Read whitespace-separated columns into a tibbleSource:
read_table() is designed to read the type of textual
data where each column is separated by one (or more) columns of space.
read_table() is like
read.table(), it allows any number of whitespace
characters between columns, and the lines can be of different lengths.
spec_table() returns the column specifications rather than a data frame.
Either a path to a file, a connection, or literal data (either a single string or a raw vector).
Files ending in
.zipwill be automatically uncompressed. Files starting with
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.
FALSEor a character vector of column names.
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.
col_namesis 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.
NA) column names will generate a warning, and be filled in with dummy names
...2etc. Duplicate column names will generate a warning and be made unique, see
name_repairto control how this is done.
NULL, all column types will be inferred from
guess_maxrows of the input, interspersed throughout the file. This is convenient (and fast), but not robust. If the guessed types are wrong, you'll need to increase
guess_maxor supply the correct types yourself.
Alternatively, you can use a compact string representation where each character represents one column:
c = character
i = integer
n = number
d = double
l = logical
f = factor
D = date
T = date time
t = time
? = guess
_ or - = skip
By default, reading a file without a column specification will print a message showing what
readrguessed they were. To remove this message, set
show_col_types = FALSEor set
options(readr.show_col_types = FALSE).
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 interpret as missing values. Set this option to
character()to indicate no missing values.
Number of lines to skip before reading data.
Maximum number of lines to read.
Maximum number of lines to use for guessing column types. Will never use more than the number of lines read. See
vignette("column-types", package = "readr")for more details.
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
A string used to identify comments. Any text after the comment characters will be silently ignored.
FALSE, do not show the guessed column types. If
TRUEalways show the column types, even if they are supplied. If
NULL(the default) only show the column types if they are not explicitly supplied by the
Should blank rows be ignored altogether? i.e. If this option is
TRUEthen blank rows will not be represented at all. If it is
FALSEthen they will be represented by
NAvalues in all the columns.
ws <- readr_example("whitespace-sample.txt") writeLines(read_lines(ws)) #> first last state phone #> John Smith WA 418-Y11-4111 #> Mary Hartford CA 319-Z19-4341 #> Evan Nolan IL 219-532-c301 read_table(ws) #> #> ── Column specification ────────────────────────────────────────────────── #> cols( #> first = col_character(), #> last = col_character(), #> state = col_character(), #> phone = col_character() #> ) #> # A tibble: 3 × 4 #> first last state phone #> <chr> <chr> <chr> <chr> #> 1 John Smith WA 418-Y11-4111 #> 2 Mary Hartford CA 319-Z19-4341 #> 3 Evan Nolan IL 219-532-c301