1. Introduction to R and RStudio, language fundamental, data structures introduction, finding documentation. 2. Data types, atomic vectors, lists, factors, matrices, variables, and basic operations. Working with packages. 3. Importing and manipulating tabular data, data frames and tibbles, data export. 4. Control structures and functions, vectorized computation, apply family of functions. Debugging 5. Base graphics and plotting, customizing plots and legends. 6. ggplot2 and tidyverse, creating and customizing scatter plots, line plots, bar plots, and histograms. 7. Data cleaning, data manipulation using tidyverse packages tidyr, dplyr and stringr 8. RMarkdown, writing reports with knitr, command line scripts Introduction to Biocondutor, Biostrings package, sequence manipulation and pattern matching. 9. The GenomicRanges package, working with genomic intervals and annotations 10. Visualizing genomic data with ggplot2, ggbio, creating tracks, density plots, and heatmaps. 11. Analysis of gene expression with RNA-Seq using edgeR and limma. Exploratory analysis of expression data, hypothesis testing, multiple testing correction, and interpretation of results. 12. Phylogenetic analysis and visualization with ape and treeio package
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Data Integration, Manipulation and Visualization of Phylogenetic Trees.
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Grolemund, G., & Wickham, H. (2023). R for Data Science. O'Reilly Media. (online edition: http://r4ds.hadley.nz/).
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Robert Gentleman (2008). R Programming for Bioinformatics. Chapman and Hall/CRC Vince Buffalo (2015). Bioinformatics Data Skills, O'Reilly Media..
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W. N. Venables, D. M. Smith and the R Core Team (2022). An Introduction to R (online edition: https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf).
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