Replication Instructions

Software Needed

There are many pieces of software used in order to create the datasets and data visualizations.Google Chrome was used to import the data and access information about the data, but any web browser will likely have the same results. GitHub was the collabration tool used to maintain code consistency between team members. RStudio is the development evironment needed to replicate the results of the lab. The programming language needed for the duplication is R. GitHub Desktop was used for pushing code and pulling code from Github.

With access to the team 02 repository, one will have access to access and analyze the code for themselves. The key function used is the here package, which allows for relative paths from the top-level directory. The renv package is also essential in recreating the results, by giving each project its own private package library and making specific packages easy to install.

Files in Folders

Data

This folder contains three sub-folders.

Raw is the the data set that needs to be analyze is obtained. This data set is raw, meaning there may be missing values and the values are often hard to analyze, until the data go through a set of data transformations.

Wrangling is the step where the data is changed into that data that can be analyzed. This involves filtering and mutating data. The step leads to the final data set, however, these intermediary data sets are not used in the final analysis.

Rodeo is the data set that can be analyze as all the necessary transformations steps have been completed. This is the final data set that is be analyzing in the report, giving us meaningful insights about the data, such as correlations.

Analysis

This folder contains a executive summary document, which contains information about the research question and the overview of the neighborhood change and tax credits. More information about the data and methodology is also in this document.

Labs

Each week a different piece of analysis was done, in order to achieve the final product. As the final week is for creating this website, there are six different labs. Each lab is given its own sub-folders within the labs folder. Additional files are in the lab folders, such as project_data_steps.R and utilities.R, which helped created the raw data.

Assets and Sass

These folders are needed for the creation of the website and have no effect on the datasets. Our website uses a minimal theme, specifically the jekyll-theme-minimal. These folders function as the front-end of the website design, using CSS code.

Docs

This folder contains a code of conduct when using GitHub. The code of conduct talks about GitHub's pledge to foster a positive environment for collaboration. In addition to standards expected of the community, project maintainers have rights and reponsibilities to align with this code of conduct. There are other files, such as information about contributing to GitHub themes and information on how to get help with GitHub.

Renv

This folder contains three files.

.gitignore separates the files that need to be sync with GitHub and the files that GitHub should ignore when sync.

activate.R is needed so that the project library to be used for R sessions when a new one is opened.

settings.dcf allows for setting changes for renv, but none were made for this project.

Citations

Abravanel, M. D., Pindus, N. M., Theodos, B.(2013, April). New Markets Tax Credit (NMTC) Program Evaluation. Urban Institute. https://www.urban.org/sites/default/files/2022-05/412958-new-markets-tax-credit-nmtc-program-evaluation.pdf

Baum-Snow, N., & Hartley, D. (2022, May). Accounting for central neighborhood change, 1980–2010. Journal of Urban Economics.https://www.sciencedirect.com/science/article/abs/pii/S0094119019301056?via%3Dihub

Cohen, M., & Pettit, K.L.S. (2019, April). Guide to Measuring Neighborhood Change to Understand and Prevent Displacement. Urban Institute. https://www.urban.org/sites/default/files/publication/100135/guide_to_measuring_neighborhood_change_to_understand_and_prevent_displacement.pdf

Guhathakurta, S. (2017, May 4). Predicting Revitalization. Georgia Tech theses and dissertations. https://smartech.gatech.edu/handle/1853/3739

Herriges, D. (2019, August 1). Untangling gentrification and displacement: A new NYU study helps. https://www.strongtowns.org/journal/2019/8/1/untangling-gentrification-and-displacement

Lecy, J. (2022, October 12). Watts-College/cpp-528-fall-2022. GitHub. https://github.com/Watts-College/cpp-528-fall-2022

Logan, John R., Zengwang Xu, and Brian J. Stults. (2020). Census geography: Bridging data for census tracts across time. https://s4.ad.brown.edu/Projects/Diversity/Researcher/Bridging.htm

Thomas, T., Hartmann, C., Driscoll, A., & Chapple, K. (2020, October). The Urban Displacement Replication Project. https://www.urbandisplacement.org/wp-content/uploads/2021/07/udp_replication_project_methodology_10.16.2020-converted.pdf