Lab 01 - R Basics

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Lab Goal: The simple goal of this lab is to get you thinking about R and RStudio again.

This lab can be completed with only R and without RStudio, but you should really use RStudio. For reference material, see Applied Statistics with R chapter two through six.

Basic Calculations

[Exercise] Calculate \(e^2\).

Solution:

exp(2)

## [1] 7.389056

[Exercise] Calculate the natural log of 3.

Solution:

log(3)

## [1] 1.098612

Getting Help

y = c(0, 2, NA, 3, 4, 1, 9, 0)

[Exercise] Calculate the mean of y with the missing values removed. Use only the mean() function.

• Hint 1: Check the documentation for mean().
• Hint 2: If you’re really stuck, there always this approach.

Solution:

mean(y, na.rm = TRUE)

## [1] 2.714286

Packages

[Exercise] Run the following code:

ggplot(mpg, aes(x = reorder(class, hwy), y = hwy, fill = class)) + 
  geom_boxplot() +
  xlab("class") +
  theme(legend.position = "none")

To do so, you will need to make sure the ggplot2 package is installed, and loaded.

Solution:

# install.packages("ggplot2")
library(ggplot2)
ggplot(mpg, aes(x = reorder(class, hwy), y = hwy, fill = class)) + 
  geom_boxplot() +
  xlab("class") +
  theme(legend.position = "none")

[Exercise] Modify the following code to run, but without loading the entire MASS library. The lda() function is from the MASS package.

lda(Species ~ ., data = iris)$means

Solution:

MASS::lda(Species ~ ., data = iris)$means

##            Sepal.Length Sepal.Width Petal.Length Petal.Width
## setosa            5.006       3.428        1.462       0.246
## versicolor        5.936       2.770        4.260       1.326
## virginica         6.588       2.974        5.552       2.026

Vectors and Lists

x = 1:100

[Exercise] Calculate

\[ \sum_{i = 1}^{n} \ln(x_i). \]

That is, sum the log of each element of x.

Solution:

sum(log(x))

## [1] 363.7394

[Exercise] After running the following code, how many of the elements of some_vector are larger than 1? A good solution will use only one line of code.

set.seed(42)
some_vector = rnorm(100)

Solution:

sum(some_vector > 1)

## [1] 17

Probability

[Exercise] Consider a random variable \(X\) that has a normal distribution with a mean of 5 and a variance of 9. Calculate

\[ P[X > c], \]

for \(c = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10.\)

Solution:

c = 1:10
pnorm(c, mean = 5, sd = 3, lower.tail = FALSE)

##  [1] 0.90878878 0.84134475 0.74750746 0.63055866 0.50000000 0.36944134
##  [7] 0.25249254 0.15865525 0.09121122 0.04779035