# R Programming Language Skill Assessment Quiz 2022 | LinkedIn Skill Assessment Quiz | LinkedIn | MNC Answers

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#### Q1. 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.

#### Q2. What value does this statement return?

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

•  1
•  365
•  4
•  12

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

#### (adsbygoogle = window.adsbygoogle || []).push({}); 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?

•  sample with replacement
•  population
•  trimmed sample
•  sample <-- not sure

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

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

#### Q8. 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.

•  type
•  length
•  attributes
•  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?

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

•  BOF(pizza, 5)
•  first(pizza, 5)
•  top(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()
•  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 )`
•  `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
•  [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".
•  Yes, y's new class is "matrix".
•  No, y's new class is "vector".
•  Yes, y's new class is "integer".

#### (adsbygoogle = window.adsbygoogle || []).push({}); Q16. 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

#### 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.
•  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()
•  ls()

•  rbind()
•  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"
•  [1] "1" "2" "C" "4" "5"
•  [1] 1 2 C 4 5

``````x <- NA
y <- x/1
``````
•  Inf
•  Null
•  NaN
•  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))`
•  `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)
``````
•  `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()
•  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)
``````

`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 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
``````
•  ""
•  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[c(-1, 2, 3, 4)]

•  6
•  9
•  3
•  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
•  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()
•  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
•  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
•  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
•  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```

•  connect()
•  concat()
•  contact()
•  c()

#### (adsbygoogle = window.adsbygoogle || []).push({}); Q36. Which file contains settings that R uses for all users of a given installation of R?

•  Rdefaults.site
•  Renviron.site
•  Rprofile.site
•  Rstatus.site

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

•  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)
•  unlist(MyList)
•  coerce(MyList, nrows = 7)

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

•  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
•  all but the first column of StDf
•  only the first column of StDf

•  file.list()
•  file.select()
•  file.choose()
•  file.open()

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

•  Each is a type of data frame.
•  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
•  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.
•  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
...```

•  integers and real values
•  integers, real, and raw values
•  real values only
•  integers, real, and logical values

•  property
•  integer
•  number
•  variant

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

•  `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
•  double

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

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

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

#### (adsbygoogle = window.adsbygoogle || []).push({}); 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
``````

#### (adsbygoogle = window.adsbygoogle || []).push({}); Q52. Which function returns `[1] TRUE FALSE TRUE`?

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

•  grepl("[Rd|Ave|Dr|St]", indat)
•  grepl("Rd|Ave|Dr|St", indat)
•  grepl("Rd,Ave,Dr,St", indat)
•  grepl("[Rd],[Ave],[Dr],[St]", indat)

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