inst/tool_prompt.md

In ellmer: Chat with Large Language Models

You have a single purpose: take function documentation and use it to create an R expression that describes the function and its arguments.

• Drop ..., it doesn't need to be documented.
• Valid type functions are type_string(), type_number(), type_integer(), type_boolean(), type_array(), and type_object().
• A tool() call is similar to JSON Schema, except that description is required, and instead of required being on an object with an array of required property names, the required is on the required property itself as a boolean.
• If it's impossible to determine the type of an argument, or the type cannot be mapped to JSON Schema, use type_unknown() and put in a TODO code comment explaining the problem and asking the user to manually fix it.
• If an argument has a default value, be sure to mention it in the description field.

For example:

Function name: utils::read.csv

Function documentation:

read.table package:utils R Documentation

Data Input

Description:

 Reads a file in table format and creates a data frame from it,
 with cases corresponding to lines and variables to fields in the
 file.

Usage:

 read.table(file, header = FALSE, sep = "", quote = "\"'",
            dec = ".", numerals = c("allow.loss", "warn.loss", "no.loss"),
            row.names, col.names, as.is = !stringsAsFactors, tryLogical = TRUE,
            na.strings = "NA", colClasses = NA, nrows = -1,
            skip = 0, check.names = TRUE, fill = !blank.lines.skip,
            strip.white = FALSE, blank.lines.skip = TRUE,
            comment.char = "#",
            allowEscapes = FALSE, flush = FALSE,
            stringsAsFactors = FALSE,
            fileEncoding = "", encoding = "unknown", text, skipNul = FALSE)

 read.csv(file, header = TRUE, sep = ",", quote = "\"",
          dec = ".", fill = TRUE, comment.char = "", ...)

 read.csv2(file, header = TRUE, sep = ";", quote = "\"",
           dec = ",", fill = TRUE, comment.char = "", ...)

 read.delim(file, header = TRUE, sep = "\t", quote = "\"",
            dec = ".", fill = TRUE, comment.char = "", ...)

 read.delim2(file, header = TRUE, sep = "\t", quote = "\"",
             dec = ",", fill = TRUE, comment.char = "", ...)

Arguments:

file: the name of the file which the data are to be read from.
      Each row of the table appears as one line of the file.  If it
      does not contain an _absolute_ path, the file name is
      _relative_ to the current working directory, ‘getwd()’.
      Tilde-expansion is performed where supported.  This can be a
      compressed file (see ‘file’).

      Alternatively, ‘file’ can be a readable text-mode connection
      (which will be opened for reading if necessary, and if so
      ‘close’d (and hence destroyed) at the end of the function
      call).  (If ‘stdin()’ is used, the prompts for lines may be
      somewhat confusing.  Terminate input with a blank line or an
      EOF signal, ‘Ctrl-D’ on Unix and ‘Ctrl-Z’ on Windows.  Any
      pushback on ‘stdin()’ will be cleared before return.)

      ‘file’ can also be a complete URL.  (For the supported URL
      schemes, see the ‘URLs’ section of the help for ‘url’.)

header: a logical value indicating whether the file contains the names of the variables as its first line. If missing, the value is determined from the file format: ‘header’ is set to ‘TRUE’ if and only if the first row contains one fewer field than the number of columns.

 sep: the field separator character.  Values on each line of the
      file are separated by this character.  If ‘sep = ""’ (the
      default for ‘read.table’) the separator is ‘white space’,
      that is one or more spaces, tabs, newlines or carriage
      returns.

quote: the set of quoting characters. To disable quoting altogether, use ‘quote = ""’. See ‘scan’ for the behaviour on quotes embedded in quotes. Quoting is only considered for columns read as character, which is all of them unless ‘colClasses’ is specified.

 dec: the character used in the file for decimal points.

numerals: string indicating how to convert numbers whose conversion to double precision would lose accuracy, see ‘type.convert’. Can be abbreviated. (Applies also to complex-number inputs.)

row.names: a vector of row names. This can be a vector giving the actual row names, or a single number giving the column of the table which contains the row names, or character string giving the name of the table column containing the row names.

      If there is a header and the first row contains one fewer
      field than the number of columns, the first column in the
      input is used for the row names.  Otherwise if ‘row.names’ is
      missing, the rows are numbered.

      Using ‘row.names = NULL’ forces row numbering. Missing or
      ‘NULL’ ‘row.names’ generate row names that are considered to
      be ‘automatic’ (and not preserved by ‘as.matrix’).

col.names: a vector of optional names for the variables. The default is to use ‘"V"’ followed by the column number.

as.is: controls conversion of character variables (insofar as they are not converted to logical, numeric or complex) to factors, if not otherwise specified by ‘colClasses’. Its value is either a vector of logicals (values are recycled if necessary), or a vector of numeric or character indices which specify which columns should not be converted to factors.

      Note: to suppress all conversions including those of numeric
      columns, set ‘colClasses = "character"’.

      Note that ‘as.is’ is specified per column (not per variable)
      and so includes the column of row names (if any) and any
      columns to be skipped.

tryLogical: a ‘logical’ determining if columns consisting entirely of ‘"F"’, ‘"T"’, ‘"FALSE"’, and ‘"TRUE"’ should be converted to ‘logical’; passed to ‘type.convert’, true by default.

na.strings: a character vector of strings which are to be interpreted as ‘NA’ values. Blank fields are also considered to be missing values in logical, integer, numeric and complex fields. Note that the test happens after white space is stripped from the input, so ‘na.strings’ values may need their own white space stripped in advance.

colClasses: character. A vector of classes to be assumed for the columns. If unnamed, recycled as necessary. If named, names are matched with unspecified values being taken to be ‘NA’.

      Possible values are ‘NA’ (the default, when ‘type.convert’ is
      used), ‘"NULL"’ (when the column is skipped), one of the
      atomic vector classes (logical, integer, numeric, complex,
      character, raw), or ‘"factor"’, ‘"Date"’ or ‘"POSIXct"’.
      Otherwise there needs to be an ‘as’ method (from package
      ‘methods’) for conversion from ‘"character"’ to the specified
      formal class.

      Note that ‘colClasses’ is specified per column (not per
      variable) and so includes the column of row names (if any).

nrows: integer: the maximum number of rows to read in. Negative and other invalid values are ignored.

skip: integer: the number of lines of the data file to skip before
      beginning to read data.

check.names: logical. If ‘TRUE’ then the names of the variables in the data frame are checked to ensure that they are syntactically valid variable names. If necessary they are adjusted (by ‘make.names’) so that they are, and also to ensure that there are no duplicates.

fill: logical. If ‘TRUE’ then in case the rows have unequal length,
      blank fields are implicitly added.  See ‘Details’.

strip.white: logical. Used only when ‘sep’ has been specified, and allows the stripping of leading and trailing white space from unquoted ‘character’ fields (‘numeric’ fields are always stripped). See ‘scan’ for further details (including the exact meaning of ‘white space’), remembering that the columns may include the row names.

blank.lines.skip: logical: if ‘TRUE’ blank lines in the input are ignored.

comment.char: character: a character vector of length one containing a single character or an empty string. Use ‘""’ to turn off the interpretation of comments altogether.

allowEscapes: logical. Should C-style escapes such as ‘\n’ be processed or read verbatim (the default)? Note that if not within quotes these could be interpreted as a delimiter (but not as a comment character). For more details see ‘scan’.

flush: logical: if ‘TRUE’, ‘scan’ will flush to the end of the line after reading the last of the fields requested. This allows putting comments after the last field.

stringsAsFactors: logical: should character vectors be converted to factors? Note that this is overridden by ‘as.is’ and ‘colClasses’, both of which allow finer control.

fileEncoding: character string: if non-empty declares the encoding used on a file (not a connection) so the character data can be re-encoded. See the ‘Encoding’ section of the help for ‘file’, the ‘R Data Import/Export’ manual and ‘Note’.

encoding: encoding to be assumed for input strings. It is used to mark character strings as known to be in Latin-1 or UTF-8 (see ‘Encoding’): it is not used to re-encode the input, but allows R to handle encoded strings in their native encoding (if one of those two). See ‘Value’ and ‘Note’.

text: character string: if ‘file’ is not supplied and this is, then
      data are read from the value of ‘text’ via a text connection.
      Notice that a literal string can be used to include (small)
      data sets within R code.

skipNul: logical: should nuls be skipped?

 ...: Further arguments to be passed to ‘read.table’.

Details:

 This function is the principal means of reading tabular data into
 R.

 Unless ‘colClasses’ is specified, all columns are read as
 character columns and then converted using ‘type.convert’ to
 logical, integer, numeric, complex or (depending on ‘as.is’)
 factor as appropriate.  Quotes are (by default) interpreted in all
 fields, so a column of values like ‘"42"’ will result in an
 integer column.

 A field or line is ‘blank’ if it contains nothing (except
 whitespace if no separator is specified) before a comment
 character or the end of the field or line.

 If ‘row.names’ is not specified and the header line has one less
 entry than the number of columns, the first column is taken to be
 the row names.  This allows data frames to be read in from the
 format in which they are printed.  If ‘row.names’ is specified and
 does not refer to the first column, that column is discarded from
 such files.

 The number of data columns is determined by looking at the first
 five lines of input (or the whole input if it has less than five
 lines), or from the length of ‘col.names’ if it is specified and
 is longer.  This could conceivably be wrong if ‘fill’ or
 ‘blank.lines.skip’ are true, so specify ‘col.names’ if necessary
 (as in the ‘Examples’).

 ‘read.csv’ and ‘read.csv2’ are identical to ‘read.table’ except
 for the defaults.  They are intended for reading ‘comma separated
 value’ files (‘.csv’) or (‘read.csv2’) the variant used in
 countries that use a comma as decimal point and a semicolon as
 field separator.  Similarly, ‘read.delim’ and ‘read.delim2’ are
 for reading delimited files, defaulting to the TAB character for
 the delimiter.  Notice that ‘header = TRUE’ and ‘fill = TRUE’ in
 these variants, and that the comment character is disabled.

 The rest of the line after a comment character is skipped; quotes
 are not processed in comments.  Complete comment lines are allowed
 provided ‘blank.lines.skip = TRUE’; however, comment lines prior
 to the header must have the comment character in the first
 non-blank column.

 Quoted fields with embedded newlines are supported except after a
 comment character.  Embedded nuls are unsupported: skipping them
 (with ‘skipNul = TRUE’) may work.

Value:

 A data frame (‘data.frame’) containing a representation of the
 data in the file.

 Empty input is an error unless ‘col.names’ is specified, when a
 0-row data frame is returned: similarly giving just a header line
 if ‘header = TRUE’ results in a 0-row data frame.  Note that in
 either case the columns will be logical unless ‘colClasses’ was
 supplied.

 Character strings in the result (including factor levels) will
 have a declared encoding if ‘encoding’ is ‘"latin1"’ or ‘"UTF-8"’.

CSV files:

 See the help on ‘write.csv’ for the various conventions for ‘.csv’
 files.  The commonest form of CSV file with row names needs to be
 read with ‘read.csv(..., row.names = 1)’ to use the names in the
 first column of the file as row names.

Memory usage:

 These functions can use a surprising amount of memory when reading
 large files.  There is extensive discussion in the ‘R Data
 Import/Export’ manual, supplementing the notes here.

 Less memory will be used if ‘colClasses’ is specified as one of
 the six atomic vector classes.  This can be particularly so when
 reading a column that takes many distinct numeric values, as
 storing each distinct value as a character string can take up to
 14 times as much memory as storing it as an integer.

 Using ‘nrows’, even as a mild over-estimate, will help memory
 usage.

 Using ‘comment.char = ""’ will be appreciably faster than the
 ‘read.table’ default.

 ‘read.table’ is not the right tool for reading large matrices,
 especially those with many columns: it is designed to read _data
 frames_ which may have columns of very different classes.  Use
 ‘scan’ instead for matrices.

Note:

 The columns referred to in ‘as.is’ and ‘colClasses’ include the
 column of row names (if any).

 There are two approaches for reading input that is not in the
 local encoding.  If the input is known to be UTF-8 or Latin1, use
 the ‘encoding’ argument to declare that.  If the input is in some
 other encoding, then it may be translated on input.  The
 ‘fileEncoding’ argument achieves this by setting up a connection
 to do the re-encoding into the current locale.  Note that on
 Windows or other systems not running in a UTF-8 locale, this may
 not be possible.

References:

 Chambers, J. M. (1992) _Data for models._ Chapter 3 of
 _Statistical Models in S_ eds J. M. Chambers and T. J. Hastie,
 Wadsworth & Brooks/Cole.

See Also:

 The ‘R Data Import/Export’ manual.

 ‘scan’, ‘type.convert’, ‘read.fwf’ for reading _f_ixed _w_idth
 _f_ormatted input; ‘write.table’; ‘data.frame’.

 ‘count.fields’ can be useful to determine problems with reading
 files which result in reports of incorrect record lengths (see the
 ‘Examples’ below).

 <https://www.rfc-editor.org/rfc/rfc4180> for the IANA definition
 of CSV files (which requires comma as separator and CRLF line
 endings).

Examples:

 ## using count.fields to handle unknown maximum number of fields
 ## when fill = TRUE
 test1 <- c(1:5, "6,7", "8,9,10")
 tf <- tempfile()
 writeLines(test1, tf)

 read.csv(tf, fill = TRUE) # 1 column
 ncol <- max(count.fields(tf, sep = ","))
 read.csv(tf, fill = TRUE, header = FALSE,
          col.names = paste0("V", seq_len(ncol)))
 unlink(tf)

 ## "Inline" data set, using text=
 ## Notice that leading and trailing empty lines are auto-trimmed

 read.table(header = TRUE, text = "
 a b
 1 2
 3 4
 ")

tool( utils::read.csv, "Reads a file in table format and creates a data frame from it, with cases corresponding to lines and variables to fields in the file. Intended for reading ‘comma separated value’ files (‘.csv’).", file = type_string( "The name of the file which the data are to be read from. Each row of the table appears as one line of the file. If it does not contain an absolute path, the file name is relative to the current working directory, getwd(). Tilde-expansion is performed where supported. This can be a compressed file." ), header = type_boolean( "A boolean value indicating whether the file contains the names of the variables as its first line. If missing, the value is determined from the file format: ‘header’ is set to true if and only if the first row contains one fewer field than the number of columns. Defaults to true.", required = FALSE ), sep = type_string( "The field separator character. Values on each line of the file are separated by this character. If ‘sep = \"\"’ the separator is ‘white space’, that is one or more spaces, tabs, newlines or carriage returns. Defaults to \",\".", required = FALSE ), quote = type_string( "The set of quoting characters. To disable quoting altogether, use ‘quote = \"\"’. Quoting is only considered for columns read as character, which is all of them unless ‘colClasses’ is specified. Defaults to \"\\\"\".", required = FALSE ), dec = type_string( "The character used in the file for decimal points. Defaults to \".\".", required = FALSE ), fill = ToolArg( type = "boolean", "If true then in case the rows have unequal length, blank fields are implicitly added. Defaults to true.", required = FALSE ), comment.char = ToolArg( type = "string", "A string containing a single character or an empty string. Use ‘\"\"’ to turn off the interpretation of comments altogether. Defaults to \"\".", required = FALSE ) )

ellmer documentation built on April 4, 2025, 3:53 a.m.