Matrices & Data Frame

Overview

Teaching: 20 min
Exercises: 0 min

Questions

• Vector in R

• How to define matrix in R?

• How to manipulate a data frame in R?

• How to read text/csv file

Objectives

• Working with Matrix

• Create data frame

• Import and export data frame

• Working with text/csv files

Vector

Typical object is a vector, that can be defined using function c() #c stands for combine

str <- c("a","b","c")
a   <- c(4,5.6,20)
b   <- c("TRUE","FALSE")

A vector having different objects: coercion

str1 <- c("a","b","c",5, 4.5)
str1
class(str1)
b1<- c(5, FALSE)
b1
class(b1)

Explicit Coercion

Convert objects from one class to another, using as. function:

a <- 0:5
class(a)
as.numeric(a)
as.logical(a)
as.character(a)

How about Nonsensical Coercion?

str <- c("a","b","c")
class(str)
as.numeric(str)
as.logical(str)
as.character(str)

Vectors Operation

a <- 3:7
b <- 20:24
a+b
a>b
a>5
a*b
a/b

Matrices

Matrics are vectors with dimension attribute. The dimension attribute is itself an integer vector of length 2 (nrow, ncol)

m <- matrix(1:12,nrow=3,ncol=4)
m <- matrix(1:12,3,4)
m
dim(m)

Another way to create matrices

m <- 1:12
dim(m) <- c(3,4)

Matrix Functions:

# Define a matrix
mr <- matrix(runif(9),3,3)
#Transpose matrics
t(mr)
#Diagonal of matrics
diag(mr)
#Determinant
det(mr)
#Inverse
solve(mr)

Merging Matrices

Merging matrices by row and column using rbind and cbind

m2 <- letters[seq(from=1,to=12)]
dim(m2) <- c(3,4)
m2
cbind(m,m2)
rbind(m,m2)

Matrices Operation

m1 <- matrix(1:9,nrow=3,ncol=3)
m2 <- matrix(rep(10,9),3,3)
m1
m2
m1+m2
m1*m2
m1 %*% m2

Factors

Factors are used to represent categorical data

m <- c("John","Mary","John","John","Jeff","Mary")
factor(m)
table(m)

Data Frames

Data frame is used to store tabular data, a table or 2-D array structure in which:

• Each column contains values of one variable and
• Each row containts one set of value from each column

Data Frame characteristics:

• Column name should not be empty
• Row name should be unique
• Data can be numeric, integer, character, factor
• Each column contains same number of data items

df <- data.frame(data=sample(12),title=LETTERS[sample(12)])
dim(df)
head(df)
names(df)
nrow(df)
ncol(df)

There are many available data frame in R, for example iris data set:

data(iris)

Names of Objects in Data Frames

Using name() function:

names(iris)
# Change name
names(iris) <- c("a", "b", "c","d","e")
head(iris)

#Change name for particular columns:
names(iris)[4] <- "new_name"

Getting data from Data Frames

Using columns or $ to get the name of Data Frames;

data(mtcars)
mpg1 <- mtcars$mpg
mpg2 <- mtcars[,1]
cylinder <- mtcars$cyl

Reading and Writing Tables

Reading Table Most popular syntax for reading table in R. Data can be read online by providing the link or read offline from the working directory

• read.table: read table in text format
• read.csv: read table in csv format
• read.xlsx: read table in excel format (require xlsx packages)
• readLines: read lines of a text file
• source: read R code

Writing Table Similarly there are syntax for writing table:

• write.table: write table in text format
• write.csv: write table in csv format
• write.xlsx: write table in excel format (require xlsx packages)
• writeLines: write lines of a text file

In this example, I perform reading online sale data and save the output to current working directory:

# Read online csv data
saledata <- read.csv('https://support.spatialkey.com/wp-content/uploads/2021/02/Sacramentorealestatetransactions.csv')
dim(saledata)
names(saledata)

# Save output to csv file
write.csv(saledata,'SaleData.csv')

Here is another example using R to read a poem online and write poem to working directory

# Reading poem
poem <- readLines("http://lib.ru/SHAKESPEARE/sonnets.txt")
poem[10:20]

# Writing poem
writeLines(poem[10:20],"Sonet1.txt")

Key Points

• Working with csv input data