- 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.
unclass(as.Date("1971-01-01"))
- 1
- 365
- 4
- 12
- remove()
- erase()
- detach()
- delete()
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
-
Var_A! -
\_VarA -
.2Var_A -
Var2_A
- 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 names 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)
- 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 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$temperature ) -
class( d.pizza[ , "temperature", drop=F ] )
a <- c(3,3,6.5,8)
b <- c(7,2,5.5,10)
c <- a < b
- [1] NaN
- [1] -4
- [1] 4 -1 -1 2
- [1] TRUE FALSE FALSE TRUE
Q15. Review the statements below. Does the use of the dim function change the class of y, and if so what is y's new class?
> y <- 1:9
> dim(y) <- c(3,3)
- No, y's new class is "array".
- Yes, y's new class is "matrix".
- No, y's new class is "vector".
- Yes, y's new class is "integer".
mydf <- data.frame(x=1:3, y=c("a","b","c"), stringAsFactors=FALSE)
- list
- string
- factor
- character vector
- Vectors are used only for numeric data, while list are useful for both numeric and string data.
- Vectors and lists are the same thing and can be used interchangeably.
- A vector contains items of a single data type, while a list can contain items of different data types.
- Vectors are like arrays, while lists are like data frames.
- list.objects()
- print.objects()
- getws()
- ls()
- rbind()
- cbind()
- bind()
- coerce()
1 mylist <- list(1,2,"C",4,5)
2 unlist(mylist)
- [1] 1 2 4 5
- "C"
- [1] "1" "2" "C" "4" "5"
- [1] 1 2 C 4 5
x <- NA
y <- x/1
- Inf
- Null
- NaN
- NA
Q22. Two variable in the mydata data frame are named Var1 and Var2. How do you tell a bivariate function, such as cor.test, which two variables you want to analyze?
-
cor.test(Var1 ~ Var2) -
cor.test(mydata$(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),]
- 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 or FALSE values, depending on whether the first character in an ournames record is C
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)]
DF1(data frame 1): DF2(data frame 2):
VarA VarB VarA VarD
1 1 2 1 18 21
2 4 5 2 19 22
3 7 8 3 20 23
- 6
- 9
- 3
- 0
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
x<-as.Date("2018-10-01")
- attr()
- months(x)
- as.month(x)
- month(x)
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
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