linkedin r programming assessment answers

LinkedIn R Programming Assessment Answers 2023 – R Programming Badge

LinkedIn R Programming Assessment Answers 2023 –R Programming Skill Badge: R Programming LinkedIn Test Answers: In this post you will get all the correct answers of LinkedIn r programming quiz answers given by the experts.

LinkedIn R Programming Assessment Answers 2023

Note: Choose the correct answers marked with [x].

LinkedIn R Programming Quiz Questions and Answers 2023

R (Programming Language)

Q1. How does a matrix differ from a data frame?

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

Q2. What value does this statement return?

unclass(as.Date("1971-01-01"))

  • [ ] 1
  • [x] 365
  • [ ] 4
  • [ ] 12

Q3. What do you use to take an object such as a data frame out of the workspace?

  • [x] remove()
  • [ ] erase()
  • [ ] detach()
  • [ ] delete()

Q4. 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”
  • [x] [1] “1” “2” “3”
  • [ ] [1] 7 9

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

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

Q6. 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?

Image
  • [ ] sample with replacement
  • [ ] population
  • [ ] trimmed sample
  • [x] sample <– not sure

Q7. Which choice does R regard as an acceptable name for a variable?

  • [ ] Var_A!
  • [ ] \_VarA
  • [ ] .2Var_A
  • [x] Var2_A

Q8. What is the principal difference between an array and a matrix?

  • [x] 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.

Q9. Which is not a property of lists and vectors?

  • [ ] type
  • [ ] length
  • [ ] attributes
  • [x] scalar

Q10. In the image below, the data frame on lines 1 through 4 is named StDf. State and Capital are both factors. Which statement returns the results shown on lines 6 and 7?

Image
  • [ ] StDf[1:2,-3]
  • [x] StDf[1:2,1]
  • [ ] StDf[1:2,]
  • [ ] StDf[1,2,]

Q11. Which function displays the first five rows of the data frame named pizza?

  • [ ] BOF(pizza, 5)
  • [ ] first(pizza, 5)
  • [ ] top(pizza, 5)
  • [x] head(pizza, 5)

Q12. 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()
  • [x] history(max.show = 25)

Q13. d.pizza is a data frame. It’s a column named temperature contains only numbers. If you 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 )
  • [x] class( d.pizza[ , "temperature", drop=F ] )

Q14. 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
  • [x] [1] TRUE FALSE FALSE TRUE

Q15. 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”.
  • [x] Yes, y’s new class is “matrix”.
  • [ ] No, y’s new class is “vector”.
  • [ ] Yes, y’s new class is “integer”.

Q16. What is mydf$y in this code?

mydf <- data.frame(x=1:3, y=c("a","b","c"), stringAsFactors=FALSE)

  • [ ] list
  • [ ] string
  • [ ] factor
  • [x] character vector

Q17. How does a vector differ from a list?

  • [ ] Vectors are used only for numeric data, while lists are useful for both numeric and string data.
  • [ ] Vectors and lists are the same thing and can be used interchangeably.
  • [x] 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.

Q18. What statement shows the objects on your workspace?

  • [ ] list.objects()
  • [ ] print.objects()
  • [ ] getws()
  • [x] ls()

Q19. What function joins two or more column vectors to form a data frame?

  • [ ] rbind()
  • [x] cbind()
  • [ ] bind()
  • [ ] coerce()

Q20. 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”
  • [x] [1] “1” “2” “C” “4” “5”
  • [ ] [1] 1 2 C 4 5

Q21. What is the value of y in this code?

x <- NA
y <- x/1
  • [ ] Inf
  • [ ] Null
  • [ ] NaN
  • [x] NA

Q22. 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$(Var1,Var2))
  • [x] cor.test(mydata$Var1,mydata$Var2)
  • [ ] cor.test(Var1,Var2, mydata)

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

Q24. How to name rows and columns in DataFrames and Matrices F in R?

  • [ ] data frame: names() and rownames() matrix: colnames() and row.names()
  • [x] 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()

Q25. 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)

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
  • [x] TRUE and FALSE values, depending on whether the first character in an ournames record is C

Q27. What is the value of names(v[4])?

v <- 1:3
names(v) <- c("a", "b", "c")
v[4] <- 4
  • [x] “”
  • [ ] d
  • [ ] NULL
  • [ ] NA

Q28. 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] x[c(-1, 2, 3, 4)]

Q29. Given DFMerged <- merge(DF1, DF2) and the image below, how manu rows are in DFMerged?

image
  • [ ] 6
  • [ ] 9
  • [ ] 3
  • [x] 0

Q30. What does R return in response to the final statement?

x<-5:8
names(x)<-letters[5:8]
x
  • [ ] e f g h
    “5” “6” “7” “8”
  • [ ] 5 6 7 8
  • [ ] e f g h
  • [x] e f g h
    5 6 7 8

Q31. How do you return “October” from x in this code?

x<-as.Date("2018-10-01")
  • [ ] attr()
  • [x] months(x)
  • [ ] as.month(x)
  • [ ] month(x)

Q32. How will R respond to the last line of this code?

fact<-factor(c("Rep","Dem","Dem","Rep"))
fact
[1] Rep Dem Dem Rep
Levels: Rep Dem
fact[2]<-"Ind"
  • [ ] >
  • [ ] [,2]Ind
  • [x] invalid factor level, NA generated
  • [ ] Ind

Q33. What does R return?

StartDate<- as.Date("2020/2/28")
StopDate<- as.Date("2020/3/1")
StopDate-StartDate
  • [ ] “1970-01-02”
  • [ ] time difference of one day
  • [x] time difference of two days
  • [ ] error in x-y: nonnumeric argument to binary operator

Q34. What does the expression mtrx * mtrx do ?

> mtrx <- matrix( c(3,5,8,4), nrow= 2,ncol=2,byrow=TRUE)
> newmat <- mtrx * mtrx
  • [ ] it transpose mtrx
  • [ ] it premultiplies the current netwmat row by the newmat column.
  • [ ] it returns the results of a matrix multiplication
  • [x] It squares each cell in mtrx
> newmat
     [,1] [,2]
[1,]    9   25
[2,]   64   16

# The `%*%` operator gives matrix multiplication
> mtrx %*% mtrx
     [,1] [,2]
[1,]   49   35
[2,]   56   56

Q35. Which function in R combines different values into a single object?

  • [ ] connect()
  • [ ] concat()
  • [ ] contact()
  • [x] c()

Q36. Which file contains settings that R uses for all users of a given installation of R?

  • [ ] Rdefaults.site
  • [ ] Renviron.site
  • [x] Rprofile.site
  • [ ] Rstatus.site

Q37. If mdf is a data frame, which statement is true ?

  • [x] ncol(mdf) equals length(mdf).
  • [ ] The number of rows must equals the number of columns.
  • [ ] The legnth of any column in mdf may differ from any other column in mdf
  • [ ] All columns must have the same data type.

Q38. A list can contain a list as an element. MyList has five columns, and the third column’s item is a list of three items. How do you put all seven values in MyList into a single vector?

  • [ ] vector(MyList, length = 7)
  • [ ] coerce(MyList, nrows = 1)
  • [x] unlist(MyList)
  • [ ] coerce(MyList, nrows = 7)

Q39. Which strings could be returned by the function ls(path = “^V”)?

  • [ ] ANOVAData, anovadata
  • [x] VisitPCA, VarX
  • [ ] VisitPCA, varx
  • [ ] Xvar, Yvar

Q40. StDf is a data frame. Based on this knowledge, what does this statement return?

StDf[, -1]
  • [ ] all but the first row and first column of StDf
  • [ ] all but the final column of StDf
  • [x] all but the first column of StDf
  • [ ] only the first column of StDf

Q41. Which statement enables you to interactively open a single file?

  • [ ] file.list()
  • [ ] file.select()
  • [x] file.choose()
  • [ ] file.open()

Q42. How are these data types alike: logical, integer, numeric, and character?

  • [ ] Each is a type of data frame.
  • [x] Each is a type of atomic vector.
  • [ ] Each is a type of complex vector.
  • [ ] Each is a type of raw vector.

Q43. What does the MyMat[ ,3] subsetting operation return for this code?

MyMat = matrix(c(7, 9, 8, 6, 10, 12),nrow=2,ncol=3, byrow = TRUE)
  • [ ]
[ ,3]
[1, ] 8
[2, ] 12
  • [x]
[1] 8 12
  • [ ]
[1] 10 12
  • [ ]
[ ,3]
[1, ] 10
[2, ] 12

Q44. What does the function power.anova.test return?

  • [ ] the probability of making a Type I error
  • [x] the probability of not making a Type II error
  • [ ] the probability of making a Type II error
  • [ ] the probability of not making a Type I error

Q45. Review the statement below. What is the effect of covariate:factor on the analysis?

result <- lm(outcome ~ covariate + factor + covariate:factor, data = testcoef)
  • [ ] It forces the intercepts of the individual regressions to zero.
  • [x] It calls for the effect of the covariate within each level of the factor.
  • [ ] It calls for the effect of each variable from covariate to factor in testcoef.
  • [ ] It forces the covariate to enter the equation before the factor levels.
# Example call to demonstrate.  `Species` is a Factor.  Petal.Length, Petal.Width are numeric.
# see `help(formula)` for more details on the formula specification.  `:` is "effect modification" or "interaction"

> summary(lm(Petal.Length ~ Petal.Width + Species + Petal.Width:Species, data = iris))
...
Petal.Width:Speciesversicolor   1.3228     0.5552   2.382   0.0185 *
Petal.Width:Speciesvirginica    0.1008     0.5248   0.192   0.8480
...

Q46. A variable whose type is numeric can contain which items?

  • [ ] integers and real values
  • [ ] integers, real, and raw values
  • [x] real values only
  • [ ] integers, real, and logical values

Q47. What is the legitimate name of a data class in R?

  • [ ] property
  • [x] integer
  • [ ] number
  • [ ] variant

Q48. How do you extract the values above the main diagonal from a square matrix named Rmat?

  • [x] Rmat[upper.tri(Rmat)]
  • [ ] upper.triangular(Rmat)
  • [ ] upper.tri(Rmat)
  • [ ] upper.diag(Rmat)

Q49. x is a vector of type integer, as shown on line 1 below. What is the type of the result returned by the statement > median(x)?

x <- c(12L, 6L, 10L, 8L, 15L, 14L, 19L, 18L, 23L, 59L)

  • [ ] numeric
  • [ ] integer
  • [ ] single
  • [x] double

Q50. A list named a is created using the statement below. Which choice returns TRUE?

a <- list("10", TRUE, 5.6)

  • [x] is.list(a[1])
  • [ ] is.numeric(a[1])
  • [ ] is.logical(a[1])
  • [ ] is.character(a[1])

Q51. How do you obtain the row numbers in a data frame named pizza for which the value of pizza$delivery_min is greater than or equal to 30?

  • [ ]
late_delivery <- pizza$delivery_min >= 30
index_late <- index(late_delivery)
index_late
  • [ ]
late_delivery <- pizza$delivery_min >= 30
rownum_late <- rownum(late_delivery)
rownum_late
  • [x]
late_delivery <- pizza$delivery_min >= 30
which_late <- which(late_delivery)
which_late
  • [x]
late_delivery <- pizza$delivery_min >= 30
late <- pizaa$late_delivery
pizza$late

Q52. Which function returns [1] TRUE FALSE TRUE?

indat <- c("Ash Rd","Ash Cir","Ash St")

  • [ ] grepl(“[Rd|Ave|Dr|St]”, indat)
  • [x] grepl(“Rd|Ave|Dr|St”, indat)
  • [ ] grepl(“Rd,Ave,Dr,St”, indat)
  • [ ] grepl(“[Rd],[Ave],[Dr],[St]”, indat)

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