validateR

validateR is a lightweight toolkit for checking data frames for common data quality issues: missing values, outliers, duplicate rows, numeric-like inconsistencies, and (optionally) date-like inconsistencies. Results are returned as a structured report object with print and plot methods, so you can quickly see what’s wrong with your data before you start analyzing it.

Installation

You can install the development version of validateR from GitHub with:

# pak::pak("UgyenNorbu/validateR")
install.packages("validateR", repos = c('https://ugyennorbu.r-universe.dev', 'https://cloud.r-project.org'))

Example

The main entry point is validate_df(), which runs several checks at once and returns a single report:

library(validateR)

messy_df <- data.frame(
  age = c(25, 30, NA, 45, 22, 31, 29, 200),
  income = c("50000", "62000", "N/A", "48000",
             "71000", "55000", "60000", "48000"),
  city = c("Thimphu", "Paro", "Thimphu", "Paro",
           "Thimphu", "Paro", "Punakha", "Paro"),
  stringsAsFactors = FALSE
)
messy_df <- rbind(messy_df, messy_df[1, ], messy_df[4, ])

report <- validate_df(messy_df)
report
#> < validation_report for 'messy_df' >
#> 10 rows, 3 columns
#> Checked: 2026-08-11 14:36:44 
#> 
#> Missing values:          1  column(s) affected
#> Outliers:                1  column(s) affected
#> Duplicates:              2  group(s) affected
#> Numeric-like issues:     1  column(s) affected
#> Date-like issues:        NOT checked 
#> 
#> For details, inspect report$results$missing, $outliers, $duplicates, $numeric_like, $date_like

You can drill into the full detail behind any of the checks:

report$results$missing
#>   column_name n_missing pct_missing
#> 1         age         1          10
#> 2      income         0           0
#> 3        city         0           0
report$results$numeric_like
#> $income
#> $income$expected_type
#> [1] "numeric"
#> 
#> $income$proportion_convertible
#> [1] 90
#> 
#> $income$bad_rows
#> [1] 3

And visualize missing values and outliers per column:

Checking date-like columns

validate_df() can also check whether character columns contain values that look like dates in a format you specify. This check is optional, since date formats are ambiguous (for example, "03/04/2023" could mean March 4th or April 3rd, depending on convention) — so rather than guessing, you tell it what format to expect for each column you want checked.

dated_df <- data.frame(
  signup_date = c("2023-01-15", "2023-02-20", "not a date",
                  "2023-03-10", "2023-04-01"),
  stringsAsFactors = FALSE
)

report_with_dates <- validate_df(
  dated_df,
  date_columns = c(signup_date = "%Y-%m-%d")
)
report_with_dates
#> < validation_report for 'dated_df' >
#> 5 rows, 1 columns
#> Checked: 2026-08-11 14:36:44 
#> 
#> Missing values:          0  column(s) affected
#> Outliers:                0  column(s) affected
#> Duplicates:              0  group(s) affected
#> Numeric-like issues:     0  column(s) affected
#> Date-like issues:        1  column(s) affected 
#> 
#> For details, inspect report$results$missing, $outliers, $duplicates, $numeric_like, $date_like

If date_columns isn’t supplied, validate_df() skips date checking entirely, and the report notes that it wasn’t checked rather than implying the column was fine:

report_without_dates <- validate_df(dated_df)
report_without_dates
#> < validation_report for 'dated_df' >
#> 5 rows, 1 columns
#> Checked: 2026-08-11 14:36:44 
#> 
#> Missing values:          0  column(s) affected
#> Outliers:                0  column(s) affected
#> Duplicates:              0  group(s) affected
#> Numeric-like issues:     0  column(s) affected
#> Date-like issues:        NOT checked 
#> 
#> For details, inspect report$results$missing, $outliers, $duplicates, $numeric_like, $date_like

Individual checks

Each check is also available on its own, if you only need one:

check_duplicates(messy_df)
#> [[1]]
#> [1] 1 9
#> 
#> [[2]]
#> [1]  4 10