Introduction to R: Assignment

This assignment accompanies the “Introduction to R” tutorial session for MCom (Economics) students attending the Intensive Statistics course at Stellenbosch University (2024).

This assignment accompanies the Introduction to R: Tutorial presented on 23 January 2024.

Download the materials needed to complete this assignment here.

This post was last updated on 22 January 2024.

Task 1

• Open R Studio and create a new project in which you will complete the assignment.

• Ensure that you are working from the new project and download the necessary assignment materials using this link. Extract the data folder from the compressed folder you have downloaded and copy it to your root directory.

Hint: Run getwd() in your console to check the file path of your working directory.

• Create a new R Script and save it as [yourstudentnumber].R in your directory. Write and execute the code necessary to complete the rest of the assignment in this R Script.

Task 2

• Using comments, create headings in your script for each of the subsequent tasks. Write your code under the appropriate headings.

Hint: In .R files, comments are created using the pound sign, i.e., #.

• Load the pacman package using install.packages("") (if necessary) and library().

• Install/load the following packages using the pacman package:

  • tidyverse
  • huxtable
  • fixest

Task 3

1. Read the ts_data.csv file and store it as a data frame in your Global Environment.

2. Use the tidyverse syntax (%>%) and mutate() to ensure that the Year column is of the class date.

3. Ensure that the data frame is arranged chronologically.

4. Subset/filter the data frame to contain only observations for France or Italy between the years 1985-2010.

Hint: When filtering, use the AND (&) and OR (|) operators.

5. Using ggplot(), create a line and scatter plot for the time series ln_consump_pc of both countries in the same figure. Move the legend to the bottom of the figure and change the label of the y-axis.

Hint: Use aes(x = Year, y = ln_consump_pc, color = Country) as the mapping for your ggplot() to distinguish between countries.

Task 4

1. Read the cs_data.csv file and store it as a data frame in your Global Environment.

2. Using the fixest package, perform the following three OLS regressions and store each of them in your Global Environment:

   • wage on x1

   • wage on x1 and x2

   • wage on x1 and x2 and the categorical variable nodegree

3. Present all the models in the same regression table using huxreg(). You are not required to perform any additional huxtable formatting operations.

Submission

Once you have completed all of the tasks successfully, please save and send your final R Script to wihanmarais@sun.ac.za for grading.