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STATS 769
STATISTICS
Data Science Practice
SECOND SEMESTER, 2016
1. [5 marks]
Figure 1 shows the content of a JSON file, "AT.json", and the following code reads this file into R and shows the resulting R object, trips.
> library(jsonlite)
> trips <- fromJSON("AT.json")
> trips
id stop_time_update.stop_sequence stop_time_update.stop_id timestamp
1 2928 20 7812 1474316682
2 2929 60 6569 1474316645
> dim(trips)
[1] 2 3
> names(trips)
[1] "vehicle" "stop_time_update" "timestamp"
Explain what sort of R object has been created and write R code to extract the stop_id information from the R object trips.
[
{
"vehicle": {
"id": "2928"
},
"stop_time_update": {
"stop_sequence": 20,
"stop_id": "7812"
},
"timestamp": 1474316682
},
{
"vehicle": {
"id": "2929"
},
"stop_time_update": {
"stop_sequence": 60,
"stop_id": "6569"
},
"timestamp": 1474316645
}
]
Figure 1: The JSON file "AT.json".
2. [5 marks]
Figure 2 shows the content of an XML file, "IRD.xml".
Write down the result of the following R code:
> library(xml2)
> ird <- read_xml("IRD.xml")
> xml_text(xml_find_all(ird, "//td[@align = ✬right✬]"))
Write R code to extract the first column of values from the table. Your code should produce the following result:
[1] "18 NCO Club" "1977 Masters Association"
[3] "1979 Reunion" "1993 Summer Camp Account"
[5] "1St Wainuiomata Venterer Unit" "44 South Travel"
[7] "81 Masters Association"
| 18 NCO Club | $142.03 |
| 1977 Masters Association | $359.77 |
| 1979 Reunion | $532.77 |
| 1993 Summer Camp Account | $1,308.78 |
| 1St Wainuiomata Venterer Unit | $431.14 |
| 44 South Travel | $489.60 |
| 81 Masters Association | $221.08 |
Figure 2: The XML file "IRD.xml".
3. [5 marks]
Explain what each of the following shell commands is doing and, where there is output, what the output means:
pmur002@sc-stat-346130:/home/paul$ ssh stats769prd01.its.auckland.ac.nz
pmur002@stats769prd01:~$ mkdir exam
pmur002@stats769prd01:~$ cd exam
pmur002@stats769prd01:~/exam$ cp /course/data/Ass2/exreg-10000.* .
pmur002@stats769prd01:~/exam$ ls -lh
total 16M
-rw-rw---- 1 pmur002 pmur002 7.8M Sep 20 12:12 exreg-10000.bin
-rw-rw---- 1 pmur002 pmur002 473 Sep 20 12:12 exreg-10000.desc
-rw-rw---- 1 pmur002 pmur002 7.7M Sep 20 12:12 exreg-10000.txt
pmur002@stats769prd01:~/exam$ wc exreg-10000.txt
10000 1010000 7979235 exreg-10000.txt
pmur002@stats769prd01:~/exam$ awk ✬NR < 1000✬ exreg-10000.txt > exreg-sub.txt
4. [5 marks]
Explain the memory usage results from the following R code and output.
What is the significance of the NULL at the end of the function definition?
> gc(reset=TRUE)
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 326457 17.5 592000 31.7 326457 17.5
Vcells 535537 4.1 1023718 7.9 535537 4.1
> x <- rnorm(1000000)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 326423 17.5 592000 31.7 328238 17.6
Vcells 1535490 11.8 2613614 20.0 1537444 11.8
> f <- function() {
+ x <- rnorm(1000000)
+ NULL
+ }
> f()
NULL
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 326438 17.5 592000 31.7 330982 17.7
Vcells 1535523 11.8 2613614 20.0 2541622 19.4
5. [5 marks]
Figure 3 shows the first few lines of a CSV file called "1987.csv". This file contains flight data from 1987 for flights within the USA.
Year,Month,DayofMonth,DayOfWeek,DepTime,CRSDepTime,ArrTime,CRSArrTime,UniqueCarr
ier,FlightNum,TailNum,ActualElapsedTime,CRSElapsedTime,AirTime,ArrDelay,DepDelay
,Origin,Dest,Distance,TaxiIn,TaxiOut,Cancelled,CancellationCode,Diverted,Carrier
Delay,WeatherDelay,NASDelay,SecurityDelay,LateAircraftDelay
1987,10,14,3,741,730,912,849,PS,1451,NA,91,79,NA,23,11,SAN,SFO,447,NA,NA,0,NA,0,
NA,NA,NA,NA,NA
1987,10,15,4,729,730,903,849,PS,1451,NA,94,79,NA,14,-1,SAN,SFO,447,NA,NA,0,NA,0,
NA,NA,NA,NA,NA
...
Figure 3: The first few lines of the CSV file "1987.csv".
The following R code and output shows the time and memory requirements involved in naively reading the file "1987.csv" into an R data frame and then calculating the average flight departure delay for each day of the week.
> system.time(f1987 <- read.csv("1987.csv"))
user system elapsed
8.785 0.176 8.967
> object.size(f1987)
152204024 bytes
> system.time(delays <- aggregate(f1987["DepDelay"],
list(DoW=f1987$DayOfWeek),
mean, na.rm=TRUE))
user system elapsed
0.997 0.004 1.001
> delays
DoW DepDelay
1 1 7.827491
2 2 9.086585
3 3 9.364805
4 4 8.143775
5 5 7.411825
6 6 6.034632
7 7 8.408912
Write R code that calls the read.csv() function, but with additional argument that would make the call run faster and create a smaller data frame than the code above. Explain why your code would be faster and use less memory.
Write R code that uses functions from the data.table package to read the file "1987.csv" into R much faster and to calculate the average departure delay for each day of the week much faster.
6. [5 marks]
The following R code and output shows the number of cores and the memory capacity of one of the virtual machines used in this course.
> detectCores()
[1] 20
> system("free")
total used free shared buffers cached
Mem: 206350080 42760988 163589092 44 262884 40465868
-/+ buffers/cache: 2032236 204317844
Swap: 1949692 131744 1817948
The following R code and output shows information about the full set of CSV files
("1987.csv" to "2008.csv") that contain US flight data over 22 years.
> system("ls -lh /course/data/ASADataExpo/*.csv")
-rw-r--r-- 1 pmur002 pmur002 122M Aug 4 14:48 /course/data/ASADataExpo/1987.csv
-rw-r--r-- 1 pmur002 pmur002 478M Aug 4 14:48 /course/data/ASADataExpo/1988.csv
-rw-r--r-- 1 pmur002 pmur002 464M Aug 4 14:48 /course/data/ASADataExpo/1989.csv
-rw-r--r-- 1 pmur002 pmur002 486M Aug 4 14:50 /course/data/ASADataExpo/1990.csv
-rw-r--r-- 1 pmur002 pmur002 469M Aug 4 14:47 /course/data/ASADataExpo/1991.csv
-rw-r--r-- 1 pmur002 pmur002 470M Aug 4 14:49 /course/data/ASADataExpo/1992.csv
-rw-r--r-- 1 pmur002 pmur002 469M Aug 4 14:49 /course/data/ASADataExpo/1993.csv
-rw-r--r-- 1 pmur002 pmur002 479M Aug 4 14:48 /course/data/ASADataExpo/1994.csv
-rw-r--r-- 1 pmur002 pmur002 507M Aug 4 14:48 /course/data/ASADataExpo/1995.csv
-rw-r--r-- 1 pmur002 pmur002 510M Aug 4 14:52 /course/data/ASADataExpo/1996.csv
-rw-r--r-- 1 pmur002 pmur002 516M Aug 4 14:49 /course/data/ASADataExpo/1997.csv
-rw-r--r-- 1 pmur002 pmur002 514M Aug 4 14:47 /course/data/ASADataExpo/1998.csv
-rw-r--r-- 1 pmur002 pmur002 528M Aug 4 14:48 /course/data/ASADataExpo/1999.csv
-rw-r--r-- 1 pmur002 pmur002 544M Aug 4 14:47 /course/data/ASADataExpo/2000.csv
-rw-r--r-- 1 pmur002 pmur002 573M Aug 4 14:48 /course/data/ASADataExpo/2001.csv
-rw-r--r-- 1 pmur002 pmur002 506M Aug 4 14:47 /course/data/ASADataExpo/2002.csv
-rw-r--r-- 1 pmur002 pmur002 598M Aug 4 14:50 /course/data/ASADataExpo/2003.csv
-rw-r--r-- 1 pmur002 pmur002 639M Aug 4 14:47 /course/data/ASADataExpo/2004.csv
-rw-r--r-- 1 pmur002 pmur002 640M Aug 4 14:49 /course/data/ASADataExpo/2005.csv
-rw-r--r-- 1 pmur002 pmur002 641M Aug 4 14:49 /course/data/ASADataExpo/2006.csv
-rw-r--r-- 1 pmur002 pmur002 671M Aug 4 14:50 /course/data/ASADataExpo/2007.csv
-rw-r--r-- 1 pmur002 pmur002 658M Aug 4 14:47 /course/data/ASADataExpo/2008.csv
The following R code could be used to read all 22 CSV files into R as a single data frame and calculate the average departure delay for each day of the week.
filenames <- paste0(1987:2008, ".csv")
flights <- do.call(rbind, lapply(filenames, read.csv))
aggregate(flights["DepDelay"],
list(DoW=flights$DayOfWeek),
mean, na.rm=TRUE)
Discuss the time and memory requirements that would be involved in running this R code and whether it would be able to run on the virtual machine.
7. [10 marks]
The function sumFile() (code not shown) takes one argument, the name of a CSV file, and calculates the sum of the departure delay column and counts the number of non-NA values in the departure delay column, for each day of the week. The result of calling this function on the file "1987.csv" is shown below.
> sumFile("1987.csv")
DoW sum count
1: 1 1457291 186176
2: 2 1692113 186221
3: 3 1757905 187714
4: 4 1619129 198818
5: 5 1356023 182954
6: 6 1037999 172007
7: 7 1498897 178251
Write R code that calls the sumFile() function in parallel across multiple cores to calculate sums and counts for all CSV files in the data set and then combines the results from all CSV files to calculate the average departure delay for each day of the week across all 22 files. The final result would look like the output below.
> meanDepDelay
[,1] [,2]
[1,] 1 7.850057
[2,] 2 6.855870
[3,] 3 7.651197
[4,] 4 9.246910
[5,] 5 10.151539
[6,] 6 6.887023
[7,] 7 8.409293
Discuss the best way to schedule the 22 calls to sumFile() across the multiple cores (hint: think about whether each call to sumFile(), which handles a different CSV file, will take the same amount of time to run).
8. [5 marks]
The following code was used to profile the sumFile() function.
> Rprof("sumFile.log")
> sumFile("1987.csv")
> Rprof(NULL)
The profile results are summarised below using summaryRprof() ...
> summaryRprof("sumFile.log")
$by.self
self.time self.pct total.time total.pct
"fread" 1.38 97.18 1.38 97.18
"!" 0.02 1.41 0.02 1.41
"forderv" 0.02 1.41 0.02 1.41
$by.total
total.time total.pct self.time self.pct
"sumFile" 1.42 100.00 0.00 0.00
"fread" 1.38 97.18 1.38 97.18
"[" 0.04 2.82 0.00 0.00
"[.data.table" 0.04 2.82 0.00 0.00
"!" 0.02 1.41 0.02 1.41
"forderv" 0.02 1.41 0.02 1.41
$sample.interval
[1] 0.02
$sampling.time
[1] 1.42
... and using profReport().
> profReport("sumFile.log")
sumFile > fread
---------------
1.38
sumFile > [ > [.data.table > !
------------------------------
0.02
sumFile > [ > [.data.table > forderv
------------------------------------
0.02
Explain the profile results and what they tell us about how the sumFile() function works and where it spent most of its time.
9. [5 marks]
i. Explain why we might need to use the functions GET() or POST() from the httr package to access a web site, rather than just the download.file() function.
ii. Explain why we might need to use the functions makeCluster() and clusterApply(), instead of the mclapply() function.
10. [10 marks]
Table 1 shows the SSresidual and adjusted R 2 values for a model selection procedure of a linear regression problem with 6 input variables, x1,..., x6. At step i, the model contains the input variable which is shown in column “Variables entered” and the variables at all previous steps. At step i, the SSresidual column shows the value of residual sum of squares after the variable in the “Variables entered” column has been added to the previous model (from step i − 1). This value is the smallest SSresidual among all possible models that add a single predictor to the previous model (from step i−1).
1. Which of the model selection procedures is used in this problem?
2. Based on Table 1, what is the best linear predictor for this problem?
Variables
Step entered SSresidual Adjusted R 2
0 Intercept 34.026 0.700
1 x4 33.789 0.707
2 x3 33.584 0.703
3 x2 33.583 0.713
4 x5 33.586 0.714
5 x1 33.590 0.713
6 x6 33.595 0.701
Table 1: Summary of the model selection procedure
11. [10 marks]
The following code has been used to fit a k-nearest neighbor classifier for given data.
> library(class)
> train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])
> test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])
> cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
> knnout <- knn(train, test, cl, k = 3)
> knnout
[1] s s s s s s s s s s s s s s s s s s s s s s s s s c c v
[29] c c c c c v c c c c c c c c c c c c c c c c v c c v v v
[57] v v v v v v v c v v v v v v v v v v v
Levels: c s v
> table(knnout,cl)
cl
knnout c s v
c 23 0 3
s 0 25 0
v 2 0 22
Based on the output of the code, calculate
1. the misclassification rate for test data.
2. the sensitivity and specificity for test data.
12. [10 marks]
Kernel density estimation is an unsupervised learning procedure that estimates the proba bility density of a new observation x0 by counting observations close to it with weights that decrease with distance from it. Formally, for a given univariate random sample x1,..., xn drawn from a probability density gX (x), kernel density estimation uses the following formula to estimate the probability density gX (x) of a new observation x0,
gˆX (x0) = nh/1n∑i=1Kh(x0, xi)
where n is the sample size and h is a tuning parameter for the kernel function K. Assume that a given learning sample L, with one input variable, contains 100 data points from population I and 100 data points from population II. We want to classify the population of a new observation based on this learning set. This problem is a classification problem with one input variable and a response with two categories. Explain a classifier algorithm which uses kernel density estimation to classify the new observation.
13. [10 marks]
Explain the k-nearest neighbor algorithm for fitting models (a) when doing prediction of a continuous response, and (b) when doing classification.
14. [10 marks]
Explain the steps of how you would build a model for a classification problem when you receive a data set of 30,000 observations, 100 inputs and a categorical response variable.