Coachella’s lineup cuts landed on Saturday and Sunday

From 2018 to 2026, Friday lost 4 artists. Saturday and Sunday lost 27 combined.

A slope chart showing that Coachella’s shrinking initial lineup contracted almost entirely on the weekend: Saturday and Sunday lost 27 acts between 2018 and 2026 while Friday lost just 4. It reframes the original’s grouped day-by-day bars as a two-point comparison that isolates where the cuts landed. Built in R with ggplot2.

Original

The original visualization comes from Coachella Lineup

Original visualization

Makeover

Figure 1: Slope chart of the number of artists on Coachella’s initial lineup announcement, by day, comparing 2018 and 2026. Friday stays nearly flat, slipping from 55 to 51, a loss of 4. Saturday drops from 57 to 44 and Sunday from 57 to 43, a combined weekend loss of 27. The two weekend lines, shown in rust, plunge steeply while Friday, in gray, holds close to level — the contraction concentrated on Saturday and Sunday.

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, ggtext, showtext, scales, glue, janitor
)
})

### |- figure size ----
camcorder::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
#| 

df_raw <- read_csv(
  here::here("data/MakeoverMonday/2026/coachella lineup.csv")) |>
  clean_names()
```

3. Examine the Data

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

glimpse(df_raw)
skimr::skim_without_charts(df_raw)
```

4. Tidy Data

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

day_levels <- c("Friday", "Saturday", "Sunday")

# Long form, one row per day-year
df_long <- df_raw |>
  select(year, friday, saturday, sunday) |>
  pivot_longer(
    cols      = c(friday, saturday, sunday),
    names_to  = "day",
    values_to = "artists"
  ) |>
  mutate(
    day = str_to_title(day),
    day = factor(day, levels = day_levels)
  )

df_slope <- df_long |>
  filter(year %in% c(2018, 2026)) |>
  mutate(year = factor(year, levels = c(2018, 2026)))

df_change <- df_slope |>
  pivot_wider(names_from = year, values_from = artists, names_prefix = "y") |>
  mutate(
    change   = y2026 - y2018,
    pct_drop = change / y2018
  )
```

5. Visualization Parameters

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

### |-  plot aesthetics ----
col_friday   <- "#8A817C" 
col_saturday <- "#9B2D20"  
col_sunday   <- "#C77B5A"   

day_pal <- c(
  Friday   = col_friday,
  Saturday = col_saturday,
  Sunday   = col_sunday
)

clrs <- get_theme_colors(
  palette = c(
    Friday   = col_friday,
    Saturday = col_saturday,
    Sunday   = col_sunday
  )
)

### |- label frames ----
df_left <- df_slope |>
  filter(year == "2018") |>
  mutate(label_y = case_when(
    day == "Friday" ~ artists - 0.5,
    day == "Saturday" ~ artists + 0.8,
    day == "Sunday" ~ artists - 0.8,
    TRUE ~ as.numeric(artists)
  ))

df_right <- df_slope |>
  filter(year == "2026") |>
  mutate(label_y = case_when(
    day == "Saturday" ~ artists + 0.6,
    day == "Sunday" ~ artists - 0.6,
    TRUE ~ as.numeric(artists)
  ))

### |- titles and caption ----
title_text <- glue(
  "Coachella's lineup cuts landed on<br>",
  "<span style='color:{col_saturday}'>**Saturday and Sunday**</span>"
)

subtitle_text <- glue(
  "From 2018 to 2026, Friday lost 4 artists. ",
  "Saturday and Sunday lost 27 combined."
)

caption_text <- create_mm_caption(
  mm_year     = 2026,
  mm_week     = 23,
  source_text = "Chartmetric via data.world<br>Counts cover the initial lineup announcement only"
)

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

### |- plot theme ----
base_theme <- create_base_theme(clrs)

weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    legend.position = "none",
    panel.grid = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks = element_blank(),
    axis.title = element_blank(),
    axis.text.x = element_text(face = "bold"),
    plot.title = element_markdown(size = rel(1.8), face = "bold", family = fonts$title_1, color = clrs$title),
    plot.subtitle = element_textbox_simple(
      width = unit(1, "npc"), margin = margin(t = 6, b = 14), size = rel(0.85),
      family = fonts$text, color = clrs$text
    ),
    plot.caption = element_markdown(
      hjust = 0, size = rel(0.55), lineheight = 1.15, margin = margin(t = 16),
      family = fonts$caption, color = clrs$caption
    ),
    plot.title.position = "plot",
    plot.margin = margin(t = 20, r = 60, b = 20, l = 30)
  )
)

theme_set(weekly_theme)
```

6. Plot

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

p <- ggplot(
  df_slope,
  aes(x = year, y = artists, group = day, color = day)
) +
  # Geoms
  geom_line(linewidth = 1.1) +
  geom_point(size = 3) +
  geom_text(
    data = df_left,
    aes(y = label_y, label = glue("{day}  {artists}")),
    hjust = 1.12, fontface = "bold", size = 4.2, show.legend = FALSE
  ) +
  geom_text(
    data = df_right,
    aes(y = label_y, label = glue("{day}   {artists}")),
    hjust = -0.10, fontface = "bold", size = 4.2, show.legend = FALSE
  ) +
  # Scales
  scale_color_manual(values = day_pal) +
  scale_x_discrete(
    expand = expansion(mult = c(0.24, 0.2))
  ) +
  scale_y_continuous(expand = expansion(mult = c(0.10, 0.08))) +
  coord_cartesian(clip = "off") +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = NULL, y = NULL
  )
```

7. Save

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

### |-  plot image ----  
save_plot(
  plot = p , 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 8, 
  height = 8
  )
```

8. Session Info

TipExpand for Session Info

R version 4.5.3 (2026-03-11 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default
  LAPACK version 3.12.1

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 utils     datasets  methods   base     

other attached packages:
 [1] here_1.0.2      janitor_2.2.1   glue_1.8.0      scales_1.4.0   
 [5] showtext_0.9-8  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
 [9] lubridate_1.9.5 forcats_1.0.1   stringr_1.6.0   dplyr_1.2.1    
[13] purrr_1.2.2     readr_2.2.0     tidyr_1.3.2     tibble_3.3.1   
[17] ggplot2_4.0.3   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.57          htmlwidgets_1.6.4  tzdb_0.5.0        
 [5] vctrs_0.7.3        tools_4.5.3        generics_0.1.4     curl_7.0.0        
 [9] parallel_4.5.3     gifski_1.32.0-2    pkgconfig_2.0.3    skimr_2.2.2       
[13] RColorBrewer_1.1-3 S7_0.2.1           lifecycle_1.0.5    compiler_4.5.3    
[17] farver_2.1.2       textshaping_1.0.5  repr_1.1.7         codetools_0.2-20  
[21] snakecase_0.11.1   litedown_0.9       htmltools_0.5.9    yaml_2.3.12       
[25] pillar_1.11.1      crayon_1.5.3       camcorder_0.1.0    magick_2.9.1      
[29] commonmark_2.0.0   tidyselect_1.2.1   digest_0.6.39      stringi_1.8.7     
[33] labeling_0.4.3     rsvg_2.7.0         rprojroot_2.1.1    fastmap_1.2.0     
[37] grid_4.5.3         cli_3.6.6          magrittr_2.0.5     base64enc_0.1-6   
[41] withr_3.0.2        bit64_4.6.0-1      timechange_0.4.0   rmarkdown_2.31    
[45] bit_4.6.0          otel_0.2.0         ragg_1.5.2         hms_1.1.4         
[49] evaluate_1.0.5     knitr_1.51         markdown_2.0       rlang_1.2.0       
[53] gridtext_0.1.6     Rcpp_1.1.1         xml2_1.5.2         svglite_2.2.2     
[57] rstudioapi_0.18.0  vroom_1.7.1        jsonlite_2.0.0     R6_2.6.1          
[61] systemfonts_1.3.2 

9. GitHub Repository

TipExpand for GitHub Repo

The complete code for this analysis is available in mm_2026_23.qmd.

For the full repository, click here.

10. References

TipExpand for References

Primary Data (Makeover Monday): 1. Makeover Monday 2026 Week 23: Coachella Lineup (2018–2026) 2. Original Chart: Chartmetric — Coachella Lineup, initial announcement 2018–2026 - Article context: The Numbers Behind Coachella’s Smallest Lineup in Years - Coverage: initial lineup poster only; artists per day (Friday, Saturday, Sunday) and total, across seven editions (2018, 2019, 2022–2026; 2020–21 not held) Source Data: 3. Chartmetric — How Music Charts - Coverage: artist counts compiled from Coachella’s initial lineup announcement posters, 2018–2026 - Unit: per-day artist counts (Friday, Saturday, Sunday) and an edition total 4. MakeoverMonday 2026 W23 — data.world - CSV file: coachella lineup.csv; 7 rows × 5 columns (year, friday, saturday, sunday, total) Note: Counts reflect the initial lineup announcement poster only — later additions, set-time fill-ins, and separately programmed stages (Do LaB, Heineken House) are excluded, matching the original chart’s own scope. The 2020 and 2021 editions were not held (pandemic) and are absent from the series, not recorded as zero. The slopegraph compares the first year of the record (2018) with the latest (2026); the day-level asymmetry holds whether anchored to 2018, to the 2022 peak, or across the full seven-edition trajectory. “Saturday and Sunday lost 27 combined” is the sum of Saturday’s drop (57→44, −13) and Sunday’s (57→43, −14); Friday fell 55→51 (−4). Per-day counts sum to the edition total exactly in 2018–2019 but run one short from 2022 onward — the difference is the unattached fourth-headliner slot introduced post-pandemic, so the three days lost 31 acts combined while the overall initial lineup fell 30 (169→139). No causal relationship is asserted: the chart describes where the contraction landed across days, not why, and “cuts” refers to fewer artists booked on the initial poster.

11. Custom Functions Documentation

Note📦 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