In the exercises below we cover the basics of data frames. Before proceeding, first read section 6.3.1 of An Introduction to R, and the help pages for the cbind, dim, str, order and cut functions.

Answers to the exercises are available here.

Exercise 1
Create the following data frame, afterwards invert Sex for all individuals.

Exercise 2
Create this data frame (make sure you import the variable Working as character and not factor).

Add this data frame column-wise to the previous one.
a) How many rows and columns does the new data frame have?
b) What class of data is in each column?

Exercise 3
Check what class of data is the (built-in data set) state.center and convert it to data frame.

Exercise 4
Create a simple data frame from 3 vectors. Order the entire data frame by the first column.

Exercise 5
Create a data frame from a matrix of your choice, change the row names so every row says id_i (where i is the row number) and change the column names to variable_i (where i is the column number). I.e., for column 1 it will say variable_1, and for row 2 will say id_2 and so on.

Exercise 6
For this exercise, we’ll use the (built-in) dataset VADeaths.

a) Make sure the object is a data frame, if not change it to a data frame.
b) Create a new variable, named Total, which is the sum of each row.
c) Change the order of the columns so total is the first variable.

Exercise 7
For this exercise we’ll use the (built-in) dataset state.x77.

a) Make sure the object is a data frame, if not change it to a data frame.
b) Find out how many states have an income of less than 4300.
c) Find out which is the state with the highest income.

Exercise 8
With the dataset swiss, create a data frame of only the rows 1, 2, 3, 10, 11, 12 and 13, and only the variables Examination, Education and Infant.Mortality.
a) The infant mortality of Sarine is wrong, it should be a NA, change it.
b) Create a row that will be the total sum of the column, name it Total.
c) Create a new variable that will be the proportion of Examination (Examination / Total)

Exercise 9
Create a data frame with the datasets state.abb, state.area, state.division, state.name, state.region. The row names should be the names of the states.

a) Rename the column names so only the first 3 letters after the full stop appear (e.g. States.abb will be abb).

Exercise 10
Add the previous data frame column-wise to state.x77
a) Remove the variable div.
b) Also remove the variables Life Exp, HS Grad, Frost, abb, and are.
c) Add a variable to the data frame which should categorize the level of illiteracy:
[0,1) is low, [1,2) is some, [2, inf) is high.
d) Find out which state from the west, with low illiteracy, has the highest income, and what that income is.