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
Appendix: Exercise answers