Question 1

Reproduce the following plots with the specified datasets.
a. Use the mtcars data set. 

ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  stat_smooth()


b. Use the mtcars data set 

ggplot(mtcars, mapping = aes(x = wt, y = mpg, color = factor(cyl))) +
  geom_point() +
  scale_color_manual(values = c("4" = "orange",
                                "6" = "green",
                                "8" = "blue"))


c. Use the airquality data set. 

ggplot(airquality, mapping = aes(y = factor(Month))) +
  geom_bar()


d. Use the airquality data set. 

ggplot(airquality, mapping = aes(x = Wind, y = Temp)) +
  geom_point() +
  stat_smooth() +
  facet_wrap(~ Month)


Question 2

Create the following functions:
a. Create a function called comp_area(radius) to calculate the area (A) of a circle for a given radius (r) using the formula 𝐴 = 𝜋 × 𝑟!. Test the function by finding the area of a circle with a radius of 5.5 cm. 

comp_area <- function(radius) {
  area <- pi * radius^2
  return(area)
}

comp_area(5.5)
## [1] 95.03318

b. Create a function, sq_dif(x, y), that doe the following:
• take two arguments x and y with default values of 2 and 3;
• take the difference of x and y values;
• square the difference;
• return the final value. 

sq_dif <- function(x = 2, y = 3) {
  difference <- x - y
  squared <- difference^2
  return(squared)
}

c. Create a function is_odd(x) that returns TRUE when x is even and FALSE otherwise. Then, using is_odd(x), write a function odd_in(vec) that returns a vector comprised of the odd integers in a vector vec of integers. 

is_odd <- function(x) {
  return(x %% 2 == 0)
}

odd_in <- function(vec) {
  return(vec[!is_odd(vec)])
}

Question 3

Create the following loops:
Create a nested for loop, where the outer for() loop increments “a” 3 times, and the inner for() loop increments “b” 3 times. The nested loop prints the values of “a” and “b” as a vector. 

for (a in 1:3) {
  for (b in 1:3) {
    print(c(a, b))
  }
}
## [1] 1 1
## [1] 1 2
## [1] 1 3
## [1] 2 1
## [1] 2 2
## [1] 2 3
## [1] 3 1
## [1] 3 2
## [1] 3 3

b. Create a while loop that prints out numbers drawn from the standard normal distribution (use rnorm()) but stops (breaks) if you get a number bigger than 1. 

while (TRUE) {
  x <- rnorm(1)
  print(x)
  
  if (x > 1) {
    break
  }
}
## [1] -0.149291
## [1] 0.4529652
## [1] -1.253267
## [1] -1.452654
## [1] 0.298133
## [1] -0.2133145
## [1] -0.383155
## [1] 1.964334

c. Using the variables below create a repeat loop that breaks off the incrementation of “i” after 6 iterations, and prints “msg” at every increment.
• msg <- c(“PSY290”)
• i <- 1 

msg <- c("PSY290")
i <- 1

repeat {
  print(msg)
  i <- i + 1
  
  if (i > 6) {
    break
  }
}
## [1] "PSY290"
## [1] "PSY290"
## [1] "PSY290"
## [1] "PSY290"
## [1] "PSY290"
## [1] "PSY290"