For certain non-rectangular data formats, it can be useful to parse the data into a melted format where each row represents a single token.

melt_delim(file, delim, quote = "\"", escape_backslash = FALSE,
  escape_double = TRUE, locale = default_locale(), na = c("", "NA"),
  quoted_na = TRUE, comment = "", trim_ws = FALSE, skip = 0,
  n_max = Inf, progress = show_progress(), skip_empty_rows = FALSE)

melt_csv(file, locale = default_locale(), na = c("", "NA"),
  quoted_na = TRUE, quote = "\"", comment = "", trim_ws = TRUE,
  skip = 0, n_max = Inf, progress = show_progress(),
  skip_empty_rows = FALSE)

melt_csv2(file, locale = default_locale(), na = c("", "NA"),
  quoted_na = TRUE, quote = "\"", comment = "", trim_ws = TRUE,
  skip = 0, n_max = Inf, progress = show_progress(),
  skip_empty_rows = FALSE)

melt_tsv(file, locale = default_locale(), na = c("", "NA"),
  quoted_na = TRUE, quote = "\"", comment = "", trim_ws = TRUE,
  skip = 0, n_max = Inf, progress = show_progress(),
  skip_empty_rows = FALSE)

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. It must contain at least one new line to be recognised as data (instead of a path) or be a vector of greater than length 1.

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

delim

Single character used to separate fields within a record.

quote

Single character used to quote strings.

escape_backslash

Does the file use backslashes to escape special characters? This is more general than escape_double as backslashes can be used to escape the delimiter character, the quote character, or to add special characters like \n.

escape_double

Does the file escape quotes by doubling them? i.e. If this option is TRUE, the value """" represents a single quote, \".

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.

quoted_na

Should missing values inside quotes be treated as missing values (the default) or strings.

comment

A string used to identify comments. Any text after the comment characters will be silently ignored.

trim_ws

Should leading and trailing whitespace be trimmed from each field before parsing it?

skip

Number of lines to skip before reading data.

n_max

Maximum number of records to read.

progress

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.

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.

Value

A tibble() of four columns:

  • row, the row that the token comes from in the original file

  • col, the column that the token comes from in the original file

  • data_type, the data type of the token, e.g. "integer", "character", "date", guessed in a similar way to the guess_parser() function.

  • value, the token itself as a character string, unchanged from its representation in the original file.

If there are parsing problems, a warning tells you how many, and you can retrieve the details with problems().

Details

melt_csv() and melt_tsv() are special cases of the general melt_delim(). They're useful for reading the most common types of flat file data, comma separated values and tab separated values, respectively. melt_csv2() uses ; for the field separator and , for the decimal point. This is common in some European countries.

See also

read_delim() for the conventional way to read rectangular data from delimited files.

Examples

# Input sources ------------------------------------------------------------- # Read from a path melt_csv(readr_example("mtcars.csv"))
#> # A tibble: 363 x 4 #> row col data_type value #> <dbl> <dbl> <chr> <chr> #> 1 1 1 character mpg #> 2 1 2 character cyl #> 3 1 3 character disp #> 4 1 4 character hp #> 5 1 5 character drat #> 6 1 6 character wt #> 7 1 7 character qsec #> 8 1 8 character vs #> 9 1 9 character am #> 10 1 10 character gear #> # ... with 353 more rows
melt_csv(readr_example("mtcars.csv.zip"))
#> # A tibble: 363 x 4 #> row col data_type value #> <dbl> <dbl> <chr> <chr> #> 1 1 1 character mpg #> 2 1 2 character cyl #> 3 1 3 character disp #> 4 1 4 character hp #> 5 1 5 character drat #> 6 1 6 character wt #> 7 1 7 character qsec #> 8 1 8 character vs #> 9 1 9 character am #> 10 1 10 character gear #> # ... with 353 more rows
melt_csv(readr_example("mtcars.csv.bz2"))
#> # A tibble: 363 x 4 #> row col data_type value #> <dbl> <dbl> <chr> <chr> #> 1 1 1 character mpg #> 2 1 2 character cyl #> 3 1 3 character disp #> 4 1 4 character hp #> 5 1 5 character drat #> 6 1 6 character wt #> 7 1 7 character qsec #> 8 1 8 character vs #> 9 1 9 character am #> 10 1 10 character gear #> # ... with 353 more rows
# NOT RUN { melt_csv("https://github.com/tidyverse/readr/raw/master/inst/extdata/mtcars.csv") # }
# Or directly from a string (must contain a newline) melt_csv("x,y\n1,2\n3,4")
#> # A tibble: 6 x 4 #> row col data_type value #> <dbl> <dbl> <chr> <chr> #> 1 1 1 character x #> 2 1 2 character y #> 3 2 1 integer 1 #> 4 2 2 integer 2 #> 5 3 1 integer 3 #> 6 3 2 integer 4
# To import empty cells as 'empty' rather than `NA` melt_csv("x,y\n,NA,\"\",''", na = "NA")
#> # A tibble: 6 x 4 #> row col data_type value #> <dbl> <dbl> <chr> <chr> #> 1 1 1 character x #> 2 1 2 character y #> 3 2 1 empty "" #> 4 2 2 missing NA #> 5 2 3 empty "" #> 6 2 4 character ''
# File types ---------------------------------------------------------------- melt_csv("a,b\n1.0,2.0")
#> # A tibble: 4 x 4 #> row col data_type value #> <dbl> <dbl> <chr> <chr> #> 1 1 1 character a #> 2 1 2 character b #> 3 2 1 double 1.0 #> 4 2 2 double 2.0
melt_csv2("a;b\n1,0;2,0")
#> Using ',' as decimal and '.' as grouping mark. Use melt_delim() for more control.
#> # A tibble: 4 x 4 #> row col data_type value #> <dbl> <dbl> <chr> <chr> #> 1 1 1 character a #> 2 1 2 character b #> 3 2 1 double 1,0 #> 4 2 2 double 2,0
melt_tsv("a\tb\n1.0\t2.0")
#> # A tibble: 4 x 4 #> row col data_type value #> <dbl> <dbl> <chr> <chr> #> 1 1 1 character a #> 2 1 2 character b #> 3 2 1 double 1.0 #> 4 2 2 double 2.0
melt_delim("a|b\n1.0|2.0", delim = "|")
#> # A tibble: 4 x 4 #> row col data_type value #> <dbl> <dbl> <chr> <chr> #> 1 1 1 character a #> 2 1 2 character b #> 3 2 1 double 1.0 #> 4 2 2 double 2.0