read_lines() reads up to n_max lines from a file. New lines are not included in the output. read_lines_raw() produces a list of raw vectors, and is useful for handling data with unknown encoding. write_lines() takes a character vector or list of raw vectors, appending a new line after each entry.

## Usage

read_lines(
file,
skip = 0,
skip_empty_rows = FALSE,
n_max = Inf,
locale = default_locale(),
na = character(),
progress = show_progress()
)

file,
skip = 0,
n_max = -1L,
progress = show_progress()
)

write_lines(
x,
file,
sep = "\n",
na = "NA",
append = FALSE,
path = deprecated()
)

## Arguments

file

Either a path to a file, a connection, or literal data (either a single string or a raw vector).

Files ending in .gz, .bz2, .xz, or .zip will be automatically uncompressed. Files starting with http://, https://, ftp://, or ftps:// will be automatically downloaded. Remote gz files can also be automatically downloaded and decompressed.

Literal data is most useful for examples and tests. To be recognised as literal data, the input must be either wrapped with I(), be a string containing at least one new line, or be a vector containing at least one string with a new line.

Using a value of clipboard() will read from the system clipboard.

skip

Number of lines to skip before reading data.

skip_empty_rows

Should blank rows be ignored altogether? i.e. If this option is TRUE then blank rows will not be represented at all. If it is FALSE then they will be represented by NA values in all the columns.

n_max

Number of lines to read. If n_max is -1, all lines in file will be read.

locale

The locale controls defaults that vary from place to place. The default locale is US-centric (like R), but you can use locale() to create your own locale that controls things like the default time zone, encoding, decimal mark, big mark, and day/month names.

na

Character vector of strings to interpret as missing values. Set this option to character() to indicate no missing values.

lazy

Read values lazily? By default the file is initially only indexed and the values are read lazily when accessed. Lazy reading is useful interactively, particularly if you are only interested in a subset of the full dataset. Note, if you later write to the same file you read from you need to set lazy = FALSE. On Windows the file will be locked and on other systems the memory map will become invalid.

The number of processing threads to use for initial parsing and lazy reading of data. If your data contains newlines within fields the parser should automatically detect this and fall back to using one thread only. However if you know your file has newlines within quoted fields it is safest to set num_threads = 1 explicitly.

progress

Display a progress bar? By default it will only display in an interactive session and not while knitting a document. The automatic progress bar can be disabled by setting option readr.show_progress to FALSE.

x

A character vector or list of raw vectors to write to disk.

sep

The line separator. Defaults to \\n, commonly used on POSIX systems like macOS and linux. For native windows (CRLF) separators use \\r\\n.

append

If FALSE, will overwrite existing file. If TRUE, will append to existing file. In both cases, if the file does not exist a new file is created.

path

Use the file argument instead.

## Value

read_lines(): A character vector with one element for each line. read_lines_raw(): A list containing a raw vector for each line. write_lines() returns x, invisibly.

## Examples

read_lines(file.path(R.home("doc"), "AUTHORS"), n_max = 10)
#>  [1] "Authors of R."
#>  [2] ""
#>  [3] "R was initially written by Robert Gentleman and Ross Ihaka—also known as \"R & R\""
#>  [4] "of the Statistics Department of the University of Auckland."
#>  [5] ""
#>  [6] "Since mid-1997 there has been a core group with write access to the R"
#>  [7] "source, currently consisting of"
#>  [8] ""
#>  [9] "Douglas Bates"
#> [10] "John Chambers"
#> [[1]]
#>  [1] 41 75 74 68 6f 72 73 20 6f 66 20 52 2e
#>
#> [[2]]
#> raw(0)
#>
#> [[3]]
#>  [1] 52 20 77 61 73 20 69 6e 69 74 69 61 6c 6c 79 20 77 72 69 74 74 65 6e
#> [24] 20 62 79 20 52 6f 62 65 72 74 20 47 65 6e 74 6c 65 6d 61 6e 20 61 6e
#> [47] 64 20 52 6f 73 73 20 49 68 61 6b 61 e2 80 94 61 6c 73 6f 20 6b 6e 6f
#> [70] 77 6e 20 61 73 20 22 52 20 26 20 52 22
#>
#> [[4]]
#>  [1] 6f 66 20 74 68 65 20 53 74 61 74 69 73 74 69 63 73 20 44 65 70 61 72
#> [24] 74 6d 65 6e 74 20 6f 66 20 74 68 65 20 55 6e 69 76 65 72 73 69 74 79
#> [47] 20 6f 66 20 41 75 63 6b 6c 61 6e 64 2e
#>
#> [[5]]
#> raw(0)
#>
#> [[6]]
#>  [1] 53 69 6e 63 65 20 6d 69 64 2d 31 39 39 37 20 74 68 65 72 65 20 68 61
#> [24] 73 20 62 65 65 6e 20 61 20 63 6f 72 65 20 67 72 6f 75 70 20 77 69 74
#> [47] 68 20 77 72 69 74 65 20 61 63 63 65 73 73 20 74 6f 20 74 68 65 20 52
#>
#> [[7]]
#>  [1] 73 6f 75 72 63 65 2c 20 63 75 72 72 65 6e 74 6c 79 20 63 6f 6e 73 69
#> [24] 73 74 69 6e 67 20 6f 66
#>
#> [[8]]
#> raw(0)
#>
#> [[9]]
#>  [1] 44 6f 75 67 6c 61 73 20 42 61 74 65 73
#>
#> [[10]]
#>  [1] 4a 6f 68 6e 20 43 68 61 6d 62 65 72 73
#>

tmp <- tempfile()

write_lines(rownames(mtcars), tmp)
#>  [1] "Mazda RX4"           "Mazda RX4 Wag"       "Datsun 710"
#>  [4] "Hornet 4 Drive"      "Hornet Sportabout"   "Valiant"
#>  [7] "Duster 360"          "Merc 240D"           "Merc 230"
#> [10] "Merc 280"            "Merc 280C"           "Merc 450SE"
#> [13] "Merc 450SL"          "Merc 450SLC"         "Cadillac Fleetwood"
#> [16] "Lincoln Continental" "Chrysler Imperial"   "Fiat 128"
#> [19] "Honda Civic"         "Toyota Corolla"      "Toyota Corona"
#> [22] "Dodge Challenger"    "AMC Javelin"         "Camaro Z28"
#> [25] "Pontiac Firebird"    "Fiat X1-9"           "Porsche 914-2"
#> [28] "Lotus Europa"        "Ford Pantera L"      "Ferrari Dino"
#> [31] "Maserati Bora"       "Volvo 142E"
#> [1] "Mazda RX4\nMazda RX4 Wag\nDatsun 710\nHornet 4 Drive\nHornet Sportabout\nValiant\nDuster 360\nMerc 240D\nMerc 230\nMerc 280\nMerc 280C\nMerc 450SE\nMerc 450SL\nMerc 450SLC\nCadillac Fleetwood\nLincoln Continental\nChrysler Imperial\nFiat 128\nHonda Civic\nToyota Corolla\nToyota Corona\nDodge Challenger\nAMC Javelin\nCamaro Z28\nPontiac Firebird\nFiat X1-9\nPorsche 914-2\nLotus Europa\nFord Pantera L\nFerrari Dino\nMaserati Bora\nVolvo 142E\n"

write_lines(airquality\$Ozone, tmp, na = "-1")
#>   [1] "41"  "36"  "12"  "18"  "-1"  "28"  "23"  "19"  "8"   "-1"  "7"
#>  [12] "16"  "11"  "14"  "18"  "14"  "34"  "6"   "30"  "11"  "1"   "11"
#>  [23] "4"   "32"  "-1"  "-1"  "-1"  "23"  "45"  "115" "37"  "-1"  "-1"
#>  [34] "-1"  "-1"  "-1"  "-1"  "29"  "-1"  "71"  "39"  "-1"  "-1"  "23"
#>  [45] "-1"  "-1"  "21"  "37"  "20"  "12"  "13"  "-1"  "-1"  "-1"  "-1"
#>  [56] "-1"  "-1"  "-1"  "-1"  "-1"  "-1"  "135" "49"  "32"  "-1"  "64"
#>  [67] "40"  "77"  "97"  "97"  "85"  "-1"  "10"  "27"  "-1"  "7"   "48"
#>  [78] "35"  "61"  "79"  "63"  "16"  "-1"  "-1"  "80"  "108" "20"  "52"
#>  [89] "82"  "50"  "64"  "59"  "39"  "9"   "16"  "78"  "35"  "66"  "122"
#> [100] "89"  "110" "-1"  "-1"  "44"  "28"  "65"  "-1"  "22"  "59"  "23"
#> [111] "31"  "44"  "21"  "9"   "-1"  "45"  "168" "73"  "-1"  "76"  "118"
#> [122] "84"  "85"  "96"  "78"  "73"  "91"  "47"  "32"  "20"  "23"  "21"
#> [133] "24"  "44"  "21"  "28"  "9"   "13"  "46"  "18"  "13"  "24"  "16"
#> [144] "13"  "23"  "36"  "7"   "14"  "30"  "-1"  "14"  "18"  "20"