Muhammad Salman Afzal (ID 4035709) Lab 2
The below mentioned code block creates a sequence of 9 continuous integers starting from 2
x = 2 : 10
x## [1] 2 3 4 5 6 7 8 9 10Displaying the length of the sequence stored in the above code block - continuous integers from 2 to 9
len=length(x)
len ## [1] 9Generating a sequence of 9 continuous integers starting from 2 using seq() command in the below code block
x= seq(2,10)
x## [1] 2 3 4 5 6 7 8 9 10Generating a vector of 20 random normal values in the below code block
x=rnorm(20)
x## [1] 1.71066470 0.19523096 -0.68995790 -0.93658251 -0.03604616 -0.84925330
## [7] 0.61819224 1.25278047 -0.66014094 2.09707067 -0.37805813 -2.38380392
## [13] 0.02852406 -0.94447302 -1.62824033 -0.45393993 -1.03539135 0.26829173
## [19] 1.60149561 0.32110778# Using seed () command to generate the same random sequence on each run and finding 20 random normal values in the below code block
set.seed(2021)
x=rnorm(20)
x## [1] -0.12245998 0.55245663 0.34864950 0.35963224 0.89805369 -1.92256952
## [7] 0.26174436 0.91556637 0.01377194 1.72996316 -1.08220485 -0.27282518
## [13] 0.18199540 1.50854179 1.60447011 -1.84147561 1.62331021 0.13138902
## [19] 1.48112247 1.51331829Generating a sequence between -pi and +pi which are equally spaced between 50 values
x=seq(-pi,pi,length.out=50)
x## [1] -3.14159265 -3.01336438 -2.88513611 -2.75690784 -2.62867957 -2.50045130
## [7] -2.37222302 -2.24399475 -2.11576648 -1.98753821 -1.85930994 -1.73108167
## [13] -1.60285339 -1.47462512 -1.34639685 -1.21816858 -1.08994031 -0.96171204
## [19] -0.83348377 -0.70525549 -0.57702722 -0.44879895 -0.32057068 -0.19234241
## [25] -0.06411414 0.06411414 0.19234241 0.32057068 0.44879895 0.57702722
## [31] 0.70525549 0.83348377 0.96171204 1.08994031 1.21816858 1.34639685
## [37] 1.47462512 1.60285339 1.73108167 1.85930994 1.98753821 2.11576648
## [43] 2.24399475 2.37222302 2.50045130 2.62867957 2.75690784 2.88513611
## [49] 3.01336438 3.14159265Using rep() function, to replicate a vector/factor by t times. Here 0 is being replicated 10 times and NA is being replicated 10 times
x=rep(0,10)
x## [1] 0 0 0 0 0 0 0 0 0 0x=rep(NA,10)
x## [1] NA NA NA NA NA NA NA NA NA NA##Graphics
Generation of a Scatter plot for 100 random normal x and y values
set.seed(2021)
x=rnorm(100)
y=rnorm(100)
plot(x,y)
plot(x,y,xlab="this is the x-axis", ylab="this is the y-axis", main="Plot of X vs Y",type="p",col="blue") # 
Matrices and Indexed Data
4*4 matrix with data from 1-16 arranged as a row format
A=matrix(1:16,4,4,byrow=TRUE) #
A## [,1] [,2] [,3] [,4]
## [1,] 1 2 3 4
## [2,] 5 6 7 8
## [3,] 9 10 11 12
## [4,] 13 14 15 16‘10’ value from the above matrix is being extracted using the below code block
B=A[3,2]
B## [1] 10Showing first row and last_column of the above created Matrix A in the below code block
along with a sub matrix of 1st 2 rows and 1st 2 columns. Further dimension of the original matrix A is also being displayed.
First_row=A[1,]
First_row## [1] 1 2 3 4Last_Column = A[,4]
Last_Column## [1] 4 8 12 16Sub_mat=A[1:2,1:2]
Sub_mat## [,1] [,2]
## [1,] 1 2
## [2,] 5 6dimension=dim(A)
dimension## [1] 4 4##Exploring Datasets
Loading of ISLR package
library(ISLR) Dimensions of the Wage dataset is provided in the below code block. Further, Structure and Summary are also provided in the below code block.
dim(Wage) ## [1] 3000 11str(Wage) ## 'data.frame': 3000 obs. of 11 variables:
## $ year : int 2006 2004 2003 2003 2005 2008 2009 2008 2006 2004 ...
## $ age : int 18 24 45 43 50 54 44 30 41 52 ...
## $ maritl : Factor w/ 5 levels "1. Never Married",..: 1 1 2 2 4 2 2 1 1 2 ...
## $ race : Factor w/ 4 levels "1. White","2. Black",..: 1 1 1 3 1 1 4 3 2 1 ...
## $ education : Factor w/ 5 levels "1. < HS Grad",..: 1 4 3 4 2 4 3 3 3 2 ...
## $ region : Factor w/ 9 levels "1. New England",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ jobclass : Factor w/ 2 levels "1. Industrial",..: 1 2 1 2 2 2 1 2 2 2 ...
## $ health : Factor w/ 2 levels "1. <=Good","2. >=Very Good": 1 2 1 2 1 2 2 1 2 2 ...
## $ health_ins: Factor w/ 2 levels "1. Yes","2. No": 2 2 1 1 1 1 1 1 1 1 ...
## $ logwage : num 4.32 4.26 4.88 5.04 4.32 ...
## $ wage : num 75 70.5 131 154.7 75 ...summary(Wage) ## year age maritl race
## Min. :2003 Min. :18.00 1. Never Married: 648 1. White:2480
## 1st Qu.:2004 1st Qu.:33.75 2. Married :2074 2. Black: 293
## Median :2006 Median :42.00 3. Widowed : 19 3. Asian: 190
## Mean :2006 Mean :42.41 4. Divorced : 204 4. Other: 37
## 3rd Qu.:2008 3rd Qu.:51.00 5. Separated : 55
## Max. :2009 Max. :80.00
##
## education region jobclass
## 1. < HS Grad :268 2. Middle Atlantic :3000 1. Industrial :1544
## 2. HS Grad :971 1. New England : 0 2. Information:1456
## 3. Some College :650 3. East North Central: 0
## 4. College Grad :685 4. West North Central: 0
## 5. Advanced Degree:426 5. South Atlantic : 0
## 6. East South Central: 0
## (Other) : 0
## health health_ins logwage wage
## 1. <=Good : 858 1. Yes:2083 Min. :3.000 Min. : 20.09
## 2. >=Very Good:2142 2. No : 917 1st Qu.:4.447 1st Qu.: 85.38
## Median :4.653 Median :104.92
## Mean :4.654 Mean :111.70
## 3rd Qu.:4.857 3rd Qu.:128.68
## Max. :5.763 Max. :318.34
## 90th Percentile of the wage attribute is shown in the below code block. Further, the table showing the tabulation of education level for workers whose wage is above the 90th percentile is also shown below.
quantile(Wage$wage,0.90) ## 90%
## 154.7036table(Wage[Wage$wage > quantile(Wage$wage, 0.90), "education"]) ##
## 1. < HS Grad 2. HS Grad 3. Some College 4. College Grad
## 0 18 28 105
## 5. Advanced Degree
## 149A plot showing correlation of each attribute with others and itself is shown here.
Note : There does not exists a single variable which has a near to 1 correlation with wage column. The only non-linear curve observe is with the LogWage column of the dataset which can not be considered worthy of correlation as it is just the log of the same attribute : wage.
plot(Wage)
####
Box Plot of race and wage is shown on the x and y-axis respectively.
plot(Wage$race, Wage$wage,xlab="Race", ylab="Wage", main="Plot of Race vs Wage",col="blue") 