detect_academic: Match messy text and email data to academic, business,...

In brandonleekramer/tidyorgs: Standardize messy text data for organizational analysis

Description

This function standardizes messy text data and/or email information to social organizations. The detect_orgs() function iterates through email domains and unstructured text to match patterns in our curated dictionaries to standardize organizations. This tool is designed to optimize pattern detection for in the linkage of multiple datasets, for bibliometric analysis, and for sector classification in social, economic, and policy analysis.

Usage

detect_academic(
  data,
  id,
  input,
  output,
  email = FALSE,
  country = FALSE,
  parent_org = FALSE,
  org_type = FALSE
)

Arguments

data	A data frame or data frame extension (e.g. a tibble).
id	A numeric or character vector unique to each entry.
input	Character vector of messy or unstructured text that will be matched to organizations from one (or all) of five economic sectors (see sector parameter).
output	Output column to be created as string or symbol.
email	Optional character vector of email or email domain information. Defaults to FALSE.
country	Optional parameter that returns country of organization when available. Defaults to FALSE.
parent_org	Optional parameter that returns the parent organization when available. For the academic sector, this value is the school system of the organization. Defaults to FALSE.
org_type	Optional parameter that returns organization type when available. Current return values include "Public", "Private for-profit", and "Private not-for-profit". Defaults to FALSE.

Examples

library(tidyverse)
library(tidyorgs)
data(github_users)

classified_users <- github_users %>%
  detect_academic(login, company, organization, email, parent_org, org_type)

brandonleekramer/tidyorgs documentation built on Dec. 19, 2021, 11:42 a.m.