Auto-detects the data format from the file extension or source pattern and
dispatches to the appropriate reader. All readers
return a tidylearn_data object, which is a
tibble subclass carrying metadata about the data
source.
Arguments
- source
A file path or
file://URL, a GitHub or Kaggle URL, ans3://orbigquery://URI, a database connection string, a directory path, or a character vector of multiple file paths. Other web URLs, and URLs with any other scheme such asftp://, are refused: download the file and read the local copy.- ...
Additional arguments passed to the format-specific reader.
- format
Optional explicit format override. One of
"csv","tsv","excel","parquet","json","rds","rdata","sqlite","postgres","mysql","bigquery","s3","github","kaggle". WhenNULL(default), the format is auto-detected from the file extension or source pattern. Note:.txtfiles default to CSV; useformat = "tsv"to override. A source with a URL scheme other thanfile://is read only with the format its scheme or host implies; to read an S3 object as a given file format, calltl_read_s3()with that format.- .quiet
Logical. If
TRUE, suppresses informational messages. Default isFALSE.
Value
A tidylearn_data object (a tibble subclass)
with attributes tl_source, tl_format, and
tl_timestamp.
Details
When source is a character vector of multiple paths, each file is read
and row-bound into a single result with a source_file column
giving each file's path below the deepest folder they share. When
source is a directory path, it is equivalent to calling
tl_read_dir(). When source is a local .zip file, it
is equivalent to calling tl_read_zip().
Examples
# The format is detected from the extension
csv <- tempfile(fileext = ".csv")
write.csv(mtcars, csv, row.names = FALSE)
tl_read(csv)
#> Reading csv data from: /tmp/Rtmp5pHJ7J/file1ab3363aa564.csv
#> Returned: 32 rows x 11 columns
#> -- tidylearn data ---------
#> Source: /tmp/Rtmp5pHJ7J/file1ab3363aa564.csv
#> Format: csv
#> Read at: 2026-10-09 07:12:06
#>
#> # A tibble: 32 × 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
#> # ℹ 22 more rows
# Several files are row-bound, with a source_file column naming each
jan <- tempfile(fileext = ".csv")
feb <- tempfile(fileext = ".csv")
write.csv(mtcars[1:16, ], jan, row.names = FALSE)
write.csv(mtcars[17:32, ], feb, row.names = FALSE)
both <- tl_read(c(jan, feb), .quiet = TRUE)
table(both$source_file)
#>
#> file1ab32d687d1.csv file1ab34d2e0af.csv
#> 16 16
# A .txt file is read as CSV unless told otherwise
txt <- tempfile(fileext = ".txt")
write.table(mtcars, txt, sep = "\t", row.names = FALSE)
tl_read(txt, format = "tsv", .quiet = TRUE)
#> -- tidylearn data ---------
#> Source: /tmp/Rtmp5pHJ7J/file1ab31d0940fd.txt
#> Format: tsv
#> Read at: 2026-10-09 07:12:06
#>
#> # A tibble: 32 × 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
#> # ℹ 22 more rows
unlink(c(csv, jan, feb, txt))