4.R
R: How to make professional and beautiful plots

1) lnstall R and R Studio

2) Reference cards for data analysis

3) R packages

How to install R Packages:
1
> install.packages("PACKAGE")
2
3
# using bioconductor.org
4
> source("https://bioconductor.org/biocLite.R")
5
> biocLite("PACKAGE")
Copied!
Common used packages
    To load data:
    RMySQL: read in data from a database or Excel.
    xlsx: read in data from a Excel.
    To manipulate data:
    stringr: easy to learn tools for regular expressions and character strings.
    reshape2: transform data between wide and long formats.
    To visualize data:
    gplots: heatmap.2
    ggplot2: plotting system for R, based on the grammar of graphics
    plotly: R package for creating interactive web-based graphs
    To model data:
    lme4, nlme: Linear and Non-linear mixed effects models
    randomForest: Random forest methods from machine learning
    glmnet: Lasso and elastic-net regression methods with cross validation
    NMF: do non-negative matrix factorization
    To do bioinformatics:
    limma, DESeq2, edgeR: do differential expression: link
    survival: Tools for survival analysis
    motifRG, MotIV, MotifDb: motif search and enrichment
    enrichR: GO term enrichment
    To write your own R packages:
    devtools: An essential suite of tools for turning your code into an R package.
    To color your figure:

4) Homework

    1.
    Plot one heatmap using function "heatmap.2" in package "gplots" or package "pheatmap" . Here are some files to help you finish this job: plotHeatmap.zip .
    2.
    Make your own plots using other packages, like plotly or ggvis.

5) Teaching video

a.Basics

@Youtube
@Bilibili

b.Advanced (by Yang Eric Li)

@Youtube
@Bilibili

6) References

Last modified 3yr ago