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Project 2.R
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Project 2.R
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rm(list=ls())
install.packages("edgar")
library(edgar)
quarter<-c(1,2)
make_URL<-function(Q) {
paste0("https://www.sec.gov/Archives/edgar/full-index/",
2018,
"/QTR",Q,
"/master.idx")
}
web.url_Q1<-make_URL(1)
web.url_Q2<-make_URL(2)
dest_file_name_Q1<-"./Data/2018Q1"
dest_file_name_Q2<-"./Data/2018Q2"
download.file(web.url_Q1,dest_file_name_Q1,mode="wb")
download.file(web.url_Q2,dest_file_name_Q2,mode="wb")
Q1<-readLines(dest_file_name_Q1, n=30000)
datalines <- Q1[-(1:11)]
separatedlines <- strsplit(datalines, "\\|") #list
edgar.matrix <- do.call(rbind, separatedlines) #matrix
edgar.dataframe <- as.data.frame(edgar.matrix)
#Choosing 10K
index.10K <- grep("^10-K$", edgar.dataframe[,3])
my_data_frame<-(edgar.dataframe[index.10K,])
#Dowloading files for Q1 and Q2 2018
for(row in 1:nrow(my_data_frame)){
my_urls<-paste0("https://www.sec.gov/Archives/", my_data_frame[row ,5])
CIK_file_name<-paste0("./Data/",
my_data_frame[row,1],".txt")
download.file(my_urls,CIK_file_name,mode="wb")
Sys.sleep (1)
}
###Building the function, where we clean the file using regular expresions and count searched expresions
###("sustainability" and "sustainable")
process_file<-function(CIK_file_name){
one_file<- readLines(CIK_file_name)
header<-grep("<TYPE>10-K", one_file, ignore.case = F)
one_file_vol2<-one_file[-(1:(header-1))]
end<-grep("?</DOCUMENT", one_file_vol2, ignore.case = F)
one_file_vol3<-one_file_vol2[1:end[1]]
#remove html tags
without_tags<-gsub("<.*?>", "", one_file_vol3)
#remove html entities
without_entities<-gsub("\\&.*?\\;","", without_tags)
#remove numbers
without_numbers<-gsub("[[:digit:]]","",without_entities)
#remove excessive white spaces
without_spaces<-gsub("[[:space:]]+"," ", without_numbers)
#remove empty lines
empty_lines<-grep("^[[:space:]]*$",without_spaces)
without_empty_lines<-without_spaces[-empty_lines]
#CIK number
CIK_number_vol1<-gsub("^.*/", "",CIK_file_name)
CIK_number_vol2<-gsub("\\.txt$","", CIK_number_vol1)
CIK_number<-strtoi(CIK_number_vol2)
#diving each line into words
words<-strsplit(without_empty_lines," ")
# let's do one line out of these words
all_words<- unlist(words)
empty_words<-grep("^[[:space:]]*$",all_words)
without_empty_words<-all_words[-(empty_words)]
#how many words
word_count<-length(without_empty_words)
key_words<-grep("(sustainability)|(sustainable)",without_empty_words, ignore.case = T )
key_words_count<-length(key_words)
#per 1000 words
per_1000<-(key_words_count/word_count)*1000
result<-list(CIK_number, key_words_count,per_1000)
return(result)
}
all_rows<-list()
for(row in 1:nrow(my_data_frame)){
print(row)
CIK_file_name<-paste0("./Data/",
my_data_frame[row,1],".txt")
new_row<-process_file(CIK_file_name)
all_rows<-append(all_rows,new_row)
}
final_data_frame<-as.data.frame(matrix(all_rows,ncol=3,byrow=T))
rm(list=ls())
save(final_data_frame, "finalDataFrame.RData")
load("finalDataFrame.Rdata")
########3filtring data ( we do not want 0 value rows)
Filtered_data_frame<-final_data_frame[final_data_frame$V3>0,]
#sorting data and changing name of header "V1"
sorted_data_frame<-final_data_frame[!is.na(final_data_frame$V3) & final_data_frame$V3>0,]
names(sorted_data_frame)[1]<-"CIK"
#reading yahoo file ("yahoo.data")
load("yahoo_data.Rdata")
#merging data
merged_data<-merge(yahoo.data, sorted_data_frame, by="CIK")
library(splines)
result_data_frame<-data.frame(x=log(merged_data$sustainability.score.yahoo),
y=log(unlist(merged_data[["V3"]])))
#regression model
#making a plot
library(ggplot2)
install.packages("ggthemes")
library(ggthemes)
ggplot(result_data_frame, aes(x =y, y =x)) +
geom_point() +
ggtitle("Sustainability scores") +
xlab("Text-based scaled score") +
ylab("Yahoo score") +
geom_smooth(method = lm)