Lab 1: R and Rmarkdown

Use the rep() function to construct the following vector: 1 1 2 2 3 3 4 4 5 5

Use rep() to construct this vector: 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

Create a vector of 1300 values evenly spaced between 1 and 100.

How many of these values are greater than 91? (Hint: see sum() as a helpful functions.)

Using the vector you created of 1300 values evenly spaced between 1 and 100,

Modify the elements greater than 90 to equal 9999.

View (not modify) the first 10 values in your vector.

View (not modify) the last 10 values in your vector.

Make a data frame with column 1: 1,2,3,4,5,6 and column 2: a,b,a,b,a,b

Select only rows with value “a” in column 2 using logical vector

mtcars is a built-in data set like iris: Extract the 4th row of the mtcars data.

Make a function called my_mean() that takes a vector of numbers as input and returns the mean of the vector.

Alter your my_mean() function to take a second argument (na.rm) with default value FALSE that removes NA values if TRUE.

Add checks to your function to make sure the input data is either numeric or logical. If it is logical convert it to numeric (Hint: look at the stopifnot() function).

The diamonds data set is included in the ggplot2 package (not by default in R). It can be read into your environment with the following function.

data("diamonds", package = "ggplot2")

Loop over the columns of the diamonds data set and apply your mean function to all of the numeric columns (Hint: look at the class() function).

Create a new Rmarkdown document.

Alter the template to specify the size to be a height of 6.

Add a caption to your figure.

Add a chunk that includes your my_mean() function and calculate the mean of \(100\) randomly generated numbers (see runif() or rnorm()).

Compile your document to pdf.