Proposal Review

[LaurieLBaker]

Proposal Review

Reviewer: Professor Baker Date: 03/14/2022

Section 1 - Introduction:

The introduction should introduce your general research question and your data (where it came from, how it was collected, what are the cases, what are the variables, etc.).

What is their general research question: What are the driving factors for renewable energy production for different countries, and what do these trends look like over time in comparison to fossil fuel usage and emissions? Is the general research question clear? If it is not clear, what questions do you have?

• Well-defined questions. I was interested in one of your subquestions where you talk about a country's capacity for renewable energy. How do you define a country's capacity for renewable energy. It might be interesting to explore this further and maybe look at where renewable energy sources have been developed compared to the potential to develop them (e.g. let's say for wind energy you might need winds of 15mph but not greater than 60mph).
• I can imagine that some of the energy trends might also be influenced by what alternative energies there are, what natural resources a country has and their ability to extract/harness energy.

Section 2 - Data:

• [X] Is the data in the /data folder?
• [X] Does the README include the dimensions and codebook for the data set?
• [X] Does the proposal include the output of glimpse() or skim() of the data frame.

Data suitability:

• [X] Does the dataset have at least 50 observations and between 10 to 20 variables (exceptions can be made).
• [X] Does the data set include a mix of categorical variables, discrete numerical variables, and continuous numerical variables.
• [X] What variables does the data include (list below):
  • see data Readme

    Section 3 - Data analysis plan:

• [X] Does the proposal include outcome (response, Y) and predictor (explanatory, X) variables they will use to answer your question? And/or the comparison groups they will use, if applicable.
• I'd also be interested in a more qualitative assessment of whether different countries follow similar trajectories and if there are similarities between countries and their energy production. It might be interesting to look at external variables like the gap of a country, the area, and the population, and the energy demand/usage, whether a country is on the coast, etc.
• I'd also be interested in seeing time lags and maybe some plots of the year that a country reached 50% of its energy coming from renewable energy sources (this could be interesting to see and compare how far behind different countries like the U.S. are).

Do the outcome and predictor variables and/or comparison groups make sense in the context of the question? Why or Why not?

• I think the

• [X] Does the proposal include some very preliminary exploratory data analysis, including some summary statistics and visualizations, along with some explanation on how they help you learn more about your data. (They can add to these later as they work on their project.)

• [X] Does the proposal include the statistical method(s) that they believe will be useful in answering your question(s). (They can update these later as they work on their project.)

  • I think looking at the trajectory will be really cool
  • geom_smooth() will be a good way to look at non-linear trends
  • you might also be interested in looking into and exploring GAMs.

• [X] Do they include what results from these specific statistical methods that are needed to support their hypothesized answer?

Reflections

1. What was something you found interesting about the project?

   • I'm really excited to see the different trajectories in countries and how their energy usage has changed. You'll want to set the scene in terms of giving an overview of how energy usage has changed over time and what the big players are.

2. What ideas/feedback do you have for other things they may explore?

   • I wondering whether there is a way to partition energy consumption (e.g. heat, electricity, transport are some main sources/needs). I'd be interested in what they say in the literature.

3. What kinds of plots should they consider to complete the project goal to create some kind of compelling visualization(s) of this data in R?

   • I think stacked bar plots and also waffle charts will come in handy to show the different proportions.
   • Time series will be useful.
   • A plot showing country on the y axis and the time point that they reached (50% or some other level renewable energy).
   • Maps.
   • You may want to do a comparison between the U.S. and a country with a similar profile in terms of natural resources and size (maybe Canada?).

4. Any additional feedback you'd like to give the other group:

   • I'm really excited to learn more! I'd be really interested to hear your thoughts about a country's potential for different renewable energy sources. Great work!

[LaurieLBaker]

Great work! @oneuman4, @lorenandrews, @jtam304 see feedback above!