Start with R

Useful web sites for R:

• Quick-R
• R-bloggers
• r-tutor
• Kickstarting R
• cran.r-project.org
• r books
• Andrew Field, youtube videos
• Simply Statistics
• idre stat
• programiz
• shiny
• learn graph
  • library: lattice, ggvis, ggplot2

About Package

• install.packages(“ggplot2”), you can also install with RStudio menu: Tools -> Install Packages…
• library(ggplot2)

About work path:

• getwd(): get the work path
• setwd(“xxx”): set the work path
• list.files(): list all the files in work path
• list(): list functions
• dir(): list files

About help:

• help(sum): get the help of a function. (“sum” is a example function name)
• example(sum): get the example of a function. (“sum” is a example function name)

load R script:

• xxx.R: standard name for R extension
• source(“xxx.R”): load the R script

Read data:

• read.csv(“xxx”)
• read.table(“xxx”, …)

Get default databases:

• >library(datasets)
  >data(mtcars,iris)

type of data:

• class(xxx)
• typeof(xxx)

create vector:

• x <- c(a,b)
• x <- vector(“numeric”, length=10)
• as.numeric(x); as.logical(x); as.character(x) : coertion

create List:

• x <- list(1, “a”, T, 1+4i)
• contrast to member can have different type

create matrices:

• m <- matrix(1:6, nrow = 2, ncol = 3)
• dim(m) : dimension
• attributes(m)
  • $dim
    2 3
• m <- 1:10
  dim(m) <- c(2,5)
• x <- 1:3
  y <- 10:12
  cbind(x,y)
  rbind(x,y)

Factors

• x <- factor( c(“yes”,”yes”,”no”,”yes”,”no”) )
• table(x): result: no 2, yes 3
• unclass(x) : result: 2 2 1 2 1
• attr(,”levels”) : result: “no”, “yes”
• x <- factor(c(“yes”,”yes”,”no”,”yes”,”no”), levels = c(“yes”, “no”) ) : “yes” will be the first level, and “no” will be the second level

Data Frames

• > df <- data.frame(vector1, vector2,…)
• > x <- data.frame(foo=1:4, bar = c(T,T,F,F))
• >data.matrix(x) # convert to matrix, all the data type must be same because of the coertion
• > nrow(x), ncol(x)
• > subset(mtcars, mtcars$cyl==8) ##Get a subset of data set “mtcars”, where column “cyl” (number of cylinders) is 8, for more information about mtcars, please use help(mtcars)
• > mtcars[mtcars$cyl==8, ]
• > head(mtcars, n=5L ) # get the first n rows of the dataset
• >tail(mtcars, n=5L) # get the last n rows of the datset
• > str(mtcars) # get the overview of the data set
• > summary(mtcars) #get the min, 1st Qua, median, mean, 3 Qua, max for each column
• > nrow(na.omit(mtcars)) #discard the rows with missing values
• > dim(mtcars) # get the dimension of the data set
• > attributes(mtcars)
• more about subset data frame

Missing Values:

• > is.na(airquality) # return a boolean vector or matrix about the missing value
• > complete.cases(vector1, vector2,…) # return a boolean vector about the missing values in every position
• > good <- complete.cases(airquality) # return a boolean vector about wether the rows has missing values
  > airquality[good, 1:6 ]
• sapply(airquality, function(x) sum(is.na(x))) #elegent check the missing values for each column
• apply(is.na(airquality),2,sum)
• length(which(is.na(airquality[1]))) or length(which(is.na(airquality[1])==T))

Names Attribute:

• names to a vector: x <- 1:3
  names(x) <- c(“foo”, “bar”, “norf”)
  names(x) : “foo” “bar” “norf”
• names to a list: x <- list(a=1, b=2, c=3)
• names to a matrix:
  m <- matrix(1:4, nrow=2, ncol=2)
  dimnames(m) <- list(c(“a”, “b”), c(“c”, “d”))

Reading Data:

• read.table(), read.csv() <-> write.table()
• readLines() <-> writeLines()
• sourse(): for reading in R code files <-> dump()
• dget():  for reading in R code files,(R object but  have been deparsed into text files) <-> dput()
• load, for reading in saved workspaces <-> save
• unseriealize <-> serialize

Textual Formats:

• dput(y)
• dput(y, file = “y.R”)
• dget(“y.R”)
• dump() and source(): deparse multiple objects and read it back:
  x <- “foo”
  y <- data.frame(a=1, b=”a”)
  dump(c(“x”,”y”), file = “data.R”)
  rm(x,y)
  source(“data.R”)

Connections:

• file(), gzfile(), bzfile(), url()
• open mode: “r” read only; “w” writing (and initializing a new file); “a” appending; “rb”, “wb”, “ab” reading, writing, or appending in binary mode (windows)

Matrix vectorized operation:

• x <- matrix(1:4, 2,2); y <- matrix(rep(10,4),2,2)
  x*y : element-wise multiplication
  x/y
  x %*% y : true matrix multiplication