Read all .csv files in a directory — read_all

Read all .csv files in a directory, with labels if specified.

Usage

read_all_csv(
  path,
  ...,
  labels_from = NULL,
  format_file = NULL,
  subdirectories = FALSE,
  read_fun = "guess",
  datetime_extraction = "guess",
  verbose = getOption("edc_read_verbose", 1),
  clean_names_fun = NULL
)

Arguments

path: [character(1)]
path to the directory containing .csv files.
...: unused
labels_from: [character(1)]
path to the file containing the labels. See section "Labels file" below.
format_file: [character(1)]
the path to the file that should be used to apply formats. See section "Format file" below. Use NULL to not apply formats.
subdirectories: [logical(1)]
whether to read subdirectories
read_fun: [function]
if "guess" doesn't work properly, a function to read the files in path, e.g. read.csv, read.csv2,...
datetime_extraction: [POSIXt(1)]
the datetime of the data extraction. Default to the most common date of last modification in path.
verbose: [numeric(1)]
one of c(0, 1, 2). The higher, the more information will be printed.
clean_names_fun: use edc_clean_names() instead.

Value

a list containing one dataframe for each .csv file in the folder, the extraction date (datetime_extraction), and a summary of all imported tables (.lookup).

Labels file

labels_from should contain the information about column labels. It should be a data file (.csv) containing 2 columns: one for the column name and the other for its associated label. Use options(edc_col_name="xxx", edc_col_label="xxx") to specify the names of the columns.

Format file

format_file should contain the information about SAS formats. It can be either:

a procformat.sas file, containing the whole PROC FORMAT
or a data file (.csv or .sas7bdat) containing 3 columns:
- FMTNAME the SAS format name (repeated)
- START the variable level
- LABEL the label associated to the level
You can get this datafile from SAS using PROC FORMAT with option CNTLOUT. Otherwise, you can use options(edc_var_format_name="xxx", edc_var_level="xxx", edc_var_label="xxx") to specify different column names.

Examples

# Create a directory with multiple csv files and a label lookup.
path = paste0(tempdir(), "/read_all_csv")
dir.create(paste0(path, "/subdir"), recursive=TRUE)
write.csv(iris, paste0(path, "/iris.csv"))
write.csv(mtcars, paste0(path, "/mtcars.csv"))
write.csv(mtcars, paste0(path, "/subdir/mtcars.csv"))
write.csv(airquality, paste0(path, "/airquality.csv"))
labs = c(iris, mtcars, airquality) %>% names()
write.csv(data.frame(name=labs, label=toupper(labs)), paste0(path, "/labels.csv"))


db = read_all_csv(path, labels_from="labels.csv", subdirectories=TRUE) %>% 
  set_project_name("My great project")
#> Warning: Option "edc_lookup" has been overwritten.
db
#> ── EDCimport database ──────────────────────────────────────────────────────────
#> Contains 4 tables: `airquality`, `iris`, `mtcars`, and `subdir_mtcars`
#> ℹ Use `EDCimport::load_database(db)` to load the tables in the global
#>   environment.
#> ℹ Use `EDCimport::edc_lookup()` to see the summary table.
edc_lookup()
#> ── Lookup table - My great project (extraction of 2025-10-25) - EDCimport v0.6.0
#>   dataset        nrow  ncol  n_id rows_per_id crfname
#>   <chr>         <dbl> <dbl> <int>       <dbl> <chr>  
#> 1 airquality      153     7     0          NA NA     
#> 2 iris            150     6     0          NA NA     
#> 3 mtcars           32    12     0          NA NA     
#> 4 subdir_mtcars    32    12     0          NA NA

Read all `.csv` files in a directory