Read and clean a CSV file

read_clean_csv() reads a CSV file, standardizes column names, optionally handles missing values, and optionally prints an ingest summary. It combines readr::read_csv(), janitor::clean_names(), and tidyr::drop_na() into a single, reproducibility-friendly step.

Usage

read_clean_csv(
  path,
  na = c("", "NA"),
  drop_na = FALSE,
  summary = FALSE,
  verbose = FALSE,
  ...
)

Arguments

path

A character string with the path to the CSV file. A relative path is read from the project root (see the Paths section).

na

A character vector of strings to treat as missing values. Passed directly to readr::read_csv(). Defaults to c("", "NA"), which matches readr's own default behavior.

drop_na

Logical or character vector. If FALSE (the default), no rows are dropped. If TRUE, drops all rows containing any missing value. If a character vector of column names, drops only rows with missing values in those columns. Always emits a cli message reporting how many rows were dropped and how many remain.

summary

Logical. If TRUE, prints a brief ingest summary after reading and cleaning: row and column counts, number of column names cleaned, and missing value totals. Reflects the final state of the tibble after any drop_na action. Defaults to FALSE.

verbose

Logical. If TRUE, displays column type messages from readr::read_csv(). Defaults to FALSE.

...

Additional arguments passed to readr::read_csv(), such as col_types, skip, or locale.

Value

A tibble with cleaned column names.

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")

# Basic usage
data <- read_clean_csv(sample_path)

# Explicit missing-value codes
data <- read_clean_csv(sample_path, na = c("", "NA", "N/A", ".", "-999"))

# Drop rows missing in any column
data <- read_clean_csv(sample_path, drop_na = TRUE)
#> Dropped 11 rows with missing values across all columns -- 333 rows remaining.

# Drop rows missing in specific columns
data <- read_clean_csv(sample_path, drop_na = c("bill_length_mm", "sex"))
#> Dropped 11 rows with missing values in columns: "bill_length_mm" and "sex" --
#> 333 rows remaining.

# Print ingest summary
data <- read_clean_csv(sample_path, summary = TRUE)
#> ✔ Read 344 rows and 8 columns.
#> ℹ 0 column names cleaned.
#> ℹ 5 columns contain missing values (19 total missing values).

# Combine arguments
data <- read_clean_csv(
  sample_path,
  na      = c("", "NA", "N/A", "."),
  drop_na = TRUE,
  summary = TRUE
)
#> Dropped 11 rows with missing values across all columns -- 333 rows remaining.
#> ✔ Read 333 rows and 8 columns.
#> ℹ 0 column names cleaned.
#> ℹ 0 columns contain missing values (0 total missing values).
# }