**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?

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?

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)?

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?

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?

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?

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?

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?

console. What does the output look like?

*Ozone Solar.R Wind Temp Month Day*

*1 41 190 7.4 67*

5 1

5 1

*2 36 118 8.0 72*

5 2

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?

console. What does the output look like?

*Ozone Solar.R Wind Temp Month Day*

*152 18 131 8.0*

76 9 29

76 9 29

*153 20 223 11.5 68*

9 30

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.

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?

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)?

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?

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

y

*else*

*z <- z +*

f

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?

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 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:

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.)

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’?

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

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)?

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?

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]

cyl]

round(abs(mtcars_dt[cyl == 4, mean_cols] – mtcars_dt[cyl == 8,

mean_cols]))

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 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?

variables?

**rnorm**

**Question 3**

**When simulating data, why is using the set.seed() function**

important?

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?

distribution function for the Poisson distribution?

**qpois**

**Explanation**

*Probability distribution functions beginning with the q prefix are used*

to evaluate the quantile function.

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?

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()?

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.

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()?

take advantage of that, then which of the following is true when using

system.time()?

**Elapsed time may be smaller than user time**