R is now a large community. Today we have 4,567 6,139 R packages. My attempt here is to jot down the code snippets based on the newer packages (by following table of available packages, sorted by date of publication)1.

The chunk of the codes will be taken either from the Vignette or from the main package documentation pdf. I also like to include stack overflow threads if there are interesting questions. These interesting and easy-to-apply codes will be applied to different data sets to develop models and assumptions. In this random version, exploration on rmarkdown is done.

Few Markdown Tips

R Markdown Reference Guide is a good source in learning rmarkdown. A must read.

Output Style

highlight="tango", "pygments", "kate", "zenburn"
theme= "cerulean", "journal", "flatly", "readable", "spacelab", "united", "cosmo"

output:
    html_document:
        toc: true
        toc_depth: 4
        theme: cerulean
        highlight: zenburn

Tables

| Item | Nuts and Bolts          |
| ------------- | ----------- |
| Why      | ~~Don't~~ why me|
| Dude     | _Don't_ dude me, Bro.    |

| Left-Aligned  | Center Aligned  | Right Aligned |
| :------------ |:---------------:| -----:|
| Left is a      | Center is not | Right is a |
| Left is a      | always        |   Right. Right is not |
| Left is a Left | centered        |   a `right`|
Item Nuts and Bolts
Why Don’t why me
Dude Don’t dude me, Bro.
Left-Aligned Center Aligned Right Aligned
Left is a Center is not Right is a
Left is a always Right. Right is not
Left is a Left centered a right 
Ordinal indicators are sometimes written as superscripts (1^st^, 2^nd^, 3^rd^, 4^th^, rather than 1st, 2nd, 3rd, 4th), although many English-language style guides recommend against this use. Other languages use a similar convention, such as 1^er^ or 2^e^ in French, or 4ª and 4º in Italian, Portuguese, and Spanish.

Also in mathematics and computing, a subscript can be used to represent the radix, or base, of a written number, especially where multiple bases are used alongside each other. For example, comparing values in hexadecimal, denary, and octal one might write C~hex~ = 12~dec~ = 14~oct~.

Ordinal indicators are sometimes written as superscripts (1st, 2nd, 3rd, 4th, rather than 1st, 2nd, 3rd, 4th), although many English-language style guides recommend against this use. Other languages use a similar convention, such as 1er or 2e in French, or 4ª and 4º in Italian, Portuguese, and Spanish.

Also in mathematics and computing, a subscript can be used to represent the radix, or base, of a written number, especially where multiple bases are used alongside each other. For example, comparing values in hexadecimal, denary, and octal one might write Chex = 12dec = 14oct.

Equation

$$
\begin{aligned}
\dot{x} & = \sigma(y-x) \\
\dot{y} & = \rho x - y - xz \\
\dot{z} & = -\beta z + xy
\end{aligned} 
$$

\[ \begin{aligned} \dot{x} & = \sigma(y-x) \\ \dot{y} & = \rho x - y - xz \\ \dot{z} & = -\beta z + xy \end{aligned} \]

Multiple Correspondence Analysis (MCA)

Five R packages are widely used for Multiple Correspondence Analysis (MCA). Part 3 compiles MCA codes.

The packages are:

Factominer, MASS, ade4, ca, homals

I have used the codes compiled by Gaston Sanchez

FactoMineR

  1. Package pdf.
  2. Website.
  3. Paper
  4. Last updated: 2014-XX-XX
library(FactoMineR)
library(ggplot2)

data(tea)
newtea = tea[, c("Tea", "How", "how", "sugar", "where", "always")]
head(newtea, 3)
##         Tea   How     how    sugar       where     always
## 1     black alone tea bag    sugar chain store Not.always
## 2     black  milk tea bag No.sugar chain store Not.always
## 3 Earl Grey alone tea bag No.sugar chain store Not.always
cats = apply(newtea, 2, function(x) nlevels(as.factor(x)))
mca1 = MCA(newtea, graph = FALSE)
mca1_vars_df = data.frame(mca1$var$coord, Variable = rep(names(cats), cats))
mca1_obs_df = data.frame(mca1$ind$coord)
ggplot(data=mca1_vars_df, aes(x = Dim.1, y = Dim.2, label = rownames(mca1_vars_df))) +
 geom_hline(yintercept = 0, colour = "gray70") +
 geom_vline(xintercept = 0, colour = "gray70") +
 geom_text(aes(colour=Variable)) +
 ggtitle("MCA plot of variables using R package FactoMineR")

ggplot(data = mca1_obs_df, aes(x = Dim.1, y = Dim.2)) +
  geom_hline(yintercept = 0, colour = "gray70") +
  geom_vline(xintercept = 0, colour = "gray70") +
  geom_point(colour = "gray50", alpha = 0.7) +
  geom_density2d(colour = "gray80") +
  geom_text(data = mca1_vars_df, aes(x = Dim.1, y = Dim.2, 
                label = rownames(mca1_vars_df), colour = Variable)) +
  ggtitle("MCA plot of variables using R package FactoMineR") +
  scale_colour_discrete(name = "Variable")

MASS

  1. Package pdf.
  2. Last updated: 2014-XX-XX
library(MASS)
## 
## Attaching package: 'MASS'
## 
## The following object is masked _by_ '.GlobalEnv':
## 
##     cats
mca2 = mca(newtea, nf = 5)
mca2$d^2
## [1] 0.2797618 0.2577477 0.2201379 0.1879296 0.1687650
mca2_vars_df = data.frame(mca2$cs, Variable = rep(names(cats), cats))
ggplot(data = mca2_vars_df, 
       aes(x = X1, y = X2, label = rownames(mca2_vars_df))) +
  geom_hline(yintercept = 0, colour = "gray70") +
  geom_vline(xintercept = 0, colour = "gray70") +
  geom_text(aes(colour = Variable)) +
  ggtitle("MCA plot of variables using R package MASS")

ade4

  1. Package pdf.
  2. Last updated: 2014-XX-XX
library(ade4)
## 
## Attaching package: 'ade4'
## 
## The following object is masked from 'package:FactoMineR':
## 
##     reconst
mca3 = dudi.acm(newtea, scannf = FALSE, nf = 5)
mca3$eig
##  [1] 0.27976178 0.25774772 0.22013794 0.18792961 0.16876495 0.16368666
##  [7] 0.15288834 0.13838682 0.11569167 0.08612637 0.06221147
mca3_vars_df = data.frame(mca3$co, Variable = rep(names(cats), cats))
ggplot(data = mca3_vars_df, 
       aes(x = Comp1, y = Comp2, label = rownames(mca3_vars_df))) +
  geom_hline(yintercept = 0, colour = "gray70") +
  geom_vline(xintercept = 0, colour = "gray70") +
  geom_text(aes(colour = Variable)) +
  ggtitle("MCA plot of variables using R package ade4")

ca

  1. Package pdf.
  2. Last updated: 2014-XX-XX
library(ca)
mca4 = mjca(newtea, lambda = "indicator", nd = 5)
mca4$sv^2
##  [1] 0.27976178 0.25774772 0.22013794 0.18792961 0.16876495 0.16368666
##  [7] 0.15288834 0.13838682 0.11569167 0.08612637 0.06221147
mca4_vars_df = data.frame(mca4$colcoord, Variable = rep(names(cats), cats))
rownames(mca4_vars_df) = mca4$levelnames
ggplot(data = mca4_vars_df, 
       aes(x = X1, y = X2, label = rownames(mca4_vars_df))) +
  geom_hline(yintercept = 0, colour = "gray70") +
  geom_vline(xintercept = 0, colour = "gray70") +
  geom_text(aes(colour = Variable)) +
  ggtitle("MCA plot of variables using R package ca")

homals

  1. Package pdf.
  2. Last updated: 2014-XX-XX
library(homals)
mca5 = homals(newtea, ndim = 5, level = "nominal")
mca5$eigenvalues
mca5$catscores

D1 = unlist(lapply(mca5$catscores, function(x) x[,1]))
D2 = unlist(lapply(mca5$catscores, function(x) x[,2]))

mca5_vars_df = data.frame(D1 = D1, D2 = D2, Variable = rep(names(cats), cats))
rownames(mca5_vars_df) = unlist(sapply(mca5$catscores, function(x) rownames(x)))
ggplot(data = mca5_vars_df, 
       aes(x = D1, y = D2, label = rownames(mca5_vars_df))) +
  geom_hline(yintercept = 0, colour = "gray70") +
  geom_vline(xintercept = 0, colour = "gray70") +
  geom_text(aes(colour = Variable)) +
  ggtitle("MCA plot of variables using R package homals")

  1. Compiled by Subasish Das↩