imputeTestbench

Organisation: The R Project for Statistical Computing

Tests

• Easy: Download the imputeTestbench package and demonstrate it with a naturally occurring time series. Document it with RMarkdown.

• Medium: Suggest possible updates or a new feature you would like to include in the next version of the imputeTestbench package.

• Hard: Develop a dummy code of 5 functions and a vignette and pass it with no Error/Warning/Note through https://win-builder.r-project.org/

Easy Test

Install the imputeTestbench package using install.packages("imputeTestbench") and load it.

library(imputeTestbench)

The dataset used is nhtemp which is a default datset in R.It contains Average Yearly Temperatures in New Haven from 1912-1971.

aa <- impute_errors(dataIn = nhtemp)
aa

# change the simulation for missing completely at random observations
aa <- impute_errors(dataIn = nhtemp, smps = 'mcar')
aa

# use one interpolation method(interp), increase number of repetitions
aa <- impute_errors(dataIn = nhtemp, methods = 'na.interp', repetition = 100)
aa

The rest of the code for the easy test can be found in the .

Medium Test

imputeTestbench is a great package for comparing various methods of imputation . This project modifies the package to work with multivariate time series data since the package currently has support for univariate time series data only. Few updates that I would suggest are:

1. Multivariate Prototype Implementation Develop a basic extension of impute_errors() to handle multivariate time series data:

• Create functions to generate missing patterns across multiple variables

• Implement correlation-aware evaluation metrics between variables

• Provide simple visualization showing imputation across multiple series simultaneously

2. Performance Enhancement with data.table Implement a performance improvement using modern data structures:

• Convert key internal operations to use data.table for efficient processing

• Add basic parallelization using future or foreach for method evaluation

• Benchmark performance gains on datasets of increasing size

3. Integration with State-of-the-Art Imputation Methods Create a prototype that connects to modern imputation approaches:

• Implement a wrapper for accessing external ML-based imputation methods

• Use reticulate to connect with Python libraries for specialized techniques

• Compare performance against traditional methods using existing metrics

Hard Test

For the final test, I created a R package called billboardsongs. It contains five functions, find_artist(), random_song(), song_lyrics(), song_properties() and spotify_playlist_url() , with documentation and tests. Then, using devtools::check(), I checked for any errors or warning, and uploaded the source file to https://win-builder.r-project.org/. It passed without errors/warnings/note.

The result of https://win-builder.r-project.org/ is included in the file(00check.log) and other test files inside the .