Syntax#

• read.csv(file, header = TRUE, sep = ”,”, quote = """, dec = ”.”, fill = TRUE, comment.char = "", …)

• read.csv2(file, header = TRUE, sep = ”;”, quote = """, dec = ”,”, fill = TRUE, comment.char = "", …)

• readr::read_csv(file, col_names = TRUE, col_types = NULL, locale = default_locale(), na = c("", “NA”), comment = "", trim_ws = TRUE, skip = 0, n_max = -1, progress = interactive())

• data.table::fread(input, sep=“auto”, sep2=“auto”, nrows=-1L, header=“auto”, na.strings=“NA”,

stringsAsFactors=FALSE, verbose=getOption(“datatable.verbose”), autostart=1L, skip=0L, select=NULL, drop=NULL, colClasses=NULL, integer64=getOption(“datatable.integer64”), # default: “integer64” dec=if (sep!=”.”) ”.” else ”,”, col.names, check.names=FALSE, encoding=“unknown”, strip.white=TRUE, showProgress=getOption(“datatable.showProgress”), # default: TRUE data.table=getOption(“datatable.fread.datatable”) # default: TRUE )

Parameters#

Input/output for other types of files is covered in several other topics, all linked from Input and output.

Importing .csv files

Importing using base R

Comma separated value files (CSVs) can be imported using read.csv, which wraps read.table, but uses sep = "," to set the delimiter to a comma.

# get the file path of a CSV included in R's utils package
csv_path <- system.file("misc", "exDIF.csv", package = "utils")

# path will vary based on installation location
csv_path
## [1] "/Library/Frameworks/R.framework/Resources/library/utils/misc/exDIF.csv"

df <- read.csv(csv_path)

df
##    Var1 Var2
## 1  2.70    A
## 2  3.14    B
## 3 10.00    A
## 4 -7.00    A

A user friendly option, file.choose, allows to browse through the directories:

df <- read.csv(file.choose())

Notes

• Unlike read.table, read.csv defaults to header = TRUE, and uses the first row as column names.
• All these functions will convert strings to factor class by default unless either as.is = TRUE or stringsAsFactors = FALSE.
• The read.csv2 variant defaults to sep = ";" and dec = "," for use on data from countries where the comma is used as a decimal point and the semicolon as a field separator.

Importing using packages

The readr package’s read_csv function offers much faster performance, a progress bar for large files, and more popular default options than standard read.csv, including stringsAsFactors = FALSE.

library(readr)

df <- read_csv(csv_path)

df
## # A tibble: 4 x 2
##    Var1  Var2
##   <dbl> <chr>
## 1  2.70     A
## 2  3.14     B
## 3 10.00     A
## 4 -7.00     A

Importing with data.table

The data.table package introduces the function fread. While it is similar to read.table, fread is usually faster and more flexible, guessing the file’s delimiter automatically.

# get the file path of a CSV included in R's utils package
csv_path <- system.file("misc", "exDIF.csv", package = "utils")

# path will vary based on R installation location
csv_path
## [1] "/Library/Frameworks/R.framework/Resources/library/utils/misc/exDIF.csv"

dt <- fread(csv_path)

dt
##     Var1 Var2
## 1:  2.70    A
## 2:  3.14    B
## 3: 10.00    A
## 4: -7.00    A

Where argument input is a string representing:

• the filename (e.g. "filename.csv"),
• a shell command that acts on a file (e.g. "grep 'word' filename"), or
• the input itself (e.g. "input1, input2 \n A, B \n C, D").

fread returns an object of class data.table that inherits from class data.frame, suitable for use with the data.table’s usage of []. To return an ordinary data.frame, set the data.table parameter to FALSE:

df <- fread(csv_path, data.table = FALSE)

class(df)
## [1] "data.frame"

df
##    Var1 Var2
## 1  2.70    A
## 2  3.14    B
## 3 10.00    A
## 4 -7.00    A

Notes

• fread does not have all same options as read.table. One missing argument is na.comment, which may lead in unwanted behaviors if the source file contains #.
• fread uses only " for quote parameter.
• fread uses few (5) lines to guess variables types.

Importing .tsv files as matrices (basic R)

Many people don’t make use of file.path when making path to a file. But if you are working across Windows, Mac and Linux machines it’s usually good practice to use it for making paths instead of paste.

FilePath <- file.path(AVariableWithFullProjectPath,"SomeSubfolder","SomeFileName.txt.gz")

Data <- as.matrix(read.table(FilePath, header=FALSE, sep ="\t"))

Generally this is sufficient for most people.

Sometimes it happens the matrix dimensions are so large that procedure of memory allocation must be taken into account while reading in the matrix, which means reading in the matrix line by line.

Take the previous example, In this case FilePath contains a file of dimension 8970 8970 with 79% of the cells containing non-zero values.

system.time(expr=Data<-as.matrix(read.table(file=FilePath,header=FALSE,sep=" ") ))

system.time says 267 seconds were taken to read the file.

   user  system elapsed
265.563   1.949 267.563

Similarly this file can be read line by line,

FilePath <- "SomeFile"
connection<- gzfile(FilePath,open="r")
TableList <- list()
Counter <- 1
system.time(expr= while ( length( Vector<-as.matrix(scan(file=connection, sep=" ", nlines=1, quiet=TRUE)) ) > 0 ) {
    TableList[[Counter]]<-Vector
    Counter<-Counter+1
})
   user  system elapsed
165.976   0.060 165.941
close(connection)
system.time(expr=(Data <- do.call(rbind,TableList)))
   user  system elapsed
  0.477   0.088   0.565

There’s also the futile.matrix package which implements a read.matrix method, the code itself will reveal itself to be the same thing as described in example 1.

Exporting .csv files

Exporting using base R

Data can be written to a CSV file using write.csv():

write.csv(mtcars, "mtcars.csv")

Commonly-specified parameters include row.names = FALSE and na = "".

Exporting using packages

readr::write_csv is significantly faster than write.csv and does not write row names.

library(readr)

write_csv(mtcars, "mtcars.csv")

Import multiple csv files

files = list.files(pattern="*.csv")
data_list = lapply(files, read.table, header = TRUE)

This read every file and adds it to a list. Afterwards, if all data.frame have the same structure they can be combined into one big data.frame:

df <- do.call(rbind, data_list)

Importing fixed-width files

Fixed-width files are text files in which columns are not separated by any character delimiter, like , or ;, but rather have a fixed character length (width). Data is usually padded with white spaces.

An example:

Column1 Column2   Column3           Column4Column5 
1647    pi        'important'       3.141596.28318
1731    euler     'quite important' 2.718285.43656
1979    answer    'The Answer.'     42     42

Let’s assume this data table exists in the local file constants.txt in the working directory.

Importing with base R

df <- read.fwf('constants.txt', widths = c(8,10,18,7,8), header = FALSE, skip = 1)

df
#>     V1     V2                 V3         V4        V5
#> 1 1647     pi         'important'   3.14159   6.28318
#> 2 1731  euler   'quite important'   2.71828   5.43656
#> 3 1979 answer       'The Answer.'   42        42.0000

Note:

• Column titles don’t need to be separated by a character (Column4Column5)
• The widths parameter defines the width of each column
• Non-separated headers are not readable with read.fwf()

Importing with readr

library(readr)

df <- read_fwf('constants.txt', 
               fwf_cols(Year = 8, Name = 10, Importance = 18, Value = 7, Doubled = 8), 
               skip = 1)
df
#> # A tibble: 3 x 5
#>    Year    Name        Importance    Value  Doubled
#>    <int>   <chr>           <chr>     <dbl>    <dbl>
#> 1  1647      pi       'important'  3.14159  6.28318
#> 2  1731   euler 'quite important'  2.71828  5.43656
#> 3  1979  answer     'The Answer.' 42.00000 42.00000

Note:

• readr’s fwf_* helper functions offer alternative ways of specifying column lengths, including automatic guessing (fwf_empty)
• readr is faster than base R
• Column titles cannot be automatically imported from data file