---
title: "datefixR"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{datefixR}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup}
library(datefixR)
```

This vignette describes the functionality of `datefixR` in more detail than the
[README](https://github.com/ropensci/datefixR) file. `DatefixR` is a lightweight
package consisting of two main user-accessible functions, `fix_date_char()` and
`fix_date_df()`, which converts dates written in different formats into R's
built-in
[`Date` class](https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/Dates).
The former is designed to modify character vectors whilst
the latter is intended for modifying columns of a data frame (or tibble).
`fix_date_app()` is a third function which allows dates to be standardized via a
Shiny app interface. You can learn more about the Shiny app in its dedicated
vignette.

Practically, this package is most useful when handling date data which has been
supplied via text boxes (instead of a date-specific input with a consistent date
format). However, this package may also be useful to validate the format of date
data (see [date and month imputation](#date-and-month-imputation)).

## Usage

### Date standardization

Firstly, we will demonstrate date standardization without imputation. We
consider a data frame with two columns of dates in differing formats
with no missing data. 

```{R, echo = TRUE}
bad.dates <- data.frame(
  id = seq(5),
  some.dates = c(
    "02/05/92",
    "01-04-2020",
    "1996/05/01",
    "2020-05-01",
    "02-04-96"
  ),
  some.more.dates = c(
    "01 03 2015",
    "2nd January 2010",
    "01/05/1990",
    "03-Dec-2012",
    "02 April 2020"
  )
)
knitr::kable(bad.dates)
```

`fix_date_df()` requires two arguments, `df`, a data frame (or tibble) object
and `col.names`, a character vector containing the names of columns to be
standardized. By default, the first column of the data frame is assumed to
contain row IDs. These IDs are used if a warning or error is raised to assist
the user with locating the source of the error. The ID column can also be
manually provided via the `id` argument. 

The output from this function is a data frame or tibble (dependent on the
object type of the the first argument, `df`) with the selected date
columns now standardized and belonging to the `Date` class. 

```{R}
fixed.dates <- fix_date_df(
  bad.dates,
  c("some.dates", "some.more.dates")
)
knitr::kable(fixed.dates)
```

`datefixR` can handle many different formats including -, /, ., or white space
separation,  year-first or day-first, and month supplied as a number, an
abbreviation or full length name.

`fix_date_char()` is similar to `fix_date_df()` but only applies to a single
character object. 

```{R}
fix_date_char("01 02 2014")
```

### Localization

`datefixR` currently supports dates being provided in English, Français
(French), Deutsch (German), español (Spanish), and Русский (Russian) by
recognizing both names of months in these languages and formatting customs.
Expected languages do not need to be specified and can be provided just like any
other date to be standardized.

 
```{R}
fix_date_char("7 de septiembre del 2014")
```

Functions in `datefixR` assume day-first instead of month-first when day, month,
and year are all given numerically (unless year is given first). However, this
behavior can be modified by passing `format = "mdy"` to function calls.  

```{R}
fix_date_char("01 02 2014", format = "mdy")
```

If the month is given by name, then `datefixR` will automatically detect the
correct format without the `format` argument needing to be specified by the user. 

```{R}
fix_date_char("July 4th, 1776")
```


### Date and month imputation

By default, `datefixR` imputes missing months as July, and missing days of the
month as the first day.  As such, "1992" converts to 

```{R}
fix_date_char("1992")
```

The argument for defaulting to July is 1st-2nd July is halfway through the year
(on a non leap year). Therefore, assuming the year supplied is indeed correct,
the imputation has a maximum potential error of 6 months from the true date.
However, this behavior can be changed by supplying the `day.impute` and 
`month.impute` arguments with an integer corresponding to the desired day and
month. For example, `day.impute = 1` and `month.impute = 1`  results in the
first day of January being imputed instead which is often a more common
imputation for missing date data. 

```{R}
fix_date_char("1992", day.impute = 1, month.impute = 1)
```

The imputation mechanism can also be modified to impute `NA` if a month or day
is missing by setting `day.impute` or `month.impute` to `NA`. This will
also result in a warning being raised.

```{R}
fix_date_char("1992", month.impute = NA)
```

Finally, imputation can be prevented by setting `day.impute` or `month.impute`
to `NULL`. This will result in an error being raised if the day or month
are missing respectively. 

```{R, eval = FALSE}
fix_date_char("1992", month.impute = NULL)
# ERROR
```

`day.impute` and `month.impute` can also be passed to `fix_date_df()` for
similar functionality. 

```{R}
example.df <- data.frame(
  id = seq(1, 3),
  some.dates = c("2014", "April 1990", "Mar 19")
)
fix_date_df(example.df, "some.dates", day.impute = 1, month.impute = 1)
```

### Converting numeric dates

By default, if a date is given numerically (I.E no separators such as "/", "-",
or white space) and is more than four character long, then this date is assumed
to have been converted from `R`'s numeric date format. If a `Date` object is
converted to a `numeric` object in R, the assigned value becomes the number of
days from `1970-01-01`. Note that the date must still be converted to a
`character` object before being passed to a `datefixR` function.

```{R}
date <- as.numeric(as.Date("2023-01-17"))
print(date)
fix_date_char(as.character(date))
```

However if a date is converted to a numeric date in Excel, the number of days
is instead counted from `1900-01-01`. If a user expects that dates to have been
converted to numerical dates in Excel, then `excel = TRUE` can be passed to a
`datefixR` function to rectify this. 

```{R}
fix_date_char("44941", excel = TRUE)
```

### Roman numeral months [![Experimental](https://lifecycle.r-lib.org/articles/figures/lifecycle-experimental.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)

Oracle Database can use Roman numerals to format months and this custom is also
used in some biological contexts. If dates in need of parsing are in this format,
`roman.numeral = TRUE` can be passed to `fix_date_char()` or `fix_date_df()`.
This implementation is currently experimental and is not guaranteed to work
alongside other date formats. 

```{R}
fix_date_char("12/IV/2019", roman.numeral = TRUE)
```

## Citation

If you use this package in your research, please consider citing `datefixR`.
An up-to-date citation can be obtained by running

```{R}
citation("datefixR")
```
