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Paste the R code for each exercise along with the answers. If: Age <- c(22, 25,...

Paste the R code for each exercise along with the answers.

  1. If:

Age <- c(22, 25, 18, 20)

Name <- c("James", "Mathew", "Olivia", "Stella")

Gender <- c("M", "M", "F", "F")

      then what is the R-code for getting the following output:

##   Age   Name Gender

## 1 22 James      M

## 2 25 Mathew      M

a.

DataFrame = data.frame(c(Age), c(Name), c(Gender))

subset(DataFrame, Gender == "M")

b.

DataFrame = data.frame(c(Age),c(Name),c(Gender))

subset(Gender=="M"), eval=FALSE

c.

DataFrame = data.frame(Age,Name,Gender)

subset(DataFrame,Gender=="M")

d.

DataFrame = data.frame(c(Age,Name,Gender))

subset(DataFrame,Gender=="M")

  1. Create three vectors x,y,z with integers and each vector has 3 elements. Combine the three vectors to become a 3×3 matrix A where each column represents a vector. Change the row names to a,b,c.
  2. mtcars is an inbuilt data set in R.

Use mtcars to create a dataframe: data <- mtcars

Look at the data: data

Extract the data for the cars with mpg greater than 15 but less than 20.

  1. Create a vector x: x<- c(“Student’s” “Are great”)

How many characters are there in x. Hint: use the nchar function.

  1. Create matrices from the vectors below, by binding them column-wise. Then check the mode of the resulting matrix (e.g., character, numeric etc.). What difference do you see and why?
  2. a <- 1:5 ; b <- 1:5
    b. a <- 1:5 ; b <- c('1', '2', '3', '4', '5')
math   Statistics-And-Probability   
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Answer #1

> #question 1
> Age <- c(22, 25, 18, 20)
> Name <- c("James", "Mathew", "Olivia", "Stella")
> Gender <- c("M", "M", "F", "F")
>
> ## Age Name Gender
> ## 1 22 James M
> ## 2 25 Mathew M
>   
> #a.
> DataFrame = data.frame(c(Age), c(Name), c(Gender))
> subset(DataFrame, Gender == "M")
c.Age. c.Name. c.Gender.
1 22 James M
2 25 Mathew M
> #b.
> DataFrame = data.frame(c(Age),c(Name),c(Gender))
> subset(Gender=="M"), eval=FALSE
Error: unexpected ',' in "subset(Gender=="M"),"
> #c.
> DataFrame = data.frame(Age,Name,Gender)
> subset(DataFrame,Gender=="M")
Age Name Gender
1 22 James M
2 25 Mathew M
> #d.
> DataFrame = data.frame(c(Age,Name,Gender))
> subset(DataFrame,Gender=="M")
c.Age..Name..Gender.
1 22
2 25
5 James
6 Mathew
9 M
10 M
>
> # correct answer c
> # question 2
>
> x<-c(1,2,3)
> y<-c(5,-2,3)
> z<-c(-1,1,2)
> A<-t(rbind(x,y,z))
> row.names(A)<-c("a","b","c")
> A
x y z
a 1 5 -1
b 2 -2 1
c 3 3 2
>
> #question 3
> #Extract the data for the cars with mpg greater than 15 but less than 20.
> data<-mtcars
> data
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
> data[(data[,1]>15)&(data[,1]<20),]
mpg cyl disp hp drat wt qsec vs am gear carb
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
>
>
> #question 4
> x<-c("Student's","Are great")
> sum(nchar(x))
[1] 18
>
> # question 5
> #Create matrices from the vectors below, by binding them column-wise. Then check the mode of the resulting matrix (e.g., character, numeric etc.). What difference do you see and why?
> a <- 1:5
> b <- 1:5
> cbind(a,b)
a b
[1,] 1 1
[2,] 2 2
[3,] 3 3
[4,] 4 4
[5,] 5 5
> a <- 1:5
> b <- c('1', '2', '3', '4', '5')
> cbind(a,b)
a b
[1,] "1" "1"
[2,] "2" "2"
[3,] "3" "3"
[4,] "4" "4"
[5,] "5" "5"
> #here the diffrence is that in the first case it is a matrix containing integers and in the second case it contains characters
> # it was because in 2nd case b is a array of characters.

R CODE

#question 1
Age <- c(22, 25, 18, 20)
Name <- c("James", "Mathew", "Olivia", "Stella")
Gender <- c("M", "M", "F", "F")

## Age Name Gender
## 1 22 James M
## 2 25 Mathew M
  
#a.
DataFrame = data.frame(c(Age), c(Name), c(Gender))
subset(DataFrame, Gender == "M")
#b.
DataFrame = data.frame(c(Age),c(Name),c(Gender))
subset(Gender=="M"), eval=FALSE
#c.
DataFrame = data.frame(Age,Name,Gender)
subset(DataFrame,Gender=="M")
#d.
DataFrame = data.frame(c(Age,Name,Gender))
subset(DataFrame,Gender=="M")

# correct answer c
# question 2

x<-c(1,2,3)
y<-c(5,-2,3)
z<-c(-1,1,2)
A<-t(rbind(x,y,z))
row.names(A)<-c("a","b","c")
A

#question 3
#Extract the data for the cars with mpg greater than 15 but less than 20.
data<-mtcars
data
data[(data[,1]>15)&(data[,1]<20),]


#question 4
x<-c("Student's","Are great")
sum(nchar(x))

# question 5
#Create matrices from the vectors below, by binding them column-wise. Then check the mode of the resulting matrix (e.g., character, numeric etc.). What difference do you see and why?
a <- 1:5
b <- 1:5
cbind(a,b)
a <- 1:5
b <- c('1', '2', '3', '4', '5')
cbind(a,b)
#here the diffrence is that in the first case it is a matrix containing integers and in the second case it contains characters
# it was because in 2nd case b is a array of characters.

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