Branches of mechanical engineering: Creating Sample Datasets – Exercises + Solutions

(This article was commencement published on R-exercises, together with kindly contributed to R-bloggers)

Creating sample information is a mutual chore performed inwards many dissimilar scenarios.

R has several base of operations functions that brand the sampling procedure quite slow together with fast.

Below is an explanation of the principal functions used inwards the electrical flow laid of exercices:

1. set.seed() – Although R executes a random machinery of sample creation, set.seed() role allows us to reproduce the exact sample each fourth dimension nosotros execute a random-related function.

2. sample() – Sampling function. The arguments of the role are:
x – a vector of values,
size – sample size
replace – Either work a chosen value to a greater extent than than i time or not
prob – the probabilities of each value inwards the input vector.

3. seq()/seq.Date() – Create a sequence of values/dates, ranging from a ‘start’ to an ‘end’ value.

4. rep() – Repeat a value/vector n times.

5. rev() – Revert the values inside a vector.

You tin become additional explanations for those functions past times adding a ‘?’ prior to each function’s name.

Answers to the exercises are available here.
If yous convey dissimilar solutions, experience gratis to postal service them.

Exercise 1
1. Set seed amongst value 1235
2. Create a Bernoulli sample of 100 ‘fair coin’ flippings.
Populate a variable called fair_coin with the sample results.

Exercise 2
1. Set seed amongst value 2312
2. Create a sample of 10 integers, based on a vector ranging from 8 thru 19.
Allow the sample to convey repeated values.
Populate a variable called hourselect1 with the sample results

Exercise 3
1. Create a vector variable called probs with the next probabilities:
‘0.05,0.08,0.16,0.17,0.18,0.14,0.08,0.06,0.03,0.03,0.01,0.01’
2. Make certain the total of the vector equals 1.

Exercise 4
1. Set seed amongst value 1976
2. Create a sample of 10 integers, based on a vector ranging from 8 thru 19.
Allow the sample to convey repeated values together with work the probabilities defined inwards the previous question.
Populate a variable called hourselect2 with the sample results

Exercise 5
Let’s gear upward the variables for a biased coin:
1. Populate a variable called coin with v zeros inwards a row together with v ones inwards a row
2. Populate a variable called probs having v times value ‘0.08’ inwards a row together with v times value ‘0.12’ inwards a row.
3. Make certain the total of probabilities on probs variable equals 1.

Exercise 6
1. Set seed amongst value 345124
2. Create a biased sample of length 100, having equally input the coin vector, together with equally probabilities probs vector of probabilities.
Populate a variable called biased_coin with the sample results.

Exercise 7
Compare the total of values in fair_coin and biased_coin

Exercise 8
1. Create a ‘Date’ variable called startDate with value ninth of Feb 2010 together with a minute ‘Date’ variable called endDate with value ninth of Feb 2005
2. Create a descending sequence of dates having all 9th’s of the calendar month betwixt those 2 dates. Populate a variable called seqDates with the sequence of dates.

Exercise 9
Revert the sequence of dates created inwards the previous question, together with then they are inwards ascending guild together with house them inwards a variable called RevSeqDates

Exercise 10
1. Set seed amongst value 10
2. Create a sample of xx unique values from the RevSeqDates vector.

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Below are the solutions to these exercises on creating a sample dataset.

#################### #                  # #    Exercise 1    # #                  # #################### set.seed(1235) fair_coin <- sample(c(0,1), 100, replace = TRUE)   #################### #                  # #    Exercise 2    # #                  # #################### set.seed(2312) hourselect1 <- sample(c(8:19),10,replace=TRUE) hourselect1 

##  [1] xiv nineteen xvi xviii thirteen fifteen  8 10 10 xvi 

#################### #                  # #    Exercise three    # #                  # #################### probs <- c(0.05,0.08,0.16,0.17,0.18,0.14,0.08,0.06,0.03,0.03,0.01,0.01) sum(probs) 

## [1] 1 

#################### #                  # #    Exercise iv    # #                  # #################### set.seed(1976) hourselect2 <- sample(c(8:19),10,replace=TRUE,prob = probs) hourselect2 

##  [1] fifteen xi 12 fifteen 12  nine xiv 12 10  nine 

#################### #                  # #    Exercise v    # #                  # #################### coin <- rep(c(0,1),each=5) coin 

##  [1] 0 0 0 0 0 1 1 1 1 1 

probs <- rep(c(0.08,0.12),each=5) sum(probs) 

## [1] 1 

#################### #                  # #    Exercise half dozen    # #                  # #################### set.seed(345124) biased_coin <- sample(coin, 100, replace = TRUE,prob=probs)   #################### #                  # #    Exercise seven    # #                  # #################### sum(fair_coin) 

## [1] 52 

sum(biased_coin) 

## [1] 63 

#################### #                  # #    Exercise 8    # #                  # #################### startDate <- as.Date("2010-02-09") endDate <- as.Date("2005-02-09") seqDates <- seq.Date(startDate, endDate, by = "-1 month")   #################### #                  # #    Exercise nine    # #                  # #################### RevSeqDates <- rev(seqDates)   #################### #                  # #    Exercise 10   # #                  # #################### set.seed(10) sample(RevSeqDates,20,replace=FALSE) 

##  [1] "2007-08-09" "2006-08-09" "2007-03-09" "2008-06-09" "2005-06-09" ##  [6] "2006-02-09" "2006-05-09" "2006-04-09" "2007-10-09" "2006-12-09" ## [11] "2007-11-09" "2007-06-09" "2005-07-09" "2009-03-09" "2006-06-09" ## [16] "2006-09-09" "2005-04-09" "2006-01-09" "2006-07-09" "2008-01-09"

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