R Language quiz questions

R Language interview questions

  • 1.

    Which of the following will reverse the order of values in x ?

    1. rev(x)

    2. max(x, na.rm=TRUE)

    3. all(x)

    4. x%in%y

    Answer
  • 2.

    Which of the following produces the variance covariance matrix ?

    1. sd(x, na.rm=TRUE)

    2. mad(x, na.rm=TRUE)

    3. fivenum(x, na.rm=TRUE)

    4. var(x, na.rm=TRUE)

    Answer
  • 3.

    Which of the following finds the maximum value in the vector x, exclude missing values

    1. rm(x)

    2. max(x, na.rm=TRUE)

    3. all(x)

    4. x%in%y

    Answer
  • 4.

    Which of the following tests each element of x for membership in y ?

    1. y%in%x

    2. all(x)

    3. any(x)

    4. x%in%y

    Answer
  • 5.

    Which of the following truncates real x to integers ?

    1. as.numeric(x)

    2. as.integer(x)

    3. as.order(x)

    4. All of the mentioned

    Answer
  • 6.

    Which of the following is uniform distribution ?

    1. dunif(x, min=0, max=1, log = FALSE)

    2. punif(q, min=0, max=1, lower.tail = TRUE, log.p = FALSE)

    3. qunif(p, min=0, max=1, lower.tail = TRUE, log.p = FALSE)

    4. runif(n, min=0, max=1)

    Answer
  • 7.

    which of the following statement gives cumulative sum ?

    1. cumsum(x,na=rm=TRUE)

    2. cumprod(x)

    3. cummax(x)

    4. cummin(x) 

    Answer
  • 8.

    Which of the following statement is normal distribution ?

    1. dnorm(x, mean=0, sd=1, log = FALSE)

    2. pnorm(q, mean=0, sd=1, lower.tail = TRUE, log.p = FALSE)

    3. qnorm(p, mean=0, sd=1, lower.tail = TRUE, log.p = FALSE)

    4. rnorm(n, mean=0, sd=1)

    Answer
  • 9.

    Which of the following statement find cases with no missing values ?

    1. complete <- subset(data.df,complete.cases(data.df)

    2. complete <- sub(data.df,complete.cases(data.df)

    3. complete <- subset(data.df,completeall.cases(data.df)

    4. new <- old[n1:n2,n3:n4]

    Answer
  • 10.

    Which of the following code drop the ith and jth column ?

    1. new <- old[-n,]

    2. new <- old[,-n]

    3. new <- old[,-c(i,j)] 

    4. new <- subset(old,logical)

    Answer
  • 11.

    Which of the following code will drop the nth column ?

    1. new <- old[-n,]

    2. new <- old[,-n]

    3. new <- old[,-c(i,j)]

    4. new <- subset(old,logical)

    Answer
  • 12.

    ___________ remove all the variables from the workspace

    1. rm(x) 

    2. rm(list=ls())

    3. ls()

    4. attach(mat)

    Answer
  • 13.

    _____ list the variables in the workspace

    1. rm(x)

    2. rm(list=ls())

    3. ls()

    4. attach(mat)

    Answer
  • 14.

    Which of the following is Mac menu command ?

    1. browse.workspace

    2. browse.works

    3. browser.workspace

    4. a statistical transformation

    Answer
  • 15.

    Which of the following sort a dataframe by the order of the elements in B

    1. x[rev(order(x$B)),]

    2. x[ordersort(x$B),]

    3. x[order(x$B),]

    4. All of the mentioned

    Answer
  • 16.

    Which of the following statement is another way to get a subset ?

    1. subsetcon(dataset,logical)

    2. data.df[data.df=logical]

    3. sub(dataset,logical)

    4. None of the mentioned 

    Answer
  • 17.

    which of the following statement chose those objects meeting a logical criterion ?

    1. sub(dataset,logical)

    2. subset(dataset,logical)

    3. subsetcon(dataset,logical)

    4. None of the mentioned

    Answer
  • 18.

    Which of the following statement read a tab or space delimited file ?

    1. read.table(filename,header=TRUE)

    2. read.CSV(filename,header=TRUE)

    3. read.table(filename,header=FALSE)

    4. read.tableall(filename,header=TRUE)

    Answer
  • 19.

    Which of the following statement can read csv files ?

    1. read.table(filename,header=TRUE,sep=',')

    2. read.csv(filename,header=TRUE,sep=',')

    3. read.tab(filename,header=TRUE,sep=',')

    4. None of the mentioned

    Answer
  • 20.

    Which of the following code create a n item vector of random normal deviates ?

    1. x1 <- c(snorm(n))

    2. x1 <- c(pnorm(n))

    3. x1 <- c(rnorm(n))

    4. All of the mentioned

    Answer

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