9.2 Exercise 2. Numeric vector manipulation

9.2.1 Exercise 2a.

Create the script “exercise2.R” and save it to the “Rcourse/Module1” directory: you will save all the commands of exercise 2 in that script.
Remember you can comment the code using #.

1- Go to Rcourse/Module1 First check where you currently are with getwd(); then go to Rcourse/Module1 with setwd()

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getwd()
setwd("Rcourse/Module1")
setwd("~/Rcourse/Module1")

2- Create a numeric vector “y” which contains the numbers from 2 to 11, both included.
Show y in the console.

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y <- c(2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
# same as
y <- 2:11
# show in console:
y

##  [1]  2  3  4  5  6  7  8  9 10 11

3- How many elements are in y? I.e what is the length of vector y ?

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length(y)

## [1] 10

4- Show the 2nd element of y.

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y[2]

## [1] 3

5- Show the 3rd and the 6th elements of y.

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y[c(3,6)]

## [1] 4 7

6- Remove the 4th element of y: reassign. What is now the length of y ?

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# remove 4th element and reassign
y <- y[-4]
# length of y
length(y)

## [1] 9

7- Show all elements of y that are inferior to 7.

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# which elements of y are less than 7:
y < 7

## [1]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE

# show those elements 
y[ y < 7 ]

## [1] 2 3 4 6

8- Show all elements of y that are superior or equal to 4 and inferior to 9.

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y[ y >= 4 & y < 9 ]

## [1] 4 6 7 8

9- Create the vector x of 1000 random numbers from the normal distribution:
First read the help page of the rnorm() function.

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# help page for the rnorm function
help(rnorm)
# produce a vector of 1000 random numbers from the normal distribution
x <- rnorm(1000)

10. What are the mean, median, minimum and maximum values of x?

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mean(x); median(x); min(x); max(x)

## [1] -0.04611927

## [1] -0.04431017

## [1] -3.032328

## [1] 4.079495

11- Run the summary() function on x.
What additional information do you obtain ?

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summary(x)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -3.03233 -0.74507 -0.04431 -0.04612  0.66838  4.07949

12- Create vector y2 as:

y2 <- c(1, 11, 5, 62,  18, 2, 8)

13- Which elements of y2 are also present in y ?
Note: remember the %in% operator.

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y2[ y2 %in% y ]

## [1] 11  2  8

14- Multiply each element of y2 by 1.5: reassign.

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y2 <- y2 * 1.5

15- Use the function any() to check if the number 3 is present.
Check the help page of any()!

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# "Given a set of logical vectors, is at least one of the values true?"
any( y2 == 3 )

## [1] TRUE

9.2.2 Exercise 2b.

1- Create the vector myvector as:

myvector <- c(1, 2, 3, 1, 2, 3, 1, 2, 3)

Create the same vector using the rep.int() function (?rep.int)

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myvector <- rep.int(x=1:3, times=3)

2- Reassign the 5th, 6th and 7th position of myvector with the values 8, 12 and 32, respectively.

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# reassign one by one
myvector[5] <- 8
myvector[6] <- 12
myvector[7] <- 32
# or reassign all at once
myvector[5:7] <- c(8, 12, 32)

3- Calculate the fraction/percentage of each element of myvector (relative to the sum of all elements of the vector).
Note:sum() can be useful…

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# sum of all elements of the vector
mytotal <- sum(myvector)
# divide each element by the sum
myvector / mytotal

## [1] 0.015625 0.031250 0.046875 0.015625 0.125000 0.187500 0.500000 0.031250
## [9] 0.046875

# multiply by 100 to get a percentage
(myvector / mytotal) * 100

## [1]  1.5625  3.1250  4.6875  1.5625 12.5000 18.7500 50.0000  3.1250  4.6875

4- Add vector c(2, 4, 6, 7) to myvector (combining both vectors): reassign!

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# create the new vector
newvector <- c(2, 4, 6, 7)
# combine both myvector and newvector
c(myvector, newvector)

##  [1]  1  2  3  1  8 12 32  2  3  2  4  6  7

# reassign myvector
myvector <- c(myvector, newvector)