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Tool.R
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library(NLP)
library(xml2)
library(proto)
library(RSQLite)
library(readr)
library(tm)
library(SnowballC)
library(RWeka)
library(sqldf)
library(dplyr)
library(data.table)
library(ggmap)
library(RODBC)
library(XML)
library(rvest)
library(xml2)
library(openxlsx)
library(RPostgreSQL)
library(stringr)
library(data.table)
library(stringr)
library(bitops)
library(RCurl)
library(rjson)
library(sp)
library(postGIStools)
library(rstudioapi)
rm(list=ls())
##PRE - PROCESSING OF DOCUMENTS
## Step 1: To convert the document from PDF to .txt by opening the PDF file in Microsoft WORD and then save it as .txt
## Step 2: To eliminate whitespaces and tap spaces by using the program Note++: (1) To enter tool "Search" (2) To enter tool "Replace" (3) To write the regular expression [\t\r\n\s]+ in the field: "Find what"
## Step 3: To name the documents in the form: doc[#].txt e.g. doc1.txt
## Step 4: To copy the processed files in the folder "C:/Users/xxx/Desktop/WETLANDS_EXTRATION/Documents"
## Step 5: To set the working directory with the location of the files - Folder: "C:/xxx/xxx/xxx/WETLANDS_EXTRATION/Documents"
current_path = rstudioapi::getActiveDocumentContext()$path
setwd(paste0(dirname(current_path),"/Documents"))
##Extracted parameters:
##1- Parameter_1: Location - COUNTRY
##2- Parameter_2: Location - COUNTRY_CODE
##3- Parameter_3: Location - MUNICIPALITY
##4- Parameter_4: Location - LONGITUDE
##5- Parameter_5: Location - LATITUDE
##6- Parameter_6: TYPE OF WETLAND
##7- Parameter_7: TYPE OF WASTEWATER
##8- Parameter_8: TYPE OF PLANTS
##9- Parameter_9: BOD5 INFLUENT
##10- Parameter_10:BOD5 EFLUENT
##11- Parameter_11:JOURNAL NAME
##CLEANING AND CONSTRUCTION OF TEXT MATRIX
## Step 1: To read the files - Folder:
files_names<-list.files(getwd())
doc.list<-lapply(files_names,read_file)
N.docs<-length(doc.list)
## Step 2: To construct Corpus of documents
my.docs<-VectorSource(c(doc.list))
my.docs$names<-sub(".txt","",files_names)
mydocs<-Corpus(my.docs)
## Step 3: To clean special characters
removeSpecialChars<-function(x) gsub("[^-a-zA-z0-9. ]","",x)
mydocs<-tm_map(mydocs,removeSpecialChars)
## Step 4: To clean stopwords()
mydocs<-tm_map(mydocs,removeWords,stopwords('en'))
## Step 5: To clean doble whitespaces between words
mydocs<-tm_map(mydocs,stripWhitespace)
## Step 6: To clean information before the word "Abstract"
removeAbstract<-function(x) gsub("(.*?)A(?i)bstract(?-i)","",x)
mydocs<-tm_map(mydocs,removeAbstract)
## Step 7: To clean information after the word "Conclusions"
removeConclusions<-function(x) sub("C(?i)onclusion(?-i)(.*)","",x)
mydocs<-tm_map(mydocs,removeConclusions)
## Step 8: To divide the text in four main parts: Abstract, Introduction, Materials and Methods and Results
## Step 8.1: Abstract
removeIntroduction_down<-function(x) sub("I(?i)ntroduction(?-i)(.*)","",x)
str1<-tm_map(mydocs,removeIntroduction_down)
## Step 8.2: Introduction
removeIntroduction_up<-function(x) sub("(.*?)I(?i)ntroduction(?-i)","",x)
str2_fs1.1<-tm_map(mydocs,removeIntroduction_up)
removeMethods_down<-function(x) sub("M(?i)ethods(?-i)(.*)","",x)
str2_fs1.2<-tm_map(str2_fs1.1,removeMethods_down)
removeMethods_down<-function(x) sub("M(?i)aterial(?-i)(.*)","",x)
str2<-tm_map(str2_fs1.2,removeMethods_down)
## Step 8.3: Materials and Methods
removeMethods_up<-function(x) sub("(.*?)M(?i)aterial(?-i)","",x)
str3_fs1.1<-tm_map(mydocs,removeMethods_up)
removeMethods_up<-function(x) sub("(.*?)M(?i)ethods(?-i)","",x)
str3_fs1.2<-tm_map(str3_fs1.1,removeMethods_up)
removeResults_down<-function(x) sub("R(?i)esults(?-i)(.*)","",x)
str3<-tm_map(str3_fs1.2,removeResults_down)
## Step 8.4: Results
removeResults_up<-function(x) sub("(.*?)R(?i)esults(?-i)","",x)
str4<-tm_map(mydocs,removeResults_up)
## Step 9: To save information in data.frames for posterior construction of VCorpus
dataframe_docs<-data.frame(text=sapply(mydocs, as.character),stringsAsFactors = FALSE)
extract_str1<-data.frame(text=sapply(str1, as.character),stringsAsFactors = FALSE)
extract_str2<-data.frame(text=sapply(str2, as.character),stringsAsFactors = FALSE)
extract_str3<-data.frame(text=sapply(str3, as.character),stringsAsFactors = FALSE)
extract_str4<-data.frame(text=sapply(str4, as.character),stringsAsFactors = FALSE)
## Step 10: To create VCorpus for N-gram analysis
df<-data.frame(doc_id=seq(1:N.docs),text=dataframe_docs$text,stringsAsFactors = F)
VCorpus_docs<-VCorpus(DataframeSource(df))
df_str1<-data.frame(doc_id=seq(1:N.docs),text=extract_str1$text,stringsAsFactors = F)##Abstract
VCorpus_docs1<-VCorpus(DataframeSource(df_str1))
df_str2<-data.frame(doc_id=seq(1:N.docs),text=extract_str2$text,stringsAsFactors = F)##Introduction
VCorpus_docs2<-VCorpus(DataframeSource(df_str2))
df_str3<-data.frame(doc_id=seq(1:N.docs),text=extract_str3$text,stringsAsFactors = F)##Materials and methods
VCorpus_docs3<-VCorpus(DataframeSource(df_str3))
df_str4<-data.frame(doc_id=seq(1:N.docs),text=extract_str4$text,stringsAsFactors = F)##Results
VCorpus_docs4<-VCorpus(DataframeSource(df_str4))
##PARAMETER_1 AND PARAMETER_2: COUNTRY AND COUNTRY_CODE
## Step 1: To read the dataset of countries in the world - Folder:
setwd(paste0(dirname(current_path),"/Documents_backup"))
countries<-read.delim("GEODATASOURCE-COUNTRY.txt", header = TRUE, sep = "\t")
countries_names<-as.vector(countries[,4])
countries_codes<-as.vector(countries[,1])
## Step 2: To extract the number of countries in the dataset
l_countries<-length(countries_names)
country_code<- c(rep(NA,N.docs))
country<-c(rep(NA,N.docs))
## Step 3: To extract the country name
## Rules
## 1- SP's: Abstract, Materials and Methods
## 2- Tokanizer: min=1, max=2
for (m in 1:N.docs){
Tokenizer<-function(x) NGramTokenizer(x,Weka_control(min=1,max=2))
head(NGramTokenizer(VCorpus_docs3[m],Weka_control(min=1,max=2)))
dtm3<-TermDocumentMatrix(VCorpus_docs3[m],control=list(tokenize=Tokenizer,tolower=FALSE))
bio3<-as.data.frame(t(as.matrix(dtm3)))
keywords3<-colnames(bio3)
kw_country3<-as.data.frame(keywords3)
colnames(kw_country3)[1]<-"keywords_country"
Tokenizer<-function(x) NGramTokenizer(x,Weka_control(min=1,max=2))
head(NGramTokenizer(VCorpus_docs1[m],Weka_control(min=1,max=2)))
dtm1<-TermDocumentMatrix(VCorpus_docs1[m],control=list(tokenize=Tokenizer,tolower=FALSE))
bio1<-as.data.frame(t(as.matrix(dtm1)))
keywords1<-colnames(bio1)
kw_country1<-as.data.frame(keywords1)
colnames(kw_country1)[1]<-"keywords_country"
kw_country<-rbind(kw_country3,kw_country1)
l_keywords<-length(keywords1)+length(keywords3)
extract_country<-as.data.frame(lapply(kw_country, function(x) lapply(countries_names,function(y) grepl(y,x))))
for (i in 1:l_keywords){
for (j in 1:l_countries){
if (extract_country[i,j]=="TRUE"){
country[m]<-countries_names[j]
country_code[m]<-countries_codes[j]
}
}
}
}
##PARAMETER_3: MUNICIPALITY
## Step 1: To read the dataset of cities in the world - Folder:
cities<-fread("GEODATASOURCE-CITIES-FREE.txt", header = TRUE, sep = "\t")
cities_data<-as.data.frame(cities)
## Step 2: To extract the parameter City_name
## Rules
## 1- SP's: Materials and Methods
## 2- Tokanizer: min=1, max=4
MUNICIPALITY<-c(rep(NA,N.docs))
for (m in 1:N.docs){
if (is.na(country_code[m])==FALSE){
Tokenizer_city<-function(x) NGramTokenizer(x,Weka_control(min=1,max=4))
head(NGramTokenizer(VCorpus_docs3[m],Weka_control(min=1,max=4)))
dtm_city<-TermDocumentMatrix(VCorpus_docs3[m],control=list(tokenize=Tokenizer_city,tolower=FALSE))
bio_city<-as.data.frame(t(as.matrix(dtm_city)))
keywords_city<-colnames(bio_city)
kw_city<-as.vector(keywords_city)
l_keywords_city<-length(keywords_city)
filtration_cities<-subset(cities_data,CC_FIPS==country_code[m])
filtration_cities<-as.vector(filtration_cities[,2])
l_cities<-length(filtration_cities)
filtration_cities<-paste("^",filtration_cities,"$",sep="")
extract_cities<-lapply(filtration_cities,function(x)grep(x,kw_city,value=TRUE))
extract_cities<-extract_cities[lapply(extract_cities,length)>0]
if ((NROW(extract_cities)==1)&(length(extract_cities[[1]])==1)){
MUNICIPALITY[m]<-extract_cities[[1]][1]
}
}
}
##PARAMETER_4 AND PARAMETER_5: COORDINATES (LATITUDE,LONGITUDE)
##Step 1.1: To extract coordinates using API from Gogle Maps based on the name of the city (MUNICIPALITY)
LONGITUDE<-c(rep(NA,N.docs))
LATITUDE<-c(rep(NA,N.docs))
getGeoData <- function(location){
location <- gsub(' ','+',location)
geo_data <- getURL(paste("https://maps.googleapis.com/maps/api/geocode/json?address=",location,"&key=AIzaSyD3r4Ckm47KQ0luZEPy7TuaH8TxMyRC0GQ", sep=""))
raw_data_2 <- fromJSON(geo_data)
return(raw_data_2)
}
for (m in 1:N.docs){
if (is.na(MUNICIPALITY[m])=="FALSE"){
coordinates<-unlist(getGeoData(MUNICIPALITY[m]))
for (a in 1:length(coordinates)){
if (names(coordinates)[a]=="results.geometry.location.lat"){
LATITUDE[m]<-unname(coordinates[a])
}
if (names(coordinates)[a]=="results.geometry.location.lng"){
LONGITUDE[m]<-unname(coordinates[a])
}
}
}
##Step 1.2: To extract coordinates directly from the text
## Rules
## 1- SP's: Materials and Methods
## 2- Tokanizer: min=1, max=1
if (is.na(MUNICIPALITY[m])=="TRUE"){
Tokenizer<-function(x) NGramTokenizer(x,Weka_control(min=1,max=1))
head(NGramTokenizer(VCorpus_docs3[m],Weka_control(min=1,max=1)))
dtm_coordinates<-TermDocumentMatrix(VCorpus_docs3[m],control=list(tokenize=Tokenizer,tolower=FALSE))
bio_coordinates<-as.data.frame(t(as.matrix(dtm_coordinates)))
keywords_coordinates<-colnames(bio_coordinates)
kw_coordinates<-as.data.frame(keywords_coordinates)
longitud_filter_E<-as.vector(filter(kw_coordinates, grepl(pattern="^[0-9]{5}E",kw_coordinates$keywords_coordinates))[1,])
longitud_filter_W<-as.vector(filter(kw_coordinates, grepl(pattern="^[0-9]{5}W",kw_coordinates$keywords_coordinates))[1,])
latitud_filter_N<-as.vector(filter(kw_coordinates, grepl(pattern="^[0-9]{5}N",kw_coordinates$keywords_coordinates))[1,])
latitud_filter_S<-as.vector(filter(kw_coordinates, grepl(pattern="^[0-9]{5}S",kw_coordinates$keywords_coordinates))[1,])
if (is.na(longitud_filter_E[1])=="FALSE"){
lon_number<-sub("(^[^0-9]*)(\\d+)([^0-9].*)", "\\2", longitud_filter_E[1])
grade_longitud<-as.numeric(sub('([0-9]{2}).*', '\\1', lon_number))
minutes_longitud<-as.numeric(sub(".*(\\d+{2}).*$", "\\1", lon_number))
LONGITUDE[m]<-grade_longitud+minutes_longitud/60
}
if (is.na(latitud_filter_N[1])=="FALSE"){
lat_number<-sub("(^[^0-9]*)(\\d+)([^0-9].*)", "\\2", latitud_filter_N[1])
grade_latitud<-as.numeric(sub('([0-9]{2}).*', '\\1', lat_number))
minutes_latitud<-as.numeric(sub(".*(\\d+{2}).*$", "\\1", lat_number))
LATITUDE[m]<-grade_latitud+minutes_latitud/60
}
if (is.na(longitud_filter_W[1])=="FALSE"){
lon_number<-sub("(^[^0-9]*)(\\d+)([^0-9].*)", "\\2", longitud_filter_W[1])
grade_longitud<-as.numeric(sub('([0-9]{2}).*', '\\1', lon_number))
minutes_longitud<-as.numeric(sub(".*(\\d+{2}).*$", "\\1", lon_number))
LONGITUDE[m]<-(grade_longitud+minutes_longitud/60)*(-1)
}
if (is.na(latitud_filter_S[1])=="FALSE"){
lat_number<-sub("(^[^0-9]*)(\\d+)([^0-9].*)", "\\2", latitud_filter_S[1])
grade_latitud<-as.numeric(sub('([0-9]{2}).*', '\\1', lat_number))
minutes_latitud<-as.numeric(sub(".*(\\d+{2}).*$", "\\1", lat_number))
LATITUDE[m]<-(grade_latitud+minutes_latitud/60)*-1
}
}
}
##PARAMETER_6: TYPE OF WETLAND
##Step 1: To define matrix of keywords
matrix_keyword<-c("^VSSF$",
"^HSSF$",
"^VFCW$",
"^HFCW$",
"^(?i)vertical(?-i)$",
"^(?i)surface flow(?-i)$",
"^(?i)subsurface flow(?-i)$")
matrix_keyword_nregex<-c("VSSF",
"HSSF",
"VFCW",
"HFCW",
"vertical",
"surface flow",
"subsurface flow")
##Step 2: To define cluster of expressions in matrix of keywords
cluster<-c("VF",
"HSSF",
"VF",
"HF",
"VF",
"FWS",
"HSSF")
##Step 3: To extract parameter
## Rules
## 1- SP's: Abstract, Materials and Methods, Results
## 2- Tokanizer: min=1, max=2
n.words<-length(matrix_keyword)
TYPE_WETLANDS<-vector(mode="character", length=N.docs)
variable_cluster<-vector(mode="character", length=10)
for (m in 1:N.docs){
Tokanizer_typewetlands3<-function(x) NGramTokenizer(x,Weka_control(min=1,max=2))
head(NGramTokenizer(VCorpus_docs3[m],Weka_control(min=1,max=2)))
dtm_typewetlands3<-TermDocumentMatrix(VCorpus_docs3[m],control = list(tokenize=Tokanizer_typewetlands3,tolower=FALSE))
bio_typewetlands3<-as.data.frame(t(as.matrix(dtm_typewetlands3)))
keywords_typewetlands3<-colnames(bio_typewetlands3)
kw_typewetlands3<-as.data.frame(keywords_typewetlands3)
colnames(kw_typewetlands3)[1]<-"keywords_typewetlands"
Tokanizer_typewetlands4<-function(x) NGramTokenizer(x,Weka_control(min=1,max=2))
head(NGramTokenizer(VCorpus_docs4[m],Weka_control(min=1,max=2)))
dtm_typewetlands4<-TermDocumentMatrix(VCorpus_docs4[m],control = list(tokenize=Tokanizer_typewetlands4,tolower=FALSE))
bio_typewetlands4<-as.data.frame(t(as.matrix(dtm_typewetlands4)))
keywords_typewetlands4<-colnames(bio_typewetlands4)
kw_typewetlands4<-as.data.frame(keywords_typewetlands4)
colnames(kw_typewetlands4)[1]<-"keywords_typewetlands"
Tokanizer_typewetlands1<-function(x) NGramTokenizer(x,Weka_control(min=1,max=2))
head(NGramTokenizer(VCorpus_docs1[m],Weka_control(min=1,max=2)))
dtm_typewetlands1<-TermDocumentMatrix(VCorpus_docs1[m],control = list(tokenize=Tokanizer_typewetlands4,tolower=FALSE))
bio_typewetlands1<-as.data.frame(t(as.matrix(dtm_typewetlands1)))
keywords_typewetlands1<-colnames(bio_typewetlands1)
kw_typewetlands1<-as.data.frame(keywords_typewetlands1)
colnames(kw_typewetlands1)[1]<-"keywords_typewetlands"
kw_typewetlands<-rbind(kw_typewetlands3,kw_typewetlands4,kw_typewetlands1)
c<-0
for(i in 1:n.words){
length_filtered_terms_type<-nrow(filter(kw_typewetlands, grepl(pattern=matrix_keyword[i],kw_typewetlands$keywords_typewetlands)))
if((length_filtered_terms_type>=1)&(TYPE_WETLANDS[m]!=""))
{
con<-0
for (l in 1:c){
if (variable_cluster[l]==cluster[i]){
con<-1
}
}
if (con==0){
c<-c+1
variable_cluster[c]<-cluster[i]
TYPE_WETLANDS[m]<-paste0(TYPE_WETLANDS[m],"-",cluster[i])
}
}
if((length_filtered_terms_type>=1)&(TYPE_WETLANDS[m]==""))
{
c<-c+1
variable_cluster[c]<-cluster[i]
TYPE_WETLANDS[m]<-cluster[i]
}
}
}
##PARAMETER_6: AREA
##Step 1: To extract parameter
## Rules
## 1- SP's: Abstract, Materials and Methods
## 2- Tokanizer: min=2, max=2
AREA<-vector(mode="character", length=N.docs)
NLPBigramTokenizer <- function(x) {
unlist(lapply(ngrams(words(x), 2), paste, collapse = " "), use.names = FALSE)
}
for (m in 1:N.docs){
dtm_area<-TermDocumentMatrix(VCorpus_docs1[m],control = list(tokenize=NLPBigramTokenizer))
bio_area<-as.data.frame(t(as.matrix(dtm_area)))
keywords_area<-colnames(bio_area)
kw_area<-as.data.frame(keywords_area)
filtered_terms_area1 <- filter(kw_area, grepl(pattern="[0-9]m2.$",kw_area$keywords_area))
filtered_terms_area2 <- filter(kw_area, grepl(pattern="[0-9] m2.$",kw_area$keywords_area))
filtered_terms_area3 <- filter(kw_area, grepl(pattern="[[0-9]m2$",kw_area$keywords_area))
filtered_terms_area4 <- filter(kw_area, grepl(pattern="[0-9] m2$",kw_area$keywords_area))
filtered_terms_area<-rbind(filtered_terms_area1,filtered_terms_area2,filtered_terms_area3,filtered_terms_area4)
filtered_terms_area<-as.vector(filtered_terms_area$keywords_area)
if (length(filtered_terms_area)>0){
for(i in 1:length(filtered_terms_area)){
if(AREA[m]!="")
{
filtered_terms_area[i]<-sub("m(.*)","",gsub(gsub("[0-9].*","",filtered_terms_area[i]),"",filtered_terms_area[i]))
filtered_terms_area[i]<-gsub("[[:space:]]+","",filtered_terms_area[i])
AREA[m]<-paste0(AREA[m],"-",filtered_terms_area[i])
}
if(AREA[m]=="")
{
filtered_terms_area[i]<-sub("m(.*)","",gsub(gsub("[0-9].*","",filtered_terms_area[i]),"",filtered_terms_area[i]))
filtered_terms_area[i]<-gsub("[[:space:]]+","",filtered_terms_area[i])
AREA[m]<-filtered_terms_area[i]
}
}
}
if (length(filtered_terms_area)==0){
dtm_area<-TermDocumentMatrix(VCorpus_docs3[m],control = list(tokenize=NLPBigramTokenizer))
bio_area<-as.data.frame(t(as.matrix(dtm_area)))
keywords_area<-colnames(bio_area)
kw_area<-as.data.frame(keywords_area)
filtered_terms_area1 <- filter(kw_area, grepl(pattern="[0-9]m2.$",kw_area$keywords_area))
filtered_terms_area2 <- filter(kw_area, grepl(pattern="[0-9] m2.$",kw_area$keywords_area))
filtered_terms_area3 <- filter(kw_area, grepl(pattern="[[0-9]m2$",kw_area$keywords_area))
filtered_terms_area4 <- filter(kw_area, grepl(pattern="[0-9] m2$",kw_area$keywords_area))
filtered_terms_area<-rbind(filtered_terms_area1,filtered_terms_area2,filtered_terms_area3,filtered_terms_area4)
filtered_terms_area<-as.vector(filtered_terms_area$keywords_area)
if (length(filtered_terms_area)!=0){
for(i in 1:length(filtered_terms_area)){
if(AREA[m]!="")
{
filtered_terms_area[i]<-sub("m(.*)","",gsub(gsub("[0-9].*","",filtered_terms_area[i]),"",filtered_terms_area[i]))
filtered_terms_area[i]<-gsub("[[:space:]]+","",filtered_terms_area[i])
AREA[m]<-paste0(AREA[m],"-",filtered_terms_area[i])
}
if(AREA[m]=="")
{
filtered_terms_area[i]<-sub("m(.*)","",gsub(gsub("[0-9].*","",filtered_terms_area[i]),"",filtered_terms_area[i]))
filtered_terms_area[i]<-gsub("[[:space:]]+","",filtered_terms_area[i])
AREA[m]<-filtered_terms_area[i]
}
}
}
}
}
##PARAMETER_7: TYPE OF WASTEWATER
##Step 1: To define matrix of keywords
matrix_keyword<-c("^(?i)domestic(?-i)$",
"^(?i)industrial(?-i)$",
"^(?i)urban runoff(?-i)$",
"^(?i)food processing industry(?-i)$",
"^(?i)agriculture(?-i)$",
"^(?i)agricultural(?-i)$",
"^(?i)eutrophic lake water(?-i)$",
"^(?i)Dariy milking parlor(?-i)$",
"^(?i)Potato starch processing(?-i)$",
"^(?i)swine urine(?-i)$")
matrix_keyword_nregex<-c("domestic",
"industrial",
"urban runoff",
"food processing industry",
"agriculture",
"agricultural",
"eutrophic lake water",
"Dariy milking parlor",
"Potato starch processing",
"swine urine")
##Step 2: To define cluster of expressions in matrix of keywords
cluster<-c("MUNICIPAL",
"INDUSTRIAL",
"STORMWATER",
"INDUSTRIAL",
"AGRICULTURAL",
"AGRICULTURAL",
"INDUSTRIAL",
"INDUSTRIAL",
"INDUSTRIAL")
##Step 3: To extract parameter
## Rules
## 1- SP's: Materials and Methods
## 2- Tokanizer: min=1, max=3
variable_cluster<-vector(mode="character", length=10)
n.words<-length(matrix_keyword)
TYPE_WASTEWATER<-c(rep(NA,N.docs))
for (m in 1:N.docs){
Tokanizer_typewastewater<-function(x) NGramTokenizer(x,Weka_control(min=1,max=3))
head(NGramTokenizer(VCorpus_docs3[m],Weka_control(min=1,max=3)))
dtm_typewastewater<-TermDocumentMatrix(VCorpus_docs3[m],control = list(tokenize=Tokanizer_typewastewater,tolower=FALSE))
bio_typewastewater<-as.data.frame(t(as.matrix(dtm_typewastewater)))
keywords_typewastewater<-colnames(bio_typewastewater)
kw_typewastewater<-as.data.frame(keywords_typewastewater)
c<-0
for(i in 1:n.words){
length_filtered_terms_type<-nrow(filter(kw_typewastewater, grepl(pattern=paste0("(?i)",matrix_keyword[i],"(?-i)"),kw_typewastewater$keywords_typewastewater)))
if((length_filtered_terms_type>=1)&(is.na(TYPE_WASTEWATER[m])=="FALSE"))
{
con<-0
for (l in 1:c){
if (variable_cluster[l]==cluster[i]){
con<-1
}
}
if (con==0){
c<-c+1
variable_cluster[c]<-cluster[i]
TYPE_WASTEWATER[m]<-paste0(TYPE_WASTEWATER[m],"-",cluster[i])
}
}
if((length_filtered_terms_type>=1)&(is.na(TYPE_WASTEWATER[m])=="TRUE"))
{
c<-c+1
variable_cluster[c]<-cluster[i]
TYPE_WASTEWATER[m]<-cluster[i]
}
}
}
##PARAMETER_8: TYPE OF PLANTS
##Step 1: To set the folder:
setwd(paste0(dirname(current_path),"/Documents_backup"))
##Step 2: To read matrix of keywords
matrix<-as.data.frame(read.xlsx("Type_plants.xlsx"))
matrix_keywords<-as.vector(matrix[,1])
##Step 3: To read cluster of expressions in matrix of keywords
variable_cluster<-vector(mode="character", length=10)
cluster<-as.vector(matrix[,2])
##Step 4: To extract parameter
## Rules
## 1- SP's: Materials and Methods
## 2- Tokanizer: min=1, max=4
n.words<-length(matrix_keywords)
TYPE_PLANT<-vector(mode="character", length=N.docs)
for (m in 1:N.docs){
Tokanizer_typeplant<-function(x) NGramTokenizer(x,Weka_control(min=1,max=4))
head(NGramTokenizer(VCorpus_docs3[m],Weka_control(min=1,max=4)))
dtm_typeplant<-TermDocumentMatrix(VCorpus_docs3[m],control = list(tokenize=Tokanizer_typeplant))
bio_typeplant<-as.data.frame(t(as.matrix(dtm_typeplant)))
keywords_typeplant<-colnames(bio_typeplant)
kw_typeplant<-as.data.frame(keywords_typeplant)
c<-0
for(i in 1:n.words){
length_filtered_terms_type<-nrow(filter(kw_typeplant, grepl(pattern=paste0("(?i)",matrix_keywords[i],"(?-i)"),kw_typeplant$keywords_typeplant)))
if((length_filtered_terms_type>=1)&(TYPE_PLANT[m]!=""))
{
con<-0
for (l in 1:c){
if (variable_cluster[l]==cluster[i]){
con<-1
}
}
if (con==0){
c<-c+1
variable_cluster[c]<-cluster[i]
TYPE_PLANT[m]<-paste0(TYPE_PLANT[m],";",cluster[i])
}
}
if((length_filtered_terms_type>=1)&(TYPE_PLANT[m]==""))
{
c<-c+1
variable_cluster[c]<-cluster[i]
TYPE_PLANT[m]<-cluster[i]
}
}
}
##PARAMETER_9: BOD5 INFLUENT, BOD5 EFLUENT, JOURNAL NAME, PUBLISHER, YEAR OF PUBLICATION, TITLE
##The following approach works for pre-reviwed articles, which information is available in HTML version e.g. ELSEVIER
## Step 1: To read the dataset of links - Folder:
setwd(paste0(dirname(current_path),"/HTML_LINKS"))
HTML_links<-read.xlsx("HTML_links.xlsx")
HTML_names<-as.vector(HTML_links[,1])
HTML_url<-as.vector(HTML_links[,2])
## Step 2: To set PHANTOM folder:
setwd(paste0(dirname(current_path),"/Phantom/phantomjs-2.1.1-windows/bin"))
## Step 3: To extract the parameters
BOD_inlist<-vector(mode="list",length=N.docs)
BOD_outlist<-vector(mode="list",length=N.docs)
BOD_removallist<-vector(mode="list",length=N.docs)
CITATION<-c(rep(NA,N.docs))
URL<-c(rep(NA,N.docs))
JOURN_NAME<-c(rep(NA,N.docs))
PUBLISHER<-c(rep(NA,N.docs))
LIT_TYPE<-c(rep(NA,N.docs))
YEAR<-c(rep(NA,N.docs))
TITLE<-vector(mode="character", length=N.docs)
for (m in 1:N.docs){
LIT_TYPE[m]<-"PRE-REVIEW ARTICLE"
for (j in 1:nrow(HTML_links)){
if (my.docs$names[m]==HTML_names[j]){
url<-HTML_url[j]
URL[m]<-url
name<-paste0("doc",m,".js")
writeLines(sprintf("var page = require('webpage').create();
page.open('%s', function () {
console.log(page.content); //page source
phantom.exit();
});", url), con=name)
name_phantom<-paste0("phantomjs doc",m,".js")
name_html<-paste0("doc",m,".html")
write(readLines(pipe(name_phantom, "r")), name_html)
journal_elsevier<-grepl("sciencedirect",url,ignore.case=FALSE)##Journal: ELSEVIERR
journal_IWA<-grepl("iwaponline",url,ignore.case=FALSE)##Journal:IWA
journal_researchgate<-grepl("researchgate",url,ignore.case=FALSE)##Journal:Researchgate
journal_ncbi<-grepl("ncbi",url,ignore.case=FALSE)##Journal:NCBI
page_html<- read_html(name_html)
table<-page_html %>% html_nodes("table") %>% html_table(fill=TRUE)
head_lines<-page_html %>% html_nodes("span")%>%html_text()
if (journal_elsevier=="TRUE"){
title_article<-page_html %>% html_nodes("span.title-text")%>%html_text()
TITLE[m]<-title_article
surname<-page_html %>% html_nodes("span.text.surname")%>%html_text()
name<-page_html %>% html_nodes("span.text.given-name")%>%html_text()
doi<-page_html %>% html_nodes("a.doi")%>%html_text()
doi<-sub("(.*?)doi.org","doi:",doi)
journal_name<-page_html %>% html_nodes("h2#publication-title.publication-title")%>%html_text()
publication_year<-as.data.frame(page_html %>% html_nodes("div.text-xs")%>%html_text())
colnames(publication_year)[1]<-"YEAR"
publication_year<-as.vector(filter(publication_year,grepl(pattern="(?i)volume(?-i)",publication_year$YEAR))[,1])
name<-sub("ä","ä",name)
name<-sub("õ","õ",name)
name<-sub("Ã\u009c","Ü",name)
surname<-sub("Ã\u0096ö","Öö",surname)
for (z in 1:length(surname)){
if (z==1){
authors<-paste0(surname[1]," ",name[1],",")
}
if ((z!=1)&(z!=length(surname))){
authors<-paste0(authors,name[z]," ",surname[z],",")
}
if(z==length(surname)){
authors<-paste0(authors,"and ",name[z]," ",surname[z],".")
}
}
PUBLISHER[m]<-authors
publication_year[1]<-sub("â\u0080\u0093","-",publication_year[1])
part1<-sub("(.*?)[,]","",publication_year[1])
part2<-sub("(.*?)[,] ","",part1)
part3<-sub("[,](.*)","",part2)
YEAR[m]<-str_sub(part3, start= -4)
p<-as.numeric(YEAR[m])
if ((is.na(p)=="TRUE")|(p<0)){
part1<-sub("(.*?)[,]","",publication_year[1])
part2<-sub("[,](.*)","",part1)
YEAR[m]<-str_sub(part2, start= -4)
}
JOURN_NAME[m]<-journal_name
CITATION[m]<-paste0(authors,'"',title_article,'"',".",journal_name," ",publication_year[1],".",doi)
}
if (journal_IWA=="TRUE"){
title_article<-page_html %>% html_nodes("h1#page-title.highwire-cite-title")%>%html_text()
TITLE[m]<-title_article
journal_name<-page_html %>% html_nodes("div.region-inner.region-branding-inner")%>%html_text()
JOURN_NAME[m]<-journal_name
name<-page_html %>% html_nodes("span.highwire-citation-authors")%>%html_text()
PUBLISHER[m]<-name[1]
doi_publicationyear<-page_html %>% html_nodes("div.highwire-cite-metadata")%>%html_text()
CITATION[m]<-paste0(name[1],".",'"',title_article,'"',".Water Sci Technol.",doi_publicationyear[1])
}
if (journal_ncbi=="TRUE"){
journal_name<-page_html %>% html_nodes("h1")%>%html_text()
TITLE[m]<-journal_name[2]
name<-page_html %>% html_nodes("div.auths")%>%html_text()
PUBLISHER[m]<-name
publication_year<-page_html %>% html_nodes("div.cit")%>%html_text()
JOURN_NAME[m]<-sub("[.](.*)","",publication_year[1])
part1<-sub("(.*?)[.] ","",publication_year[1])
part2<-sub("[;](.*)","",part1)
YEAR[m]<-gsub("[a-zA-z ]","",part2)
PMID<-page_html %>% html_nodes("dd")%>%html_text()
CITATION[m]<-paste0(name,'"',journal_name[2],'"',publication_year,"PMID: ",PMID[2])
}
head_tables<-vector(mode="character", length=20)
a<-0
for (i in 1:length(head_lines)){
regex_head<-grepl("Table",head_lines[i],ignore.case=FALSE)
if (regex_head=="TRUE"){
a<-a+1
head_tables[a]<-head_lines[i]
}
}
head_tables<-head_tables[head_tables!=""]
head_tables_clean<-vector(mode="character", length=20)
c<-0
for (i in 1:length(head_tables)){
number<-length(unlist(str_extract_all(head_tables[i], "\\w+")))
if (number>2){
c<-c+1
head_tables_clean[c]<-head_tables[i]
}
}
head_tables_clean<-head_tables_clean[head_tables_clean!=""]
BOD_IN<-vector(mode="integer", length=20)
BOD_OUT<-vector(mode="integer", length=20)
BOD_REMOVAL<-vector(mode="integer", length=20)
sec_in<-0
sec_out<-0
sec_removal<-0
if(length(table)>0){
for(b in 1:length(table)){
con5<-0
con6<-0
con2<-0
con3<-0
con4<-0
regex_influent<-grepl("(?i)influent(?-i)",head_tables_clean[b],ignore.case=FALSE)|grepl("(?i)inflow(?-i)",head_tables_clean[b],ignore.case=FALSE)
if (regex_influent==TRUE){
con2<-1
}
regex_efluent<-grepl("(?i)efluent(?-i)",head_tables_clean[b],ignore.case=FALSE)|grepl("(?i)outflow(?-i)",head_tables_clean[b],ignore.case=FALSE)
if (regex_efluent==TRUE){
con3<-1
}
regex_removal<-grepl("(?i)removal(?i)",head_tables_clean[b],ignore.case=FALSE)
if (regex_removal==TRUE){
con4<-1
}
con<-0
row<-nrow(table[[b]])
col<-ncol(table[[b]])
for(i in 1:row){
for (j in 1:col){
table[[b]][i,j]<-sub("(?i)Â(.*)(?-i)","",table[[b]][i,j])
regex<-grepl("BOD",table[[b]][i,j],ignore.case=FALSE)|grepl("BOD",colnames(table[[b]])[j],ignore.case=FALSE)
if (regex=="TRUE"){
con<-con+1
}
}
}
table_numeric<-data.frame(table[[b]],stringsAsFactors=FALSE)
table_numeric<- as.data.frame(sapply(table_numeric, as.numeric))
elimination_colum<-vector(mode="integer", length=10)
elimination_row<-vector(mode="integer", length=10)
if (con>=1){
for (j in 1:col){
regex_deviation<-grepl("(?i)deviation(?-i)",table[[b]][1,j],ignore.case=FALSE)
regex_deviation_names<-grepl("(?i)deviation(?-i)",colnames(table[[b]][j]),ignore.case=FALSE)
if((regex_deviation==TRUE)|(regex_deviation_names==TRUE)){
con5<-con5+1
elimination_colum[con5]<-j
}
}
for (i in 1:row){
regex_deviation<-grepl("(?i)deviation(?-i)",table[[b]][i,1],ignore.case=FALSE)
if(regex_deviation==TRUE){
con6<-con6+1
elimination_row[con6]<-i
}
}
elimination_colum<-elimination_colum[elimination_colum!=0]
elimination_row<-elimination_row[elimination_row!=0]
if ((length(elimination_colum)!=0)&(length(elimination_row)!=0)){
table[[b]]<-table[[b]][-elimination_row,-elimination_colum]
}
if ((length(elimination_colum)==0)&(length(elimination_row)!=0)){
table[[b]]<-table[[b]][-elimination_row,]
}
if ((length(elimination_colum)!=0)&(length(elimination_row)==0)){
table[[b]]<-table[[b]][,-elimination_colum]
}
}
row<-nrow(table[[b]])
col<-ncol(table[[b]])
if ((con2>=1)&(con>=1)){
for (i in 1:2){
for (j in 1:col){
regex_in<-grepl("BOD",table[[b]][i,j],ignore.case=FALSE)
k<-i+1
if(k<=row){
if ((regex_in=="TRUE")&(is.numeric(table_numeric[k,j])==TRUE)&(is.na(table_numeric[k,j])==FALSE)){
p<-i+1
for (u in p:row){
sec_in<-sec_in+1
BOD_IN[sec_in]<-table[[b]][u,j]
}
}
}
}
}
if (BOD_IN[1]==0){
for (j in 1:col){
regex_in<-grepl("BOD",colnames(table[[b]])[j],ignore.case=FALSE)&grepl("(?i)in(?-i)",table[[b]][1,j],ignore.case=FALSE)
if (regex_in=="TRUE"){
for (i in 2:row){
sec_in<-sec_in+1
BOD_IN[sec_in]<-table[[b]][i,j]
}
}
}
}
for (i in 1:row) {
regex_in<-grepl("BOD",table[[b]][i,1],ignore.case=FALSE)
if ((regex_in=="TRUE")&(is.numeric(table_numeric[i,2])==TRUE)&(is.na(table_numeric[k,j])==FALSE)){
for (u in 2:col){
sec_in<-sec_in+1
BOD_IN[sec_in]<-table[[b]][i,u]
}
}
}
BOD_IN<-BOD_IN[BOD_IN!=0]
BOD_inlist[[m]]<-BOD_IN
}
if ((con3>=1)&(con>=1)){
for (i in 1:2){
for (j in 1:col){
regex_out<-grepl("BOD",table[[b]][i,j],ignore.case=FALSE)
k<-i+1
if(k<=row){
if ((regex_out=="TRUE")&(is.numeric(table_numeric[k,j])==TRUE)&(is.na(table_numeric[k,j])==FALSE)){
p<-i+1
for (u in p:row){
sec_out<-sec_out+1
BOD_OUT[sec_out]<-table[[b]][u,j]
}
}
}
}
}
if (BOD_OUT[1]==0){
for (j in 1:col){
regex_out<-grepl("BOD",colnames(table[[b]])[j],ignore.case=FALSE)&grepl("(?i)out(?-i)",table[[b]][1,j],ignore.case=FALSE)
if (regex_out=="TRUE"){
for (i in 2:row){
sec_out<-sec_out+1
BOD_OUT[sec_out]<-table[[b]][i,j]
}
}
}
}
for (i in 1:row) {
regex_out<-grepl("BOD",table[[b]][i,1],ignore.case=FALSE)
if ((regex_out=="TRUE")&(is.numeric(table_numeric[i,2])==TRUE)&(is.na(table_numeric[k,j])==FALSE)){
for (u in 2:col){
sec_out<-sec_out+1
BOD_OUT[sec_out]<-table[[b]][i,u]
}
}
}
BOD_OUT<-BOD_OUT[BOD_OUT!=0]
BOD_outlist[[m]]<-BOD_OUT
}
if ((con4>=1)&(con>=1)){
sec<-0
for (i in 1:2){
for (j in 1:col){
regex_removal<-grepl("BOD",table[[b]][i,j],ignore.case=FALSE)
k<-i+1
if(k<=row){
if ((regex_removal=="TRUE")&(is.numeric(table_numeric[k,j])==TRUE)&(is.na(table_numeric[k,j])==FALSE)){
p<-i+1
for (u in p:row){
sec_removal<-sec_removal+1
BOD_REMOVAL[sec_removal]<-table[[b]][u,j]
}
}
}
}
}
for (i in 1:row) {
regex_removal<-grepl("BOD",table[[b]][i,1],ignore.case=FALSE)
if ((regex_removal=="TRUE")&(is.numeric(table_numeric[i,2])==TRUE)&(is.na(table_numeric[k,j])==FALSE)){
for (u in 2:col){
sec_removal<-sec_removal+1
BOD_REMOVAL[sec_removal]<-table[[b]][i,u]
}
}
}
BOD_REMOVAL<-BOD_REMOVAL[BOD_REMOVAL!=0]
BOD_removallist[[m]]<-BOD_REMOVAL
}
}
}
}
}
}
for (i in 1:N.docs){
if (length(BOD_inlist[[i]])==0){
BOD_inlist[[i]]<-c(NA)
}
if (length(BOD_outlist[[i]])==0){
BOD_outlist[[i]]<-c(NA)
}
if (length(BOD_removallist[[i]])==0){
BOD_removallist[[i]]<-c(NA)
}
BOD_removallist[[i]]<-as.numeric(BOD_removallist[[i]])
BOD_outlist[[i]]<-as.numeric(BOD_outlist[[i]])
BOD_inlist[[i]]<-as.numeric(BOD_inlist[[i]])
}
##CREATION MATRIX OF PARAMETER: The parameters extracted from the text are integrated in a data.frame
##Step 1: To open the database in PostgreSQL
pw <- {
"mauricio"
}##pw=password for accesing to the database
drv <- dbDriver("PostgreSQL")
con <- dbConnect(drv, dbname = "postgres",
host = "localhost", port = 5432,
user = "openpg", password = pw)
##Step 2: To read the tables in the PostgreSQL database: SITES, SYSTEMS, C_PLANTS, LITERATURE, C_ORG, CELLS, COUNTRY,LITERATURE
con2=dbConnect(PostgreSQL(),user = "postgres",password="wetlands",dbname = "postgres")
SITES <- as.data.frame(dbGetQuery(con2, 'SELECT * from "SITES"'))
SITES$CTRY_CODE[is.na(SITES$CTRY_CODE)=="TRUE"]<-"NULL"
SITES$MUNI[is.na(SITES$MUNI)=="TRUE"]<-"NULL"
SITES$LONG[is.na(SITES$LONG)=="TRUE"]<-0
SYSTEMS<-as.data.frame(dbGetQuery(con2, 'SELECT * from "SYSTEMS"'))
C_PLANTS<-as.data.frame(dbGetQuery(con2, 'SELECT * from "C_PLANTS"'))
C_PLANTS$PLANT_SPEC[is.na(C_PLANTS$PLANT_SPEC)=="TRUE"]<-"NULL"
LITERATURE<-as.data.frame(dbGetQuery(con2, 'SELECT * from "LITERATURE"'))
LITERATURE$TITLE[is.na(LITERATURE$TITLE)=="TRUE"]<-"NULL"
C_ORG<-as.data.frame(dbGetQuery(con2, 'SELECT * from "C_ORG"'))
CELLS<-as.data.frame(dbGetQuery(con2, 'SELECT * from "CELLS"'))
COUNTRY<-as.data.frame(dbGetQuery(con2, 'SELECT * from "COUNTRY"'))
LITERATURE_comparison<-data.frame(LIT_ID=LITERATURE$LIT_ID,TITLE=LITERATURE$TITLE)
C_PLANTS_comparison<-data.frame(PLANT_ID=C_PLANTS$PLANT_ID,PLANT_SPEC=C_PLANTS$PLANT_SPEC)
SITES_comparison<-data.frame(SITE_ID=SITES$SITE_ID,CTRY_CODE=SITES$CTRY_CODE,MUNI=SITES$MUNI,LONG=SITES$LONG)
##Step 3: To read the length of the tables in the PostgreSQL database
row_C_ORG<-nrow(C_ORG)
row_LITERATURE<-nrow(LITERATURE)
row_PLANT<-nrow(C_PLANTS)