How to process many txt files with my code in R - r

Question

I'm quite a novice, but I've successfully managed to make some code do what I want.
Right now my code does what I want for one file at a time.
I want to make my code automate this process for 600 files.
I kind of have an idea, that I need to put the list of files in a vector, then maybe use lapply and a function, but I'm not sure how to do this. The syntax and code are beyond me at the moment.
Here's my code...
#Packages are callled
library(tm) #text mining
library(SnowballC) #stemming - reducing words to their root
library(stringr) #for str_trim
library(plyr)
library(dplyr)
library(readtext)
#this is my code to run the code on a bunch of text files. Obviously it's unfinished, and I'm not sure if this is the right approach. Where do I put this? Will it even work?
data_files <- list.files(path = "data/", pattern = '*.txt', full.names = T, recursive = T)
lapply(
#
# where do I put this chunk of code?
# do I need to make all the code below a function?
##this bit cleans the document
company <- "CompanyXReport2015"
txt_raw = readLines("data/CompanyXReport2015.txt")
# remove all extra white space, also splits on lines
txt_format1 <- gsub(" *\\b[[:alpha:]]{1,2}\\b *", " ", txt_raw)
txt_format1.5 <- gsub("^ +| +$|( ) +", "\\1", txt_format1)
# recombine now that all white space is stripped
txt_format2 <- str_c(txt_format1.5, collapse=" ")
#split strings on space now to get a list of all words
txt_format3 <- str_split(txt_format2," ")
txt_format3
# convert to vector
txt_format4 <- unlist(txt_format3)
# remove empty strings and those with words shorter than 3 length
txt_format5 <- txt_format4[str_length(txt_format4) > 3]
# combine document back to single string
cleaned <- str_c(txt_format5, collapse=" ")
head(cleaned, 2)
##import key words and run analysis on frequency for the document
s1_raw = readLines("data/stage1r.txt")
str(s1_raw)
s2_raw = readLines("data/stage2r.txt")
str(s2_raw)
s3_raw = readLines("data/stage3r.txt")
str(s3_raw)
s4_raw = readLines("data/stage4r.txt")
str(s4_raw)
s5_raw = readLines("data/stage5r.txt")
str(s5_raw)
# str_count(cleaned, "legal")
# apply str_count function using each stage vector
level1 <- sapply(s1_raw, str_count, string=cleaned)
level2 <- sapply(s2_raw, str_count, string=cleaned)
level3 <- sapply(s3_raw, str_count, string=cleaned)
level4 <- sapply(s4_raw, str_count, string=cleaned)
level5 <- sapply(s5_raw, str_count, string=cleaned)
#make a vector from this for the report later
wordcountresult <- c(level1,level2,level3,level4,level5)
# convert to dataframes
s1 <- as.data.frame(level1)
s2 <- as.data.frame(level2)
s3 <- as.data.frame(level3)
s4 <- as.data.frame(level4)
s5 <- as.data.frame(level5)
# add a count column that each df shares
s1$count <- s1$level1
s2$count <- s2$level2
s3$count <- s3$level3
s4$count <- s4$level4
s5$count <- s5$level5
# add a stage column to identify what stage the word is in
s1$stage <- "Stage 1"
s2$stage <- "Stage 2"
s3$stage <- "Stage 3"
s4$stage <- "Stage 4"
s5$stage <- "Stage 5"
# drop the unique column
s1 <- s1[c("count","stage")]
s2 <- s2[c("count","stage")]
s3 <- s3[c("count","stage")]
s4 <- s4[c("count","stage")]
s5 <- s5[c("count","stage")]
# s1
df <- rbind(s1, s2,s3, s4, s5)
df
#write the summary for each company to a csv
#Making the report
#Make a vector to put in the report
#get stage counts and make a vector
s1c <- sum(s1$count)
s2c <- sum(s2$count)
s3c <- sum(s3$count)
s4c <- sum(s4$count)
s5c <- sum(s5$count)
stagesvec <- c(s1c,s2c,s3c,s4c,s5c)
names(stagesvec) <- c("Stage1","Stage2","Stage3","Stage4","Stage5")
#get the company report name for a vector
companyvec <- c(company)
names(companyvec) <- c("company")
# combine the vectors for the vector row to be inserted into the report
reportresult <- c(companyvec, wordcountresult, stagesvec)
rrdf <- data.frame(t(reportresult))
newdf <- data.frame(t(reportresult))
#if working file exists-use it
if (file.exists("data/WordCount12.csv")){
write.csv(
rrdf,
"data/WordCountTemp12.csv", row.names=FALSE
)
rrdf2 <-
read.csv("data/WordCountTemp12.csv")
df2 <-
read.csv("data/WordCount12.csv")
df2 <- rbind(df2, rrdf2)
write.csv(df2,
"data/WordCount12.csv", row.names=FALSE)
}else{ #if NO working file exists-make it
write.csv(newdf,
"data/WordCount12.csv", row.names=FALSE)
}

Answer 1

Hello :) Here is an example of workflow, you might find better ones but I started with it when learning.
listoftextfiles = list.files(...)
analysis1 = function(an element of listoftextfiles){
# your 1st analysis
}
res1 = lapply(listoftextfile, analysis1) # results of the 1st analysis
analysis2 = function(an element of res1){
# your 2nd analysis
}
res2 = lapply(res1, analysis2) # results of the 2nd analysis
# ect.
You will find many tutorials about custom functions on internet.