Exercise 2

In this practical, you will play around with the different types of R objects.

1 Creating Data Objects

Create two vectors

  1. One named vec1 containing the integers 1 through 6
  2. One named vec2 with letters A through F

Create two matrices

  1. One named mat1 from vec1
  2. One named mat2 from vec2

Define both matrices to have 3 rows and 2 columns.

Inspect vec1, vec2, mat1, and mat2

Are they all numeric?

Make a 6 \(\times\) 2 matrix named mat3 from both vec1 and vec2

 1
  1. Use vec1 to populate the first column
  2. Use vec2 to populate the second column

Inspect this matrix.

Make a two-column data frame called dat3

 2
  1. Use function data.frame().
  2. Set vec1 and vec2 as the columns.
  3. Name the columns v1 and v2, respectively.

2 Data Manipulation

Make a 2-column data frame called dat3b

 3
  1. Use the as.data.frame() function with the matrix created in  1 as input.
  2. Set the argument stringsAsFactors = TRUE.
  3. Name the columns v1 and v2, respectively.

Are the types of the v1 and v2 columns the same as the types of vec1 and vec2, respectively?

Check if the first columns in the data frames from  2 and  3 are numeric

If these columns are not numeric, determine their type.

Select the following elements from the data frame you created in  2

  1. The third row
  2. The second column
  3. The intersection of the above

Inspect the structure of the data frame that you created in  2.

The structure function, str(), allows us to inspect the structure of an R object. Try using it here.

Let’s pretend the first variable (v1) in the data frame you created in  2 (dat3) is not coded correctly, and it actually represents grouping information about cities.

Convert the v1 variable into a factor with the levels:

  • Utrecht
  • New York,
  • London
  • Singapore
  • Rome
  • Cape Town

3 Working with Real Data

Load the workspace boys.RData

You can download the boys.RData workspace here.

Most R packages ship with datasets included (these datasets are most often used for examples to demonstrate the functionality of the package). Since you have not yet learned how to load packages, you get the boys data (which comes from the mice package) as a stand-alone workspace.

View the boys dataset two ways

  1. By executing boys in the console
  2. By using the View() function

Find the dimensions of the boys dataset

Inspect the first 6 cases and the final 6 cases in the dataset

Check if the boys data are sorted on age

Inspect the boys dataset with str()

Use one or more functions to generate numeric summaries of each variable’s distribution

At least show the minimum, the maximum, the mean, and the median for all of the variables.

Give the standard deviations for age and bmi

Tip: Use the help (?) and help search (??) functionality in R, if you get stuck.

4 Logical Subsetting

Create a new data frame containing only the boys that are 20 years old or older

How many boys are at least 20 years old?

Select all boys that are older than 19 but younger than 19.5

How many boys are between the ages of 19 and 19.5?

Compute the mean age of boys younger than 15 years old that do not live in region north

In this exercise, you have learned some basic R usage. The approaches we used for this exercise offer tremendous flexibility but may also be inefficient in complex analyses or data manipulation. Doing advanced operations in basic R can require lots of code. In the next exercise, we will start using packages that allow us to do more complicated operations with fewer lines of code.

As you start using R in your own research, you will quickly find yourself in need of packages that are not part of the default R installation. The beauty of R is that its functionality is community-driven. Anyone can add packages to CRAN, and other people can use and improve these packages. There’s a good chance that a function and/or package has been already developed for the analysis or operation you need. If not, maybe you’re interested in filling the gap by submitting your own package?

End of Exercise 2

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Copyright Hanne Oberman, 2025 - CC BY-NC-SA 4.0
Materials developed by Amices team - Methodology & Statistics - Utrecht University