Lists (practice)

Primary exercises

Create and investigate a list.
Three students received different sets of grades (Amy: 1,6,7,9,10; Bob: 6,7,4,3,5,2,2,1,4; Dan: 9,9,10).
In a variable scores create a list (the names of the list elements should be the names of the students and the values should be the corresponding grades).
Print the list, its class, length and structure (str) of scores.
Add an element, change an element.
Reuse scores from the previous exercise.
Add there grades for Eve (7,3,5,8,8,9) and print the list.
Then, for Dan merge new grades (8,8,6,7) with the existing grades (hint: use the combine function c to combine existing Dan’s grades with the new grades then put the result back to scores; do not type again 9,9,10).
Single and double bracket operators.
Reuse scores from the previous exercises.
Investigate the difference between scores[[ "Bob" ]] and scores[ "Bob" ].
Look at what is printed and what is the class of each result.
Then compare scores[[ c( "Amy", "Bob" ) ]] with scores[ c( "Amy", "Bob" ) ].
Understand, why the error is reported.
Dollar operator.
Reuse scores from the previous exercises.
Investigate the (lack of) difference between scores$Bob and scores[[ "Bob" ]].
Look at what is printed and what is the class of each result.
Then compare scores$Bo with scores[[ "Bo" ]].
Understand, why the NULL is returned.

Extra exercises

A list returned by a function; test for association/correlation.
For this exercise we need two random numerical vectors.
Let’s create x and y, each of 30 elements sampled from the normal distribution: x <- rnorm( 30 ) and y <- rnorm( 30 ).
Print these vectors. You may also produce a scatter plot: plot( x, y ).

The function cor.test tests for association between corresponding elements of two vectors.
Use h <- cor.test( x, y ) and print h to see a report of the association test.
Internally h is stored as a list. Print names of the elements stored in h.
Now, read Help for cor.test. In the section Value you will see the description of the h elements.
Get directy the values of elements estimate and p.value.
A nested list.
Let’s extend the concept of scores to describe various topics (see the code below).
Check class and str of scores.
Calculate how many students are in the scores list.
Get Dan’s scores in physics.

scores <- list(
  Amy = list(
    math = c( 1,6,7,9,10 ),
    biology = c( 7,6,8 )
  ),
  Bob = list(
    math = c( 6,7,4,3,5,2,2,1,4 ),
    physics = c( 8,7 )
  ),
  Dan = list(
    math = c( 9,9,10 ),
    physics = c( 10, 10, 10 ),
    biology = c( 3, 5, 7 )
  )
)

Multitopic exercises

(ADV) Split a table into list of tables by a column factor; merge back.
Some functions might require an input to be provided as a list of tables.
Let’s assume that the pulse table should be split into a list of table parts based on the exercise argument.
Load the pulse.csv data to variable pulse.
Try l <- pulse %>% split( .$exercise ) and investigate the class, length and names of the result l.
Use double square bracket to extract the part for exercise being low.
Finally, check that with bind_rows applied to l you can recreate the pulse table (but with a different order of rows).
(ADV) Split a table by a column and write each part to a different file.
Continue with the setup of the previous exercise.
Study/type/exectute the following example.
Find the newly created files in your filesystem.

l <- pulse %>% split( .$exercise )
exercises <- names( l )                   # name in l of each table chunk
for( exercise in exercises ) {            # exercise will be a name of a single chunk
  fileName <- paste0( "pulse_", exercise, ".csv" )  # name of the file for the chunk
  message( "Writing file '", fileName, "'..." )
  write_csv( l[[ exercise ]], file = fileName )
}

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