datefixR standardizes dates in different formats or with missing data: for example dates which have been provided from free text web forms.
There are many different formats dates are commonly represented with: the order of day, month, or year can differ, different separators (“-”, “/”, “.”, or whitespace) can be used, months can be numerical, names, or abbreviations and year given as two digits or four.
datefixR takes dates in all these different formats and converts them to R’s built-in date class. If
datefixR cannot standardize a date, such as because it is too malformed, then the user is told which date cannot be standardized and the corresponding ID for the row.
datefixR also allows the imputation of missing days and months with user-controlled behavior.
datefixR also supports dates provided in different languages and provides translated warning and error messages. The following languages are currently supported:
- Français (French)
- Deutsch (German)
- español (Spanish)
- Pусский (Russian)
Not familiar with R or want to quickly try out
datefixR? Check out the shiny app here.
datefixR is now available on CRAN.
The most up-to-date (hopefully) stable version of
datefixR can be installed via r-universe
# Enable universe(s) by ropensci options(repos = c( ropensci = "https://ropensci.r-universe.dev", CRAN = "https://cloud.r-project.org" )) install.packages("datefixR")
If you wish to live on the cutting edge of
datefixR development, then the development version can be installed via
datefixR has a “Getting Started” vignette which describes how to use this package in more detail than this page. View the vignette by either calling
or visiting the vignette on the package website
datefixR is most commonly used to standardize columns of date data in a data frame or tibble. For this demonstration, we will use an example toy dataset provided alongside the package,
|1||02 05 92||2015|
|6||le 3 mars 2013||22.07.1977|
|7||7 de septiembre de 2014||13821|
We can standardize these date columns by using the
fix_date_df() function and passing the data frame/tibble object and a character vector of column names for the corresponding columns to fix.
datefixR imputes missing days of the month as 01, and missing months as 07 (July). However, this behavior can be modified via the
example.df <- data.frame(example = "1994") fix_date_df(example.df, "example", month.impute = 1) #> example #> 1 1994-01-01
datefixR assume day-first instead of month-first when day, month, and year are all given (unless year is given first). However this behavior can be modified by passing
format = "mdy" to function calls.
Numeric representations of dates, as used by either Excel or by R, are also supported.
Date and time data are often reported together in the same variable (known as “datetime”). However datetime formats are not supported by
datefixR. The current rationale is this package is mostly used to handle dates entered via free text web forms and it is much less common for both date and time to be reported together in this input method. However, if there is significant demand for support for datetime data in the future this may added.
The package is written solely in R and seems fast enough for my current use cases (a few hundred rows). However, I may convert the core for loop to C++ in the future if speed becomes an issue.
- When a date fails to parse in lubridate then the user is simply told how many dates failed to parse. In
datefixRthe user is told the ID (assumed to be the first column by default but can be user-specified) corresponding to the date which failed to parse and reports the considered date: making it much easier to figure out which dates supplied failed to parse and why.
- When imputing a missing day or month, there is no user-control over this behavior. For example, when imputing a missing month, the user may wish to impute July, the middle of the year, instead of January. However, January will always be imputed in lubridate. In
datefixR, this behavior can be controlled by the
- These functions require all possible date formats to be specified in the
ordersargument, which may result in a date format not being considered if the user forgets to list one of the possible formats. By contrast,
datefixRonly needs a format to be specified if month-first is to be preferred over day-first when guessing a date.
However, lubridate of course excels in general date manipulation and is an excellent tool to use alongside
An alternative function is
anytime::anydate() which also attempts to convert dates to a consistent format (POSIXct). However anytime assumes year, month, and day have all been provided and does not permit imputation. Moreover, if a date cannot be parsed, then the date is converted to an NA object and no warning is raised- which may lead to issues later in the analysis.
parsedate::parse_date() also attempts to solve the problem of handling arbitrary dates and parses dates into the
POSIXct type. Unfortunately,
parse_date() cannot handle years before 1970 – instead imputing the year using the current year without raising a warning.
parsedate::parse_date("april 15 1969") #>  "2023-04-15 UTC"
parse_date() assumes dates are in MDY format and does not allow the user to specify otherwise. However, parsedate has excellent support for handling dates in ISO 8601 formats.
These packages all use ICU library when parsing dates (via
clock::date_parse()) and therefore all behave very similarly. Notably, all of these functions require the date format to be specified including specifying a priori if a date is missing. Ultimately, this makes these packages unsuitable when numerous dates in different formats must be parsed.
readr::parse_date("02/2010", "%m/%Y") #>  "2010-02-01"
However, these packages have support for weekdays and months in around 211 locales whereas
datefixR supports much fewer languages due to support for additional languages needing to be implemented individually by hand.
These alternative packages all use compiled code and therefore have the potential to be orders of magnitude faster than
datefixR. However, in my own testing, I found anytime to actually be slower than
datefixR: consistently being over 3 times slower (testing up to 10,000 dates).
lubridate::parse_date_time() (which is written in R) is an order of magnitude of time faster than
lubridate::parse_date_time2(), which is written in C but only allows numeric dates, is even faster. If you are don’t mind not having control over imputation, do not expect to have to deal with many dates which fail to parse, are confident you will specify all potential formats the supplied dates will be in, and you have many many dates to standardize (hundreds of thousands or more), lubridate’s functions may be a better option than
If speed is an absolute priority and limited control over date parsing is acceptable, then
clock are all excellent choices as they are around 105 times faster than
If you are interested in contributing to
datefixR, please read our contributing guide.
Please note that this package is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
If you use this package in your research, please consider citing
datefixR! An up-to-date citation can be obtained by running