Math Camp field notebook

Question, code, evidence, judgment

Policy question

Write one descriptive or associational question. Name the population, outcome, comparison, and time period.

Data and definitions

  • Source: World Bank World Development Indicators
  • Retrieval or snapshot date:
  • Unit of observation: country-year
  • Indicators and units:
  • Known limitations:
library(tidyverse)

wdi <- read_csv(
  "../data/derived/math-camp-wdi-2000-2022.csv",
  show_col_types = FALSE
)

First inspection

Before using an agent, predict what each check should reveal.

glimpse(wdi)
Rows: 4,991
Columns: 15
$ iso3c                       <chr> "ABW", "ABW", "ABW", "ABW", "ABW", "ABW", …
$ country                     <chr> "Aruba", "Aruba", "Aruba", "Aruba", "Aruba…
$ region                      <chr> "Latin America & Caribbean", "Latin Americ…
$ income_level                <chr> "High income", "High income", "High income…
$ lending_type                <chr> "Not classified", "Not classified", "Not c…
$ year                        <dbl> 2000, 2001, 2002, 2003, 2004, 2005, 2006, …
$ adolescent_fertility        <dbl> 43.729, 40.463, 38.185, 37.807, 38.761, 40…
$ carbon_intensity            <dbl> 0.07890653, 0.07658411, 0.08445188, 0.0962…
$ electricity_access          <dbl> 91.7, 100.0, 100.0, 100.0, 100.0, 100.0, 1…
$ female_secondary_enrollment <dbl> 91.73772, 97.35983, 98.05524, 101.51862, 9…
$ gdp_per_capita_growth       <dbl> 6.51922377, 3.21240569, -1.62809915, -0.03…
$ gdp_per_capita_ppp          <dbl> 37618.993, 38827.467, 38195.318, 38182.393…
$ internet_use                <dbl> 15.442822948, 17.100000000, 18.800000000, …
$ renewable_electricity       <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.…
$ under5_mortality            <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
count(wdi, year)
# A tibble: 23 × 2
    year     n
   <dbl> <int>
 1  2000   217
 2  2001   217
 3  2002   217
 4  2003   217
 5  2004   217
 6  2005   217
 7  2006   217
 8  2007   217
 9  2008   217
10  2009   217
# ℹ 13 more rows
summarise(wdi, economies = n_distinct(iso3c))
# A tibble: 1 × 1
  economies
      <int>
1       217

Analysis sample

Explain every filter and transformation in plain language.

# Replace this example with the variables for your selected policy track.
analysis_data <- wdi |>
  select(iso3c, country, year, income_level,
         gdp_per_capita_ppp, under5_mortality) |>
  filter(year >= 2000) |>
  mutate(log_income = log(gdp_per_capita_ppp))

stopifnot(
  !anyDuplicated(analysis_data[c("iso3c", "year")])
)

Comparison

ggplot(analysis_data, aes(log_income, under5_mortality)) +
  geom_point(alpha = 0.4, na.rm = TRUE) +
  labs(
    x = "Log GDP per capita, PPP",
    y = "Under-five deaths per 1,000 live births"
  ) +
  theme_minimal()

Replace this caption with the population, variables, years, and units.

Write the strongest descriptive or associational statement supported by the output.

What this does not show

Name at least one limitation related to measurement, missingness, comparison, or causal interpretation.

AI-use note

I used [Codex / Claude Code / other] to [explain, generate, debug, review, or update]. I provided it with [public project context]. I verified its contribution by [tests, source comparison, clean render, or manual review]. I revised or rejected [important example]. I remain responsible for the analysis and interpretation.

Verification record

Agent claim or change Check performed Result
Confirmed / contradicted / unresolved