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_chunked(file, callback, chunk_size = 10000, delim,
  quote = "\"", escape_backslash = FALSE, escape_double = TRUE,
  locale = default_locale(), na = c("", "NA"), quoted_na = TRUE,
  comment = "", trim_ws = FALSE, skip = 0,
  progress = show_progress(), skip_empty_rows = FALSE)

melt_csv_chunked(file, callback, chunk_size = 10000,
  locale = default_locale(), na = c("", "NA"), quoted_na = TRUE,
  quote = "\"", comment = "", trim_ws = TRUE, skip = 0,
  progress = show_progress(), skip_empty_rows = FALSE)

melt_csv2_chunked(file, callback, chunk_size = 10000,
  locale = default_locale(), na = c("", "NA"), quoted_na = TRUE,
  quote = "\"", comment = "", trim_ws = TRUE, skip = 0,
  progress = show_progress(), skip_empty_rows = FALSE)

melt_tsv_chunked(file, callback, chunk_size = 10000,
  locale = default_locale(), na = c("", "NA"), quoted_na = TRUE,
  quote = "\"", comment = "", trim_ws = TRUE, skip = 0,
  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.

callback

A callback function to call on each chunk

chunk_size

The number of rows to include in each chunk

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.

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.

Details

melt_delim_chunked() and the specialisations melt_csv_chunked(), melt_csv2_chunked() and melt_tsv_chunked() read files by a chunk of rows at a time, executing a given function on one chunk before reading the next.

See also

Examples

# Cars with 3 gears f <- function(x, pos) subset(x, data_type == "integer") melt_csv_chunked(readr_example("mtcars.csv"), DataFrameCallback$new(f), chunk_size = 5)
#> # A tibble: 218 x 4 #> row col data_type value #> <dbl> <dbl> <chr> <chr> #> 1 2 1 integer 21 #> 2 2 2 integer 6 #> 3 2 3 integer 160 #> 4 2 4 integer 110 #> 5 2 8 integer 0 #> 6 2 9 integer 1 #> 7 2 10 integer 4 #> 8 2 11 integer 4 #> 9 3 1 integer 21 #> 10 3 2 integer 6 #> # ... with 208 more rows