Radial Warmth: San Juan’s Temperature Profile

Monthly Temperature Patterns in San Juan, Puerto Rico (2014-2024)

Exploring San Juan’s temperature patterns through a circular heatmap visualization. This radial chart reveals the seasonal cycle of temperatures in Puerto Rico’s capital from 2014-2024, highlighting the consistent tropical climate with subtle variations across months and years.

Author

Steven Ponce

Published

April 3, 2025

Figure 1: A circular heatmap showing San Juan’s monthly temperature patterns from 2014-2024. The visualization arranges months in a circle with years radiating outward. Temperature is displayed through a purple-to-yellow color gradient, with cooler winter temperatures (December-February) in purple and warmer summer temperatures (July-September) in yellow. The pattern reveals San Juan’s consistent tropical seasonal cycle.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
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
  viridis,        # Color palettes
  fs,             # For file handling
  camcorder       # Record Your Plot History
)
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  7,
    height =  8,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code

# Directory where all CSV files are stored
data_dir <- here::here('data/30DayChartChallenge/2025/')

# Get a list of all CSV files for San Juan from 2014-2024
sj_files <- fs::dir_ls(data_dir, regexp = "78526011641_20[0-9]{2}\\.csv$")

# Function to read and process each file
process_file <- function(file_path) {
  # Extract year from filename
  year <- str_extract(file_path, "20[0-9]{2}")
  
  # Read file and add year column
  read_csv(file_path) |>
    clean_names() |>
    mutate(file_year = year)
}

# Read and combine all files
pri_temperature_all <- map_df(sj_files, process_file)

3. Examine the Data

Show code

glimpse(pri_temperature_all)
skim(pri_temperature_all)

4. Tidy Data

Show code

### |- Tidy ----
# Process temperature data
san_juan_temp <- pri_temperature_all |>
  # Extract date information
  mutate(
    year = year(date),
    month = month(date),
    month_name = month(date, label = TRUE, abbr = TRUE)
  ) |>
  # Select relevant columns
  select(date, year, month, month_name, temp)

# Calculate monthly averages
monthly_temp <- san_juan_temp |>
  group_by(year, month, month_name) |>
  summarize(
    avg_temp = mean(temp, na.rm = TRUE),
    .groups = "drop"
  ) |>
  # Round temperature
  mutate(avg_temp = round(avg_temp, 1))

# Filter for complete years
years_to_include <- 2014:2024
monthly_temp <- monthly_temp |>
  filter(year %in% years_to_include)

# Prepare data for visualization
monthly_temp <- monthly_temp |>
  mutate(
    # Convert to factors to ensure proper ordering
    month_name = factor(month_name, levels = month.abb),
    year = as.factor(year)
  )

# Set temperature scale
temp_min <- floor(min(monthly_temp$avg_temp, na.rm = TRUE))
temp_max <- ceiling(max(monthly_temp$avg_temp, na.rm = TRUE))

5. Visualization Parameters

Show code

### |- plot aesthetics ---- 
colors <- get_theme_colors(palette = NULL)

### |-  titles and caption ----
# text
title_text    <- str_glue("Radial Warmth: San Juan's Temperature Profile") 
subtitle_text <- str_glue("Monthly Temperature Patterns in San Juan, Puerto Rico (2014-2024)")

# Create caption
caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 03,
  source_text =  "NOAA" 
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Text styling 
    plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)),
    plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)),
    
    # Axis elements
    axis.title.y = element_text(color = colors$text, size = rel(0.8),
                              hjust = 1, vjust = 0.5, angle = 90),
    axis.title.x = element_blank(),
    axis.text.x = element_text(color = colors$text, size = rel(0.7)),
    axis.text.y = ggtext::element_markdown(),
  
    # Grid elements
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
 
    # Legend elements
    legend.position = "bottom",
    legend.title = element_text(family = fonts$text, size = rel(0.8)),
    legend.text = element_text(family = fonts$text, size = rel(0.7)),
    
    # Plot margins 
    plot.margin = margin(t = 10, r = 15, b = 10, l = 15),
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code

### |-  Plot ----
p <- ggplot(monthly_temp, aes(x = month_name, y = year, fill = avg_temp)) +
  # Geoms
  geom_tile(color = "white", size = 0.1) +
  # Scales
  coord_polar() +
  scale_fill_viridis(
    option = "plasma",
    name = "Temperature (°F)",
    limits = c(temp_min, temp_max),
    breaks = seq(temp_min, temp_max, by = 2)
  ) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = NULL,
    y = NULL
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size          = rel(1.6),
      family        = fonts$title,
      face          = "bold",
      color         = colors$title,
      margin        = margin(t = 5, b = 5)
    ),
    plot.subtitle   = element_markdown(
      size          = rel(0.9),
      family        = fonts$subtitle,
      color         = colors$subtitle,
      lineheight    = 1.1,
      margin        = margin(t = 5, b = 20)
    ),
    plot.caption    = element_markdown(
      size          = rel(.6),
      family        = fonts$caption,
      color         = colors$caption,
      lineheight    = 0.65,
      hjust         = 0.5,
      halign        = 0.5,
      margin        = margin(t = 10, b = 5)
    ),
  )

7. Save

Show code

### |-  plot image ----  

save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2025, 
  day = 03, 
  width = 7, 
  height = 8
  )

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1        camcorder_0.1.0   fs_1.6.5          viridis_0.6.5    
 [5] viridisLite_0.4.2 scales_1.3.0      skimr_2.1.5       janitor_2.2.0    
 [9] showtext_0.9-7    showtextdb_3.0    sysfonts_0.8.9    ggtext_0.1.2     
[13] lubridate_1.9.3   forcats_1.0.0     stringr_1.5.1     dplyr_1.1.4      
[17] purrr_1.0.2       readr_2.1.5       tidyr_1.3.1       tibble_3.2.1     
[21] ggplot2_3.5.1     tidyverse_2.0.0  

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      xfun_0.49         htmlwidgets_1.6.4 tzdb_0.4.0       
 [5] vctrs_0.6.5       tools_4.4.0       generics_0.1.3    curl_6.0.0       
 [9] parallel_4.4.0    gifski_1.32.0-1   fansi_1.0.6       pacman_0.5.1     
[13] pkgconfig_2.0.3   lifecycle_1.0.4   farver_2.1.2      compiler_4.4.0   
[17] textshaping_0.4.0 munsell_0.5.1     repr_1.1.7        codetools_0.2-20 
[21] snakecase_0.11.1  htmltools_0.5.8.1 yaml_2.3.10       crayon_1.5.3     
[25] pillar_1.9.0      magick_2.8.5      commonmark_1.9.2  tidyselect_1.2.1 
[29] digest_0.6.37     stringi_1.8.4     rsvg_2.6.1        rprojroot_2.0.4  
[33] fastmap_1.2.0     grid_4.4.0        colorspace_2.1-1  cli_3.6.3        
[37] magrittr_2.0.3    base64enc_0.1-3   utf8_1.2.4        withr_3.0.2      
[41] bit64_4.5.2       timechange_0.3.0  rmarkdown_2.29    bit_4.5.0        
[45] gridExtra_2.3     ragg_1.3.3        hms_1.1.3         evaluate_1.0.1   
[49] knitr_1.49        markdown_1.13     rlang_1.1.4       gridtext_0.1.5   
[53] Rcpp_1.0.13-1     glue_1.8.0        xml2_1.3.6        renv_1.0.3       
[57] vroom_1.6.5       svglite_2.1.3     rstudioapi_0.17.1 jsonlite_1.8.9   
[61] R6_2.5.1          systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in 30dcc_2025_03.qmd.

For the full repository, click here.

10. References

Expand for References

Data Sources:
- NOAA - National Centers for Environmental Information, Global Surface Summary of the Day - GSOD SAN JUAN L M MARIN INTERNATIONAL AIRPORT, PR US (78526011641.csv), 2014 - 2024
Citation:
- NOAA National Centers of Environmental Information. 1999. Global Surface Summary of the Day - GSOD. 1.0. [SAN JUAN L M MARIN INTERNATIONAL AIRPORT, PR US (78526011641.csv), 2014 - 2024]. NOAA National Centers for Environmental Information. Accessed [2025-03-21].

--- title: "Radial Warmth: San Juan's Temperature Profile" subtitle: "Monthly Temperature Patterns in San Juan, Puerto Rico (2014-2024)" description: "Exploring San Juan's temperature patterns through a circular heatmap visualization. This radial chart reveals the seasonal cycle of temperatures in Puerto Rico's capital from 2014-2024, highlighting the consistent tropical climate with subtle variations across months and years." author: "Steven Ponce" date: "2025-04-03" categories: ["30DayChartChallenge", "Data Visualization", "R Programming", "2025"] tags: [ "ggplot2", "circular visualization", "weather data", "climate patterns", "heatmap", "NOAA", "Puerto Rico", "tropical climate", "seasonal patterns", "temperature analysis", "radial chart" ] image: "thumbnails/30dcc_2025_03.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" self-contained: true theme: light: [flatly, assets/styling/custom_styles.scss] dark: [darkly, assets/styling/custom_styles_dark.scss] editor_options: chunk_output_type: inline execute: freeze: true cache: true error: false message: false warning: false eval: true # filters: # - social-share # share: # permalink: "https://stevenponce.netlify.app/data_visualizations/30DayChartChallenge/2025/30dcc_2025_03.html" # description: "Radial Warmth: Visualizing a decade of San Juan's temperature patterns in a circular heatmap for the #30DayChartChallenge. See how Puerto Rico's tropical climate cycles through the seasons." # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ![A circular heatmap showing San Juan's monthly temperature patterns from 2014-2024. The visualization arranges months in a circle with years radiating outward. Temperature is displayed through a purple-to-yellow color gradient, with cooler winter temperatures (December-February) in purple and warmer summer temperatures (July-September) in yellow. The pattern reveals San Juan's consistent tropical seasonal cycle.](30dcc_2025_03.png){#fig-1} ### <mark> **Steps to Create this Graphic** </mark> #### 1. Load Packages & Setup ```{r} #| label: load #| warning: false #| message: false #| results: "hide" ## 1. LOAD PACKAGES & SETUP ---- suppressPackageStartupMessages({ 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 viridis, # Color palettes fs, # For file handling camcorder # Record Your Plot History ) }) ### |- figure size ---- gg_record( dir = here::here("temp_plots"), device = "png", width = 7, height = 8, units = "in", dpi = 320 ) # Source utility functions suppressMessages(source(here::here("R/utils/fonts.R"))) source(here::here("R/utils/social_icons.R")) source(here::here("R/utils/image_utils.R")) source(here::here("R/themes/base_theme.R")) ``` #### 2. Read in the Data ```{r} #| label: read #| include: true #| eval: true #| warning: false # Directory where all CSV files are stored data_dir <- here::here('data/30DayChartChallenge/2025/') # Get a list of all CSV files for San Juan from 2014-2024 sj_files <- fs::dir_ls(data_dir, regexp = "78526011641_20[0-9]{2}\\.csv$") # Function to read and process each file process_file <- function(file_path) { # Extract year from filename year <- str_extract(file_path, "20[0-9]{2}") # Read file and add year column read_csv(file_path) |> clean_names() |> mutate(file_year = year) } # Read and combine all files pri_temperature_all <- map_df(sj_files, process_file) ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(pri_temperature_all) skim(pri_temperature_all) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- Tidy ---- # Process temperature data san_juan_temp <- pri_temperature_all |> # Extract date information mutate( year = year(date), month = month(date), month_name = month(date, label = TRUE, abbr = TRUE) ) |> # Select relevant columns select(date, year, month, month_name, temp) # Calculate monthly averages monthly_temp <- san_juan_temp |> group_by(year, month, month_name) |> summarize( avg_temp = mean(temp, na.rm = TRUE), .groups = "drop" ) |> # Round temperature mutate(avg_temp = round(avg_temp, 1)) # Filter for complete years years_to_include <- 2014:2024 monthly_temp <- monthly_temp |> filter(year %in% years_to_include) # Prepare data for visualization monthly_temp <- monthly_temp |> mutate( # Convert to factors to ensure proper ordering month_name = factor(month_name, levels = month.abb), year = as.factor(year) ) # Set temperature scale temp_min <- floor(min(monthly_temp$avg_temp, na.rm = TRUE)) temp_max <- ceiling(max(monthly_temp$avg_temp, na.rm = TRUE)) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- colors <- get_theme_colors(palette = NULL) ### |- titles and caption ---- # text title_text <- str_glue("Radial Warmth: San Juan's Temperature Profile") subtitle_text <- str_glue("Monthly Temperature Patterns in San Juan, Puerto Rico (2014-2024)") # Create caption caption_text <- create_dcc_caption( dcc_year = 2025, dcc_day = 03, source_text = "NOAA" ) ### |- fonts ---- setup_fonts() fonts <- get_font_families() ### |- plot theme ---- # Start with base theme base_theme <- create_base_theme(colors) # Add weekly-specific theme elements weekly_theme <- extend_weekly_theme( base_theme, theme( # Text styling plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)), plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)), # Axis elements axis.title.y = element_text(color = colors$text, size = rel(0.8), hjust = 1, vjust = 0.5, angle = 90), axis.title.x = element_blank(), axis.text.x = element_text(color = colors$text, size = rel(0.7)), axis.text.y = ggtext::element_markdown(), # Grid elements panel.grid.major = element_blank(), panel.grid.minor = element_blank(), # Legend elements legend.position = "bottom", legend.title = element_text(family = fonts$text, size = rel(0.8)), legend.text = element_text(family = fonts$text, size = rel(0.7)), # Plot margins plot.margin = margin(t = 10, r = 15, b = 10, l = 15), ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot ---- p <- ggplot(monthly_temp, aes(x = month_name, y = year, fill = avg_temp)) + # Geoms geom_tile(color = "white", size = 0.1) + # Scales coord_polar() + scale_fill_viridis( option = "plasma", name = "Temperature (°F)", limits = c(temp_min, temp_max), breaks = seq(temp_min, temp_max, by = 2) ) + # Labs labs( title = title_text, subtitle = subtitle_text, caption = caption_text, x = NULL, y = NULL ) + # Theme theme( plot.title = element_text( size = rel(1.6), family = fonts$title, face = "bold", color = colors$title, margin = margin(t = 5, b = 5) ), plot.subtitle = element_markdown( size = rel(0.9), family = fonts$subtitle, color = colors$subtitle, lineheight = 1.1, margin = margin(t = 5, b = 20) ), plot.caption = element_markdown( size = rel(.6), family = fonts$caption, color = colors$caption, lineheight = 0.65, hjust = 0.5, halign = 0.5, margin = margin(t = 10, b = 5) ), ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot( p, type = "30daychartchallenge", year = 2025, day = 03, width = 7, height = 8 ) ``` #### 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 The complete code for this analysis is available in [`30dcc_2025_03.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/30dcc_2025_03.qmd). For the full repository, [click here](https://github.com/poncest/personal-website/). ::: #### 10. References ::: {.callout-tip collapse="true"} ##### Expand for References 1. Data Sources: - NOAA - National Centers for Environmental Information, Global Surface Summary of the Day - GSOD [SAN JUAN L M MARIN INTERNATIONAL AIRPORT, PR US (78526011641.csv), 2014 - 2024](https://www.ncei.noaa.gov/access/search/data-search/global-summary-of-the-day?dataTypes=TEMP&pageNum=1&bbox=18.516,-67.951,17.883,-65.221&place=Country:241&startDate=2014-01-01T00:00:00&endDate=2024-12-03T23:59:59&stations=78526011641) 2. Citation: - NOAA National Centers of Environmental Information. 1999. Global Surface Summary of the Day - GSOD. 1.0. [SAN JUAN L M MARIN INTERNATIONAL AIRPORT, PR US (78526011641.csv), 2014 - 2024]. NOAA National Centers for Environmental Information. Accessed [2025-03-21]. :::