(pd-missing-data-checklist-resources)=
# Checklist and Resources
## Checklist
- Identify whether your dataset has missing data; use visualisation tools to help you {ref}`pd-missing-data-visualising-missingness`
- Try to determine what type of missing data there is (MCAR/MAR/MNAR) as described in {ref}`pd-missing-data-structures`, and don't forget about {ref}`pd-missing-data-structured-missingness`
- Choose an appropriate missing data handling method; you can use {ref}`pd-missing-data-methods` as a starting point for ideas
- Apply the missing data handling method and then continue with your analyses!
## References
Coding segments of this chapter were in part created thanks to several online tutorials which were used as a reference:
- [Visualizing Missing Data](https://www.kaggle.com/code/selahattinsanli/visualizing-missing-data/notebook): A python notebook exploring the use of the missingno library.
- [Gallery of Missing Data Visualisations](https://cran.r-project.org/web/packages/naniar/vignettes/naniar-visualisation.html): A tutorial on missing data visualisation in R.
- [Imputing Missing Data with R; MICE package](https://datascienceplus.com/imputing-missing-data-with-r-mice-package/)
- [Intro to MICE: An Imputation Strategy](https://www.kaggle.com/code/shilongzhuang/intro-to-mice-an-imputation-strategy/notebook): A short notebook introducing implementing MICE in python.
- Lastly, the scikit-learn documentation is incredibly helpful and detailed with regards to implementing missing data handling in python:
- [6.4. Imputation of missing values](https://scikit-learn.org/stable/modules/impute.html)
- [Imputing missing values with variants of IterativeImputer](https://scikit-learn.org/stable/auto_examples/impute/plot_iterative_imputer_variants_comparison.html)
Other textbook and paper references used, that have not been previously directly cited:
- On types of missing data {cite:ps}`Bland2015medicalstatistics,Mack2018patientregistries`
- On multiple imputation {cite:ps}`deGoeij2013multipleimputation,Austin2021multipleimputation`
(pd-missing-data-learn)=
## What to Learn Next
If you happen to be handling sensitive data in your project, check out the {ref}`pd-sdpw` chapter.
Alternatively, if you want to make your research project and data analysis pipeline more reproducible, see the chapter on {ref}`rr-make`, a build automation tool.
(pd-missing-data-reading)=
## Further Reading
- [Flexible Imputation of Missing Data](https://stefvanbuuren.name/fimd/): This is a much more in-depth look at missing data imputation that goes into further characterising data, including mathematical definitions, and describing data imputation methods.
- [Getting Started with naniar](https://naniar.njtierney.com/articles/naniar.html#tidy-missing-data-the-shadow-matrix): More R functions to visualise Data Missingness, including one using decision trees to map out the proportion of missingness in a variable based on all other variables.
- The papers cited throughout this chapter are all good resources for further reading. The original paper on MICE {cite:ps}`vanBuuren2011mice` and the review papers on missing data handling {cite:ps}`Pigott2001missingdatamethods, Joel2022missingdatahandling` are especially great resources.
- For more R visualisation and imputation packages see:
- [mi](https://cran.r-project.org/web/packages/mi/index.html)
- [Amelia](https://cran.r-project.org/web/packages/Amelia/index.html)
- [missforest](https://cran.r-project.org/web/packages/missForest/index.html)
- The Turing-Roche partnership has some resources on structured missingness:
- See *#ExplainToMe: The Problem of Structured Missing Data* for a great animated overview
- Papers on structured missingness (that were cited previously): {cite:ps}`Mitra2023structuredmissingness` and {cite:ps}`Jackson2023structuredmissingness`
- For more in-depth recordings from the Turing-Roche Knowledge Series see:
- *Modern Topics on Missing Data*, which also provides a brief overview of missing data:
- *Structured Missingness Challenges in Data Integration*: