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knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

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library(readxl)
library(gapminder)
library(DT)

# get a copy of the data to mutate/play with
my_gap <- gapminder

Excerpted Analysis from Data Wrangling Lab

What is the maximum and minimum GDP per capita for each continent from the Gapminder dataset?

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min_max_gdp <- my_gap %>%
  filter(year == 2007) %>% # trying to only make comparisons across one point in time, most recent is 2007
  group_by(continent) %>%
  arrange(gdpPercap) %>%
  slice(1, n())

Table

The table is nice because it provides a lot of information about the countries involved, and enables the viewer to make some of their own inferences. However, I think some of the impact in the differences of wealth gaps (or life expectancy) lose their impact among the numbers. A visualization would be useful here to isolate a singular point and drive it home.

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datatable(
  min_max_gdp
  , colnames = c("Country", "Continent", "Year", "Life Expectancy", "Population", "GDP per Capita")
  )

Companion Graph

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min_max_gdp %>%
  ggplot() + 
  aes(
    x = continent
    , y = gdpPercap
  ) +
  geom_point(
    size = 3
  ) +
  geom_text(
    aes(
      label = country
      , hjust = "left"
    )
    , nudge_x = 0.2
    , nudge_y = 0.2
    , angle = 15
  ) +
  scale_y_continuous(
    limits = c(0, 75000)
    , labels = scales::comma
  ) + 
  labs(
    x = "Continent"
    , y = "GDP per Capita"
    , title = "Oceania had the narrowest gap in per capita GDP in 2007"
  ) +
  coord_flip() +
  theme_minimal()

Description/Writeup

I decided to focus this visual on the differences between each continent’s highest and lowest GDP per capita countries. I think a scatter plot/point graph is appropriate because I will be able to compare both countries from each continent linearly, without needing to stagger their values (in say, a bar graph). The linear comparison should emphasize the differences (or lack thereof), and facilitate comparison across each continent (with an “implied bar” kind of like a gestalt principle).

This post was excerpted from Lab 4 of BMI 625