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Score %0 (0 correct0 incorrect68 unanswered)

#### 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)`

#### 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

• 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)

#### 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()

• 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)]

• 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()

#### 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

#### 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

• 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)

• 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]
``````

• %OPTION% ­

• %OPTION% ­

• %OPTION% ­

• %OPTION% [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