LinkedIn Skill Assessment | R Programming Assessment Answers 2021

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These are the answers to LinkedIn Skill Assessment | R Programming Assessment Answers 2021 free certification course. These answers have been updated recently and are 100% correct. The final exam is on Monday, April. All answers are 100%.

Q1. What do you use to take an object such as a data frame out of the workspace?

  •  remove()
  •  erase()
  •  detach()
  •  delete()

Q2. Which is not a property of lists and vectors?

  •  type
  •  length
  •  attributes
  •  scalar

Q3. Review the following code. What is the result of line 3?
xvect<-c(1,2,3)xvect[2] <- “2”xvect

  •  [1] 1 2 3
  •  [1] “1” 2 “3”
  •  [1] “1” “2” “3”
  •  [1] 7 9

Q4. What value does this statement return?
unclass(as.Date(“1971-01-01”))

  •  1
  •  365
  •  4
  •  12

Q5. The variable height is a numeric vector in the code below. Which statement returns the value 35?

  •  height(length(height))
  •  height[length(height)]
  •  height[length[height]]
  •  height(5)

Q6. In the image below, the data frame on lines 1 through 4 is names StDf. State and Capital are both factors. Which statement returns the results shown on lines 6 and 7?

LinkedIn R Programming Quiz Answers
  •  StDf[1:2,-3]
  •  StDf[1:2,1]
  •  StDf[1:2,]
  •  StDf[1,2,]

Q7. In the image below, the data frame is named rates. The statement sd(rates[, 2]) returns 39. As what does R regard Ellen’s product ratings?

R Programming Language Assessment LinkedIn Answers
  •  sample with replacement
  •  population
  •  trimmed sample
  •  sample <– not sure

Q8. Which choice does R regard as an acceptable name for a variable?

  •  Var_A!
  •  \_VarA
  •  .2Var_A
  •  Var2_A

Q9. How does a matrix differ from a data frame?

  •  A matrix may contain numeric values only.
  •  A matrix must not be singular.
  •  A data frame may contain variables that have different modes.
  •  A data frame may contain variables of different lengths.

Q10. What is the principal difference between an array and a matrix?

  •  A matrix has two dimensions, while an array can have three or more dimensions.
  •  An array is a subtype of the data frame, while a matrix is a separate type entirely.
  •  A matrix can have columns of different lengths, but an array’s columns must all be the same length.
  •  A matrix may contain numeric values only, while an array can mix different types of values.

Q11. You accidentally display a large data frame on the R console, losing all the statements you entered during the current session. What is the best way to get the prior 25 statements back?

  •  console(-25)
  •  console(reverse=TRUE)
  •  history()
  •  history(max.show = 25)

Q12. What is mydf$y in this code?
mydf <- data.frame(x=1:3, y=c(“a”,”b”,”c”), stringAsFactors=FALSE)

  •  list
  •  string
  •  factor
  •  character vector

Q13. d.pizza is a data frame. It’s column named temperature contains only numbers. If u extract temperature using the [] accessors, its class defaults to numeric. How can you access temperature so that it retains the class of data.frame?
> class( d.pizza[ , “temperature” ] )> “numeric”

  •  class( d.pizza( , “temperature” ) )
  •  class( d.pizza[ , “temperature” ] )
  •  class( d.pizza$temperature )
  •  class( d.pizza[ , “temperature”, drop=F ] )

Q14. Which function displays the first five rows of the data frame named pizza?

  •  BOF(pizza, 5)
  •  first(pizza, 5)
  •  top(pizza, 5)
  •  head(pizza, 5)

Q15. What does c contain?
a <- c(3,3,6.5,8)b <- c(7,2,5.5,10)c <- a < b

  •  [1] NaN
  •  [1] -4
  •  [1] 4 -1 -1 2
  •  [1] TRUE FALSE FALSE TRUE

Q16. What statement shows the objects on your workspace?

  •  list.objects()
  •  print.objects()
  •  getws()
  •  ls()

Q17. Review the statements below. Does the use of the dim function change the class of y, and if so what is y’s new class?
> y <- 1:9> dim(y) <- c(3,3)

  •  No, y’s new class is “array”.
  •  Yes, y’s new class is “matrix”.
  •  No, y’s new class is “vector”.
  •  Yes, y’s new class is “integer”.

Q18. Review line 1 below. What does the statement in line 2 return?
1 mylist <- list(1,2,”C”,4,5)2 unlist(mylist)

  •  [1] 1 2 4 5
  •  “C”
  •  [1] “1” “2” “C” “4” “5”
  •  [1] 1 2 C 4 5

Q19. How does a vector differ from a list?

  •  Vectors are used only for numeric data, while list are useful for both numeric and string data.
  •  Vectors and lists are the same thing and can be used interchangeably.
  •  A vector contains items of a single data type, while a list can contain items of different data types.
  •  Vectors are like arrays, while lists are like data frames.

Q20. What function joins two or more column vectors to form a data frame?

  •  rbind()
  •  cbind()
  •  bind()
  •  coerce()

Q21. Two variable in the mydata data frame are named Var1 and Var2. How do you tell a bivariate function, such as cor.test, which two variables you want to analyze?

  •  cor.test(Var1 ~ Var2)
  •  cor.test(mydata$(Var1,Var2))
  •  cor.test(mydata$Var1,mydata$Var2)
  •  cor.test(Var1,Var2, mydata)

Q22. Which set of two statements-followed by the cbind() function-results in a data frame named vbound?

  • [ ] v1<-list(1,2,3)
    v2<-list(c(4,5,6))
    vbound<-cbind(v1,v2)
  • [ ] v1<-c(1,2,3)
    v2<-list(4,5,6))
    vbound<-cbind(v1,v2)
  • [ ] v1<-c(1,2,3)
    v2<-c(4,5,6))
    vbound<-cbind(v1,v2)

Q23. A data frame named d.pizza is part of the DescTools package. A statement is missing from the following R code and an error is therefore likely to occur. Which statement is missing?
library(DescTools)deliver <- aggregate(count,by=list(area,driver), FUN=mean)
print(deliver)

  •  attach(d.pizza)
  •  summarize(deliver)
  •  mean <- rbind(d.pizza,count)
  •  deliver[!complete.cases(deliver),]

Q24. What is the value of y in this code?
x <- NAy <- x/1

  •  Inf
  •  Null
  •  NaN
  •  NA

Q25. How to name rows and columns in DataFrames and Matrices F in R?

  •  data frame: names() and rownames() matrix: colnames() and row.names()
  •  data frame: names() and row.names() matrix: dimnames() (not sure)
  •  data frame: colnames() and row.names() matrix: names() and rownames()
  •  data frame: colnames() and rownames() matrix: names() and row.names()

Q26. ournames is a character vector. What values does the statement below return to Cpeople?Cpeople <- ournames %in% grep(“^C”, ournames, value=TRUE)

  •  records where the first character is a C
  •  any record with a value containing a C
  •  TRUE or FALSE, depending on whether any character in ournames is C
  •  TRUE or FALSE values, depending on whether the first character in an ournames record is C

Q27. Given DFMerged <- merge(DF1, DF2) and the image below, how manu rows are in DFMerged?
DF1(data frame 1): DF2(data frame 2):VarA VarB VarA VarD
1 1 2 1 18 212 4 5 2 19 223 7 8 3 20 23

  •  6
  •  9
  •  3
  •  0

Q28. What is the value of names(v[4])?v <- 1:3names(v) <- c(“a”, “b”, “c”)
v[4] <- 4

  •  “”
  •  d
  •  NULL
  •  NA

Q29. Which of the following statements doesn’t yield the code output below. Review the following code. What is the result of line 3?
x <- c(1, 2, 3, 4)Output: [1] 2 3 4

  •  x[c(2, 3, 4)]
  •  x[-1]
  •  x[c(-1, 0, 0, 0)]
  •  x[c(-1, 2, 3, 4)]

LinkedIn R Programming Assessment

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