diff --git a/DESCRIPTION b/DESCRIPTION index 78ee691..6da5008 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,7 +2,7 @@ Type: Package Package: refsplitr Title: author name disambiguation, author georeferencing, and mapping of coauthorship networks with 'Web of Science' data -Version: 1.2.0 +Version: 1.2.2 Authors@R: c(person(given = "Auriel M.V.", family = "Fournier", @@ -14,7 +14,7 @@ Authors@R: person(given = "Forrest R.", family = "Stevens", role = "aut"), - person(given = "Emilio", + person(given = "Emilio M.", family = "Bruna", role = c("aut", "cre"), email = "embruna@ufl.edu"), diff --git a/NEWS.md b/NEWS.md index fbc6d04..8fb15f0 100644 --- a/NEWS.md +++ b/NEWS.md @@ -7,9 +7,9 @@ refsplitr 1.2.2 (2026-06-16) ### MINOR IMPROVEMENTS - * authors_clean() would sometimes combine authors with very similar or - identical names in the same groupID even if they had different ORCID ID - numbers. A tweak in `authors_match` now eliminates these 'false positives'. + * `authors_clean()` would sometimes combine authors with very similar or + identical names in the same `groupID` even if they had different ORCID ID + numbers. A tweak in `authors_match()` now eliminates these 'false positives'. refsplitr 1.2.1 (2026-06-15) @@ -17,7 +17,7 @@ refsplitr 1.2.1 (2026-06-15) ### MINOR IMPROVEMENTS - * modified the section of `authors_parse` that identifies corresponding author + * modified the section of `authors_parse()` that identifies corresponding author and reprint address (RP field code). There are cases where there is more than one corresponding author, with names separated by a ';'. This was causing both authors to be ignored. The revision treats only the first name provided as the @@ -40,7 +40,7 @@ refsplitr 1.2.1 (2026-06-15) ### BUG FIXES - * corrected an error in `authors_address` that was changing "USA" to "NA" when + * corrected an error in `authors_address()` that was changing "USA" to "NA" when parsing countries. @@ -58,9 +58,9 @@ refsplitr 1.2 (2025-04-25) The Google Maps API is still an option, but users should be aware that their georeferencing request may exceed the lower limit of free queries. - * The `authors_addresses` function has been updated and is now more efficient. + * The `authors_addresses()` function has been updated and is now more efficient. - * In `plot_net_address`: the deprecated function `fortify` has been replaced + * In `plot_net_address()`: the deprecated function `fortify` has been replaced with `sf_convert` @@ -70,19 +70,19 @@ refsplitr 1.0.2 (2024-08-12) ### NEW FEATURES - * `references_read` now extracts additional fields from Web of Science + * `references_read()` now extracts additional fields from Web of Science records: WE (Source Database), C3 (all author affiliations, equivalent to the Scopus `affiliations` field code), EI (eISSN), OA (Open Access), and RID (the original version of the Thomson-Reuters ResearcherID (RI); authors of some older publications might have an RID but not an RI). These are not - included in the default output of `references_read`; to include + included in the default output of `references_read()`; to include them use `include_all = TRUE`. - * `references_read` no longer extracts some rarely used field codes: + * `references_read()` no longer extracts some rarely used field codes: GE, LT, MC, MI, and TA * The following field codes are now returned by default when using - `references_read`: DT (Document Type), ID (Keywords Plus), IS (Issue), + `references_read()`: DT (Document Type), ID (Keywords Plus), IS (Issue), JI (ISO abbreviated source code), and NR (number of references cited by the article). @@ -95,7 +95,7 @@ refsplitr 1.0.1 (2024-07-23) ### NEW FEATURES * output of `plot_net_country()` now includes a list of any authors that have - a lat-lon but no country (called with `products$fixable_countries`).Users can + a lat-lon but no country (called with `products$fixable_countries()`).Users can correct these and re-run the visualization to include them in the graph. ### DEPRECATED AND DEFUNCT diff --git a/README.Rmd b/README.Rmd index b072a9d..be6fabe 100644 --- a/README.Rmd +++ b/README.Rmd @@ -91,8 +91,8 @@ Fournier, Auriel M.V., Matthew E. Boone, Forrest R. Stevens, and Emilio M. Bruna and mapping of coauthorship networks with Web of Science data.}, author = {Fournier, Auriel M.V., Matthew E. Boone, Forrest R. Stevens, and Emilio M. Bruna}, - year = {2020}, - note = {R package version 1.2.0.}, + year = {2026}, + note = {R package version 1.2.2.}, url ={https://github.com/ropensci/refsplitr} } diff --git a/README.html b/README.html index 8518a81..5cd20b0 100644 --- a/README.html +++ b/README.html @@ -471,8 +471,8 @@

Citation: Refsplitr Package

and mapping of coauthorship networks with Web of Science data.}, author = {Fournier, Auriel M.V., Matthew E. Boone, Forrest R. Stevens, and Emilio M. Bruna}, -year = {2020}, -note = {R package version 1.2.0.}, +year = {2026}, +note = {R package version 1.2.2.}, url ={https://github.com/ropensci/refsplitr} } diff --git a/README.md b/README.md index 1b1980c..6c44b83 100644 --- a/README.md +++ b/README.md @@ -109,8 +109,8 @@ Auriel M.V. Fournier, Matthew E. Boone, Forrest R. Stevens, and title = {refsplitr: author name disambiguation, author georeferencing, and mapping of coauthorship networks with Web of Science data.}, author = {Fournier, Auriel M.V., Matthew E. Boone, Forrest R. Stevens, and Emilio M. Bruna}, - year = {2020}, - note = {R package version 1.2.0.}, + year = {2026}, + note = {R package version 1.2.2.}, url={https://github.com/ropensci/refsplitr} } diff --git a/codemeta.json b/codemeta.json index 0b4ada4..03a5b61 100644 --- a/codemeta.json +++ b/codemeta.json @@ -8,7 +8,7 @@ "codeRepository": "https://github.com/ropensci/refsplitr", "issueTracker": "https://github.com/ropensci/refsplitr/issues", "license": "https://spdx.org/licenses/GPL-3.0", - "version": "1.0", + "version": "1.2.2", "programmingLanguage": { "@type": "ComputerLanguage", "name": "R", diff --git a/vignettes/refsplitr.Rmd b/vignettes/refsplitr.Rmd index e4793d3..beb6a97 100644 --- a/vignettes/refsplitr.Rmd +++ b/vignettes/refsplitr.Rmd @@ -1,7 +1,7 @@ --- title: "refsplitr" author: "Auriel M. V. Fournier, Matthew E. Boone, Forrest R. Stevens, Emilio M. Bruna" -date: "2025-03-25" +date: "2026-06-16" output: rmarkdown::html_vignette: fig_width: 6 diff --git a/vignettes/refsplitr.Rmd.orig b/vignettes/refsplitr.Rmd.orig index bd6cc11..beb6a97 100644 --- a/vignettes/refsplitr.Rmd.orig +++ b/vignettes/refsplitr.Rmd.orig @@ -1,7 +1,7 @@ --- title: "refsplitr" author: "Auriel M. V. Fournier, Matthew E. Boone, Forrest R. Stevens, Emilio M. Bruna" -date: "`r Sys.Date()`" +date: "2026-06-16" output: rmarkdown::html_vignette: fig_width: 6 @@ -12,16 +12,8 @@ vignette: > \usepackage[utf8]{inputenc} --- -```{r setup, include = FALSE} -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>" -) -library(refsplitr) -``` - ## 1. Introduction The Science of Science (SciSci) is an emerging, trans-disciplinary approach for using large and disparate data-sets to study the emergence, dissemination, and impact of scientific research (Fortunato et al. 2018). Bibliometric databases such as the [Web of Science](https://clarivate.com/products/web-of-science/) are rich sources of data for SciSci studies (Sugimoto and Larivière 2018). In recent years the type and scope of questions addressed with data gathered from these databases has expanded tremendously (Forutnato et al. 2018). This is due in part to their expanding coverage and greater accessibility, but also because advances in computational power make it possible to analyze data-sets comprising millions of bibliographic records (e.g., Larivière et al. 2013, Smith et al. 2014). @@ -60,7 +52,8 @@ The output of `references_read()` is an object in the R workspace. Each line of a. To import and process a single file, set dir=FALSE and set data equal to the file path. For example, if the file "example_data.txt" were saved in the "data" folder of the RStudio project, you would import and process the data file as follows: -```{r,eval=FALSE} + +``` r example_refs <- references_read(data = "./data/example_data.txt", dir=FALSE, include_all = FALSE) @@ -69,7 +62,8 @@ example_refs <- references_read(data = "./data/example_data.txt", b. To import and process multiple files, set "dir = TRUE" and use "data=" to indicate the folder containing the files. For instance, if the files were saved in a folder called "UF_data" inside the "data" folder of the RStudio project, they would be imported and processed as follows: -```{r,eval=FALSE} + +``` r example_refs <- references_read(data = "./data/UF_data", dir=TRUE, include_all = FALSE) @@ -78,7 +72,8 @@ example_refs <- references_read(data = "./data/UF_data", c. The sample data used in the examples below can be loaded and processed as follows: -```{r, results="hide", message=FALSE} + +``` r example_refs <- references_read(data = system.file("extdata",package = "refsplitr"), dir = TRUE, include_all = FALSE) @@ -86,13 +81,15 @@ example_refs <- references_read(data = system.file("extdata",package = "refsplit d. The processed references can then be saved as a .csv file in the "output" folder of the RStudio project: -```{r,eval=FALSE} + +``` r write.csv(example_refs,"./output/example_refs.csv") ``` -```{r, echo=FALSE, fig.pos="H", out.width="500px"} -knitr::include_graphics("images/references_read.png", error = F) -``` +
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**Figure 1** An image of the .csv file showing a subset of the rows and columns from the output of `references_read()`. @@ -111,20 +108,23 @@ Once disambiguation is complete, users can accept `refsplitr`'s preliminary resu a. To disambiguate the authors of the references in the dataset: -```{r, results="hide", message=FALSE} + +``` r example_a_clean <- authors_clean(example_refs) ``` b. To save the resulting list elements -- 'prelim' and 'review' -- in the "output" folder of the RStudio project as .csv files: -```{r,eval=FALSE} + +``` r write.csv(example_a_clean$prelim,"./output/example_a_clean_prelim.csv") write.csv(example_a_clean$review,"./output/example_a_clean_review.csv") ``` -```{r, echo=FALSE, fig.pos="H", out.width="500px"} -knitr::include_graphics("images/authors_clean_prelim.png", error = F) -``` +
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**Figure 2** A subset of the .csv file showing the rows and columns in the 'prelim' output from `authors_clean()`. @@ -146,14 +146,16 @@ The output of `authors_refine()` is an object in the R workspace. We recommend s a. To accept the results of author disambiguation _**without**_ manual review (with default values for `sim_score` and `confidence`): -```{r, results="hide", message=FALSE} + +``` r example_a_refined <- authors_refine(example_a_clean$review, example_a_clean$prelim) ``` b. To accept the results of author disambiguation _**without**_ manual review (with user-modified values for `sim_score` and `confidence`): -```{r, results="hide", message=FALSE} + +``` r example_a_refined_mod <- authors_refine(example_a_clean$review, example_a_clean$prelim, sim_score = 0.70, @@ -162,7 +164,8 @@ example_a_refined_mod <- authors_refine(example_a_clean$review, c. to save the final disambiguated (i.e., 'refined') dataset to the "output" folder of the RStudio Project: -```{r,eval=FALSE} + +``` r write.csv(example_a_refined,"./output/example_a_refined.csv") ``` @@ -182,33 +185,37 @@ These corrections are uploaded using the ```authors_refine()``` function, which Figure 3 is an example of the the first few rows and columns of the 'review' element of `authors_clean()`. Each row is the author of a paper, with their name as it is on the author list (AF). Each author has been assigned a unique authorID number as well assigned to a groupID; the "match_name" column provides the name under which the algorithm has grouped all of an author’s putative name variants when assigning the groupID number. -```{r, echo=FALSE, fig.pos="H", out.width="500px"} -knitr::include_graphics("images/review_file.png", error = F) -``` +
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**Figure 3:** First rows and columns from the review element of `authors_clean()` A review of this output indicates that `refsplitr` assigned three authors sharing the last name "Bailey" and first initial "J" to groupID number 982: John Bailey (authorID 2240), JW Bailey (authorID 982), and J Bailey (authorID 1231; Figure 4). However, we know that J Bailey at Moon University is a distinct individual that should not be in the same group as the other two. Their incorrect groupID number (982) should be replaced with their authorID number (1231). -```{r, echo=FALSE, fig.pos="H", out.width="500px"} -knitr::include_graphics("images/grouped_should_not_be.png", error = F) -``` +
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**Figure 4:** review element of `authors_clean()` highlighting three authors sharing the same last and assigned the same groupID. Further review reveals that there are two putative authors named LW Wise -- one at U of Middle Earth (groupID 89) and one at U of the Shire (groupID 90; Figure 5). However, an online search reveals that this is actually the same author, who recently moved from one university to the other. The groupID for all of these records should therefore be changed to "89". -```{r, echo=FALSE, fig.pos="H", out.width="500px"} -knitr::include_graphics("images/not_grouped_should_be.png", error = F) -``` +
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**Figure 5:** review element of `authors_clean()` highlighting the same author incorrectly assigned to different groupID numbers. Once these corrections have been made (Figure 6) and saved, the changes can be incorporated using the ```authors_refine()``` function (see Section 2.2.3). -```{r, echo=FALSE, fig.pos="H", out.width="500px"} -knitr::include_graphics("images/corrected_review.png", error = F) -``` +
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**Figure 6:** Corrected version of the 'review' element from `authors_clean()`. @@ -230,32 +237,37 @@ The output of `authors_refine()` is an object in the R workspace, which can be s a. To merge the changes made to the disambiguations, first load the .csv file with the corrections: -```{r eval=FALSE} + +``` r example_a_corrected <- read.csv("correctedfile.csv") ``` The changes are then merged into the preliminary disambiguation: -```{r,eval=FALSE} + +``` r example_a_refined <-authors_refine(example_a_corrected, example_a_clean$prelim) ``` b. to save the final disambiguated (i.e., 'refined') dataset to the "output" folder of the RStudio project: -```{r,eval=FALSE} + +``` r write.csv(example_a_refined,"./output/example_a_refined.csv") ``` -```{r, echo=FALSE, fig.pos="H", out.width="500px"} -knitr::include_graphics("images/AuthorMatch.PNG", error = F) -``` +
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**Figure 7:** `authors_refine()` output. c. User-selected values for `sim_score` and `confidence` can be used to merge the changes made to the "review" file by adding the two relevant arguments to the `authors_refine()` function: -```{r,eval=FALSE} + +``` r example_a_refined_mod <- authors_refine(example_a_corrected, example_a_clean$prelim, sim_score = 0.70, @@ -286,7 +298,8 @@ The output of `authors_georef()` is a list with three elements: (1) `addresses` a. to georeference author institutions: -```{r,eval=FALSE} + +``` r example_georef <-authors_georef(data=example_a_refined, address_column = "address", @@ -295,7 +308,8 @@ example_georef <-authors_georef(data=example_a_refined, Note that while this function is being executed a message will be printed every time a location is geoprocessed. These messages can be suppressed by modifying the function call as follows: -```{r, results="hide", message=FALSE} + +``` r example_georef <-suppressMessages(authors_georef( data=example_a_refined, @@ -309,7 +323,8 @@ example_georef <-suppressMessages(authors_georef( 1. Install and load the `ggmap` package -```{r example2, eval=FALSE} + +``` r install.packages("ggmap") library(ggmap) @@ -320,7 +335,8 @@ library(ggmap) 3. Once you have your API key, add it to your `~/.Renviron` with the following: -```{r example3, eval=FALSE} + +``` r `ggmap::register_google(key = "[your key]", write = TRUE)` ``` @@ -349,10 +365,16 @@ The output of `plot_addresses_country()` is plot from the `rworldmap` package. a. Author records plotted on a world map -```{r, fig.pos="H", out.width="500px",message=FALSE,results="hide"} + +``` r plot_addresses_country <- plot_addresses_country(example_georef$addresses) ``` +
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+ **Figure 8:** Plot of the countries in which the authors in the dataset are based, with shading to indicate the number of authors based in each of country. #### 2.4.2. Visualization 2: Author locations @@ -371,25 +393,35 @@ The output of `authors_georef()` is a ggplot object. a. Mapped location of author institutions (global). -```{r, fig.pos="H", out.width="500px"} + +``` r plot_addresses_points <- plot_addresses_points(example_georef$addresses) plot_addresses_points ``` +
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+ b. Mapped location of author institutions (national). -```{r, echo=FALSE, fig.pos="H", out.width="500px"} -#knitr::include_graphics("images/plot_addresses_points.png") -``` + **Figure 9:** Figure indicating the georefeenced locations of all authors in the dataset -```{r, fig.pos="H", out.width="500px"} + +``` r plot_addresses_points <- plot_addresses_points(example_georef$addresses, mapCountry = "Brazil") plot_addresses_points ``` +
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+ **Figure 10:** Figure indicating the georefeenced locations of authors in the dataset with institutional addresses in Brazil. #### 2.4.3. Visualization 3: Base coauthorship network @@ -405,9 +437,30 @@ This function has one output, a plot, built in `igraph`. a. Coauthorship networked based on the country in which coauthors are based. -```{r, fig.pos="H", out.width="500px"} + +``` r plot_net_coauthor <- plot_net_coauthor(example_georef$addresses) +``` + +
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+ +``` r plot_net_coauthor +#> IGRAPH 59d2e4d UNW- 15 27 -- +#> + attr: name (v/c), label (v/c), label.color (v/c), label.cex +#> | (v/n), size (v/n), frame.color (v/l), color (v/c), weight +#> | (e/n) +#> + edges from 59d2e4d (vertex names): +#> [1] argentina--mexico argentina--usa +#> [3] australia--brazil australia--germany +#> [5] australia--mexico australia--usa +#> [7] belgium --brazil belgium --usa +#> [9] brazil --could not be extracted brazil --germany +#> [11] brazil --mexico brazil --usa +#> + ... omitted several edges ``` **Figure 11:** Plot of the coauthorship network for authors of articles in the dataset. @@ -437,9 +490,12 @@ The output of `plot_net_country()` is a list in the R workspace. The `$plot` ele a. Mapped coauthorship network based on the countries in which authors are based. -```{r fig.pos="H", out.width="500px"} + +``` r plot_net_country <- plot_net_country(example_georef$addresses) +#> Error in Ops.data.frame(fromC, toC): '+' only defined for equally-sized data frames plot_net_country$plot +#> Error in plot_net_country$plot: object of type 'closure' is not subsettable ``` **Figure 12:** Map showing the coauthorship connections between countries. @@ -467,11 +523,17 @@ The output of `plot_net_address()` is a list in the R workspace. The `$plot` ele a. Coauthorship network based on the geographic locations of coauthor institutions. -```{r, fig.pos="H", out.width="500px"} + +``` r plot_net_address <- plot_net_address(example_georef$addresses) plot_net_address$plot ``` +
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+ **Figure 13:** Plot showing the network between individual author locations. ## Acknowledgments @@ -501,22 +563,59 @@ Westgate, M. J. (2018). revtools: bibliographic data visualization for evidence ## **Appendix 1:** Guide to downloading reference records from the Web of Science. -```{r, echo=FALSE, fig.pos="H", out.width="250px"} -# knitr::include_graphics("images/Appendix1b.jpg") -knitr::include_graphics("images/app1-1.png", error = F) -knitr::include_graphics("images/app1-2.png", error = F) - -``` +
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**Figure 13:** Web of Science Download Instructions ## **Appendix 2:** Web of Science Data Field Definitions -```{r echo = FALSE, results = 'asis'} -App2Table1<-read.csv("App2_Table1.csv",dec=".", header = TRUE, sep = ",", check.names=FALSE) -knitr::kable(App2Table1, caption = "Definition of column headings in the output of read_references()^1^. Most are [Web of Science Core Collection Field Tags](https://images.webofknowledge.com/images/help/WOS/hs_advanced_fieldtags.html) associated with different data types.") -``` + +Table: Definition of column headings in the output of read_references()^1^. Most are [Web of Science Core Collection Field Tags](https://images.webofknowledge.com/images/help/WOS/hs_advanced_fieldtags.html) associated with different data types. + +|Column Heading |Definition | +|:--------------|:----------------------------------------------------------------------| +|filename |file from which records were imported | +|AB |Abstract | +|AF |Author Full Name | +|AU |Authors | +|CA |Consortium Author | +|BP |Beginning Page | +|C1 |Author Address | +|CR |Cited References | +|DE |Author Keywords | +|DI |Digital Object Identifier (DOI) | +|EM |E-mail Address | +|EP |Ending Page | +|FN |File Name | +|FU |Funding Agency and Grant Number | +|PD |Publication Date | +|PG |Page Count | +|PT |Publication Type (J=Journal; B=Book; S=Series; P=Patent) | +|PU |Publisher | +|PY |Publication Year | +|RI |ResearcherID Number | +|OI |Open Researcher and Contributor ID Number (ORCID ID) | +|PM |PubMed ID | +|RP |Reprint Address | +|SC |Research Areas | +|SN |International Standard Serial Number (ISSN) | +|SO |Publication Name | +|TC |Web of Science Core Collection Times Cited Count | +|TI |Document Title | +|UT |Accession Number | +|VL |Volume | +|WC |Web of Science Categories | +|Z9 |Total Times Cited Count^2^ | +|refID |a unique identifier for each article in the dataset assigned by refnet | + + ^1^the following Web of Science data fields are only included if users select the `include_all=TRUE` option in `references_read()`: CC, CH, CL, CT, CY, DT, FX, GA, GE, ID, IS, J9, JI, LA, LT, MC, MI, NR, PA, PI, PN, PS, RID, SU, TA, VR. @@ -528,12 +627,36 @@ knitr::kable(App2Table1, caption = "Definition of column headings in the output The information in the Table below is provided by the `authors_clean()` function to help users assess the validity of groupID assignments made by `refsplitr`'s disambiguation algorithm. However, finding any errors in disambiguation becomes increasigly challegning as the number of references processed increases. This is because the number of names on the _review.csv file will increase as more authors, and hence author name variants, are identified. We **strongly** recommend using [code we have written](https://github.com/embruna/refsplitr_simplify_authors_review) to streamline the process of reviewing the output of ```authors_refine()```. This code divides the list of names to review into more manageable subgroups; any errors identified are then corrected on the "_review.csv" file. The code and instructions for using it are available at [https://github.com/embruna/refsplitr_simplify_authors_review](https://github.com/embruna/refsplitr_simplify_authors_review). -```{r echo = FALSE, results = 'asis'} -App3Table1<-read.csv("App3_Table1.csv",dec=".", header = TRUE, sep = ",", check.names=FALSE) -knitr::kable(App3Table1, caption = "Information provided by the `authors_clean()` function to help users assess the validity of groupID assignments made by `refsplitr`'s disambiguation algorithm.") -``` +Table: Information provided by the `authors_clean()` function to help users assess the validity of groupID assignments made by `refsplitr`'s disambiguation algorithm. + +|Field |Definition | +|:-------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +|authorID |AuthorID is a unique identifier for each name in the database (i.e. every author of every paper; the initial assumption of refnet's disambiguation algorithm is that all authors of all articles are different individuals). | +|AU |Authors | +|AF |Author Full Name | +|groupID |This indicates which names (i.e. AuthorID numbers) have been grouped together under a single groupID number because they are believed to be the same person. Because disambiguation is performed iteratively. The lowest authorID number in a group will always be used as the groupID. | +|match_name |The name under which the algorithm groups all of an author's putative name variants. | +|similarity |The similarity score between the two records. | +|author_order |The location on the article's list of authors where this specific author is found | +|address |The author's complete address as listed in the record for an article. | +|university |The author's department, if one is listed in the address | +|department |The author's department, if one is listed in the address | +|short_address |The author's street address | +|postal_code |The author's postal code | +|country |The country in which an author's institution is based | +|RP_address |The reprint address, if present | +|RI |The author's Thomson-Reuters Researcher ID number in the WOS record for an article (if they have one). | +|OI |The author's ORCID ID number in the record for an article (if they have one). | +|EM |The author's email address in the WOS record for an article (if it lists one). | +|UT |Accession Number | +|refID |An id number given to each reference | +|PT |Publication Type (J=Journal; B=Book; S=Series; P=Patent) | +|PY |Publication Year | +|PU |Publisher | + + ## **Appendix 4:** Overview of the `refsplitr` author name disambiguation algorithm. @@ -551,10 +674,10 @@ The first step in our disambiguation process is matching all groups together wit Below is an example of how the algorithm processes a sample data set. -```{r, echo=FALSE, fig.pos="H", out.width="500px"} -knitr::include_graphics("images/appendix3.1.png", error = F) - -``` +
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**Figure 14:** Example dataset. @@ -570,9 +693,10 @@ To lower the number of Type II errors we build a dataset of possible matches for **Entry 1.** In our test data we will start trying to match the first entry "Smith, J" in row 1. By subsetting with the above rules, we'd be matching the first row against rows 2, 3, 4, 5, 6, 7, 9: -```{r, echo=FALSE, fig.pos="H", out.width="500px"} -knitr::include_graphics("images/appendix3.2.png", error = F) -``` +
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**Figure 15:** Figure for entry 1 @@ -584,9 +708,10 @@ In our test data, there is only one piece of information we can match against - **Entry 3.** Row 3 has 2 unique identifying pieces of information: A middle initial and an email. This subset is smaller because we have a middle initial to filter out the Smith, J.L entries: -```{r, echo=FALSE, fig.pos="H", out.width="500px"} -knitr::include_graphics("images/appendix3.3.png", error = F) -``` +
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**Figure 16:** Figure for entry 3 @@ -594,9 +719,10 @@ Matching this information against our subset, the two possible matches are Row 2 **Entry 4** - This entry gets assigned groupID = 2 as well because it has a matching middle initial with Row 2 and Row 3: -```{r, echo=FALSE, fig.pos="H", out.width="500px"} -knitr::include_graphics("images/appendix3.4.png", error = F) -``` +
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**Figure 16:** Figure for entry 4 @@ -606,9 +732,10 @@ knitr::include_graphics("images/appendix3.4.png", error = F) **Entry 7** - Entry 7 has one unique identifier: an email address. It gets matched to the entry in Row 3 and therefore is assigned groupID = 2. -```{r, echo=FALSE, fig.pos="H", out.width="500px"} -knitr::include_graphics("images/appendix3.5.png", error = F) -``` +
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**Figure 17:** Figure for entry 7 @@ -616,9 +743,10 @@ After these first 7 entries, we've correctly matched all likely 'Smith, Jon Karl **Entry 8** - This novel entry has two unique pieces of information: a middle initial and an ORCID. We know the ORCID did not match any previous entries, and the middle initial does not match up with any of the 'Smith' names in our record. -```{r, echo=FALSE, fig.pos="H", out.width="500px"} -knitr::include_graphics("images/appendix3.6.png", error = F) -``` +
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**Figure 18:** Figure for entry 8 @@ -628,17 +756,19 @@ Because there are no suitable matches using initial criteria, we instead match t **Entry 10** - This entry has no matching names and results in no change to the groupID number. -```{r, echo=FALSE, fig.pos="H", out.width="500px"} -knitr::include_graphics("images/appendix3.7.png", error = F) -``` +
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**Figure 19:** Figure for Entry 10 Thus our final results are: -```{r, echo=FALSE, fig.pos="H", out.width="500px"} -knitr::include_graphics("images/appendix3.8.png", error = F) -``` +
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**Figure 20:** Final Results.