Stellar Observations by the Hubble Space Telescope

Timeline of the 10 Most Frequently Observed Celestial Targets

Analyzing Hubble Space Telescope observation patterns of stellar objects from 2015-2023, revealing scientific priorities and long-term monitoring programs through time series visualization.

Published

April 22, 2025

Figure 1: Time series visualization of Hubble Space Telescope observations from 2016-2024, showing the 10 most frequently observed celestial targets. Gold dots represent observations, with NGC104-WFC having the most consistent observations over time. M-51 shows a concentrated period of observations in 2017-2018, while NGC5139-WFC observations only begin around 2020.

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
  camcorder       # Record Your Plot History
  )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 8,
    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

hubble_raw <- read_csv(here::here(
  "data/30DayChartChallenge/2025/HST_2025-04-05T07_06_02-04_00.csv")
  ) |> 
    clean_names()

3. Examine the Data

Show code

glimpse(hubble_raw)
skim(hubble_raw)

4. Tidy Data

Show code

### |- Tidy ----
# Filter for the top 10 targets
top_stars <- hubble_raw |>
  filter(sci_targname != "ANY") |>  
  count(sci_targname, sort = TRUE) |>
  slice_head(n = 10)

# Filter for those top targets
top_observations <- hubble_raw |>
  filter(sci_targname %in% top_stars$sci_targname)

# Prepare the timeline data
observations_timeline <- top_observations |>
  mutate(
    clean_name = str_replace(sci_targname, "^\\d+\\s*", "")           
  )

5. Visualization Parameters

Show code

### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = c("#FFD700")
)

### |-  titles and caption ----
# text
title_text    <- str_wrap("Stellar Observations by the Hubble Space Telescope",
                          width = 55) 

subtitle_text <- str_wrap("Timeline of the 10 Most Frequently Observed Celestial Targets",
                          width = 85)

caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 22,
  source_text =  "Barbara A. Mikulski Archive for Space Telescopes (MAST)" 
)

### |-  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(
    
    plot.background = element_rect(fill = "black", color = NA),
    panel.background = element_rect(fill = "black", color = NA),
    
    text = element_text(color = "white"),
    axis.text = element_text(color = "white"),
    axis.title = element_text(color = "white"),
    
    panel.grid.major = element_line(color = "gray30", linetype = "dotted"),
    panel.grid.minor = element_line(color = "gray20", linetype = "dotted"),

    plot.margin = margin(t = 10, r = 20, b = 10, l = 20),
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code

### |-  Plot ----
p <- ggplot(
  observations_timeline, 
  aes(x = sci_start_time, y = clean_name)
  ) +
  # Geoms
  geom_point(
    color = colors$palette,  
    size = 2,
    alpha = 0.8
  ) +
  # Annotate
  annotate(
    "text", 
    x = as.POSIXct("2018-02-01"), 
    y = "M-51", 
    label = paste(                        
      "Search Parameters:",
      "Objects: star | Radius: 3 arcminutes | Date: 2015-2023",
      "Instrument: ACS | Exposure Time: >60s | Science observations only",
      sep = "\n"
    ),
    hjust = 0,
    vjust = 1.3,  
    color = "white",
    size = 3,
    lineheight = 0.9,
    fontface = "italic"
  ) +
  # Scale
  scale_x_datetime(
    labels = date_format("%Y"),
    breaks = breaks_width("2 years")
  ) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = "Observation Date",
    y = "Target Name",
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.8),
      family = fonts$title,
      face = "bold",
      color = 'white',
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_text(
      size = rel(0.85),
      family = fonts$subtitle,
      color = 'white',
      lineheight = 1.2,
      margin = margin(t = 5, b = 15)
    ),
    plot.caption = element_markdown(
      size = rel(0.6),
      family = fonts$caption,
      color = alpha('white', 0.8),
      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 = 22, 
  width = 8, 
  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 scales_1.3.0    skimr_2.1.5    
 [5] janitor_2.2.0   showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9 
 [9] ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1  
[13] dplyr_1.1.4     purrr_1.0.2     readr_2.1.5     tidyr_1.3.1    
[17] tibble_3.2.1    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] ragg_1.3.3        hms_1.1.3         evaluate_1.0.1    knitr_1.49       
[49] markdown_1.13     rlang_1.1.4       gridtext_0.1.5    Rcpp_1.0.13-1    
[53] glue_1.8.0        xml2_1.3.6        renv_1.0.3        svglite_2.1.3    
[57] rstudioapi_0.17.1 vroom_1.6.5       jsonlite_1.8.9    R6_2.5.1         
[61] systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in 30dcc_2025_22.qmd.

For the full repository, click here.

10. References

Expand for References

Data Sources:
- Barbara A. Mikulski Archive for Space Telescopes (MAST)

--- title: "Stellar Observations by the Hubble Space Telescope" subtitle: "Timeline of the 10 Most Frequently Observed Celestial Targets" description: "Analyzing Hubble Space Telescope observation patterns of stellar objects from 2015-2023, revealing scientific priorities and long-term monitoring programs through time series visualization." date: "2025-04-22" categories: ["30DayChartChallenge", "Data Visualization", "R Programming", "2025"] tags: [ "Astronomy", "Hubble", "Timeseries", "Space", "ggplot2", "Stars", "HST", "MAST", "Telescope", "Celestial Objects" ] image: "thumbnails/30dcc_2025_22.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_22.html" # description: "Day 22 of #30DayChartChallenge: Timeseries of stellar observations by the Hubble Space Telescope, revealing patterns of scientific interest across cosmic targets." # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ![Time series visualization of Hubble Space Telescope observations from 2016-2024, showing the 10 most frequently observed celestial targets. Gold dots represent observations, with NGC104-WFC having the most consistent observations over time. M-51 shows a concentrated period of observations in 2017-2018, while NGC5139-WFC observations only begin around 2020.](30dcc_2025_22.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 camcorder # Record Your Plot History ) }) ### |- figure size ---- gg_record( dir = here::here("temp_plots"), device = "png", width = 8, 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 hubble_raw <- read_csv(here::here( "data/30DayChartChallenge/2025/HST_2025-04-05T07_06_02-04_00.csv") ) |> clean_names() ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(hubble_raw) skim(hubble_raw) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- Tidy ---- # Filter for the top 10 targets top_stars <- hubble_raw |> filter(sci_targname != "ANY") |> count(sci_targname, sort = TRUE) |> slice_head(n = 10) # Filter for those top targets top_observations <- hubble_raw |> filter(sci_targname %in% top_stars$sci_targname) # Prepare the timeline data observations_timeline <- top_observations |> mutate( clean_name = str_replace(sci_targname, "^\\d+\\s*", "") ) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- colors <- get_theme_colors( palette = c("#FFD700") ) ### |- titles and caption ---- # text title_text <- str_wrap("Stellar Observations by the Hubble Space Telescope", width = 55) subtitle_text <- str_wrap("Timeline of the 10 Most Frequently Observed Celestial Targets", width = 85) caption_text <- create_dcc_caption( dcc_year = 2025, dcc_day = 22, source_text = "Barbara A. Mikulski Archive for Space Telescopes (MAST)" ) ### |- fonts ---- setup_fonts() fonts <- get_font_families() ### |- plot theme ---- # Start with base theme base_theme <- create_base_theme(colors) # Add weekly-specific theme elements weekly_theme <- extend_weekly_theme( base_theme, theme( plot.background = element_rect(fill = "black", color = NA), panel.background = element_rect(fill = "black", color = NA), text = element_text(color = "white"), axis.text = element_text(color = "white"), axis.title = element_text(color = "white"), panel.grid.major = element_line(color = "gray30", linetype = "dotted"), panel.grid.minor = element_line(color = "gray20", linetype = "dotted"), plot.margin = margin(t = 10, r = 20, b = 10, l = 20), ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot ---- p <- ggplot( observations_timeline, aes(x = sci_start_time, y = clean_name) ) + # Geoms geom_point( color = colors$palette, size = 2, alpha = 0.8 ) + # Annotate annotate( "text", x = as.POSIXct("2018-02-01"), y = "M-51", label = paste( "Search Parameters:", "Objects: star | Radius: 3 arcminutes | Date: 2015-2023", "Instrument: ACS | Exposure Time: >60s | Science observations only", sep = "\n" ), hjust = 0, vjust = 1.3, color = "white", size = 3, lineheight = 0.9, fontface = "italic" ) + # Scale scale_x_datetime( labels = date_format("%Y"), breaks = breaks_width("2 years") ) + # Labs labs( title = title_text, subtitle = subtitle_text, caption = caption_text, x = "Observation Date", y = "Target Name", ) + # Theme theme( plot.title = element_text( size = rel(1.8), family = fonts$title, face = "bold", color = 'white', margin = margin(t = 5, b = 5) ), plot.subtitle = element_text( size = rel(0.85), family = fonts$subtitle, color = 'white', lineheight = 1.2, margin = margin(t = 5, b = 15) ), plot.caption = element_markdown( size = rel(0.6), family = fonts$caption, color = alpha('white', 0.8), 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 = 22, width = 8, 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_22.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/30dcc_2025_22.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: - Barbara A. Mikulski Archive for Space Telescopes [(MAST)](https://archive.stsci.edu/hst/search.php) :::