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Practice Task
Sameer Nair-Desai edited this page Oct 25, 2023
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- Read through RA manual carefully.
- Make sure your computer is properly set up. First, check that you have installed the softwares listed on the RA manual setup page. Then, verify that your computer satisfies the requirements of the template per its ReadMe.
Make sure you follow all procedures in the RA manual (e.g., issue threads and branching).
- Create your own public repository from the gentzkow template and invite the practice task assigner as a collaborator on your GitHub repo. Make sure you fork this repository, do not click "Use this template". Doing so will create errors in the filters for
git-lfs
files. - Run the repository from beginning to end to replicate the output.
- Open a new issue following the guidelines described in the RA manual and modify the code in the
data
folder so that the histogram inoutput/chips_sold.pdf
displays percentages instead of counts. To update thedata
folder with your new histogram as an output, you should rundata/make.py
. Then, to make sure your changes are reflected in the paper, you should runpaper_slides/make.py
. Commit this file with a helpful tag following the RA manual's format. - Once you are done, close the issue with a summary and deliverables. Open a Pull Request to merge your work into the master repository following the PR guidelines. On the PR, ask the practice task assigner for a peer review (see peer review guidelines).
- Repeat steps 3 and 4, this time to modify the code in
analysis/code/analyze_data.py
to run an additional regression only on the subset of years greater or equal to 1960. Export the model coefficient, standard errors and p-value into a new table output (similarly to what is currently done in the code for the full set of years) and add your new table to the paper. - Complete the task.
- Thoroughly review the purpose and features of Sherlock in the Research Clusters section of the RA Manual Wiki.
- Log on to Sherlock following the instructions here. Ensure you have access to the correct partitions (
$OAK
,$HOME
,$GROUP_HOME
,$SCRATCH
, and$GROUP_SCRATCH
) with thesh_part
command. If you do not have access to$OAK
, ask a lab member for access. - Set up your personal directory under
$OAK
. You can review the Research Clusters section of the ra-manual if needed. - Set up Dropbox and Rclone on Sherlock. You can follow the instructions here and here.
- Initialize GitHub usage on Sherlock by generating a new SSH key and adding it to Sherlock.
- Clone the remote
template
repository to your personal directory in$OAK
. - Set up the repository following the instructions in the ReadMe. To install
miniconda
, see the instructions in the Research Clusters section (this installation process is distinct from the standardtemplate
setup). - To check that the
template
repository is operating as expected, run thedata
module within your terminal. You should see the standard output appear in your$OAK
directory.
-
Note: You will not need to set up command line usage on Sherlock - simply load the relevant applications with the
module spider
andml <application/version>
commands. For more on this, see Setting up the environment in the Research Clusters section.
- Create a new issue following the guidelines in
lab-manual
, and modify the code inanalysis/code/analyze_data.py
to cluster standard errors at the county-level.
-
Note: You may either follow the instructions in the Research Clusters section to make these file edits directly in Sherlock, or make the edits locally and pull to your Sherlock directory from a remote
Git
repository.
- Add a new regression table capturing the revised outputs to the paper in
paper_slides/code/paper.lyx
. Caption the table mentioning the two-way fixed effects the model uses, and that the std. errors are clustered at the county level. - Submit a job to run the full repository with these edits, following the instructions here.
- Practice Task
- Autofilling Values
- Overleaf Workflow
- IT Support
- Research Clusters
- Legacy Tools
- Style Guides
- Mothballing Projects
- Recruiting on Social Media
- PhD Applications
- Gentzkow-Shapiro Lab Notes
- Allcott-Gentzkow Lab Notes