I have multiple files (30, tab delimited) that look like the one below:
|target_id | length| eff_length| est_counts| tpm|
|:------------|------:|----------:|----------:|--------:|
|LmjF.27.1250 | 966| 823.427| 2932| 94.7314|
|LmjF.09.0430 | 1410| 1267.430| 3603| 75.6304|
|LmjF.13.0210 | 2001| 1858.430| 4435| 63.4897|
|LmjF.28.0530 | 4083| 3940.430| 7032| 47.4778|
|LmjF.16.1400 | 591| 448.577| 1163| 68.9761|
|LmjF.29.2570 | 1506| 1363.430| 11135| 217.2770|
I am trying to cut the fifth column from all of these files 30 files with a command such as:
fifth_colum_file1 = file1.csv[ , 5]
But I want to make the process more automatised.
The files that I want to work with have all the pattern "bs_abundance", therefore I think a good starting point would be to either load all the files I want to work with with such a command:
temp = list.files(pattern="*bs_abundance")
Or perhaps I can also load all the tables I want to work with directly into the working space already:
for(i in temp) {
x <- read.table(i, header=TRUE, comment.char = "A", sep="\t")
assign(i,x)
}
Then, as explained, I want to cut the fifth column of each of the files to later bind them all to another table of same number of rows.
Put the files into a folder. For this example let's call it temp. Set your working directory appropriately or specify the full path for the example below.
cols <- as.character()
files <- dir("temp")
for(i in files){
# You didn't mention a file type, but let's say it's csv
tmp <- read.csv(files[i], header = T)
tmp <- tmp[, 5]
cols <- cbind(cols, tmp)
}
Then you can just cbind the columns in cols with your final data object.
Here is a method using lapply that assumes each file in the folder has the same number of rows.
# get file names
files <- dir("temp")
# remove one file
files <- files[-which(files == "removeFileName")]
# get list of vectors from 29 files
myList <- lapply(files, function(i) {temp <- read.csv(i); temp[, 5]})
# get new data.frame
dfDone <- do.call(data.frame, myList)
Related
Sorry for the generic question. I'm looking for pointers for sorting out a data folder, in which I have numerous .txt files. All of them have different titles, and for the vast majority of them, the files have the same dimension, that is the column numbers are the same. However, the pain is some of the files, despite having the same number of columns, have different column names. That is in those files, some other variables were measured.
I want to weed out these files, and I cannot do by simply comparing column numbers. Is there any method that I can pass a name of the column and check how many files in the directory have that column, so that I can remove them into a different folder?
UPDATE:
I have created a dummy folder to have files to reflect the problem
please see link below to access the files on my google drive. In this folder, I have took 4 files that have the problem columns.
https://drive.google.com/drive/folders/1IDq7BwfQNkGb9y3RvwlLE3FeMQc38taD?usp=sharing
The problems is the code seem to be able to find files matching the selection criteria, aka the actual name of problem columns, but I cannot extract the real index of such files in the list. Any pointers?
library(data.table)
#read in the example file that have the problem column content
df_var <- read.delim("ctrl_S3127064__3S_DMSO_00_none.TXT", header = T, sep = "\t")
#read in a file that I want to use as reference
df_standard <- read.delim("ctrl__S162465_20190111_T8__3S_2DG_3mM_none.TXT", header = T, sep = "\t")
#get the names of columns of each file
standar.names <- names(df_standard)
var.names <- names(df_var)
same.titles <- var.names %in% standar.names
dff.titles <- !var.names %in% standar.names
#confirm the only 3 columns of problem is column 129,130 and 131
mismatched.names <- colnames(df_var[129:131])
#visual check the names of the problematic columns
mismatched.names
# get current working directory and list all files in this directory
wd <- getwd()
files_in_wd <- list.files(wd)
# create an empty list and read in all files from wd
l_files <- list()
for(i in seq_along(files_in_wd)){
l_files[[i]] <- read.delim(file = files_in_wd[i],
sep = "\t",
header = T,
nrows = 2)
}
# get column names of all files
column_names <- lapply(l_files, names)
# get unique names of files
unique_names <- unique(mismatched.names)
unique_names[1]
# decide which files to remove
#here there the "too_keep" returns an integer vector that I don't undestand
#I thought the numbers should represent the ID/index of the elements
#but I have less than 10 files, but the numbers in to_keep are around 1000
#this is probably because it's matching the actually index of the unlisted list
#but if I use to_keep <- which(column_names%in% unique_names[1]) it returns empty vector
to_keep <- which(unlist(column_names)%in% unique_names[1])
#now if I want to slice the file using to_keep the files_to_keep returns NA NA NA
files_to_keep <- files_in_wd[to_keep]
#once I have a list of targeted files, I can remove them into a new folder by using file.remove
library(filesstrings)
file.move(files_to_keep, "C:/Users/mli/Desktop/weeding/need to reanalysis" )
If you can distinguish the files you'd like to keep from those you'd like to drop depending on the column names, you could use something along these lines:
# set working directory to folder with generic text files
setwd("C:/Users/tester/Desktop/generic-text-files")
# get current working directory and list all files in this directory
wd <- getwd()
files_in_wd <- list.files(wd)
# create an empty list and read in all files from wd
l_files <- list()
for(i in seq_along(files_in_wd)){
l_files[[i]] <- read.delim(file = files_in_wd[i],
sep = ';',
header = T,
nrows = 2)
}
# get column names of all files
column_names <- lapply(l_files, names)
# get unique names of files
unique_names <- unique(column_names)
# decide which files to keep
to_keep <- which(column_names %in% unique_names[1])
files_to_keep <- files_in_wd[to_keep]
If you have many files you should probably avoid the loop or just read in the header of the corresponding file.
edit after your comment:
by adding nrows = 2 the code only reads the first 2 rows + the header.
I assume that the first file in the folder has the structure that you'd like to keep, that's why column_names is checked against unique_names[1].
the files_to_keep contains the names of the files you'd like to keep
you could try to run that on a subset of your data and see if it works and worry about efficiency later. A vectorized approach might work better I think.
edit:
This code works with your dummy-data.
library(filesstrings)
# set working directory to folder with generic text files
setwd("C:/Users/tester/Desktop/generic-text-files/dummyset")
# get current working directory and list all files in this directory
wd <- getwd()
files_in_wd <- list.files(wd)
# create an empty list and read in all files from wd
l_files <- list()
for(i in seq_along(files_in_wd)){
l_files[[i]] <- read.delim(file = files_in_wd[i],
sep = "\t",
header = T,
nrows = 2,
encoding = "UTF-8",
check.names = FALSE
)
}
# get column names of all files
column_names <- lapply(l_files, names)
# decide which files to keep
to_keep <- column_names[[1]] # e.g. column names of file #1 are ok
# check if the other files have the same header:
df_filehelper <- data.frame('fileindex' = seq_along(files_in_wd),
'filename' = files_in_wd,
'keep' = NA)
for(i in 2:length(files_in_wd)){
df_filehelper$keep[i] <- identical(to_keep, column_names[[i]])
}
df_filehelper$keep[1] <- TRUE # keep the original file used for selecting the right columns
# move files out of the current folder:
files_to_move <- df_filehelper$filename[!df_filehelper$keep] # selects file that are not to be kept
file.move(files_to_move, "C:/Users/tester/Desktop/generic-text-files/dummyset/testsubfolder/")
Due to the large number and size of files it might be worth looking at alternatives to R, e.g. in bash:
for f in ctrl*.txt
do
if [[ "$(head -1 ctrl__S162465_20190111_T8__3S_2DG_3mM_none.txt | md5)" != "$(head -1 $f | md5)" ]]
then echo "$f"
fi
done
This command compares the column names of the 'good file' to the column names of every file and prints out the names of files that do not match.
Before I dive into the question, here is a similar problem asked but there is not yet a solution.
So, I am working in R, and there is a folder in my working directory called columns that contains 198 similar .csv files with the name format of a 6-digit integer (e.g. 100000) that increases inconsistently (since the name of those files are actually names for each variable).
Now, I have would like to full join them, but somehow I have to import all of those files into R and then join them. Naturally, I thought about using a list to contain those files and then use a loop to join them. This is the code I tried to use:
#These are the first 3 columns containing identifiers
matrix_starter <- read_csv("files/matrix_starter.csv")
## import_multiple_csv_files_to_R
# Purpose: Import multiple csv files to the Global Environment in R
# set working directory
setwd("columns")
# list all csv files from the current directory
list.files(pattern=".csv$") # use the pattern argument to define a common pattern for import files with regex. Here: .csv
# create a list from these files
list.filenames <- list.files(pattern=".csv$")
#list.filenames
# create an empty list that will serve as a container to receive the incoming files
list.data <- list()
# create a loop to read in your data
for (i in 1:length(list.filenames))
{
list.data[[i]] <- read.csv(list.filenames[i])
list.data[[i]] <- list.data[[i]] %>%
select(`Occupation.Title`,`X2018.Employment`) %>%
rename(`Occupation title` = `Occupation.Title`) #%>%
#rename(list.filenames[i] = `X2018.Employment`)
}
# add the names of your data to the list
names(list.data) <- list.filenames
# now you can index one of your tables like this
list.data$`113300.csv`
# or this
list.data[1]
# source: https://www.edureka.co/community/1902/how-can-i-import-multiple-csv-files-into-r
The chunk above solve the importing part. Now I have a list of .csv files. Next, I would like to join them:
for (i in 1:length(list.filenames)){
matrix_starter <- matrix_starter %>% full_join(list.data[[i]], by = `Occupation title`)
}
However, this does not work nicely. I end up with somewhere around 47,000 rows, to which I only expect around 1700 rows. Please let me know your opinion.
Reading the files into R as a list and including the file name as a column can be done like this:
files <- list.files(path = path,
full.names = TRUE,
all.files = FALSE)
files <- files[!file.info(files)$isdir]
data <- lapply(files,
function(x) {
data <- read_xls(
x,
sheet = 1
)
data$File_name <- x
data
})
I am assuming now that all your excel files have the same structure: the same columns and column types.
If that is the case you can use dplyr::bind_rows to create one combined data frame.
You could off course loop through the list and left_join the list elements. E.g. by using Reduce and merge.
Update based on mihndang's comment. Is this what you are after when you say: Is there a way to use the file name to name the column and also not include the columns of file names?
library(dplyr)
library(stringr)
path <- "./files"
files <- list.files(path = path,
full.names = TRUE,
all.files = FALSE)
files <- files[!file.info(files)$isdir]
data <- lapply(files,
function(x) {
read.csv(x, stringsAsFactors = FALSE)
})
col1 <- paste0(str_sub(basename(files[1]), start = 1, end = -5), ": Values")
col2 <- paste0(str_sub(basename(files[1]), start = 1, end = -5), ": Character")
df1 <- data[[1]] %>%
rename(!!col1 := Value,
!!col2 := Character)
I created two simple .csv files in ./files: file1.csv and file2.csv. I read them into a list. I extract the first list element (the DF) and work out column names in a variable. I then rename the columns in the DF by passing the two variables to them. The column name includes the file name.
Result:
> View(df1)
> df1
file1: Values file1: Character
1 1 a
2 2 b
3 3 c
4 4 d
5 5 e
6 6 f
7 7 g
8 8 h
9 9 i
10 10 j
I guess you are looking for :
result <- Reduce(function(x, y) merge(x, y, by = `Occupation title`, all = TRUE), list.data)
which can be done using purrrs Reduce as well :
result <- purrr::reduce(list.data, dplyr::full_join, by = `Occupation title`)
When you do full join it adds every combination and gives us the tables. if you are looking for unique records then you might want to use left join where keep dataframe/table on left whose all columns you want keep as reference and keep the file you want to join on right.
Hope this helps.
I have figured out some part of the code, I will describe below, but I find it hard to iterate (loop) the function over a list of files:
library(Hmisc)
filter_173 <- c("kp|917416", "kp|835898", "kp|829747", "kp|767311")
# This is a vector of values that I want to exclude from the files
setwd("full_path_of_directory_with_desired_files")
filepath <- "//full_path_of_directory_with_desired_files"
list.files(filepath)
predict_files <- list.files(filepath, pattern="predict.txt")
# all files that I want to filter have _predict.txt in them
predict_full <- file.path(filepath, predict_files)
# generates full pathnames of all desired files I want to filter
sample_names <- sample_names <- sapply(strsplit(predict_files , "_"), `[`, 1)
Now here is an example of a simple filtering I want to do with one specific example file, this works great. How do I repeat this in a loop on all filenames in predict_full
test_predict <- read.table("a550673-4308980_A05_RepliG_rep2_predict.txt", header = T, sep = "\t")
# this is a file in my current working directory that I set with setwd above
test_predict_filt <- test_predict[test_predict$target_id %nin% filter_173]
write.table(test_predict_filt, file = "test_predict")
Finally how do I place the filtered files in a folder with the same name as original with the suffix filtered?
predict_filt <- file.path(filepath, "filtered")
# Place filtered files in
filtered/ subdirectory
filtPreds <- file.path(predict_filt, paste0(sample_names, "_filt_predict.txt"))
I always get stuck at looping! It is hard to share a 100% reproducible example as everyone's working directory and file paths will be unique though all the code I shared works if you adapt it to an appropriate path name on your machine.
This should work to loop through each of the files and write them out to the new location with the filename specifications you needed. Just be sure to change the directory paths first.
filter_173 <- c("kp|917416", "kp|835898", "kp|829747", "kp|767311") #This is a vector of values that I want to exclude from the files
filepath <- "//full_path_of_directory_with_desired_files"
filteredpath <- "//full_path_of_directory_with_filtered_results/"
# Get vector of predict.txt files
predict_files <- list.files(filepath, pattern="predict.txt")
# Get vector of full paths for predict.txt files
predict_full <- file.path(filepath, predict_files)
# Get vector of sample names
sample_names <- sample_names <- sapply(strsplit(predict_files , "_"), `[`, 1)
# Set for loop to go from 1 to the number of predict.txt files
for(i in 1:length(predict_full))
{
# Load the current file into a dataframe
df.predict <- read.table(predict_full[i], header=T, sep="\t")
# Filter out the unwanted rows
df.predict <- df.predict[!(df.predict$target_id %in% filter_173)]
# Write the filtered dataframe to the new directory
write.table(df.predict, file = file.path(filteredpath, paste(sample_names[i],"_filt_predict.txt",sep = "")))
}
I’m looking to do the following in R.
I have 250+ csv files of chromatographic data structured similarly to the example below, but with 21 rows instead of three:
1 4.708252 BB 9.946890 7.830349 0.01982016 4.684836 4.742056
2 4.970352 BB 1.792341 1.497008 0.01896829 4.945352 5.005390
3 6.393414 BB 6.599891 5.309925 0.01950091 6.368413 6.428723
What I want to do is read a subset of the data in all 250 files into a single data frame, which is easy enough — but I also need to restructure it a fair bit.
Every row in the table above is a peak. I only want the data from the first and fourth columns (which are ‘peak number’ and ‘area under the peak’, respectively), and in the output I need to make each peak an individual column, rather than a row as above, with the peak number as the header. Finally, I want to create a new column where each row (that is, the data from each individual csv file) is given the same name as the csv file name.
So, imagine I have 3 files: ABC1.csv, ABC2.csv, and ABC3.csv. Each file looks like my example above. I want to automatically take all those files and merge them into a single data frame such as the one below.
ID 1 2 3
ABC1 9.94689 1.792341 6.599891
ABC2 9.76651 1.932332 6.600022
ABC3 8.99193 2.556471 6.718934
I hope I’ve made this clear enough. I’ve been able to manage most of the steps but haven’t been successful writing them into a single script. And I have no idea how, if there is any way, to make the file name into a variable.
Cheers
I am assuming the working directory is set to where the files are. Then you can get the list of files below.
filenames <- list.files()
Have a helper function to read a file and keep just columns 1 and 4.
readdata <- function(filename) {
df <- read.csv(filename)
vec <- df[, 4]
names(vec) <- df[, 1]
return(vec)
}
Loop over all of the files and rbind them
result <- do.call(rbind, lapply(filenames, readdata))
Name them as you like
row.names(result) <- filenames
this following code can probably be of some help, though the file name is still not working properly -
path <- "C:\\Users\\Vidyut\\"
filenames <- list.files(path = path,pattern = ".csv")
l <- data.frame(ID=character(),col1=numeric(),col2=numeric(),col3=numeric(),stringsAsFactors=FALSE)
for (i in filenames) {
#i = filenames[1]
full = paste(path,i,sep="")
m <- read.csv(full, header=F)
# extract the subset of rows required from each file
# m <- m[c(),]
n<- m[,c(1,4)]
y <- gsub('.csv','',i)
print("y=")
print(y)
d <- list(ID=as.character(y),col1=n[1,2],col2=n[2,2],col3=n[3,2])
print("d=")
print(d)
l <- rbind.data.frame(l,d)
print("l=")
print(l)
}
Mind you, this is not very pretty code - just something hacked together to get the job done (visible from the multiple print lines scattered across).
Here's a solution for you. This only works if we can assume that there are exactly 21 peaks in each file and they are in order 1:21. If that's not the case a few changes to the code should remedy this.
folder = "c:/temp/"
files <- dir(folder)
first_loop <- TRUE
for (file in files) {
# Read one file, only the first and fourth columns
temp <- read.csv(file=paste0(folder,file),
header = FALSE,
colClasses = c("integer", "NULL", "NULL", "numeric", "NULL", "NULL", "NULL", "NULL"))
# Transpose the data
temp <- data.frame(t(temp))
# Remove the peak number
temp <- temp[2,]
# Concatenate the dataframes together
temp$file <- file
if (first_loop) {
data <- temp
first_loop <- FALSE
} else {
data <- rbind(data, temp)
}
}
data
I have a directory containing a large number of csv files. I would like to load the data into R and apply a function to every possible pair combination of csv files in the directory, then write the output to file.
The function that I would like to apply is matchpt() from the biobase library which compares locations between two data frames.
Here is an example of what I would like to do (although I have many more files than this):
Three files in directory: A, B and C
Perform matchpt on each pairwise combination:
nn1 = matchpt(A,B)
nn2 = matchpt(A,C)
nn3 = matchpt(B,C)
Write nn1, nn2 and nn3 to csv file.
I have not been able to find any solutions for this yet and would appreciate any suggestions. I am really not sure where to go from here but I am assuming that some sort of nested for loop is required to somehow cycle sequentially through all pairwise combinations of files. Below is a beginning at something but this only compares the first file with all the others in the directory so does not work!
library("Biobase")
# create two lists of identical filenames stored in the directory:
filenames1 = list.files(path=dir, pattern="csv$", full.names=FALSE, recursive=FALSE)
filenames2 = list.files(path=dir, pattern="csv$", full.names=FALSE, recursive=FALSE)
for(i in 1:length(filenames2)){
# load the first data frame in list 1
df1 <- lapply(filenames1[1], read.csv, header=TRUE, stringsAsFactors=FALSE)
df1 <- data.frame(df1)
# load a second data frame from list 2
df2 <- lapply(filenames2[i], read.csv, header=TRUE, stringsAsFactors=FALSE)
df2 <- data.frame(df2)
# isolate the relevant columns from within the two data frames
dat1 <- as.matrix(df1[, c("lat", "long")])
dat2 <- as.matrix(df2[, c("lat", "long")])
# run the matchpt function on the two data frames
nn <- matchpt(dat1, dat2)
#Extract the unique id code in the two filenames (for naming the output file)
file1 = filenames1[1]
code1 = strsplit(file1,"_")[[1]][1]
file2 = filenames2[i]
code2 = strsplit(file2,"_")[[1]][1]
outname = paste(code1, code2, sep=”_”)
outfile = paste(code, "_nn.csv", sep="")
write.csv(nn, file=outname, row.names=FALSE)
}
Any suggestions on how to solve this problem would be greatly appreciated. Many thanks!
You could do something like:
out <- combn( list.files(), 2, FUN=matchpt )
write.table( do.call( rbind, out ), file='output.csv', sep=',' )
This assumes that matchpt is expecting 2 strings with the names of the files and that the result is the same structure each time so that the rbinding makes sense.
You could also write your own function to pass to combn that takes the 2 file names, runs matchpt and then appends the results to the csv file. Remember that if you pass an open filehandle to write.table then it will append to the file instead of overwriting what is there.
Try this example:
#dummy filenames
filenames <- paste0("file_",1:5,".txt")
#loop through unique combination
for(i in 1:(length(filenames)-1))
for(j in (i+1):length(filenames))
{
flush.console()
print(paste("i=",i,"j=",j,"|","file1=",filenames[i],"file2=",filenames[j]))
}
In response to my question I seem to have found a solution. The below uses a for loop to perform every pairwise combination of files in a common directory (this seems to work and gives EVERY combination of files i.e. A & B and B & A):
# create a list of filenames
filenames = list.files(path=dir, pattern="csv$", full.names=FALSE, recursive=FALSE)
# For loop to compare the files
for(i in 1:length(filenames)){
# load the first data frame in the list
df1 = lapply(filenames[i], read.csv, header=TRUE, stringsAsFactors=FALSE)
df1 = data.frame(df1)
file1 = filenames[i]
code1 = strsplit(file1,"_")[[1]][1] # extract unique id code of file (in case where the id comes before an underscore)
# isolate the columns of interest within the first data frame
d1 <- as.matrix(df1[, c("lat_UTM", "long_UTM")])
# load the comparison file
for (j in 1:length(filenames)){
# load the second data frame in the list
df2 = lapply(filenames[j], read.csv, header=TRUE, stringsAsFactors=FALSE)
df2 = data.frame(df2)
file2 = filenames[j]
code2 = strsplit(file2,"_")[[1]][1] # extract uniqe id code of file 2
# isolate the columns of interest within the second data frame
d2 <- as.matrix(df2[, c("lat_UTM", "long_UTM")])
# run the comparison function on the two data frames (in this case matchpt)
out <- matchpt(d1, d2)
# Merge the unique id code in the two filenames (for naming the output file)
outname = paste(code1, code2, sep="_")
outfile = paste(outname, "_out.csv", sep="")
# write the result to file
write.csv(out, file=outfile, row.names=FALSE)
}
}