R is a free software programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software and data analysis (source). More about R popularity you can see here.

I am going to install R language and RStudio as IDE.

# arch yaourt -S r rstudio-desktop-bin # ubuntu sudo apt-get install r-base

The latest version of RStudio for Ubuntu can be found here. Download it and install with next command

sudo dpkg -i rstudio-0.98.501-amd64.deb

**Data types**

R has such data types: numbers, strings, logical, factor, vector, list, matrix, data frame. Almost all data structures are built upon these five types. Hadley Wickham, in his book Advanced R, provided an easy-to-comprehend segregation of these five data structures

# number n <- 7 # string s <- "string" # concatenate two strings s1 <- "Hello, "; s2 <- "world!" paste(s1, s2) # format string sprintf("Hello, %s. It's %d am.", "user", 10) # logical l <- TRUE

A **vector** is a sequence of data elements of the same basic type. A vector can contain any number of elements. However, all the elements must be of the same type; for instance, a vector cannot contain both numbers and text.

v1 <- c(1, 2, 3, 4, 5) [1] 1 2 3 4 5 # sequence v2 <- seq(1,5) [1] 1 2 3 4 5 v3 <- 1:5 [1] 1 2 3 4 5 v3 <- 5:1 [1] 5 4 3 2 1 # sequence with step v4 <- seq(from = 1, to = 5, by = 0.5) [1] 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 # sequence with boundaries, increment is calculated automaticaly v5 <- seq(from=1, to=5, length.out=6) [1] 1.0 1.8 2.6 3.4 4.2 5.0 # a series of repeated values v6 <- rep(1, times=5) v7 <- rep(1:3, times=3) [1] 1 2 3 1 2 3 1 2 3 # repeat 1 time 2 and 3 time 4 v8 <- rep(c(2,4), c(1,3)) [1] 2 4 4 4 # the number of members in a vector length(v1)

Indexing vector

v1 <- c(1, 2, 3, 4, 5) v1[3] [1] 3 # range of items v1[2:4] [1] 2 3 4 # items by order v1[c(1,3,5)] [1] 1 3 5 # exclude 2nd and 4th items v1[-c(2,4)] # logical vector indicating whether each item should be included v1[c(TRUE, TRUE, FALSE, FALSE, TRUE)] # all items greater than 3 v1[v1 > 3] [1] 4 5 # get indexes of all items which values is greater than 3 which(v1 > 3) [1] 4 5 # all values that is greater 1 and less 5 > v1[v1 > 1 & v1 < 5] [1] 2 3 4 # indexing by name names(v1) <- c("one", "two", "three", "four", "five") v1["one"] one 1 v1[c("one", "three")] one three 1 3

Vector arithmetic

v1 <- 1:5 v2 <- 5:10 # multiple each member by 5 v1 * 5 # the sum would be a vector whose members are the sum of the corresponding members from v1 and v2 v3 <- v1 + v2

Sort vector

sort(v1, decreasing=TRUE) five four three two one 5 4 3 2 1

A **matrix** is a collection of data elements, of the same basic type, arranged in a two-dimensional rectangular layout.

Create matrix with 4 rows and 4 columns (4x4).

m <- matrix(seq(1, 16), nrow = 4, ncol = 4) # fill matrix by rows m <- matrix(seq(1, 16), nrow = 4, ncol = 4, byrow = TRUE) # create matrix from vector m <- 1:16 dim(m) <- c(4,4)

Matrix indexing

# element at 1st row, 2nd column m[1, 2] # the 3rd row m[3,] # the 3rd column m[,3] # the 3rd and 4th columns m[, c(3,4)]

Matrix arithmetic

# multiple 1st and 4th columns m[, 1] * m[, 4] # transpose matrix t(m)

A **list** is used for storing an ordered set of values. However, unlike a vector that requires all elements to be the same type, a list allows different types of values to be collected. When a *list* is constructed, you have the option of providing names (*fullname*), for each value in the sequence of items.

# create three vectors v1 <- c("a", "b", "c") v2 <- seq(1, 5) v3 <- c(FALSE, TRUE, TRUE, FALSE) # combine three vectors to list with names l <- list(Text=v1, Number=v2, Logic=v3) # indexing 1st member l[[1]] l$Text

A **data frame** is used for storing data tables. It is a list of vectors of equal length. For example, the following variable *tbl* is a data frame containing three vectors *car, year, owner*.

car <- c("Ford", "BMW", "Audio") year <- c(1972, 1976, 1983) owner <- c("John", "Lisa", "Lisa") tbl <- data.frame(Car=car, Year=year, Owner=owner, stringsAsFactors=FALSE) # display the structure str(tbl) # indexing by column tbl$Car # get 3rd item from Year column tbl$Year[3] # extract several columns from a data frame tbl[c("Year", "Owner")] # extract the value in the 1st row and 2nd column tbl[1, 2] # get range from Year column tbl$Year[1:3] # get range from Year column where year is biger than 1972 tbl$Year[tbl$Year > 1972] # get all cars which belongs to Lisa tbl$Car[tbl$Owner == "Lisa"] # lookup # first 3 rows head(tbl, n=3) # last 3 rows tail(tbl, n=3)

Import data frame from file

# from CSV file tbl <- read.csv(file="table.csv", header=TRUE, sep=","); names(tbl) # from Excel file library(gdata) tbl <- read.xls("table.xls")

**Plot data**

There are many external packages for charts.

Following are internal abilities of R.

# random sequence for plot v <- sample(1:10, 15, replace=TRUE)

Strip chart plots the data in order along a line with each data point represented as a box.

stripchart(v)

Histogram plots the frequencies that data appears within certain ranges.

hist(v, help="Distribution of v", xlab="v")

A boxplot provides a graphical view of the median, quartiles, maximum, and minimum of a data set.

boxplot(v)

A scatter plot provides a graphical view of the relationship between two sets of numbers.

v2 < sample(1:10, 15, replace=TRUE) plot(v, v2)

**Statistics with R**

The following commands can be used to get the mean, median, quantiles, minimum, maximum, variance, and standard deviation of a set of numbers:

# random sequence v <- sample(1:10, 15, replace=TRUE) mean(v) median(v) # quantiles quantile(v) # minimum min(v) # maximum max(v) # variance var(v) # standard deviation sd(v) # filter missing values append(v, NA) mean(v, na.rm=TRUE) sd(v, na.rm=TRUE)

Finally, the summary command will print out the min, max, mean, median, and quantiles:

summary(v) Min. 1st Qu. Median Mean 3rd Qu. Max. 1.0 5.0 8.0 6.8 10.0 10.0

The `cor`

and `cov`

functions can calculate the correlation and covariance, respectively, between two vectors:

# random sequence v1 <- sample(1:10, 15, replace=TRUE) v2 <- sample(1:10, 15, replace=TRUE) cor(v1, v2) cov(v1, v2)

The *correlation* between two variables is a number that indicates how closely their relationship follows a straight line. Without additional qualification, correlation typically refers to *Pearson's correlation coefficient*, which was developed by the 20th century mathematician Karl Pearson. The correlation ranges between -1 and +1. The extreme values indicate a perfectly linear relationship, while a correlation close to zero indicates the absence of a linear relationship.

*Covariance* is a measure of the linear relationship between two continuous variables. Covariance is scale dependent, meaning that the value depends on the units of measurements used for the variables. For this reason, it is difficult to directly interpret the covariance value. The higher the absolute covariance between two variables, the greater the association. Positive values indicate positive association and negative values indicate negative association.

**Import/Export data**

# save variables to file save(v, v1, v2, file="myvar.RData") # load variables from file load("myvar.RData") # save data frame to CSV write.csv(df, file="df.csv") # load data frame from CSV df <- read.csv("df.csv", stringsAsFactors = FALSE, header = FALSE)

Also, we can import data from SQL databases (PostgreSQL, MySQL, and so on) with help of *RODBC package*.

# install and load library install.packages("RODBC") library(RODBC) # create connection mydb <- odbcConnect("DSN", uid="username", pwd="password") # create query and execute it query <- "select * from table where status = 1" df <- sqlQuery(channel=mydb, query=query, stringsAsFactors=FALSE) # close connection odbcClose(mydb)

**Packages**

To install package

install.packages("package name")

To update packages

update.packages()

To see which packages are already installed on the computer, enter

installed.packages()

**Help**

Use help to display the documentation for the function:

help(functionname) # or ?(function)

Use args for a quick reminder of the function arguments:

args(functionname)

Use example to see examples of using the function:

example(functionname)

**Useful links**

Any learning algorithm is only as good as its input data ...
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