NISR R Training for Interns 2025-2026
2026-01-07
Introduction to R programming
1 .Arthematic operations
1.1 .Addition
1+2 # addition## [1] 32 .Data type
can be checked using class() function as well as
typeof() function.
n<- 4 # = is same as <-
class(n)## [1] "numeric"# == this is equal
1==2## [1] FALSEstring <- "I am Learning R"
class(string)## [1] "character""LeaRning" == "Learning"## [1] FALSE3 .Factors
string_vec<-c("Female", "Male","Female", "Male","Female","Female","Transgender")
string_vec## [1] "Female" "Male" "Female" "Male" "Female"
## [6] "Female" "Transgender"fact<-factor(string_vec)
print(fact)## [1] Female Male Female Male Female Female
## [7] Transgender
## Levels: Female Male Transgenderclass(fact)## [1] "factor"fact_1<-as.character(fact) # datatype conversion from factor to character
class(fact_1)## [1] "character"# checking data type of a given variable
is.character(fact_1)## [1] TRUEtypeof(fact_1)## [1] "character"string_vec<-c("Female", "Male","Female", "Male","Female","Female","Transgender")
string_vec## [1] "Female" "Male" "Female" "Male" "Female"
## [6] "Female" "Transgender"string_vec[1] ## [1] "Female"4 .Contigency table usually used for categorical variables
tb1<-table(string_vec) # this function perform count for each category
tb1## string_vec
## Female Male Transgender
## 4 2 1print("Number of female:")## [1] "Number of female:"print(tb1[[1]]) # displaying number of female## [1] 4# this function return proportions.
round(prop.table(table(string_vec))*100,2) # retuns percentages for each category## string_vec
## Female Male Transgender
## 57.14 28.57 14.29# creating list of these values
rep_1<-rep(c(0, 1), c(50,40))
rep_1## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39] 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [77] 1 1 1 1 1 1 1 1 1 1 1 1 1 1# count values
print("counts:")## [1] "counts:"table(rep_1)## rep_1
## 0 1
## 50 40print("Proportions:")## [1] "Proportions:"round(prop.table(table(rep_1))*100,digits=2)## rep_1
## 0 1
## 55.56 44.44sum(rep_1==1) # count values of rep_1 whereby rep_1==1## [1] 405 .Data Sctructure
A data structure is a particular way of organizing data in a computer so that it can be used effectively. The idea is to reduce the space and time complexities of different tasks. Data structures in R programming are tools for holding multiple values.
5.1 .Vectors
A vector is an ordered collection of basic data types of a given length. The only key thing here is all the elements of a vector must be of the identical data type e.g homogeneous data structures. Vectors are one-dimensional data structures.
X = c(1, 3, 5, 7, 8)
print(X)## [1] 1 3 5 7 85.2 .List
A list is a generic object consisting of an ordered collection of objects. Lists are heterogeneous data structures. These are also one-dimensional data structures.
value1<-c(1:20)
list1<-list(value1)
list1## [[1]]
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20list1[[1]] # accessing list values using list name.## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20value2=c(30:50)
list2<-list(val1=value1,
val2=value2)
list2## $val1
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
##
## $val2
## [1] 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50#exercise access 34 from list2#exercise access 34 from list2
list2$val2[5]## [1] 345.3 .Arrays
Arrays are the R data objects which store the data in more than two dimensions. Arrays are n-dimensional data structures.
array1<-array(c(2:30)) #1D array
array1## [1] 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
## [26] 27 28 29 30arr<-array(c(1:5),
dim = c(2,4)) #2D array is a matrix
arr## [,1] [,2] [,3] [,4]
## [1,] 1 3 5 2
## [2,] 2 4 1 3arr[1,2] # accessing value at a specified position## [1] 3arr2<-array(c(1:5),
dim = c(2,4,2)) #3D array
arr2## , , 1
##
## [,1] [,2] [,3] [,4]
## [1,] 1 3 5 2
## [2,] 2 4 1 3
##
## , , 2
##
## [,1] [,2] [,3] [,4]
## [1,] 4 1 3 5
## [2,] 5 2 4 1arr2[1,2,1] # accessing element from 3D array at specified position## [1] 35.4 .Matrix
A matrix is a rectangular arrangement of numbers in rows and columns. In a matrix, as we know rows are the ones that run horizontally and columns are the ones that run vertically. Matrices are two-dimensional, homogeneous data structures.
matrix<-matrix(c(1:8),
nrow = 2,
ncol = 4) # it is 2D
matrix## [,1] [,2] [,3] [,4]
## [1,] 1 3 5 7
## [2,] 2 4 6 85.5 .Data frame
Data frames are generic data objects of R which are used to store the tabular data. * They are two-dimensional, heterogeneous data structures. These are lists of vectors of equal lengths.
Data frames have the following constraints placed upon them:
- A data-frame must have column names and every row should have a unique name.
- Each column must have the identical number of items.
- Each item in a single column must be of the same data type.
- Different columns may have different data types
#Data frame
val1<-c(2,3,4,6)
val2<-c(4,7,8,9)
df<-data.frame(val1=val1,
val2=val2)
df## val1 val2
## 1 2 4
## 2 3 7
## 3 4 8
## 4 6 9ls<-list(val1=val1,
val2=val2)
ls## $val1
## [1] 2 3 4 6
##
## $val2
## [1] 4 7 8 95.5.1 .Difference btn
list() and data.frame()
is.list(df)## [1] TRUEis.data.frame(ls)## [1] FALSE5.6 .Factors
Factors are the data objects which are used to categorize the data and store it as levels. They are useful for storing categorical data. They are useful in data analysis for statistical modeling.
# Creating factor using factor()
fac = factor(c("Male", "Female", "Male",
"Male", "Female", "Male", "Female"))
print(fac)## [1] Male Female Male Male Female Male Female
## Levels: Female Maleprint(typeof(fac))## [1] "integer"print(class(fac))## [1] "factor"6 List
- change item value
- length of list
- check if item is exists in a list using
%in%operator - add item to list using
append - remove item from list [-1]
- join two list using
c()function - loop through a list using
forloop
fruits<-list("banana","pineaple","avocado","mango")
fruits[1] # accessing item from list## [[1]]
## [1] "banana"length(fruits) # length of list## [1] 4#fruits[1]<-"beans" #changing item that we have in a list
fruits[1]## [[1]]
## [1] "banana"fruits## [[1]]
## [1] "banana"
##
## [[2]]
## [1] "pineaple"
##
## [[3]]
## [1] "avocado"
##
## [[4]]
## [1] "mango"fruits2<-fruits[-1] # removing item from a list
fruits2## [[1]]
## [1] "pineaple"
##
## [[2]]
## [1] "avocado"
##
## [[3]]
## [1] "mango"fruits<-list("banana","pineaple","avocado","mango")
# using in operator
"banana" %in% fruits## [1] TRUE# adding item to list
fruits3<-append(fruits,"apple",after = 1) # after argument help you to specify a position you want to add item
length(fruits)## [1] 4length(fruits3)## [1] 5# Joining two list to form one list
f_name<-list("Kagabo","Mugabo","Ntawuyirushintege")
l_name<-list("Jean","Emmanuel","Andre")
names<- c(f_name,l_name)
names## [[1]]
## [1] "Kagabo"
##
## [[2]]
## [1] "Mugabo"
##
## [[3]]
## [1] "Ntawuyirushintege"
##
## [[4]]
## [1] "Jean"
##
## [[5]]
## [1] "Emmanuel"
##
## [[6]]
## [1] "Andre"length(names) # this return 6 as addition of elements of two list (ie, they are combined)## [1] 66.1 Loop statement
- for loop
- while loop
# for loop
vect1<-c(1:5)
for (i in vect1) {
print(i)
}## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 56.2 If statement
num1<-20
num2<-30
if (num1>num2) {
print("numb1 is less than num2")
}else{
print("num2 is greather than num1")
}## [1] "num2 is greather than num1"num1<-20
num2<-20
if (num1>num2) {
print("numb1 is less than num2")
}else if(num1==num2){
print("num2 is equal to num1")
}else{
print("numb1 is less than num2")
}## [1] "num2 is equal to num1"#break
vect2<-c(1:20)
for (i in vect2) {
print(i)
if (i==14) {
break
}
}## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10
## [1] 11
## [1] 12
## [1] 13
## [1] 14vect2<-c(1:20)
for (i in vect2) {
if (i==13 | i==14) {
next
}else if(i==15){
next
}
print(i)
}## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10
## [1] 11
## [1] 12
## [1] 16
## [1] 17
## [1] 18
## [1] 19
## [1] 20# Concantinating two list
f_name<-list("Kagabo","Mugabo","Ntawuyirushintege")
l_name<-list("Jean","Emmanuel","Andre")
# First way
for(i in seq_along(f_name)){
print(paste(f_name[i],l_name[i]))
}## [1] "Kagabo Jean"
## [1] "Mugabo Emmanuel"
## [1] "Ntawuyirushintege Andre"# second way
paste(f_name,l_name)## [1] "Kagabo Jean" "Mugabo Emmanuel"
## [3] "Ntawuyirushintege Andre"6.3 Function
- Function in R is defined using keyword
function
# function definition
names<-function(){
f_name<-list("Kagabo","Mugabo","Ntawuyirushintege")
l_name<-list("Jean","Emmanuel","Andre")
for(i in seq_along(f_name)){
print(paste(f_name[i],l_name[i]))
}
}
names() # call a function## [1] "Kagabo Jean"
## [1] "Mugabo Emmanuel"
## [1] "Ntawuyirushintege Andre"# define a function that take a parameter and return a results
add_numbers<-function(num1, num2){
print("numbers are entered")
results<-num1+num2
print("addition has completed")
return(results)
}add_numbers(2,5) # function call## [1] "numbers are entered"
## [1] "addition has completed"## [1] 7c<-add_numbers(2,5) # function call## [1] "numbers are entered"
## [1] "addition has completed"c # this variable holds only value we returned. (i,e. 7)## [1] 76.3.1 Making a calculator
# exercise
# Do function called calculator that perform addition,substraction,multiplication, division
calculator<-function(a,b,operator){
if (operator=="+") {
return(results=a+b)
}else if(operator=="-"){
return(results=a-b)
}else if(operator=="*"){
return(results=a*b)
}else if(operator=="/"){
return(results=a/b)
}
}calculator(3,5,"+")## [1] 86.4 while
loop
i<-1
f_name<-c("Kagabo","Mugabo","Ntawuyirushintege")
while (i<=length(f_name)) {
print(f_name[i])
i<-i+1
}## [1] "Kagabo"
## [1] "Mugabo"
## [1] "Ntawuyirushintege"7 Importing a dataset
#Dataset
df<-read.csv("D:/NISR Training/Python_NISR_tutorial_Training/car price data.csv")
head(df)## Car_Name Year Selling_Price Present_Price Kms_Driven Fuel_Type
## 1 ritz 2014 3.35 5.59 27000 Petrol
## 2 sx4 2013 4.75 9.54 43000 Diesel
## 3 ciaz 2017 7.25 9.85 6900 Petrol
## 4 wagon r 2011 2.85 4.15 5200 Petrol
## 5 swift 2014 4.60 6.87 42450 Diesel
## 6 vitara brezza 2018 9.25 9.83 2071 Diesel
## Seller_Type Transmission Owner
## 1 Dealer Manual 0
## 2 Dealer Manual 0
## 3 Dealer Manual 0
## 4 Dealer Manual 0
## 5 Dealer Manual 0
## 6 Dealer Manual 07.1 slicing a dataset
df[1:3,c(1,2)] # c("Car_Name","Year")## Car_Name Year
## 1 ritz 2014
## 2 sx4 2013
## 3 ciaz 20177.2 Adding new column to a dataset
df$Age<-2026-df$Year # adding new column to dataset
#colnames(df)
df## Car_Name Year Selling_Price Present_Price Kms_Driven
## 1 ritz 2014 3.35 5.590 27000
## 2 sx4 2013 4.75 9.540 43000
## 3 ciaz 2017 7.25 9.850 6900
## 4 wagon r 2011 2.85 4.150 5200
## 5 swift 2014 4.60 6.870 42450
## 6 vitara brezza 2018 9.25 9.830 2071
## 7 ciaz 2015 6.75 8.120 18796
## 8 s cross 2015 6.50 8.610 33429
## 9 ciaz 2016 8.75 8.890 20273
## 10 ciaz 2015 7.45 8.920 42367
## 11 alto 800 2017 2.85 3.600 2135
## 12 ciaz 2015 6.85 10.380 51000
## 13 ciaz 2015 7.50 9.940 15000
## 14 ertiga 2015 6.10 7.710 26000
## 15 dzire 2009 2.25 7.210 77427
## 16 ertiga 2016 7.75 10.790 43000
## 17 ertiga 2015 7.25 10.790 41678
## 18 ertiga 2016 7.75 10.790 43000
## 19 wagon r 2015 3.25 5.090 35500
## 20 sx4 2010 2.65 7.980 41442
## 21 alto k10 2016 2.85 3.950 25000
## 22 ignis 2017 4.90 5.710 2400
## 23 sx4 2011 4.40 8.010 50000
## 24 alto k10 2014 2.50 3.460 45280
## 25 wagon r 2013 2.90 4.410 56879
## 26 swift 2011 3.00 4.990 20000
## 27 swift 2013 4.15 5.870 55138
## 28 swift 2017 6.00 6.490 16200
## 29 alto k10 2010 1.95 3.950 44542
## 30 ciaz 2015 7.45 10.380 45000
## 31 ritz 2012 3.10 5.980 51439
## 32 ritz 2011 2.35 4.890 54200
## 33 swift 2014 4.95 7.490 39000
## 34 ertiga 2014 6.00 9.950 45000
## 35 dzire 2014 5.50 8.060 45000
## 36 sx4 2011 2.95 7.740 49998
## 37 dzire 2015 4.65 7.200 48767
## 38 800 2003 0.35 2.280 127000
## 39 alto k10 2016 3.00 3.760 10079
## 40 sx4 2003 2.25 7.980 62000
## 41 baleno 2016 5.85 7.870 24524
## 42 alto k10 2014 2.55 3.980 46706
## 43 sx4 2008 1.95 7.150 58000
## 44 dzire 2014 5.50 8.060 45780
## 45 omni 2012 1.25 2.690 50000
## 46 ciaz 2014 7.50 12.040 15000
## 47 ritz 2013 2.65 4.890 64532
## 48 wagon r 2006 1.05 4.150 65000
## 49 ertiga 2015 5.80 7.710 25870
## 50 ciaz 2017 7.75 9.290 37000
## 51 fortuner 2012 14.90 30.610 104707
## 52 fortuner 2015 23.00 30.610 40000
## 53 innova 2017 18.00 19.770 15000
## 54 fortuner 2013 16.00 30.610 135000
## 55 innova 2005 2.75 10.210 90000
## 56 corolla altis 2009 3.60 15.040 70000
## 57 etios cross 2015 4.50 7.270 40534
## 58 corolla altis 2010 4.75 18.540 50000
## 59 etios g 2014 4.10 6.800 39485
## 60 fortuner 2014 19.99 35.960 41000
## 61 corolla altis 2013 6.95 18.610 40001
## 62 etios cross 2015 4.50 7.700 40588
## 63 fortuner 2014 18.75 35.960 78000
## 64 fortuner 2015 23.50 35.960 47000
## 65 fortuner 2017 33.00 36.230 6000
## 66 etios liva 2014 4.75 6.950 45000
## 67 innova 2017 19.75 23.150 11000
## 68 fortuner 2010 9.25 20.450 59000
## 69 corolla altis 2011 4.35 13.740 88000
## 70 corolla altis 2016 14.25 20.910 12000
## 71 etios liva 2014 3.95 6.760 71000
## 72 corolla altis 2011 4.50 12.480 45000
## 73 corolla altis 2013 7.45 18.610 56001
## 74 etios liva 2011 2.65 5.710 43000
## 75 etios cross 2014 4.90 8.930 83000
## 76 etios g 2015 3.95 6.800 36000
## 77 corolla altis 2013 5.50 14.680 72000
## 78 corolla 2004 1.50 12.350 135154
## 79 corolla altis 2010 5.25 22.830 80000
## 80 fortuner 2012 14.50 30.610 89000
## 81 corolla altis 2016 14.73 14.890 23000
## 82 etios gd 2015 4.75 7.850 40000
## 83 innova 2017 23.00 25.390 15000
## 84 innova 2015 12.50 13.460 38000
## 85 innova 2005 3.49 13.460 197176
## 86 camry 2006 2.50 23.730 142000
## 87 land cruiser 2010 35.00 92.600 78000
## 88 corolla altis 2012 5.90 13.740 56000
## 89 etios liva 2013 3.45 6.050 47000
## 90 etios g 2014 4.75 6.760 40000
## 91 corolla altis 2009 3.80 18.610 62000
## 92 innova 2014 11.25 16.090 58242
## 93 innova 2005 3.51 13.700 75000
## 94 fortuner 2015 23.00 30.610 40000
## 95 corolla altis 2008 4.00 22.780 89000
## 96 corolla altis 2012 5.85 18.610 72000
## 97 innova 2016 20.75 25.390 29000
## 98 corolla altis 2017 17.00 18.640 8700
## 99 corolla altis 2013 7.05 18.610 45000
## 100 fortuner 2010 9.65 20.450 50024
## 101 Royal Enfield Thunder 500 2016 1.75 1.900 3000
## 102 UM Renegade Mojave 2017 1.70 1.820 1400
## 103 KTM RC200 2017 1.65 1.780 4000
## 104 Bajaj Dominar 400 2017 1.45 1.600 1200
## 105 Royal Enfield Classic 350 2017 1.35 1.470 4100
## 106 KTM RC390 2015 1.35 2.370 21700
## 107 Hyosung GT250R 2014 1.35 3.450 16500
## 108 Royal Enfield Thunder 350 2013 1.25 1.500 15000
## 109 Royal Enfield Thunder 350 2016 1.20 1.500 18000
## 110 Royal Enfield Classic 350 2017 1.20 1.470 11000
## 111 KTM RC200 2016 1.20 1.780 6000
## 112 Royal Enfield Thunder 350 2016 1.15 1.500 8700
## 113 KTM 390 Duke 2014 1.15 2.400 7000
## 114 Mahindra Mojo XT300 2016 1.15 1.400 35000
## 115 Royal Enfield Classic 350 2015 1.15 1.470 17000
## 116 Royal Enfield Classic 350 2015 1.11 1.470 17500
## 117 Royal Enfield Classic 350 2013 1.10 1.470 33000
## 118 Royal Enfield Thunder 500 2015 1.10 1.900 14000
## 119 Royal Enfield Classic 350 2015 1.10 1.470 26000
## 120 Royal Enfield Thunder 500 2013 1.05 1.900 5400
## 121 Bajaj Pulsar RS200 2016 1.05 1.260 5700
## 122 Royal Enfield Thunder 350 2011 1.05 1.500 6900
## 123 Royal Enfield Bullet 350 2016 1.05 1.170 6000
## 124 Royal Enfield Classic 350 2013 1.00 1.470 46500
## 125 Royal Enfield Classic 500 2012 0.95 1.750 11500
## 126 Royal Enfield Classic 500 2009 0.90 1.750 40000
## 127 Bajaj Avenger 220 2017 0.90 0.950 1300
## 128 Bajaj Avenger 150 2016 0.75 0.800 7000
## 129 Honda CB Hornet 160R 2017 0.80 0.870 3000
## 130 Yamaha FZ S V 2.0 2017 0.78 0.840 5000
## 131 Honda CB Hornet 160R 2017 0.75 0.870 11000
## 132 Yamaha FZ 16 2015 0.75 0.820 18000
## 133 Bajaj Avenger 220 2017 0.75 0.950 3500
## 134 Bajaj Avenger 220 2016 0.72 0.950 500
## 135 TVS Apache RTR 160 2017 0.65 0.810 11800
## 136 Bajaj Pulsar 150 2015 0.65 0.740 5000
## 137 Honda CBR 150 2014 0.65 1.200 23500
## 138 Hero Extreme 2013 0.65 0.787 16000
## 139 Honda CB Hornet 160R 2016 0.60 0.870 15000
## 140 Bajaj Avenger 220 dtsi 2015 0.60 0.950 16600
## 141 Honda CBR 150 2013 0.60 1.200 32000
## 142 Bajaj Avenger 150 street 2016 0.60 0.800 20000
## 143 Yamaha FZ v 2.0 2015 0.60 0.840 29000
## 144 Yamaha FZ v 2.0 2016 0.60 0.840 25000
## 145 Bajaj Pulsar NS 200 2014 0.60 0.990 25000
## 146 TVS Apache RTR 160 2012 0.60 0.810 19000
## 147 Hero Extreme 2014 0.55 0.787 15000
## 148 Yamaha FZ S V 2.0 2015 0.55 0.840 58000
## 149 Bajaj Pulsar 220 F 2010 0.52 0.940 45000
## 150 Bajaj Pulsar 220 F 2016 0.51 0.940 24000
## 151 TVS Apache RTR 180 2011 0.50 0.826 6000
## 152 Hero Passion X pro 2016 0.50 0.550 31000
## 153 Bajaj Pulsar NS 200 2012 0.50 0.990 13000
## 154 Bajaj Pulsar NS 200 2013 0.50 0.990 45000
## 155 Yamaha Fazer 2014 0.50 0.880 8000
## 156 Honda Activa 4G 2017 0.48 0.510 4300
## 157 TVS Sport 2017 0.48 0.520 15000
## 158 Yamaha FZ S V 2.0 2015 0.48 0.840 23000
## 159 Honda Dream Yuga 2017 0.48 0.540 8600
## 160 Honda Activa 4G 2017 0.45 0.510 4000
## 161 Bajaj Avenger Street 220 2011 0.45 0.950 24000
## 162 TVS Apache RTR 180 2014 0.45 0.826 23000
## 163 Bajaj Pulsar NS 200 2012 0.45 0.990 14500
## 164 Bajaj Avenger 220 dtsi 2010 0.45 0.950 27000
## 165 Hero Splender iSmart 2016 0.45 0.540 14000
## 166 Activa 3g 2016 0.45 0.540 500
## 167 Hero Passion Pro 2016 0.45 0.550 1000
## 168 TVS Apache RTR 160 2014 0.42 0.810 42000
## 169 Honda CB Trigger 2013 0.42 0.730 12000
## 170 Hero Splender iSmart 2015 0.40 0.540 14000
## 171 Yamaha FZ S 2012 0.40 0.830 5500
## 172 Hero Passion Pro 2015 0.40 0.550 6700
## 173 Bajaj Pulsar 135 LS 2014 0.40 0.640 13700
## 174 Activa 4g 2017 0.40 0.510 1300
## 175 Honda CB Unicorn 2015 0.38 0.720 38600
## 176 Hero Honda CBZ extreme 2011 0.38 0.787 75000
## 177 Honda Karizma 2011 0.35 1.050 30000
## 178 Honda Activa 125 2016 0.35 0.570 24000
## 179 TVS Jupyter 2014 0.35 0.520 19000
## 180 Honda Karizma 2010 0.31 1.050 213000
## 181 Hero Honda Passion Pro 2012 0.30 0.510 60000
## 182 Hero Splender Plus 2016 0.30 0.480 50000
## 183 Honda CB Shine 2013 0.30 0.580 30000
## 184 Bajaj Discover 100 2013 0.27 0.470 21000
## 185 Bajaj Pulsar 150 2008 0.25 0.750 26000
## 186 Suzuki Access 125 2008 0.25 0.580 1900
## 187 TVS Wego 2010 0.25 0.520 22000
## 188 Honda CB twister 2013 0.25 0.510 32000
## 189 Hero Glamour 2013 0.25 0.570 18000
## 190 Hero Super Splendor 2005 0.20 0.570 55000
## 191 Bajaj Pulsar 150 2008 0.20 0.750 60000
## 192 Bajaj Discover 125 2012 0.20 0.570 25000
## 193 Hero Hunk 2007 0.20 0.750 49000
## 194 Hero Ignitor Disc 2013 0.20 0.650 24000
## 195 Hero CBZ Xtreme 2008 0.20 0.787 50000
## 196 Bajaj ct 100 2015 0.18 0.320 35000
## 197 Activa 3g 2008 0.17 0.520 500000
## 198 Honda CB twister 2010 0.16 0.510 33000
## 199 Bajaj Discover 125 2011 0.15 0.570 35000
## 200 Honda CB Shine 2007 0.12 0.580 53000
## 201 Bajaj Pulsar 150 2006 0.10 0.750 92233
## 202 i20 2010 3.25 6.790 58000
## 203 grand i10 2015 4.40 5.700 28200
## 204 i10 2011 2.95 4.600 53460
## 205 eon 2015 2.75 4.430 28282
## 206 grand i10 2016 5.25 5.700 3493
## 207 xcent 2017 5.75 7.130 12479
## 208 grand i10 2015 5.15 5.700 34797
## 209 i20 2017 7.90 8.100 3435
## 210 grand i10 2015 4.85 5.700 21125
## 211 i10 2012 3.10 4.600 35775
## 212 elantra 2015 11.75 14.790 43535
## 213 creta 2016 11.25 13.600 22671
## 214 i20 2011 2.90 6.790 31604
## 215 grand i10 2017 5.25 5.700 20114
## 216 verna 2012 4.50 9.400 36100
## 217 eon 2016 2.90 4.430 12500
## 218 eon 2016 3.15 4.430 15000
## 219 verna 2014 6.45 9.400 45078
## 220 verna 2012 4.50 9.400 36000
## 221 eon 2017 3.50 4.430 38488
## 222 i20 2013 4.50 6.790 32000
## 223 i20 2014 6.00 7.600 77632
## 224 verna 2015 8.25 9.400 61381
## 225 verna 2013 5.11 9.400 36198
## 226 i10 2011 2.70 4.600 22517
## 227 grand i10 2015 5.25 5.700 24678
## 228 i10 2011 2.55 4.430 57000
## 229 verna 2012 4.95 9.400 60000
## 230 i20 2012 3.10 6.790 52132
## 231 verna 2013 6.15 9.400 45000
## 232 verna 2017 9.25 9.400 15001
## 233 elantra 2015 11.45 14.790 12900
## 234 grand i10 2013 3.90 5.700 53000
## 235 grand i10 2015 5.50 5.700 4492
## 236 verna 2017 9.10 9.400 15141
## 237 eon 2016 3.10 4.430 11849
## 238 creta 2015 11.25 13.600 68000
## 239 verna 2013 4.80 9.400 60241
## 240 eon 2012 2.00 4.430 23709
## 241 verna 2012 5.35 9.400 32322
## 242 xcent 2015 4.75 7.130 35866
## 243 xcent 2014 4.40 7.130 34000
## 244 i20 2016 6.25 7.600 7000
## 245 verna 2013 5.95 9.400 49000
## 246 verna 2012 5.20 9.400 71000
## 247 i20 2012 3.75 6.790 35000
## 248 verna 2015 5.95 9.400 36000
## 249 i10 2013 4.00 4.600 30000
## 250 i20 2016 5.25 7.600 17000
## 251 creta 2016 12.90 13.600 35934
## 252 city 2013 5.00 9.900 56701
## 253 brio 2015 5.40 6.820 31427
## 254 city 2014 7.20 9.900 48000
## 255 city 2013 5.25 9.900 54242
## 256 brio 2012 3.00 5.350 53675
## 257 city 2016 10.25 13.600 49562
## 258 city 2015 8.50 13.600 40324
## 259 city 2015 8.40 13.600 25000
## 260 amaze 2014 3.90 7.000 36054
## 261 city 2016 9.15 13.600 29223
## 262 brio 2016 5.50 5.970 5600
## 263 amaze 2015 4.00 5.800 40023
## 264 jazz 2016 6.60 7.700 16002
## 265 amaze 2015 4.00 7.000 40026
## 266 jazz 2017 6.50 8.700 21200
## 267 amaze 2014 3.65 7.000 35000
## 268 city 2016 8.35 9.400 19434
## 269 brio 2017 4.80 5.800 19000
## 270 city 2015 6.70 10.000 18828
## 271 city 2011 4.10 10.000 69341
## 272 city 2009 3.00 10.000 69562
## 273 city 2015 7.50 10.000 27600
## 274 jazz 2010 2.25 7.500 61203
## 275 brio 2014 5.30 6.800 16500
## 276 city 2016 10.90 13.600 30753
## 277 city 2015 8.65 13.600 24800
## 278 city 2015 9.70 13.600 21780
## 279 jazz 2016 6.00 8.400 4000
## 280 city 2014 6.25 13.600 40126
## 281 brio 2015 5.25 5.900 14465
## 282 city 2006 2.10 7.600 50456
## 283 city 2014 8.25 14.000 63000
## 284 city 2016 8.99 11.800 9010
## 285 brio 2013 3.50 5.900 9800
## 286 jazz 2016 7.40 8.500 15059
## 287 jazz 2016 5.65 7.900 28569
## 288 amaze 2015 5.75 7.500 44000
## 289 city 2015 8.40 13.600 34000
## 290 city 2016 10.11 13.600 10980
## 291 amaze 2014 4.50 6.400 19000
## 292 brio 2015 5.40 6.100 31427
## 293 jazz 2016 6.40 8.400 12000
## 294 city 2010 3.25 9.900 38000
## 295 amaze 2014 3.75 6.800 33019
## 296 city 2015 8.55 13.090 60076
## 297 city 2016 9.50 11.600 33988
## 298 brio 2015 4.00 5.900 60000
## 299 city 2009 3.35 11.000 87934
## 300 city 2017 11.50 12.500 9000
## 301 brio 2016 5.30 5.900 5464
## Fuel_Type Seller_Type Transmission Owner Age
## 1 Petrol Dealer Manual 0 12
## 2 Diesel Dealer Manual 0 13
## 3 Petrol Dealer Manual 0 9
## 4 Petrol Dealer Manual 0 15
## 5 Diesel Dealer Manual 0 12
## 6 Diesel Dealer Manual 0 8
## 7 Petrol Dealer Manual 0 11
## 8 Diesel Dealer Manual 0 11
## 9 Diesel Dealer Manual 0 10
## 10 Diesel Dealer Manual 0 11
## 11 Petrol Dealer Manual 0 9
## 12 Diesel Dealer Manual 0 11
## 13 Petrol Dealer Automatic 0 11
## 14 Petrol Dealer Manual 0 11
## 15 Petrol Dealer Manual 0 17
## 16 Diesel Dealer Manual 0 10
## 17 Diesel Dealer Manual 0 11
## 18 Diesel Dealer Manual 0 10
## 19 CNG Dealer Manual 0 11
## 20 Petrol Dealer Manual 0 16
## 21 Petrol Dealer Manual 0 10
## 22 Petrol Dealer Manual 0 9
## 23 Petrol Dealer Automatic 0 15
## 24 Petrol Dealer Manual 0 12
## 25 Petrol Dealer Manual 0 13
## 26 Petrol Dealer Manual 0 15
## 27 Petrol Dealer Manual 0 13
## 28 Petrol Individual Manual 0 9
## 29 Petrol Dealer Manual 0 16
## 30 Diesel Dealer Manual 0 11
## 31 Diesel Dealer Manual 0 14
## 32 Petrol Dealer Manual 0 15
## 33 Diesel Dealer Manual 0 12
## 34 Diesel Dealer Manual 0 12
## 35 Diesel Dealer Manual 0 12
## 36 CNG Dealer Manual 0 15
## 37 Petrol Dealer Manual 0 11
## 38 Petrol Individual Manual 0 23
## 39 Petrol Dealer Manual 0 10
## 40 Petrol Dealer Manual 0 23
## 41 Petrol Dealer Automatic 0 10
## 42 Petrol Dealer Manual 0 12
## 43 Petrol Dealer Manual 0 18
## 44 Diesel Dealer Manual 0 12
## 45 Petrol Dealer Manual 0 14
## 46 Petrol Dealer Automatic 0 12
## 47 Petrol Dealer Manual 0 13
## 48 Petrol Dealer Manual 0 20
## 49 Petrol Dealer Manual 0 11
## 50 Petrol Dealer Automatic 0 9
## 51 Diesel Dealer Automatic 0 14
## 52 Diesel Dealer Automatic 0 11
## 53 Diesel Dealer Automatic 0 9
## 54 Diesel Individual Automatic 0 13
## 55 Petrol Individual Manual 0 21
## 56 Petrol Dealer Automatic 0 17
## 57 Petrol Dealer Manual 0 11
## 58 Petrol Dealer Manual 0 16
## 59 Petrol Dealer Manual 1 12
## 60 Diesel Dealer Automatic 0 12
## 61 Petrol Dealer Manual 0 13
## 62 Petrol Dealer Manual 0 11
## 63 Diesel Dealer Automatic 0 12
## 64 Diesel Dealer Automatic 0 11
## 65 Diesel Dealer Automatic 0 9
## 66 Diesel Dealer Manual 0 12
## 67 Petrol Dealer Automatic 0 9
## 68 Diesel Dealer Manual 0 16
## 69 Petrol Dealer Manual 0 15
## 70 Petrol Dealer Manual 0 10
## 71 Diesel Dealer Manual 0 12
## 72 Diesel Dealer Manual 0 15
## 73 Petrol Dealer Manual 0 13
## 74 Petrol Dealer Manual 0 15
## 75 Diesel Dealer Manual 0 12
## 76 Petrol Dealer Manual 0 11
## 77 Petrol Dealer Manual 0 13
## 78 Petrol Dealer Automatic 0 22
## 79 Petrol Dealer Automatic 0 16
## 80 Diesel Dealer Automatic 0 14
## 81 Diesel Dealer Manual 0 10
## 82 Diesel Dealer Manual 0 11
## 83 Diesel Dealer Automatic 0 9
## 84 Diesel Dealer Manual 0 11
## 85 Diesel Dealer Manual 0 21
## 86 Petrol Individual Automatic 3 20
## 87 Diesel Dealer Manual 0 16
## 88 Petrol Dealer Manual 0 14
## 89 Petrol Dealer Manual 0 13
## 90 Petrol Dealer Manual 0 12
## 91 Petrol Dealer Manual 0 17
## 92 Diesel Dealer Manual 0 12
## 93 Petrol Dealer Manual 0 21
## 94 Diesel Dealer Automatic 0 11
## 95 Petrol Dealer Automatic 0 18
## 96 Petrol Dealer Manual 0 14
## 97 Diesel Dealer Automatic 0 10
## 98 Petrol Dealer Manual 0 9
## 99 Petrol Dealer Manual 0 13
## 100 Diesel Dealer Manual 0 16
## 101 Petrol Individual Manual 0 10
## 102 Petrol Individual Manual 0 9
## 103 Petrol Individual Manual 0 9
## 104 Petrol Individual Manual 0 9
## 105 Petrol Individual Manual 0 9
## 106 Petrol Individual Manual 0 11
## 107 Petrol Individual Manual 1 12
## 108 Petrol Individual Manual 0 13
## 109 Petrol Individual Manual 0 10
## 110 Petrol Individual Manual 0 9
## 111 Petrol Individual Manual 0 10
## 112 Petrol Individual Manual 0 10
## 113 Petrol Individual Manual 0 12
## 114 Petrol Individual Manual 0 10
## 115 Petrol Individual Manual 0 11
## 116 Petrol Individual Manual 0 11
## 117 Petrol Individual Manual 0 13
## 118 Petrol Individual Manual 0 11
## 119 Petrol Individual Manual 0 11
## 120 Petrol Individual Manual 0 13
## 121 Petrol Individual Manual 0 10
## 122 Petrol Individual Manual 0 15
## 123 Petrol Individual Manual 0 10
## 124 Petrol Individual Manual 0 13
## 125 Petrol Individual Manual 0 14
## 126 Petrol Individual Manual 0 17
## 127 Petrol Individual Manual 0 9
## 128 Petrol Individual Manual 0 10
## 129 Petrol Individual Manual 0 9
## 130 Petrol Individual Manual 0 9
## 131 Petrol Individual Manual 0 9
## 132 Petrol Individual Manual 0 11
## 133 Petrol Individual Manual 0 9
## 134 Petrol Individual Manual 0 10
## 135 Petrol Individual Manual 0 9
## 136 Petrol Individual Manual 0 11
## 137 Petrol Individual Manual 0 12
## 138 Petrol Individual Manual 0 13
## 139 Petrol Individual Manual 0 10
## 140 Petrol Individual Manual 0 11
## 141 Petrol Individual Manual 0 13
## 142 Petrol Individual Manual 0 10
## 143 Petrol Individual Manual 0 11
## 144 Petrol Individual Manual 0 10
## 145 Petrol Individual Manual 0 12
## 146 Petrol Individual Manual 0 14
## 147 Petrol Individual Manual 0 12
## 148 Petrol Individual Manual 0 11
## 149 Petrol Individual Manual 0 16
## 150 Petrol Individual Manual 0 10
## 151 Petrol Individual Manual 0 15
## 152 Petrol Individual Manual 0 10
## 153 Petrol Individual Manual 0 14
## 154 Petrol Individual Manual 0 13
## 155 Petrol Individual Manual 0 12
## 156 Petrol Individual Automatic 0 9
## 157 Petrol Individual Manual 0 9
## 158 Petrol Individual Manual 0 11
## 159 Petrol Individual Manual 0 9
## 160 Petrol Individual Automatic 0 9
## 161 Petrol Individual Manual 0 15
## 162 Petrol Individual Manual 0 12
## 163 Petrol Individual Manual 0 14
## 164 Petrol Individual Manual 0 16
## 165 Petrol Individual Manual 0 10
## 166 Petrol Individual Automatic 0 10
## 167 Petrol Individual Manual 0 10
## 168 Petrol Individual Manual 0 12
## 169 Petrol Individual Manual 0 13
## 170 Petrol Individual Manual 0 11
## 171 Petrol Individual Manual 0 14
## 172 Petrol Individual Manual 0 11
## 173 Petrol Individual Manual 0 12
## 174 Petrol Individual Automatic 0 9
## 175 Petrol Individual Manual 0 11
## 176 Petrol Individual Manual 0 15
## 177 Petrol Individual Manual 0 15
## 178 Petrol Individual Automatic 0 10
## 179 Petrol Individual Automatic 0 12
## 180 Petrol Individual Manual 0 16
## 181 Petrol Individual Manual 0 14
## 182 Petrol Individual Manual 0 10
## 183 Petrol Individual Manual 0 13
## 184 Petrol Individual Manual 0 13
## 185 Petrol Individual Manual 1 18
## 186 Petrol Individual Automatic 0 18
## 187 Petrol Individual Automatic 0 16
## 188 Petrol Individual Manual 0 13
## 189 Petrol Individual Manual 0 13
## 190 Petrol Individual Manual 0 21
## 191 Petrol Individual Manual 0 18
## 192 Petrol Individual Manual 1 14
## 193 Petrol Individual Manual 1 19
## 194 Petrol Individual Manual 1 13
## 195 Petrol Individual Manual 0 18
## 196 Petrol Individual Manual 0 11
## 197 Petrol Individual Automatic 0 18
## 198 Petrol Individual Manual 0 16
## 199 Petrol Individual Manual 1 15
## 200 Petrol Individual Manual 0 19
## 201 Petrol Individual Manual 0 20
## 202 Diesel Dealer Manual 1 16
## 203 Petrol Dealer Manual 0 11
## 204 Petrol Dealer Manual 0 15
## 205 Petrol Dealer Manual 0 11
## 206 Petrol Dealer Manual 1 10
## 207 Petrol Dealer Manual 0 9
## 208 Petrol Dealer Automatic 0 11
## 209 Petrol Dealer Manual 0 9
## 210 Diesel Dealer Manual 0 11
## 211 Petrol Dealer Manual 0 14
## 212 Diesel Dealer Manual 0 11
## 213 Petrol Dealer Manual 0 10
## 214 Petrol Dealer Manual 0 15
## 215 Petrol Dealer Manual 0 9
## 216 Petrol Dealer Manual 0 14
## 217 Petrol Dealer Manual 0 10
## 218 Petrol Dealer Manual 0 10
## 219 Petrol Dealer Manual 0 12
## 220 Petrol Dealer Manual 0 14
## 221 Petrol Dealer Manual 0 9
## 222 Petrol Dealer Automatic 0 13
## 223 Diesel Dealer Manual 0 12
## 224 Diesel Dealer Manual 0 11
## 225 Petrol Dealer Automatic 0 13
## 226 Petrol Dealer Manual 0 15
## 227 Petrol Dealer Manual 0 11
## 228 Petrol Dealer Manual 0 15
## 229 Diesel Dealer Manual 0 14
## 230 Diesel Dealer Manual 0 14
## 231 Diesel Dealer Manual 0 13
## 232 Petrol Dealer Manual 0 9
## 233 Petrol Dealer Automatic 0 11
## 234 Diesel Dealer Manual 0 13
## 235 Petrol Dealer Manual 0 11
## 236 Petrol Dealer Manual 0 9
## 237 Petrol Dealer Manual 0 10
## 238 Diesel Dealer Manual 0 11
## 239 Petrol Dealer Manual 0 13
## 240 Petrol Dealer Manual 0 14
## 241 Diesel Dealer Manual 0 14
## 242 Petrol Dealer Manual 1 11
## 243 Petrol Dealer Manual 0 12
## 244 Petrol Dealer Manual 0 10
## 245 Diesel Dealer Manual 0 13
## 246 Diesel Dealer Manual 0 14
## 247 Petrol Dealer Manual 0 14
## 248 Petrol Dealer Manual 0 11
## 249 Petrol Dealer Manual 0 13
## 250 Petrol Dealer Manual 0 10
## 251 Diesel Dealer Manual 0 10
## 252 Petrol Dealer Manual 0 13
## 253 Petrol Dealer Automatic 0 11
## 254 Diesel Dealer Manual 0 12
## 255 Petrol Dealer Manual 0 13
## 256 Petrol Dealer Manual 0 14
## 257 Petrol Dealer Manual 0 10
## 258 Petrol Dealer Manual 0 11
## 259 Petrol Dealer Manual 0 11
## 260 Petrol Dealer Manual 0 12
## 261 Petrol Dealer Manual 0 10
## 262 Petrol Dealer Manual 0 10
## 263 Petrol Dealer Manual 0 11
## 264 Petrol Dealer Manual 0 10
## 265 Petrol Dealer Manual 0 11
## 266 Petrol Dealer Manual 0 9
## 267 Petrol Dealer Manual 0 12
## 268 Diesel Dealer Manual 0 10
## 269 Petrol Dealer Manual 0 9
## 270 Petrol Dealer Manual 0 11
## 271 Petrol Dealer Manual 0 15
## 272 Petrol Dealer Manual 0 17
## 273 Petrol Dealer Manual 0 11
## 274 Petrol Dealer Manual 0 16
## 275 Petrol Dealer Manual 0 12
## 276 Petrol Dealer Automatic 0 10
## 277 Petrol Dealer Manual 0 11
## 278 Petrol Dealer Manual 0 11
## 279 Petrol Dealer Manual 0 10
## 280 Petrol Dealer Manual 0 12
## 281 Petrol Dealer Manual 0 11
## 282 Petrol Dealer Manual 0 20
## 283 Diesel Dealer Manual 0 12
## 284 Petrol Dealer Manual 0 10
## 285 Petrol Dealer Manual 0 13
## 286 Petrol Dealer Automatic 0 10
## 287 Petrol Dealer Manual 0 10
## 288 Petrol Dealer Automatic 0 11
## 289 Petrol Dealer Manual 0 11
## 290 Petrol Dealer Manual 0 10
## 291 Petrol Dealer Manual 0 12
## 292 Petrol Dealer Manual 0 11
## 293 Petrol Dealer Manual 0 10
## 294 Petrol Dealer Manual 0 16
## 295 Petrol Dealer Manual 0 12
## 296 Diesel Dealer Manual 0 11
## 297 Diesel Dealer Manual 0 10
## 298 Petrol Dealer Manual 0 11
## 299 Petrol Dealer Manual 0 17
## 300 Diesel Dealer Manual 0 9
## 301 Petrol Dealer Manual 0 10#[], $, [[]] accessing element(column) from dataset
df["Year"]## Year
## 1 2014
## 2 2013
## 3 2017
## 4 2011
## 5 2014
## 6 2018
## 7 2015
## 8 2015
## 9 2016
## 10 2015
## 11 2017
## 12 2015
## 13 2015
## 14 2015
## 15 2009
## 16 2016
## 17 2015
## 18 2016
## 19 2015
## 20 2010
## 21 2016
## 22 2017
## 23 2011
## 24 2014
## 25 2013
## 26 2011
## 27 2013
## 28 2017
## 29 2010
## 30 2015
## 31 2012
## 32 2011
## 33 2014
## 34 2014
## 35 2014
## 36 2011
## 37 2015
## 38 2003
## 39 2016
## 40 2003
## 41 2016
## 42 2014
## 43 2008
## 44 2014
## 45 2012
## 46 2014
## 47 2013
## 48 2006
## 49 2015
## 50 2017
## 51 2012
## 52 2015
## 53 2017
## 54 2013
## 55 2005
## 56 2009
## 57 2015
## 58 2010
## 59 2014
## 60 2014
## 61 2013
## 62 2015
## 63 2014
## 64 2015
## 65 2017
## 66 2014
## 67 2017
## 68 2010
## 69 2011
## 70 2016
## 71 2014
## 72 2011
## 73 2013
## 74 2011
## 75 2014
## 76 2015
## 77 2013
## 78 2004
## 79 2010
## 80 2012
## 81 2016
## 82 2015
## 83 2017
## 84 2015
## 85 2005
## 86 2006
## 87 2010
## 88 2012
## 89 2013
## 90 2014
## 91 2009
## 92 2014
## 93 2005
## 94 2015
## 95 2008
## 96 2012
## 97 2016
## 98 2017
## 99 2013
## 100 2010
## 101 2016
## 102 2017
## 103 2017
## 104 2017
## 105 2017
## 106 2015
## 107 2014
## 108 2013
## 109 2016
## 110 2017
## 111 2016
## 112 2016
## 113 2014
## 114 2016
## 115 2015
## 116 2015
## 117 2013
## 118 2015
## 119 2015
## 120 2013
## 121 2016
## 122 2011
## 123 2016
## 124 2013
## 125 2012
## 126 2009
## 127 2017
## 128 2016
## 129 2017
## 130 2017
## 131 2017
## 132 2015
## 133 2017
## 134 2016
## 135 2017
## 136 2015
## 137 2014
## 138 2013
## 139 2016
## 140 2015
## 141 2013
## 142 2016
## 143 2015
## 144 2016
## 145 2014
## 146 2012
## 147 2014
## 148 2015
## 149 2010
## 150 2016
## 151 2011
## 152 2016
## 153 2012
## 154 2013
## 155 2014
## 156 2017
## 157 2017
## 158 2015
## 159 2017
## 160 2017
## 161 2011
## 162 2014
## 163 2012
## 164 2010
## 165 2016
## 166 2016
## 167 2016
## 168 2014
## 169 2013
## 170 2015
## 171 2012
## 172 2015
## 173 2014
## 174 2017
## 175 2015
## 176 2011
## 177 2011
## 178 2016
## 179 2014
## 180 2010
## 181 2012
## 182 2016
## 183 2013
## 184 2013
## 185 2008
## 186 2008
## 187 2010
## 188 2013
## 189 2013
## 190 2005
## 191 2008
## 192 2012
## 193 2007
## 194 2013
## 195 2008
## 196 2015
## 197 2008
## 198 2010
## 199 2011
## 200 2007
## 201 2006
## 202 2010
## 203 2015
## 204 2011
## 205 2015
## 206 2016
## 207 2017
## 208 2015
## 209 2017
## 210 2015
## 211 2012
## 212 2015
## 213 2016
## 214 2011
## 215 2017
## 216 2012
## 217 2016
## 218 2016
## 219 2014
## 220 2012
## 221 2017
## 222 2013
## 223 2014
## 224 2015
## 225 2013
## 226 2011
## 227 2015
## 228 2011
## 229 2012
## 230 2012
## 231 2013
## 232 2017
## 233 2015
## 234 2013
## 235 2015
## 236 2017
## 237 2016
## 238 2015
## 239 2013
## 240 2012
## 241 2012
## 242 2015
## 243 2014
## 244 2016
## 245 2013
## 246 2012
## 247 2012
## 248 2015
## 249 2013
## 250 2016
## 251 2016
## 252 2013
## 253 2015
## 254 2014
## 255 2013
## 256 2012
## 257 2016
## 258 2015
## 259 2015
## 260 2014
## 261 2016
## 262 2016
## 263 2015
## 264 2016
## 265 2015
## 266 2017
## 267 2014
## 268 2016
## 269 2017
## 270 2015
## 271 2011
## 272 2009
## 273 2015
## 274 2010
## 275 2014
## 276 2016
## 277 2015
## 278 2015
## 279 2016
## 280 2014
## 281 2015
## 282 2006
## 283 2014
## 284 2016
## 285 2013
## 286 2016
## 287 2016
## 288 2015
## 289 2015
## 290 2016
## 291 2014
## 292 2015
## 293 2016
## 294 2010
## 295 2014
## 296 2015
## 297 2016
## 298 2015
## 299 2009
## 300 2017
## 301 20167.3 Removing a certain rows and columns from a dataset
## removing a certain rows and columns from a dataset.
df2<-df[-c(1,3),-1]
df2## Year Selling_Price Present_Price Kms_Driven Fuel_Type Seller_Type
## 2 2013 4.75 9.540 43000 Diesel Dealer
## 4 2011 2.85 4.150 5200 Petrol Dealer
## 5 2014 4.60 6.870 42450 Diesel Dealer
## 6 2018 9.25 9.830 2071 Diesel Dealer
## 7 2015 6.75 8.120 18796 Petrol Dealer
## 8 2015 6.50 8.610 33429 Diesel Dealer
## 9 2016 8.75 8.890 20273 Diesel Dealer
## 10 2015 7.45 8.920 42367 Diesel Dealer
## 11 2017 2.85 3.600 2135 Petrol Dealer
## 12 2015 6.85 10.380 51000 Diesel Dealer
## 13 2015 7.50 9.940 15000 Petrol Dealer
## 14 2015 6.10 7.710 26000 Petrol Dealer
## 15 2009 2.25 7.210 77427 Petrol Dealer
## 16 2016 7.75 10.790 43000 Diesel Dealer
## 17 2015 7.25 10.790 41678 Diesel Dealer
## 18 2016 7.75 10.790 43000 Diesel Dealer
## 19 2015 3.25 5.090 35500 CNG Dealer
## 20 2010 2.65 7.980 41442 Petrol Dealer
## 21 2016 2.85 3.950 25000 Petrol Dealer
## 22 2017 4.90 5.710 2400 Petrol Dealer
## 23 2011 4.40 8.010 50000 Petrol Dealer
## 24 2014 2.50 3.460 45280 Petrol Dealer
## 25 2013 2.90 4.410 56879 Petrol Dealer
## 26 2011 3.00 4.990 20000 Petrol Dealer
## 27 2013 4.15 5.870 55138 Petrol Dealer
## 28 2017 6.00 6.490 16200 Petrol Individual
## 29 2010 1.95 3.950 44542 Petrol Dealer
## 30 2015 7.45 10.380 45000 Diesel Dealer
## 31 2012 3.10 5.980 51439 Diesel Dealer
## 32 2011 2.35 4.890 54200 Petrol Dealer
## 33 2014 4.95 7.490 39000 Diesel Dealer
## 34 2014 6.00 9.950 45000 Diesel Dealer
## 35 2014 5.50 8.060 45000 Diesel Dealer
## 36 2011 2.95 7.740 49998 CNG Dealer
## 37 2015 4.65 7.200 48767 Petrol Dealer
## 38 2003 0.35 2.280 127000 Petrol Individual
## 39 2016 3.00 3.760 10079 Petrol Dealer
## 40 2003 2.25 7.980 62000 Petrol Dealer
## 41 2016 5.85 7.870 24524 Petrol Dealer
## 42 2014 2.55 3.980 46706 Petrol Dealer
## 43 2008 1.95 7.150 58000 Petrol Dealer
## 44 2014 5.50 8.060 45780 Diesel Dealer
## 45 2012 1.25 2.690 50000 Petrol Dealer
## 46 2014 7.50 12.040 15000 Petrol Dealer
## 47 2013 2.65 4.890 64532 Petrol Dealer
## 48 2006 1.05 4.150 65000 Petrol Dealer
## 49 2015 5.80 7.710 25870 Petrol Dealer
## 50 2017 7.75 9.290 37000 Petrol Dealer
## 51 2012 14.90 30.610 104707 Diesel Dealer
## 52 2015 23.00 30.610 40000 Diesel Dealer
## 53 2017 18.00 19.770 15000 Diesel Dealer
## 54 2013 16.00 30.610 135000 Diesel Individual
## 55 2005 2.75 10.210 90000 Petrol Individual
## 56 2009 3.60 15.040 70000 Petrol Dealer
## 57 2015 4.50 7.270 40534 Petrol Dealer
## 58 2010 4.75 18.540 50000 Petrol Dealer
## 59 2014 4.10 6.800 39485 Petrol Dealer
## 60 2014 19.99 35.960 41000 Diesel Dealer
## 61 2013 6.95 18.610 40001 Petrol Dealer
## 62 2015 4.50 7.700 40588 Petrol Dealer
## 63 2014 18.75 35.960 78000 Diesel Dealer
## 64 2015 23.50 35.960 47000 Diesel Dealer
## 65 2017 33.00 36.230 6000 Diesel Dealer
## 66 2014 4.75 6.950 45000 Diesel Dealer
## 67 2017 19.75 23.150 11000 Petrol Dealer
## 68 2010 9.25 20.450 59000 Diesel Dealer
## 69 2011 4.35 13.740 88000 Petrol Dealer
## 70 2016 14.25 20.910 12000 Petrol Dealer
## 71 2014 3.95 6.760 71000 Diesel Dealer
## 72 2011 4.50 12.480 45000 Diesel Dealer
## 73 2013 7.45 18.610 56001 Petrol Dealer
## 74 2011 2.65 5.710 43000 Petrol Dealer
## 75 2014 4.90 8.930 83000 Diesel Dealer
## 76 2015 3.95 6.800 36000 Petrol Dealer
## 77 2013 5.50 14.680 72000 Petrol Dealer
## 78 2004 1.50 12.350 135154 Petrol Dealer
## 79 2010 5.25 22.830 80000 Petrol Dealer
## 80 2012 14.50 30.610 89000 Diesel Dealer
## 81 2016 14.73 14.890 23000 Diesel Dealer
## 82 2015 4.75 7.850 40000 Diesel Dealer
## 83 2017 23.00 25.390 15000 Diesel Dealer
## 84 2015 12.50 13.460 38000 Diesel Dealer
## 85 2005 3.49 13.460 197176 Diesel Dealer
## 86 2006 2.50 23.730 142000 Petrol Individual
## 87 2010 35.00 92.600 78000 Diesel Dealer
## 88 2012 5.90 13.740 56000 Petrol Dealer
## 89 2013 3.45 6.050 47000 Petrol Dealer
## 90 2014 4.75 6.760 40000 Petrol Dealer
## 91 2009 3.80 18.610 62000 Petrol Dealer
## 92 2014 11.25 16.090 58242 Diesel Dealer
## 93 2005 3.51 13.700 75000 Petrol Dealer
## 94 2015 23.00 30.610 40000 Diesel Dealer
## 95 2008 4.00 22.780 89000 Petrol Dealer
## 96 2012 5.85 18.610 72000 Petrol Dealer
## 97 2016 20.75 25.390 29000 Diesel Dealer
## 98 2017 17.00 18.640 8700 Petrol Dealer
## 99 2013 7.05 18.610 45000 Petrol Dealer
## 100 2010 9.65 20.450 50024 Diesel Dealer
## 101 2016 1.75 1.900 3000 Petrol Individual
## 102 2017 1.70 1.820 1400 Petrol Individual
## 103 2017 1.65 1.780 4000 Petrol Individual
## 104 2017 1.45 1.600 1200 Petrol Individual
## 105 2017 1.35 1.470 4100 Petrol Individual
## 106 2015 1.35 2.370 21700 Petrol Individual
## 107 2014 1.35 3.450 16500 Petrol Individual
## 108 2013 1.25 1.500 15000 Petrol Individual
## 109 2016 1.20 1.500 18000 Petrol Individual
## 110 2017 1.20 1.470 11000 Petrol Individual
## 111 2016 1.20 1.780 6000 Petrol Individual
## 112 2016 1.15 1.500 8700 Petrol Individual
## 113 2014 1.15 2.400 7000 Petrol Individual
## 114 2016 1.15 1.400 35000 Petrol Individual
## 115 2015 1.15 1.470 17000 Petrol Individual
## 116 2015 1.11 1.470 17500 Petrol Individual
## 117 2013 1.10 1.470 33000 Petrol Individual
## 118 2015 1.10 1.900 14000 Petrol Individual
## 119 2015 1.10 1.470 26000 Petrol Individual
## 120 2013 1.05 1.900 5400 Petrol Individual
## 121 2016 1.05 1.260 5700 Petrol Individual
## 122 2011 1.05 1.500 6900 Petrol Individual
## 123 2016 1.05 1.170 6000 Petrol Individual
## 124 2013 1.00 1.470 46500 Petrol Individual
## 125 2012 0.95 1.750 11500 Petrol Individual
## 126 2009 0.90 1.750 40000 Petrol Individual
## 127 2017 0.90 0.950 1300 Petrol Individual
## 128 2016 0.75 0.800 7000 Petrol Individual
## 129 2017 0.80 0.870 3000 Petrol Individual
## 130 2017 0.78 0.840 5000 Petrol Individual
## 131 2017 0.75 0.870 11000 Petrol Individual
## 132 2015 0.75 0.820 18000 Petrol Individual
## 133 2017 0.75 0.950 3500 Petrol Individual
## 134 2016 0.72 0.950 500 Petrol Individual
## 135 2017 0.65 0.810 11800 Petrol Individual
## 136 2015 0.65 0.740 5000 Petrol Individual
## 137 2014 0.65 1.200 23500 Petrol Individual
## 138 2013 0.65 0.787 16000 Petrol Individual
## 139 2016 0.60 0.870 15000 Petrol Individual
## 140 2015 0.60 0.950 16600 Petrol Individual
## 141 2013 0.60 1.200 32000 Petrol Individual
## 142 2016 0.60 0.800 20000 Petrol Individual
## 143 2015 0.60 0.840 29000 Petrol Individual
## 144 2016 0.60 0.840 25000 Petrol Individual
## 145 2014 0.60 0.990 25000 Petrol Individual
## 146 2012 0.60 0.810 19000 Petrol Individual
## 147 2014 0.55 0.787 15000 Petrol Individual
## 148 2015 0.55 0.840 58000 Petrol Individual
## 149 2010 0.52 0.940 45000 Petrol Individual
## 150 2016 0.51 0.940 24000 Petrol Individual
## 151 2011 0.50 0.826 6000 Petrol Individual
## 152 2016 0.50 0.550 31000 Petrol Individual
## 153 2012 0.50 0.990 13000 Petrol Individual
## 154 2013 0.50 0.990 45000 Petrol Individual
## 155 2014 0.50 0.880 8000 Petrol Individual
## 156 2017 0.48 0.510 4300 Petrol Individual
## 157 2017 0.48 0.520 15000 Petrol Individual
## 158 2015 0.48 0.840 23000 Petrol Individual
## 159 2017 0.48 0.540 8600 Petrol Individual
## 160 2017 0.45 0.510 4000 Petrol Individual
## 161 2011 0.45 0.950 24000 Petrol Individual
## 162 2014 0.45 0.826 23000 Petrol Individual
## 163 2012 0.45 0.990 14500 Petrol Individual
## 164 2010 0.45 0.950 27000 Petrol Individual
## 165 2016 0.45 0.540 14000 Petrol Individual
## 166 2016 0.45 0.540 500 Petrol Individual
## 167 2016 0.45 0.550 1000 Petrol Individual
## 168 2014 0.42 0.810 42000 Petrol Individual
## 169 2013 0.42 0.730 12000 Petrol Individual
## 170 2015 0.40 0.540 14000 Petrol Individual
## 171 2012 0.40 0.830 5500 Petrol Individual
## 172 2015 0.40 0.550 6700 Petrol Individual
## 173 2014 0.40 0.640 13700 Petrol Individual
## 174 2017 0.40 0.510 1300 Petrol Individual
## 175 2015 0.38 0.720 38600 Petrol Individual
## 176 2011 0.38 0.787 75000 Petrol Individual
## 177 2011 0.35 1.050 30000 Petrol Individual
## 178 2016 0.35 0.570 24000 Petrol Individual
## 179 2014 0.35 0.520 19000 Petrol Individual
## 180 2010 0.31 1.050 213000 Petrol Individual
## 181 2012 0.30 0.510 60000 Petrol Individual
## 182 2016 0.30 0.480 50000 Petrol Individual
## 183 2013 0.30 0.580 30000 Petrol Individual
## 184 2013 0.27 0.470 21000 Petrol Individual
## 185 2008 0.25 0.750 26000 Petrol Individual
## 186 2008 0.25 0.580 1900 Petrol Individual
## 187 2010 0.25 0.520 22000 Petrol Individual
## 188 2013 0.25 0.510 32000 Petrol Individual
## 189 2013 0.25 0.570 18000 Petrol Individual
## 190 2005 0.20 0.570 55000 Petrol Individual
## 191 2008 0.20 0.750 60000 Petrol Individual
## 192 2012 0.20 0.570 25000 Petrol Individual
## 193 2007 0.20 0.750 49000 Petrol Individual
## 194 2013 0.20 0.650 24000 Petrol Individual
## 195 2008 0.20 0.787 50000 Petrol Individual
## 196 2015 0.18 0.320 35000 Petrol Individual
## 197 2008 0.17 0.520 500000 Petrol Individual
## 198 2010 0.16 0.510 33000 Petrol Individual
## 199 2011 0.15 0.570 35000 Petrol Individual
## 200 2007 0.12 0.580 53000 Petrol Individual
## 201 2006 0.10 0.750 92233 Petrol Individual
## 202 2010 3.25 6.790 58000 Diesel Dealer
## 203 2015 4.40 5.700 28200 Petrol Dealer
## 204 2011 2.95 4.600 53460 Petrol Dealer
## 205 2015 2.75 4.430 28282 Petrol Dealer
## 206 2016 5.25 5.700 3493 Petrol Dealer
## 207 2017 5.75 7.130 12479 Petrol Dealer
## 208 2015 5.15 5.700 34797 Petrol Dealer
## 209 2017 7.90 8.100 3435 Petrol Dealer
## 210 2015 4.85 5.700 21125 Diesel Dealer
## 211 2012 3.10 4.600 35775 Petrol Dealer
## 212 2015 11.75 14.790 43535 Diesel Dealer
## 213 2016 11.25 13.600 22671 Petrol Dealer
## 214 2011 2.90 6.790 31604 Petrol Dealer
## 215 2017 5.25 5.700 20114 Petrol Dealer
## 216 2012 4.50 9.400 36100 Petrol Dealer
## 217 2016 2.90 4.430 12500 Petrol Dealer
## 218 2016 3.15 4.430 15000 Petrol Dealer
## 219 2014 6.45 9.400 45078 Petrol Dealer
## 220 2012 4.50 9.400 36000 Petrol Dealer
## 221 2017 3.50 4.430 38488 Petrol Dealer
## 222 2013 4.50 6.790 32000 Petrol Dealer
## 223 2014 6.00 7.600 77632 Diesel Dealer
## 224 2015 8.25 9.400 61381 Diesel Dealer
## 225 2013 5.11 9.400 36198 Petrol Dealer
## 226 2011 2.70 4.600 22517 Petrol Dealer
## 227 2015 5.25 5.700 24678 Petrol Dealer
## 228 2011 2.55 4.430 57000 Petrol Dealer
## 229 2012 4.95 9.400 60000 Diesel Dealer
## 230 2012 3.10 6.790 52132 Diesel Dealer
## 231 2013 6.15 9.400 45000 Diesel Dealer
## 232 2017 9.25 9.400 15001 Petrol Dealer
## 233 2015 11.45 14.790 12900 Petrol Dealer
## 234 2013 3.90 5.700 53000 Diesel Dealer
## 235 2015 5.50 5.700 4492 Petrol Dealer
## 236 2017 9.10 9.400 15141 Petrol Dealer
## 237 2016 3.10 4.430 11849 Petrol Dealer
## 238 2015 11.25 13.600 68000 Diesel Dealer
## 239 2013 4.80 9.400 60241 Petrol Dealer
## 240 2012 2.00 4.430 23709 Petrol Dealer
## 241 2012 5.35 9.400 32322 Diesel Dealer
## 242 2015 4.75 7.130 35866 Petrol Dealer
## 243 2014 4.40 7.130 34000 Petrol Dealer
## 244 2016 6.25 7.600 7000 Petrol Dealer
## 245 2013 5.95 9.400 49000 Diesel Dealer
## 246 2012 5.20 9.400 71000 Diesel Dealer
## 247 2012 3.75 6.790 35000 Petrol Dealer
## 248 2015 5.95 9.400 36000 Petrol Dealer
## 249 2013 4.00 4.600 30000 Petrol Dealer
## 250 2016 5.25 7.600 17000 Petrol Dealer
## 251 2016 12.90 13.600 35934 Diesel Dealer
## 252 2013 5.00 9.900 56701 Petrol Dealer
## 253 2015 5.40 6.820 31427 Petrol Dealer
## 254 2014 7.20 9.900 48000 Diesel Dealer
## 255 2013 5.25 9.900 54242 Petrol Dealer
## 256 2012 3.00 5.350 53675 Petrol Dealer
## 257 2016 10.25 13.600 49562 Petrol Dealer
## 258 2015 8.50 13.600 40324 Petrol Dealer
## 259 2015 8.40 13.600 25000 Petrol Dealer
## 260 2014 3.90 7.000 36054 Petrol Dealer
## 261 2016 9.15 13.600 29223 Petrol Dealer
## 262 2016 5.50 5.970 5600 Petrol Dealer
## 263 2015 4.00 5.800 40023 Petrol Dealer
## 264 2016 6.60 7.700 16002 Petrol Dealer
## 265 2015 4.00 7.000 40026 Petrol Dealer
## 266 2017 6.50 8.700 21200 Petrol Dealer
## 267 2014 3.65 7.000 35000 Petrol Dealer
## 268 2016 8.35 9.400 19434 Diesel Dealer
## 269 2017 4.80 5.800 19000 Petrol Dealer
## 270 2015 6.70 10.000 18828 Petrol Dealer
## 271 2011 4.10 10.000 69341 Petrol Dealer
## 272 2009 3.00 10.000 69562 Petrol Dealer
## 273 2015 7.50 10.000 27600 Petrol Dealer
## 274 2010 2.25 7.500 61203 Petrol Dealer
## 275 2014 5.30 6.800 16500 Petrol Dealer
## 276 2016 10.90 13.600 30753 Petrol Dealer
## 277 2015 8.65 13.600 24800 Petrol Dealer
## 278 2015 9.70 13.600 21780 Petrol Dealer
## 279 2016 6.00 8.400 4000 Petrol Dealer
## 280 2014 6.25 13.600 40126 Petrol Dealer
## 281 2015 5.25 5.900 14465 Petrol Dealer
## 282 2006 2.10 7.600 50456 Petrol Dealer
## 283 2014 8.25 14.000 63000 Diesel Dealer
## 284 2016 8.99 11.800 9010 Petrol Dealer
## 285 2013 3.50 5.900 9800 Petrol Dealer
## 286 2016 7.40 8.500 15059 Petrol Dealer
## 287 2016 5.65 7.900 28569 Petrol Dealer
## 288 2015 5.75 7.500 44000 Petrol Dealer
## 289 2015 8.40 13.600 34000 Petrol Dealer
## 290 2016 10.11 13.600 10980 Petrol Dealer
## 291 2014 4.50 6.400 19000 Petrol Dealer
## 292 2015 5.40 6.100 31427 Petrol Dealer
## 293 2016 6.40 8.400 12000 Petrol Dealer
## 294 2010 3.25 9.900 38000 Petrol Dealer
## 295 2014 3.75 6.800 33019 Petrol Dealer
## 296 2015 8.55 13.090 60076 Diesel Dealer
## 297 2016 9.50 11.600 33988 Diesel Dealer
## 298 2015 4.00 5.900 60000 Petrol Dealer
## 299 2009 3.35 11.000 87934 Petrol Dealer
## 300 2017 11.50 12.500 9000 Diesel Dealer
## 301 2016 5.30 5.900 5464 Petrol Dealer
## Transmission Owner Age
## 2 Manual 0 13
## 4 Manual 0 15
## 5 Manual 0 12
## 6 Manual 0 8
## 7 Manual 0 11
## 8 Manual 0 11
## 9 Manual 0 10
## 10 Manual 0 11
## 11 Manual 0 9
## 12 Manual 0 11
## 13 Automatic 0 11
## 14 Manual 0 11
## 15 Manual 0 17
## 16 Manual 0 10
## 17 Manual 0 11
## 18 Manual 0 10
## 19 Manual 0 11
## 20 Manual 0 16
## 21 Manual 0 10
## 22 Manual 0 9
## 23 Automatic 0 15
## 24 Manual 0 12
## 25 Manual 0 13
## 26 Manual 0 15
## 27 Manual 0 13
## 28 Manual 0 9
## 29 Manual 0 16
## 30 Manual 0 11
## 31 Manual 0 14
## 32 Manual 0 15
## 33 Manual 0 12
## 34 Manual 0 12
## 35 Manual 0 12
## 36 Manual 0 15
## 37 Manual 0 11
## 38 Manual 0 23
## 39 Manual 0 10
## 40 Manual 0 23
## 41 Automatic 0 10
## 42 Manual 0 12
## 43 Manual 0 18
## 44 Manual 0 12
## 45 Manual 0 14
## 46 Automatic 0 12
## 47 Manual 0 13
## 48 Manual 0 20
## 49 Manual 0 11
## 50 Automatic 0 9
## 51 Automatic 0 14
## 52 Automatic 0 11
## 53 Automatic 0 9
## 54 Automatic 0 13
## 55 Manual 0 21
## 56 Automatic 0 17
## 57 Manual 0 11
## 58 Manual 0 16
## 59 Manual 1 12
## 60 Automatic 0 12
## 61 Manual 0 13
## 62 Manual 0 11
## 63 Automatic 0 12
## 64 Automatic 0 11
## 65 Automatic 0 9
## 66 Manual 0 12
## 67 Automatic 0 9
## 68 Manual 0 16
## 69 Manual 0 15
## 70 Manual 0 10
## 71 Manual 0 12
## 72 Manual 0 15
## 73 Manual 0 13
## 74 Manual 0 15
## 75 Manual 0 12
## 76 Manual 0 11
## 77 Manual 0 13
## 78 Automatic 0 22
## 79 Automatic 0 16
## 80 Automatic 0 14
## 81 Manual 0 10
## 82 Manual 0 11
## 83 Automatic 0 9
## 84 Manual 0 11
## 85 Manual 0 21
## 86 Automatic 3 20
## 87 Manual 0 16
## 88 Manual 0 14
## 89 Manual 0 13
## 90 Manual 0 12
## 91 Manual 0 17
## 92 Manual 0 12
## 93 Manual 0 21
## 94 Automatic 0 11
## 95 Automatic 0 18
## 96 Manual 0 14
## 97 Automatic 0 10
## 98 Manual 0 9
## 99 Manual 0 13
## 100 Manual 0 16
## 101 Manual 0 10
## 102 Manual 0 9
## 103 Manual 0 9
## 104 Manual 0 9
## 105 Manual 0 9
## 106 Manual 0 11
## 107 Manual 1 12
## 108 Manual 0 13
## 109 Manual 0 10
## 110 Manual 0 9
## 111 Manual 0 10
## 112 Manual 0 10
## 113 Manual 0 12
## 114 Manual 0 10
## 115 Manual 0 11
## 116 Manual 0 11
## 117 Manual 0 13
## 118 Manual 0 11
## 119 Manual 0 11
## 120 Manual 0 13
## 121 Manual 0 10
## 122 Manual 0 15
## 123 Manual 0 10
## 124 Manual 0 13
## 125 Manual 0 14
## 126 Manual 0 17
## 127 Manual 0 9
## 128 Manual 0 10
## 129 Manual 0 9
## 130 Manual 0 9
## 131 Manual 0 9
## 132 Manual 0 11
## 133 Manual 0 9
## 134 Manual 0 10
## 135 Manual 0 9
## 136 Manual 0 11
## 137 Manual 0 12
## 138 Manual 0 13
## 139 Manual 0 10
## 140 Manual 0 11
## 141 Manual 0 13
## 142 Manual 0 10
## 143 Manual 0 11
## 144 Manual 0 10
## 145 Manual 0 12
## 146 Manual 0 14
## 147 Manual 0 12
## 148 Manual 0 11
## 149 Manual 0 16
## 150 Manual 0 10
## 151 Manual 0 15
## 152 Manual 0 10
## 153 Manual 0 14
## 154 Manual 0 13
## 155 Manual 0 12
## 156 Automatic 0 9
## 157 Manual 0 9
## 158 Manual 0 11
## 159 Manual 0 9
## 160 Automatic 0 9
## 161 Manual 0 15
## 162 Manual 0 12
## 163 Manual 0 14
## 164 Manual 0 16
## 165 Manual 0 10
## 166 Automatic 0 10
## 167 Manual 0 10
## 168 Manual 0 12
## 169 Manual 0 13
## 170 Manual 0 11
## 171 Manual 0 14
## 172 Manual 0 11
## 173 Manual 0 12
## 174 Automatic 0 9
## 175 Manual 0 11
## 176 Manual 0 15
## 177 Manual 0 15
## 178 Automatic 0 10
## 179 Automatic 0 12
## 180 Manual 0 16
## 181 Manual 0 14
## 182 Manual 0 10
## 183 Manual 0 13
## 184 Manual 0 13
## 185 Manual 1 18
## 186 Automatic 0 18
## 187 Automatic 0 16
## 188 Manual 0 13
## 189 Manual 0 13
## 190 Manual 0 21
## 191 Manual 0 18
## 192 Manual 1 14
## 193 Manual 1 19
## 194 Manual 1 13
## 195 Manual 0 18
## 196 Manual 0 11
## 197 Automatic 0 18
## 198 Manual 0 16
## 199 Manual 1 15
## 200 Manual 0 19
## 201 Manual 0 20
## 202 Manual 1 16
## 203 Manual 0 11
## 204 Manual 0 15
## 205 Manual 0 11
## 206 Manual 1 10
## 207 Manual 0 9
## 208 Automatic 0 11
## 209 Manual 0 9
## 210 Manual 0 11
## 211 Manual 0 14
## 212 Manual 0 11
## 213 Manual 0 10
## 214 Manual 0 15
## 215 Manual 0 9
## 216 Manual 0 14
## 217 Manual 0 10
## 218 Manual 0 10
## 219 Manual 0 12
## 220 Manual 0 14
## 221 Manual 0 9
## 222 Automatic 0 13
## 223 Manual 0 12
## 224 Manual 0 11
## 225 Automatic 0 13
## 226 Manual 0 15
## 227 Manual 0 11
## 228 Manual 0 15
## 229 Manual 0 14
## 230 Manual 0 14
## 231 Manual 0 13
## 232 Manual 0 9
## 233 Automatic 0 11
## 234 Manual 0 13
## 235 Manual 0 11
## 236 Manual 0 9
## 237 Manual 0 10
## 238 Manual 0 11
## 239 Manual 0 13
## 240 Manual 0 14
## 241 Manual 0 14
## 242 Manual 1 11
## 243 Manual 0 12
## 244 Manual 0 10
## 245 Manual 0 13
## 246 Manual 0 14
## 247 Manual 0 14
## 248 Manual 0 11
## 249 Manual 0 13
## 250 Manual 0 10
## 251 Manual 0 10
## 252 Manual 0 13
## 253 Automatic 0 11
## 254 Manual 0 12
## 255 Manual 0 13
## 256 Manual 0 14
## 257 Manual 0 10
## 258 Manual 0 11
## 259 Manual 0 11
## 260 Manual 0 12
## 261 Manual 0 10
## 262 Manual 0 10
## 263 Manual 0 11
## 264 Manual 0 10
## 265 Manual 0 11
## 266 Manual 0 9
## 267 Manual 0 12
## 268 Manual 0 10
## 269 Manual 0 9
## 270 Manual 0 11
## 271 Manual 0 15
## 272 Manual 0 17
## 273 Manual 0 11
## 274 Manual 0 16
## 275 Manual 0 12
## 276 Automatic 0 10
## 277 Manual 0 11
## 278 Manual 0 11
## 279 Manual 0 10
## 280 Manual 0 12
## 281 Manual 0 11
## 282 Manual 0 20
## 283 Manual 0 12
## 284 Manual 0 10
## 285 Manual 0 13
## 286 Automatic 0 10
## 287 Manual 0 10
## 288 Automatic 0 11
## 289 Manual 0 11
## 290 Manual 0 10
## 291 Manual 0 12
## 292 Manual 0 11
## 293 Manual 0 10
## 294 Manual 0 16
## 295 Manual 0 12
## 296 Manual 0 11
## 297 Manual 0 10
## 298 Manual 0 11
## 299 Manual 0 17
## 300 Manual 0 9
## 301 Manual 0 107.4 Filtering a data
observations based on a certain
condition.
7.4.1 And then calculate
a mean.
# filtering by conditions
df[df$Fuel_Type=="Diesel",]## Car_Name Year Selling_Price Present_Price Kms_Driven Fuel_Type
## 2 sx4 2013 4.75 9.54 43000 Diesel
## 5 swift 2014 4.60 6.87 42450 Diesel
## 6 vitara brezza 2018 9.25 9.83 2071 Diesel
## 8 s cross 2015 6.50 8.61 33429 Diesel
## 9 ciaz 2016 8.75 8.89 20273 Diesel
## 10 ciaz 2015 7.45 8.92 42367 Diesel
## 12 ciaz 2015 6.85 10.38 51000 Diesel
## 16 ertiga 2016 7.75 10.79 43000 Diesel
## 17 ertiga 2015 7.25 10.79 41678 Diesel
## 18 ertiga 2016 7.75 10.79 43000 Diesel
## 30 ciaz 2015 7.45 10.38 45000 Diesel
## 31 ritz 2012 3.10 5.98 51439 Diesel
## 33 swift 2014 4.95 7.49 39000 Diesel
## 34 ertiga 2014 6.00 9.95 45000 Diesel
## 35 dzire 2014 5.50 8.06 45000 Diesel
## 44 dzire 2014 5.50 8.06 45780 Diesel
## 51 fortuner 2012 14.90 30.61 104707 Diesel
## 52 fortuner 2015 23.00 30.61 40000 Diesel
## 53 innova 2017 18.00 19.77 15000 Diesel
## 54 fortuner 2013 16.00 30.61 135000 Diesel
## 60 fortuner 2014 19.99 35.96 41000 Diesel
## 63 fortuner 2014 18.75 35.96 78000 Diesel
## 64 fortuner 2015 23.50 35.96 47000 Diesel
## 65 fortuner 2017 33.00 36.23 6000 Diesel
## 66 etios liva 2014 4.75 6.95 45000 Diesel
## 68 fortuner 2010 9.25 20.45 59000 Diesel
## 71 etios liva 2014 3.95 6.76 71000 Diesel
## 72 corolla altis 2011 4.50 12.48 45000 Diesel
## 75 etios cross 2014 4.90 8.93 83000 Diesel
## 80 fortuner 2012 14.50 30.61 89000 Diesel
## 81 corolla altis 2016 14.73 14.89 23000 Diesel
## 82 etios gd 2015 4.75 7.85 40000 Diesel
## 83 innova 2017 23.00 25.39 15000 Diesel
## 84 innova 2015 12.50 13.46 38000 Diesel
## 85 innova 2005 3.49 13.46 197176 Diesel
## 87 land cruiser 2010 35.00 92.60 78000 Diesel
## 92 innova 2014 11.25 16.09 58242 Diesel
## 94 fortuner 2015 23.00 30.61 40000 Diesel
## 97 innova 2016 20.75 25.39 29000 Diesel
## 100 fortuner 2010 9.65 20.45 50024 Diesel
## 202 i20 2010 3.25 6.79 58000 Diesel
## 210 grand i10 2015 4.85 5.70 21125 Diesel
## 212 elantra 2015 11.75 14.79 43535 Diesel
## 223 i20 2014 6.00 7.60 77632 Diesel
## 224 verna 2015 8.25 9.40 61381 Diesel
## 229 verna 2012 4.95 9.40 60000 Diesel
## 230 i20 2012 3.10 6.79 52132 Diesel
## 231 verna 2013 6.15 9.40 45000 Diesel
## 234 grand i10 2013 3.90 5.70 53000 Diesel
## 238 creta 2015 11.25 13.60 68000 Diesel
## 241 verna 2012 5.35 9.40 32322 Diesel
## 245 verna 2013 5.95 9.40 49000 Diesel
## 246 verna 2012 5.20 9.40 71000 Diesel
## 251 creta 2016 12.90 13.60 35934 Diesel
## 254 city 2014 7.20 9.90 48000 Diesel
## 268 city 2016 8.35 9.40 19434 Diesel
## 283 city 2014 8.25 14.00 63000 Diesel
## 296 city 2015 8.55 13.09 60076 Diesel
## 297 city 2016 9.50 11.60 33988 Diesel
## 300 city 2017 11.50 12.50 9000 Diesel
## Seller_Type Transmission Owner Age
## 2 Dealer Manual 0 13
## 5 Dealer Manual 0 12
## 6 Dealer Manual 0 8
## 8 Dealer Manual 0 11
## 9 Dealer Manual 0 10
## 10 Dealer Manual 0 11
## 12 Dealer Manual 0 11
## 16 Dealer Manual 0 10
## 17 Dealer Manual 0 11
## 18 Dealer Manual 0 10
## 30 Dealer Manual 0 11
## 31 Dealer Manual 0 14
## 33 Dealer Manual 0 12
## 34 Dealer Manual 0 12
## 35 Dealer Manual 0 12
## 44 Dealer Manual 0 12
## 51 Dealer Automatic 0 14
## 52 Dealer Automatic 0 11
## 53 Dealer Automatic 0 9
## 54 Individual Automatic 0 13
## 60 Dealer Automatic 0 12
## 63 Dealer Automatic 0 12
## 64 Dealer Automatic 0 11
## 65 Dealer Automatic 0 9
## 66 Dealer Manual 0 12
## 68 Dealer Manual 0 16
## 71 Dealer Manual 0 12
## 72 Dealer Manual 0 15
## 75 Dealer Manual 0 12
## 80 Dealer Automatic 0 14
## 81 Dealer Manual 0 10
## 82 Dealer Manual 0 11
## 83 Dealer Automatic 0 9
## 84 Dealer Manual 0 11
## 85 Dealer Manual 0 21
## 87 Dealer Manual 0 16
## 92 Dealer Manual 0 12
## 94 Dealer Automatic 0 11
## 97 Dealer Automatic 0 10
## 100 Dealer Manual 0 16
## 202 Dealer Manual 1 16
## 210 Dealer Manual 0 11
## 212 Dealer Manual 0 11
## 223 Dealer Manual 0 12
## 224 Dealer Manual 0 11
## 229 Dealer Manual 0 14
## 230 Dealer Manual 0 14
## 231 Dealer Manual 0 13
## 234 Dealer Manual 0 13
## 238 Dealer Manual 0 11
## 241 Dealer Manual 0 14
## 245 Dealer Manual 0 13
## 246 Dealer Manual 0 14
## 251 Dealer Manual 0 10
## 254 Dealer Manual 0 12
## 268 Dealer Manual 0 10
## 283 Dealer Manual 0 12
## 296 Dealer Manual 0 11
## 297 Dealer Manual 0 10
## 300 Dealer Manual 0 9# exercise
#calculate selling price mean of diesel car
#mean(df[df$Fuel_Type=="Diesel",]$Selling_Price)
val1<-df[df$Fuel_Type=="Diesel",]$Selling_Price
#first way
print(mean(df[df$Fuel_Type=="Diesel",]$Selling_Price))## [1] 10.2785#second way
x=0
for (values in val1) {
x<-(x+values)
}
print(x/length(val1))## [1] 10.2785#third way
meanss<-sum(val1)/length(val1)
print(meanss)## [1] 10.27857.5 Import dataset called
iris and apply() functions
data<-iris
#View(data)
data## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica# apply()
apply(data[-5], MARGIN=2,FUN = summary)## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. 4.300000 2.000000 1.000 0.100000
## 1st Qu. 5.100000 2.800000 1.600 0.300000
## Median 5.800000 3.000000 4.350 1.300000
## Mean 5.843333 3.057333 3.758 1.199333
## 3rd Qu. 6.400000 3.300000 5.100 1.800000
## Max. 7.900000 4.400000 6.900 2.500000summary(data$Sepal.Length)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.300 5.100 5.800 5.843 6.400 7.900#lapply() sapply(), vapply(),tapply()
lapply(data[-c(4,5)],mean)## $Sepal.Length
## [1] 5.843333
##
## $Sepal.Width
## [1] 3.057333
##
## $Petal.Length
## [1] 3.758sapply(data[-c(4,5)],mean)## Sepal.Length Sepal.Width Petal.Length
## 5.843333 3.057333 3.758000vapply(data[-c(4,5)],mean,FUN.VALUE = numeric(1))## Sepal.Length Sepal.Width Petal.Length
## 5.843333 3.057333 3.758000tapply(iris$Sepal.Length, iris[[5]], mean) # this like groupby function## setosa versicolor virginica
## 5.006 5.936 6.5887.6 Installing and
loading tidyverse package.
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.6
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.1 ✔ tibble 3.3.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.2
## ✔ purrr 1.2.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors7.7 Data Visualization
with ggplot2
library(palmerpenguins)
penguins <- penguins
dim(penguins)## [1] 344 8# take a look on the data set at first
glimpse(penguins)## Rows: 344
## Columns: 8
## $ species <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
## $ island <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
## $ bill_length_mm <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
## $ bill_depth_mm <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
## $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
## $ body_mass_g <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
## $ sex <fct> male, female, female, NA, female, male, female, male…
## $ year <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…# bodymass vs flipper_length
plot101<- ggplot(data = penguins,
mapping = aes(x = flipper_length_mm, y = body_mass_g)
) +
geom_point(mapping = aes(color = species, shape = species)) +
geom_smooth( method = "lm") +
labs(
title = "Penguin Vibes",
subtitle = "Body Mass Vs Flipper Length",
x = "Flipper length (mm)",
y = "Body mass (g)"
)
plot101## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Data manipulations using dplyr package.
# select, filter, mutate, etc
df<-data %>% select("Species","Sepal.Width") %>% filter(Species=="virginica")
df## Species Sepal.Width
## 1 virginica 3.3
## 2 virginica 2.7
## 3 virginica 3.0
## 4 virginica 2.9
## 5 virginica 3.0
## 6 virginica 3.0
## 7 virginica 2.5
## 8 virginica 2.9
## 9 virginica 2.5
## 10 virginica 3.6
## 11 virginica 3.2
## 12 virginica 2.7
## 13 virginica 3.0
## 14 virginica 2.5
## 15 virginica 2.8
## 16 virginica 3.2
## 17 virginica 3.0
## 18 virginica 3.8
## 19 virginica 2.6
## 20 virginica 2.2
## 21 virginica 3.2
## 22 virginica 2.8
## 23 virginica 2.8
## 24 virginica 2.7
## 25 virginica 3.3
## 26 virginica 3.2
## 27 virginica 2.8
## 28 virginica 3.0
## 29 virginica 2.8
## 30 virginica 3.0
## 31 virginica 2.8
## 32 virginica 3.8
## 33 virginica 2.8
## 34 virginica 2.8
## 35 virginica 2.6
## 36 virginica 3.0
## 37 virginica 3.4
## 38 virginica 3.1
## 39 virginica 3.0
## 40 virginica 3.1
## 41 virginica 3.1
## 42 virginica 3.1
## 43 virginica 2.7
## 44 virginica 3.2
## 45 virginica 3.3
## 46 virginica 3.0
## 47 virginica 2.5
## 48 virginica 3.0
## 49 virginica 3.4
## 50 virginica 3.07.8 Checking column names of a dataset.
colnames(data)## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"