dataPreparation (version 0.4.3)

identifyDates: Identify date columns

Description

Function to identify dates columns and give there format. It use a bunch of default formats. But you can also add your own formats.

Usage

identifyDates(
  dataSet,
  cols = "auto",
  formats = NULL,
  n_test = 30,
  ambiguities = "IGNORE",
  verbose = TRUE
)

Arguments

dataSet

Matrix, data.frame or data.table

cols

List of column(s) name(s) of dataSet to look into. To check all all columns, set it to "auto". (characters, default to "auto")

formats

List of additional Date formats to check (see strptime)

n_test

Number of non-null rows on which to test (numeric, default to 30)

ambiguities

How ambiguities should be treated (see details in ambiguities section) (character, default to IGNORE)

verbose

Should the algorithm talk? (Logical, default to TRUE)

Value

A named list with names being col names of dataSet and values being formats.

Ambiguity

Ambiguities are often present in dates. For example, in date: 2017/01/01, there is no way to know if format is YYYY/MM/DD or YYYY/DD/MM. Some times ambiguity can be solved by a human. For example 17/12/31, a human might guess that it is YY/MM/DD, but there is no sure way to know. To be safe, findAndTransformDates doesn't try to guess ambiguities. To answer ambiguities problem, param ambiguities is now available. It can take one of the following values

• IGNORE function will then take the first format which match (fast, but can make some mistakes)

• WARN function will try all format and tell you - via prints - that there are multiple matches (and won't perform date transformation)

• SOLVE function will try to solve ambiguity by going through more lines, so will be slower. If it is able to solve it, it will transform the column, if not it will print the various acceptable formats.

Details

This function is looking for perfect transformation. If there are some mistakes in dataSet, consider setting them to NA before. In the unlikely case where you have numeric higher than as.numeric(as.POSIXct("1990-01-01")) they will be considered as timestamps and you might have some issues. On the other side, if you have timestamps before 1990-01-01, they won't be found, but you can use setColAsDate to force transformation.

Examples

# NOT RUN {
# Load exemple set
data(messy_adult)
head(messy_adult)
# using the findAndTransformDates
identifyDates(messy_adult, n_test = 5)
# }