read_table() and read_table2() are designed to read the type of textual
data where each column is #' separate by one (or more) columns of space.
read_table2() is like read.table(), it allows any number of whitespace
characters between columns, and the lines can be of different lengths.
read_table() is more strict, each line must be the same length,
and each field is in the same position in every line. It first finds empty columns and then
parses like a fixed width file.
spec_table() and spec_table2() return
the column specifications rather than a data frame.
read_table(file, col_names = TRUE, col_types = NULL,
locale = default_locale(), na = "NA", skip = 0, n_max = Inf,
guess_max = min(n_max, 1000), progress = show_progress(), comment = "")read_table2(file, col_names = TRUE, col_types = NULL,
locale = default_locale(), na = "NA", skip = 0, n_max = Inf,
guess_max = min(n_max, 1000), progress = show_progress(), comment = "")
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. It must contain at least one new line to be recognised as data (instead of a path).
Either 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.
One of 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.
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.
Character vector of strings to use for missing values. Set this
option to character() to indicate no missing values.
Number of lines to skip before reading data.
Maximum number of records to read.
Maximum number of records to use for guessing column types.
Display a progress bar? By default it will only display
in an interactive session and not while knitting a document. The display
is updated every 50,000 values and will only display if estimated reading
time is 5 seconds or more. The automatic progress bar can be disabled by
setting option readr.show_progress to FALSE.
A string used to identify comments. Any text after the comment characters will be silently ignored.
read_fwf() to read fixed width files where each column
is not separated by whitespace. read_fwf() is also useful for reading
tabular data with non-standard formatting.
# NOT RUN {
# One corner from http://www.masseyratings.com/cf/compare.htm
massey <- readr_example("massey-rating.txt")
cat(read_file(massey))
read_table(massey)
# Sample of 1978 fuel economy data from
# http://www.fueleconomy.gov/feg/epadata/78data.zip
epa <- readr_example("epa78.txt")
cat(read_file(epa))
read_table(epa, col_names = FALSE)
# }
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