Last articles - STHDA : R packages

Plotly: Create Interactive Plots in R

Tue, 08 Aug 2017 20:25:00 +0200

Plotly R package makes interactive, publication-quality graphs online. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, and 3D (WebGL based) charts.

You can read more at: Plotly R Library.

In this document, we’ll show how to create an interactive ggplot2-based graphics using plotly.

Install required packages

install.packages("ggplot2")
install.packages("plotly")

Usage

library(ggplot2)
library(plotly)

set.seed(100)
d <- diamonds[sample(nrow(diamonds), 1000), ]

p <- ggplot(data = d, aes(x = carat, y = price)) +
  geom_point(aes(text = paste("Clarity:", clarity)), size = .5) +
  geom_smooth(aes(colour = cut, fill = cut)) + facet_wrap(~ cut)

ggplotly(p)

The plots look like this:

gganimate: Create Animations with ggplot2

Tue, 08 Aug 2017 19:05:00 +0200

The gganimate package is an extension to ggplot2 for creating an animated plot. It provides a supplementary aesthetic called frame, just like x, y, size, color, or so on. Thus, a variable in your data can be mapped to frame just as others are mapped to x or y.

Install

if(!require(devtools)) install.packages("devtools")
devtools::install_github("dgrtwo/gganimate")

Demo data sets

We’ll use the gapminder data set available in the package of the same name.

To install gapminder, type this:

install.packages("gapminder")

Inspect the data set:

library(gapminder)
head(gapminder)

##       country continent year lifeExp      pop gdpPercap
## 1 Afghanistan      Asia 1952    28.8  8425333       779
## 2 Afghanistan      Asia 1957    30.3  9240934       821
## 3 Afghanistan      Asia 1962    32.0 10267083       853
## 4 Afghanistan      Asia 1967    34.0 11537966       836
## 5 Afghanistan      Asia 1972    36.1 13079460       740
## 6 Afghanistan      Asia 1977    38.4 14880372       786

Simple animation

Plot the variable lifeExp (life expectancy at birth) by the variable gdpPercap (GDP per capita).
Make animation by year using the aesthetic frame = year.

# Load required package
library(gapminder)
library(ggplot2)
library(gganimate)
# Basic scatter plot
mapping <- aes(x = gdpPercap, y = lifeExp, 
               size = pop, color = continent,
               frame = year) 
p <- ggplot(gapminder, mapping = mapping) +
  geom_point() +
  scale_x_log10()
# Animate
gganimate(p)

To animate across continent, use frame = continent.
For faster animation use the option interval, for example interval = 0.2

Save the animation

Allowed format: an GIF, video, or an animated webpage.

gganimate(p, "output.gif")
gganimate(p, "output.mp4")
gganimate(p, "output.swf")
gganimate(p, "output.html")

System requirement: ffmpeg and ImageMagick drivers should be installed on your computer.

Customize

Animate some of the plot layers by highlighting particular points:

p2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  geom_point(aes(frame = year), color = "red") +
  scale_x_log10()
gganimate(p2)

Cumulative layer using the aesthetic cumulative = TRUE.

p3 <- ggplot(gapminder, aes(gdpPercap, lifeExp, frame = year)) +
  geom_path(aes(cumulative = TRUE, group = country)) +
  scale_x_log10() +
  facet_wrap(~continent)
gganimate(p3)

karyoploteR: Plot Karyotype and Visualize Genomic Data

Tue, 01 Aug 2017 21:33:00 +0200

karyoploteR is an R/Bioconductor package to to plot virtually anything along the genome.

Installation:

Code R :

 
source("https://bioconductor.org/biocLite.R")
biocLite("karyoploteR")

Basic workflow to create a karyoplot:

1) start with an empty plot with no data apart from the ideograms themselves using the function plotKaryotype() .

2) add the data plots as required. To add the data there are functions based on the R base graphics: kpPoints(), kpLines(), kpSegments(), kpRects(), … - that can be used to plot virtually anything along the genome and other functions at a higher level useful to plot more specific genomic data types -e.g. kpPlotRegions(), kpPlotCoverage(), …-.

A simple example is to plot a set of regions representing copy-number gains and losses using the kpPlotRegions() function:

Code R :

 
  gains <- makeGRangesFromDataFrame(data.frame(chr=c("chr1", "chr5", "chr17", "chr22"), start=c(1, 1000000, 4000000, 1),
                      end=c(5000000, 3200000, 80000000, 1200000)))
  losses <- makeGRangesFromDataFrame(data.frame(chr=c("chr3", "chr9", "chr17"), start=c(80000000, 20000000, 1),
                       end=c(170000000, 30000000, 25000000)))
  kp <- plotKaryotype(genome="hg19")
  kpPlotRegions(kp, gains, col="#FFAACC")
  kpPlotRegions(kp, losses, col="#CCFFAA")

ggjoy: Create a ggplot2-based Joyplots

Tue, 25 Jul 2017 19:20:00 +0200

Joyplots consist of partially overlapping line plots for visualizing changes in distribution over time or space. Joyplots can be easily created in R using the ggjoy package, which is an extension of ggplot2.

Install ggjoy

install.packages("ggjoy")

Use ggjoy

In the example below, we’ll use the iris demo data set. We’ll visualize the distribution of the variable Sepal.Length by groups (“Species”).

library(ggplot2)
library(ggjoy)
ggplot(iris, aes(x = Sepal.Length, y = Species)) + 
  geom_joy() + 
  theme_joy()

Control the overlap of the different density plots. Use the argument scale.

ggplot(iris, aes(x = Sepal.Length, y = Species)) + 
  geom_joy(scale = 0.9) + 
  theme_joy()

FactoInvestigate R Package: Automatic Reports and Interpretation of Principal Component Analyses

Wed, 19 Jul 2017 20:20:00 +0200

FactoInvestigate makes it easy to generate an automatic report and interpretation of FactoMineR - based principal compoent analyses. The main function provided by the package is the function Investigate(), which can be used to create either a Word, PDF or a HTML report.

The report include the:

detection of existing outliers,
identification of the major first principal components,
plots,
description of dimensions

Install and load Required packages

install.packages("FactoMineR", "FactoInvestigate")

Demo examples

# Load Packages
library("FactoMineR")
library("FactoInvestigate")
# Principal component analyses
data(decathlon)
res.pca <- PCA(decathlon, quanti.sup = 11:12, quali.sup = 13, graph = FALSE)
Investigate(res.pca, file = "PCA.Rmd", document = "pdf_document", 
            parallel = TRUE)
# Correpondance analysis
data(children)
res.ca = CA(children, row.sup = 15:18, col.sup = 6:8, graph = FALSE)
Investigate(res.ca, file = "CA.Rmd", document = "html_document")
# Multiple correspondance analysis
data(tea)
res.mca = MCA(tea, quanti.sup = 19,quali.sup = 20:36, graph = FALSE)
Investigate(res.mca, file = "MCA.Rmd", document = c("word_document", "pdf_document"))

The output looks like this:

FactoInvestigate R Package de kassambara