Week 14 - Gapminder

Goal 1: Business Scenario

First, create your own context for the lab. This should be a business use-case such as “a real estate firm aims to present housing trends (and recommendations) for their clients in Ames, Iowa”.

You do not need to solve the problem, you only need to define it.

Your scenario should include the following:

• Customer or Audience: who exactly will use your results?
  • We represent a non-profit taking the place of USAID and we are looking for where our services would do the most good. We only have funding, time, and personel to place ourselves into three countries to start, thanks to a grant we recieved from a weathy benefactor. Depending on our work, we have the opportunity to extend the grant, which we will be able to use to expand our operations and move towards helping more people.
• Problem Statement: reference this article to help you write a SMART problem statement.
  • Which three countries would allow us to have the highest year per dollar return on our investment in the first five years of our grant?
• Scope: What variables from the data (in the lab) can address the issue presented in your problem statement? What analyses would you use? You’ll need to define any assumptions you feel need to be made before you move forward.
  • Our response variable will be life expectancy, and our target variables will be country and gdp per capita.
  • Linear Regression, with a hint of weighted least square means.
  • Assumptions
    • GDP per capita is related to life expectancy.
    • Life expectancy is a good measure of where people need additional resources and there is an opportunity to help.
    • All else being equal, the country specifically does not have an effect on life expectancy.
    • All else being equal, the continent the people exist on does have an impact on their life expectancy.
    • Life expectancy is positively correlated with year.
    • The United Kingdom is a valid representative country in infant mortality for a developed country.
    • Afganistan is a valid representative country in infant mortality for an under-developed country.
• Objective: Define your success criteria. In other words, suppose you started working on this problem in earnest; how will you know when you are done? For example, you might want to “identify the factors that most influence <some variable>.”
  • Identify which variables help determine which countries have the lowest life expectancies so that we can determine which countries would therefore benefit from our charity’s services and support.

Goal 2: Model Critique

Since this is a class, and not a workplace, we need to be careful not to present too much information to you all at once. For this reason, our labs are often not as analytically rigorous or thorough as they might be in practice … So here, your goal is to:

Present a list of at least 3 (improved) analyses you would recommend for your business scenario. Each proposed analysis should be accompanied by a “proof of concept” R implementation. (As usual, execute R code blocks here in the RMarkdown file.)

In the lab your group has been assigned, consider issues with models, statistical improvements, interpretations, analyses, visualizations, etc. Use this notebook as a sandbox for trying out different code, and investigating the data from a different perspective. Take notes on all the issues you see, and propose your solutions (even if you might need to request more data or resources to accomplish those solutions).

You’ll want to consider the following:

• Analytical issues, such as the current model assumptions.

  • Central limit theory allows us to bootstrap the mean, but not the median.
  • There is an assumption of accuracy of the data.
  • There are interesting, but inscrutable assumptions made around continent categorization, such as what is occurring with Oceania and FSU.

• Issues with the data itself.

  • Mean life expectancy is being used, instead of median, and no information on infant mortality is included or considered. Infant mortality is a big factor which skews the mean life expectancy data. Because we’re predicting the mean life expectancy, there is a possibility this is a poisson distribution and not situation where a least squared mean is most appropriate.

• Statistical improvements; what do we know now that we didn’t know (or at least didn’t use) then? Are there other methods that would be appropriate?

  • The original model used confidence intervals, but we are interested in causality which will allow us to better understand which elements are related to our response variable.

• Are there better visualizations which could have been used?

  • Overall, the visualizations used for the notebook are appropriate for confidence intervals, but less so for the content/information we are seeking.

  • Better Visualizations for this problem question include:

    • Life expectancy for varying levels of infant mortality.
    • Frequency of each country’s representation in the data set to show how some countries are over/under represented.

  gapminder$infant <- ifelse(gapminder$country == "United Kingdom", 3.9, NA)
  gapminder$infant <- ifelse(gapminder$country == "Afghanistan", 50, gapminder$infant)
  
  gapminder_filter <- gapminder %>%
    filter(country %in% c("United Kingdom", "Afghanistan"))
  
  
  ggplot(data = gapminder_filter, aes(x = year, y = lifeExp)) + 
    geom_point(aes(color = country)) + 
     labs(x = "Year",
              y = "Life Expectancy",
              title = "Life Expectancy for Countries with Low and High Infant Mortality")

  

gap_group <- gapminder %>%
  group_by(country) %>%
  summarise(n_records = n(), 
            continent = continent) %>%
  distinct()

## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
## dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
##   always returns an ungrouped data frame and adjust accordingly.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## `summarise()` has grouped output by 'country'. You can override using the
## `.groups` argument.

ggplot(data = gap_group, aes(x = reorder(country, n_records), y = n_records)) + 
  geom_point(aes(color = continent)) + 
  theme(axis.text.x=element_blank()) + 
   labs(x = "",
            y = "Survey Records",
            title = "Number of Observations by Continent")

Feel free to use the reading for the week associated with your assigned lab to help refresh your memory on the concepts presented.

Goal 3: Ethical and Epistemological Concerns

Review the materials from the Week 5 lesson on Ethics and Epistemology. This includes lecture slides, the lecture video, or the reading. You should also consider doing supplementary research on the topic at hand (e.g., news outlets, historical articles, etc.). Some issues you might want to consider include:

• Overcoming biases (existing or potential).
  • There is no information in the data set that details if a country wad involved in a (civil) war, epidemic, or other mass casualty event that skews the median life expectancy.
  • There is an assumption that all countries in the data set are universally recognized with no areas in dispute or under occupation.
  • There may be an overrepresentation in the data of people living in urban areas in lieu of those living in rural areas due to ease of collecting data.
• Possible risks or societal implications.
  • If you do the analysis wrong, you could get an underwhelming or under expectations result based on potential investment.
• Crucial issues which might not be measurable.
  • Quality of life.
• Who would be affected by this project, and how does that affect your critique?
  • Donors and philanthropic individuals who seek to gain the most well-being per dollar of donation who rely on analyses like this to make informed decisions when making a donation.