LinkedIn skill assessment answers and questions — R (Programming Language)

“R has established itself as a leading programming language in the realm of data analysis and statistical computing, renowned for its powerful capabilities and extensive library of packages. In this comprehensive guide, we’re delighted to present a curated collection of skill assessment questions and answers for R.

Whether you’re a data scientist looking to enhance your analytical skills or a beginner eager to delve into the world of data science, this resource is tailored to help you become proficient in R and its applications. Join us as we explore the core concepts of R programming, including data manipulation, visualization, statistical modelling, and more, empowering you to unlock the full potential of this versatile language.”

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

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

•  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)

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.

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

•  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,]

Q11. 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)

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”.

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()

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

•  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

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

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)

•  ­ none

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 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)]

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

•  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

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

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

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”)?

•  ANOVAData, anovadata
•  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

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

•  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

•  :

[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
...

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

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

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

•  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])

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

•  :

late_delivery <- pizza$delivery_min >= 30
which_late <- which(late_delivery)
which_late

•  :

late_delivery <- pizza$delivery_min >= 30
late <- piza$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)
•  grepl(“Rd|Ave|Dr|St”, indat)
•  grepl(“Rd,Ave,Dr,St”, indat)
•  grepl(“[Rd],[Ave],[Dr],[St]”, indat)

Q53. Which statement returns the fourth row of a data frame named fish?

•  fish[4, ]
•  fish( ,4)
•  fish(4, )
•  fish{4, }

Q54. What is the value of csum?

a <- c(1.2, 2, 3.5, 4)
b <- c(1.2, 2.2, 3.5, 4)
csum <-sum(a == b)

•  8
•  3
•  0.2
•  21.6

Q54. 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])

Q55. What is the result of these three lines of code?

vect1 <- c(1:4)
vect2 <- c(1:2)
vect1 * vect2

•  [1] 1 4 3 8
•  ERROR
•  [1] 1 2 3 4 1 2
•  [1] 1 2 3 4 2 4 6 8

Q56. Which choice returns [1] “2019-09-28”?

•  format(as.POSIXct(“Sep-28-2019 07:54:31 AM”,format=’%b%d%Y’))
•  as.POSIXlt(“Sep-28-2019 07:54:31 AM”,format=’%b-%d-%Y’)
•  as.POSIXct(“Sep-28-2019 07:54:31 AM UTC”)
•  format(as.POSIXct(“Sep-28-2019 07:54:31 AM UTC”,format=’%b-%d-%Y’))

Q57. The variable potus is a character vector, as shown in line 1 below. Wich statement returns the results shown?

1 potus <- c("GHW Bush", "Clinton", "GW Bush", "Obama")

Results: [1] "GHW BUsh" "Clinton" "Obama"

•  potus[-“GW Bush”]
•  potus[1:2 4]
•  potus[-3]
•  potus[1,2,4]

Q58. A data frame contains two factor -fact1 and fact2- and a numerical outcome variable. Which statement returns results that do NOT include an interaction term?

•  anova(lm(outcome ~ fact1 : fact2))
•  anova(lm(outcome ~ fact1 * fact2))
•  anova(lm(outcome ~ fact1 + fact2))
•  anova(lm(outcome ~ fact1 + fact2 + fact1 : fact2))

Q59. Review line 1 below. What does the statement on line 2 return?

1 myvect <- c(-2,-1,0)
2 as.logical(myvect)

•  [1]-2 -1 0
•  [1]TRUE TRUE FALSE
•  [1]FALSE FALSE TRUE
•  [1]NA NA NA

Q60. Which option setting can cause difficulty if you want to add to a variable’s possible values after you have designed an object’s initial data structure?

•  ()OPTIONS(colnames(x)<-NULL)
•  ()OPTIONS(max.print=5)
•  ()OPTIONS(continue=”… “,
•  ()OPTIONS(stringAsFactors=TRUE

Q61. In this image below, the data frame on lines 1 through 4 is named StDf. StDf contains no factors. Why does statement on line 6 return “character” while the statement on line 7 returns “data.frame”?

•  Each value in the first row is a character value, but the values in the third column include both character and numeric values.
•  By specifying the final row, 3, and no column specified, StDf[3, ] calls for the complete structure.
•  Columns in a data frame are vectors generally containing a single type of data. Rows in a data frame are lists, but they belong to a structure that has multiple rows: the data frame.
•  Each value in the first column is a character value, but the values in the third row include both character and numeric values.

Q62. Review line 1. What does the statement on line 3 return?

mtrx <- matrix(1:6, 3, 2)

mtrx[, -1]

•  ­

•  ­

•  ­

•  [1] 4 5 6

Q63. Why does sum(!is.na(pizza$week)) return the number of rows with valid, non-NA values in the column named week?

•  The exclamation point in !is.na(pizza$week) reverses the meaning of the test it precedes.
•  !is.na(pizza$week) counts the number of NA values in the column.
•  !is.na(pizza$week) returns a vector of TRUE/FALSE values, in which TRUE is treated as a 0 and FALSE as a 1.
•  !is.na(pizza$week) counts the number of non-missing values in the column.

Q64. How do you get documentation of an installed and loaded R package named dplyr and packages with dplyr as an alias?

•  help(dplyr)
•  ? dplyr
•  ?? dplyr
•  Press the F1 key.

Q65. In the image below, the data frame named iris includes a numeric vector named Petal.Length. Do the functions labeled Pair 1 and Pair 2 return the same information?

•  No, both the length and the class of the returned structures are different.
•  Yes, both pairs of statements return an object with the same length and class.
•  No, the length is the same but the class is different.
•  No, the class is the same but the length is different.

Q66. The _ for R are the main feature that make it different from the original S language.

•  closure rules
•  scoping rules
•  environment rules
•  None of the above

reference

Q67. Which of the following is a base package for R programming ?

•  tools
•  util
•  lang
•  All of the above

reference