--- 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" date: "2024-10-21" categories: - "#TidyTuesday" 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" self-contained: true 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" ):: p_load (# Easily Install and Load the 'Tidyverse' # Improved Text Rendering Support for 'ggplot2' # Using Fonts More Easily in R Graphs # Simple Tools for Examining and Cleaning Dirty Data # Compact and Flexible Summaries of Data # Scale Functions for Visualization # Make Dealing with Dates a Little Easier # Interpreted String Literals # The Composer of Plots # A Simpler Way to Find Your Files # Correlogram # Record plots ### |- figure size ---- :: 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 <- tidytuesdayR:: tt_load (2024 , week = 06 ) <- tidytuesdayR:: tt_load (2024 , week = 43 ) <- tt$ cia_factbook |> clean_names () :: 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 <- 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 <- 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 ---- <- colorspace:: lighten ('#f7f5e9' , 0.05 ) <- "gray20" <- "gray20" <- "gray30" <- "gray20" <- paletteer:: paletteer_d ("NineteenEightyR::miami1" )[c (1 ,3 ,5 )] # show_col(col_palette) ### |- titles and caption ---- # icons <- str_glue ("#TidyTuesday: { 2024 } Week { 43 } • Source: usdatasets R package<br>" )<- str_glue ("<span style='font-family:fa6-brands'></span>" )<- str_glue ("<span style='font-family:fa6-brands'></span>" )<- str_glue ("<span style='font-family:fa6-brands'></span>" )# text <- str_glue ("Exploring Relationships Among CIA Factbook Variables" )<- str_glue ("This figure shows correlations between various CIA Factbook variables. \n Large circles indicate strong correlations, with positive values in teal and negative values in orange." )<- 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" )) <- 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 <- 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 <- 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 + # 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---- :: image_read (here:: here ("data_visualizations/TidyTuesday/2024/tt_2024_43.png" )) |> :: image_resize (geometry = "400" ) |> :: image_write (here:: here ("data_visualizations/TidyTuesday/2024/thumbnails/tt_2024_43.png" ))``` #### 8. Session Info ##### Expand for Session Info ```{r, echo = FALSE} #| eval: true #| warning: false sessionInfo() ``` #### 9. GitHub Repository ##### Expand for GitHub Repo [ Access the GitHub repository here ](https://github.com/poncest/personal-website/) 