I have a text file that I read like this:
file=read.table("file.txt",skip="1",sep="")
The first line of this text file contains information about the file then it is followed by the observations.
I want to extract the first line and write it out to a new text file.
To read the first line of a file, you can do:
con <- file("file.txt","r")
first_line <- readLines(con,n=1)
close(con)
To write it out, there are many options. Here is one:
cat(first_line,file="first_line.txt")
Another way to do it is to read it with read.table() like this:
read.table(file = 'file.txt',header = F,nrows = 1)
This is a simple way to do it, and you can get your data separated into columns which makes it easier to work with.
Related
I am trying to assign a comment to a data frame to store some relevant metadata. I have an unstructured text file wrapped in quote marks, with several line breaks ('\n').
WHO_comment<-read.table(file="WHO comment.txt", sep="\t")
comment(WHO)<-WHO_comment #Read in the comment from .txt due to its length
cat(comment(WHO)) #Database metadata
However, the readout comes in one large block with '\n' read as literal strings. Converting it with as.character() only returns the row name (i.e. '1').
How can I read in this file correctly?
read.table is the wrong function to read a text file. As the name suggests, its purpose is to read tabular data. To read a text file, use readLines, and then paste the individual lines together:
comment(data) = paste(readLines('WHO comment.txt'), collapse = '\n')
Solved it - I need to use stringsAsFactors=FALSE to read the file in correctly. This code now does what I wanted it to, which is assign a comment from a .txt file.
WHO_comment<-read.table(file="WHO comment.txt", sep="\t",stringsAsFactors=FALSE)
comment(WHO)<-WHO_comment #Read in the comment from .txt due to its length
cat(comment(WHO)) #Database metadata
I have an Excel with data, like this (but then N=1.000):
p_evar7_CO.main.
p_evar7_CP.acquistion..sign_up.start
p_evar7_CP.main.
p_evar7_CP.main.facial_stylers00
I want to put it in a vector, but with simple copy/pasting it goes wrong. I want this as result:
Excel <- c("p_evar7_CO.", "p_evar7_CP.acquistion..sign_up.start", "p_evar7_CP.main.","p_evar7_CP.main.facial_stylers00")
So basically: How can I paste a big data file into R, and automatically separate it with a Comma and Quote each row?
EDIT I don't want to load in an Excel data file, but only pasting columns names (and have them as a vector).
Looks like you could do a simple scan().
scan(file, what = "")
where file is your file name as a character string. If you are working with copied text, then you can enter "clipboard" as the file name.
scan("clipboard", what = "")
For example, I copied the file text from your question for the following code.
scan("clipboard", what="")
# Read 4 items
# [1] "p_evar7_CO.main." "p_evar7_CP.acquistion..sign_up.start"
# [3] "p_evar7_CP.main." "p_evar7_CP.main.facial_stylers00"
I have a .txt file with one column consisting of 1040 lines (including a header). However, when loading it into R using the read.table() command, it's showing 1044 lines (including a header).
The snippet of the file looks like
L*H
no
H*L
no
no
no
H*L
no
Might it be an issue with R?
When opened in Excel it doesn't show any errors as well.
EDIT
The problem was that R read a line like L + H* as three separated lines L + H*.
I used
table <- read.table(file.choose(), header=T, encoding="UTF-8", quote="\n")
You can try readLines() to see how many lines are there in your file. And feel free to use read.csv() to import it again to see it gets the expected return. Sometimes, the file may be parsed differently due to extra quote, extra return, and potentially some other things.
possible import steps:
look at your data with text editor or readLines() to figure out the delimiter and file type
Determine an import method (type read and press tab, you will see the import functions for import. Also check out readr.)
customize your argument. For example, if you have a header or not, or if you want to skip the first n lines.
Look at the data again in R with View(head(data)) or View(tail(data)). And determine if you need to repeat step 2,3,4
Based on the data you have provided, try using sep = "\n". By using sep = "\n" we ensure that each line is read as a single column value. Additionally, quote does not need to be used at all. There is no header in your example data, so I would remove that argument as well.
All that said, the following code should get the job done.
table <- read.table(file.choose(), sep = "\n")
The program I am exporting my data from (PowerBI) saves the data as a .csv file, but the first line of the file is sep=, and then the second line of the file has the header (column names).
Sample fake .csv file:
sep=,
Initiative,Actual to Estimate (revised),Hours Logged,Revised Estimate,InitiativeType,Client
FakeInitiative1 ,35 %,320.08,911,Platform,FakeClient1
FakeInitiative2,40 %,161.50,400,Platform,FakeClient2
I'm using this command to read the file:
initData <- read.csv("initData.csv",
row.names=NULL,
header=T,
stringsAsFactors = F)
but I keep getting an error that there are the wrong number of columns (because it thinks the first line tells it the number of columns).
If I do header=F instead then it loads, but then when I do names(initData) <- initData[2,] then the names have spaces and illegal characters and it breaks the rest of my program. Obnoxious.
Does anyone know how to tell R to ignore that first line? I can go into the .csv file in a text editor and just delete the first line manually before I load it each time (if I do that, everything works fine) but I have to export a bunch of files and this is a bit stupid and tedious.
Any help would be much appreciated.
There are many ways to do that. Here's one:
all_content = readLines("initData.csv")
skip_first_line = all_content[-1]
initData <- read.csv(textConnection(skip_first_line),
row.names=NULL,
header=T,
stringsAsFactors = F)
Your file could be in a UTF-16 encoding. See hrbrmstr's answer in how to read a UTF-16 file:
I'm new, and I have a problem:
I got a dataset (csv file) with the 15 columns and 33,000 rows.
When I view the data in Excel it looks good, but when I try to load the data
into R- studio I have a problem:
I used the code:
x <- read.csv(file = "1energy.csv", head = TRUE, sep="")
View(x)
The result is that the columnnames are good, but the data (row 2 and further) are
all in my first column.
In the first column the data is separated with ; . But when i try the code:
x1 <- read.csv(file = "1energy.csv", head = TRUE, sep=";")
The next problem is: Error in read.table(file = file, header = header, sep = sep, quote = quote, :
duplicate 'row.names' are not allowed
So i made the code:
x1 <- read.csv(file = "1energy.csv", head = TRUE, sep=";", row.names = NULL)
And it looks liked it worked.... But now the data is in the wrong columns (for example, the "name" column contains now the "time" value, and the "time" column contains the "costs" value.
Does anybody know how to fix this? I can rename columns but i think that is not the best way.
Excel, in its English version at least, may use a comma as separator, so you may want to try
x1 <- read.csv(file = "1energy.csv", head = TRUE, sep=",")
I once had a similar problem where header had a long entry that contained a character that read.csv mistook for column separator. In reality, it was a part of a long name that wasn’t quoted properly.
Try skipping header and see if the problem persists
x1 <- read.csv(file = "1energy.csv", skip = 1, head = FALSE, sep=";")
In reply to your comment:
Two things you can do. Simplest one is to assign names manually:
myColNames <- c(“col1.name”,”col2.name”)
names(x1) <- myColNames
The other way is to read just the name row (the first line in your file)
read only the first line, split it into a character vector
nameLine <- readLines(con="1energy.csv", n=1)
fileColNames <- unlist(strsplit(nameLine,”;”))
then see how you can fix the problem, then assign names to your x1 data frame. I don’t know what exactly is wrong with your first line, so I can’t tell you how to fix it.
Yet another cruder option is to open your csv file using a text editor and edit column names.
It happens because of Exel's specifics. The easy solution is just to copy all your data Ctrl+C to Notepad and Save it again from Notepad as filename.csv (don't forget to remove .txt if necessary). It worked well for me. R opened this newly created csv file correctly, all data was separated at columns right.
Open your file in text edit and see if it really is separated with commas...
Sometimes .csv files are separated with tabs instead of commas or semicolon and when opening in excel it has no problem but in R you have to specify the separator like this:
x <- read.csv(file = "1energy.csv", head = TRUE, sep="\t")
I once had the same problem, this was my solution. Hope it works for you.
This problem can arise due to regional settings on the excel application where the .csv file was created.
While in most places a "," separates the columns in a COMMA separated file (which makes sense), in other places it is a ";"
Depending on your regional settings, you can experiment with:
x1 <- read.csv(file = "1energy.csv", head = TRUE, sep=",") #used in North America
or,
x1 <- read.csv(file = "1energy.csv", head = TRUE, sep=";") #used in some parts of Asia and Europe
You could use -
df <- read.csv("filename.csv", sep = ";", quote = "")
It solved one my problems similar to yours.
So i made the code:
x1 <- read.csv(file = "1energy.csv", head = TRUE, sep=";", row.names =
NULL) And it looks liked it worked.... But now the data is in the
wrong columns (for example, the "name" column contains now the "time"
value, and the "time" column contains the "costs" value.
Does anybody know how to fix this? I can rename columns but i think
that is not the best way.
I had the exact same issue. Did quite some research and found out, that the CSV was ill-formed.
In the header line of the CSV there were all the labels (separated by the separator) and then a line break.
Starting with line 2, there was an additional separator at the end of each line. So an example of such an ill-formed CSV file looks like this:
Field1;Field2 <-- see the *missing* semicolon at the end
12;23; <-- see the *trailing* semicolon in each of the data lines
34;67;
45;56;
Such ill-formatted files are even harder to spot for TAB-separated files.
Excel does not care about that, when importing CSV files.
But R does care.
When you use skip=1 you skip the header line that contains part of the mismatch. The data frame will be imported well, but there will be a column of "NA" at the end of each row. And obviously you will not have column names, as these were skipped.
Easiest solution: edit the CSV file and either add an additional separator at the end of the header line as well, or remove the trailing delimiters in the data lines. You can also use generic read and write functions in R for text files to automate that editing.
You can transform the data by arranging the data into many cells corresponding to columns.
1.Open your csv file
2.copy the content and paste it into txt file save and copy its content
3.open new excell file
4.in excell go to the section responsible for data . it is acually called "Data"
5.then on the left side go to external data query , in german "externe Daten abfragen"
6.go ahead step by step and seperate by commas
7. save your file as csv
I had the same problem and it was frustrating...
However, I found the ultimate solution
First take this (csv file) and then convert it online to Json file and download it ... then redo the whole thing backwards (re-convert Jason to csv) online... download the converted file... give it a name...
then put it on your Rstudio
file name <- read.csv(file='name your file.csv')
... took me 4 days to think out of the box... 🙂🙂🙂