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R Basics

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R Packages

Repositories

Packages are the fundamental units of R code. They include R functions, documentation, and sample data. R packages can be found in repositories and these are the most popular ones:

  • CRAN - official R repository
  • Bioconductor - topic specific R repository
  • Github - most popular repository for open source projects, not R specific

Installing Packages via devtools

The different repositories have unique ways to install a package. This might be a bit confusing if you are using packages from different repositories. You could use devtools package instead to unify this process. For more information, visit the devtools readme page.

Package Installation

The commands for the installation of R packages depends on the repository.

### CRAN Repository

install.packages("package")
install.packages(c("packageA", "packageB"))

### Bioconductor Repository

## R version 3.6+
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("package")

## Older R versions
source("https://bioconductor.org/biocLite.R")
biocLite("package")

### GitHub
library(devtools)
devtools::install_github("link/to/package")

Find Packages

# Searching for packages on CRAN
install.packages("packagefinder"); library(packagefinder)
findPackage("tea")
packageDetails("tea")

Package Info/Help

It is a superb idea to look at the package information.

packageDescription("package")
help(package = "package")

Manage Packages

# List all installed packages
installed.packages()
# Get Package version
packageVersion("fun")
# Update a package
update.packages("fun")
# Load a package
library("fun")
# Un-load a package
detach("package:fun", unload=TRUE)
# Remove a package
remove.packages("fun")

Example(s)

## Alternative package version

# Install dplyr by installing tidyverse (collection of data science tools):
install.packages("tidyverse")
search()

# Alternatively, install just dplyr:
install.packages("dplyr")

# Or the development version from GitHub:
install.packages("devtools")
devtools::install_github("tidyverse/dplyr")

## Load multiple CRAN packages

# Package list
package.list = c("ggplot2","RColorBrewer","ggpubr")

package.manager <- lapply(
  package.list,
  FUN <- function(x) {
    # Load multiple packages and
    # install missing packages
    if (!require(x, character.only = TRUE)) {
      install.packages(x, dependencies = TRUE)
      library(x, character.only = TRUE)
    }
  }
)

R Code with Style

Style Guides

xkcd.com webcomic Picture Source: https://xkcd.com

Style Packages

Code Folding

RStudio supports both automatic and user-defined folding for regions of code. Code folding allows you to easily show and hide blocks of code to make it easier to navigate your source file and focus on the coding task at hand.

To insert a new code section you can use the Code > Insert Section command. Alternatively, any comment line which includes at least four trailing dashes (-), equal signs (=), or pound signs (#) automatically creates a code section.

## Setup ----

## clean/reset environment
rm(list=ls())

## R and Bioconductor libraries
library(ggplot2)

## Data Import ----

otufile <- "ZOTU_c99_Count_Sintax.txt"
mapfile <- "MapFile.txt"

## Import into Phyloseq
d.ZOTU <- import_qiime(otufilename = otufile, mapfilename = mapfile)
d.ZOTU