# Specification for reading a record from a text file with delimited values

Specification for reading a record from a text file with delimited values

delim_record_spec(example_file, delim = ",", skip = 0, names = NULL,
types = NULL, defaults = NULL)

csv_record_spec(example_file, skip = 0, names = NULL, types = NULL,
defaults = NULL)

tsv_record_spec(example_file, skip = 0, names = NULL, types = NULL,
defaults = NULL)

## Arguments

 example_file File that provides an example of the records to be read. If you don't explicitly specify names and types (or defaults) then this file will be read to generate default values. delim Character delimiter to separate fields in a record (defaults to ",") skip Number of lines to skip before reading data. Note that if names is explicitly provided and there are column names witin the file then skip should be set to 1 to ensure that the column names are bypassed. names Character vector with column names (or NULL to automatically detect the column names from the first row of example_file). If names is 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 datset. Note that if the underlying text file also includes column names in it's first row, this row should be skipped explicitly with skip = 1. If NULL, the first row of the example_file will be used as the column names, and will be skipped when reading the dataset. types Column types. If NULL and defaults is specified then types will be imputed from the defaults. Otherwise, all column types will be imputed from the first 1000 rows of the example_file. This is convenient (and fast), but not robust. If the imputation fails, you'll need to supply the correct types yourself. Types can be explicitliy specified in a character vector as "integer", "double", and "character" (e.g. col_types = c("double", "double", "integer"). Alternatively, you can use a compact string representation where each character represents one column: c = character, i = integer, d = double (e.g. types = ddi). defaults List of default values which are used when data is missing from a record (e.g. list(0, 0, 0L). If NULL then defaults will be automatically provided based on types (0 for numeric columns and ""` for character columns).