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A.10: R packages

( \newcommand{\kernel}{\mathrm{null}\,}\)

This page describes basic steps for package installation from a CRAN mirror site and how to update installed packages following installation of a new version of R. See at the end of this page for a list of packages described in Mike’s Biostatistics Book.

Adding packages to base R installation

Installing R packages is straightforward, assuming the package is part of CRAN. Select a CRAN mirror site, e.g., 0-Cloud, RStudio’s mirror site.

chooseCRANmirror()

To find out what CRAN mirror was set for the current session use

findCRANmirror()

A list of mirror sites is stored on your computer once R is installed, see CRAN_mirrors.csv in the doc folder, e.g., ~/R-4.3.1/doc.

Once the CRAN mirror is selected, and assuming you have the name of the package, e.g., package.name, then

install.packages("package.name")

will work.

Useful additional command options include

install.packages("package.name", dependencies=TRUE)

which will also download and install any additional packages required, and

install.packages("package.name", quiet=TRUE)

cuts down on the amount of screen output during installation.

If you receive the following warning message,

Warning: package 'package.name' is not available (for R version 4.3.2)

it may be possible that the package has not yet become available, but first double-check for typos.

Another warning message may be that a binary version is available, but a more recent source version is available, prompted by the question, Do you want to install from sources the package which needs compilation? In most cases, the answer is no. R will install a previous binary version. In order to install from source, RTools must be installed.

Update R packages after installing new R version

After updating to new version of R you’ll need to download and update the user installed packages again. If you are running RStudio, see instructions here. For Win11 users you can download and run a package called installr, for macOS users download and install updateR, which will assist you to update R packages.

I prefer to run a script, modified from R-Bloggers.com. This script works on any operating system, but updates only CRAN packages (e.g., not github or Bioconductor).

Before installing the new version of base R, start up your current R installation and set your working directory, setwd(). Enter the following script to gather and save all installed R packages. Select CRAN mirror when prompted.

tmp <- installed.packages()
installedpkgs <- as.vector(tmp[is.na(tmp[,"Priority"]), 1])
save(installedpkgs, file="installed_old.rda")

Shutdown R, then install and start the new version of R (see Install R for help).

In the new version of R, set your working directory as above. Enter the following script

load(file="installed_old.rda")
tmp <- installed.packages()
installedpkgs.new <- as.vector(tmp[is.na(tmp[,"Priority"]), 1])
missing <- setdiff(installedpkgs, installedpkgs.new)
install.packages(missing)
update.packages(ask=FALSE)

Should be good to go. You can remove old R version installation.

Note:

To check installed packages, just view the object installedpkgs created earlier.

R packages used in Mike’s Biostatistics Book

list updated 12 August 2024

package chapter
agRee 16.5 – Instrument reliability and validity
ape 20.11 - Plot a Newick tree
baseline 20.3 - Baseline correction
BiocManager 20.11 - Plot a Newick tree
Bioconductor 20.11 - Plot a Newick tree
BiodiversityR 5.6 - Sampling from Populations
boot 19.2 - Bootstrap sampling
bootstrap 19.1 - Jackknife sampling
BSDA 11.4 - Two-sample effect size
cairoDevice 13.3 - Test assumption of normality
car 4.3 - Box plots
carData 4.1 - Bar (column) charts
cholera 2.3 - A brief history of (bio)statistics
clipr 4 - How to report statistics
combinat 6.3 - Combinations and permutations
confintr 19.2 - Bootstrap sampling
contingencytables 9.6 - McNemar’s test
correlation 16.6 - Similarity and Distance
cranlogs 2.2 - Why do we use R Software?
datasets 4.5 - Scatter plots
digitize 12.3 - Fixed effects, random effects, and ICC
drc 20.10 - Growth equations and dose response calculations
effectsize 12.5 – Effect size for ANOVA
effsize 11.4 - Two-sample effect size
epiR 5.4 - Clinical trials
epitools 7.4 – Epidemiology: Relative risk and absolute risk, explained
exact2x2 9.6 – McNemar’s test
factoextra 20.6 – Dimensional analysis
findpeaks 20.2 - Peak detection
forecast 20.5 - Time series
geepack 20.1 - Area under the curve
geeM 20.1 - Area under the curve
geodist 16.6 - Similarity and Distance
ggplot2 4.1 - Bar (column) charts
ggtree 20.11 - Plot a Newick tree
gplots 4.1 - Bar (column) charts
gtools 6.3 - Combinations and permutations
GrapheR 4.10 - Graph software
HH 12.4 - ANOVA from "sufficient statistics"
HistData 3.2 - Measures of Central Tendency
lattice 4.10 - Graph software
lmboot 19.1 - Jackknife sampling
irr 12.3 - Fixed effects, random effects, and ICC
MASS 12.4 - ANOVA from "sufficient statistics"
Matrix 20.1 - Area under the curve
mcp 12.6 - ANOVA post-hoc tests
MESS 20.1 - Area under the curve
mlr3misc 8.2 – The controversy over proper hypothesis testing
modeest 3.2 - Measures of Central Tendency
multcomp 12.6 - ANOVA posthoc tests
NCStats 3.3 - Measures of dispersion
nlopt 20.10 - Growth equations and dose response calculations
nortest 13.3 – Test assumption of normality
PairedData 10.3 – Paired t-test
peakDetection 20.2 - Peak detection
Phylotools 20.11 - Plot a Newick tree
Phytools 20.11 - Plot a Newick tree
plotly 4.10 - Graph software
plyr 4.1 - Bar (column) charts
polychor 16.4 – Spearman and other correlations
propCIs 7.6 - Confidence intervals
psa 20.6 – Dimensional analysis
psy 12.3 - Fixed effects, random effects, and ICC
psych 3.2 - Measures of Central Tendency
pwr 11.5 - Power analysis in R
random 6.6 - Continuous distributions
rattle 13.3 - Test assumption of normality
Rcmdr 1.1 - A quick look at R and R Commander
RcmdrMisc 1.1 - A quick look at R and R Commander
RcmdrPlugin.EBM 4.4 - Mosaic plots
RcmdrPlugin.EZR 11.5 - Power analysis in R
RcmdrPlugin.HH 12.4 - ANOVA from "sufficient statistics"
RcmdrPlugin.KMggplot2 4.1 - Bar (column) charts
RcmdrPlugin.mosaic 4.4 - Mosaic plots
RcmdrPlugin.survival 20.9 - Survival analysis
Rcolorbrewer 4.4 - Mosaic plots
reshape2 4.6 - Adding a second Y axis
rgl 18.1 - Multiple Linear Regression
Rmisc 3.5 - Statistics of error
ROCR 20.1 - Area under the curve
rptR 12.3 - Fixed effects, random effects, and ICC
RGtk2 13.3 - Test assumption of normality
season 20.5 – Time series
shotGroups 3.5 - Statistics of error
stats 4 – How to report statistics
survival 3.1 - Data types
tanggle 20.11 - Plot a Newick tree
Ternary 4.8 - Ternary plots
testequavar 13.4 - Tests for Equal Variances
tidyverse 4.3 - Box plot
tigerstats 8.4 - Tails of a test
timeseries 20.5 - Time series
TOSTER 16.1 - Product-moment correlation
vegan 20.8 - Diversity indexes
WRS2 3.3 - Measures of dispersion

This page titled A.10: R packages is shared under a CC BY-NC-SA 4.0 license and was authored, remixed, and/or curated by Michael R Dohm via source content that was edited to the style and standards of the LibreTexts platform.

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