I have a list of 50 text files all beginning with NEW.
I want to loop through each textfile/dataframe and run some function and then output the results via the write.table function. Therefore for each file, a function is applied and then an output should be created containing the original name with output at the end.
Here is my code.
fileNames <- Sys.glob("*NEW.*")
for (fileName in fileNames) {
df <- read.table(fileName, header = TRUE)
FUNCTION (not shown as this works)
...
result <-print(chr1$results) #for each file a result would be printed.
write.table(result, file = paste0(fileName,"_output.txt"), quote = F, sep = "\t", row.names = F, col.names = T)
#for each file a new separate file is created with the original output name retained.
}
However, I only get one output rather than 50 output files. It seems like its only looping through one file. What am I doing wrong?
readme <- function(folder_name = "my_texts"){
file_list <- list.files(path = folder_name, pattern = "*.txt",
recursive = TRUE, full.names = TRUE).
#list files with .txt ending
textdata <- lapply(file_list, function(x) {.
paste(readLines(x), collapse=" ").
}).
#apply readlines over the file list.
data.table::setattr(textdata, "names", file_list) .
#add names attribute to textdata from file_list.
lapply(names(file_list), function(x){.
lapply(names(file_list[[x]]), function(y) setattr(DT[[x]], y,
file_list[[x]][[y]])).
}).
#set names attribute over the list.
df1 <- data.frame(doc_id = rep(names(textdata), lengths(textdata)),
doc_text = unlist(textdata), row.names = NULL).
#convert to dataframe where names attribute is doc_id and textdata is text.
return(df1).
}
Related
I am trying to add the name of each file in a list of .csv as the last column with all values also equal to the name. I am getting it, but the result show the files like transposed or something. I don´t know how to fix it, I have tried data.frame, unlist, but nothing.
This is the code:
workbooks <- list.files(pattern="*.csv", full.names= T)
read_workbooks <- lapply(workbooks, read.csv)
for (i in 1:length(workbooks)){
name_of_file[i] <- str_replace_all(str_sub(workbooks[i], 3,
end = unlist(gregexpr("-current",workbooks[i]))-1),"_"," ")
temp_workbook <- cbind(read_workbooks[i],"Filer Name" = name_of_file[i])
write.csv(temp_workbook, file = paste(name_of_file[i],".csv",sep = ""),
row.names = F)
}
You can do this in the same lapply call with the help of an anonymous function.
workbooks <- list.files(pattern="*.csv", full.names= TRUE)
lapply(workbooks, function(x) {
write.csv(transform(read.csv(x), file_name = basename(x)),sprintf('new_%s.csv',
tools::file_path_sans_ext(basename(x))), row.names = FALSE)
})
Read each csv in workbooks, add a new column name in each file which is the name of the file and write the new csv.
Some background for my question: This is an R script that a previous research assistant wrote, but he did not provide any guidance to me on using it for myself. After working through an R textbook, I attempted to use the code on my data files.
What this code is supposed to do is load multiple .csv files, delete certain items/columns from them, and then write the new cleaned .csv files to a specified directory.
Currently, the files are being created in the right directory with the right file name, but the .csv files that are being created are empty.
I am currently getting the following error message:
Warning in
fread(input = paste0("data/", str_match(pattern = "CAFAS|PECFAS",: Starting data input on line 2 and discarding line 1 because it has too few or too many items to be column names or data: (variable names).
This is my code:
library(data.table)
library(magrittr)
library(stringr)
# create a function to delete unnecessary variables from a CAFAS or PECFAS
data set and save the reduced copy
del.items <- function(file){
data <- fread(input = paste0("data/", str_match(pattern = "CAFAS|PECFAS",
string = file) %>% tolower, "/raw/", file), sep = ",", header = TRUE,
na.strings = "", stringsAsFactors = FALSE, skip = 0, colClasses =
"character", data.table = FALSE)
data <- data[-grep(pattern = "^(CA|PEC)FAS_E[0-9]+(TR?(Initial|[0-
9]+|Exit)|SP[a-z])_(G|S|Item)[0-9]+$", x = names(data))]
write.csv(data, file = paste0("data/", str_match(pattern = "CAFAS|PECFAS",
string = file) %>% tolower, "/items-del/", sub(pattern = "ExportData_", x =
file, replacement = "")) %>% tolower, row.names = FALSE)
}
# delete items from all cafas data sets
cafas.files <- list.files("data/cafas/raw", pattern = ".csv")
for (file in cafas.files){
del.items(file)
}
# delete items from all pecfas data sets
pecfas.files <- list.files("data/pecfas/raw", pattern = ".csv")
for (file in pecfas.files){
del.items(file)
}
I'm quite new at R and a bit stuck on what I feel is likely a common operation to do. I have a number of files (57 with ~1.5 billion rows cumulatively by 6 columns) that I need to perform basic functions on. I'm able to read these files in and perform the calculations I need no problem but I'm tripping up in the final output. I envision the function working on 1 file at a time, outputting the worked file and moving onto the next.
After calculations I would like to output 57 new .txt files named after the file the input data first came from. So far I'm able to perform the calculations on smaller test datasets and spit out 1 appended .txt file but this isn't what I want as a final output.
#list filenames
files <- list.files(path=, pattern="*.txt", full.names=TRUE, recursive=FALSE)
#begin looping process
loop_output = lapply(files,
function(x) {
#Load 'x' file in
DF<- read.table(x, header = FALSE, sep= "\t")
#Call calculated height average a name
R_ref= 1647.038203
#Add column names to .las data
colnames(DF) <- c("X","Y","Z","I","A","FC")
#Calculate return
DF$R_calc <- (R_ref - DF$Z)/cos(DF$A*pi/180)
#Calculate intensity
DF$Ir_calc <- DF$I * (DF$R_calc^2/R_ref^2)
#Output new .txt with calcuated columns
write.table(DF, file=, row.names = FALSE, col.names = FALSE, append = TRUE,fileEncoding = "UTF-8")
})
My latest code endeavors have been to mess around with the intial lapply/sapply function as so:
#begin looping process
loop_output = sapply(names(files),
function(x) {
As well as the output line:
#Output new .csv with calcuated columns
write.table(DF, file=paste0(names(DF), "txt", sep="."),
row.names = FALSE, col.names = FALSE, append = TRUE,fileEncoding = "UTF-8")
From what I've been reading the file naming function during write.table output may be one of the keys I don't have fully aligned yet with the rest of the script. I've been viewing a lot of other asked questions that I felt were applicable:
Using lapply to apply a function over list of data frames and saving output to files with different names
Write list of data.frames to separate CSV files with lapply
to no luck. I deeply appreciate any insights or paths towards the right direction on inputting x number of files, performing the same function on each, then outputting the same x number of files. Thank you.
The reason the output is directed to the same file is probably that file = paste0(names(DF), "txt", sep=".") returns the same value for every iteration. That is, DF must have the same column names in every iteration, therefore names(DF) will be the same, and paste0(names(DF), "txt", sep=".") will be the same. Along with the append = TRUE option the result is that all output is written to the same file.
Inside the anonymous function, x is the name of the input file. Instead of using names(DF) as a basis for the output file name you could do some transformation of this character string.
example.
Given
x <- "/foo/raw_data.csv"
Inside the function you could do something like this
infile <- x
outfile <- file.path(dirname(infile), gsub('raw', 'clean', basename(infile)))
outfile
[1] "/foo/clean_data.csv"
Then use the new name for output, with append = FALSE (unless you need it to be true)
write.table(DF, file = outfile, row.names = FALSE, col.names = FALSE, append = FALSE, fileEncoding = "UTF-8")
Using your code, this is the general idea:
require(purrr)
#list filenames
files <- list.files(path=, pattern="*.txt", full.names=TRUE, recursive=FALSE)
#Call calculated height average a name
R_ref= 1647.038203
dfTransform <- function(file){
colnames(file) <- c("X","Y","Z","I","A","FC")
#Calculate return
file$R_calc <- (R_ref - file$Z)/cos(file$A*pi/180)
#Calculate intensity
file$Ir_calc <- file$I * (file$R_calc^2/R_ref^2)
return(file)
}
output <- files %>% map(read.table,header = FALSE, sep= "\t") %>%
map(dfTransform) %>%
map(write.table, file=paste0(names(DF), "txt", sep="."),
row.names = FALSE, col.names = FALSE, append = TRUE,fileEncoding = "UTF-8")
I have a list of approximately 500 csv files each with a filename that consists of a six-digit number followed by a year (ex. 123456_2015.csv). I would like to append all files together that have the same six-digit number. I tried to implement the code suggested in this question:
Import and rbind multiple csv files with common name in R but I want the appended data to be saved as new csv files in the same directory as the original files are currently saved. I have also tried to implement the below code however the csv files produced from this contain no data.
rm(list=ls())
filenames <- list.files(path = "C:/Users/smithma/Desktop/PM25_test")
NAPS_ID <- gsub('.+?\\([0-9]{5,6}?)\\_.+?$', '\\1', filenames)
Unique_NAPS_ID <- unique(NAPS_ID)
n <- length(Unique_NAPS_ID)
for(j in 1:n){
curr_NAPS_ID <- as.character(Unique_NAPS_ID[j])
NAPS_ID_pattern <- paste(".+?\\_(", curr_NAPS_ID,"+?)\\_.+?$", sep = "" )
NAPS_filenames <- list.files(path = "C:/Users/smithma/Desktop/PM25_test", pattern = NAPS_ID_pattern)
write.csv(do.call("rbind", lapply(NAPS_filenames, read.csv, header = TRUE)),file = paste("C:/Users/smithma/Desktop/PM25_test/MERGED", "MERGED_", Unique_NAPS_ID[j], ".csv", sep = ""), row.names=FALSE)
}
Any help would be greatly appreciated.
Because you're not doing any data manipulation, you don't need to treat the files like tabular data. You only need to copy the file contents.
filenames <- list.files("C:/Users/smithma/Desktop/PM25_test", full.names = TRUE)
NAPS_ID <- substr(basename(filenames), 1, 6)
Unique_NAPS_ID <- unique(NAPS_ID)
for(curr_NAPS_ID in Unique_NAPS_ID){
NAPS_filenames <- filenames[startsWith(basename(filenames), curr_NAPS_ID)]
output_file <- paste0(
"C:/Users/nwerth/Desktop/PM25_test/MERGED_", curr_NAPS_ID, ".csv"
)
for (fname in NAPS_filenames) {
line_text <- readLines(fname)
# Write the header from the first file
if (fname == NAPS_filenames[1]) {
cat(line_text[1], '\n', sep = '', file = output_file)
}
# Append every line in the file except the header
line_text <- line_text[-1]
cat(line_text, file = output_file, sep = '\n', append = TRUE)
}
}
My changes:
list.files(..., full.names = TRUE) is usually the best way to go.
Because the digits appear at the start of the filenames, I suggest substr. It's easier to get an idea of what's going on when skimming the code.
Instead of looping over the indices of a vector, loop over the values. It's more succinct and less likely to cause problems if the vector's empty.
startsWith and endsWith are relatively new functions, and they're great.
You only care about copying lines, so just use readLines to get them in and cat to get them out.
You might consider something like this:
##will take the first 6 characters of each file name
six.digit.filenames <- substr(filenames, 1,6)
path <- "C:/Users/smithma/Desktop/PM25_test/"
unique.numbers <- unique(six.digit.filenames)
for(j in unique.numbers){
sub <- filenames[which(substr(filenames,1,6) == j)]
data.for.output <- c()
for(file in sub){
##now do your stuff with these files including read them in
data <- read.csv(paste0(path,file))
data.for.output <- rbind(data.for.output,data)
}
write.csv(data.for.output,paste0(path,j, '.csv'), row.names = F)
}
I have 900 text files in my directory as seen in the following figure below
each file consists of data in the following format
667869 667869.000000
580083 580083.000000
316133 316133.000000
11065 11065.000000
I would like to extract fourth row from each text file and store the values in an array, any suggestions are welcome
This sounds more like a StackOverflow question, similar to
Importing multiple .csv files into R
You can try something like:
setwd("/path/to/files")
files <- list.files(path = getwd(), recursive = FALSE)
head(files)
myfiles = lapply(files, function(x) read.csv(file = x, header = TRUE))
mydata = lapply(myfiles, FUN = function(df){df[4,]})
str(mydata)
do.call(rbind, mydata)
A lazy answer is:
array <- c()
for (file in dir()) {
row4 <- read.table(file,
header = FALSE,
row.names = NULL,
skip = 3, # Skip the 1st 3 rows
nrows = 1, # Read only the next row after skipping the 1st 3 rows
sep = "\t") # change the separator if it is not "\t"
array <- cbind(array, row4)
}
You can further keep the name of the files
colnames(array) <- dir()