Assisting with Data Wrangling online test 25MCQ and 1 short answer
MATH2349 Exam Semester 1, 2020 MATH2349 Exam Semester 2, 2020 Example Exam Questions (with Answers) MATH2349 Final Exam Information • Exam Date & Time - TBA. • The final exam will be an “Open-Book” online exam via Canvas. • There will be 25 multiple-choice questions (3.6 marks each) and 1 short answer question (10 marks), 100 marks total. • The exam will cover Modules 1 - 8. • Weight: 15% • Duration of exam will be 1 hours. • Once you open the exam in Canvas the timer starts. The timer will continue to run whether you are logged into the quiz or not. • If you lose your internet connection, try to log back in as quickly as possible to resume the quiz. • Canvas will automatically submit the quiz at the designated end time and the system will terminate your access to the quiz/exam. • Any questions that were not answered will be scored as incorrect. • During the exam, you are NOT allowed to share the questions/answers with your fellow classmates using any platform (i.e. Email, Slack, text, . . . ). You will be in breach of exam conditions if such an action is detected. 1 Exam Questions (Answers are given on Page 8 and 9) Answer the following multiple choice questions on Canvas. Choose the one, best response from the alternatives provided. 1. Which one of the following is a task in “Scan” step of Data Preprocessing? (a) Preprocessing time and characters/strings (b) Data scaling, centering, standardising and normalising (c) Identify and handle outliers, missing values (d) Importing data into R from different data sources 2. Which one of the following is the reason for the error given below? cars <- read_csv("cars.csv")="" #="" error="" in="" read_csv("cars.csv")="" :="" could="" not="" find="" function="" "read_csv"="" (a)="" the="" “cars.csv”="" is="" not="" in="" the="" working="" directory="" (b)="" the="" “cars.csv”="" file="" is="" corrupted="" (c)="" the="" relevant="" package="" is="" missing="" (d)="" no="" need="" to="" use="" quotations="" "="" "="" 4.="" which="" one="" of="" the="" following="" functions="" stores="" only="" a="" single="" object?="" i.="" rdata="" ii.="" rds="" iii.="" rda="" (a)="" i="" and="" ii="" (b)="" i="" and="" iii="" (c)="" only="" ii="" (d)="" i,="" ii="" and="" iii="" 2="" 3.="" which="" one="" of="" the="" following="" packages="" is="" used="" to="" export="" data="" to="" excel?="" (a)="" library(readr)="" (b)="" library(xlsx)="" (c)="" library(readxl)="" (d)="" base="" r="" functions="" 5.="" what="" is="" the="" result="" of="" the="" following="" r="" code?="" x=""><- 5="" y=""><- is.finite(x=""> 0) y (a) 5 (b) TRUE (c) FALSE (d) 1 6. What is the class of z? z <- c("a",="" "b",="" "c",="" 1,="" 2,="" 3)="" class(z)="" (a)="" logical="" (b)="" numeric="" (c)="" factor="" (d)="" character="" 7.="" the="" header="" and="" the="" structure="" of="" the="" city="" data="" frame="" are="" given="" in="" the="" following="" output.="" which="" one="" of="" the="" following="" will="" subset="" x2016.rank="" and="" x2015.rank="" from="" the="" city="" data="" frame?="" head(city)="" ##="" x="" x2016.rank="" x2015.rank="" x2014.rank="" city="" country="" index.2010="" ##="" 1="" 1="" 1="" 1="" 1="" vienna="" austria="" 108.6="" ##="" 2="" 2="" 2="" 2="" 2="" zurich="" switzerland="" 108.0="" ##="" 3="" 3="" 3="" 3="" 3="" auckland="" new="" zealand="" 107.4="" ##="" 4="" 4="" 4="" 4="" 4="" munich="" germany="" 107.0="" ##="" 5="" 5="" 5="" 5="" 5="" vancouver="" canada="" 107.4="" ##="" 6="" 6="" 6="" 6="" 6="" dusseldorf="" germany="" 107.2="" str(city)="" ##="" 'data.frame':="" 51="" obs.="" of="" 7="" variables:="" ##="" $="" x="" :="" int="" 1="" 2="" 3="" 4="" 5="" 6="" 7="" 8="" 9="" 10="" ...="" ##="" $="" x2016.rank:="" int="" 1="" 2="" 3="" 4="" 5="" 6="" 7="" 8="" 9="" 10="" ...="" ##="" $="" x2015.rank:="" int="" 1="" 2="" 3="" 4="" 5="" 6="" 7="" 8="" 9="" 10="" ...="" ##="" $="" x2014.rank:="" int="" 1="" 2="" 3="" 4="" 5="" 6="" 7="" 8="" 9="" 10="" ...="" ##="" $="" city="" :="" factor="" w/="" 51="" levels="" "adelaide","amsterdam",..:="" 47="" 51="" 3="" 31="" 46="" 15="" 17="" 18="" 13="" 43="" ...="" ##="" $="" country="" :="" factor="" w/="" 22="" levels="" "australia","austria",..:="" 2="" 20="" 14="" 8="" 4="" 8="" 8="" 20="" 5="" 1="" ...="" ##="" $="" index.2010:="" num="" 109="" 108="" 107="" 107="" 107="" ...="" (a)="" city$x2016.rank="" (b)="" city$x2015.rank="" (c)="" city[,="" c(2,3)]="" (d)="" city[c(2,3),="" ]="" 3="" 8.="" the="" header="" and="" the="" structure="" of="" the="" city="" data="" frame="" are="" given="" in="" the="" following="" output.="" which="" one="" of="" the="" following="" will="" subset="" german="" cities="" from="" the="" city="" data="" frame?="" head(city)="" ##="" x="" x2016.rank="" x2015.rank="" x2014.rank="" city="" country="" index.2010="" ##="" 1="" 1="" 1="" 1="" 1="" vienna="" austria="" 108.6="" ##="" 2="" 2="" 2="" 2="" 2="" zurich="" switzerland="" 108.0="" ##="" 3="" 3="" 3="" 3="" 3="" auckland="" new="" zealand="" 107.4="" ##="" 4="" 4="" 4="" 4="" 4="" munich="" germany="" 107.0="" ##="" 5="" 5="" 5="" 5="" 5="" vancouver="" canada="" 107.4="" ##="" 6="" 6="" 6="" 6="" 6="" dusseldorf="" germany="" 107.2="" str(city)="" ##="" 'data.frame':="" 51="" obs.="" of="" 7="" variables:="" ##="" $="" x="" :="" int="" 1="" 2="" 3="" 4="" 5="" 6="" 7="" 8="" 9="" 10="" ...="" ##="" $="" x2016.rank:="" int="" 1="" 2="" 3="" 4="" 5="" 6="" 7="" 8="" 9="" 10="" ...="" ##="" $="" x2015.rank:="" int="" 1="" 2="" 3="" 4="" 5="" 6="" 7="" 8="" 9="" 10="" ...="" ##="" $="" x2014.rank:="" int="" 1="" 2="" 3="" 4="" 5="" 6="" 7="" 8="" 9="" 10="" ...="" ##="" $="" city="" :="" chr="" "vienna"="" "zurich"="" "auckland"="" "munich"="" ...="" ##="" $="" country="" :="" chr="" "austria"="" "switzerland"="" "new="" zealand"="" "germany"="" ...="" ##="" $="" index.2010:="" num="" 109="" 108="" 107="" 107="" 107="" ...="" (a)="" city[city$country="="Germany",]" (b)="" city[city$city="="Germany"," ]="" (c)="" city[,="" city$country="="Germany"]" (d)="" city[,="" city$city="="Germany"]" 9.="" consider="" the="" following="" list.="" which="" command="" would="" you="" use="" to="" extract="" the="" second="" row="" of="" m2?=""><-list(v1=c(1,3,5), rep(c(14,2,1,3),2),="" list(c(true,="" false,="" true),="" m2="matrix(1:9," nrow="3," ncol="3)))" (a)="" ls$m2="" (b)="" ls$m2[2,]="" (c)="" ls[[3]]="" (d)="" ls[[3]]$m2[2,]="" 10.="" there="" are="" three="" interrelated="" rules="" which="" make="" a="" data="" set="" tidy.="" which="" one="" of="" the="" following="" is="" not="" one="" of="" them?="" (a)="" each="" variable="" must="" have="" its="" own="" column.="" (b)="" each="" observation="" must="" have="" its="" own="" row.="" (c)="" each="" value="" must="" have="" its="" own="" cell.="" (d)="" each="" data="" set="" must="" have="" one="" factor="" variable.="" 11.="" which="" command="" would="" you="" use="" to="" convert="" the="" following="" long-formatted="" data="" frame="" (df)="" into="" a="" wide-="" format?="" df="" ##="" #="" a="" tibble:="" 12="" x="" 4="" ##="" country="" year="" type="" count="" ##="">
## 1 Afghanistan 1999 cases 745 4 ## 2 Afghanistan 1999 population 19987071 ## 3 Afghanistan 2000 cases 2666 ## 4 Afghanistan 2000 population 20595360 ## 5 Brazil 1999 cases 37737 ## 6 Brazil 1999 population 172006362 ## 7 Brazil 2000 cases 80488 ## 8 Brazil 2000 population 174504898 ## 9 China 1999 cases 212258 ## 10 China 1999 population 1272915272 ## 11 China 2000 cases 213766 ## 12 China 2000 population 1280428583 (a) df %>% gather(key = type, value = count) (b) df %>% unite(key = type, value = count) (c) df %>% spread(key = type, value = count) (d) df %>% split(key = type, value = count) 12. Consider the following data frame (df) and its structure: df ## country 1999 2000 ## 1 Afghanistan 745 2666 ## 2 Brazil 37737 80488 ## 3 China 212258 213766 str(df) ## 'data.frame': 3 obs. of 3 variables: ## $ country: chr "Afghanistan" "Brazil" "China" ## $ 1999 : int 745 37737 212258 ## $ 2000 : int 2666 80488 213766 Why does the following code fail? df %>% gather(1999, 2000, key = "year", value = "cases") #> Error in inds_combine(.vars, ind_list): Position must be between 0 and n (a) Because the relevant package for gather() is missing (b) Because the relevant package for pipe operator (%>%) is missing (c) Because the columns 1999 and 2000 have non-syntactic names (they don’t start with a letter) so we have to surround them in backticks. (d) Because the arguments inside gather() are not in the correct order. 13. According to the following code, what will be the result of y? x <- c(1:3,="" na,="" 5,="" na)="" y=""><- is.na(x)="" y="" (a)="" false="" (b)="" true="" (c)="" false="" false="" false="" true="" false="" true="" (d)="" na="" 5="" 14.="" according="" to="" the="" following="" code,="" what="" will="" be="" the="" result="" of="" x?="" df=""><- data.frame(col1="c(1:3," na),="" col2="c("this"," na,"is",="" "text"),="" col3="c(TRUE," false,="" true,="" true),="" col4="c(2.5," 4.2,="" 3.2,="" na))="" x=""><- sum(is.na(df))="" x="" (a)="" true="" (b)="" 3="" (c)="" 2="" (d)="" na="" 15.="" according="" to="" the="" following="" code,="" what="" will="" be="" the="" result="" of="" y?="" x=""><- c(1:3,="" na,="" 5)="" y=""><- mean(x)="" y="" (a)="" na="" (b)="" 3="" (c)="" 2.75="" (d)="" 5="" 16.="" which="" one="" of="" the="" following="" is="" not="" a="" common="" cause="" of="" outliers?="" (a)="" data="" processing="" errors="" (b)="" errors="" due="" to="" data="" type="" conversions="" (c)="" measurement="" errors="" (d)="" intentional="" error="" 17.="" which="" of="" the="" following="" can="" be="" used="" to="" deal="" with="" outliers?="" (a)="" capping="" (b)="" transforming="" (c)="" imputing="" (d)="" all="" of="" the="" above="" 18.="" x="" values="" and="" its="" z="" scores="" z_x="" are="" given="" in="" the="" following="" data="" frame="" (df):="" df="" ##="" x="" z_x="" ##="" 1="" 1.2="" -0.3010569="" ##="" 2="" 1243.0="" 3.0151091="" ##="" 3="" 1.8="" -0.2994547="" ##="" 4="" 1.2="" -0.3010569="" ##="" 5="" 2.2="" -0.2983865="" ##="" 6="" 0.8="" -0.3021251="" ##="" 7="" 0.3="" -0.3034603="" ##="" 8="" 0.2="" -0.3037274="" ##="" 9="" 0.2="" -0.3037274="" ##="" 10="" 1.2="" -0.3010569="" ##="" 11="" 1.2="" -0.3010569="" 6="" according="" to="" the="" given="" df,="" what="" is="" the="" result="" of="" the="" following="" code?="" which(abs(df$z_x)="">3) (a) 2 (b) integer(0) (c) 1243 (d) 3.0151091 19. Which one of the following is NOT a reason for applying data transformation? (a) To change the scale of a variable for better understanding. (b) To convert the non-linear relation into a linear one. (c) To convert non-normal data into a normal distribution. (d) To filter out unwanted effect of missing values. 20. Which one of the following is a data reduction technique? (a) Feature filtering (b) logarithmic transformation (c) z-score standardisation (d) Box-Cox transformation 21. Which one of the following codes will convert x into a date format? x <- c("08/03/2018",="" "23/03/2016",="" "30/01/2018")="" (a)="" as.date(x,="" format="%Y-%m-%d" )="" (b)="" as.date(x,="" format="%d/%m/%Y" )="" (c)="" as.date(x,="" format="%Y/%m/%d" )="" (d)="" as.date(x,="" format="%d-%m-%Y" )="" 22.="" the="" header="" of="" the="" flights="" data="" frame="" is="" given="" in="" the="" following="" output.="" which="" one="" of="" the="" following="" can="" be="" used="" to="" extract="" the="" year="" information="" of="" the="" flights$departure_time?="" head(flights)="" ##="" #="" a="" tibble:="" 6="" x="" 1="" ##="" departure_time="" ##=""> ## 1 2013-01-01 05:15:00 ## 2 2013-01-01 05:29:00 ## 3 2013-01-01 05:40:00 ## 4 2013-01-01 05:45:00 ## 5 2013-01-01 06:00:00 ## 6 2013-01-01 05:58:00 (a) mday(Flights$departure_time) (b) month(Flights$departure_time) (c) year(Flights$departure_time) (d) hour(Flights$departure_time) 23. What is the result of the following R code? paste0("red", "orange", "yellow", "green", "blue") 7 (a) redorangeyellowgreenblue (b) orangeyellowgreenbluered (c) orange yellow green blue red (d) red orange yellow green blue 24. Have a look at