---
freeze: true
title: "Exploring Relationships Among CIA Factbook Variables"
subtitle: "This figure shows correlations between various CIA Factbook variables.\nLarge circles indicate strong correlations, with positive values in teal and negative values in orange."
author: "Steven Ponce"
description: "Correlations among CIA World Factbook variables, with circle size showing strength and color showing direction. Built in R."
date: "2024-10-21"
categories: ["TidyTuesday", "2024"]
image: "thumbnails/tt_2024_43.png"
format:
html:
toc: true
toc-depth: 5
code-link: true
code-fold: true
code-tools: true
code-summary: "Show code"
editor_options:
chunk_output_type: inline
execute:
error: false
message: false
warning: false
eval: true
# share:
# permalink: "https://stevenponce.netlify.app/data_visualizations.png"
# linkedin: true
# twitter: true
# email: true
---
{#fig-1}
### <mark> __Steps to Create this Graphic__ </mark>
#### 1. Load Packages & Setup
```{r}
#| label: load
#| warning: false
## 1. LOAD PACKAGES & SETUP ----
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
tidyverse, # Easily Install and Load the 'Tidyverse'
ggtext, # Improved Text Rendering Support for 'ggplot2'
showtext, # Using Fonts More Easily in R Graphs
janitor, # Simple Tools for Examining and Cleaning Dirty Data
skimr, # Compact and Flexible Summaries of Data
scales, # Scale Functions for Visualization
lubridate, # Make Dealing with Dates a Little Easier
glue, # Interpreted String Literals
patchwork, # The Composer of Plots
here, # A Simpler Way to Find Your Files
ggcorrplot, # Correlogram
camcorder # Record plots
)
### |- figure size ----
camcorder::gg_record(
dir = here::here("temp_plots"),
device = "png",
width = 10,
height = 10,
units = "in",
dpi = 320
)
### |- resolution ----
showtext_opts(dpi = 320, regular.wt = 300, bold.wt = 800)
```
#### 2. Read in the Data
```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false
tt <-tidytuesdayR::tt_load(2024, week = 06)
tt <-tidytuesdayR::tt_load(2024, week = 43)
cia_factbook <- tt$cia_factbook |> clean_names()
tidytuesdayR::readme(tt)
rm(tt)
```
#### 3. Examine the Data
```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false
glimpse(cia_factbook)
skim(cia_factbook)
```
#### 4. Tidy Data
```{r}
#| label: tidy
#| warning: false
### |- tidy data ----
# Select numeric variables and compute correlation matrix
cor_matrix <- cia_factbook |>
select(where(is.numeric)) |>
cor(use = "complete.obs")
# Clean column names for better readability in plots
colnames(cor_matrix) <- gsub("_", " ", colnames(cor_matrix)) |> str_to_title()
rownames(cor_matrix) <- gsub("_", " ", rownames(cor_matrix)) |> str_to_title()
# Convert correlation matrix to a long format for plotting labels
cor_df <- as.data.frame(as.table(cor_matrix)) |>
rename(Var1 = Var1, Var2 = Var2, Correlation = Freq) |>
filter(!is.na(Correlation))
```
#### 5. Visualization Parameters
```{r}
#| label: params
#| include: true
#| warning: false
### |- plot aesthetics ----
bkg_col <- colorspace::lighten('#f7f5e9', 0.05)
title_col <- "gray20"
subtitle_col <- "gray20"
caption_col <- "gray30"
text_col <- "gray20"
col_palette <- paletteer::paletteer_d("NineteenEightyR::miami1")[c(1,3,5)]
# show_col(col_palette)
### |- titles and caption ----
# icons
tt <- str_glue("#TidyTuesday: { 2024 } Week { 43 } • Source: usdatasets R package<br>")
li <- str_glue("<span style='font-family:fa6-brands'></span>")
gh <- str_glue("<span style='font-family:fa6-brands'></span>")
mn <- str_glue("<span style='font-family:fa6-brands'></span>")
# text
title_text <- str_glue("Exploring Relationships Among CIA Factbook Variables")
subtitle_text <- str_glue("This figure shows correlations between various CIA Factbook variables.\nLarge circles indicate strong correlations, with positive values in teal and negative values in orange.")
caption_text <- str_glue("{tt} {li} stevenponce • {mn} @sponce1(graphic.social) {gh} poncest • #rstats #ggplot2")
### |- fonts ----
font_add("fa6-brands", here::here("fonts/6.4.2/Font Awesome 6 Brands-Regular-400.otf"))
font_add_google("Oswald", regular.wt = 400, family = "title")
font_add_google("Merriweather Sans", regular.wt = 400, family = "subtitle")
font_add_google("Merriweather Sans", regular.wt = 400, family = "text")
font_add_google("Noto Sans", regular.wt = 400, family = "caption")
font_add_google("Shadows Into Light", regular.wt = 400, family = "anotation")
showtext_auto(enable = TRUE)
### |- plot theme ----
theme_set(theme_minimal(base_size = 14, base_family = "text"))
cor_theme <- function() {
theme(
plot.title.position = "plot",
plot.caption.position = "plot",
plot.background = element_rect(fill = bkg_col, color = bkg_col),
panel.background = element_rect(fill = bkg_col, color = bkg_col),
plot.margin = margin(t = 20, r = 20, b = 20, l = 20),
panel.grid.minor = element_blank(),
panel.grid.major = element_line(color = "gray80", size = 0.4, linetype = 'dotted'),
axis.title = element_blank(),
axis.text.y = element_text(color = text_col, family = "text", size = rel(1)),
axis.text.x = element_text(color = text_col, family = "text", size = rel(1), angle = 30, hjust = 1),
axis.ticks.x = element_line(color = text_col),
axis.line.x = element_line(color = "#252525", linewidth = .2)
)
}
```
#### 6. Plot
```{r}
#| label: plot
#| warning: false
### |- initial plot ----
# Create correlation heatmap with annotations
p <- cor_matrix |>
# Geoms
ggcorrplot(
method = "circle", type = "lower",
hc.order = TRUE, lab = FALSE, show.legend = TRUE,
colors = col_palette, outline.color = "black",
ggtheme = cor_theme()
) +
# Scales
scale_x_discrete() +
scale_y_discrete() +
scale_fill_stepsn(
colors = col_palette,
transform = "identity",
guide = guide_colorsteps(
barheight = unit(16, "lines"),
barwidth = unit(.5, "lines")
)
) +
# Labs
labs(
x = NULL,
y = NULL,
title = title_text,
subtitle = subtitle_text,
caption = caption_text
) +
# Theme
theme(
plot.title = element_text(
size = rel(2),
family = "title",
face = "bold",
color = title_col,
lineheight = 1.1,
margin = margin(t = 5, b = 5)
),
plot.subtitle = element_text(
size = rel(1),
family = "subtitle",
color = subtitle_col,
lineheight = 1.1,
margin = margin(t = 5, b = 5)
),
plot.caption = element_markdown(
size = rel(0.75),
family = "caption",
color = caption_col,
lineheight = 1.1,
hjust = 0.5,
halign = 1,
margin = margin(t = 15, b = 5)
)
)
### |- Annotated plot ----
# Add annotation text
annotation_text <- str_glue(
"Variables like birth rate and infant mortality rate
show a strong positive correlation, whereas others,
such as life expectancy at birth and infant mortality rate,
exhibit a strong negative correlation.
These relationships suggest that countries
with higher birth rates also tend to have
higher infant mortality, indicating possible
healthcare challenges."
)
p <- p +
# text
annotate(
"text",
x = 1, y = 7.3, label = annotation_text,
family = "anotation",
colour = text_col,
size = 5,
lineheight = 1.2,
hjust = 0
)
```
#### 7. Save
```{r}
#| label: save
#| warning: false
### |- plot image ----
# Save the plot as PNG
ggsave(
filename = here::here("data_visualizations/TidyTuesday/2024/tt_2024_43.png"),
plot = p,
width = 10, height = 10, units = "in", dpi = 320
)
### |- plot thumbnail----
magick::image_read(here::here("data_visualizations/TidyTuesday/2024/tt_2024_43.png")) |>
magick::image_resize(geometry = "400") |>
magick::image_write(here::here("data_visualizations/TidyTuesday/2024/thumbnails/tt_2024_43.png"))
```
#### 8. Session Info
::: {.callout-tip collapse="true"}
##### Expand for Session Info
```{r, echo = FALSE}
#| eval: true
#| warning: false
sessionInfo()
```
:::
#### 9. GitHub Repository
::: {.callout-tip collapse="true"}
##### Expand for GitHub Repo
[Access the GitHub repository here](https://github.com/poncest/personal-website/)
:::
