Scans a directory for files matching a pattern or format, reads each one,
and row-binds them into a single tidylearn_data object with a
source_file column identifying the origin of each row.
Arguments
- path
Path to a directory.
- pattern
Optional regex pattern to filter file names (e.g.,
"sales_.*\\.csv$"). IfNULL, files are filtered byformatinstead.- format
File format to read. If
NULLandpatternisNULL, all recognized data files are read. If specified, only files with matching extensions are read..txtfiles are read only when selected withpattern.- recursive
Logical. Should subdirectories be scanned? Default is
FALSE.- .quiet
Suppress messages. Default is
FALSE.- ...
Additional arguments passed to the format-specific reader.
Value
A tidylearn_data object with an additional
source_file column giving each row's file as a path below
path, such as "2024/sales.csv".
Examples
dir <- tempfile("sales_")
dir.create(file.path(dir, "2024"), recursive = TRUE)
write.csv(mtcars[1:16, ], file.path(dir, "jan.csv"), row.names = FALSE)
write.csv(mtcars[17:32, ], file.path(dir, "2024", "feb.csv"),
row.names = FALSE)
# Only the top level unless asked to recurse
tl_read_dir(dir, format = "csv")
#> Found 1 file(s) in /tmp/Rtmp5pHJ7J/sales_1ab35880b125
#> Reading 1 files...
#> Combined: 16 rows x 12 columns from 1 files
#> -- tidylearn data ---------
#> Source: 1 files
#> Format: csv
#> Read at: 2026-10-09 07:12:09
#>
#> # A tibble: 16 × 12
#> mpg cyl disp hp drat wt qsec vs am gear carb source_file
#> * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 jan.csv
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 jan.csv
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 jan.csv
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 jan.csv
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 jan.csv
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 jan.csv
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 jan.csv
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 jan.csv
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 jan.csv
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 jan.csv
#> 11 17.8 6 168. 123 3.92 3.44 18.9 1 0 4 4 jan.csv
#> 12 16.4 8 276. 180 3.07 4.07 17.4 0 0 3 3 jan.csv
#> 13 17.3 8 276. 180 3.07 3.73 17.6 0 0 3 3 jan.csv
#> 14 15.2 8 276. 180 3.07 3.78 18 0 0 3 3 jan.csv
#> 15 10.4 8 472 205 2.93 5.25 18.0 0 0 3 4 jan.csv
#> 16 10.4 8 460 215 3 5.42 17.8 0 0 3 4 jan.csv
# Files in subfolders are labelled by their path below dir
all_months <- tl_read_dir(dir, recursive = TRUE, .quiet = TRUE)
table(all_months$source_file)
#>
#> 2024/feb.csv jan.csv
#> 16 16
# Or select files by a regular expression
tl_read_dir(dir, pattern = "^jan", .quiet = TRUE)
#> -- tidylearn data ---------
#> Source: 1 files
#> Format: multi
#> Read at: 2026-10-09 07:12:09
#>
#> # A tibble: 16 × 12
#> mpg cyl disp hp drat wt qsec vs am gear carb source_file
#> * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 jan.csv
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 jan.csv
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 jan.csv
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 jan.csv
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 jan.csv
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 jan.csv
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 jan.csv
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 jan.csv
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 jan.csv
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 jan.csv
#> 11 17.8 6 168. 123 3.92 3.44 18.9 1 0 4 4 jan.csv
#> 12 16.4 8 276. 180 3.07 4.07 17.4 0 0 3 3 jan.csv
#> 13 17.3 8 276. 180 3.07 3.73 17.6 0 0 3 3 jan.csv
#> 14 15.2 8 276. 180 3.07 3.78 18 0 0 3 3 jan.csv
#> 15 10.4 8 472 205 2.93 5.25 18.0 0 0 3 4 jan.csv
#> 16 10.4 8 460 215 3 5.42 17.8 0 0 3 4 jan.csv
unlink(dir, recursive = TRUE)