# How to use GetLattesData

#### 2017-11-28

ATTENTION: The package is not working as of 2017-11-26. The Lattes website, where the xml files were available, is down.

Lattes is an unique and largest platform for academic curriculumns. There you can find information about the academic work of all Brazilian scholars. It includes institution of PhD, current employer, field of work, all publications metadata and more. It is an unique and reliable source of information for bibliometric studies.

I’ve been working with Lattes data for some time. Here I present a short list of papers that have used this data.

Package GetLattesData is a wrap up of functions I’ve been using for accessing the dataset. It’s main innovation is the possibility of downloading data directly from Lattes, without any manual work or captcha solving.

# 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', 'K4440252H7', '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)

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

names(l.out)

The first is a dataframe with information about researchers:

tpesq <- l.out$tpesq str(tpesq) The second dataframe contains information about all published publications, including Qualis and SJR: dplyr::glimpse(l.out$tpublic.published)

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

## An application of GetLattesData

GetLattesData makes it easy to create academic reports for a large number of researchers. See next, where we plot the number of publications for each researcher, conditioning on Qualis ranking.

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)

We can also use dplyr to do some simple assessment of academic productivity:

library(dplyr)

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)