Creating the environment

library(tidyverse)
library(tidygraph)
library(igraph)
library(bibliometrix)
library(tosr)
library(here)
library(lubridate)
# library(sjrdata)
library(openxlsx)
library(zoo)
library(RSQLite)
library(journalabbr)
library(ggraph)
library(openxlsx)
library(XML)
library(plyr)
library(readxl)
source("verbs.R")
windowsFonts("Times" = windowsFont("Times"))
windowsFonts("Times New Roman" = windowsFont("Times New Roman"))

giant.component <- function(graph) {
  cl <- igraph::clusters(graph)
  igraph::induced.subgraph(graph, 
                           which(cl$membership == which.max(cl$csize)))
}

Data getting

library(readxl)
library(httr)
tf<-"C:/Core_OF_Science/all_data_luis_supply.xlsx"
wos_scopus <- readxl::read_excel(tf,sheet =  1)
wos <- readxl::read_excel(tf, sheet = 2)
scopus <- readxl::read_excel(tf, sheet = 3)
reference_df <- readxl::read_excel(tf,sheet =  4)
journal_df <- readxl::read_excel(tf, sheet = 5)
author_df <- readxl::read_excel(tf, sheet = 6)
TC_all <- readxl::read_excel(tf,sheet =  7)
figure_1_data <- readxl::read_excel(tf, sheet = 8)
table_2_country <- readxl::read_excel(tf, sheet = 10)
figure_2_country_wos_scopus <- readxl::read_excel(tf, sheet = 11)
figure_2_country_wos_scopus_1 <-
  readxl::read_excel(tf, sheet = 12) |>
  tidygraph::as_tbl_graph(directed = FALSE) |>
  activate(nodes) |>
  dplyr::mutate(community = tidygraph::group_louvain(),
                degree = tidygraph::centrality_degree(),
                community = as.factor(community))
table_3_journal <- readxl::read_excel(tf, sheet = 13)
table_4_authors <- readxl::read_excel(tf, sheet = 14)
AU_CO_links <- readxl::read_excel(tf, sheet = 15)
tos <- readxl::read_excel(tf, sheet = 16)
edges_tos <- readxl::read_excel(tf, sheet = 17)
nodes_tos <- readxl::read_excel(tf, sheet = 18)
SO_edges <- readxl::read_excel(tf, sheet = 19)
SO_nodes <- readxl::read_excel(tf, sheet = 20)
AU_ego_edges <- readxl::read_excel(tf, sheet = 21)
AU_ego_nodes <- readxl::read_excel(tf, sheet = 22)

Summary of WoS and Scopus

table_1 <- 
  tibble(wos = length(wos$AU), # Create a dataframe with the values.
         scopus = length(scopus$AU), 
         total = length(wos_scopus$AU))
table_1 %>% 
  DT::datatable(class = "cell-border stripe", 
                rownames = F, 
                filter = "top", 
                editable = FALSE, 
                extensions = "Buttons", 
                options = list(dom = "Bfrtip",
                               buttons = c("copy",
                                           "csv",
                                           "excel", 
                                           "pdf", 
                                           "print")))
wos_scopus %>% 
  tidyr::separate_rows(DT, sep = ";") %>% 
  dplyr::count(DT, sort = TRUE)%>% 
  dplyr::mutate(percentage = n /sum(n),
                percentage = percentage * 100,
                percentage = round(percentage, digits = 2)) %>%
  dplyr::rename(total = n) %>% 
  DT::datatable(class = "cell-border stripe", 
                rownames = F, 
                filter = "top", 
                editable = FALSE, 
                extensions = "Buttons", 
                options = list(dom = "Bfrtip",
                               buttons = c("copy",
                                           "csv",
                                           "excel", 
                                           "pdf", 
                                           "print")))

Resutls

Scientometric Analysis

3.1 Scientific Production

Figure 1a - Scopus + WoS

Combine charts using Python Matplotlib & Reticulate

library(reticulate)
# create a new environment
#conda_create("r-reticulate")
# install Matplotlib
#conda_install("r-reticulate", "matplotlib")
# import Matplotlib (it will be automatically discovered in "r-reticulate")
plt <- import("matplotlib")
np <- import("numpy")
# From Double get integers 
# TC y
TC_all$TC_sum_all <- as.integer(TC_all$TC_sum_all)
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import FuncFormatter
# ax=axes
fig, ax = plt.subplots()
# First plot Total Publications - time series
ax.plot(tpx, tpy, color='r',marker='o', label='Total Publications')
ax.set_xlabel('Year')
ax.set_ylabel('Total Publications', color='r')
# Customization for bar charts
barw = 0.5
ax.bar(sx, sy, color='g', label = 'Scopus', alpha = 0.5, width=barw)
ax.bar(wx1, wy, color='orange', label = 'WoS', alpha=0.8, width=barw)
# Y2 - Total citations
twin_axes = ax.twinx()
twin_axes.plot(tcx, tcy, color = 'purple',marker='o', label='Total Citations')
twin_axes.set_ylabel('Total Citations', color='purple')
# Customize
plt.title('Total Scientific Production vs. Total Citations')
# y2 Total Citation label location
plt.legend(loc='center left')
# True or False to get the grid at the background
ax.grid(False)
# y1 label location
ax.legend(loc='upper left')
# Y2 limit depends of tcy scale in this case 1400 improves label location
plt.ylim(0,1500) #########  <-----Important--------- """"Change Y2 Coordinate"""""
## (0.0, 1500.0)
# plt.annotate() customize numbers for each position
for i, label in enumerate(tcy):
  plt.annotate(label, (tcx[i], tcy[i] + 0.5), color='purple', size=8)

for i, label in enumerate(tpy):
  ax.annotate(label, (tpx[i], tpy[i] + 0.8), color='red', size=8)

for i, label in enumerate(wy):
  ax.annotate(label, (wx1[i], wy[i] + 0.1), color='brown', size=8)

for i, label in enumerate(sy):
  ax.annotate(label, (sx[i], sy[i] + 0.2),color='green', size=8)

# Rotate x ticks
plt.xticks(tpx)
## ([<matplotlib.axis.XTick object at 0x000002C4353625D0>, <matplotlib.axis.XTick object at 0x000002C43539A9C0>, <matplotlib.axis.XTick object at 0x000002C43530F710>, <matplotlib.axis.XTick object at 0x000002C43556CDD0>, <matplotlib.axis.XTick object at 0x000002C43556D8B0>, <matplotlib.axis.XTick object at 0x000002C43539B0B0>, <matplotlib.axis.XTick object at 0x000002C43556C410>, <matplotlib.axis.XTick object at 0x000002C43556E450>, <matplotlib.axis.XTick object at 0x000002C43556ED50>, <matplotlib.axis.XTick object at 0x000002C43556F6B0>, <matplotlib.axis.XTick object at 0x000002C43556F380>, <matplotlib.axis.XTick object at 0x000002C43556FD70>, <matplotlib.axis.XTick object at 0x000002C435594710>, <matplotlib.axis.XTick object at 0x000002C4355950A0>], [Text(2023.0, 0, '2023'), Text(2022.0, 0, '2022'), Text(2021.0, 0, '2021'), Text(2020.0, 0, '2020'), Text(2019.0, 0, '2019'), Text(2018.0, 0, '2018'), Text(2017.0, 0, '2017'), Text(2016.0, 0, '2016'), Text(2015.0, 0, '2015'), Text(2014.0, 0, '2014'), Text(2013.0, 0, '2013'), Text(2012.0, 0, '2012'), Text(2011.0, 0, '2011'), Text(2010.0, 0, '2010')])
fig.autofmt_xdate(rotation = 70)
# The Y1 ticks depends from tpy scale limits
yticks = [0,5,10,15,20,25,30,35,40,45,50] ########## <-----Important---- Choose scale .. just specify which numbers you want
ax.set_yticks(yticks)
# Export Figure as SVG
plt.savefig("innovation.svg")

plt.show()