final_proj.Rmd

---
title: "Final Project"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
library(kableExtra)
```


Each student will produce and report on a thoughtful and thorough analysis of a dataset. You should start with a research question that relates to your interests. You then should obtain a data set, analyze it (with full consideration of issues including checking your assumptions, model selection, parameter estimation, and prediction), and report on your findings.

You’ll deliver a final presentation and produce a final report, each of which should include an Introduction, Methods, Results, and Discussion. Your attention to detail should be at the level expected for a conference presentation or a paper you would submit for publication (even if the question and depth of introduction and discussion are not). When thinking about your project, remember our class themes: moving from a question to a model or models, and moving from a model or model(s) to inference. I want to clearly see your thought process.

There will be 3 components to your project: a project plan, a presentation, and a final report. All three of these elements will contribute a total of 30% toward your final grade.

## 1. Plan

#### Project plans are due by 11:59 PM on May 11 and constitute 3% of your final grade.

Write a 1-page description of your project idea that includes 

1. the question(s) of interest;  
2. the data you will use; and  
3. your general approach to analyzing the data.  

Things to consider:

* What sort of model do you expect to build?  
* Will it be linear or otherwise?  
* Might there be random effects?  
* How will you go about developing one or more models, analyzing them, and making inference?

## 2. Presentation

#### Presentations will be during week of June 1-5 and make up 9% of your final grade.

Each student will be expected to present their project to the class. The instructor will randomly assign each student to a time slot for their presentation, either during class on June 1, June 3, or June 5, or during lab on June 5. You will have 10 minutes for your presentation, and there will be 2 minutes for questions. I will be quite strict with time. Unfortunately, we will not be able to gather together to watch everyone's presentations, so you will have to take turns presenting remotely via **Zoom**. Students will have an opportunity for a "dry run" if you would like.

Your presentation should include

* a *brief* introduction of your question and why you chose it;  
* a *synopsis* of the models and assumptions;  
* the results of your analsysis; and
* a discussion of the implications and possible next steps.

## 3. Report

#### Reports are due by 11:59 PM on June 9 and constitute 18% of your final grade.

Please use the [R Markdown report template files](https://github.com/QERM514/website/tree/master/project) for your final report, which will facilitate proper formatting of text, equations, code, figures, references, etc. At a minimum, you will need these two files:

1) `report_template.Rmd`

2) `report_title_page_template.tex`

You will also need these two files:

1) `jpe.csl`

2) `example_refs.bib`

if you want to use BibTeX as reference manager for your report. All of these files will need to be located in the same folder or directory. See the report template for instructions.

Please bear in mind that your report should detail a fully reproducible workflow of your analysis. As such, please provide a copy of any necessary data file(s). However, I’ll only consult this as a last resort if there seems to be an error or I can’t seem to work out what you did. Otherwise, all of the relevant information should be contained within your written report. Your report is restricted to 2000 words (exclusive of code chunks and figure/table captions) plus no more than 2 tables and 2 figures, although you should not feel obligated to hit that limit.

## Schedule for presentations

Here is the schedule for presentations. Days and times were randomly assigned.

```{r present_order, echo = FALSE}
set.seed(514)
## get class roster
roster <- read.csv("QERM_514_2020_students.csv", stringsAsFactors = FALSE)
## get last names
l_names <- roster$LastName
## fix spelling
l_names[l_names == "Mcmonagle"] <- "McMonagle"
## auditors
auditors <- c("Oxborrow")
## names of students asking to go early
early <- c("Aeluro", "Duvall")
## names of students asking to go late
later <- c("Tournay", "Mistry")
## rest of class
r_names <- l_names[!(l_names %in% c(auditors, early, later))]
## order of presentations
p_order <- c(sample(early, length(early)),
             sample(r_names, length(r_names)),
             sample(later, length(later)))

## insert 15 min break on Fri
p_order <- c(p_order[1:14], "[ Break ]", p_order[15:length(p_order)])

## first day of presentations
start_date <- as.POSIXct("2020-06-01 10:30")
## dates in seconds past `start_date`
dates_sec <- c(0, 48, 96, 97, 98) * 3600
## presentation times in seconds past the hour
p_times <- c(0, 15, 30, 45) * 60
## presentation dates
p_dates <- start_date + dates_sec
## presentation dates & times
dates_times <- sapply(p_dates, function(x) x + p_times)
## nicer formatting
dates_times <- as.POSIXct(dates_times, origin = "1970-01-01")
dates_times <- format(dates_times, "%d %B @ %H:%M")
## drop leading 0
dates_times <- sub("0(\\d)", "\\1", dates_times)

## full schedule
df <- data.frame(date_time = dates_times, name = p_order)
colnames(df) <- c("Date & time", "Last name")

## generate table
kable(df, format = "html", caption = " ",
                  align = "cc", escape = FALSE,
                  fixed_thead = TRUE) %>%
  kable_styling(bootstrap_options = "striped",
                full_width = FALSE,
                position = "left") %>%
  column_spec(1, width = "10em") %>%
  column_spec(2, width = "15em") 
```