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Write a cleaned data frame to a CSV file

Source: R/write-clean-csv.R
write_clean_csv.Rd

write_clean_csv() writes a data frame to a CSV file using readr::write_csv() and emits a cli confirmation message reporting the number of rows and columns written. It is the natural counterpart to read_clean_csv(), reinforcing the convention that data-raw/ holds original inputs and data/ holds cleaned, analysis-ready outputs.

Usage

write_clean_csv(data, path, overwrite = FALSE, ...)

Arguments

data

A data frame or tibble to write.

path

A character string with the path to the output CSV file. A relative path is written under the project root (see the Paths section), and its folder is created if it does not exist.

overwrite

Logical. If FALSE (the default), errors if the file already exists. Set to TRUE to overwrite an existing file.

...

Additional arguments passed to readr::write_csv(), such as append, col_names, or quote.

Value

Invisibly returns path, resolved from the project root when it was relative.

Details

If column names are not already clean, write_clean_csv() applies janitor::clean_names() before writing and emits a warning listing the affected columns.

Paths

A relative path is read from the project root, not from the working directory. So save_output(fit, "output/fit.rds", saveRDS) writes into the project's own output/ folder whether you call it from the project root, from a document under reports/, or from a job on an HTCondor execute node. You never need to wrap a path in here::here(). Doing so still works, because here::here() returns an absolute path and absolute paths are used as given.

The project root is found afresh on every call, in this order:

  1. The job's scratch directory, when _CONDOR_SCRATCH_DIR names one. HTCondor sets that variable itself, inside every job.

  2. The nearest folder above the working directory that carries a project marker: the .here file init_project() writes, an .Rproj file, a package DESCRIPTION, or a version control folder such as .git.

  3. The working directory, when neither of the above applies.

Absolute paths (including those starting with ~, or a drive letter on Windows) and URLs such as https:// or s3:// are used as given. .. and ./ are not interpreted, so "../data/x.csv" means the folder above the root; write an absolute path to leave the project. A bare "." means the root itself.

Functions that read are forgiving about older code. A relative path that does not exist at the project root but does exist relative to the working directory is read from there, and a message says so. Functions that write never guess: a relative path is always written under the root.

A few arguments are deliberately left alone:

  • init_project() reads path, config and use_rprofile from the working directory, because the project does not exist yet and so has no root.

  • Under Rscript, resolve_input_path() reads the command line argument as given. Run Rscript from the project root or pass an absolute path.

  • Arguments passed on through ... to another function reach it untouched.

  • Where a default is "beside the input" (qmd_to_r() and generate_kb_xml()), the output follows wherever the input was read from.

Two limits are worth knowing. A project nested inside another project is not supported: the nearest marker wins, which can surprise you when you work from the outer one. And on an execute node ~ and $HOME cannot be trusted ($HOME can be the submit node's path), so prefer relative paths in job scripts.

Examples

# \donttest{
sample_path <- system.file("templates", "sample.csv", package = "toolero")
data <- read_clean_csv(sample_path)

# Write to a temp file
out <- tempfile(fileext = ".csv")
write_clean_csv(data, out)
#> ✔ Wrote 344 rows and 8 columns to /tmp/RtmpVdwYt8/file1a89582606d5.csv.

# Overwrite an existing file
write_clean_csv(data, out, overwrite = TRUE)
#> ✔ Wrote 344 rows and 8 columns to /tmp/RtmpVdwYt8/file1a89582606d5.csv.

# Dirty names are cleaned automatically with a warning
dirty <- data.frame("First Name" = "Jane", "Last Name" = "Doe",
                    check.names = FALSE)
write_clean_csv(dirty, tempfile(fileext = ".csv"))
#> Warning: Column names were not clean -- applying `janitor::clean_names()` before
#> writing. Affected columns: "First Name" and "Last Name"
#> ✔ Wrote 1 row and 2 columns to /tmp/RtmpVdwYt8/file1a894eb6b11.csv.
# }