
    ngrams = TRUE,
    min_n = 2,
    language = "English"
  )
#> Loading required namespace: stopwords

taggedkeywords <-
  litsearchr::extract_terms(
    keywords = naiveresults$keywords,
    method = "tagged",
    min_freq = 2,
    ngrams = TRUE,
    min_n = 2,
    language = "English"
  )
```

--------------------------------

### Check recall with litsearchr::check_recall

Source: https://elizagrames.github.io/litsearchr/litsearchr_vignette.html

Compares the gold standard titles against the retrieved article titles using litsearchr::check_recall. The output shows a matrix with the gold standard titles, best matches, and similarity scores, indicating which articles were found.

```R
articles_found <- litsearchr::check_recall(true_hits = gold_standard,
                                           retrieved = retrieved_articles$title)

articles_found
#>      Title                                                                                                  
#> [1,] "Black-backed woodpecker occupancy in burned and beetle killed forests: disturbance agent matters"     
#> [2,] "Nest site selection and nest survival of Black-backed Woodpeckers after wildfire"                     
#> [3,] "Cross scale occupancy dynamics of a postfire specialist in response to variation across a fire regime"
#>      Best_Match                                                                                        
#> [1,] "Black-backed woodpecker occupancy in burned and beetle-killed forests: Disturbance agent matters"
#> [2,] "Nest site selection and nest survival of Black-backed Woodpeckers after wildfire"                
#> [3,] "The ecological importance of severe wildfires: Some like it hot"                                 
#>      Similarity         
#> [1,] "0.588235294117647"
#> [2,] "1"                
#> [3,] "0.134969325153374"
```

--------------------------------

### Create gold standard title search with litsearchr::write_title_search

Source: https://elizagrames.github.io/litsearchr/litsearchr_vignette.html

Defines a gold_standard vector of article titles and uses litsearchr::write_title_search to create a simple search string for those titles. This is used to check if the articles are indexed in bibliographic databases.

```R
gold_standard <-
  c(
    "Black-backed woodpecker occupancy in burned and beetle killed forests: disturbance agent matters",
    "Nest site selection and nest survival of Black-backed Woodpeckers after wildfire",
    "Cross scale occupancy dynamics of a postfire specialist in response to variation across a fire regime"
  )

title_search <- litsearchr::write_title_search(titles=gold_standard)
```

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