• Data processing, quality checking & interpretation with R
  • 1 Introduction
    • 1.1 How to read this book
    • 1.2 Getting started
    • 1.3 A quick refresher
  • 2 Data Preparation
    • 2.1 Data Lifecycle & Management Plan
    • 2.2 Good Data Protocol
    • 2.3 Further Reading
  • 3 Reproducible reporting in R
    • 3.1 Literate programming
    • 3.2 Open a new knitR document
  • 4 Importing and reformatting data files
    • 4.1 Importing data
    • 4.2 Reformat the file
    • 4.3 Transform data
    • 4.4 Normalise data
    • 4.5 Export data
  • 5 Data manipulation - extracting and exporting records
    • 5.1 Removing samples
    • 5.2 Remove uninformative measurements
    • 5.3 Extracting specific measurements
  • 6 Integrating datasets
    • 6.1 Combining two files with identical row identifiers
    • 6.2 Combining two files with rows in common
  • 7 Charts and graphical output
    • 7.1 Refresher - standard R plotting functions
      • 7.1.1 Plot() function
      • 7.1.2 Boxplots
      • 7.1.3 Heatmaps
      • 7.1.4 PCA plots
    • 7.2 ggplot2
  • 8 Generating report with R
    • 8.1 Other reporting formats
    • 8.2 Interactive reporting with Shiny
  • 9 Conclusion
  • Appendix: Exercise answers
    • 4 Importing and reformatting data files
      • 4.1 Read in the datafile
      • 4.2 Reformat the file
      • 4.3 Transform data
      • 4.4 Normalise data
      • 4.5 Export data
    • 5.1 Data manipulation
      • 5.1.2 Removing samples
      • 5.1.2 Remove uninformative measurements
      • 5.1.3 Extracting specific measurements
    • 5.2 An alternative practice data set
    • 6 Integrating and summarising datasets
      • 6.1.1 Combining two files with identical row identifiers
      • 6.1.2 Combining two files with rows in common
      • 6.1.3 Linking in reference data
    • 6.2 Summarising data
      • 6.2.1 Table summaries
    • 7 Charts and graphical output
      • 7.1.1 Plot
      • 7.1.2 Further timecourse plotting practice
      • 7.1.3 Heatmaps
      • 7.1.4 PCA plots
    • 7.2 ggplot2
  • Appendix - SessionInfo for this version of the course material
  • Published with bookdown

Data preparation, processing and interpretation with R

2.3 Further Reading

  • Key Data Concepts
  • Wickham, H.. (2014) Tidy Data. JStatSoft
  • Hulley S. B. et al. (2013) Designing Clinical Research. Ch16-17
  • McFadden E.. (2007) Management of Data in Clinical Trials