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.

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.

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).
# }