For undergrads…
Code like a pro…
Undergrad students come to me from all subjects and courses. For example, I have taught undergrads within economics, psychology, oceanography, political science, criminology, veterinary science (dog behaviour) and many more topics. Many if not most science courses now involve working with R studio, usually within data science or statistics modules.
Undergrad assignments often involve the following, which I can definitely help you with:
Getting familiar with R studio (interface, working directories, different R file types).
Reading in data from excel or a .txt file
Data cleaning - removing NA values and/or outliers, changing data types (e.g. from character to numeric), correcting typos in the data.
Data processing / mutations - filtering a column for values of interest, adding new columns that are based on existing columns, performing calculations etc.
Research questions - often assignments give you a dataset and ask you to come up with your own interesting questions. Discussions can help here to form questions that are interesting but can actually be answered with the data you have.
Plots and tables - every kind of visualisation you can imagine, whether in ggplot2 or another library (e.g. boxplots, pie charts, correlation plots, NMDS plots, maps, the list is pretty much endless). The ideal visualisations depend on the data you have and the questions you are asking.
Statistics and models - sometimes assignments require a specific test to be conducted, and sometimes you are given flexibility to choose amongst different tests presented in your course. However, most students are asked to perform linear or logistic regression to examine the effect of changing the levels of predictors (the independent variables) on a response (the dependent variable). Results from these tests go hand-in-hand with plots, and both are normally presented in reports. Post-hoc tests allow you to go further in understand patterns.
Writing the report - this is often done in Word or even better in R markdown (a type of file in R). By using Rmd files, you can do all the analyses and all the writing without leaving R Studio, and this can save your time and avoid copy-paste errors.