options(readr.num_columns) can be used to modify this (a value of 0
turns off printing).
spec_delim(file, delim, quote = "\"", escape_backslash = FALSE, escape_double = TRUE, col_names = TRUE, col_types = NULL, locale = default_locale(), na = c("", "NA"), quoted_na = TRUE, comment = "", trim_ws = FALSE, skip = 0, n_max = 0, guess_max = 1000, progress = interactive())
spec_csv(file, col_names = TRUE, col_types = NULL, locale = default_locale(), na = c("", "NA"), quoted_na = TRUE, comment = "", trim_ws = TRUE, skip = 0, n_max = 0, guess_max = 1000, progress = interactive())
spec_csv2(file, col_names = TRUE, col_types = NULL, locale = default_locale(), na = c("", "NA"), quoted_na = TRUE, comment = "", trim_ws = TRUE, skip = 0, n_max = 0, guess_max = 1000, progress = interactive())
spec_tsv(file, col_names = TRUE, col_types = NULL, locale = default_locale(), na = c("", "NA"), quoted_na = TRUE, comment = "", trim_ws = TRUE, skip = 0, n_max = 0, guess_max = 1000, progress = interactive()) 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 &
decompressed.
Literal data is most useful for examples and tests. It must contain at least one new line to be recognised as data (instead of a path).
escape_double as backslashes
can be used to escape the delimeter character, the quote characer, or
to add special characters like \n.TRUE, the value """" represents
a single quote, \".TRUE, FALSE or a character vector
of column names. If TRUE, the first row of the input will be used as the column
names, and will not be included in the data frame. If FALSE, column
names will be generated automatically: X1, X2, X3 etc.
If col_names is a character vector, the values will be used as the
names of the columns, and the first row of the input will be read into
the first row of the output data frame.
Missing (NA) column names will generate a warning, and be filled
in with dummy names X1, X2 etc. Duplicate column names
will generate a warning and be made unique with a numeric prefix.
NULL, a cols specification, or
a string. See vignette("column-types") for more details. If NULL, all column types will be imputed from the first 1000 rows
on the input. This is convenient (and fast), but not robust. If the
imputation fails, you'll need to supply the correct types yourself.
If a column specification created by cols, it must contain
one column specification for each column. If you only want to read a
subset of the columns, use cols_only.
Alternatively, you can use a compact string representation where each
character represents one column:
c = character, i = integer, n = number, d = double,
l = logical, D = date, T = date time, t = time, ? = guess, or
_/- to skip the column.
locale to create your own locale that controls things like
the default time zone, encoding, decimal mark, big mark, and day/month
names.character() to indicate no missing values.col_spec generated for the file.
# Input sources -------------------------------------------------------------
# Retrieve specs from a path
spec_csv(system.file("extdata/mtcars.csv", package = "readr"))
spec_csv(system.file("extdata/mtcars.csv.zip", package = "readr"))
# Or directly from a string (must contain a newline)
spec_csv("x,y\n1,2\n3,4")
# Column types --------------------------------------------------------------
# By default, readr guess the columns types, looking at the first 1000 rows.
# You can specify the number of rows used with guess_max.
spec_csv(system.file("extdata/mtcars.csv", package = "readr"), guess_max = 20)
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