Coursera Cerficate Course_1200_630

Coursera R Programming ALL Weeks Quiz & Answer

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R Programming Week 1 Quiz Answer

 
 
Question 1)
R was developed by statisticians working at…
  • The University of Auckland
 
 
Question 2)
The definition of free software consists of four freedoms (freedoms 0
through 3). Which of the following is NOT one of the freedoms that are
part of the definition?
  • The freedom to sell the software for any price.
 
 
Question 3)
In R the following are all atomic data types EXCEPT
  • matrix
 
 
Question 4)
If I execute the expression x <- 4 in R, what is the class of the
object ‘x’ as determined by the `class()’ function?
numeric
 
x <- 4
class(x)
 
 
Question 5)
What is the class of the object defined by x <- c(4, TRUE)?
numeric
 
x <- c(4, TRUE)
class(x)


Question 6
If I have two vectors x <- c(1,3, 5) and y <- c(3, 2, 10), what
is produced by the expression cbind(x, y)?
  • a 3 by 2 numeric matrix
 
x <- c(1,3, 5)
y <- c(3, 2, 10)
cbind(x, y)
 
 
Question 7
A key property of vectors in R is that
  • elements of a vector all must be of the same class
 
 
Question 8)
Suppose I have a list defined as x <- list(2, “a”, “b”, TRUE). What
does x[[1]] give me?
  • a numeric vector containing the element 2
 
x <- list(2, “a”, “b”, TRUE)
x[[1]]

class(x[[1]])
 
 
 
Question 9)
Suppose I have a vector x <- 1:4 and a vector y <- 2. What is
produced by the expression x + y?
  • a numeric vector with elements 3, 4, 5, 6.
 
x <- 1:4
y <- 2
x + y
 
class(x + y)
 
Question 10)
Suppose I have a vector x <- c(17, 14, 4, 5, 13, 12, 10) and I want
to set all elements of this vector that are greater than 10 to be equal
to 4. What R code achieves this?
x[x >= 11] <- 4
 
x <- c(17, 14, 4, 5, 13, 12, 10)
x[x >= 11] <- 4
x
 
 
Question 11
In the dataset provided for this Quiz, what are the column names of the
dataset?
  • Ozone, Solar.R, Wind, Temp, Month, Day
# install package if doesnt exist 
install.packages(“data.table”)
library(“data.table”)

# Reading in data
quiz_data <- fread(‘hw1_data.csv’)

# Column names of the dataset 
names(quiz_data)



Question 12)
Extract the first 2 rows of the data frame and print them to the
console. What does the output look like?
  Ozone Solar.R Wind Temp Month Day
1    41     190  7.4   67
5   1
2    36     118  8.0   72
5   2
# First two rows 
quiz_data[c(1,2),]

OR

# First two rows
head(quiz_data,2)
 
 
 
 
Question 13
How many observations (i.e. rows) are in this data frame?
  • 153
nrow(quiz_data)


Question 14)
Extract the last 2 rows of the data frame and print them to the
console. What does the output look like?
Ozone Solar.R Wind Temp Month Day
152    18     131  8.0
76     9  29
153    20     223 11.5   68
9  30
tail(quiz_data, 2)
 
 
Question 15)
What is the value of Ozone in the 47th row?
  • 21
 
quiz_data[47, ‘Ozone’]
 
 
 
Question 16)
How many missing values are in the Ozone column of this data frame?
  • 37
 
 
Question 17)
What is the mean of the Ozone column in this dataset? Exclude missing
values (coded as NA) from this calculation.
  • 42.1
 
 
Question 18)
Extract the subset of rows of the data frame where Ozone values are
above 31 and Temp values are above 90. What is the mean of Solar.R in
this subset?
  • 212.8
 
 
Question 19)
What is the mean of “Temp” when “Month” is equal to 6?
  • 79.1
 
 
Question 20)
What was the maximum ozone value in the month of May (i.e. Month =
5)?
  • 115

R Programming Week 2 Quiz Answer

 
 

Week 2 Quiz

Question 1)
Suppose I define the following function in R
 
cube <- function(x, n) {
        x^3
}
 
What is the result of running cube(3) in R after defining this
function?
  • The number 27 is returned
 
 
Question 2)
The following code will produce a warning in R.
 
x <- 1:10
if(x > 5) {
        x <- 0
}
 
Why?
  • ‘x’ is a vector of length 10 and ‘if’ can only test a single logical
    statement.
 
 
Question 3)
Consider the following function
 
f <- function(x) {
        g <- function(y) {
                y + z
        }
        z <- 4
        x + g(x)
}
If I then run in R

z <- 10
f(3)
 
What value is returned?
  • 10

 
Question 4)
Consider the following expression:
 
x <- 5
y <- if(x < 3) {
        NA
} else {
        10
}
 
What is the value of ‘y’ after evaluating this expression?
  • 10
 
 
Question 5)
Consider the following R function
 
h <- function(x, y = NULL, d = 3L) {
        z <- cbind(x, d)
        if(!is.null(y))
                z <- z +
y
        else
                z <- z +
f
        g <- x + y / z
        if(d == 3L)
                return(g)
        g <- g + 10
        g
}
Which symbol in the above function is a free variable?
  • f
 
 
Question 6)
What is an environment in R?
  • a collection of symbol/value pairs
 
Question 7)
The R language uses what type of scoping rule for resolving free
variables?
  • lexical scoping
 
 
Question 8)
How are free variables in R functions resolved?
  • The values of free variables are searched for in the environment in
    which the function was defined
 
 
Question 9)
What is one of the consequences of the scoping rules used in R?
  • All objects must be stored in memory Correct 1.00
 
 
Question 10
In R, what is the parent frame?
  • It is the environment in which a function was called

R Programming Week 3 Quiz Answer

In this article i am gone to share Coursera Course R Programming
Week 3 Quiz Answer with you..
 
 

Week 3 Quiz

 
Question 1)
Take a look at the ‘iris’ dataset that comes with R. The data can be
loaded with the code:
 
library(datasets)
data(iris)
 
A description of the dataset can be found by running
?iris
 
There will be an object called ‘iris’ in your workspace. In this
dataset, what is the mean of ‘Sepal.Length’ for the species virginica?
(Please only enter the numeric result and nothing else.)
  • 6.588
# if you don’t have data.table installed
# install.packages(“data.table”)

library(data.table)
iris_dt <- as.data.table(iris)
iris_dt[Species == “virginica”,round(mean(Sepal.Length)) ]
 

 
 
Question 2)
Continuing with the ‘iris’ dataset from the previous Question, what R
code returns a vector of the means of the variables ‘Sepal.Length’,
‘Sepal.Width’, ‘Petal.Length’, and ‘Petal.Width’?
  • apply(iris[, 1:4], 2, mean)
 
 
Question 3
Load the ‘mtcars’ dataset in R with the following code
 
library(datasets)
data(mtcars)
 
There will be an object names ‘mtcars’ in your workspace. You can find
some information about the dataset by running
 
?mtcars
 
How can one calculate the average miles per gallon (mpg) by number of
cylinders in the car (cyl)?
with(mtcars, tapply(mpg, cyl, mean))
tapply(mtcars$mpg, mtcars$cyl, mean)
sapply( split(mtcars$mpg, mtcars$cyl) , mean)
 
 
 
Question 4)
Continuing with the ‘mtcars’ dataset from the previous Question, what
is the absolute difference between the average horsepower of 4-cylinder
cars and the average horsepower of 8-cylinder cars?
  • 126.5779
 
mtcars_dt <- as.data.table(mtcars)
mtcars_dt <- mtcars_dt[,  .(mean_cols = mean(hp)), by =
cyl]
round(abs(mtcars_dt[cyl == 4, mean_cols] – mtcars_dt[cyl == 8,
mean_cols]))
 
 
 
 
Question 5)
If you run
 
debug(ls)
what happens when you next call the ‘ls’ function?
  • Execution of ‘ls’ will suspend at the beginning of the function and
    you will be in the browser.

R Programming Week 4 Quiz Answer

In this article i am gone to share Coursera Course R Programming
Week 4 Quiz Answer with you..
 
 

Week 4 Quiz

Question 1
What is produced at the end of this snippet of R code?
set.seed(1)
rpois(5, 2)
  • A vector with the numbers 1, 1, 2, 4, 1
Question 2)
What R function can be used to generate standard Normal random
variables?
  • rnorm
Question 3
When simulating data, why is using the set.seed() function
important?
  • It ensures that the sequence of random numbers starts in a specific
    place and is therefore reproducible.
Question 4)
Which function can be used to evaluate the inverse cumulative
distribution function for the Poisson distribution?
  • qpois
Explanation
Probability distribution functions beginning with the q prefix are used
to evaluate the quantile function.
Question 5)
What does the following code do?
set.seed(10)
x <- rbinom(10, 10, 0.5)
e <- rnorm(10, 0, 20)
y <- 0.5 + 2 * x + e
  • Generate data from a Normal linear model
Question 6)
What R function can be used to generate Binomial random variables?
  • rbinom
Question 7)
What aspect of the R runtime does the profiler keep track of when an R
expression is evaluated?
  • the function call stack
Question 8)
Consider the following R code
library(datasets)
Rprof()
fit <- lm(y ~ x1 + x2)
Rprof(NULL)
(Assume that y, x1, and x2 are present in the workspace.) Without
running the code, what percentage of the run time is spent in the lm
function, based on the by.total method of normalization shown in
summaryRprof()?
  • 100%
Explanation
When using by.total normalization, the top-level function (in this
case, lm()) always takes 100% of the time.
Question 9)
When using system.time(), what is the user time?
  • It is the time spent by the CPU evaluating an expression
Question 10)
If a computer has more than one available processor and R is able to
take advantage of that, then which of the following is true when using
system.time()?
  • Elapsed time may be smaller than user time

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