In brandonleekramer/standardizeR: Package to standardize institutions and location data from messy strings.

knitr::opts_chunk$set(
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suppressPackageStartupMessages(library(ggplot2))
theme_set(theme_light())

standardizeR: R package for standardizing messy institution and location strings

Authors: Brandon L. Kramer
License: MIT

Installation

The most current version of the textnets package is currently available on GitHub. To install standardizeR, the devtools package must also be installed.

install.packages("devtools")
library(devtools)
install_github("brandonleekramer/standardizeR")
library(standardizeR)

Overview

The standardizeR package compiles a number of products from an ongoing collaboration between the University of Virginia's Biocomplexity Institute and the National Center for Science and Engineering Statistics dedicated to tracking the source and impact of open-source software. One goal of this project was to take GitHub contributor data and classify users into economic sectors. To do this, our team developed a series of functions to standardize the self-reported user data while compiling and cleaning datasets used to match GitHub contributors to institutions within each

This package includes four core functions:

• clean_academic and clean_gov clean messy string data from academic and government institutions
• clean_county_code converts messy location strings to a standardized country code
• sectorize classifies cleaned institutional string data into one of five economic sectors, including the academic, government, corporate, nonprofit and household sectors

The package also includes n cleaned datasets:

• The hipo_labs dataset includes over n academic institutions and email domains from around the world
• The full

Standardizing messy government institution strings

library(dplyr)
data("usgov_azindex")

usgov_clean <- usgov_azindex %>% 
  clean_gov(agency) %>% 
  select(agency, institution)

usgov_clean

Standardizing messy academic institution strings

data("hipo_labs")

academic_clean <- hipo_labs %>% 
  clean_gov(agency) %>% 
  select(agency, institution)

academic_clean

Standardizing messy strings from self-reported country data

Classify data into economic sectors based

TO DO:

Use these repos as guidelines for detailing data and functions https://github.com/specialistgeneralist/geodiverse https://github.com/cbail/textnets https://b-rodrigues.github.io/modern_R/package-development.html https://github.com/juliasilge/tidytext

brandonleekramer/standardizeR documentation built on Oct. 4, 2020, 12:15 a.m.