Kids’ June 2025 Streaming Top 10

Netflix leads June with 4 of the top 10 programs, totaling 3.3B minutes viewed

An analysis of Nielsen’s kids’ streaming data (ages 6-17) for June 2025, revealing Netflix’s dominance with 4 of the top 10 programs. The visualization highlights viewing patterns and raises questions about age-appropriate content, as several Teen/Mature-rated shows rank highly among young viewers.

Original

The original visualization comes from School’s out and the TV’s on: What kids in the U.S. watched in June

Original visualization

Makeover

Figure 1: Horizontal bar chart showing Kids’ June 2025 Streaming Top 10 programs by total minutes viewed. Netflix dominates with four programs (shown in red): Ginny & Georgia leads at 1.43 billion minutes, followed by Squid Game (837M), Stranger Things (617M), and Alvin! and the Chipmunks (466M). Other platforms in gray include Disney+ (Bluey - 895M, Phineas and Ferb - 748M), Paramount+ (SpongeBob - 801M), Hulu (Gumball - 626M), Max/Netflix (Young Sheldon - 533M), and Peacock (Love Island USA - 473M). Notable: Several Teen/Mature-rated shows rank highly despite a 6-17 age demographic.

Steps to Create this Graphic

1. Load Packages & Setup

```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
  if (!require("pacman")) install.packages("pacman")
  pacman::p_load(
  tidyverse,     # Easily Install and Load the 'Tidyverse'
  janitor,       # Simple Tools for Examining and Cleaning Dirty Data
  skimr,         # Compact and Flexible Summaries of Data
  scales,        # Scale Functions for Visualization
  ggtext,        # Improved Text Rendering Support for 'ggplot2'
  showtext,      # Using Fonts More Easily in R Graphs
  glue           # Interpreted String Literals
)
})

### |- figure size ----
camcorder::gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 10,
    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
#|

top_10 <- readxl::read_excel(
  here::here("data/MakeoverMonday/2025/Top 10 Streaming (Kids 6 17).xlsx")) |>
  clean_names()
```

3. Examine the Data

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(top_10)
```

4. Tidy Data

```{r}
#| label: tidy
#| warning: false

top_10_clean <- top_10 |>
  mutate(
    # Standardize program names
    program_clean = case_when(
      str_detect(program, "Spongebob") ~ "SpongeBob SquarePants",
      TRUE ~ program
    )
  )

# SOURCES:
# - Content Ratings: IMDb (www.imdb.com) - displays TV Parental Guidelines ratings
# - Show Type & Genre: IMDb and streaming service platforms (Netflix, Disney+, etc.)
# - Additional verification: Common Sense Media (www.commonsensemedia.org)
#
# NOTE: These ratings reflect US TV Parental Guidelines:
#   TV-Y   = All Children
#   TV-Y7  = Directed to Older Children (7+)
#   TV-G   = General Audience
#   TV-PG  = Parental Guidance Suggested
#   TV-14  = Parents Strongly Cautioned (14+)
#   TV-MA  = Mature Audience Only (17+)

show_info <- tribble(
  ~program_clean,                ~type,         ~rating,   ~genre,          ~imdb_id,
  "Ginny & Georgia",             "Live-action", "TV-14",   "Drama",         "tt10624432",
  "Bluey",                       "Animation",   "TV-Y",    "Comedy",        "tt7678620",
  "Squid Game",                  "Live-action", "TV-MA",   "Thriller",      "tt10919420",
  "SpongeBob SquarePants",       "Animation",   "TV-Y7",   "Comedy",        "tt0206512",
  "Phineas and Ferb",            "Animation",   "TV-G",    "Comedy",        "tt0852863",
  "The Amazing World of Gumball","Animation",   "TV-PG",   "Comedy",        "tt1942683",
  "Stranger Things",             "Live-action", "TV-14",   "Sci-Fi",        "tt4574334",
  "Young Sheldon",               "Live-action", "TV-PG",   "Comedy",        "tt6226232",
  "Love Island USA",             "Reality",     "TV-14",   "Reality",       "tt8230780",
  "Alvin! and the Chipmunks",    "Animation",   "TV-Y7",   "Comedy",        "tt0084119"
)

## |-  join and create analysis variables ----
top_10_analysis <- top_10_clean |>
  left_join(show_info, by = "program_clean") |>
  mutate(
    mins_millions = round(mins_viewed / 1e6, 0),
    age_appropriate = case_when(
      rating %in% c("TV-Y", "TV-Y7", "TV-G") ~ "Kids",
      rating == "TV-PG" ~ "Family",
      rating == "TV-14" ~ "Teen",
      rating == "TV-MA" ~ "Mature",
      TRUE ~ "Unknown"
    ),
    age_appropriate = factor(age_appropriate,
                             levels = c("Kids", "Family", "Teen", "Mature")
    ),
    program_with_provider = glue("{program_clean}<br><span style='font-size:8pt;color:gray50'>({svod_provider})</span>"),
    program_ordered = fct_reorder(program_with_provider, -rank)
  )
```

5. Visualization Parameters

```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(
  palette = list(
    netflix       = "#B20710", 
    neutral_dark  = "#9E9E9E"          
  )
)

### |-  Main titles ----
title_text <- "Kids' June 2025 Streaming Top 10"
subtitle_text <- str_glue(
  "Netflix leads June with 4 of the top 10 programs, totaling 3.3B minutes viewed<br>",
  "Nielsen measures household accounts with viewers 6–17; programming reflects actual viewing, not age-appropriateness."
)

### |-  Data source caption ----
caption_text <- create_mm_caption(
  mm_year = 2025,
  mm_week = 46,
  source_text = str_glue(
    "Nielsen National TV Panel<br>",
    "**Note:** Kids 6-17, June 2025 (05/26/25 - 06/29/25). Metadata from IMDb."
  )
)

### |-  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(
      size = rel(1.5), family = fonts$title, face = "bold",
      color = colors$title, lineheight = 1.1, hjust = 0,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.9), family = fonts$subtitle, face = "italic",
      color = alpha(colors$subtitle, 0.9), lineheight = 1.1,
      margin = margin(t = 0, b = 20)
    ),

    # Legend formatting
    legend.position = "plot",
    legend.justification = "top",
    legend.margin = margin(l = 12, b = 5),
    legend.key.size = unit(0.8, "cm"),
    legend.box.margin = margin(b = 10),
    # legend.title = element_text(face = "bold"),

    # Axis formatting
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5),
    axis.title.x = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(t = 10)
    ),
    axis.title.y = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(r = 10)
    ),
    axis.text.x = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = colors$text
    ),
    axis.text.y = element_markdown(
      size = rel(0.85), family = fonts$subtitle,
      color = colors$text
    ),

    # Grid lines
    panel.grid.minor = element_line(color = "#ecf0f1", linewidth = 0.2),
    panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4),

    # Margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

```{r}
#| label: plot
#| warning: false

## |-  prepare data for plotting ----
plot_data <- top_10_analysis |>
  mutate(
    # Netflix highlighting
    is_netflix = svod_provider == "Netflix",
    bar_color = ifelse(is_netflix, colors$palette$netflix, colors$palette$neutral_dark),
    text_color = ifelse(is_netflix, "white", "gray30"),
  ) |>
  mutate(
    mins_billions = mins_millions / 1000,
    mins_label_smart = ifelse(
      mins_billions >= 1,
      paste0(round(mins_billions, 2), "B"),
      paste0(mins_millions, "M")
    )
  )

### |-  main plot ----
p <- 
  plot_data |>
  ggplot(aes(x = mins_millions, y = program_ordered)) +
  # Geoms
  geom_col(aes(fill = bar_color), width = 0.65) +
  geom_text(
    aes(x = 25, label = paste0(genre, " | ", type), color = text_color),
    hjust = 0,
    size = 2.5
  ) +
  geom_label(
    aes(x = mins_millions - 40, label = rating),
    hjust = 1,
    size = 2.5,
    fontface = "bold",
    fill = "white",
    label.size = 0,
    label.padding = unit(0.15, "lines")
  ) +
  geom_text(
    aes(label = mins_label_smart),
    hjust = -0.1,
    size = 3.5,
    fontface = "bold"
  ) +
  # Annotate
  annotate(
    "text", x = 850, y = 3,
    label = "60% of the Top 10 are Kids/Family titles,\nwhile several Teen/Mature shows still rank highly\namong households with viewers 6–17.",
    hjust = 0, size = 3.2, lineheight = 1.1,
    family = fonts$subtitle, color = "gray30"
  ) +
  # Scales
  scale_x_continuous(
    position = "top",
    expand = expansion(mult = c(0.02, 0.1)),
    labels = label_comma(suffix = "M")
  ) +
  scale_fill_identity() +
  scale_color_identity() +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    x = "Total Minutes Viewed (Millions)",
    y = NULL,
    caption = caption_text
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.85),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      lineheight = 1.1,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.8),
      family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.9),
      lineheight = 1.5,
      margin = margin(t = 5, b = 25)
    ),
    plot.caption = element_markdown(
      size = rel(0.5),
      family = fonts$caption,
      color = colors$caption,
      hjust = 0,
      lineheight = 1.2,
      margin = margin(t = 10)
    ),
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.ticks = element_blank()
  )
```

7. Save

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot(
  plot = p, 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 10, 
  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 26100)

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      glue_1.8.0      showtext_0.9-7  showtextdb_3.0 
 [5] sysfonts_0.8.9  ggtext_0.1.2    scales_1.3.0    skimr_2.1.5    
 [9] janitor_2.2.0   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 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      xfun_0.49         htmlwidgets_1.6.4 tzdb_0.5.0       
 [5] vctrs_0.6.5       tools_4.4.0       generics_0.1.3    curl_6.0.0       
 [9] gifski_1.32.0-1   fansi_1.0.6       pkgconfig_2.0.3   readxl_1.4.3     
[13] lifecycle_1.0.4   farver_2.1.2      compiler_4.4.0    textshaping_0.4.0
[17] munsell_0.5.1     repr_1.1.7        codetools_0.2-20  snakecase_0.11.1 
[21] htmltools_0.5.8.1 yaml_2.3.10       pillar_1.9.0      camcorder_0.1.0  
[25] magick_2.8.5      commonmark_1.9.2  tidyselect_1.2.1  digest_0.6.37    
[29] stringi_1.8.4     labeling_0.4.3    rsvg_2.6.1        rprojroot_2.0.4  
[33] fastmap_1.2.0     grid_4.4.0        colorspace_2.1-1  cli_3.6.4        
[37] magrittr_2.0.3    base64enc_0.1-3   utf8_1.2.4        withr_3.0.2      
[41] timechange_0.3.0  rmarkdown_2.29    cellranger_1.1.0  ragg_1.3.3       
[45] hms_1.1.3         evaluate_1.0.1    knitr_1.49        markdown_1.13    
[49] rlang_1.1.6       gridtext_0.1.5    Rcpp_1.0.13-1     xml2_1.3.6       
[53] renv_1.0.3        svglite_2.1.3     rstudioapi_0.17.1 jsonlite_1.8.9   
[57] R6_2.5.1          systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in mm_2025_46.qmd.

For the full repository, click here.

10. References

Expand for References

1. Data:

• Makeover Monday 2025 Week 46: School’s out and the TV’s on: What kids in the U.S. watched in June

2. Article

• School’s out and the TV’s on: What kids in the U.S. watched in June

3. Citation:
   • Nielsen. (2025). School’s out and the TV’s on: What kids in the U.S. watched in June. Nielsen Insights. Retrieved from https://www.nielsen.com/insights/2025/what-us-kids-watched-tv-streaming-summer-june/

11. Custom Functions Documentation

📦 Custom Helper Functions

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

• fonts.R: setup_fonts(), get_font_families() - Font management with showtext
• social_icons.R: create_social_caption() - Generates formatted social media captions
• image_utils.R: save_plot() - Consistent plot saving with naming conventions
• base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils