Report how a CSV file would be read

Runs the first of read_csv()'s two passes and returns what it learned — the header verdict, the shape, the column names and the inferred column types — without building any columns. Use it when you want to see the decisions before committing to them, or to pin them down so a later run cannot drift:

Usage

sniff_csv(file = NULL, delimiter = ",", na = c("", "NA"), text = NULL)

Arguments

file

Path to a local file, as a single string. ~ is expanded. Supply this or text, not both.

delimiter

The field delimiter, as a single ASCII character. Defaults to ",". Not detected — zucsv never guesses the delimiter.

na

Character vector of cell values to read as missing, or NULL to disable missing-value matching. Affects the inferred types, so pass here whatever you will pass to read_csv().

text

The CSV itself, as a character vector, instead of a path, with the same meaning as in read_csv().

Value

A list with five elements:

header

TRUE if the first record is names, FALSE if it is data, by the rule documented under header in read_csv().

ncol

Number of columns.

nrow

Number of data rows — exact, not an estimate, because the pass counts every record.

col_names

The names the file would produce: the first record when header is TRUE, otherwise V1, V2, ...

col_types

Inferred type of each column, suitable for passing straight back as col_types.

An empty file gives header = FALSE, zero columns and zero rows.

Details

s <- sniff_csv(path)
read_csv(path, header = s$header, col_types = s$col_types)

That second call reads the file strictly: nothing about it is inferred, so it gives the same answer next month even if the data changes shape.

Sniffing reads the whole file and applies exactly the validation read_csv() applies, so a ragged row, an embedded NUL or invalid UTF-8 is an error here too. If sniff_csv() succeeds, read_csv() on the same file will not fail on content.

It costs one pass rather than two, but that pass is the expensive one: do not call it before every read as a matter of course. Its purpose is to let you stop inferring, not to infer twice.

See also

read_csv()

Examples

path <- tempfile(fileext = ".csv")
writeLines(c("id,name,score", "1,Ada,9.5", "2,Grace,8"), path)

sniff_csv(path)
#> $header
#> [1] TRUE
#> 
#> $ncol
#> [1] 3
#> 
#> $nrow
#> [1] 2
#> 
#> $col_names
#> [1] "id"    "name"  "score"
#> 
#> $col_types
#> [1] "integer"   "character" "double"   
#> 

# Pin the decisions, then read without inferring anything
s <- sniff_csv(path)
read_csv(path, header = s$header, col_types = s$col_types)
#>   id  name score
#> 1  1   Ada   9.5
#> 2  2 Grace   8.0

unlink(path)