What folder does R look at when trying to import a file? I'm using the read.csv command.

Question

What folder does R look at when trying to import a file?  I'm using the read.csv command.

Rimantas Venckus · Answer

I have very interesting question about file import into R.  I have a file mydata.cvs   This file is updating by the software every 2 seconds and data is added. How do you work out this problem ? I mean how to refresh data in R ?  Do you use some file read/write ? I just wondering why doesn't exist stream data package in such useful language ? I know that R doesnt support streams by itself, but would be nice to have one. Nowadays the data changes  very quickly and it will be very usefull

Michael Hochster · Answer

You can use [code]getwd()[/code] to see where R is looking, and [code]setwd()[/code] to change it. Or you can use the full path of the filename.

Patrick Burns · Answer

If you are on Windows, you can also use:
file.choose()
where the filename goes and a window pops up in which you can make a choice.

Florian May · Answer

Are you trying to learn R from scratch? Google “r tutorial”, and learn the basic operations before trying to solve problems where you get stuck at each step. You're on the right path, keep going! An excellent resource is R for Data Science [ https://r4ds.had.co.nz/ ]. Happy learning!

Are you a bot artificially filling Quora feeds? Please stop. Quora mods?

Are you anything than a beginner who got stuck? Behold: https://duckduckgo.com/ (and other search engines) will change your life! Beyond my mild sarcasm, searching is faster than asking.

Anonymous · Answer

Hope this answers your question:

[code]library(DT)
library(shiny)
ui %3C- fluidPage(
   fileInput('file1', 'Choose file to upload',
              accept = c(
                'text/csv',
                'text/comma-separated-values',
                'text/tab-separated-values',
                'text/plain',
                '.csv',
                '.tsv'
              )
      ),
   tags$hr(),
  checkboxInput('header', 'Header', TRUE),
  radioButtons('sep', 'Separator',
               c(Comma=',',
                 Semicolon=';',
                 Tab='	'),
               ','),
  radioButtons('quote', 'Quote',
               c(None='',
                 'Double Quote'='"',
                 'Single Quote'="'"),
               '"'),
  tags$hr(),
  p('If you want a sample .csv or .tsv file to upload,',
    'you can first download the sample',
    a(href = 'mtcars.csv', 'mtcars.csv'), 'or',
    a(href = 'pressure.tsv', 'pressure.tsv'),
    'files, and then try uploading them.'
  ),
  DT::dataTableOutput('contents')
)

server %3C- function(input, output,session) {

output$contents %3C- DT::renderDataTable({

inFile %3C- input$file1
    
    if (is.null(inFile))
      return(NULL)
    
    k%3C-read.csv(inFile$datapath, header = input$header,
             sep = input$sep, quote = input$quote)
     })

}

shinyApp(ui = ui, server = server)
[/code]Output:

1)

2)

3)

Hemant Kaithwas · Answer

The process of importing and exporting CSV (Comma Separated Values) files in R is straightforward. R provides several packages and functions to import and export CSV files.

To import a CSV file in R, you can use the read.csv function from the base R package. This function is used to read CSV files and returns a data frame. Here's an example of how to use this function:

Code: data %3C- read.csv("file.csv")

* In this example, "file.csv" is the name of the file you want to import, and "data" is the name of the object that will hold the data from the file.
 * If your CSV file has a header, you can specify it using the header argument:
Code: data %3C- read.csv("file.csv", header = TRUE)

* If your CSV file has a different separator, such as a tab or a semicolon, you can specify it using the sep argument:
Code: data %3C- read.csv("file.tsv", sep = "	", header = TRUE)

Code: data %3C- read.csv("file.csv", sep = ";", header = TRUE)

* To export a CSV file in R, you can use the write.csv function from the base R package. This function is used to write data frames to CSV files. Here's an example of how to use this function:
Code: write.csv(data, "file.csv")

* In this example, "data" is the name of the object that holds the data you want to export, and "file.csv" is the name of the file you want to create.
 * If you want to use a different separator, you can use the sep argument:
Code: write.csv(data, "file.tsv", sep = "	")

Code: write.csv(data, "file.csv", sep = ";")

* You can also use the row.names argument to specify whether to include row names in the output file:
Code: write.csv(data, "file.csv", row.names = FALSE)

In conclusion, importing and exporting CSV files in R is a simple process, and the read.csv and write.csv functions make it easy to handle these operations. You can also use other packages, such as data.table and readr, to import and export CSV files with more options and better performance.

Jon Wayland · Answer

Yes. There’s a few ways to do it and it depends on your needs:

1. You can read all csv files into R with [code ]read.csv()[/code], change the column names with [code ]colnames(df)%3C-c(“column1”, “column2”,…)[/code], and then combine them with [code ]rbind()[/code]. Assuming the variable names are different but the variables are not.
2. You can read all csv files into R with [code ]read.csv()[/code], and write each one back to a single csv file on separate sheets with [code ]write.xlsx()[/code] from the [code ]xlsx[/code] library. Assuming they do not need to be on the same sheet.
3. You can read all csv files into R with [code ]read.csv()[/code], and join them together with either [code ]merge()[/code] given a unique joining key or [code ]cbind()[/code] if the datasets are all the same length and ordered the same way. Assuming the variable names and variables are different, but the data is either relational or from the same population.

Vincent Jiang · Answer

Simple upload them to Acho Studio [ https://www.acho.io/ ] as “Multiple CSV”, and the software will union/merge the files for you automatically.

Make sure that the column names are exactly the same, and the schema for each column (integer, float, boolean, string..) are the same as well.

Shriram Bhat · Answer

I tried this with for loop and it is working

df=NULL

Combined=NULL

a %3C- list.files(path=”File_path”,pattern="*.csv",recursive = T,full.names = T)

ind %3C- 0

for (i in a){

ind %3C- ind +1

b %3C- read.csv(i, skip = 0, header = F, nrows = 1, Ás fasteignasala [ http://as.is ] = T)

df=read.csv(i, skip = 1, header = T)

df=df %%3E% mutate( URL = b$V1 )

Combined=rbind(Combined, df)

}

Rupkatha Ghosh · Answer

The standard C library also offers a higher level fread function. Unlike the read function, you can set a buffer size. Buffers can be good or bad. On the one hand, they reduce the number of disk accesses. On the other hand, they introduce an intermediate step between the disk and you data. That is, they may cause the data to be copied needlessly. Buffers usually makes software faster because copying data in memory is much faster than reading it from disk.

For sequential access, both fread and ifstream are equally fast. Unbuffered IO (read) is slower, as expected. Memory mapping is not beneficial.
Thanks for the A2A :)

For sequential access, both fread and ifstream are equally fast.  Unbuffered IO (read) is slower, as expected.  Memory mapping is not  beneficial.
Thanks for the A2A :)

Joe Bologna · Answer

Let’s assume you want to parse CSV files that do not contain “,” characters in the fields. (Otherwise things get complicated because it’s necessary to parsed optionally quoted fields).

[code]The simplest way, using just Bash is by setting the IFS shell variable. This changes how lines are tokenized. Using IFS=“,” causes the shell to create a new token when a “,” is seen. (Normally tokens separators are whitespace).
[/code]For example, let’s start with a simple CSV file:

[code]% cat %3C%3CEOF %3E blah.csv
row1,field1,field2
row2,field1,field2
row3,field1,field2
EOF
[/code]Now let’s parse it with Bash:

[code]% (IFS=,;while read a b c; do echo $a: $b = $c;done %3C blah.csv)
row1: field1 = field2
row2: field1 = field2
row3: field1 = field2
[/code]Using Bash for parsing works, but it is pretty slow. Almost all Unix/Linux systems have awk installed. Awk is an awesome high performance scripting language. Here’s how to parse CSV using awk:

[code]% awk -F , '{printf "%s: %s = %s
", $1, $2, $3}' blah.csv
row1: field1 = field2
row2: field1 = field2
row3: field1 = field2
[/code]Awk is specifically designed for parsing. The script above is very simple. Awk can do much, much more. If you are parsing stuff, I highly recommend learning it.

Let’s assume you have something line NodeJS installed. This is where things get interesting. NodeJS have NPM modules for all sorts of stuff, including parsing CSV files. When an NPM module is installed, the command line interface to the module is usually installed.

I’ll install the csv-parser module so we can use it to convert CSV files with headers into newline-delimited JSON.

[code]% yarn add csv-parser
% cat %3C%3CEOF %3E blah2.csv
ROW,FIELD1,FIELD2
1,"contents of field 1,1","contents of field 1,2"
EOF
% node_modules/csv-parser/bin/csv-parser blah2.csv
{"ROW":"1","FIELD1":"contents of field 1,1","FIELD2":"contents of field 1,2"}
{"ROW":"2","FIELD1":"contents of field 2,1","FIELD2":"contents of field 2,2"}
[/code]Notice how the first row is used as the name of the attribute. Also notice how the quoted field parsed. Using quotes allows embedding a “,” in the field.

David Lee · Answer

If the data appears clean when you look at it in a spreadsheet program (column headers are the proper attribute names, the columns appear "tidy" with the right values in the right columns, etc.) then I would suggest try opening the .csv file in a text editor, and use the options in "save as" (or perhaps in a format menu) to confirm that the character encoding is UTF-8.  The text and file should appear the same to your eye.  This has solved problems similar to yours for me many times, and I never could figure out where the change occurred in the process, so this is just one of those things that I check when we are having problems.

Zhu Victor · Answer

Yes for sure.

you can first read it with Pandas and produce a dataframe, and then convert it to a numpy array:

import pandas as pd

df = pd.read_csv('data.csv')

[code]import pandas as pd
df = pd.read_csv('data.csv').to_numpy()
[/code]Or just with numpy:

[code]from numpy import genfromtxt
my_data = genfromtxt('my_file.csv', delimiter=',')
[/code]If your csv has multiple columns and those columns fall in different data types, I would suggest you to stick with pandas df, it give you more flexibility in data types, while with numpy array, you can only have one data type:

[code]mport pandas as pd
import numpy as np
df = pd.read_csv(%3Cfile-name%3E, dtype={'Col_A': np.int64, 'Col_B': np.float64})
[/code]

Dinesh Kumar · Answer

You can read file using following methods:

readr::read_csv(“path/filename”)

read.csv(“path/filename”,header = T)

You can use Rstudio to import dataset without writing any code by using file menu and importing the type of dataset.

Rugved Modak · Answer

CSV stands for comma separated values. Which means it is just a text file, given form of spreadsheet, by separating columns by commas. But in some languages like French comma is used to represent floating point values. That is, in French 3.14 will be written as 3,14. In such cases, you cannot split columns using commas as it might split values too. So in such cases a semicolon ; is used to separate values. Such CSV file has extension CSV2. Sometimes you can also set the custom separator in encoding like | or — . Well now you know what csv2 is for.

I hope that helped. Good luck.

Chaithanya Kumar · Answer

R has built in functions to handle csv files.

Reading is as simple as

[code]df = read.csv("filename.csv',header=T)
[/code]To plot a histogram, please check hist() command in help for more fine-grained control.

[code]hist(df$col_name)
[/code]Hope this helps.

Daniel Nebdal · Answer

Oops, I didn’t see your comments on the question. This is how I’d have written it:

[code]filenames = Sys.glob("*.csv")
contents = lapply(filenames, read.csv)
merged = Reduce(rbind, contents)
[/code]As for why what you have doesn’t work, I don’t think [code ]""[/code] is a legal path for list.files. The default for path is [code ]"."[/code] (the current directory), so just drop the path argument.

I don’t have r.bind, only rbind without the dot; try that spelling.

Sai Teja Nannapaneni · Answer

First set working directory using setwd( )

Setwd(“filepath”)

Next use read.csv to read file

Data= read.csv(“filename.csv”,header=T)

If ur file has seperator as ; , : like that u can use read.csv2 function

Data =read.csv2(“filename.csv”,sep=:,header=T)

For more usage about this function u can refer to help page in R studio.

Helene HT · Answer

[code]mydata = read.csv("myfile.csv",skip=1)
firstrow = read.csv("myfile.csv",nrows=0)
mydata$extracolumn = unlist(firstrow[1,])
[/code]

Wayne Brehob · Answer

I use bash for reading CSV files with something like this.

[code]#!/bin/sh
awk -F, '{ about a hundred lines of code }' $1
[/code]I use bash for things like reading the command-line arguments, providing a Usage statement if they’re not right, and checking the existence of the file, but I use ‘awk’ to process the CSV file.

Bash in itself is not very good at text processing, but there are many other tools that do it well, such as ‘awk’ and bash is good at calling those.

There may be even better tools, such as python, but I personally haven’t tried python for tasks like this.