This is a demonstration on how the number of papers can be reduced using additional keywords to control the number of results returned.
library(petro.One)
# provide two different set of keywords to combine as vectors
major <- c("water injection", "water flooding")
minor <- c("machine-learning", "intelligent")
lesser <- c("neural network", "SVM", "genetic", "algorithm")
p.df <- join_keywords(major, minor, lesser, get_papers = TRUE)
#> 1 45 'water+injection'AND'machine-learning'AND'neural+network'
#> 2 20 'water+flooding'AND'machine-learning'AND'neural+network'
#> 3 106 'water+injection'AND'intelligent'AND'neural+network'
#> 4 40 'water+flooding'AND'intelligent'AND'neural+network'
#> 5 6 'water+injection'AND'machine-learning'AND'SVM'
#> 6 6 'water+flooding'AND'machine-learning'AND'SVM'
#> 7 3 'water+injection'AND'intelligent'AND'SVM'
#> 8 5 'water+flooding'AND'intelligent'AND'SVM'
#> 9 66 'water+injection'AND'machine-learning'AND'genetic'
#> 10 30 'water+flooding'AND'machine-learning'AND'genetic'
#> 11 162 'water+injection'AND'intelligent'AND'genetic'
#> 12 84 'water+flooding'AND'intelligent'AND'genetic'
#> 13 109 'water+injection'AND'machine-learning'AND'algorithm'
#> 14 53 'water+flooding'AND'machine-learning'AND'algorithm'
#> 15 437 'water+injection'AND'intelligent'AND'algorithm'
#> 16 216 'water+flooding'AND'intelligent'AND'algorithm'
p.df
#> $keywords
#> # A tibble: 16 x 6
#> Var1 Var2 Var3 paper_count sf url
#> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 water injection machine-learning neural network 45.0 'wat~ "htt~
#> 2 water flooding machine-learning neural network 20.0 'wat~ "htt~
#> 3 water injection intelligent neural network 106 'wat~ "htt~
#> 4 water flooding intelligent neural network 40.0 'wat~ "htt~
#> 5 water injection machine-learning SVM 6.00 'wat~ "htt~
#> 6 water flooding machine-learning SVM 6.00 'wat~ "htt~
#> 7 water injection intelligent SVM 3.00 'wat~ "htt~
#> 8 water flooding intelligent SVM 5.00 'wat~ "htt~
#> 9 water injection machine-learning genetic 66.0 'wat~ "htt~
#> 10 water flooding machine-learning genetic 30.0 'wat~ "htt~
#> 11 water injection intelligent genetic 162 'wat~ "htt~
#> 12 water flooding intelligent genetic 84.0 'wat~ "htt~
#> 13 water injection machine-learning algorithm 109 'wat~ "htt~
#> 14 water flooding machine-learning algorithm 53.0 'wat~ "htt~
#> 15 water injection intelligent algorithm 437 'wat~ "htt~
#> 16 water flooding intelligent algorithm 216 'wat~ "htt~
#>
#> $papers
#> # A tibble: 1,388 x 7
#> title_data paper_id source type year author1_data keyword
#> <chr> <chr> <chr> <chr> <int> <chr> <chr>
#> 1 Recovery Incre~ " ~ " ~ " ~ 2001 Brouwer, D.R.,~ 'water+~
#> 2 Ensemble-Based~ " ~ " ~ " ~ 2011 Pajonk, Oliver~ 'water+~
#> 3 Dynamic Optimi~ " ~ " ~ " ~ 2002 Brouwer, D.R.,~ 'water+~
#> 4 Developing a S~ " ~ " ~ " ~ 2017 Alenezi, Faisa~ 'water+~
#> 5 Proactive Opti~ " ~ " ~ " ~ 2016 Haghighat Sefa~ 'water+~
#> 6 Production Opt~ " ~ " ~ " ~ 2007 Emerick, Alexa~ 'water+~
#> 7 Effective well~ " ~ " ~ " ~ 2013 Jamal, Mariam ~ 'water+~
#> 8 Efficient Well~ " ~ " ~ " ~ 2008 Sarma, Pallav,~ 'water+~
#> 9 Application Of~ " ~ " ~ " ~ 2002 Zheng, Jian, S~ 'water+~
#> 10 An Adaptive Hi~ " ~ " ~ " ~ 2013 Oliveira, D.F.~ 'water+~
#> # ... with 1,378 more rows
# provide two different set of keywords to combine as vectors
m <- c("water injection", "water flooding")
n <- c("machine-learning", "machine learning", "intelligent")
p <- c("neural network", "SVM", "genetic")
q <- c("algorithm")
p.df <- join_keywords(m, n, p, q, get_papers = TRUE)
#> 1 37 'water+injection'AND'machine-learning'AND'neural+network'AND'algorithm'
#> 2 16 'water+flooding'AND'machine-learning'AND'neural+network'AND'algorithm'
#> 3 37 'water+injection'AND'machine+learning'AND'neural+network'AND'algorithm'
#> 4 16 'water+flooding'AND'machine+learning'AND'neural+network'AND'algorithm'
#> 5 70 'water+injection'AND'intelligent'AND'neural+network'AND'algorithm'
#> 6 30 'water+flooding'AND'intelligent'AND'neural+network'AND'algorithm'
#> 7 5 'water+injection'AND'machine-learning'AND'SVM'AND'algorithm'
#> 8 3 'water+flooding'AND'machine-learning'AND'SVM'AND'algorithm'
#> 9 5 'water+injection'AND'machine+learning'AND'SVM'AND'algorithm'
#> 10 3 'water+flooding'AND'machine+learning'AND'SVM'AND'algorithm'
#> 11 2 'water+injection'AND'intelligent'AND'SVM'AND'algorithm'
#> 12 1 'water+flooding'AND'intelligent'AND'SVM'AND'algorithm'
#> 13 62 'water+injection'AND'machine-learning'AND'genetic'AND'algorithm'
#> 14 24 'water+flooding'AND'machine-learning'AND'genetic'AND'algorithm'
#> 15 62 'water+injection'AND'machine+learning'AND'genetic'AND'algorithm'
#> 16 24 'water+flooding'AND'machine+learning'AND'genetic'AND'algorithm'
#> 17 145 'water+injection'AND'intelligent'AND'genetic'AND'algorithm'
#> 18 75 'water+flooding'AND'intelligent'AND'genetic'AND'algorithm'
p.df
#> $keywords
#> # A tibble: 18 x 7
#> Var1 Var2 Var3 Var4 paper_count sf url
#> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 water injection machine-learning neura~ algo~ 37.0 'wat~ "https~
#> 2 water flooding machine-learning neura~ algo~ 16.0 'wat~ "https~
#> 3 water injection machine learning neura~ algo~ 37.0 'wat~ "https~
#> 4 water flooding machine learning neura~ algo~ 16.0 'wat~ "https~
#> 5 water injection intelligent neura~ algo~ 70.0 'wat~ "https~
#> 6 water flooding intelligent neura~ algo~ 30.0 'wat~ "https~
#> 7 water injection machine-learning SVM algo~ 5.00 'wat~ "https~
#> 8 water flooding machine-learning SVM algo~ 3.00 'wat~ "https~
#> 9 water injection machine learning SVM algo~ 5.00 'wat~ "https~
#> 10 water flooding machine learning SVM algo~ 3.00 'wat~ "https~
#> 11 water injection intelligent SVM algo~ 2.00 'wat~ "https~
#> 12 water flooding intelligent SVM algo~ 1.00 'wat~ "https~
#> 13 water injection machine-learning genet~ algo~ 62.0 'wat~ "https~
#> 14 water flooding machine-learning genet~ algo~ 24.0 'wat~ "https~
#> 15 water injection machine learning genet~ algo~ 62.0 'wat~ "https~
#> 16 water flooding machine learning genet~ algo~ 24.0 'wat~ "https~
#> 17 water injection intelligent genet~ algo~ 145 'wat~ "https~
#> 18 water flooding intelligent genet~ algo~ 75.0 'wat~ "https~
#>
#> $papers
#> # A tibble: 617 x 7
#> title_data paper_id source type year author1_data keyword
#> <chr> <chr> <chr> <chr> <int> <chr> <chr>
#> 1 Application Of ~ " ~ " ~ " ~ 2002 Zheng, Jian, ~ 'water+f~
#> 2 Adopting Simple~ " ~ " ~ " ~ 2011 Al-Mudhafer, ~ 'water+f~
#> 3 Proactive Optim~ " ~ " ~ " ~ 2016 Haghighat Sef~ 'water+f~
#> 4 Application of ~ " ~ " ~ " ~ 2012 Al-Mudhafer, ~ 'water+f~
#> 5 Efficient Well ~ " ~ " ~ " ~ 2008 Sarma, Pallav~ 'water+f~
#> 6 Novel Applicati~ " ~ " ~ " ~ 2017 Prakasa, Bona~ 'water+f~
#> 7 An Optimization~ " ~ " ~ " ~ 2013 Yan, Xia, Uni~ 'water+f~
#> 8 Real-Time Optim~ " ~ " ~ " ~ 2015 Temizel, Cenk~ 'water+f~
#> 9 Comparisons Of ~ " ~ " ~ " ~ 2010 Samier, Pierr~ 'water+f~
#> 10 Optimizing Wate~ " ~ " ~ " ~ 2014 Prada Mejía, ~ 'water+f~
#> # ... with 607 more rows
# provide two different set of keywords to combine as vectors
major <- c("waterflooding")
minor <- c("machine-learning", "artificial intelligence")
lesser <- c("algorithm")
another <- c("data-mining")
more <- c("data-driven")
p.df <- join_keywords(major, minor, lesser, another, more, get_papers = TRUE)
#> 1 14 'waterflooding'AND'machine-learning'AND'algorithm'AND'data-mining'AND'data-driven'
#> 2 10 'waterflooding'AND'artificial+intelligence'AND'algorithm'AND'data-mining'AND'data-driven'
p.df
#> $keywords
#> # A tibble: 2 x 8
#> Var1 Var2 Var3 Var4 Var5 paper_count sf url
#> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 waterflooding machi~ algor~ data~ data~ 14.0 'waterf~ "https://w~
#> 2 waterflooding artif~ algor~ data~ data~ 10.0 'waterf~ "https://w~
#>
#> $papers
#> # A tibble: 24 x 7
#> title_data paper_id source type year author1_data keyword
#> <chr> <chr> <chr> <chr> <int> <chr> <chr>
#> 1 Practical Appli~ " ~ " ~ " ~ 2015 Amirian, Ehs~ 'waterflo~
#> 2 Data-Driven Mod~ " ~ " ~ " ~ 2013 Dzurman, Pet~ 'waterflo~
#> 3 Turning Data in~ " ~ " ~ " ~ 2016 Temizel, Cen~ 'waterflo~
#> 4 Predicting Wate~ " ~ " ~ " ~ 2002 Fedenczuk, L~ 'waterflo~
#> 5 Predicting Wate~ " ~ " ~ " ~ 2006 Fedenczuk, L~ 'waterflo~
#> 6 Developing a Sm~ " ~ " ~ " ~ 2017 Alenezi, Fai~ 'waterflo~
#> 7 Holistic Workfl~ " ~ " ~ " ~ 2011 Zangl, Georg~ 'waterflo~
#> 8 Applying Analyt~ " ~ " ~ " ~ 2014 Bravo, Cesar~ 'waterflo~
#> 9 Intelligent Pro~ " ~ " ~ " ~ 2011 Khazaeni, Ya~ 'waterflo~
#> 10 Water Productio~ " ~ " ~ " ~ 2011 Hermann, Rol~ 'waterflo~
#> # ... with 14 more rows