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Introduction to R

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Intoduction to strings and data structures

Overview

Time: min
Objectives

  • understand basics of strings and string manipulation

  • understanding different data structure

  • learn about functions of each data structure

Strings

Rule for String in R

# input code string concatenation
Count number of characters 
x1 <- "Olivia"
x2 <- "Jhon"
x3 <- "William"

#checking number of characters
nchar(x1)
nchar(x2)
nchar(x3)

# Letters using vector function in R
# Check the sequence of letters
letters
letters[4]
letters[1:5]

# String Concatenation
# Paste function is used with syntax below:

x <- paste("Hello","World","!",sep = " ")
x

y <- paste(x1,x2,x3,"is happy.")
y

z<- paste("Hello","everyone","!", sep =" ")
z
# Vectors

c() # concatenate function

x4 <- c("Olivia","Jhon","William")
y1 <- paste(x4,"is happy.")
y1

z1 <- c("Please bring me","a few ")
z2 <- c("some vegetables","fruits")
z <- paste(z1,z2,collapse = " and ")
z

# output
 # input code string concatenation
> Count number of characters 
Error: unexpected symbol in "Count number"
> x1 <- "Olivia"
> x2 <- "Jhon"
> x3 <- "William"
> 
> #checking number of characters
> nchar(x1)
[1] 6
> nchar(x2)
[1] 4
> nchar(x3)
[1] 7
> 
> # Letters using vector function in R
> # Check the sequence of letters
> letters
 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v" "w" "x"
[25] "y" "z"
> letters[4]
[1] "d"
> letters[1:5]
[1] "a" "b" "c" "d" "e"
> 
> # String Concatenation
> # Paste function is used with syntax below:
> 
> x <- paste("Hello","World","!",sep = " ")
> x
[1] "Hello World !"
> 
> y <- paste(x1,x2,x3,"is happy.")
> y
[1] "Olivia Jhon William is happy."
> 
> z<- paste("Hello","everyone","!", sep =" ")
> z
[1] "Hello everyone !"
> 
> x4 <- c("Olivia","Jhon","William")
> y1 <- paste(x4,"is happy.")
> y1
[1] "Olivia is happy."  "Jhon is happy."    "William is happy."
> 
> z1 <- c("Please bring me","a few ")
> z2 <- c("some vegetables","fruits")
> z <- paste(z1,z2,collapse = " and ")
> z
[1] "Please bring me some vegetables and a few  fruits"

String Manipulation

-it’s the process of corecing, slicing, pasting, or analyzing strings

x <- "William is happy today"
x

# Converting all words to upper case using toupper() function
toupper(x)

# Converting all words to lower case using tolower() function
tolower(x)

x1 <- "Henry is A hardworker. He owns A house and A car."
x1
chartr("A", "a", x1)

z <- "I widd gq tq market tqmqrrqw."
chartr("dq","lo", z)

x2 <- "Henry puts in all his good efforts"
x2
substr(x2, start = 22, stop = 27)

#split function

x4 <- "Henry puts in all his good efforts"
class(x4)
y1 <- strsplit(x4, split = " ")
y1
class(y1)
#either create a variable like y1 or direct use the function in case of Mason 
strsplit("Mason", split ="") 
x4
y2 <- unlist(strsplit(x4, split = " "))
y2
class(y2)


#output 
> x <- "William is happy today"
> x
[1] "William is happy today"
> 
> # Converting all words to upper case using toupper() function
> toupper(x)
[1] "WILLIAM IS HAPPY TODAY"
> 
> # Converting all words to lower case using tolower() function
> tolower(x)
[1] "william is happy today"
> 
> x1 <- "Henry is A hardworker. He owns A house and A car."
> x1
[1] "Henry is A hardworker. He owns A house and A car."
> chartr("A", "a", x1)
[1] "Henry is a hardworker. He owns a house and a car."
> 
> z <- "I widd gq tq market tqmqrrqw."
> chartr("dq","lo", z)
[1] "I will go to market tomorrow."
> 
> x2 <- "Henry puts in all his good efforts"
> x2
[1] "Henry puts in all his good efforts"
> substr(x2, start = 22, stop = 27)
[1] " good "
> 
> #split function
> 
> x4 <- "Henry puts in all his good efforts"
> class(x4)
[1] "character"
> y1 <- strsplit(x4, split = " ")
> y1
[[1]]
[1] "Henry"   "puts"    "in"      "all"     "his"     "good"    "efforts"

> class(y1)
[1] "list"
> #either create a variable like y1 or direct use the function in case of Mason 
> strsplit("Mason", split ="") 
[[1]]
[1] "M" "a" "s" "o" "n"

> x4
[1] "Henry puts in all his good efforts"
> y2 <- unlist(strsplit(x4, split = " "))
> y2
[1] "Henry"   "puts"    "in"      "all"     "his"     "good"    "efforts"
> class(y2)
[1] "character"

Data Structure

A data structure is essentially a way to organize data in a system to facilitate effective usage of the same.Data structures are the objects that are manipulated regularly in R. They are used to store data in an organized fashion to make data manipulation and other data operations more efficient. R has many data structure which are as follows

Vector

Vectors are the basic data structure of R. Vectors can hold multiple values together using the concatenate c() function. The type of data inside a vector can be determined by using the type of() function and the length (or) number of elements in a vector can be found with the length() function.

R uses one indexing unlike python, hence the position of the first component in a vector can be accessed by vector name [1]

A vector will always contain data of the same data type. If a vector contains multiple data types the vector will convert all its values to the same data type in the below order of precedence:

# input code

v1 <- c(1, 2, 3, 4, 5)
v1
is.vector(v1)

v2 <- c("a", "b", "c")
v2
is.vector(v2)

v3 <- c (TRUE, TRUE, FALSE, FALSE, TRUE)
v3
is.vector(v3)

v4<- c (TRUE, TRUE, "a", 5)
v4
typeof(v4)

v5<- c(6,7 ,8.8,23L)
v5
typeof(v5)


# output
> v1 <- c(1, 2, 3, 4, 5)
> v1
[1] 1 2 3 4 5
> is.vector(v1)
[1] TRUE
>
> v2 <- c("a", "b", "c")
> v2
[1] "a" "b" "c"
> is.vector(v2)
[1] TRUE
>
> v3 <- c (TRUE, TRUE, FALSE, FALSE, TRUE)
> v3
[1]  TRUE  TRUE FALSE FALSE  TRUE
> is.vector(v3)
[1] TRUE
>
> v4<- c (TRUE, TRUE, "a", 5)
> v4
[1] "TRUE" "TRUE" "a"    "5"   
> typeof(v4)
[1] "character"

> v5<- c(6,7 ,8.8,23L)
> v5
[1]  6.0  7.0  8.8 23.0
> typeof(v5)
[1] "double"

Analyzing a Vector

class(vector_name) - Type of data present inside the vector

str(vector_name) - Structure of the vector

is.na(vector_name) - Checks if each element of vector is “NA”

is.null(vector_name) - Checks if the entire vector is empty

length(vector_name) - Number of elements present inside the vector

> x <- c(1,2,3,4)
> class(x)
[1] "numeric"
> str(x)
 num [1:4] 1 2 3 4
> length(x)
[1] 4
>
> x<- c(1,2,3,4)
> is.na(x)
[1] FALSE FALSE FALSE FALSE
> is.null(x)
[1] FALSE
>
> x<- c(TRUE, FALSE, TRUE, TRUE)
> class(x)
[1] "logical"
> str(x)
 logi [1:4] TRUE FALSE TRUE TRUE
> length(x)
[1] 4
>
> x<- c(1,2,3,4,NA)
> is.na(x)
[1] FALSE FALSE FALSE FALSE  TRUE
> x<- c()
> is.null(x)
[1] TRUE

Subsetting a vector

R uses one-indexing mechanism where the elements in the vector start with an index number of one instead of a zero.

vector_name[4] - Element at the fourth position (index) in the vector

vector_name[1:4] - Elements from positions 1 to 4 in the vector

vector_name[c(1,4)] - Elements at positions 1 & 4 only in the vector

vector_name[-c(1,4)] - All elements except those at positions 1 & 4 in the vector

> x <- c("A", "B", "C", "D", "E")
> x[1]
[1] "A"
> x[4]
[1]"D"

> x[1:4]
[1] "A", "B", "C", "D"

> x[c(1,4)]
[1] "A" "D"

> x[-c(1,4)]
[1] "B", "C", "E"

Sorting a vector

Sorting of a vector can be performed using two different functions

sort(vector) - Sorts the vector numerically or alphabetically based on vector type (ascending by default)

order(vector) - Returns the indices of the vector in the order they would appear when the vector is sorted (ascending by default)

> x<- c("D","B","A","E","C")
> sort(x)
[1] "A" "B" "C" "D" "E"
> order(x)
[1] 3 2 5 1 4
> x[order(x)]
[1] "A" "B" "C" "D" "E"
> sort (x, decreasing = TRUE)
[1] "E" "D" "C" "B" "A"
> order(x, decreasing = TRUE)
[1] 4 1 5 2 3
> x[order(x, decreasing = TRUE)]
[1] "E" "D" "C" "B" "A"

Key Points

  • understanding strings and vectors

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