Interface to the World Database on Protected Areas

Fetch and clean data from the World Database on Protected Areas (WDPA) and the World Database on Other Effective Area-Based Conservation Measures (WDOECM). Data is obtained from Protected Planet < https://www.protectedplanet.net/en>. To augment data cleaning procedures, users can install the 'prepr' R package (available at < https://github.com/dickoa/prepr>). For more information on this package, see Hanson (2022) .

R Wrapper for the 'OneMap.Sg API'

An R wrapper for the 'OneMap.Sg' API < https://www.onemap.gov.sg/docs/>. Functions help users query data from the API and return raw JSON data in "tidy" formats. Support is also available for users to retrieve data from multiple API calls and integrate results into single dataframes, without needing to clean and merge the data themselves. This package is best suited for users who would like to perform analyses with Singapore's spatial data without having to perform excessive data cleaning.

Sites, Population, and Records Cleaning Skills

Data cleaning including 1) generating datasets for time-series and case-crossover analyses based on raw hospital records, 2) linking individuals to an areal map, 3) picking out cases living within a buffer of certain size surrounding a site, etc. For more information, please refer to Zhang W,etc. (2018) .

GIS Integration

Designed to facilitate the preprocessing and linking of GIS (Geographic Information System) databases < https://www.sciencedirect.com/topics/computer-science/gis-database>, the R package 'GISINTEGRATION' offers a robust solution for efficiently preparing GIS data for advanced spatial analyses. This package excels in simplifying intrica procedures like data cleaning, normalization, and format conversion, ensuring that the data are optimally primed for precise and thorough analysis.

Economics and Pricing Tools

Functions to aid in micro and macro economic analysis and handling of price and currency data. Includes extraction of relevant inflation and exchange rate data from World Bank API, data cleaning/parsing, and standardisation. Inflation adjustment calculations as found in Principles of Macroeconomics by Gregory Mankiw et al (2014). Current and historical end of day exchange rates for 171 currencies from the European Central Bank Statistical Data Warehouse (2020) < https://sdw.ecb.europa.eu/curConverter.do>.

Simple Data Frames

Provides a 'tbl_df' class (the 'tibble') with stricter checking and better formatting than the traditional data frame.

Tidy Messy Data

Tools to help to create tidy data, where each column is a variable, each row is an observation, and each cell contains a single value. 'tidyr' contains tools for changing the shape (pivoting) and hierarchy (nesting and 'unnesting') of a dataset, turning deeply nested lists into rectangular data frames ('rectangling'), and extracting values out of string columns. It also includes tools for working with missing values (both implicit and explicit).

Draw Stratified Samples from the VADIR Database

Affords researchers the ability to draw stratified samples from the U.S. Department of Veteran's Affairs/Department of Defense Identity Repository (VADIR) database according to a variety of population characteristics. The VADIR database contains information for all veterans who were separated from the military after 1980. The central utility of the present package is to integrate data cleaning and formatting for the VADIR database with the stratification methods described by Mahto (2019) < https://CRAN.R-project.org/package=splitstackshape>. Data from VADIR are not provided as part of this package.

Miscellaneous Functions for the Analysis of Educational Assessments

Miscellaneous functions for data cleaning and data analysis of educational assessments. Includes functions for descriptive analyses, character vector manipulations and weighted statistics. Mainly a lightweight dependency for the packages 'eatRep', 'eatGADS', 'eatPrep' and 'eatModel' (which will be subsequently submitted to 'CRAN'). The function for defining (weighted) contrasts in weighted effect coding refers to te Grotenhuis et al. (2017) . Functions for weighted statistics refer to Wolter (2007) .

Develop Text Prediction Models Based on N-Grams

A framework for developing n-gram models for text prediction. It provides data cleaning, data sampling, extracting tokens from text, model generation, model evaluation and word prediction. For information on how n-gram models work we referred to: "Speech and Language Processing" < https://web.stanford.edu/~jurafsky/slp3/3.pdf>. For optimizing R code and using R6 classes we referred to "Advanced R" < https://adv-r.hadley.nz/r6.html>. For writing R extensions we referred to "R Packages", < https://r-pkgs.org/index.html>.