Example of usage

Let’s consider a simple example of downloading information for a group of scholars. I selected a couple of coleagues at my university. Their Lattes id can be easilly found in Lattes website. After searching for a name, notice the internet address of the resulting CV, such as http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4713546D3. Lattes ID is the final 10 digit code of this address. In our example, it is 'K4713546D3'.

Since we all work in the business department of UFRGS, the impact of our publications is localy set by the Qualis ranking of Management, Accounting and Tourism ('ADMINISTRAÇÃO PÚBLICA E DE EMPRESAS, CIÊNCIAS CONTÁBEIS E TURISMO'). Qualis is the local journal ranking in Brazil. You can read more about Qualis in Wikipedia and here.

Now, based on the two sets of information, vector of ids and field of Qualis, we use GetLattesData to download all up to date information available in Lattes:

library(GetLattesData)

# ids from EA-UFRGS
my.ids <- c('K4713546D3','K4723925J2')

# qualis for the field of management
field.qualis = 'ADMINISTRAÇÃO PÚBLICA E DE EMPRESAS, CIÊNCIAS CONTÁBEIS E TURISMO'

l.out <- gld_get_lattes_data(id.vec = my.ids, field.qualis = field.qualis)

## 
## Downloading file  /tmp/RtmpRygTqV/K4713546D3_2018-08-13.zip
## Downloading file  /tmp/RtmpRygTqV/K4723925J2_2018-08-13.zip
## Reading  K4713546D3_2018-08-13.zip -  Marcelo Scherer Perlin
##  Found 21 published papers
##  Found 0 accepted paper(s)
##  Found 10 supervisions
##  Found 2 published books
##  Found 0 book chapters
##  Found 17 conference papers
## Reading  K4723925J2_2018-08-13.zip -  Denis Borenstein
##  Found 75 published papers
##  Found 2 accepted paper(s)
##  Found 96 supervisions
##  Found 1 published books
##  Found 6 book chapters
##  Found 89 conference papers

The output my.l is a list with the following dataframes:

names(l.out)

## [1] "tpesq"             "tpublic.published" "tpublic.accepted" 
## [4] "tsupervisions"     "tbooks"            "tconferences"

The first is a dataframe with information about researchers:

tpesq <- l.out$tpesq
str(tpesq)

## 'data.frame':    2 obs. of  9 variables:
##  $ name           : chr  "Marcelo Scherer Perlin" "Denis Borenstein"
##  $ last.update    : Date, format: "2018-07-13" "2018-07-19"
##  $ phd.institution: chr  "University of Reading" "University of Strathclyde"
##  $ phd.start.year : num  2007 1991
##  $ phd.end.year   : num  2010 1995
##  $ country.origin : chr  "Brasil" "Brasil"
##  $ major.field    : chr  "CIENCIAS_SOCIAIS_APLICADAS" "ENGENHARIAS"
##  $ minor.field    : chr  "Administração" "Engenharia de Produção"
##  $ id.file        : chr  "K4713546D3_2018-08-13.zip" "K4723925J2_2018-08-13.zip"

The second dataframe contains information about all published publications, including Qualis and SJR:

dplyr::glimpse(l.out$tpublic.published)

## Observations: 96
## Variables: 11
## $ name          <chr> "Marcelo Scherer Perlin", "Marcelo Scherer Perli...
## $ article.title <chr> "Teoria do Caos aplicada aos Contratos de Café n...
## $ year          <dbl> 2006, 2009, 2007, 2011, 2013, 2013, 2013, 2013, ...
## $ language      <chr> "Português", "Inglês", "Inglês", "Inglês", "Port...
## $ journal.title <chr> "READ - Revista Eletrônica da Administração (UFR...
## $ ISSN          <chr> "-", "1753-9641", "1413-2311", "1749-9135", "167...
## $ order.aut     <dbl> 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 2, 1, 1, 3, 1, ...
## $ n.authors     <dbl> 2, 1, 2, 2, 1, 3, 3, 3, 2, 2, 3, 2, 4, 5, 3, 2, ...
## $ qualis        <chr> NA, NA, "B1", NA, "B1", "A2", "B1", "A1", "B1", ...
## $ SJR           <dbl> NA, 0.213, NA, NA, NA, 0.886, NA, 0.429, NA, NA,...
## $ H.SJR         <int> NA, 6, NA, NA, NA, 17, NA, 38, NA, NA, NA, NA, 4...

Other dataframes in l.out included information about accepted papers, supervisions, books and conferences.

tpublic.published <- l.out$tpublic.published

library(ggplot2)

p <- ggplot(tpublic.published, aes(x = qualis)) +
  geom_bar(position = 'identity') + facet_wrap(~name) +
  labs(x = paste0('Qualis: ', field.qualis))
print(p)

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

my.tab <- tpublic.published %>%
  group_by(name) %>%
  summarise(n.papers = n(),
            max.SJR = max(SJR, na.rm = T),
            mean.SJR = mean(SJR, na.rm = T),
            n.A1.qualis = sum(qualis == 'A1', na.rm = T),
            n.A2.qualis = sum(qualis == 'A2', na.rm = T),
            median.authorship = median(as.numeric(order.aut), na.rm = T ))

knitr::kable(my.tab)