Motivation

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Reproducible data gathering

Data and metadata should be retrieved programmatically.

Here is an example from the OECD API documentation. Other providers have their own API for example Eurostat.

url <- "https://sdmx.oecd.org/public/rest/data/OECD.SDD.STES,DSD_STES@DF_CLI/.M.LI...AA...H?startPeriod=2023-02&dimensionAtObservation=AllDimensions&format=csvfilewithlabels"

df <-  read.csv(url)

df |>
  dplyr::select(sample(seq(1,30), size=10)) |>
  tibble::glimpse(width=30)

Rows: 770
Columns: 10
$ Observation.value       <lgl> …
$ Unit.of.measure         <chr> …
$ Time.horizon            <chr> …
$ TIME_HORIZ              <chr> …
$ Reference.area          <chr> …
$ FREQ                    <chr> …
$ STRUCTURE_NAME          <chr> …
$ Calculation.methodology <chr> …
$ TRANSFORMATION          <chr> …
$ Observation.status      <chr> …

Note that in your report, data gathering chunks should go to the appendix.

200 countries, 50 years, 20 lines of code

You should build your report and your presentation around plots and tables.

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Display tables with style (no kitsch please).

Country	Continent	Year	Life Expectancy	Population (Millions Inhab.)	gdpPercap
Philippines	Asia	1957	51.3	26.1	$1,548
Mongolia	Asia	2002	65.0	2.7	$2,141
United Kingdom	Europe	1977	72.8	56.2	$17,429
France	Europe	1992	77.5	57.4	$24,704
Zambia	Africa	1977	51.4	5.2	$1,589
Niger	Africa	1977	41.3	5.7	$809
Uruguay	Americas	1967	68.5	2.7	$5,445
Argentina	Americas	1957	64.4	19.6	$6,857
Australia	Oceania	1987	76.3	16.3	$21,889
New Zealand	Oceania	1997	77.5	3.7	$21,050

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Visualization matters

Static with plotly

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Interaction and animation shine

Wizardry

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Appendix

The appendix should contain the code that helped you create the document

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# Load the data
#
gapminder <- gapminder::gapminder
#
#
# Using package gt 
#
gapminder |>
  dplyr::slice_sample(n=2, by= continent) |>
  dplyr::mutate(pop=pop/1e6) |>
  gt::gt() |>
  gt::fmt_number(columns=c(pop, lifeExp),decimals=1)|>
  gt::fmt_currency(columns=gdpPercap, use_subunits=FALSE) |>
  gt::cols_label(
    country = "Country",
    continent = "Continent",
    year = "Year",
    lifeExp = "Life Expectancy",
    pop = "Population (Millions Inhab.)", 
    .fn =  stringr::str_to_title
    )
#
#
# A neater color scale 
#
neat_color_scale <-
      c("Africa" = "#01d4e5",
        "Americas" = "#7dea01" ,
        "Asia" = "#fc5173",
        "Europe" = "#fde803",
        "Oceania" = "#536227")
#
#
a_year <- sample(gapminder$year, 1)

p <- gapminder |>
    filter(year==a_year) |>
    ggplot() +
    aes(x = gdpPercap) +
    aes(y = lifeExp) +
    aes(size = pop) +
    aes(text = country) +                   #
    aes(fill = continent) +
    aes(color= continent) +
    aes(frame = year) +                     #
    geom_point(alpha=.5) +
    scale_x_log10() +
    scale_size_area(max_size = 15,
                    labels= scales::label_number(scale=1/1e6,
                                                suffix=" M")) +
    scale_fill_manual(values = neat_color_scale) +
    labs(title= glue("Gapminder  {a_year}"),
        x = "Yearly Income per Capita",
        y = "Life Expectancy",
        caption="From sick  and poor (bottom left) to healthy and rich (top right)")

p 

(p + theme(legend.position = "none")) |> 
    plotly::ggplotly(height = 500, width=750)

(p %+% 
    gapminder + 
    theme(legend.position = "none") +
    ggtitle("Gapminder")) |> 
    plotly::ggplotly(height = 500, width=750)