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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,69 @@ | ||
| #' Completeness of bibliographic metadata | ||
| #' | ||
| #' It calculates the percentage of missing data in the metadata of a bibliographic data frame. | ||
| #' | ||
| #' Each metadata is assigned a status c("Excellent," "Good," "Acceptable", "Poor", "Critical," "Completely missing") | ||
| #' depending on the percentage of missing data. In particular, the column *status* classifies the percentage of missing | ||
| #' value in 5 categories: "Excellent" (0%), "Good" (0.01% to 10.00%), "Acceptable" (from 10.01% to 20.00%), | ||
| #' "Poor" (from 20.01% to 50.00%), "Critical" (from 50.01% to 99.99%), "Completely missing" (100%). | ||
| #' | ||
| #' The results of the function allow us to understand which analyses can be performed with bibliometrix | ||
| #' and which cannot based on the completeness (or status) of different metadata. | ||
| #' @param M is a bibliographic data frame obtained by \code{\link{convert2df}} function. | ||
| #' | ||
| #' @return The function \code{missingData} returns a list containing two objects: | ||
| #' \tabular{lll}{ | ||
| #' \code{allTags} \tab \tab is a data frame including results for all original metadata tags from the collection\cr | ||
| #' \code{mandatoryTags}\tab \tab is a data frame that included only the tags needed for analysis with bibliometrix and biblioshiny.} | ||
| #' | ||
| #' @examples | ||
| #' data(scientometrics, package = "bibliometrixData") | ||
| #' res <- missingData(scientometrics) | ||
| #' print(res$mandatoryTags) | ||
| #' | ||
| #' @export | ||
| #' | ||
| missingData <- function(M) { | ||
| cols <- names(M) | ||
| missing_counts <- sapply(cols, function(x){ | ||
| sum(is.na(M[,x]) | M[,x] %in% c("NA,0000,NA","NA","")) | ||
| }) | ||
| missing_pct <- round(missing_counts/nrow(M) * 100, 2) | ||
| df_all <- data.frame(cols, missing_counts, missing_pct) | ||
|
|
||
| tag <- unlist( | ||
| strsplit( | ||
| "AB,AU,C1,CR,DE,DI,DT,ID,LA,NR,PY,RP,SO,TC,TI,WC","," | ||
| ) | ||
| ) | ||
| description <- trimws(unlist( | ||
| strsplit( | ||
| "Abstract, Author,Affiliation,Cited References,Keywords,DOI,Document Type,Keywords Plus,Language,Number of Cited References, | ||
| Publication Year,Corresponding Author, Journal, Total Citation, Title, Science Categories", "," | ||
| ) | ||
| )) | ||
|
|
||
| df_all <- df_all %>% | ||
| mutate(status = status(missing_pct)) %>% | ||
| replace_na(replace = list(missing_counts = nrow(M), missing_pct = 100)) | ||
|
|
||
| df_tags <- data.frame(tag, description) %>% | ||
| left_join(df_all, by = c("tag" = "cols")) %>% | ||
| replace_na(replace = list(missing_counts = nrow(M), missing_pct = 100, status = "Completely missing")) %>% | ||
| arrange(missing_pct,description) | ||
|
|
||
| results <- list(allTags=df_all, mandatoryTags=df_tags) | ||
| return(results) | ||
| } | ||
|
|
||
| status <- function(x){ | ||
| y <- character(length(x)) | ||
| y[x==0] <- "Excellent" | ||
| y[x>0 & x<= 10] <- "Good" | ||
| y[x>10 & x<= 20] <- "Acceptable" | ||
| y[x>20 & x<=50] <- "Poor" | ||
| y[x>50 & x<100] <- "Critical" | ||
| y[is.na(x) | x==100] <- "Completely missing" | ||
| return(y) | ||
| } | ||
|
|
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,3 +1,3 @@ | ||
| toUpper <- function(D){ | ||
| stringr::str_to_upper(D, locale = "en") | ||
| stringi::stri_trans_toupper(D, locale = "en") | ||
| } |
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