Description
read_csv() and read_tsv() are special cases of the more generalread_delim(). They're useful for reading the most common types offlat file data, comma separated values and tab separated values,respectively. read_csv2() uses ; for the field separator and , for thedecimal point. This format is common in some European countries.
Usage
read_delim( file, delim = NULL, quote = "\"", escape_backslash = FALSE, escape_double = TRUE, col_names = TRUE, col_types = NULL, col_select = NULL, id = NULL, locale = default_locale(), na = c("", "NA"), quoted_na = TRUE, comment = "", trim_ws = FALSE, skip = 0, n_max = Inf, guess_max = min(1000, n_max), name_repair = "unique", num_threads = readr_threads(), progress = show_progress(), show_col_types = should_show_types(), skip_empty_rows = TRUE, lazy = should_read_lazy())read_csv( file, col_names = TRUE, col_types = NULL, col_select = NULL, id = NULL, locale = default_locale(), na = c("", "NA"), quoted_na = TRUE, quote = "\"", comment = "", trim_ws = TRUE, skip = 0, n_max = Inf, guess_max = min(1000, n_max), name_repair = "unique", num_threads = readr_threads(), progress = show_progress(), show_col_types = should_show_types(), skip_empty_rows = TRUE, lazy = should_read_lazy())
read_csv2( file, col_names = TRUE, col_types = NULL, col_select = NULL, id = NULL, locale = default_locale(), na = c("", "NA"), quoted_na = TRUE, quote = "\"", comment = "", trim_ws = TRUE, skip = 0, n_max = Inf, guess_max = min(1000, n_max), progress = show_progress(), name_repair = "unique", num_threads = readr_threads(), show_col_types = should_show_types(), skip_empty_rows = TRUE, lazy = should_read_lazy())
read_tsv( file, col_names = TRUE, col_types = NULL, col_select = NULL, id = NULL, locale = default_locale(), na = c("", "NA"), quoted_na = TRUE, quote = "\"", comment = "", trim_ws = TRUE, skip = 0, n_max = Inf, guess_max = min(1000, n_max), progress = show_progress(), name_repair = "unique", num_threads = readr_threads(), show_col_types = should_show_types(), skip_empty_rows = TRUE, lazy = should_read_lazy())
Value
A tibble(). If there are parsing problems, a warning will alert you.You can retrieve the full details by calling problems() on your dataset.
Arguments
Either a path to a file, a connection, or literal data(either a single string or a raw vector). Files ending in Literal data is most useful for examples and tests. To be recognised asliteral data, the input must be either wrapped with Using a value of Single character used to separate fields within a record. Single character used to quote strings. Does the file use backslashes to escape specialcharacters? This is more general than Does the file escape quotes by doubling them?i.e. If this option is Either .gz, .bz2, .xz, or .zip willbe automatically uncompressed. Files starting with http://,https://, ftp://, or ftps:// will be automaticallydownloaded. Remote gz files can also be automatically downloaded anddecompressed.I(), be a stringcontaining at least one new line, or be a vector containing at least onestring with a new line.clipboard() will read from the system clipboard.escape_double as backslashescan be used to escape the delimiter character, the quote character, orto add special characters like \\n.TRUE, the value """" representsa single quote, \".TRUE, FALSE or a character vectorof column names.
If TRUE, the first row of the input will be used as the columnnames, and will not be included in the data frame. If FALSE, columnnames will be generated automatically: X1, X2, X3 etc.
If col_names is a character vector, the values will be used as thenames of the columns, and the first row of the input will be read intothe first row of the output data frame.
Missing (NA) column names will generate a warning, and be filledin with dummy names ...1, ...2 etc. Duplicate column nameswill generate a warning and be made unique, see name_repair to controlhow this is done.
One of NULL, a cols() specification, ora string. See vignette("readr") for more details.
If NULL, all column types will be inferred from guess_max rows of theinput, interspersed throughout the file. This is convenient (and fast),but not robust. If the guessed types are wrong, you'll need to increaseguess_max or supply the correct types yourself.
Column specifications created by list() or cols() must containone column specification for each column. If you only want to read asubset of the columns, use cols_only().
Alternatively, you can use a compact string representation where eachcharacter 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 amessage showing what readr guessed they were. To remove this message,set show_col_types = FALSE or set options(readr.show_col_types = FALSE).
Columns to include in the results. You can use the samemini-language as dplyr::select() to refer to the columns by name. Usec() to use more than one selection expression. Although thisusage is less common, col_select also accepts a numeric column index. See?tidyselect::language for full details on theselection language.
The name of a column in which to store the file path. This isuseful when reading multiple input files and there is data in the filepaths, such as the data collection date. If NULL (the default) no extracolumn is created.
The locale controls defaults that vary from place to place.The default locale is US-centric (like R), but you can uselocale() to create your own locale that controls things likethe default time zone, encoding, decimal mark, big mark, and day/monthnames.
Character vector of strings to interpret as missing values. Set thisoption to character() to indicate no missing values.
Should missing valuesinside quotes be treated as missing values (the default) or strings. Thisparameter is soft deprecated as of readr 2.0.0.
A string used to identify comments. Any text after thecomment characters will be silently ignored.
Should leading and trailing whitespace (ASCII spaces and tabs) be trimmed fromeach field before parsing it?
Number of lines to skip before reading data. If comment issupplied any commented lines are ignored after skipping.
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.
Handling of column names. The default behaviour is toensure column names are "unique". Various repair strategies aresupported:
"minimal": No name repair or checks, beyond basic existence of names."unique"(default value): Make sure names are unique and not empty."check_unique": No name repair, but check they areunique."unique_quiet": Repair with theuniquestrategy, quietly."universal": Make the namesuniqueand syntactic."universal_quiet": Repair with theuniversalstrategy, quietly.A function: Apply custom name repair (e.g.,
name_repair = make.namesfor names in the style of base R).A purrr-style anonymous function, see
rlang::as_function().
This argument is passed on as repair to vctrs::vec_as_names().See there for more details on these terms and the strategies usedto enforce them.
The number of processing threads to use for initialparsing and lazy reading of data. If your data contains newlines withinfields the parser should automatically detect this and fall back to usingone thread only. However if you know your file has newlines within quotedfields it is safest to set num_threads = 1 explicitly.
Display a progress bar? By default it will only displayin an interactive session and not while knitting a document. The automaticprogress bar can be disabled by setting option readr.show_progress toFALSE.
If FALSE, do not show the guessed column types. IfTRUE always show the column types, even if they are supplied. If NULL(the default) only show the column types if they are not explicitly suppliedby the col_types argument.
Should blank rows be ignored altogether? i.e. If thisoption is TRUE then blank rows will not be represented at all. If it isFALSE then they will be represented by NA values in all the columns.
Read values lazily? By default, this is FALSE, because thereare special considerations when reading a file lazily that have tripped upsome users. Specifically, things get tricky when reading and then writingback into the same file. But, in general, lazy reading (lazy = TRUE) hasmany benefits, especially for interactive use and when your downstream workonly involves a subset of the rows or columns.
Learn more in should_read_lazy() and in the documentation for thealtrep argument of vroom::vroom().
Examples
# Input sources -------------------------------------------------------------# Read from a pathread_csv(readr_example("mtcars.csv"))read_csv(readr_example("mtcars.csv.zip"))read_csv(readr_example("mtcars.csv.bz2"))if (FALSE) {# Including remote pathsread_csv("https://github.com/tidyverse/readr/raw/main/inst/extdata/mtcars.csv")}# Read from multiple file paths at oncecontinents <- c("africa", "americas", "asia", "europe", "oceania")filepaths <- vapply( paste0("mini-gapminder-", continents, ".csv"), FUN = readr_example, FUN.VALUE = character(1))read_csv(filepaths, id = "file")# Or directly from a string with `I()`read_csv(I("x,y\n1,2\n3,4"))# Column selection-----------------------------------------------------------# Pass column names or indexes directly to select themread_csv(readr_example("chickens.csv"), col_select = c(chicken, eggs_laid))read_csv(readr_example("chickens.csv"), col_select = c(1, 3:4))# Or use the selection helpersread_csv( readr_example("chickens.csv"), col_select = c(starts_with("c"), last_col()))# You can also rename specific columnsread_csv( readr_example("chickens.csv"), col_select = c(egg_yield = eggs_laid, everything()))# Column types --------------------------------------------------------------# By default, readr guesses the columns types, looking at `guess_max` rows.# You can override with a compact specification:read_csv(I("x,y\n1,2\n3,4"), col_types = "dc")# Or with a list of column types:read_csv(I("x,y\n1,2\n3,4"), col_types = list(col_double(), col_character()))# If there are parsing problems, you get a warning, and can extract# more details with problems()y <- read_csv(I("x\n1\n2\nb"), col_types = list(col_double()))yproblems(y)# Column names --------------------------------------------------------------# By default, readr duplicate name repair is noisyread_csv(I("x,x\n1,2\n3,4"))# Same default repair strategy, but quietread_csv(I("x,x\n1,2\n3,4"), name_repair = "unique_quiet")# There's also a global option that controls verbosity of name repairwithr::with_options( list(rlib_name_repair_verbosity = "quiet"), read_csv(I("x,x\n1,2\n3,4")))# Or use "minimal" to turn off name repairread_csv(I("x,x\n1,2\n3,4"), name_repair = "minimal")# File types ----------------------------------------------------------------read_csv(I("a,b\n1.0,2.0"))read_csv2(I("a;b\n1,0;2,0"))read_tsv(I("a\tb\n1.0\t2.0"))read_delim(I("a|b\n1.0|2.0"), delim = "|")Run the code above in your browser using DataLab
