# R Notes¶

Author: Emre Şahin <12294 - Tue 08:23>

These are the notes I took from here and there, including Coursera Data Analysis course and R’s online help, with help.start

## Basics¶

=R= objects have attributes which can be observed by attributes() functions.

=<-= is the assignment operator

=:= is used to create integer sequences. 1:4 = 1 2 3 4

=c= function can be used to create vectors from different kinds of objects. (concatenate) c(TRUE, FALSE) creates a logical vector, c(1+3i, 4+8i, 3-5i) creates a complex vector. Coercion happens if different kinds of objects are mixed.

=as.*= functions can be used to coerce different kinds of data. as.numeric(TRUE) returns 1.

=matrix(ncol = 3, nrow = 4)= creates a matrix.

=cbind= and rbind are other options to create a matrix from vectors by binding them to columns or rows.

=factor=s are categorized data, like male///female. They are created using factor function. table and unclass functions can also be used to get information and change the factor into a numeric table.

=levels= parameter to factor function can be used to determined the factor <-> number correspondence.

=is.nan= and is.na functions are used to check whether vector values are NaN or NA.

=data.frame=s are used to store tabular data like matrices. but unlike matrices, they can store different types of data in each column. first column can be numeric, second row can be factor, third can be logical etc.

=data.frame=s are created usually by read.table and read.csv functions. each row has a name which can be accessed by row.names It can be converted to matrix with data.matrix.

=nrow= and ncol functions can be used to get row and columns sizes of data.table.

=str= and summary functions can be used to get a concise information about a structure.

Use getwd to report the working directory, and use setwd to change it:

The ls function displays the names of objects in your workspace:

> x <- 10
> y <- 50
> z <- c("three", "blind", "mice")
26 | Chapter 2: Some Basics
> f <- function(n,p) sqrt(p*(1-p)/n)
> ls()
[1] "f" "x" "y" "z"


=rm= function removes, permanently, one or more objects from the workspace:

### Scripts¶

R will execute the .Rprofile script when it starts. The placement of .Rprofile depends upon your platform: In Linux, or Unix Save the file in your home directory ~/.Rprofile

The source function instructs R to read the text file and execute its contents:

On the command line, this can be run as

Managing the various objects used in R can be challenging. As mentioned in Starting Up, using the directory structure to sort these objects into sensible categories can be a big help. Instead of starting the R session in a particular directory, you may wish to keep a directory of R scripts and allow these to change the working directory to suit whatever task they perform.

=par(ask=TRUE)= requires you to hit before each plot is displayed.

=readline(“Press <Enter> to continue”)= presents a prompt where you like.

### Vectors¶

Vectors are created like v <- c(1.1, 2.2, 3.3). Vectors can be used in arithmetic expressions. x <- v + 2 * w A shorter vector is cycled until it reaches the length of longer vector in arithmetic expressions.

=range= returns the minimum and maximum elements of a vector.

=sort= sorts a vector in increasing order.

=sqrt(-17)= return NaN but sqrt(-17+0i) returns a result.

Regular sequences are generated by : operator. 4:10 returns [4, 5, 6, 7, 8, 9, 10]. This is a syntactic sugar for seq function which also receives step size and length parameters.

=rep= function repeats the supplied elements to create a vector.

### Arrays¶

If z is a vector with 1500 elements (e.g. z <- 1:1500), then dim(z) <- c(3, 5, 100) makes it a 3D array with the respective boundaries.

Another way to create an array is like x <- array(1:20, dim=c(4,5))

### Matrices¶

Two matrices A and B can be multiplied like A %*% B

A linear equation of the for b <- A %*% x can be solved by solve(A, b)

### Lists¶

A list can be created by list function. List elements don’t have to be of the same type. They can be anything from characters to vectors.

=> mylist <- list(name=”Fred”, no.children=3, child.ages=c(4, 7, 9))=



### Method 2: Install from CRAN directly¶

Type the following command in R console to install it to /my/own/R-packages/ directly from CRAN:

> install.packages("mypkg", lib="/my/own/R-packages/")


Type the following command in R console to load the package

> library("mypkg", lib.loc="/my/own/R-packages/")


## density¶

Use the density function to approximate the sample density; then use lines to draw the approximation:

> hist(x, prob=T)
> lines(density(x))


## Creating Data Frame¶

> points <- data.frame(label=c("Low", "Mid", "High"),
lbound=c(0, 0.67, 1.64),
ubound=c(0.674, 1.64, 2.33))


## The print function¶

lets you vary the number of printed digits using the digits parameter:

> print(pi, digits=4)


## The cat function does not give you direct control over formatting.¶

Instead, use the format function to format your numbers before calling cat:

> cat(pi, "\n")


## The list.files function shows the contents of your working directory:¶

> list.files()


## The write.csv function can write a CSV file:¶

> write.csv(x, file="filename", row.names=FALSE)


## ~ notation for relations between variables¶

R has a special notation for describing relationships between variables. Suppose that you are assuming a linear model for a variable y, predicted from the variables x_1, x_2,...,x_n. (Statisticians usually refer to y as the dependent variable, and x_1,x_2,..., x_n as the independent variables.) In equation form, this implies a relationship like: In R, you would write the relationship as y ~ x1 + x2 + ... + xn, which is a formula object.

## Factor analysis¶

Factor analysis is available in R through the function factanal in the stats package:

factanal(x, factors, data = NULL, covmat = NULL, n.obs = NA,
subset, na.action, start = NULL,
scores = c("none", "regression", "Bartlett"),
rotation = "varimax", control = NULL, ...)


## PCA¶

Principal components analysis breaks a set of (possibly correlated) variables into a set of un- correlated variables. In R, principal components analysis is available through the function prcomp in the stats package:

## distributions in R¶

Binomial binom n = number of trials; p = probability of success for one trial Geometric geom p = probability of success for one trial Hypergeometric hyper m = number of white balls in urn; n = number of black balls in urn; k = number of balls drawn from urn 177 Discrete distribution R name Parameters Negative binomial (NegBinomial) nbinom size = number of successful trials; either prob = probability of successful trial or mu = mean Poisson pois lambda = mean Table 8-2. Continuous distributions Continuous distribution R name Parameters Beta beta shape1; shape2 Cauchy cauchy location; scale Chi-squared (Chisquare) chisq df = degrees of freedom Exponential exp rate F f df1 and df2 = degrees of freedom Gamma gamma rate; either rate or scale Log-normal (Lognormal) lnorm meanlog = mean on logarithmic scale; sdlog = standard deviation on logarithmic scale Logistic logis location; scale Normal norm mean; sd = standard deviation Student’s t (TDist) t df = degrees of freedom Uniform unif min = lower limit; max = upper limit Weibull weibull shape; scale Wilcoxon wilcox m = number of observations in first sample; n = number of observations in second sample

## combination calculation¶

A common problem in computing probabilities of discrete variables is counting combinations: the number of distinct subsets of size k that can be created from n items. The number is given by \$\frac{n!}{r!(n − r)!}, but it’s much more convenient to use the choose function—especially as n and k grow larger:

> choose(5,3) # How many ways can we select 3 items from 5 items?
[1] 10
> choose(50,3) # How many ways can we select 3 items from 50 items?
[1] 19600
> choose(50,30) # How many ways can we select 30 items from 50 items?
[1] 4.712921e+13


These numbers are also known as binomial coefficients.

## generating combinations¶

When you want to generate all combinations of n items taken k at a time. Use the combn function:

> combn(items, k)


## selecting n items from a vector¶

The sample function will randomly select n items from a vector:

> sample(vec, n)


## dotplot() of lattice¶

The dotplot() function in library(lattice) is useful for displaying labeled quantitative values

## measuring intelligence¶

The classic examples come from the social sciences. Suppose that you wanted to measure intelligence. It’s not possible to directly measure an abstract concept like intelligence, but it is possible to measure performance on different tests. You could use factor analysis to analyze a set of test scores (the observed values) to try to determine intelligence (the hidden value).

## correlation¶

Correlation measures range between −1 and 1; 1 means that one variable is a (positive) linear function of the other, 0 means the two variables aren’t correlated at all, and −1 means that one variable is a negative linear function of the other (the two move in completely opposite directions;

## bootstrapping¶

Would we get a similar result if we were to omit a few points? What are the range of values for the statistic? It is possible to answer this question for an arbitrary statistic using a technique called bootstrapping. Formally, bootstrap resampling is a technique for estimating the bias of an estimator. An estimator is a statistic calculated from a data sample that provides an estimate of a true underlying value, often a mean, standard deviation, or a hidden parameter. Bootstrapping works by repeatedly selecting random observations from a data sam- ple (with replacement) and recalculating the statistic. In R, you can use bootstrap resampling through the boot function in the boot package:

## paste¶

The paste function allows you to concatenate multiple character vectors into a single vector.

## chi-squared distribution¶

The chi-squared distribution allows for statistical tests of categorical data. Among these tests are those for goodness of fit and independence.

## chi-square test¶

The chi-square test for homogeneity does a similar analysis as the chi-square test for independence. For each cell it computes an expected amount and then uses this to compare to the frequency.

## plotting the regression line¶

To plot the regression line You make a plot of the data, and then add a line with the abline command

> plot(x,y)
> abline(lm.result)


## coefficients of regression¶

To access the coefficients The coef function will return a vector of coefficients.

> coef(lm.result)
(Intercept)            x
210.0484584 -0.7977266


## anova¶

For example, to test if there is a difference between control and treatment groups. The method called analysis of variance (ANOVA) allows one to compare means for more than 2 independent samples.

## regression analysis¶

Regression analysis is used for explaining or modeling the relationship between a single variable y, called the response, output or dependent variable; and one or more predictor, input, independent or explanatory variables, x_1,...,X_p. When p=1, it is called simple regression but when p>1 it is called multiple regression or sometimes multivariate regression. When there is more than one y, then it is called multivariate multiple regression

## i.i.d¶

independent and identically distributed (i.i.d.)

## linear models¶

Linear models seem rather restrictive, but because the predictors can be transformed and combined in any way, they are actually very flexible. The term linear is often used in everyday speech as almost a synonym for simplicity. This gives the casual observer the impression that linear models can only handle small simple datasets. This is far from the truth—linear models can easily be expanded and modified to handle complex datasets. Linear is also used to refer to straight lines, but linear models can be curved.

## nonlinear models¶

Truly nonlinear models are rarely absolutely necessary and most often arise from a theory about the relationships between the variables, rather than an empirical investigation.

## failing to reject the null hypothesis¶

A failure to reject the null hypothesis is not the end of the game—you must still investigate the possibility of nonlinear transformations of the variables and of outliers which may obscure the relationship. Even then, you may just have insufficient data to demonstrate a real effect, which is why we must be careful to say “fail to reject” the null rather than “accept” the null. It would be a mistake to conclude that no real relationship exists.

## Statistical inference is based on the assumption that¶

none of the expected counts is smaller than 1 and most (80%) are bigger than 5. As well, the data must be independent and identically distributed – that is multinomial with some specified probability distribution. If these assumptions are satisfied, then the χ2 statistic is approximately χ2 distributed with n − 1 degrees of freedom

## using null hypothesis for independence testing¶

The same statistic can also be used to study if two rows in a contingency table are “independent”. That is, the null hypothesis is that the rows are independent and the alternative hypothesis is that they are not independent.