Assignment 2: Data Management

Assignment 2: Data Management#

Introduction#

In this assignment you will work on a small but realistic data management project. As always, you will collaborate via git.

To avoid disappointments, here are a few rules for all tasks:

  1. Write good commit messages and commit frequently. Use git for the entire process. Do not hesitate to commit unfinished or broken code. Git should be the only way to share code with your peers.

  2. You only get points if you contribute. If you don’t commit at all or your only commit trivial stuff (like fixing a typo in a comment) you will not get points, even if your group provides a good solution.

  3. All functions need docstrings

  4. Functions must not have side effects on inputs

  5. Never overwrite the original data

  6. Do not commit generated files (e.g. cleaned datasets)

  7. Follow the rules for working with file paths

  8. Use the “modern pandas” settings for all exercises

The deadline is November 18, 11:59 pm

The entire solution has to be in .py files. If you find it easier, you can prototype some of the functions in jupyter notebooks. In that case, it is a good idea if each group member has their own notebook, so you do not get merge conflicts.

Background#

In this assignment you will do data management for the paper Estimating the Technology of Cognitive and Noncognitive Skill Formation by Cunha, Heckman and Schennach (CHS), Econometrica, 2010.

Doing the complete data management of such a complicated project is not possible in one assignment (it often takes weeks or months). Therefore, you will only work with a small subset of the variables needed to replicate the paper. Moreover, we will save you some of the most painful steps by providing a pre-processed version of the dataset and csv files that will help you to harmonize variable names between panel waves.

We will focus on the Behavior Problem Index that is used to measure non-cognitive skills. This index has the subscales antisocial behavior, anxiety, dependence, headstrong, hyperactive and peer problems. Here is an overview.

The assignment repository contains a file called src/original_data/original_data.zip, with four files in it:

  • BEHAVIOR_PROBLEMS_INDEX.dta: Contains the main data you will work with. It is in wide format and the variable names are not informative. Moreover, the names do not contain the survey year in which the question was asked.

  • bpi_variable_info.csv: Contains information that will help you to decompose the main dataset into datasets for each year and to rename the variables such that the same questions get the same name across periods. In a real project you would have to generate this information yourself.

  • BEHAVIOR_PROBLEMS_INDEX.cdb: The codebook of the dataset. If you have any questions about the data, the answers are probably in the codebook.

  • chs_data.dta: The data file used in the original paper by Cunha Heckman and Schennach.

The chs_data is taken from the online appendix of the paper. bpi_variable_info.csv was created by us. The other files were downloaded using the NLS Investigator

Task 1#

Follow this link, create the repository for your group and clone it to your computers.

Task 2#

Make sure you have seen the screencast on functional data management and worked through the functional data management example.

Answer the following questions in your README file:

  • In the imperative way the DataFrame is changed in-place many times and variables of the same name changed their content. Describe why this is a problem, especially when working in a jupyter notebook.

  • Read the code in the functional way. Does any of the functions have a side effect on its inputs?

  • The functional way contains three functions. One of them could be called the main function and the others are helper functions. Which one is the main function and why?

Note: It is a good idea to mark the helper functions by starting their name with an underscore (_)

Task 3#

For this task you will work in unzip.py and store the results in the bld directory.

Use pathlib to check if the bld directory exists and create it otherwise.

Modify your .gitignore file to make sure that all files in the bld directory are ignored.

Unzip the the file src/original_data/original_data.zip. This stackoverflow post tells you how. Since the unzipped files are generated, they should not be under version control.

Task 4#

For this task you will work in clean_chs_data.py

Implement the function clean_chs_data. The function should take a DataFrame and return a DataFrame.

Use as many helper functions as you need. Give them good names and mark them as helper functions by starting the name with an underscore.

The cleaned data should contain the following columns:

  • “bpiA”, “bpiB”, “bpiC”, “bpiD”, “bpiE”. They are cleaned versions of columns with the same name in the raw data. Cleaned means that missing are coded as pandas missings (NA or NaN) instead of negative numbers.

  • The column “momid” based on the original column “momid”. Choose a suitable dtype.

  • “age”, just copied over from the raw data. This is an integer as it was discretized to two year bins.

Set the index to [“childid”, “year”] and choose suitable dtypes for both index variables

At the bottom of the py file you find an if __name__ == '__main__' clause. You can watch a video on why it’s necessary if you want.

Add the code suggested by the comments inside the if condition.

For those who watched the video: You do not have to put everything you do inside the condition into a main function. Putting multiple function calls into the if condition is completely fine.

Don’t forget to write docstrings for all functions. A one-liner is enough for helper functions

Task 5#

For this task you will work in clean_nlsy_data.py.

Implement the function clean_year_data. The function takes the entire raw nlsy dataset, a year (between 1986 and 2010, both inclusive and only even year numbers) and the bpi variable info (as a DataFrame).

It returns a DataFrame with the cleaned data of the requested year.

Use as many helper functions as you need. Give them good names and mark them as helper functions by starting the name with an underscore.

The cleaned data should contain the following variables:

  • Clean versions of all variables that make up the BPI. They have an ordered categorical dtype with the values “not true” < “sometimes true” < “often true”. The variables have the name indicated in the bpi_variable_info file. Missings are coded as pandas missings (NA or NaN) instead of negative numbers. The dataset contains surprises such as variables that take values you did not expect or category labels that are similar but not identical across variables. Try to find good solutions for each of them.

  • Scores for each subscale of the behavioral problems index (bpi) that are calculated by averaging the items of that subscale. Before averaging, you need to convert the categorical variables to numbers. For this, the answers ‘sometimes true’ and ‘often true’, are counted as 1; ‘not true’ is counted as 0. Use the names “antisocial”, “anxiety”, “headstrong”, “hyperactive”, “dependence” and “peer”.

Set the same index as for the chs_data.

Don’t forget to write docstrings for all functions. A one-liner is enough for helper functions

Task 6#

You continue to work in clean_nlsy_data.py

Implement the function clean_and_reshape_nlsy_data. This function takes the entire raw nlsy dataset and variable info as DataFrames. It calls clean_year_data to create a list of cleaned yearly datasets and concatenates them into one DataFrame in long format.

Only keep the data for even years between 1986 and 2010.

As before, add data loading and function calls in the if __name__ == '__main__ condition.

Task 7#

For this task you will work in merge.py.

Merge the clean chs data with the clean nlsy data. Only keep observations that are present in the chs data. Before you merge, check that there are no overlaps in column names between the two datasets.

Restrict the merged data sets to the age groups 5 to 13 (both inclusive)

Use an if __name__ == '__main__' condition for all function calls.

Task 8#

For this task you will work in plot.py

You will plot your scores against the ones in the chs data – for each score and age.

Make a grid of regression plots for each score that show how your score relates to the corresponding score in the chs data. Each grid contains 5 subplots, one for each age group.

Note: Making such grids is really easy in plotly. Search for the word facet in the documentation of px.scatter

The names of their score relate to your names as follows:

{
    "antisocial": "bpiA",
    "anxiety": "bpiB",
    "headstrong": "bpiC",
    "hyperactive": "bpiD",
    "peer": "bpiE",
}

The dependence scale has no counterpart in the chs data. If you did everything correctly you should see a perfectly negative correlation for some variables and a strong but not perfectly negative correlation for other variables.

Save the plots under suitable file names in the bld folder. .png is the preferred format. If you are on Windows and have trouble to export static plotly plots (even with the workaround) you can switch to .html instead.

Use an if __name__ == '__main__' condition for all function calls.