Homework assignments

Assignments will be posted here.

Try R Markdown

Try to write your report using R Markdown. The advantage is that R code chunks written inline are executed when you make the html file.

An example using R Markdown file is found here. Save example files to a folder on your hard disk and then open with RStudio. To render it, click “Knit” at the top of the Source file pane in RStudio.

Email the TA for the course if you have questions.

Assignment 1: Improve a graph

This assignment is due Feb 7.

Find a graph drawn from data and published by your thesis supervisor. If your supervisor is flawless, pick another published graph, eg from a paper published from your lab or department.
Choose a graph that has plenty of room for improvement. Too little improvement means we can’t assign many marks.
Students from the same lab: don’t choose the same or very similar graphs.
In your report, explain the study.
Analyze the graph. What is its goal? Explain what patterns the graph is intended to show. Explain why you think it is not successful. Explain the flaws in the graph. Why do they interfere with the goals of the graph?
Make a new graph in R using principles of effective display (review lecture notes).
Try to obtain and make use of the raw data, otherwise extract them from the graph or simulate raw data.
Analyze your new graph according to principles of good graph design. Remind us of the goal of the graph. Explain how your improvements achieve the goal more effectively than the original. Why does your graph succeed?
Attach your R script at the end (or include as code chunks inline if you are using R Markdown)
Email paper to me as a single .pdf file: LASTNAME.FIRSTNAME.ASSIGNMENT1.PDF
Grade will be based on: the quality of your analysis of the original graph; the magnitude of improvement of the new graph; your interpretation of it and explanation of how it is improved; the quality of your R script.

Assignment 2: Analyze linear model

This assignment is due Friday, March 14.

Obtain a data set and analyze it by fitting a linear, mixed, or generalized linear model in R.

Obtain a data set from your supervisor or online data depository (e.g. datadryad.org).
Include just one response variable.
For the explanatory variables, include at least one categorical fixed factor, such as an experimental or observational treatment.
Include at least 1, and no more than 2, additional explanatory variables (random or fixed factors, blocks, covariates, etc).

Prepare a thorough report on the analysis and interpretation of the data. Below I list some of the things to include in your report, but note that the list might not be complete.

Include all your writing and graphs in a single pdf file (titled LASTNAME.FIRSTNAME.ASSIGNMENT2.PDF) and email to me.

Explain (in a paragraph) the purpose of the study that yielded the data.
Explain the specific data set you are using. For example, say where the data are from, give the meaning of the variables, and so on.
Illustrate and describe the main patterns revealed in the data.
State what parameters (magnitudes) you will estimate with these data.
State what hypotheses you will test with these data.
Fit a linear model to the data in R. Explain in words the model you fit.
Interpret the output. To assess biological significance, explain the parameter estimates (magnitudes). What do they mean and what are your conclusions based on these parameter estimates. To assess statistical significance, explain the null hypotheses and interpret the test results.
Visualize the model fit to the data. Explain what the graph is showing.
Address how well the statistical assumptions of your analysis were met. How did you handle violations?
State the overall conclusions reached from your analyses of biological and statistical significance.
Include your clean R code in an appendix.