Exercises

Begin this practical exercise by setting the maximum line length in R-Studio to 80 characters. Go to RStudio’s Preferences (or Global Options under Tools) –> Code –> Display, and tick the show margin box. Make sure that the margin column is set to 80

Exercise 1-5

  1. Install package mice.

Go to Tools > Install Packages in RStudio. If you are connected to the internet, select Repository under Install From and type mice under Packages. Leave the Install to Library at default and make sure that Install Dependencies is selected. Click install. If you are not connected to the internet, select Package Archive File under “Install from” and navigate to the respective file on your drive.

Some packages depend on other packages, meaning that their functionality may be limited if their dependencies are not installed. Installing dependencies is therefor recommended, but internet connectivity is required.

If all is right, you will receive a message in the console that the package has been installed (as well as its dependencies).

ALternatively, if you know the name of the package you would like to install - in this case mice - you can also call install.packages("mice") in the console window.

  1. Load package mice. Loading packages can be done through functions library() and require().
# type your code here

If you use require() within a function, and the required package is not available, require() will yield a warning and the remainder of the function is still executed, whereas library() will yield an error and terminate all executions. The use of library() when not doing too complicated things is preferred - require() would result in more computational overhead because it calls library() itself.

  1. Most packages have datasets included. Open the mammalsleep dataset from package mice by typing mammalsleep in the console, and subsequently by using the function View().

Using View() is preferred for inspecting datasets that are large. View() opens the dataset in a spreadsheet-like window (conform MS Excel, or SPSS). If you View() your own datasets, you can even edit the datasets’ contents.

  1. Write the mammalsleep dataset from package mice to the work directory (= the directory of your R project) as a tab-delimited text file with . as a decimal separator. Use the function write.table() and name the file mammalsleep.txt.
# type your code here
  1. Import the mammalsleep.txt file with read.table(). 
# type your code here

Exercise 6-10

  1. The dataset we’ve just imported contains the sleepdata by Allison & Cicchetti (1976). Inspect the sleepdata and make yourself familiar with it.

If you would like to know more about this dataset, you can open the help for the mammalsleep dataset in package mice through ?mammalsleep.

Inspect the data set to obtain information about the variables, the number of observations. Some summary statistics.

# type your code here
  1. Save the current workspace. Name the workspace Practical_C.RData. Also, save the sleepdata file as a separate workspace called Sleepdata.RData.

Now that we have imported our data, it may be wise to save the current workspace, i.e. the current state of affairs. Saving the workspace will leave everything as is, so that we can continue from this exact state at a later time, by simply opening the workspace file. To save everything in the current workspace, type:

# To save the entire workspace:
save.image("Practical_C.RData")

To save just the data set sleepdata, and nothing else, type:

# To save the data set only.
save(sleepdata, file = "Sleepdata.RData")

With the save functions, any object in the workspace can be saved.

  1. Some animals were not used in the calculations by Allison and Cicchetti. Exclude the following animals from the sleepdata data set: Echidna, Lesser short-tailed shrew and Musk shrew. Save the data set as sleepdata2. Tip: use the square brackets to indicate [rows, columns] or use the function filter() from dplyr.
# type your code here
  1. Plot brain weight as a function of species. 
# type your code here
  1. Some animals have much heavier brains than other animals. Find out the names of the animals that have a brain weight larger than 1 standard deviation above the mean brain weight. Replicate the plot from Question 9 with only these animals and do not plot any information about the other animals.

To find out which animals have a brain weight larger than 1 standard deviation above the mean brain weight:

# type your code here

To plot these animals:

# type your code here

The downside is that it still prints all the animals on the x-axis. This is due to the factor labels for species being copied to the smaller subset of the data. Plot automatically takes over the labels. For example,

sleepdata2$species[which]

returns only 3 mammals, but still has 62 factor levels. To get rid of the unused factor levels, we can use function factor():

sleepdata3 <- sleepdata2[which, ]
sleepdata3$species <- factor(sleepdata3$species)
sleepdata3$species

To plot the graph that we wanted:

# type your code here

End of exercise