prep_query: Prepare variable names and types in query data frame for...

In vivainsights: Analyze and Visualize Data from 'Microsoft Viva Insights'

Prepare variable names and types in query data frame for analysis

Description

For applying to data frames that are read into R using any other method other than import_query(), this function cleans variable names by replacing special characters and converting the relevant variable types so that they are compatible with the rest of the functions in vivainsights.

Usage

prep_query(data, convert_date = TRUE, date_format = "%m/%d/%Y")

Arguments

data	A Standard Person Query dataset in the form of a data frame. You should pass the data frame that is read into R using any other method other than import_query(), as import_query() automatically performs the same variable operations.
convert_date	Logical. Defaults to TRUE. When set to TRUE, any variable that matches true with is_date_format() gets converted to a Date variable. When set to FALSE, this step is skipped.
date_format	String specifying the date format for converting any variable that may be a date to a Date variable. Defaults to "%m/%d/%Y".

Value

A tibble with the cleaned data frame is returned.

Examples

The following shows when and how to use prep_query():

 pq_df <- read.csv("path_to_query.csv")
 cleaned_df <- pq_df |> prep_query()

You can then run checks to see that the variables are of the correct type:

dplyr::glimpse(cleaned_df)

See Also

Other Import and Export: copy_df(), create_dt(), export(), import_query()

vivainsights documentation built on April 3, 2025, 9:25 p.m.