Working with matrices, lists, and data frames

  1. Assign to the variable n_dims a single random integer between 3 and 10.

    • Create a vector of consecutive integers from 1 to \(\mbox{n_dims}^2\).

    • Use the sample function to randomly reshuffle these values.

    • create a square matrix with these elements.

    • print out the matrix.

    • find a function in r to transpose the matrix.

    • print it out again and note how it has changed.

    • calculate the sum and the mean of the elements in the first row and then the last row.

    • read about the eigen() function and use it on your matrix

    • look carefully at the elements of $values and $vectors in the output. What kind of numbers are these?

    • dig in with the typeof() function to figure out their type.

    • if have set your code up properly, you should be able to re-run it and create a matrix of different size because n_dims will change.

  2. Create a list with the following named elements:

    • my_matrix, which is a 4 x 4 matrix filled with random uniform values
    • my_logical which is a 100-element vector of TRUE or FALSE values. Do this efficiently by setting up a vector of random values and then applying an inequality to it.
    • my_letters, which is a 26-element vector of all the lower-case letters in random order.

    Then, complete the following steps:

    • create a new list, which has the element[2,2] from the matrix, the second element of the logical vector, and the second element of the letters vector.
    • use the typeof() function to confirm the underlying data types of each component in this list
    • combine the underlying elements from the new list into a single atomic vector with the c() function.
    • what is the data type of this vector?

  3. Create a data frame with the two variables (= columns) and 26 cases (= rows) below:

    • call the first variable my_unis and fill it with 26 random uniform values from 0 to 10

    • call the second variable my_letters and fill it with 26 capital letters in random order.

    • for the first variable, use a single line of code in R to select 4 random rows and replace the numerical values in those rows with NA.

    • for the first variable, write a single line of R code to identify which rows have the missing values.

    • re-order the entire data frame to arrange the second variable in alphabetical order

    • calculate the column mean for the first variable.

Resources

Matrices, Lists, & Data Frames Lecture Notes