16.5 Test your R might!

  1. Captain Jack is convinced that he can predict how much gold he will find on an island with the following equation: (a * b) - c * 324 + log(a), where a is the area of the island in square meters, b is the number of trees on the island, and c is how drunk he is on a scale of 1 to 10. Create a function called Jacks.Equation that takes a, b, and c as arguments and returns Captain Jack’s predictions. Here is an example of Jacks.Equation in action:
Jacks.Equation(a = 1000, b = 30, c = 7)
## [1] 27739
  1. Write a function called standardize.me that takes a vector x as an argument, and returns a vector that standardizes the values of x (standardization means subtracting the mean and dividing by the standard deviation). Here is an example of standardize.me in action:
standardize.me(c(1, 2, 1, 100))
## [1] -0.51 -0.49 -0.51  1.50
  1. Often times you will need to recode values of a dataset. For example, if you have a survey of age data, you may want to convert any crazy values (like anything below 0 or above 100) to NA. Write a function called recode.numeric() with 3 arguments: x, lb, and ub. We’ll assume that x is a numeric vector. The function should look at the values of x, convert any values below lb and above ub to NA, and then return the resulting vector. Here is the function in action:
recode.numeric(x = c(5, 3, -5, 4, 3, 97),
               lb = 0,
               ub = 10)
## [1]  5  3 NA  4  3 NA

  1. Create a function called plot.advanced that creates a scatterplot with the following arguments:

    • add.regression, a logical value indicating whether or not to add a regression line to the plot.
    • add.means, a logical value indicating whether or not to add a vertical line at the mean x value and a horizontal line at mean y value.
    • add.test, a logical value indicating whether or not to add text to the top margin of the plot indicating the result of a correlation test between x and y. (Hint: use mtext() and paste() to add the text)

Here is my version of plot.advanced() in action:

plot.advanced(x = diamonds$weight,
              y = diamonds$value,
              add.regression = TRUE,
              add.means = TRUE,
              add.test = TRUE)