Writing Reproducible Data Preparation Scripts

Why Make Data Preparation Explicit?

A regression analysis depends on the data that reach the model. If an analyst changes a spreadsheet by hand, recodes values in an undocumented way, or joins two files without checking their keys, the resulting data may be difficult to reproduce and easy to misinterpret. A data-preparation script makes these operations visible: it states which files are read, what is changed, how records are combined, and what checks establish that the result is as intended.

The goal is not simply to make code run. A useful script also checks the shape and contents of the data at important steps. If an input file loses a column, a code value changes, or a join unexpectedly duplicates participants, a check should make the problem apparent before the prepared data are used in a regression analysis.

The examples use R-style commands and hypothetical data. Their purpose is to show how to express and verify preparation steps; they do not describe a real study. In each example, the checks are part of the script, not informal observations made after it runs.

Import Data and Check What Arrived

An import step should name the source file and make important expectations explicit. At a minimum, check that the file was read with the columns needed for the next operation. When the number of rows is known in advance, or when a known identifier set provides a stronger check, verify that as well. A successful import command alone does not establish that the correct file or variables were imported.

For example, an R-style script might read a comma-separated file with dat = read.csv("participants.csv"). It can then check required columns with stopifnot(all(c("id", "arm_code", "weight_lb") %in% names(dat))). If the identifier is expected to be unique, it can check stopifnot(!anyDuplicated(dat$id)). These checks turn assumptions about the incoming file into conditions that the script can test.

A check should be chosen for a specific reason. If a data provider regularly adds extra, irrelevant columns, testing for required columns is generally more useful than requiring an exact column list. If the analysis requires a particular identifier or measurement, however, failing loudly when it is absent is safer than continuing with an incomplete data set.

Make Transformations and Recodings Unambiguous

A transformation calculates a variable from one or more existing variables. A recoding maps recorded values to new values, often changing numeric codes into meaningful labels. In either case, the script should state the rule and the output name clearly. Names such as weight_kg communicate more than a generic name such as weight2, and recording units in variable names or accompanying documentation helps prevent later confusion.

The input values also need checking. If a recode is written to handle only 0 and 1, check that all nonmissing input codes belong to that set. If missing values are allowed, decide how the language’s recoding function treats them and verify that the intended missingness is preserved. Do not turn an unexpected code into a familiar category simply to make the script complete.

Checks can cover both structure and values. For example, after calculating a change score, verify that the new column exists and that its values equal the specified subtraction for records with both measurements observed. A small set of hand-calculated values is useful during development. Once the code is established, automated checks can guard against future changes in the input.

Join Tables by Checked Keys

A join combines columns from separate tables using one or more matching variables, called keys. Before joining, identify what one row represents in each table and check whether the key is unique where it is supposed to be unique. Also check that key variables have compatible formats. For instance, the text value "031" is not necessarily equivalent to the number 31; converting identifiers casually can remove meaningful leading zeros.

A common operation is a left join: retain every row in a left-hand table and add matching columns from a right-hand table. If no right-hand row matches a left-hand key, the added columns are missing for that record. The result should be checked for the intended records and for missing joined values when a match was expected.

Why Key Checks Protect the Record Count

The record-count check for a left join follows from what happens to each left-hand row. If the right-hand key is unique, any left-hand key has either one matching right-hand row or no matching row. The left join therefore contributes exactly one output row for each left-hand row: a matched row in the first case, or a row with missing right-hand values in the second. Thus, the number of output rows equals the number of left-hand rows.

If a right-hand key occurs \(k\) times, where \(k > 1\), a left-hand row with that key may instead produce \(k\) output rows. This explains why checking uniqueness before a join is not a cosmetic step: it tests a condition that protects the intended row structure. A matching row count is helpful, but it does not replace checking that the right-hand key is unique or that the resulting IDs are the expected ones.

Build a Preparation Sequence With Checkpoints

A clear preparation script separates major operations so that each step can be inspected and checked before the next one begins. The following sequence is useful for many regression data sets:

For example, a final check might verify that the prepared table contains an ID, treatment label, outcome, and required adjustment variables; that the ID is unique if one row per participant is intended; and that a hand-calculated transformed value agrees with the script’s result. These checks do not establish that the scientific model is appropriate. They establish that the data passed to later analysis are the data the preparation steps were meant to create.

Common Mistake: Treating a Successful Join as a Correct Join

A frequent mistake is to assume that a join is correct because the software ran without an error. That reasoning is insufficient: the join can run while duplicate keys multiply records, a key mismatch leaves missing values, or an unintended key combines the wrong rows. The correction is to state what one row represents, identify the join key, check the relevant key uniqueness before joining, and compare the output records and values with expectations.

Another pitfall is to fix unexpected values manually in a spreadsheet after the script runs. That creates a result that the script cannot reproduce. Instead, investigate why the input or rule did not match expectations, make the intended change explicit in the script, and add a check that would reveal the same problem on a future run.