2.4 Ejemplo WoS data

Ejemplo wosdata.R en wosdata.zip. Ver Apéndice ??.

# library(dplyr)
# library(stringr)
# https://rubenfcasal.github.io/scimetr/articles/scimetr.html
# library(scimetr)

db <- readRDS("data/wosdata/db_udc_2015.rds")
str(db, 1)
## List of 11
##  $ Docs      :'data.frame':  856 obs. of  26 variables:
##  $ Authors   :'data.frame':  4051 obs. of  4 variables:
##  $ AutDoc    :'data.frame':  5511 obs. of  2 variables:
##  $ Categories:'data.frame':  189 obs. of  2 variables:
##  $ CatDoc    :'data.frame':  1495 obs. of  2 variables:
##  $ Areas     :'data.frame':  121 obs. of  2 variables:
##  $ AreaDoc   :'data.frame':  1364 obs. of  2 variables:
##  $ Addresses :'data.frame':  3655 obs. of  5 variables:
##  $ AddAutDoc :'data.frame':  7751 obs. of  3 variables:
##  $ Journals  :'data.frame':  520 obs. of  12 variables:
##  $ label     : chr ""
##  - attr(*, "variable.labels")= Named chr [1:62] "Publication type" "Author" "Book authors" "Editor" ...
##   ..- attr(*, "names")= chr [1:62] "PT" "AU" "BA" "BE" ...
##  - attr(*, "class")= chr "wos.db"
variable.labels <- attr(db, "variable.labels")
knitr::kable(as.data.frame(variable.labels),
             caption = "Variable labels")
Tabla 2.1: Variable labels
variable.labels
PT Publication type
AU Author
BA Book authors
BE Editor
GP Group author
AF Author full
BF Book authors fullname
CA Corporate author
TI Title
SO Publication name
SE Series title
BS Book series
LA Language
DT Document type
CT Conference title
CY Conference year
CL Conference place
SP Conference sponsors
HO Conference host
DE Keywords
ID Keywords Plus
AB Abstract
C1 Addresses
RP Reprint author
EM Author email
RI Researcher id numbers
OI Orcid numbers
FU Funding agency and grant number
FX Funding text
CR Cited references
NR Number of cited references
TC Times cited
Z9 Total times cited count
U1 Usage Count (Last 180 Days)
U2 Usage Count (Since 2013)
PU Publisher
PI Publisher city
PA Publisher address
SN ISSN
EI eISSN
BN ISBN
J9 Journal.ISI
JI Journal.ISO
PD Publication date
PY Year published
VL Volume
IS Issue
PN Part number
SU Supplement
SI Special issue
MA Meeting abstract
BP Beginning page
EP Ending page
AR Article number
DI DOI
D2 Book DOI
PG Page count
WC WOS category
SC Research areas
GA Document delivery number
UT Access number
PM Pub Med ID

Veamos ahora un par de ejemplos, en el primero se buscan los documentos correspondientes a revistas (que contiene Chem en el título de la revista journal). Para ello utilizamos la función grepl() que busca las coincidencias con el patrón Chem dentro de cada elemento de un vector de caracteres.

# View(db$Journals)
iidj <- with(db$Journals, idj[grepl('Chem', JI)])
db$Journals$JI[iidj]
##  [1] "J. Am. Chem. Soc."                 
##  [2] "Inorg. Chem."                      
##  [3] "J. Chem. Phys."                    
##  [4] "J. Chem. Thermodyn."               
##  [5] "J. Solid State Chem."              
##  [6] "Chemosphere"                       
##  [7] "Antimicrob. Agents Chemother."     
##  [8] "Trac-Trends Anal. Chem."           
##  [9] "Eur. J. Med. Chem."                
## [10] "J. Chem. Technol. Biotechnol."     
## [11] "J. Antimicrob. Chemother."         
## [12] "Food Chem."                        
## [13] "Cancer Chemother. Pharmacol."      
## [14] "Int. J. Chem. Kinet."              
## [15] "Chem.-Eur. J."                     
## [16] "J. Phys. Chem. A"                  
## [17] "New J. Chem."                      
## [18] "Chem. Commun."                     
## [19] "Chem. Eng. J."                     
## [20] "Comb. Chem. High Throughput Screen"
## [21] "Mini-Rev. Med. Chem."              
## [22] "Phys. Chem. Chem. Phys."           
## [23] "Org. Biomol. Chem."                
## [24] "J. Chem Inf. Model."               
## [25] "ACS Chem. Biol."                   
## [26] "Environ. Chem. Lett."              
## [27] "Anal. Bioanal. Chem."              
## [28] "J. Cheminformatics"                
## [29] "J. Mat. Chem. B"
idd <- with(db$Docs, idj %in% iidj)
which(idd)
##  [1]   2   4  16  23  43  69 119 126 138 175 188 190 203 208
## [15] 226 240 272 337 338 341 342 357 382 385 386 387 388 394
## [29] 411 412 428 460 483 518 525 584 600 604 605 616 620 665
## [43] 697 751 753 775 784 796 806 808 847 848
# View(db$Docs[idd, ])
head(db$Docs[idd, -3])
##    idd idj      PT      DT  NR TC Z9 U1 U2     PD   PY VL
## 2    2  37 Journal Article  45  5  5  0  0 DEC 21 2015 54
## 4    4 272 Journal Article  78  2  2  4 21 DEC 14 2015 21
## 16  16 195 Journal Article  34  2  2  0  0    DEC 2015 70
## 23  23 436 Journal Article  48  3  3  0  4    DEC 2015 10
## 43  43 455 Journal  Review 214  0  0  0  8    DEC 2015 13
## 69  69  37 Journal Article  86  2  2  8 28  NOV 2 2015 54
##    IS PN SU SI MA    BP    EP AR
## 2  24             11680 11687   
## 4  51             18662 18670   
## 16 12              3222  3229   
## 23 12              2850  2860   
## 43  4               413   430   
## 69 21             10342 10350   
##                               DI D2 PG           UT an
## 2  10.1021/acs.inorgchem.5b01652     8 367118100013  9
## 4         10.1002/chem.201502937     9 368280400026  8
## 16            10.1093/jac/dkv262     8 368246800008 10
## 23    10.1021/acschembio.5b00624    11 366875400020 10
## 43     10.1007/s10311-015-0526-2    18 365096700004  2
## 69 10.1021/acs.inorgchem.5b01719     9 364175000028  8

En este segundo ejemplo, se buscan los documentos correspondientes a autores (que contiene Abad en su nombre):

# View(db$Authors)
iida <- with(db$Authors, ida[grepl('Abad', AF)])
db$Authors$AF[iida]
## [1] "Mato Abad, Virginia" "Abad, Maria-Jose"   
## [3] "Abad Vicente, J."    "Abada, Sabah"
idd <- with(db$AutDoc, idd[ida %in% iida])
idd
## [1] 273 291 518 586
# View(db$Docs[idd, ])
head(db$Docs[idd, -3])
##     idd idj      PT               DT  NR TC Z9 U1 U2     PD
## 273 273 282 Journal          Article 107  8  8  0  0    SEP
## 291 291 141 Journal Meeting Abstract   0  0  0  0  1  AUG 1
## 518 518 272 Journal          Article 103  4  4  0  0 APR 20
## 586 586 311 Journal          Article  32  2  2  2 19    APR
##       PY VL    IS PN SU SI   MA   BP   EP AR
## 273 2015 42 15-16               6205 6214   
## 291 2015 36           1    P167    9    9   
## 518 2015 21    17               6535 6546   
## 586 2015 26     4                369  375   
##                             DI D2 PG           UT an
## 273 10.1016/j.eswa.2015.03.011    10 355063700018  7
## 291                                1 361205101026 10
## 518     10.1002/chem.201500155    12 352796100030 10
## 586           10.1002/pat.3462     7 351472700012  6