---
title: "Season 35 power rankings (weekly) \u2014 provisional"
subtitle: "Data with the Stars · An Artometrics project"
date: 2026-11-03
format:
  html:
    toc: true
    embed-resources: true
    theme: cosmo
  pdf:
    documentclass: article
    geometry: margin=1in
execute:
  echo: false
  warning: false
  message: false
---


> **Note:** data-quality note

**Finding.** Provisional judge ranking through week 8; S35 name scrape is garbled \u2014 repair cast map before publishing as power rankings.

```{r}
#| label: setup
#| include: false
source("R/_setup.R")
```

## Charts

```{r}
#| label: 03_s35_cumulative
#| fig-cap: "03_s35_cumulative"
#| echo: false
# Rebuild chart with ggplot2 from bundled CSVs / story data.csv
story_dir <- file.path(root, "public/downloads/stories", "03_season35_power_rankings")
df <- read_csv(file.path(story_dir, "data.csv"), show_col_types = FALSE)
# Prefer a pre-defined plot expression file if present; else smart default
plot_r <- file.path(root, "reports/qmd/R/plots", "03_s35_cumulative.R")
if (file.exists(plot_r)) {
  source(plot_r, local = TRUE)
  # expects object `p`
} else {
  # Default: if typical named mean columns, bar them
  num_cols <- names(df)[sapply(df, is.numeric)]
  chr_cols <- names(df)[sapply(df, function(z) is.character(z) || is.factor(z))]
  ycol <- intersect(c("mean","wins","delta","boost","composite","accuracy","n","perfects","share","lift","pro_mean","resid","streak","guest_mean","field_mean","pts"), num_cols)
  ycol <- if (length(ycol)) ycol[[1]] else num_cols[[1]]
  xcol <- intersect(c("dance_style","pro","celebrity","judge","bucket","theme_key","hometown_state","checkpoint","label","song","professional","role","era","bin","metric"), chr_cols)
  xcol <- if (length(xcol)) xcol[[1]] else if (length(chr_cols)) chr_cols[[1]] else NULL
  if (!is.null(xcol)) {
    dd <- df
    if (nrow(dd) > 20) dd <- dd[1:20,]
    dd[[xcol]] <- as.character(dd[[xcol]])
    dd <- dd[!duplicated(dd[[xcol]]), , drop = FALSE]
    dd[[xcol]] <- factor(dd[[xcol]], levels = rev(unique(dd[[xcol]])))
    p <- ggplot(dd, aes(.data[[ycol]], .data[[xcol]], fill = .data[[ycol]])) +
      geom_col() + scale_fill_gradient(low = "#A8DADC", high = "#6B4CE6") +
      labs(title = "03_s35_cumulative", x = ycol, y = NULL) + theme_dwts() + theme(legend.position = "none")
  } else {
    p <- ggplot(df, aes(.data[[num_cols[[1]]]])) + geom_histogram(bins = 30, fill = "#6B4CE6", color = "white") +
      labs(title = "03_s35_cumulative") + theme_dwts()
  }
}
save_both(p, "03_s35_cumulative")
p
```

[PNG](charts/03_s35_cumulative.png){download="03_s35_cumulative.png"} · [SVG](charts/03_s35_cumulative.svg){download="03_s35_cumulative.svg"}


```{r}
#| label: 03_s35_field
#| fig-cap: "03_s35_field"
#| echo: false
# Rebuild chart with ggplot2 from bundled CSVs / story data.csv
story_dir <- file.path(root, "public/downloads/stories", "03_season35_power_rankings")
df <- read_csv(file.path(story_dir, "data.csv"), show_col_types = FALSE)
# Prefer a pre-defined plot expression file if present; else smart default
plot_r <- file.path(root, "reports/qmd/R/plots", "03_s35_field.R")
if (file.exists(plot_r)) {
  source(plot_r, local = TRUE)
  # expects object `p`
} else {
  # Default: if typical named mean columns, bar them
  num_cols <- names(df)[sapply(df, is.numeric)]
  chr_cols <- names(df)[sapply(df, function(z) is.character(z) || is.factor(z))]
  ycol <- intersect(c("mean","wins","delta","boost","composite","accuracy","n","perfects","share","lift","pro_mean","resid","streak","guest_mean","field_mean","pts"), num_cols)
  ycol <- if (length(ycol)) ycol[[1]] else num_cols[[1]]
  xcol <- intersect(c("dance_style","pro","celebrity","judge","bucket","theme_key","hometown_state","checkpoint","label","song","professional","role","era","bin","metric"), chr_cols)
  xcol <- if (length(xcol)) xcol[[1]] else if (length(chr_cols)) chr_cols[[1]] else NULL
  if (!is.null(xcol)) {
    dd <- df
    if (nrow(dd) > 20) dd <- dd[1:20,]
    dd[[xcol]] <- as.character(dd[[xcol]])
    dd <- dd[!duplicated(dd[[xcol]]), , drop = FALSE]
    dd[[xcol]] <- factor(dd[[xcol]], levels = rev(unique(dd[[xcol]])))
    p <- ggplot(dd, aes(.data[[ycol]], .data[[xcol]], fill = .data[[ycol]])) +
      geom_col() + scale_fill_gradient(low = "#A8DADC", high = "#6B4CE6") +
      labs(title = "03_s35_field", x = ycol, y = NULL) + theme_dwts() + theme(legend.position = "none")
  } else {
    p <- ggplot(df, aes(.data[[num_cols[[1]]]])) + geom_histogram(bins = 30, fill = "#6B4CE6", color = "white") +
      labs(title = "03_s35_field") + theme_dwts()
  }
}
save_both(p, "03_s35_field")
p
```

[PNG](charts/03_s35_field.png){download="03_s35_field.png"} · [SVG](charts/03_s35_field.svg){download="03_s35_field.svg"}


```{r}
#| label: 03_s35_power
#| fig-cap: "03_s35_power"
#| echo: false
# Rebuild chart with ggplot2 from bundled CSVs / story data.csv
story_dir <- file.path(root, "public/downloads/stories", "03_season35_power_rankings")
df <- read_csv(file.path(story_dir, "data.csv"), show_col_types = FALSE)
# Prefer a pre-defined plot expression file if present; else smart default
plot_r <- file.path(root, "reports/qmd/R/plots", "03_s35_power.R")
if (file.exists(plot_r)) {
  source(plot_r, local = TRUE)
  # expects object `p`
} else {
  # Default: if typical named mean columns, bar them
  num_cols <- names(df)[sapply(df, is.numeric)]
  chr_cols <- names(df)[sapply(df, function(z) is.character(z) || is.factor(z))]
  ycol <- intersect(c("mean","wins","delta","boost","composite","accuracy","n","perfects","share","lift","pro_mean","resid","streak","guest_mean","field_mean","pts"), num_cols)
  ycol <- if (length(ycol)) ycol[[1]] else num_cols[[1]]
  xcol <- intersect(c("dance_style","pro","celebrity","judge","bucket","theme_key","hometown_state","checkpoint","label","song","professional","role","era","bin","metric"), chr_cols)
  xcol <- if (length(xcol)) xcol[[1]] else if (length(chr_cols)) chr_cols[[1]] else NULL
  if (!is.null(xcol)) {
    dd <- df
    if (nrow(dd) > 20) dd <- dd[1:20,]
    dd[[xcol]] <- as.character(dd[[xcol]])
    dd <- dd[!duplicated(dd[[xcol]]), , drop = FALSE]
    dd[[xcol]] <- factor(dd[[xcol]], levels = rev(unique(dd[[xcol]])))
    p <- ggplot(dd, aes(.data[[ycol]], .data[[xcol]], fill = .data[[ycol]])) +
      geom_col() + scale_fill_gradient(low = "#A8DADC", high = "#6B4CE6") +
      labs(title = "03_s35_power", x = ycol, y = NULL) + theme_dwts() + theme(legend.position = "none")
  } else {
    p <- ggplot(df, aes(.data[[num_cols[[1]]]])) + geom_histogram(bins = 30, fill = "#6B4CE6", color = "white") +
      labs(title = "03_s35_power") + theme_dwts()
  }
}
save_both(p, "03_s35_power")
p
```

[PNG](charts/03_s35_power.png){download="03_s35_power.png"} · [SVG](charts/03_s35_power.svg){download="03_s35_power.svg"}


## Data

Analysis CSV: [data.csv](../../public/downloads/stories/03_season35_power_rankings/data.csv){download="03_season35_power_rankings-data.csv"}

Source tables: `performances.csv`, `scores.csv`, `contestants.csv`, `seasons.csv` under `/downloads/`.